model

package
v0.0.0-...-2887456 Latest Latest
Warning

This package is not in the latest version of its module.

 Go to latest Published: Jul 5, 2022 License: MIT Imports: 0 Imported by: 0

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

This section is empty.

Types

type ACenturyOfWorkAndLeisureRameyAndFrancis2009Dataset 

type ACenturyOfWorkAndLeisureRameyAndFrancis2009Dataset struct {
	AverageWeeklyHoursWorkedPerPersonByDemographicGroupRameyAndFrancis2009 *float64 `json:"average_weekly_hours_worked_per_person_by_demographic_group_ramey_and_francis_2009"`
	AverageWeeklyHoursDevotedToSchoolRameyAndFrancis2009                   *float64 `json:"average_weekly_hours_devoted_to_school_ramey_and_francis_2009"`
	WeeklyHomeProductionTimeByDemographicGroupRameyAndFrancis2009          *float64 `json:"weekly_home_production_time_by_demographic_group_ramey_and_francis_2009"`
	AverageWeeklyLeisureEstimatesByAgeRameyAndFrancis2009                  *float64 `json:"average_weekly_leisure_estimates_by_age_ramey_and_francis_2009"`
}

type AbsoluteDeathsFromAmbientPm25AirPollutionStateOfGlobalAirDataset 

type AbsoluteDeathsFromAmbientPm25AirPollutionStateOfGlobalAirDataset struct {
	AbsoluteDeathsFromAmbientPm25AirPollutionStateOfGlobalAir *float64 `json:"absolute_deaths_from_ambient_pm25_air_pollution_state_of_global_air"`
}

type AbsolutePopulationChangeOwidBasedOnHydeAndUnDataset 

type AbsolutePopulationChangeOwidBasedOnHydeAndUnDataset struct {
	AbsoluteIncreaseInPopulationOwidBasedOnHydeAndUn        *float64 `json:"absolute_increase_in_population_owid_based_on_hyde_and_un"`
	ProjectedAbsolutePopulationIncreaseOwidBasedOnHydeAndUn *float64 `json:"projected_absolute_population_increase_owid_based_on_hyde_and_un"`
}

Data is calculated by OurWorldinData as the net change in population from one year to the next e.g. figures for 1900 represent the net change in population between 1900 and 1901.

These calculations are based on the 'OurWorldinData'-series derived from various sources. The data for the period before 1900 are taken from the History Database of the Global Environment (HYDE). The History Database of the Global Environment (HYDE) collected the data by earlier publications. The data for the World Population between 1900 and 1940 is taken from the UN publication 'The World at Six Billion'. The annual data for the World Population between 1950 and 2015 is taken from the United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/

Data from 2016 onwards is based on the above UN source's 'medium variant' projection to the year 2100.

The HYDE Database is available at: http://themasites.pbl.nl/tridion/en/themasites/hyde/basicdrivingfactors/population/index-2.html

The full 'OurWorldinData' series is available to download at: https://ourworldindata.org/wp-content/uploads/2013/05/WorldPopulationAnnual12000years_interpolated_HYDEandUNto2015.csv

type AccessToFinancialAccountOrServicesPercWorldBank2014Dataset 

type AccessToFinancialAccountOrServicesPercWorldBank2014Dataset struct {
	AccessToFinancialAccountOrServicesPercWorldBank2014 *float64 `json:"access_to_financial_account_or_services_perc_world_bank_2014"`
}

This data series extends from 2005-2014, and has been created through the merging of two independent estimates of access to financial services.

Data for 2011 and 2014 has been sourced from the World Bank's Global Findex (Global Financial Inclusion) Database [available at: http://databank.worldbank.org/data/reports], which provides several measures of financial access. The dataset presented here is 'Account at a financial institution (% age 15+)'. This dataset is predominantly sourced from household and census survey data. The World Bank defines this parameter as:

"the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution; having a debit card in their own name; receiving wages, government transfers, or payments for agricultural products into an account at a financial institution in the past 12 months; paying utility bills or school fees from an account at a financial institution in the past 12 months; or receiving wages or government transfers into a card in the past 12 months (% age 15+).

Earlier global estimates of global access to financial services are less certain. To present earlier estimates, we have relied on composite measures of financial access reported in the World Bank's publication 'Finance for All?: Policies and Pitfalls in Expanding Access' (2008). The sources from which the World Bank draws upon for this composite indicator are referenced below.

The World Bank defines this composite indicator as measuring "the percentage of the adult population with access to an account with a financial intermediary. The indicator is constructed as follows: for any country with data on access from a household survey, the surveyed percentage is given. For other countries, the percentage is constructed as a function of the estimated number and average size of bank accounts as discussed in Honohan (2007). These numbers are subject to estimation error."

It should also be noted that while the majority of data is reported for the year 2005, some are sourced from earlier (thus the measurement period should be considered to extend from 2000-2005). References: World Bank. 2008. Finance for All? A World Bank Policy Research Report. Available at: http://siteresources.worldbank.org/INTFINFORALL/Resources/4099583-1194373512632/FFA_book.pdf [accessed 25/05/2017]

Honohan. 2006. “Household Financial Assets in the Process of Development.” Policy Research Working Paper 3965, World Bank, Washington DC. Christen, Robert Peck, Veena Jayadeva, and Richard Rosenberg. 2004. “Financial Institutions with a Double Bottom Line: Implications for the Future of Microfi nance.” CGAP Occasional Paper 8, Consultative Group to Assist the Poorest, Washington DC.

Beck, Thorsten, Aslı Demirgüç-Kunt, and Maria Soledad Martinez Peria. 2007. “Reaching Out: Access to and Use of Banking Services across Countries.” Journal of Financial Economics 85 (1): 234–66.

Peachey, Stephen, and Alan Roe. 2006. “Access to Finance: Measuring the Contribution of Savings Banks.” World Savings Banks Institute, Brussels, Belgium.

type AdjustedNetSavingsPerCapitaWorldBankWdi2017Dataset 

type AdjustedNetSavingsPerCapitaWorldBankWdi2017Dataset struct {
	AdjustedNetSavingsPerCapitaWorldBank2017 *float64 `json:"adjusted_net_savings_per_capita_world_bank_2017"`
}

To calculate adjusted net savings per capita, adjusted net savings was divided by the World Bank's population, total.

type AdultLiteracyProficiencyWorldBankEdstatsAndStepSkillsMeasurementProgramDataset 

type AdultLiteracyProficiencyWorldBankEdstatsAndStepSkillsMeasurementProgramDataset struct {
	MeanAdultLiteracyProficiencyWorldBankEdstatsAndStepSkillsMeasurementProgram *float64 `json:"mean_adult_literacy_proficiency_world_bank_edstats_and_step_skills_measurement_program"`
}

To calculate the average score in adult literacy proficiency from the STEP surveys we have: <ul><li>Calculated the mean literacy score for each individual in the survey (over 10 plausible value scores)</li><li>Taken the sum of the mean literacy scores over all individuals</li><li>Divided the aggregate literacy score by the number of individuals</li></ul>The STEP data for calculated average adult literacy (8 additional observations) is appended to the PIAAC scores from World Bank EdStats dataset. 8 STEP observations include: Armenia (2013); Bolivia (2012); Colombia (2012); Georgia (2013); Ghana (2013); Kenya (2013); Ukraine (2012); and Vietnam (2012)As the STEP methodology note outlines "The STEP literacy assessment has been developed specifically for use in the context of developing countries, and it includes sets of questions taken from PIAAC, the International Adult Literacy Survey, and the Adult Literacy and Life Skills Survey. This overlap allows countries participating in the STEP program to compare their literacy results with those of over 30 other countries."Full citation: “Pierre, Gaelle; Sanchez Puerta, Maria Laura; Valerio, Alexandria; Rajadel, Tania. 2014. STEP Skills Measurement Surveys : Innovative Tools for Assessing Skills. Social protection and labor discussion paper;no. 1421. World Bank Group, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/19985 License: CC BY 3.0 IGO.”

type AdultObesityByRegionFao2017Dataset 

type AdultObesityByRegionFao2017Dataset struct {
	PrevalenceOfObesityInAdults18YearsOldFao2017 *float64 `json:"prevalence_of_obesity_in_adults_18_years_old_fao_2017"`
}

Obesity is defined as having a body-mass index (BMI) greater than 30 kg per m2.This data presents the prevalence of obesity in adults (male and female) aged 18 and above.

type AffordabilityOfDietsSofi2021Dataset 

type AffordabilityOfDietsSofi2021Dataset struct {
	CostOfCalorieSufficientDiet2017UsdPerDay              *float64 `json:"cost_of_calorie_sufficient_diet_2017_usd_per_day"`
	CostOfNutrientAdequateDiet2017UsdPerDay               *float64 `json:"cost_of_nutrient_adequate_diet_2017_usd_per_day"`
	CostOfHealthyDiet2017UsdPerDay                        *float64 `json:"cost_of_healthy_diet_2017_usd_per_day"`
	CalorieSufficientDietCostPercOfMoney120PovertyLine    *float64 `json:"calorie_sufficient_diet_cost_perc_of_money120_poverty_line"`
	NutrientAdequateDietCostPercOfMoney120PovertyLine     *float64 `json:"nutrient_adequate_diet_cost_perc_of_money120_poverty_line"`
	HealthyDietCostPercOfMoney120PovertyLine              *float64 `json:"healthy_diet_cost_perc_of_money120_poverty_line"`
	CalorieSufficientDietCostPercOfAverageFoodExpenditure *float64 `json:"calorie_sufficient_diet_cost_perc_of_average_food_expenditure"`
	NutrientAdequateDietCostPercOfAverageFoodExpenditure  *float64 `json:"nutrient_adequate_diet_cost_perc_of_average_food_expenditure"`
	HealthyDietCostPercOfAverageFoodExpenditure           *float64 `json:"healthy_diet_cost_perc_of_average_food_expenditure"`
	CalorieSufficientDietCostPercCannotAfford             *float64 `json:"calorie_sufficient_diet_cost_perc_cannot_afford"`
	NutrientAdequateDietCostPercCannotAfford              *float64 `json:"nutrient_adequate_diet_cost_perc_cannot_afford"`
	HealthyDietCostPercCannotAfford                       *float64 `json:"healthy_diet_cost_perc_cannot_afford"`
	CalorieSufficientDietCostNumberCannotAfford           *float64 `json:"calorie_sufficient_diet_cost_number_cannot_afford"`
	NutrientAdequateDietCostNumberCannotAfford            *float64 `json:"nutrient_adequate_diet_cost_number_cannot_afford"`
	HealthyDietCostNumberCannotAfford                     *float64 `json:"healthy_diet_cost_number_cannot_afford"`
}

This data is sourced from the work of Hereforth et al. (2020), which is a background paper for the UN FAO State of Food Security and Nutrition in the World report. It is based on data on prices for locally available food items from the World Bank's International Comparison Program (ICP) (https://icp.worldbank.org/) atched to other data on food composition and dietary requirements.The nutritional requirements used in this study are in line with the WHO's recommendations for the median woman of reproductive age. The authors note two key two reasons for this:(1) Requirements fall roughly at the median of the entire population distribution, in the sense that least-cost diets to meet energy and nutrient requirements for people in this reference group approximate the median level of least costs for all sex-age groups over the entire life cycle. This reference group is therefore a good representation of the population as a whole.(2) Women of reproductive age are typically a nutritionally vulnerable population group, as seen in their increased risk of dietary inadequacies (due to social practices and norms that often disadvantage them in terms of access to food), which have important consequences for themselves and their children. Previous studies have also based their analyses on this reference group.

type AgriculturalPolicySupportAgrimonitor2017Dataset 

type AgriculturalPolicySupportAgrimonitor2017Dataset struct {
	ProducerSupportIdbAgrimonitor        *float64 `json:"producer_support_idb_agrimonitor"`
	TotalSupportIdbAgrimonitor           *float64 `json:"total_support_idb_agrimonitor"`
	GeneralServicesSupportIdbAgrimonitor *float64 `json:"general_services_support_idb_agrimonitor"`
}

The IDB Agrimonitor defines these variables as the following.Producer Support (%): The annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm-gate level, arising from policy measures that support agriculture, regardless of their nature, objectives or impacts on farm production or income.Total Support (%): The annual monetary value of all gross transfers from taxpayers and consumers arising from policy measures that support agriculture, net of associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products.General Support Services (%): The annual monetary value of gross transfers to general services provided to agricultural producers collectively (such as research, development, training, inspection, marketing and promotion), arising from policy measures that support agriculture regardless of their nature, objectives and impacts on farm production, income, or consumption. The GSSE does not include any transfers to individual producers.

type AgriculturalTotalFactorProductivityUsdaDataset 

type AgriculturalTotalFactorProductivityUsdaDataset struct {
	Tfp                  *float64 `json:"tfp"`
	Output               *float64 `json:"output"`
	Inputs               *float64 `json:"inputs"`
	AgLandIndex          *float64 `json:"ag_land_index"`
	LaborIndex           *float64 `json:"labor_index"`
	CapitalIndex         *float64 `json:"capital_index"`
	MaterialsIndex       *float64 `json:"materials_index"`
	OutputQuantity       *float64 `json:"output_quantity"`
	CropOutputQuantity   *float64 `json:"crop_output_quantity"`
	AnimalOutputQuantity *float64 `json:"animal_output_quantity"`
	FishOutputQuantity   *float64 `json:"fish_output_quantity"`
	AgLandQuantity       *float64 `json:"ag_land_quantity"`
	LaborQuantity        *float64 `json:"labor_quantity"`
	CapitalQuantity      *float64 `json:"capital_quantity"`
	MachineryQuantity    *float64 `json:"machinery_quantity"`
	LivestockQuantity    *float64 `json:"livestock_quantity"`
	FertilizerQuantity   *float64 `json:"fertilizer_quantity"`
	AnimalFeedQuantity   *float64 `json:"animal_feed_quantity"`
	CroplandQuantity     *float64 `json:"cropland_quantity"`
	PastureQuantity      *float64 `json:"pasture_quantity"`
	IrrigationQuantity   *float64 `json:"irrigation_quantity"`
}

type AgricultureInEngland12701870BankOfEngland2017Dataset 

type AgricultureInEngland12701870BankOfEngland2017Dataset struct {
	TotalArableAndSownAcreage                  *float64 `json:"total_arable_and_sown_acreage"`
	WheatOutput                                *float64 `json:"wheat_output"`
	RyeOutput                                  *float64 `json:"rye_output"`
	BarleyOutput                               *float64 `json:"barley_output"`
	OatsOutput                                 *float64 `json:"oats_output"`
	PulsesOutput                               *float64 `json:"pulses_output"`
	PotatoesOutput                             *float64 `json:"potatoes_output"`
	NumberOfCattle                             *float64 `json:"number_of_cattle"`
	NumberOfSheep                              *float64 `json:"number_of_sheep"`
	NumberOfPigs                               *float64 `json:"number_of_pigs"`
	MilkOutput                                 *float64 `json:"milk_output"`
	BeefOutput                                 *float64 `json:"beef_output"`
	VealOutput                                 *float64 `json:"veal_output"`
	MuttonOutput                               *float64 `json:"mutton_output"`
	PorkOutput                                 *float64 `json:"pork_output"`
	WoolOutput                                 *float64 `json:"wool_output"`
	HidesOutput                                *float64 `json:"hides_output"`
	HayOutput                                  *float64 `json:"hay_output"`
	ShareOfArableOutputInCurrentPrices         *float64 `json:"share_of_arable_output_in_current_prices"`
	ShareOfLivestockOutputInCurrentPrices      *float64 `json:"share_of_livestock_output_in_current_prices"`
	ShareOfArableOutputInConstant1700Prices    *float64 `json:"share_of_arable_output_in_constant_1700_prices"`
	ShareOfLivestockOutputInConstant1700Prices *float64 `json:"share_of_livestock_output_in_constant_1700_prices"`
}

This dataset is produced using ‘A3. Eng. Agriculture 1270-1870’ sheet of the BoE dataset. To maintain consistency, all measurements are converted into either hectares or tonnes. We use the following sources to convert units:

1 Acre = 0.404686 hectare Bushels to tonne (for grain): Agriculture statistics, Govt. of Canada. Available at https://www.gov.mb.ca/agriculture/market-prices-and-statistics/yearbook-and-state-of-agriculture/pubs/crop_conversion_factors.pdf Bushels to tonne (for potatoes): Potato weights & volumes, Northern Plains Potato Growers Association, Minnesota, USA. Available at http://nppga.org/crop_science/measurements.php 1 UK gallon = 0.004546 tonnes. Available at http://convert-to.com/conversion/water-weight-volume/convert-metric-tonne-t-water-weight-to-uk-gal-of-water-volume.html 1 UK ton = 1.01605 tonne 1 Pound = 0.000454 tonne

type AirPollutantEmissionsOecdDataset 

type AirPollutantEmissionsOecdDataset struct {
	CarbonMonoxide                              *float64 `json:"carbon_monoxide"`
	Nox                                         *float64 `json:"nox"`
	NonMethaneVolatileOrganicCompoundsVocs      *float64 `json:"non_methane_volatile_organic_compounds_vocs"`
	Pm10                                        *float64 `json:"pm10"`
	Pm25                                        *float64 `json:"pm25"`
	So2                                         *float64 `json:"so2"`
	CarbonMonoxideIndex                         *float64 `json:"carbon_monoxide_index"`
	NoxIndex                                    *float64 `json:"nox_index"`
	NonMethaneVolatileOrganicCompoundsVocsIndex *float64 `json:"non_methane_volatile_organic_compounds_vocs_index"`
	Pm10Index                                   *float64 `json:"pm10_index"`
	Pm25Index                                   *float64 `json:"pm25_index"`
	So2Index                                    *float64 `json:"so2_index"`
}

Air pollutant emissions reported for OECD countries, where data is available, measured in tonnes per year. Indexed figures relate to changes since the year 1990 (1990 is assumed equal to 100). A figure lower than 100 indicates a reduction in emissions (e.g. 40 indicates a 60% reduction since 1990). Indexed figures are only available for countries with data extending to 1990.

type AirPollutionByCityFouquetAndDpcc2011Dataset 

type AirPollutionByCityFouquetAndDpcc2011Dataset struct {
	SmokeFouquetAndDpcc2011                         *float64 `json:"smoke_fouquet_and_dpcc_2011"`
	SuspendedParticulateMatterSpmFouquetAndDpcc2011 *float64 `json:"suspended_particulate_matter_spm_fouquet_and_dpcc_2011"`
}

Raw data and extension of this set to 2016 was generously supplied through personal communication with the author.Data trends for Delhi have a shorter coverage, and are not available post-2010 (later measurements are now reported based on particle size rather than total suspended particulate matter).

type AirPollutionSourcesInTheUkDefraDataset 

type AirPollutionSourcesInTheUkDefraDataset struct {
	EnergyProduction                      *float64 `json:"energy_production"`
	ManufacturingIndustryAndConstruction  *float64 `json:"manufacturing_industry_and_construction"`
	RoadTransport                         *float64 `json:"road_transport"`
	NonRoadTransport                      *float64 `json:"non_road_transport"`
	SmallNonRoadMobileSourcesAndMachinery *float64 `json:"small_non_road_mobile_sources_and_machinery"`
	MilitaryAircraftAndNavalShipping      *float64 `json:"military_aircraft_and_naval_shipping"`
	FugitiveEmissions                     *float64 `json:"fugitive_emissions"`
	IndustrialProcesses                   *float64 `json:"industrial_processes"`
	Agriculture                           *float64 `json:"agriculture"`
	Waste                                 *float64 `json:"waste"`
	Other                                 *float64 `json:"other"`
	Total                                 *float64 `json:"total"`
}

Data refers to the national total emissions of specific air pollutants by source, as reported by DEFRA. 'Small non-road mobile sources & machinery' refers to mobile machinery and sources such as residential, commercial, agricultural and fishery machinery.

type AirTravelTripsPerCapitaAirbus2019Dataset 

type AirTravelTripsPerCapitaAirbus2019Dataset struct {
	AirTravelTripsPerCapita *float64 `json:"air_travel_trips_per_capita"`
}

type AirlineHijackingAviationSafetyNetworkDataset 

type AirlineHijackingAviationSafetyNetworkDataset struct {
	NumberOfAirlineHijackingsAviationSafetyNetwork       *float64 `json:"number_of_airline_hijackings_aviation_safety_network"`
	FatalitiesDueToAirlineHijackingAviationSafetyNetwork *float64 `json:"fatalities_due_to_airline_hijacking_aviation_safety_network"`
}

Number of hijackings, fatalities of hijacking

type AlcoholConsumptionByTypeSince1890AlexanderAndHolmes2017Dataset 

type AlcoholConsumptionByTypeSince1890AlexanderAndHolmes2017Dataset struct {
	WinePercTotalAlcohol    *float64 `json:"wine_perc_total_alcohol"`
	BeerPercTotalAlcohol    *float64 `json:"beer_perc_total_alcohol"`
	SpiritsPercTotalAlcohol *float64 `json:"spirits_perc_total_alcohol"`
}

Breakdown of alcohol consumption by type (wine, beer and spirits) in select high-income countries where data is available. This is presented as each beverage's share of total pure alcohol consumption.The original paper presents these trends as 5-year averages (e.g. 1980–1984). In this data we allocate a single year to each 5-year average – the last year of the period (1984 in this case).

type AlcoholConsumptionInUsaSince1850NiaaaDataset 

type AlcoholConsumptionInUsaSince1850NiaaaDataset struct {
	BeerNiaaa         *float64 `json:"beer_niaaa"`
	WineNiaaa         *float64 `json:"wine_niaaa"`
	SpiritsNiaaa      *float64 `json:"spirits_niaaa"`
	AllBeveragesNiaaa *float64 `json:"all_beverages_niaaa"`
}

Liters of ethanol per capita (differentiated by beverage type), based on population aged 15 and older prior to 1970 and on population aged 14 and older thereafter.The period 1920–1933 marks a period of alcohol prohibition in the United States. Here it is assumed consumption was zero by the NIAAA.Data prior to 1977 are from: HYMAN, M.; ZIMMERMAN, M.; GURIOLI, C.; and HELRICH, A. Drinkers, Drinking and Alcohol–Related Mortality and Hospitalizations: A Statistical Compendium, 1980 Edition. New Brunswick, NJ: Rutgers University, 1980.

type AlcoholConsumptionSince1890AlexanderAndHolmes2017Dataset 

type AlcoholConsumptionSince1890AlexanderAndHolmes2017Dataset struct {
	AlcoholConsumptionSince1890AlexanderAndHolmes2017 *float64 `json:"alcohol_consumption_since_1890_alexander_and_holmes_2017"`
}

Average per capita alcohol consumption in select high-income countries from 1890 to 2014. This is given as the average per capita level of consumption (not level per average adult); it may therefore not be directly comparable with modern statistics of per capita consumption for populations 15+ years.Estimates in the original paper are given for particular decades e.g. "1920s", "1940s". For this dataset we have allocated this figure to the first year in this period e.g. 1920 and 1940.

type AlcoholExpenditureInTheUsaLongTermUsda2018Dataset 

type AlcoholExpenditureInTheUsaLongTermUsda2018Dataset struct {
	LiquorStoresAtHomeUsda2018             *float64 `json:"liquor_stores_at_home_usda_2018"`
	FoodStoresAtHomeUsda2018               *float64 `json:"food_stores_at_home_usda_2018"`
	OtherAtHomeUsda2018                    *float64 `json:"other_at_home_usda_2018"`
	TotalAtHomeUsda2018                    *float64 `json:"total_at_home_usda_2018"`
	RestaurantsAndBarsAwayFromHomeUsda2018 *float64 `json:"restaurants_and_bars_away_from_home_usda_2018"`
	HotelsAndMotelsAwayFromHomeUsda2018    *float64 `json:"hotels_and_motels_away_from_home_usda_2018"`
	AllOtherAwayFromHomeUsda2018           *float64 `json:"all_other_away_from_home_usda_2018"`
	TotalAwayFromHome                      *float64 `json:"total_away_from_home"`
	TotalAlcoholExpenditureUsda2018        *float64 `json:"total_alcohol_expenditure_usda_2018"`
}

Data is sourced from the USDA ERS dataset 'Nominal food expenditures, with taxes and tips, from previously-published estimates', available at: https://www.ers.usda.gov/data-products/food-expenditures.aspxFigures are measured in constant 1998 US$. Values also include taxes and tips.

type AnnualShareOfCo2EmissionsOwidBasedOnGcp2017Dataset 

type AnnualShareOfCo2EmissionsOwidBasedOnGcp2017Dataset struct {
	AnnualShareOfCo2EmissionsOwidBasedOnGcp2017 *float64 `json:"annual_share_of_co2_emissions_owid_based_on_gcp_2017"`
}

Each country's share of global carbon dioxide (CO₂) has been calculated by Our World in Data based on annual national emissions data published by the Global Carbon Project (GCP). This is calculated as each country's share of the sum of all country emissions; this does not include international aviation and shipping ('bunkers') and 'statistical differences'.Raw emissions data was from tonnes of carbon to tonnes of carbon dioxide (CO₂) using a conversion factor of 3.664.Archived data is held at the Carbon Dioxide Information Analysis Centre (CDIAC). Reference: Tom Boden and Bob Andres (Oak Ridge National Laboratory); Gregg Marland (Appalachian State University). Available at: http://cdiac.ornl.gov/

type AnnualWorldPopulationGrowthRateOwidDataset 

type AnnualWorldPopulationGrowthRateOwidDataset struct {
	AnnualPopulationGrowthRateOwid *float64 `json:"annual_population_growth_rate_owid"`
}

type AntibioticUseInLivestock2030BoeckelEtAl2017Dataset 

type AntibioticUseInLivestock2030BoeckelEtAl2017Dataset struct {
	GlobalAntibioticUse *float64 `json:"global_antibiotic_use"`
}

Boeker et al. (2017) project global antibiotic use under business-as-usual antibiotic concentrations and projected meat consumption.

The authors also project a number of potential reduction scenarios: - a global limit of 50 milligrams of antibiotic use per kilogram of meat production. - a limit of 50 milligrams of antibiotic use per kilogram of meat production for OECD countries & China. - global per capita meat consumption of 40 grams per day (the equivalent of an average burger patty) - global per capita meat consumption of 165 grams per day (the projected EU average in 2030)

Projected global antibiotic use for livestock are shown for each of these potential reduction scenarios.

type AntibioticUseInLivestockEuropeanCommissionAndVanBoeckelEtAlDataset 

type AntibioticUseInLivestockEuropeanCommissionAndVanBoeckelEtAlDataset struct {
	AntibioticUseInLivestock *float64 `json:"antibiotic_use_in_livestock"`
}

Data on antibiotic use in livestock was derived from multiple sources.Antibiotic use across Europe is typically well-reported by the European Commission, allowing for the use of time-series trends. Time-series data from 2010-2015 for European countries was therefore sourced from: European Medicines Agency, European Surveillance of Veterinary Antimicrobial Consumption, 2017.‘Sales of veterinary antimicrobial agents in 30 European countries in 2015’. (EMA/184855/2017). Available at: http://www.ema.europa.eu/docs/en_GB/document_library/Report/2017/10/WC500236750.pdf.Additional data for the United Kingdom in 2016 was sourced from: UK-VARSS 2016; UK – Veterinary Antibiotic Resistanceand Sales Surveillance Report (2017). Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/655403/_1274590_VARSS_2016_report.PDFGlobal-level estimates by country for the year 2010 were published by: Van Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., ... & Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences, 112(18), 5649-5654. Available at: http://www.pnas.org/content/112/18/5649.full.pdfFull data from Boeckel et al. (2015) is available by the Center for Disease Dynamics, Economics & Policy (CDDEP). Available at: https://resistancemap.cddep.org/AnimalUse.phpAntibiotic use is normalised to the average consumption per kilogram of meat production. This correction for livestock population sizes and types is termed 'population-corrected unit' (PCU). Data is reported as the milligrams of total antibiotic ingredient per PCU (mg/PCU), which translates to mg per kilogram of meat production (mg/kg meat).

type ArableLandPerCropOutputPinFao2019Dataset 

type ArableLandPerCropOutputPinFao2019Dataset struct {
	ArableLandPerCropOutputPinFao2019 *float64 `json:"arable_land_per_crop_output_pin_fao_2019"`
}

This metric measures the index of arable land needed to produce a fixed quantity of crops (where values in 1961 are equal to 1.0). This is calculated as arable land divided by the crop production index (PIN). The crop production index (PIN) here is the sum of crop commodities produced (after deductions of quantities used as seed and feed). It is weighted by the commodity prices. Values for arable land use and the crop production index (PIN) is sourced from the UN Food and Agriculture Organization (FAO).

type ArchaeologicalLandUseStephensEtAl2019Dataset 

type ArchaeologicalLandUseStephensEtAl2019Dataset struct {
	CumSumRegions               *float64 `json:"cum_sum_regions"`
	GlobalShareLandUseExtent    *float64 `json:"global_share_land_use_extent"`
	Foraging                    *float64 `json:"foraging"`
	ExtensiveAgriculture        *float64 `json:"extensive_agriculture"`
	IntensiveAgriculture        *float64 `json:"intensive_agriculture"`
	Pastoralism                 *float64 `json:"pastoralism"`
	Urban                       *float64 `json:"urban"`
	ForagingOnsetDate           *float64 `json:"foraging_onset_date"`
	ForagingDeclineDate         *float64 `json:"foraging_decline_date"`
	ExtensiveAgricultureOnset   *float64 `json:"extensive_agriculture_onset"`
	ExtensiveAgricultureDecline *float64 `json:"extensive_agriculture_decline"`
	PastoralismOnset            *float64 `json:"pastoralism_onset"`
	PastoralismDecline          *float64 `json:"pastoralism_decline"`
	IntensiveAgricultureOnset   *float64 `json:"intensive_agriculture_onset"`
	IntensiveAgricultureDecline *float64 `json:"intensive_agriculture_decline"`
	UrbanOnset                  *float64 `json:"urban_onset"`
	UrbanDecline                *float64 `json:"urban_decline"`
}

The authors present a global assessment of archaeological expert knowledge on land use from 10,000 years before the present (yr B.P.) to 1850 CE. To assess and integrate archaeological knowledge toward synthesis at a global scale, the ArchaeoGLOBE Project used a crowdsourcing approach. Archaeologists with land-use expertise were invited to contribute to a detailed questionnaire describing levels of land-use knowledge at 10 time intervals across 146 regional analytical units covering all continents except Antarctica. Contributors selected individual regions where they had expertise; 255 individual archaeologists completed a total of 711 regional questionnaires, resulting in complete, though uneven, global coverage. The result is an expert-based meta-analysis that uses semi-subjective (ranked) survey data to generate regional assessments of land use over time.They map the dating and extent of four 'agricultural' regimes across the world:1) Foraging: Foraging/hunting/gathering/fishing - subsistence based on hunting wild animals, gathering wild plants, and fishing, without deliberately modifying the reproduction of plants and animals that people exploit.2) Pastoralism: the exploitation of pasturelands for animal husbandry - including the breeding, care, and use of domesticated herd animals (e.g., sheep, goats, camels, cattle, horses, llamas, reindeer, and yaks).3) Extensive agriculture: swidden/shifting cultivation and other forms of noncontinuous cultivation.4) Intensive agriculture: all other forms of continuous cultivation (including irrigated and nonirrigated annual cropping, tropical agroforestry, flooded field farming, and industrial monocrop/plantation agriculture).

type ArmedForcesPersonnelAsAShareOfTheTotalPopulationOwidBasedOnWorldBankDataset 

type ArmedForcesPersonnelAsAShareOfTheTotalPopulationOwidBasedOnWorldBankDataset struct {
	ArmedForcesPersonnelIissViaWorldBank2017               *float64 `json:"armed_forces_personnel_iiss_via_world_bank_2017"`
	TotalPopulationWorldBank2017                           *float64 `json:"total_population_world_bank_2017"`
	ArmedForcesPersonnelPercOfTotalPopulationWorldBank2017 *float64 `json:"armed_forces_personnel_perc_of_total_population_world_bank_2017"`
}

This dataset has been constructed by Our World in Data based on two variables.– Armed forces personnel as published by the International Institute for Strategic Studies - The Military Balance. This is made available via the World Bank World Development Indicators: http://data.worldbank.org/data-catalog/world-development-indicators– The Our World in Data population series. Details on how this is constructed are available here: https://ourworldindata.org/population-sourcesArmed forces as a share of total population is then calculated by dividing total armed forces personnel by population and multiplying by 100. The input and output files, and script used to calculate this data is available on GitHub here: https://github.com/owid/notebooks/tree/main/HannahRitchie/armed-forces-----Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces.Limitations and exceptions: Data excludes personnel not on active duty, therefore it underestimates the share of the labor force working for the defense establishment. The cooperation of governments of all countries listed in “The Military Balance” has been sought by IISS and, in many cases, received. However, some data in “The Military Balance” is estimated.Statistical concept and methodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The IISS collects the data from a wide variety of sources. The numbers are based on the most accurate data available to, or on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The current WDI indicator includes active armed forces and active paramilitary (but not reservists). Armed forces personnel comprise all servicemen and women on full-time duty, including conscripts and long-term assignments from the Reserves (“Reserve” describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency). The indicator includes paramilitary forces. The source of the data (IISS) reports armed forces and paramilitary forces separately, however these figures are added for the purpose of computing this series. Home Guard units are counted as paramilitary.

type AttainableYieldsMuellerEtAl2012Dataset 

type AttainableYieldsMuellerEtAl2012Dataset struct {
	BarleyAttainable    *float64 `json:"barley_attainable"`
	CassavaAttainable   *float64 `json:"cassava_attainable"`
	CottonAttainable    *float64 `json:"cotton_attainable"`
	GroundnutAttainable *float64 `json:"groundnut_attainable"`
	MaizeAttainable     *float64 `json:"maize_attainable"`
	MilletAttainable    *float64 `json:"millet_attainable"`
	OilpalmAttainable   *float64 `json:"oilpalm_attainable"`
	PotatoAttainable    *float64 `json:"potato_attainable"`
	RapeseedAttainable  *float64 `json:"rapeseed_attainable"`
	RiceAttainable      *float64 `json:"rice_attainable"`
	RyeAttainable       *float64 `json:"rye_attainable"`
	SorghumAttainable   *float64 `json:"sorghum_attainable"`
	SoybeanAttainable   *float64 `json:"soybean_attainable"`
	SugarbeetAttainable *float64 `json:"sugarbeet_attainable"`
	SugarcaneAttainable *float64 `json:"sugarcane_attainable"`
	SunflowerAttainable *float64 `json:"sunflower_attainable"`
	WheatAttainable     *float64 `json:"wheat_attainable"`
	WheatYieldGap       *float64 `json:"wheat_yield_gap"`
	BarleyYieldGap      *float64 `json:"barley_yield_gap"`
	RyeYieldGap         *float64 `json:"rye_yield_gap"`
	MilletYieldGap      *float64 `json:"millet_yield_gap"`
	SorghumYieldGap     *float64 `json:"sorghum_yield_gap"`
	MaizeYieldGap       *float64 `json:"maize_yield_gap"`
	CassavaYieldGap     *float64 `json:"cassava_yield_gap"`
	SoybeansYieldGap    *float64 `json:"soybeans_yield_gap"`
	RapeseedYieldGap    *float64 `json:"rapeseed_yield_gap"`
	SugarbeetYieldGap   *float64 `json:"sugarbeet_yield_gap"`
	SugarcaneYieldGap   *float64 `json:"sugarcane_yield_gap"`
	PotatoYieldGap      *float64 `json:"potato_yield_gap"`
	OilpalmYieldGap     *float64 `json:"oilpalm_yield_gap"`
	GroundnutYieldGap   *float64 `json:"groundnut_yield_gap"`
	RiceYieldGap        *float64 `json:"rice_yield_gap"`
	SunflowerYieldGap   *float64 `json:"sunflower_yield_gap"`
	CottonYieldGap      *float64 `json:"cotton_yield_gap"`
}

Attainable yields are estimates of feasible crop yields calculated from high-yielding areas of similar climate. They are more conservative than biophysical ‘potential yields’, but should be achievable using current technologies and management (e.g. fertilizers and irrigation). Attainable yields are based on assessments for the year 2000. Attainable yield pre-2000 may be lower; and post-2000 may be higher than these values.Yield gaps have been calculated by Our World in Data as the difference between actual observed yields (as reported by the UN FAO: http://www.fao.org/faostat/en/) and attainable yields, as reported by Mueller et al. (2012).

type AttitudesToVaccinesWellcomeTrust2019Dataset 

type AttitudesToVaccinesWellcomeTrust2019Dataset struct {
	ShareOfPeopleWhoDisagreeVaccinesAreImportantForChildrenToHave *float64 `json:"share_of_people_who_disagree_vaccines_are_important_for_children_to_have"`
	ShareOfPeopleWhoAgreeVaccinesAreSafe                          *float64 `json:"share_of_people_who_agree_vaccines_are_safe"`
	ShareOfPeopleWhoDisagreeVaccinesAreSafe                       *float64 `json:"share_of_people_who_disagree_vaccines_are_safe"`
	ShareOfPeopleWhoAgreeVaccinesAreEffective                     *float64 `json:"share_of_people_who_agree_vaccines_are_effective"`
	ShareOfPeopleWhoDisagreeVaccinesAreEffective                  *float64 `json:"share_of_people_who_disagree_vaccines_are_effective"`
	ShareOfPeopleWhoAgreeVaccinesAreImportantForChildrenToHave    *float64 `json:"share_of_people_who_agree_vaccines_are_important_for_children_to_have"`
}

The Wellcome Global Monitor is the world’s largest study into how people around the world think and feel about science and major health challenges. It surveys over 140,000 people from more than 140 countries. Survey respondents were asked:(1) Do you strongly or somewhat agree, strongly or somewhat disagree or neither agree nor disagree with the following statements? 'Vaccines are important for children to have'.(2) Do you strongly or somewhat agree, strongly or somewhat disagree or neither agree nor disagree with the following statement? 'Vaccines are safe.'(3) Do you strongly or somewhat agree, strongly or somewhat disagree or neither agree nor disagree with the following statement? 'Vaccines are effective'.For each question we have calculated the "share of people who agree" to be the sum of those who "strongly agree" and "somewhat agree"; and the "share of people who disagree" to be the sum of those who "strongly disagree" and "somewhat disagree".

type AverageHarmonisedLearningOutcomeScore20052015AltinokAngristAndPatrinos2018Dataset 

type AverageHarmonisedLearningOutcomeScore20052015AltinokAngristAndPatrinos2018Dataset struct {
	AverageHarmonisedLearningOutcomeScoreIn2005_2015AltinokAngristAndPatrinos2018 *float64 `json:"average_harmonised_learning_outcome_score_in_2005_2015_altinok_angrist_and_patrinos_2018"`
	YearOfTheLatestLearningOutcomeScoreAltinokAngristAndPatrinos2018              *float64 `json:"year_of_the_latest_learning_outcome_score_altinok_angrist_and_patrinos_2018"`
}

This dataset covers countries over 2005–2015. The globally comparable achievement outcomes were constructed by linking standardized, psychometrically-robust international and regional achievement tests, including: <a href="https://nces.ed.gov/timss/" rel="noopener" target="_blank">TIMSS</a>, <a href="https://www.iea.nl/pirls" rel="noopener" target="_blank">PIRLS</a>, <a href="http://www.oecd.org/pisa/" rel="noopener" target="_blank">PISA</a>, <a href="https://www.iea.nl/fims" rel="noopener" target="_blank">FIMS</a>, <a href="https://www.iea.nl/fiss" rel="noopener" target="_blank">FISS</a>, <a href="https://www.capita-sims.co.uk/products-and-services/sims-assessment" rel="noopener" target="_blank">SIMS</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/SISS" rel="noopener" target="_blank">SISS</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/Six_Subject_Survey/SSS_Reading" rel="noopener" target="_blank">SRC</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/RLS" rel="noopener" target="_blank">RLS</a>, <a href="https://www.unicef.org/education/index_achievement.html" rel="noopener" target="_blank">MLA</a>, <a href="https://www.nap.edu/read/9174/chapter/9" rel="noopener" target"_blank">IAEP</a>, <a href="http://www.sacmeq.org/" rel="noopener" target="_blank" >SACMEQ</a>, <a href="https://www.epdc.org/data-about-epdc-data-epdc-learning-outcomes-data/sacmeq-and-pasec" rel="noopener" target="_blank">PASEC</a>, and <a href="http://www.unesco.org/new/en/santiago/education/education-assessment-llece/" rel="noopener" target="_blank">LLECE</a>.

type AverageMonthlyIncomesOrConsumptionByDecileAndQuintilePovcalnet2019Dataset 

type AverageMonthlyIncomesOrConsumptionByDecileAndQuintilePovcalnet2019Dataset struct {
	Welfare      *float64 `json:"welfare"`
	Mean         *float64 `json:"mean"`
	Pop          *float64 `json:"pop"`
	D1avgincome  *float64 `json:"d1avgincome"`
	D2avgincome  *float64 `json:"d2avgincome"`
	D3avgincome  *float64 `json:"d3avgincome"`
	D4avgincome  *float64 `json:"d4avgincome"`
	D5avgincome  *float64 `json:"d5avgincome"`
	D6avgincome  *float64 `json:"d6avgincome"`
	D7avgincome  *float64 `json:"d7avgincome"`
	D8avgincome  *float64 `json:"d8avgincome"`
	D9avgincome  *float64 `json:"d9avgincome"`
	D10avgincome *float64 `json:"d10avgincome"`
	Q1avgincome  *float64 `json:"q1avgincome"`
	Q2avgincome  *float64 `json:"q2avgincome"`
	Q3avgincome  *float64 `json:"q3avgincome"`
	Q4avgincome  *float64 `json:"q4avgincome"`
	Q5avgincome  *float64 `json:"q5avgincome"`
	Flag         *float64 `json:"flag"`
}

type AviationAccidentsAndFatalitiesByFlightPhaseAsn2019Dataset 

type AviationAccidentsAndFatalitiesByFlightPhaseAsn2019Dataset struct {
	EnRouteAccidents       *float64 `json:"en_route_accidents"`
	EnRouteCasualties      *float64 `json:"en_route_casualties"`
	TakeOffAccidents       *float64 `json:"take_off_accidents"`
	TakeOffCasualties      *float64 `json:"take_off_casualties"`
	InitialClimbAccidents  *float64 `json:"initial_climb_accidents"`
	InitialClimbCasualties *float64 `json:"initial_climb_casualties"`
	ApproachAccidents      *float64 `json:"approach_accidents"`
	ApproachFatalities     *float64 `json:"approach_fatalities"`
	LandingAccidents       *float64 `json:"landing_accidents"`
	LandingFatalities      *float64 `json:"landing_fatalities"`
}

The total number of aviation accidents and fatalities by flight phase (take-off, climbing, en-route, approach and landing). The figures include corporate jet and military transport accidents.

type AviationPassengerKilometresAndCo2EmissionsIcctDataset 

type AviationPassengerKilometresAndCo2EmissionsIcctDataset struct {
	PercGlobalDomesticCo2              *float64 `json:"perc_global_domestic_co2"`
	PercGlobalDomesticRpks             *float64 `json:"perc_global_domestic_rpks"`
	PercGlobalInternationalCo2         *float64 `json:"perc_global_international_co2"`
	PercGlobalInternationalRpks        *float64 `json:"perc_global_international_rpks"`
	PercGlobalTotalCo2                 *float64 `json:"perc_global_total_co2"`
	PercGlobalTotalRpks                *float64 `json:"perc_global_total_rpks"`
	DomesticRpksBillions               *float64 `json:"domestic_rpks_billions"`
	DomesticAviationCo2Mt              *float64 `json:"domestic_aviation_co2_mt"`
	InternationalRpksBillions          *float64 `json:"international_rpks_billions"`
	InternationalAviationCo2Mt         *float64 `json:"international_aviation_co2_mt"`
	TotalRpksBillions                  *float64 `json:"total_rpks_billions"`
	TotalAviationCo2Mt                 *float64 `json:"total_aviation_co2_mt"`
	PerCapitaTotalRpks                 *float64 `json:"per_capita_total_rpks"`
	PerCapitaDomesticRpks              *float64 `json:"per_capita_domestic_rpks"`
	PerCapitaInternationalRpks         *float64 `json:"per_capita_international_rpks"`
	PerCapitaAviationCo2               *float64 `json:"per_capita_aviation_co2"`
	PerCapitaDomesticAviationCo2       *float64 `json:"per_capita_domestic_aviation_co2"`
	PerCapitaInternationalAviationCo2  *float64 `json:"per_capita_international_aviation_co2"`
	InboundOutboundFlightRatio         *float64 `json:"inbound_outbound_flight_ratio"`
	PerCapitaInternationalRpksAdjusted *float64 `json:"per_capita_international_rpks_adjusted"`
	PerCapitaInternationalCo2Adjusted  *float64 `json:"per_capita_international_co2_adjusted"`
	PerCapitaTotalRpksAdjusted         *float64 `json:"per_capita_total_rpks_adjusted"`
	PerCapitaAviationCo2Adjusted       *float64 `json:"per_capita_aviation_co2_adjusted"`
}

Data on revenue passenger kilometres (RPKs) and carbon dioxide emissions from aviation are sourced from the International Council on Clean Transportation (ICCT).In its 2018 review of commercial aviation it provides data on RPKs and CO₂ emissions for domestic flights, international flights and total emissions for each country. Under this framework, 'international' RPKs and emissions are allocated to the <b>country of departure</b>.Our World in Data has calculated per capita RPKs and aviation emissions by dividing these values by population figures in 2018 for each country, sourced from the UN World Population Prospects: https://population.un.org/wpp/These per capita footprints do not necessarily reflect the amount of travel by locals in a given location. This is especially true if a country has high tourist volumes: much of the departures will result from travellers rather than local residents. We have therefore also calculated 'adjusted' aviation footprints by multiplying per capita international RPKs and CO2 emissions by an adjustment factor.This adjustment factor is taken as the ratio between inbound and outbound arrivals, sourced from the World Bank: https://datacatalog.worldbank.org/dataset/world-development-indicators.This adjustment methodology was also featured in analysis by the ICCT here: https://theicct.org/blog/staff/not-every-tonne-of-aviation-CO2

type BasicReadingAndMathsSkillsWorldDevelopmentReport2018Dataset 

type BasicReadingAndMathsSkillsWorldDevelopmentReport2018Dataset struct {
	PercentOfStudentsWhoCouldNotReadASingleWordOfAShortText *float64 `json:"percent_of_students_who_could_not_read_a_single_word_of_a_short_text"`
}

The World Development Report (2018) includes the following information regarding sources: "WDR 2018 team, using reading and mathematics data for Kenya and Uganda from Uwezo, Annual Assessment Reports, 2015 (www.uwezo.net/); reading and mathematics data for rural India from ASER Centre (2017); reading data for all other countries from U.S. Agency for International Development (USAID), Early Grade Reading Barometer, 2017, accessed May 30, 2017 (www.earlygradereadingbarometer.org/); and mathematics data for all other countries from USAID/RTI Early Grade Mathematics Assessment intervention reports, 2012–15 (shared.rti.org/sub-topic/early-grade-math-assessment-egma)."The original source notes: "These data typically pertain to selected regions in the countries and are not necessarily nationally representative. Data for India pertain to rural areas."

type BiodiversityHabitatLossWilliamsEtAl2021Dataset 

type BiodiversityHabitatLossWilliamsEtAl2021Dataset struct {
	AllSpeciesNumber               *float64 `json:"all_species_number"`
	BauHabitatLossAll              *float64 `json:"bau_habitat_loss_all"`
	CombinedHabitatLossAll         *float64 `json:"combined_habitat_loss_all"`
	DietsHabitatLossAll            *float64 `json:"diets_habitat_loss_all"`
	WasteHabitatLossAll            *float64 `json:"waste_habitat_loss_all"`
	TradeHabitatLossAll            *float64 `json:"trade_habitat_loss_all"`
	YieldsHabitatLossAll           *float64 `json:"yields_habitat_loss_all"`
	AmphibianSpeciesNumber         *float64 `json:"amphibian_species_number"`
	BauHabitatLossAmphibians       *float64 `json:"bau_habitat_loss_amphibians"`
	CombinedHabitatLossAmphibians  *float64 `json:"combined_habitat_loss_amphibians"`
	DietsHabitatLossAmphibians     *float64 `json:"diets_habitat_loss_amphibians"`
	WasteHabitatLossAmphibians     *float64 `json:"waste_habitat_loss_amphibians"`
	TradeHabitatLossAmphibians     *float64 `json:"trade_habitat_loss_amphibians"`
	YieldsHabitatLossAmphibians    *float64 `json:"yields_habitat_loss_amphibians"`
	BirdSpeciesNumber              *float64 `json:"bird_species_number"`
	BauHabitatLossBirds            *float64 `json:"bau_habitat_loss_birds"`
	CombinedHabitatLossBirds       *float64 `json:"combined_habitat_loss_birds"`
	DietsHabitatLossBirds          *float64 `json:"diets_habitat_loss_birds"`
	WasteHabitatLossBirds          *float64 `json:"waste_habitat_loss_birds"`
	TradeHabitatLossBirds          *float64 `json:"trade_habitat_loss_birds"`
	YieldsHabitatLossBirds         *float64 `json:"yields_habitat_loss_birds"`
	MammalSpeciesNumber            *float64 `json:"mammal_species_number"`
	BauHabitatLossMammals          *float64 `json:"bau_habitat_loss_mammals"`
	CombinedHabitatLossMammals     *float64 `json:"combined_habitat_loss_mammals"`
	DietsHabitatLossMammals        *float64 `json:"diets_habitat_loss_mammals"`
	WasteHabitatLossMammals        *float64 `json:"waste_habitat_loss_mammals"`
	TradeHabitatLossMammals        *float64 `json:"trade_habitat_loss_mammals"`
	YieldsHabitatLossMammals       *float64 `json:"yields_habitat_loss_mammals"`
	BauChangeCroplandKm2           *float64 `json:"bau_change_cropland_km2"`
	CombinedChangeCroplandKm2      *float64 `json:"combined_change_cropland_km2"`
	DietsChangeCroplandKm2         *float64 `json:"diets_change_cropland_km2"`
	YieldsChangeCroplandKm2        *float64 `json:"yields_change_cropland_km2"`
	WasteChangeCroplandKm2         *float64 `json:"waste_change_cropland_km2"`
	TradeChangeCroplandKm2         *float64 `json:"trade_change_cropland_km2"`
	CountriesLosing25Species       *float64 `json:"countries_losing_25_species"`
	BauChangePct                   *float64 `json:"bau_change_pct"`
	CombinedChangePct              *float64 `json:"combined_change_pct"`
	DietsChangePct                 *float64 `json:"diets_change_pct"`
	YieldsChangePct                *float64 `json:"yields_change_pct"`
	WasteChangePct                 *float64 `json:"waste_change_pct"`
	TradeChangePct                 *float64 `json:"trade_change_pct"`
	SpeciesLosingMore25pct         *float64 `json:"species_losing_more_25pct"`
	SpeciesLosingMore50pct         *float64 `json:"species_losing_more_50pct"`
	SpeciesLosingMore75pct         *float64 `json:"species_losing_more_75pct"`
	SpeciesLosingMore90pct         *float64 `json:"species_losing_more_90pct"`
	SpeciesLosingMore25pctYields   *float64 `json:"species_losing_more_25pct_yields"`
	SpeciesLosingMore50pctYields   *float64 `json:"species_losing_more_50pct_yields"`
	SpeciesLosingMore75pctYields   *float64 `json:"species_losing_more_75pct_yields"`
	SpeciesLosingMore90pctYields   *float64 `json:"species_losing_more_90pct_yields"`
	SpeciesLosingMore25pctCombined *float64 `json:"species_losing_more_25pct_combined"`
	SpeciesLosingMore50pctCombined *float64 `json:"species_losing_more_50pct_combined"`
	SpeciesLosingMore75pctCombined *float64 `json:"species_losing_more_75pct_combined"`
	SpeciesLosingMore90pctCombined *float64 `json:"species_losing_more_90pct_combined"`
}

Projected species' habitat loss is projected to 2050 under a business-as-usual, plus five intervention scenarios with changes to diets or agricultural production.Business-as-usual: This assumes population growth from UN medium projections; crop yield increases in line with historical rates of improvement; and dietary changes in line with projected rises in income.Closing yield gaps: Yields increase linearly from current yields to 80% of the estimated maximum potential by 2050. Increasing yields above 80% is rarely achieved over large areas.Halve food waste: consumer food waste and food losses in supply chains are reduced by 25% by 2030 and 50% by 2050.Healthier diets: Diets transition to the EAT-Lancet diet which is in line with healthy calorie and nutritional requirements. For richer countries this would mean a reduction (but not elimination) of meat consumption. For poorer countries, this would mean an increase.Optimize trade: agricultural production and trade is optimized to produce food in the locations with the least risk of habitat loss. Agricultural production shifts from the 25 countries projected to have the greatest mean losses of suitable habitat across all species to countries where less than 10% of species are threatened with extinction.Combined: all four interventions are combined.

type BiomassAndTaxaAbundanceBarOnEtAl2018Dataset 

type BiomassAndTaxaAbundanceBarOnEtAl2018Dataset struct {
	GlobalBiomass        *float64 `json:"global_biomass"`
	AbundanceOfOrganisms *float64 `json:"abundance_of_organisms"`
}

Estimates of the global biomass (measured in tonnes of carbon) and abundance (number of individuals) of different taxonomic groups.Due to large uncertainty for some groups, these are used as order-of-magnitude estimates and come with notable error margins.

type BirthsOutsideOfMarriageDataset 

type BirthsOutsideOfMarriageDataset struct {
	BirthsOutsideOfMarriagePercOfAllBirths *float64 `json:"births_outside_of_marriage_perc_of_all_births"`
}

Data for Australia, Japan, Korea and New Zealand refer to ex-nuptial/out-of-wedlock births, that is, where the child's parents are not registered as married to each other (or, for New Zealand only, in a civil union with each other) at the time of the birth. For all other countries, data refer to births to mothers where the mother's marital status at the time of birth was other than married. For Canada, births to mothers whose marital status is other than married as a proportion of births where the mother's marital status is recorded. In 2017, the mother's marital status was not recorded on 8% of births. For Mexico, births to mothers whose civil status is other than married as a proportion of births where the mother's civil status is recorded. In 2017, the mother's civil status was not recorded on 8% of births. For Israel, data refer to births to unmarried Jewish women as a proportion of all births to Jewish women, only.

type BooksBuringhAndVanZanden2009Dataset 

type BooksBuringhAndVanZanden2009Dataset struct {
	ManuscriptProductionPerCentury         *float64 `json:"manuscript_production_per_century"`
	ProductionOfPrintedBooksPerHalfCentury *float64 `json:"production_of_printed_books_per_half_century"`
	AnnualPerCapitaConsumptionOfBooks      *float64 `json:"annual_per_capita_consumption_of_books"`
}

type BourguignonAndMorrison2002AndWorldBankPovcalnet2015Dataset 

type BourguignonAndMorrison2002AndWorldBankPovcalnet2015Dataset struct {
	LessThan190moneyPerDayWorldBank2015 *float64 `json:"less_than_190money_per_day_world_bank_2015"`
	ExtremePovertyBm2002                *float64 `json:"extreme_poverty_bm_2002"`
	PovertyBm2002                       *float64 `json:"poverty_bm_2002"`
}

The share of people of living in poverty and extreme poverty is taken from Bourguignon and Morrison (2002), and ‘the poverty lines were calibrated so that poverty and extreme poverty headcounts in 1992 coincided roughly with estimates from other sources’. And in footnote they say ‘these definitions correspond to poverty lines equal to consumption per capita of $2 and $1 a day, expressed in 1985 PPP.’

To this I added the share of people living living below the international poverty line which, since the revision in 2015, is $1.90 at 2011 purchasing-power parity (PPP). The revisions in the definition of the poverty line and the PPP adjustment make the poverty figures in levels not comparable to earlier data – to illustrate this I have plotted both series for the time from 1981 to 1992. The World Bank data was downloaded in October 2015.

type CaloriesLostByFoodGroupAndRegionWri2013Dataset 

type CaloriesLostByFoodGroupAndRegionWri2013Dataset struct {
	ShareOfCaloriesLostOrWastedByFoodGroupWri2013 *float64 `json:"share_of_calories_lost_or_wasted_by_food_group_wri_2013"`
	ShareOfGlobalFoodLossAndWasteByRegionWri2013  *float64 `json:"share_of_global_food_loss_and_waste_by_region_wri_2013"`
	PerCapitaFoodLossOrWasteKcalPerDaywri2013     *float64 `json:"per_capita_food_loss_or_waste_kcal_per_daywri_2013"`
}

Data on food losses and waste includes all supply chain stages, from (and including) on-farm harvesting through to final food waste.Figures are based only on food intended for direct human consumption: this means crops diverted for animal feed or industrial uses are not included as losses.The original authors of this study – Lipinski et al. (2013) of the World Resources Institute – calculate these figures based on the regional percentage losses expressed in the landmark UN FAO report on food losses: FAO (2011). Global food losses and food waste – Extent, causes and prevention. Rome.This data is expressed for the year 2009 due to poor data availability and updated estimates since then. It is likely that, especially when expressed in relative terms (each region or food group's contribution to food losses) remains similar today.

type CancerDeathRatesInTheUsOverTheLongTermAmericanCancerSocietyDataset 

type CancerDeathRatesInTheUsOverTheLongTermAmericanCancerSocietyDataset struct {
	StomachMale           *float64 `json:"stomach_male"`
	ColonAndRectumMale    *float64 `json:"colon_and_rectum_male"`
	LiverMale             *float64 `json:"liver_male"`
	PancreasMale          *float64 `json:"pancreas_male"`
	LungAndBronchusMale   *float64 `json:"lung_and_bronchus_male"`
	ProstateMale          *float64 `json:"prostate_male"`
	LeukemiaMale          *float64 `json:"leukemia_male"`
	StomachFemale         *float64 `json:"stomach_female"`
	ColonAndRectumFemale  *float64 `json:"colon_and_rectum_female"`
	PancreasFemale        *float64 `json:"pancreas_female"`
	LungAndBronchusFemale *float64 `json:"lung_and_bronchus_female"`
	BreastFemale          *float64 `json:"breast_female"`
	UterusFemale          *float64 `json:"uterus_female"`
	LiverFemale           *float64 `json:"liver_female"`
}

Figures are based on long-term death rates from various cancer types (since 1930) in the United States for males and females. These death rates are age-standardized to the US population structure for males and females in the year 2000. Death rates are measured per 100,000 individuals.

type CancerDeathsGroupedOwidBasedOnIhmeDataset 

type CancerDeathsGroupedOwidBasedOnIhmeDataset struct {
	LipAndOralCavityCancerDeaths        *float64 `json:"lip_and_oral_cavity_cancer_deaths"`
	OtherPharynxCancerDeaths            *float64 `json:"other_pharynx_cancer_deaths"`
	EsophagealCancerDeaths              *float64 `json:"esophageal_cancer_deaths"`
	StomachCancerDeaths                 *float64 `json:"stomach_cancer_deaths"`
	ColonAndRectumCancerDeaths          *float64 `json:"colon_and_rectum_cancer_deaths"`
	LiverCancerDeaths                   *float64 `json:"liver_cancer_deaths"`
	GallbladderCancerDeaths             *float64 `json:"gallbladder_cancer_deaths"`
	PancreaticCancerDeaths              *float64 `json:"pancreatic_cancer_deaths"`
	LarynxCancerDeaths                  *float64 `json:"larynx_cancer_deaths"`
	TrachealBronchusAndLungCancerDeaths *float64 `json:"tracheal_bronchus_and_lung_cancer_deaths"`
	BreastCancerDeaths                  *float64 `json:"breast_cancer_deaths"`
	CervicalCancerDeaths                *float64 `json:"cervical_cancer_deaths"`
	OvarianCancerDeaths                 *float64 `json:"ovarian_cancer_deaths"`
	ProstateCancerDeaths                *float64 `json:"prostate_cancer_deaths"`
	KidneyCancerDeaths                  *float64 `json:"kidney_cancer_deaths"`
	BladderCancerDeaths                 *float64 `json:"bladder_cancer_deaths"`
	BrainAndNervousSystemCancerDeaths   *float64 `json:"brain_and_nervous_system_cancer_deaths"`
	NonHodgkinLymphomaDeaths            *float64 `json:"non_hodgkin_lymphoma_deaths"`
	LeukemiaDeaths                      *float64 `json:"leukemia_deaths"`
	OtherCancersDeaths                  *float64 `json:"other_cancers_deaths"`
}

Total annual number of deaths from cancers (termed 'Neoplasms' within the IHME, Global Burden of Disease Study). This measures cancer deaths across both sexes and all ages.

Smaller categories of cancer types have been grouped by Our World in Data into a collective category 'Other cancers'. This grouping was set based on cancer types with global annual deaths in 2016 under 100,000. This includes testicular, Hodgkin lymphoma, mesothelioma, thyroid, non-melanoma skin cancer, nasopharynx, malignant skin melanoma, uterine cancer, and multiple myeloma.

type CapitalCityPopulationUnUrbanizationProspects2018Dataset 

type CapitalCityPopulationUnUrbanizationProspects2018Dataset struct {
	CapitalCityPopulationUnUrbanizationProspects2018 *float64 `json:"capital_city_population_un_urbanization_prospects_2018"`
}

Data represents the estimated population of a given country's capital city.Where a country has multiple capital cities, here we have opted to show the population of the Administrative or Constitutional Capital (as opposed to the Seat of Government, or otherwise).

type CarbonIntensityKgco2moneyMadissonWorldBankCdiacDataset 

type CarbonIntensityKgco2moneyMadissonWorldBankCdiacDataset struct {
	CarbonIntensityKgco2moneyCdiac2017 *float64 `json:"carbon_intensity_kgco2money_cdiac_2017"`
}

Carbon intensity was calculated by dividing total national CO2 emissions by gross domestic product (2011 int-$ PPP) for a given year. These figures are presented in kgCO2 per international-$ PPP.

Annual CO2 emissions data was derived from the Carbon Dioxide Information Analysis Center (CDIAC): http://cdiac.ornl.gov/CO2_Emission/ (accessed on 20th April 2017). CDIAC generates estimates of CO2 emissions from fossil-fuel consumption and cement production at global and national levels, dating historically to 1751.

GDP figures are presented in 2011 international-$ PPP based on backward extension of World Bank and Maddison datasets. The data presented here from 1990 onwards is from the World Bank. It is total global and national GDP in 2011 international-$ as published here: http://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD (accessed on 20th April 2017). Data earlier than 1990 is backwards extended from the World Bank observation for 1990 based on the growth rates implied by Maddison data. The Maddison data is published here: http://www.ggdc.net/maddison/oriindex.htm

type CausesOfChildMortalityIhmeGlobalBurdenOfDiseaseStudy2017Dataset 

type CausesOfChildMortalityIhmeGlobalBurdenOfDiseaseStudy2017Dataset struct {
	ShareOfChildrenDyingInFirst5YearsGloballyByCauseIhme2017 *float64 `json:"share_of_children_dying_in_first_5_years_globally_by_cause_ihme_2017"`
	DeathsIhme2017                                           *float64 `json:"deaths_ihme_2017"`
}

Estimates refer to both sexes and to the global level.

All causes for which IHME published an estimate of 0 were removed from the dataset.

Ebola as a cause of death was only included in 2015 by IHME. We have assumed 0 deaths for Ebola in 1990.

type CausesOfDeathVsMediaCoverageShenDataset 

type CausesOfDeathVsMediaCoverageShenDataset struct {
	ShareOfDeaths                *float64 `json:"share_of_deaths"`
	ShareOfGoogleSearches        *float64 `json:"share_of_google_searches"`
	ShareOfNytMediaCoverage      *float64 `json:"share_of_nyt_media_coverage"`
	ShareOfGuardianMediaCoverage *float64 `json:"share_of_guardian_media_coverage"`
}

Data was compiled and published by Owen Shen, tallying comparisons between the share of deaths, google searches and media coverage across a range of causes of death.This includes the top 10 largest causes of mortality, as well as terrorism, overdoses, and homicides (the three additional causes of death which get significant media coverage). These are normalised to be given as deaths, Google searches and media coverage as a share of the total reported across these 13 causes (which does not mean the share of deaths or coverage of all causes of death).The values given represent their relative share, rather than absolute counts. This allows for comparison of the proportionality in representation across different sources.Mortality data was sourced from the Center for Disease Control and Prevention (CDC): https://wonder.cdc.gov/Google searches from Google Trends: https://github.com/GeneralMills/pytrendsThe Guardian's article database: http://open-platform.theguardian.com/The New York Times' database: https://developer.nytimes.com/

type CausesOfDeathVsMediaCoverageShenEtAl2018Dataset 

type CausesOfDeathVsMediaCoverageShenEtAl2018Dataset struct {
	ShareOfDeathsShenEtAl2018                *float64 `json:"share_of_deaths_shen_et_al_2018"`
	ShareOfGoogleSearchesShenEtAl2018        *float64 `json:"share_of_google_searches_shen_et_al_2018"`
	ShareOfNytMediaCoverageShenEtAl2018      *float64 `json:"share_of_nyt_media_coverage_shen_et_al_2018"`
	ShareOfGuardianMediaCoverageShenEtAl2018 *float64 `json:"share_of_guardian_media_coverage_shen_et_al_2018"`
}

Shen et al. (2018) compared the leading causes of death in the United States as their share of total deaths relative to Google searches and media coverage in The New York Times (NYT) and The Guardian newspaper. For this analysis they selected the top 10 causes of death in the USA in addition to terrorism, homicide, and drug overdoses (which they assumed to also receive significant media attention).Data each causes' share of total deaths in the USA was assessed based on the Centers for Disease Control and Prevention (CDC) WONDER database for public health, available at: https://wonder.cdc.gov/. This is available from 1999 to 2016. Combined, the 13 causes of death assessed in this analysis account for approximately 88% of all deaths in the USA.Data on Google searches was derived from Google Trends (available from 2004 to 2016). This was assessed on the number of searches for these terms and close synonyms.The New York Times and The Guardian media coverage was assessed from both newspapers' article databases. Here the authors searched the database for a list of all articles which contained the word anywhere (headline or body).All values are normalized to 100% so they represent their relative share of the top causes, rather than absolute counts (e.g. ‘deaths’ represents each causes’ share of deaths within the 13 categories shown rather than total deaths). This allows for us to compare the relative representation of different sources.Full methodology, notes and open-access data on GitHub are available from the original source: https://owenshen24.github.io/charting-death/.

type CausesOfInfantDeathInBoysAndGirlsIhme2018Dataset 

type CausesOfInfantDeathInBoysAndGirlsIhme2018Dataset struct {
	CausesOfDeathForInfantGirlsLess1YearIhme *float64 `json:"causes_of_death_for_infant_girls_less1_year_ihme"`
	CausesOfDeathForInfantBoysLess1YearIhme  *float64 `json:"causes_of_death_for_infant_boys_less1_year_ihme"`
}

Rates of infant mortality by sex for selected causes of death. This is based on death rates per 100,000 for infants less than one year old. This is given for a range of leading causes of death for comparison between infant mortality rates in males and females.For conciseness and clarity, we have shortened a few of the original IHME causes of death:Congenital birth defects = Birth defectsCongenital heart anomalies = Heart anomaliesDigestive congenital anomalies = Digestive anomaliesNeonatal encephalopathy due to birth asphyxia and trauma = Encephalopathy from asphyxia and traumaNeonatal preterm birth = Preterm birth

type CerealAllocationToFoodFeedFuelOwidBasedOnFaoDataset 

type CerealAllocationToFoodFeedFuelOwidBasedOnFaoDataset struct {
	CerealsAllocatedToFoodTonnesFao2020        *float64 `json:"cereals_allocated_to_food_tonnes_fao_2020"`
	CerealsAllocatedToAnimalFeedTonnesFao2020  *float64 `json:"cereals_allocated_to_animal_feed_tonnes_fao_2020"`
	CerealsAllocatedToOtherUsesTonnesFao2020   *float64 `json:"cereals_allocated_to_other_uses_tonnes_fao_2020"`
	ShareOfCerealsAllocatedToFoodFao2020       *float64 `json:"share_of_cereals_allocated_to_food_fao_2020"`
	ShareOfCerealsAllocatedToAnimalFeedFao2020 *float64 `json:"share_of_cereals_allocated_to_animal_feed_fao_2020"`
	ShareOfCerealsAllocatedToOtherUsesFao2020  *float64 `json:"share_of_cereals_allocated_to_other_uses_fao_2020"`
}

This dataset reports the quantity of cereal crops allocated directly to human food; used as animal feed; and allocated to other uses (predominantly industrial uses such as biofuel production).The dataset combines two sets of variables: in the Food Balance Sheets the FAO include data via their ‘old methodology’ from 1961 to 2013, and ‘new methodology’ separately from 2014 to 2017.The variables included were ‘Cereals – Food’, ‘Cereals – Feed’ and ‘Cereals – Other Uses’, all measured in 1000 tonnes.We have combined these two sets of data to get a complete series from 1961 to 2017 (in tonnes).The share of cereals allocated to each use (Food, Feed and Other Uses) have also been calculated by dividing each individual variable by the sum of all three. For example:Share of cereals allocated to food = [Food / (Food + Feed + Other Uses) * 100].This calculates the share of cereals available domestically (after trade) allocated to each use, excluding supply chain losses and seed resown from the crop.

type CerealYieldIndexWorldBank2017AndOwidDataset 

type CerealYieldIndexWorldBank2017AndOwidDataset struct {
	CerealYieldIndexWorldBank2017AndOwid *float64 `json:"cereal_yield_index_world_bank_2017_and_owid"`
}

Cereal yield index was derived by OurWorldinData based on original data sourced from the World Bank's World Development Indicators (WDI).The cereal yield index measures annual cereal production as an index to average yields in 1961, the first year of the original dataset. This index was calculated by dividing cereal yield values (which are measured in kilograms per hectare) in any given year by cereal yields in 1961. 1961 = 100. Values >100 indicate an increase in yield vs. 1961, and values <100 indicate a decrease.The original dataset from WDI used in this calculation was "Cereal yield (kg per hectare)", which is defined by the World Bank/FAO as: "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year."Data available at: http://data.worldbank.org/data-catalog/world-development-indicators [accessed 18th July 2017]

type Cfc11ExpectedAndMeasuredConcentrationsMontzkaEtAl2018Dataset 

type Cfc11ExpectedAndMeasuredConcentrationsMontzkaEtAl2018Dataset struct {
	NorthernHemisphere *float64 `json:"northern_hemisphere"`
	SouthernHemisphere *float64 `json:"southern_hemisphere"`
	Wmo2003            *float64 `json:"wmo_2003"`
	Wmo2010            *float64 `json:"wmo_2010"`
	Wmo2014            *float64 `json:"wmo_2014"`
}

Data denotes the measured concentrations of trichlorofluoromethane (CFC-11) gas in the Northern and Southern Hemisphere, collected via air collection and analysis automated onsite instrumentation with gas chromatography coupled with electron capture detection (GC–ECD). This allows for measurement of CFC-11 concentrations in mole fractions (parts per trillion). Data here represents the annual average for data reported in Montzka et al. (2018).The WMO (2003; 2010 and 2014) projections are those published by the World Meteorological Organization (WMO) and represent the global average expected change in CFC-11 concentrations based on reported emissions of CFC-11 from all parties to the Montreal Protocol.

type Cfc11ExpectedAndMeasuredRateOfChangeMontzkaEtAl2018Dataset 

type Cfc11ExpectedAndMeasuredRateOfChangeMontzkaEtAl2018Dataset struct {
	Cfc11Measurement *float64 `json:"cfc_11_measurement"`
	Wmo2003          *float64 `json:"wmo_2003"`
	Wmo2010          *float64 `json:"wmo_2010"`
	Wmo2014          *float64 `json:"wmo_2014"`
}

Data denotes the measured annual change in concentrations of trichlorofluoromethane (CFC-11) gas as the global average, collected via air collection and analysis automated onsite instrumentation with gas chromatography coupled with electron capture detection (GC–ECD). Data here represents the annual average for data reported in Montzka et al. (2018).The WMO (2003; 2010 and 2014) projections are those published by the World Meteorological Organization (WMO) and represent the global average expected rate of change in CFC-11 concentrations based on reported emissions of CFC-11 from all parties to the Montreal Protocol.

type ChangeInGlobalHungerIndex19922017Listed2017GlobalHungerIndex2017Dataset 

type ChangeInGlobalHungerIndex19922017Listed2017GlobalHungerIndex2017Dataset struct {
	ChangeInGlobalHungerIndex1992_2017GlobalHungerIndex2017 *float64 `json:"change_in_global_hunger_index_1992_2017_global_hunger_index_2017"`
}

type ChartbookOfEconomicInequalityGini2016Dataset 

type ChartbookOfEconomicInequalityGini2016Dataset struct {
	GiniChartbookOfEconomicInequality2016 *float64 `json:"gini_chartbook_of_economic_inequality_2016"`
}

Please note that for some countries the Chartbook includes additional time series for economic inequality.

The purpose of the Chartbook is to present consistent time-series for each country over time. Unfortunately there are important cases in which the comparability between countries is limited. The table below lists the definition of the Gini time series for each country and differences in definitions should be taken into account when making cross-country comparisons.

The addition of series x behind the definition indicates that there are several Gini time-series available for this country in the Chartbook and they can be found at the country page there.

Argentina Gini coefficient, household equivalised income Australia Gini coefficient equivalised disposable household income (*) Brazil Gini coefficient, household equivalised income Canada Gini coefficient equivalised disposable household income (*) series 3 Finland Gini coefficient, equivalised disposable household income (*), series 1 France Gini coefficient, equivalised household disposable income (*) Germany Gini coefficient, equivalised household disposable income, (*) Iceland Gini coefficient, equiv dispos household income (*) India Gini coefficient equivalent disposable income, series 2 (*) Indonesia Gini coefficient, household expenditure data Italy Gini coefficient, per capita income Japan Gini coefficient, equiv dispos household income, series 3 (*) Malaysia Gini household income, series 2 Mauritius Gini coefficient, disposable household income Netherlands Gini coefficient, equivalised disposable household income (*) New Zealand Gini coefficient, equivalised disposable household income (*) series 1 Norway Gini coefficient, equivalised disposable household income (*), series 2 Portugal Gini coefficient, equivalised disposable household income, series 3 (*) Singapore Gini coefficient among employed households (modified OECD equiv scale), income from work after government benefits and taxes, series 3 South Africa Gini coefficient, per capita income Spain Gini coefficient, equivalised disposable household income, series 2 (*) Sweden Gini coefficient, equivalised household disposable income (*) Switzerland Gini coefficient, equivalised household disposable income (*), EU-SILC Series 3 UK Gini coefficient, equivalised household disposable income (*) US Gini coefficient, equivalised household gross income

type ChildDeathsByLifeStageOwidBasedOnUnIgmeDataset 

type ChildDeathsByLifeStageOwidBasedOnUnIgmeDataset struct {
	First28DaysNumberOfDeaths  *float64 `json:"first_28_days_number_of_deaths"`
	O29Days1YearNumberOfDeaths *float64 `json:"o29_days_1_year_number_of_deaths"`
	O1_4YearsNumberOfDeaths    *float64 `json:"o1_4_years_number_of_deaths"`
}

Data represents the total number of deaths of children (under 5 years old) by life stage. This is split into three categories: - the first 28 days of life; - 29 days to 1 year; - 1-4 years.This was calculated by Our World in Data based on data on neonatal, infant and under-5 deaths published by the World Bank (based on the UN IGME).- The first 28 days of life are equally to neonatal deaths;- 29 days to 1 year are calculated as infant minus neonatal deaths;- 1-4 years calculated as under-5s minus infant deaths.

type ChildDeathsUnitedNationsPopulationDivision2015Dataset 

type ChildDeathsUnitedNationsPopulationDivision2015Dataset struct {
	ChildDeathsUnitedNationsPopulationDivision2015 *float64 `json:"child_deaths_united_nations_population_division_2015"`
}

– The original data is presented in 1,000s live births. Here it was multiplied by 1,000 for this reason.

– Importantly data refer to 5 year intervals around the indicated year. E.g. 1952 refers to 1950-1954 in the original dataset. Here the value for the 5 year interval is divided by 5 and attributed to the mid-year of the interval.

type ChildLaborInUsEconomicHistoryAssociation2017Dataset 

type ChildLaborInUsEconomicHistoryAssociation2017Dataset struct {
	ChildLaborBoysPercEconomicHistoryAssociation2017  *float64 `json:"child_labor_boys_perc_economic_history_association_2017"`
	ChildLaborGirlsPercEconomicHistoryAssociation2017 *float64 `json:"child_labor_girls_perc_economic_history_association_2017"`
	ChildLaborAllPercEconomicHistoryAssociation2017   *float64 `json:"child_labor_all_perc_economic_history_association_2017"`
}

Total/all figures on child labour have been calculated as a weighted-average based on male:female ratio of 10-14 year-olds in the US from the United States Census Bureau in each respective year.References:Whaples, Robert. “Child Labor in the United States”. EH.Net Encyclopedia, edited by Robert Whaples. October 7, 2005. Available at: http://eh.net/encyclopedia/child-labor-in-the-united-states/Carter, Susan and Richard Sutch. “Fixing the Facts: Editing of the 1880 U.S. Census of Occupations with Implications for Long-Term Labor Force Trends and the Sociology of Official Statistics.” Historical Methods 29 (1996): 5-24Historical Statistics of the United States Colonial Times to 1970 (1790-1970). Available at: https://www.census.gov/library/publications/1975/compendia/hist_stats_colonial-1970.html

type ChildLaborInUsLong1958Dataset 

type ChildLaborInUsLong1958Dataset struct {
	ChildLaborLong1958 *float64 `json:"child_labor_long_1958"`
}

The incidence of child labour in males and females aged 10-13 has been taken directly from the source; this is available for both rural and urban demographics individually.To calculate the incidence of child labour across both sexes combined, we have weighted gender-specific incidences based on the male:female ratio in the total population for this age bracket (these are based on reported gender ratios, which may incur rounding errors). The same weighting application of population in rural vs. urban areas has been carried out to provide a combined rural & urban figure of incidence.

type ChildLaborItalyHistoricTonioloGAndVecchiG2007Dataset 

type ChildLaborItalyHistoricTonioloGAndVecchiG2007Dataset struct {
	ChildLaborItalyAllHistoricTonioloGAndVecchiG2007   *float64 `json:"child_labor_italy_all_historic_toniolo_g_and_vecchi_g_2007"`
	ChildLaborItalyBoysHistoricTonioloGAndVecchiG2007  *float64 `json:"child_labor_italy_boys_historic_toniolo_g_and_vecchi_g_2007"`
	ChildLaborItalyGirlsHistoricTonioloGAndVecchiG2007 *float64 `json:"child_labor_italy_girls_historic_toniolo_g_and_vecchi_g_2007"`
}

type ChildLaborUkHistoricCunninghamHAndViazzoPp1996Dataset 

type ChildLaborUkHistoricCunninghamHAndViazzoPp1996Dataset struct {
	ChildLaborUkBoysHistoricCunninghamHAndViazzoPp1996  *float64 `json:"child_labor_uk_boys_historic_cunningham_h_and_viazzo_pp_1996"`
	ChildLaborUkGirlsHistoricCunninghamHAndViazzoPp1996 *float64 `json:"child_labor_uk_girls_historic_cunningham_h_and_viazzo_pp_1996"`
}

type ChildLaborWorld19501995Basu1999Dataset 

type ChildLaborWorld19501995Basu1999Dataset struct {
	ChildLaborWorldIloEpeap1950_1995 *float64 `json:"child_labor_world_ilo_epeap_1950_1995"`
}

type ChildLaborWorldIloIlo2017Dataset 

type ChildLaborWorldIloIlo2017Dataset struct {
	ChildLaborAllWorldIloIpec           *float64 `json:"child_labor_all_world_ilo_ipec"`
	ChildLaborBoysWorld2000_2012Ilo     *float64 `json:"child_labor_boys_world_2000_2012_ilo"`
	ChildLaborGirlsWorld2000_2012Ilo    *float64 `json:"child_labor_girls_world_2000_2012_ilo"`
	Children5_14YearsInEmploymentIlo    *float64 `json:"children_5_14_years_in_employment_ilo"`
	Children5_14YearsNotInEmploymentIlo *float64 `json:"children_5_14_years_not_in_employment_ilo"`
	ChildLaborIncidenceIlo              *float64 `json:"child_labor_incidence_ilo"`
}

type ChildMarriageUnicef2017Dataset 

type ChildMarriageUnicef2017Dataset struct {
	PercentageOfWomen20_24WhoWereFirstMarriedBefore18UnicefGlobalDatabases2016 *float64 `json:"percentage_of_women_20_24_who_were_first_married_before_18_unicef_global_databases_2016"`
}

– The source notes that in some instances observations differ from the standard definition or refer to only part of a country.

– The dates associated to each observation correspond to the end of the survey used as underlying source

type ChildMortality19502017Ihme2017Dataset 

type ChildMortality19502017Ihme2017Dataset struct {
	ChildMortality1950_2017Ihme2018 *float64 `json:"child_mortality_1950_2017_ihme_2018"`
}

Child mortality is the share of newborns who die before reaching the age of five.This data is available from 1950 to 2017 in 5-year intervals from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease study, available at: http://ghdx.healthdata.org/record/ihme-data/gbd-2017-all-cause-mortality-and-life-expectancy-1950-2017

type ChildMortalityByIncomeLevel19602012WorldBankWdi2016Dataset 

type ChildMortalityByIncomeLevel19602012WorldBankWdi2016Dataset struct {
	ChildMortalityByIncomeLevel1960_2012WorldBankWdi2016 *float64 `json:"child_mortality_by_income_level_1960_2012_world_bank_wdi_2016"`
}

type ChildMortalityDataIhme2017Dataset 

type ChildMortalityDataIhme2017Dataset struct {
	Under5MortalityRateIhme2017 *float64 `json:"under_5_mortality_rate_ihme_2017"`
}

Data from the IHME GBDx database on child mortality is defined as the probability (expressed as the rate per 1,000 live births) that children born alive will die before reaching the age of 5 years

type ChildMortalityEstimatesCmeInfo2018Dataset 

type ChildMortalityEstimatesCmeInfo2018Dataset struct {
	ChildMortalityEstimatesCmeInfo2016 *float64 `json:"child_mortality_estimates_cme_info_2016"`
}

The UN Inter-agency Group for Child Mortality Estimation (IGME).

The IGME, led by the United Nations Children's Fund (UNICEF) and the World Health Organization (WHO), also includes the World Bank and the United Nations Population Division of the Department of Economic and Social Affairs as full members.

type ChildMortalityEstimatesGapminder2015Dataset 

type ChildMortalityEstimatesGapminder2015Dataset struct {
	ChildMortalityEstimatesGapminder2015 *float64 `json:"child_mortality_estimates_gapminder_2015"`
}

This is Version 8 of the data set uploaded by Gapminder on 2015 October 18

The World series for 1800 to 1960 was calculated by Max Roser on the basis of the Gapminder estimates of child mortality and the Gapminder series on population by country. For each estimate in that period a population weighted global average was calculated. The recent annual observations of the World series (1960 and later) is taken from the World Bank (http://data.worldbank.org/indicator/SH.DYN.MORT).

type ChildMortalityGapminder2013Dataset 

type ChildMortalityGapminder2013Dataset struct {
	ChildMortalityGapminder2013 *float64 `json:"child_mortality_gapminder_2013"`
}

Gapminder dataset. Available online at http://www.gapminder.org/.

type ChildMortalityRatesCompleteGapminderV102017Dataset 

type ChildMortalityRatesCompleteGapminderV102017Dataset struct {
	ChildMortality *float64 `json:"child_mortality"`
}

Data is that of version 10 of Gapminder, the latest version as of 2019. This is the full child mortality rate dataset published by Gapminder.Gapminder's sources and methodology if well-documented in its dataset at: https://www.gapminder.org/data/It notes its data sources during three key periods of time:— 1800 to 1950: Gapminder v7 ( In some cases this is also used for years after 1950, see below.) This was compiled and documented by Mattias Lindgren from many sources, but mainly based on www.mortality.org and the series of books called International Historical Statistics by Brian R Mitchell, which often have historic estimates of Infant mortality rate which were converted to Child mortality through regression. See detailed documentation of v7 below.— 1950 to 2016: UNIGME, is a data collaboration project between UNICEF, WHO, UN Population Division and the World Bank. They released new estimates of child mortality for countries and a global estimate on October 17, 2017, which is available at www.childmortality.org. In this dataset almost all countries have estimates between 1970 and 2016, while roughly half the countries also reach back to 1950.— 1950 to 2100: UN WPP, World Population Prospects 2017 provides annual data for Child mortality rate for all countries in the interpolated demographic indicators, called WPP2017_INT_F01_ANNUAL_DEMOGRAPHIC_INDICATORS.xlsx, accessed on September 2, 2017.Version 12 of the dataset extends back to the year 1800. Version 6 of Gapminder's fertility series includes data for a few countries further than 1800. We have included more historic data from Version 6 for Finland, the United Kingdom and Sweden. All data from 1800 onwards is from Version 12; data from pre-1800 is from Version 6.There are significant uncertainties in data for many countries pre-1950. To develop full series back to 1800 for all countries, Gapminder combines published estimates within the academic literature and national statistics, with their own guesstimates and extrapolations for countries without published estimates. This series presents the full Gapminder dataset: both those from published estimates and estimates made by Gapminder with high uncertainty. This is provided so users have access to the full dataset.However, for our main long-term series on child mortality rates at Our World in Data we exclude the highly uncertain data points which are not backed up with published estimates within the literature. Users looking for a series with less uncertainty should refer to that instead.

type ChildMortalityRatesSelectedGapminderV102017Dataset 

type ChildMortalityRatesSelectedGapminderV102017Dataset struct {
	ChildMortalitySelectGapminderV10_2017 *float64 `json:"child_mortality_select_gapminder_v10_2017"`
}

Dataset comes from Gapminder - Child mortality (version 10), the latest version as of 2019.Gapminder's sources and methodology is well-documented in its dataset at: https://www.gapminder.org/data/documentation/gd005/It notes its data sources during three key periods of time:— 1800 to 1950: Gapminder v7 ( In some cases this is also used for years after 1950, see below.) This was compiled and documented by Mattias Lindgren from many sources, but mainly based on www.mortality.org and the series of books called International Historical Statistics by Brian R Mitchell, which often have historic estimates of Infant mortality rate which were converted to Child mortality through regression.— 1950 to 2016: UNIGME, is a data collaboration project between UNICEF, WHO, UN Population Division and the World Bank. They released new estimates of child mortality for countries and a global estimate on October 17, 2017, which is available at www.childmortality.org. In this dataset almost all countries have estimates between 1970 and 2016, while roughly half the countries also reach back to 1950.— 1950 to 2100: UN WPP, World Population Prospects 2017 provides annual data for Child mortality rate for all countries in the interpolated demographic indicators, called WPP2017_INT_F01_ANNUAL_DEMOGRAPHIC_INDICATORS.xlsx, accessed on September 2, 2017.There are significant uncertainties in data for many countries pre-1950. To develop full series back to 1800 for all countries, Gapminder combines published estimates within the academic literature and national statistics, with their own guesstimates and extrapolations for countries without published estimates. This series presents the selective Gapminder dataset: we have removed data points which were estimated by Gapminder with high uncertainty and instead only include those from published sources or the United Nations dataset. We also publish the full dataset from Gapminder for users looking for a complete series. However, we should highlight that some of these estimates have a high degree of uncertainty. This dataset can be accessed here: https://ourworldindata.org/grapher/child-mortality-complete

type ChildViolenceEndingViolenceInChildhoodReport2017Dataset 

type ChildViolenceEndingViolenceInChildhoodReport2017Dataset struct {
	Children1_14WhoExperiencedAnyViolentDiscipline                                     *float64 `json:"children_1_14_who_experienced_any_violent_discipline"`
	Children13_15WhoReportedBeingBullied                                               *float64 `json:"children_13_15_who_reported_being_bullied"`
	Children13_15WhoReportedBeingInAPhysicalFightDuringPastYear                        *float64 `json:"children_13_15_who_reported_being_in_a_physical_fight_during_past_year"`
	Girls15_19WhoExperiencedAnyPhysicalViolenceSinceAge15                              *float64 `json:"girls_15_19_who_experienced_any_physical_violence_since_age_15"`
	Girls15_19WhoEverExperiencedForcedSexualIntercourseOrAnyOtherForcedSexualActs      *float64 `json:"girls_15_19_who_ever_experienced_forced_sexual_intercourse_or_any_other_forced_sexual_acts"`
	NumberOfHomicideVictimsAmongChildren0_19Per100_000Population                       *float64 `json:"number_of_homicide_victims_among_children_0_19_per_100_000_population"`
	Women15AndAboveWhoExperiencedAnyIntimatePartnerPhysicalAndorSexualViolenceLastYear *float64 `json:"women_15_and_above_who_experienced_any_intimate_partner_physical_andor_sexual_violence_last_year"`
}

type ChildrenThatDiedBefore5YearsOfAgePerWomanGapminder2017Dataset 

type ChildrenThatDiedBefore5YearsOfAgePerWomanGapminder2017Dataset struct {
	ChildrenThatDiedBefore5YearsOfAgePerWomanBasedOnGapminder2017 *float64 `json:"children_that_died_before_5_years_of_age_per_woman_based_on_gapminder_2017"`
}

The number of children that died before 5 years of age per women is calculated by multiplying the child mortality rate by the fertility rate of a country for a given year.Child mortality is the share of newborns who die before reaching the age of 5. The dataset comes from version 10 of Gapminder, the latest version as of 2019. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year. The dataset comes from version 12 of Gapminder, the latest version as of 2019.

type ChildrenThatSurvivedPast5YearsOfAgePerWomanGapminder2017Dataset 

type ChildrenThatSurvivedPast5YearsOfAgePerWomanGapminder2017Dataset struct {
	ChildrenThatSurvivedPast5YearsOfAgePerWomanBasedOnGapminder2017 *float64 `json:"children_that_survived_past_5_years_of_age_per_woman_based_on_gapminder_2017"`
}

The number of children that survived past 5 years of age per women is calculated by multiplying the child survival rate by the fertility rate of a country for a given year.Child survival rate is 1 minus the child mortality rate. The dataset comes from version 10 of Gapminder, the latest version as of 2019. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year. The dataset comes from version 12 of Gapminder, the latest version as of 2019.

type ChinaShareOfWorldPovertyWorldBankWdi2017Dataset 

type ChinaShareOfWorldPovertyWorldBankWdi2017Dataset struct {
	WorldWorldBankWdi2017                  *float64 `json:"world_world_bank_wdi_2017"`
	ChinaWorldBankWdi2017                  *float64 `json:"china_world_bank_wdi_2017"`
	WorldNotIncludingChinaWorldBankWdi2017 *float64 `json:"world_not_including_china_world_bank_wdi_2017"`
}

Definitions:

Our definition of poverty is defined using World Bank's poverty headcount. People are considered poor if they live with less than 1.90$ per day (2011 int $ PPP). For more information on how the OurWorldInData team defines poverty following the World Bank's definitions see our entry on "Global Extreme Poverty".

type CityPopulations19502035UnUrbanizationProspects2018Dataset 

type CityPopulations19502035UnUrbanizationProspects2018Dataset struct {
	CityPopulationUnUrbanizationProspects2018          *float64 `json:"city_population_un_urbanization_prospects_2018"`
	ProjectedCityPopulationUnUrbanizationProspects2018 *float64 `json:"projected_city_population_un_urbanization_prospects_2018"`
}

Data on city populations is available for the world's largest 30 cities by population (in 2015).Estimates are available in 5-year intervals from 1950 to 2015. Projections are also available to the year 2035 as published by the UN World Urbanization Prospects (2018) based on its median fertility scenario and urbanization trends.

type ClarkFlecheAndSenikHappinessInequalityDataset 

type ClarkFlecheAndSenikHappinessInequalityDataset struct {
	StandardDeviationOfLifeSatisfaction *float64 `json:"standard_deviation_of_life_satisfaction"`
}

type Co2EmissionFactorsIpcc2006Dataset 

type Co2EmissionFactorsIpcc2006Dataset struct {
	Co2EmissionsFactorIpcc2006 *float64 `json:"co2_emissions_factor_ipcc_2006"`
}

Figures represent the carbon dioxide emission factors as used in offical greenhouse gas reporting methodologies, defined by the Intergovernmental Panel on Climate Change (IPCC).

Data has been converted from kilograms per terajoule to kilograms per MWh (kg/MWh) using a conversion factor of 277.778.

type Co2EmissionsByCityC40Cities2018Dataset 

type Co2EmissionsByCityC40Cities2018Dataset struct {
	PopulationDensity               *float64 `json:"population_density"`
	Co2EmissionsPerCapita           *float64 `json:"co2_emissions_per_capita"`
	TransportEmissionsPerCapitaTco2 *float64 `json:"transport_emissions_per_capita_tco2"`
	WasteEmissionsPerCapita         *float64 `json:"waste_emissions_per_capita"`
}

type Co2EmissionsBySectorCait2020Dataset 

type Co2EmissionsBySectorCait2020Dataset struct {
	BuildingCait                                  *float64 `json:"building_cait"`
	IntlAviationAndShippingCait                   *float64 `json:"intl_aviation_and_shipping_cait"`
	ElectricityAndHeatCait                        *float64 `json:"electricity_and_heat_cait"`
	EnergyCait                                    *float64 `json:"energy_cait"`
	FugitiveEmissionsCait                         *float64 `json:"fugitive_emissions_cait"`
	IndustryCait                                  *float64 `json:"industry_cait"`
	LandUseChangeAndForestryCait                  *float64 `json:"land_use_change_and_forestry_cait"`
	ManufacturingAndConstructionCait              *float64 `json:"manufacturing_and_construction_cait"`
	OtherFuelCombustionCait                       *float64 `json:"other_fuel_combustion_cait"`
	TotalExcludingLucfCait                        *float64 `json:"total_excluding_lucf_cait"`
	TotalIncludingLucfCait                        *float64 `json:"total_including_lucf_cait"`
	TransportCait                                 *float64 `json:"transport_cait"`
	BuildingsPerCapitaCait                        *float64 `json:"buildings_per_capita_cait"`
	ElectricityAndHeatPerCapitaCait               *float64 `json:"electricity_and_heat_per_capita_cait"`
	FugitiveEmissionsPerCapitaCait                *float64 `json:"fugitive_emissions_per_capita_cait"`
	IndustryPerCapitaCait                         *float64 `json:"industry_per_capita_cait"`
	InternationalAviationAndShippingPerCapitaCait *float64 `json:"international_aviation_and_shipping_per_capita_cait"`
	LandUseChangeAndForestryPerCapitaCait         *float64 `json:"land_use_change_and_forestry_per_capita_cait"`
	ManufacturingAndConstructionPerCapitaCait     *float64 `json:"manufacturing_and_construction_per_capita_cait"`
	TotalExcludingLucfPerCapitaCait               *float64 `json:"total_excluding_lucf_per_capita_cait"`
	TotalIncludingLucfPerCapitaCait               *float64 `json:"total_including_lucf_per_capita_cait"`
	TransportPerCapitaCait                        *float64 `json:"transport_per_capita_cait"`
}

Carbon dioxide (CO₂) emissions broken down by sector, measured in tonnes per year. Further information on sector definitions is available here: https://ourworldindata.org/ghg-emissions-by-sectorThis data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type Co2EmissionsBySectorCait2021Dataset 

type Co2EmissionsBySectorCait2021Dataset struct {
	AviationAndShipping                   *float64 `json:"aviation_and_shipping"`
	AviationAndShippingPerCapita          *float64 `json:"aviation_and_shipping_per_capita"`
	Buildings                             *float64 `json:"buildings"`
	BuildingsPerCapita                    *float64 `json:"buildings_per_capita"`
	ElectricityAndHeat                    *float64 `json:"electricity_and_heat"`
	ElectricityAndHeatPerCapita           *float64 `json:"electricity_and_heat_per_capita"`
	Energy                                *float64 `json:"energy"`
	EnergyPerCapita                       *float64 `json:"energy_per_capita"`
	FugitiveEmissions                     *float64 `json:"fugitive_emissions"`
	FugitiveEmissionsPerCapita            *float64 `json:"fugitive_emissions_per_capita"`
	Industry                              *float64 `json:"industry"`
	IndustryPerCapita                     *float64 `json:"industry_per_capita"`
	LandUseChangeAndForestry              *float64 `json:"land_use_change_and_forestry"`
	LandUseChangeAndForestryPerCapita     *float64 `json:"land_use_change_and_forestry_per_capita"`
	ManufacturingAndConstruction          *float64 `json:"manufacturing_and_construction"`
	ManufacturingAndConstructionPerCapita *float64 `json:"manufacturing_and_construction_per_capita"`
	OtherFuelCombustion                   *float64 `json:"other_fuel_combustion"`
	OtherFuelCombustionPerCapita          *float64 `json:"other_fuel_combustion_per_capita"`
	TotalExcludingLucf                    *float64 `json:"total_excluding_lucf"`
	TotalExcludingLucfPerCapita           *float64 `json:"total_excluding_lucf_per_capita"`
	TotalIncludingLucf                    *float64 `json:"total_including_lucf"`
	TotalIncludingLucfPerCapita           *float64 `json:"total_including_lucf_per_capita"`
	Transport                             *float64 `json:"transport"`
	TransportPerCapita                    *float64 `json:"transport_per_capita"`
}

Carbon dioxide (CO₂) emissions broken down by sector, measured in tonnes per year. Further information on sector definitions is available here: https://ourworldindata.org/ghg-emissions-by-sectorThis data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type Co2EmissionsBySourceCdiac2016Dataset 

type Co2EmissionsBySourceCdiac2016Dataset struct {
	SolidFuelConsumptionCdiac2016  *float64 `json:"solid_fuel_consumption_cdiac_2016"`
	LiquidFuelConsumptionCdiac2016 *float64 `json:"liquid_fuel_consumption_cdiac_2016"`
	GasFuelConsumptionCdiac2016    *float64 `json:"gas_fuel_consumption_cdiac_2016"`
	CementProductionCdiac2016      *float64 `json:"cement_production_cdiac_2016"`
	GasFlaringCdiac2016            *float64 `json:"gas_flaring_cdiac_2016"`
}

China only refers to Mainland China; France is including Monaco. As in the original source (CDIAC), emissions from bunker fuels are not included in these totals.

type Co2EmissionsInTradeAsPercProductionGlobalCarbonProject2014Dataset 

type Co2EmissionsInTradeAsPercProductionGlobalCarbonProject2014Dataset struct {
	Co2TradedAsPercentageOfDomesticEmissionsProductionGlobalCarbonProject2014 *float64 `json:"co2_traded_as_percentage_of_domestic_emissions_production_global_carbon_project_2014"`
}

Original data on production-based emissions, and emissions transfers in trade from Peters et al. (2012) via the Global Carbon Project (http://www.globalcarbonproject.org/carbonbudget/16/data.htm) have been manipulated to calculate the emissions transfers as a percentage of production-based emissions, presented in this chart.

type Co2FootprintBreakdownPerCapitaGoodall2011Dataset 

type Co2FootprintBreakdownPerCapitaGoodall2011Dataset struct {
	Co2FootprintBreakdownPerCapitaGoodall2011 *float64 `json:"co2_footprint_breakdown_per_capita_goodall_2011"`
}

The data sourced from Chris Goodall's book is representative of the composition of the average carbon footprint in the United Kingdom at the time of writing (reprinted in 2011). Significant variability between individual lifestyles in the UK, and across comparable high-income countries is to be expected. This breakdown does not include estimates of per capita contribution to national services, manufacturing and infrastructure; Goodall estimated this additional component to comprise approximately 35% of an individual's footprint.

type Co2FromCementCdiac2017Dataset 

type Co2FromCementCdiac2017Dataset struct {
	Co2FromCementCdiac2017 *float64 `json:"co2_from_cement_cdiac_2017"`
}

CDIAC data originally sourced from:

T.A. Boden, G. Marland, and R.J. Andres. 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Available at: doi:10.3334/CDIAC/00001_V2010

Data originally reported in units of carbon. This has been converted to units of CO2 using a conversion factor of 3.67.

type Co2FromFlaringCdiac2017Dataset 

type Co2FromFlaringCdiac2017Dataset struct {
	Co2FromFlaringCdiac2017 *float64 `json:"co2_from_flaring_cdiac_2017"`
}

CDIAC data originally sourced from:

T.A. Boden, G. Marland, and R.J. Andres. 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Available at: doi:10.3334/CDIAC/00001_V2010

Data originally reported in units of carbon. This has been converted to units of CO2 using a conversion factor of 3.67.

type Co2FromGasCdiac2017Dataset 

type Co2FromGasCdiac2017Dataset struct {
	Co2FromGasCdiac2017 *float64 `json:"co2_from_gas_cdiac_2017"`
}

CDIAC data originally sourced from:

T.A. Boden, G. Marland, and R.J. Andres. 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Available at: doi:10.3334/CDIAC/00001_V2010

Data originally reported in units of carbon. This has been converted to units of CO2 using a conversion factor of 3.67.

type Co2FromLiquidCdiac2017Dataset 

type Co2FromLiquidCdiac2017Dataset struct {
	Co2FromLiquidCdiac2017 *float64 `json:"co2_from_liquid_cdiac_2017"`
}

CDIAC data originally sourced from:

T.A. Boden, G. Marland, and R.J. Andres. 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Available at: doi:10.3334/CDIAC/00001_V2010

Data originally reported in units of carbon. This has been converted to units of CO2 using a conversion factor of 3.67.

type Co2FromSolidFuelCdiac2017Dataset 

type Co2FromSolidFuelCdiac2017Dataset struct {
	Co2FromSolidFuelCdiac2017 *float64 `json:"co2_from_solid_fuel_cdiac_2017"`
}

CDIAC data originally sourced from:

T.A. Boden, G. Marland, and R.J. Andres. 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Available at: doi:10.3334/CDIAC/00001_V2010

Data originally reported in units of carbon. This has been converted to units of CO2 using a conversion factor of 3.67.

type Co2GdpCouplingOwidBasedOnWorldBankDataset 

type Co2GdpCouplingOwidBasedOnWorldBankDataset struct {
	GdpPerCapitaOwidBasedOnWorldBank              *float64 `json:"gdp_per_capita_owid_based_on_world_bank"`
	GdpOwidBasedOnWorldBank                       *float64 `json:"gdp_owid_based_on_world_bank"`
	PopulationOwidBasedOnWorldBank                *float64 `json:"population_owid_based_on_world_bank"`
	Co2EmissionsOwidBasedOnWorldBank              *float64 `json:"co2_emissions_owid_based_on_world_bank"`
	Co2EmissionsPerCapitaOwidBasedOnWorldBank     *float64 `json:"co2_emissions_per_capita_owid_based_on_world_bank"`
	MethaneEmissionsOwidBasedOnWorldBank          *float64 `json:"methane_emissions_owid_based_on_world_bank"`
	MethaneEmissionsPerCapitaOwidBasedOnWorldBank *float64 `json:"methane_emissions_per_capita_owid_based_on_world_bank"`
	NitrousOxideEmissionsOwidBasedOnWorldBank     *float64 `json:"nitrous_oxide_emissions_owid_based_on_world_bank"`
	NitrousOxidePerCapitaOwidBasedOnWorldBank     *float64 `json:"nitrous_oxide_per_capita_owid_based_on_world_bank"`
	EnergyUsePerCapitaOwidBasedOnWorldBank        *float64 `json:"energy_use_per_capita_owid_based_on_world_bank"`
	EnergyUseOwidBasedOnWorldBank                 *float64 `json:"energy_use_owid_based_on_world_bank"`
}

type Co2IntensityOfTransportByModeUkBeisDataset 

type Co2IntensityOfTransportByModeUkBeisDataset struct {
	GhgEmissionsGco2ekm    *float64 `json:"ghg_emissions_gco2ekm"`
	Co2EmissionsGco2km     *float64 `json:"co2_emissions_gco2km"`
	NonCo2EmissionsGco2ekm *float64 `json:"non_co2_emissions_gco2ekm"`
}

type Co2MitigationCurvesFor15cAndrewsAndGcp2019Dataset 

type Co2MitigationCurvesFor15cAndrewsAndGcp2019Dataset struct {
	Co2MitigationCurvesFor15cAndrewsAndGcp2019 *float64 `json:"co2_mitigation_curves_for_15c_andrews_and_gcp_2019"`
}

Data denotes the range of CO2 mitigation curves for a range of 'start year scenarios': scenarios are based on the annual emission reductions necessary to keep global temperature rise below 1.5C if emissions mitigation was to start in a given year. For example, 'Start in 2010' marks the necessary future emissions pathway to have a >66% chance of keeping global average temperatures below 1.5C warming if global CO2 emissions mitigation had started in 2010, very quickly peaking then falling. Data is sourced from Robbie Andrew, and available for download here: http://folk.uio.no/roberan/t/global_mitigation_curves.shtmlHistorical emissions to 2017 are sourced from CDIAC/Global Carbon Project, projection to 2018 from Global Carbon Project (Le Quéré et al. 2018).Global cumulative CO2 emissions budgets are from the IPCC Special Report on 1.5°C (Rogelj et al 2018): 420 GtCO2 for a 66% of 1.5°C and 1170 GtCO2 for a 66% of 2°C. Mitigation curves describe approximately exponential decay pathways such that the quota is never exceeded (see Raupach et al., 2014).

type Co2MitigationCurvesFor2cAndrewsAndGcp2019Dataset 

type Co2MitigationCurvesFor2cAndrewsAndGcp2019Dataset struct {
	Co2MitigationCurvesFor2cAndrewsAndGcp2019 *float64 `json:"co2_mitigation_curves_for_2c_andrews_and_gcp_2019"`
}

Data denotes the range of CO2 mitigation curves for a range of 'start year scenarios': scenarios are based on the annual emission reductions necessary to keep global temperature rise below 2C if emissions mitigation was to start in a given year. For example, 'Start in 2010' marks the necessary future emissions pathway to have a >66% chance of keeping global average temperatures below 2C warming if global CO2 emissions mitigation had started in 2010, very quickly peaking then falling. Data is sourced from Robbie Andrew, and available for download here: http://folk.uio.no/roberan/t/global_mitigation_curves.shtmlHistorical emissions to 2017 are sourced from CDIAC/Global Carbon Project, projection to 2018 from Global Carbon Project (Le Quéré et al. 2018).Global cumulative CO2 emissions budgets are from the IPCC Special Report on 1.5°C (Rogelj et al 2018): 420 GtCO2 for a 66% of 1.5°C and 1170 GtCO2 for a 66% of 2°C. Mitigation curves describe approximately exponential decay pathways such that the quota is never exceeded (see Raupach et al., 2014).

type Co2PerYearByRegionCdiac2017Dataset 

type Co2PerYearByRegionCdiac2017Dataset struct {
	Co2PerYearByRegionCdiac2017 *float64 `json:"co2_per_year_by_region_cdiac_2017"`
}

Emissions data have been converted from units of carbon to carbon dioxide (CO2) using a conversion factor of 3.67. Regions denoted "other" are given as regional totals minus emissions from the EU-28, USA, China and India. Here, we have rephrased the general term "bunker (fuels)" as "international aviation and maritime transport" for clarity.

CDIAC denote a "statistical difference" component which has been included in this data. This statistical difference represents the difference between estimated global CO2 emissions and the sum of national totals. Estimates of CO2 emissions show that the global total of emissions is not equal to the sum of emissions from all countries. This is introduced in several cases: emissions within international territories, which are included in global totals but not attributed to individual countries; inconsistent national reporting where global import and export data is imbalanced; and differing treatment of non-fuel uses of hydrocarbons.

Full methodology on global, regional, national and statistical difference estimations can be found in Le Quere et al. (2016): Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., ... & Keeling, R. F. (2016). Global carbon budget 2016. Earth System Science Data, 8(2), 605. Available at: doi:10.5194/essd-8-605-2016.

type CoalOutputAndEmploymentInUkBeis2020Dataset 

type CoalOutputAndEmploymentInUkBeis2020Dataset struct {
	CoalOutputDecc2018                          *float64 `json:"coal_output_decc_2018"`
	EmploymentInCoalIndustryDecc2018            *float64 `json:"employment_in_coal_industry_decc_2018"`
	CoalProductionPerWorkerDecc2018             *float64 `json:"coal_production_per_worker_decc_2018"`
	PercentageOfWorkforceInCoalIndustryDecc2018 *float64 `json:"percentage_of_workforce_in_coal_industry_decc_2018"`
	CoalImportsDecc2018                         *float64 `json:"coal_imports_decc_2018"`
	DeepminedCoalOutputDecc2018                 *float64 `json:"deepmined_coal_output_decc_2018"`
	OpencastCoalOutputDecc2018                  *float64 `json:"opencast_coal_output_decc_2018"`
	CoalByEndUseCollieries                      *float64 `json:"coal_by_end_use_collieries"`
	CoalByEndUseElectricity                     *float64 `json:"coal_by_end_use_electricity"`
	CoalByEndUseGas                             *float64 `json:"coal_by_end_use_gas"`
	CoalByEndUseCokeOvensAndSolidFuels          *float64 `json:"coal_by_end_use_coke_ovens_and_solid_fuels"`
	CoalByEndUseRailways                        *float64 `json:"coal_by_end_use_railways"`
	CoalByEndUseDomestic                        *float64 `json:"coal_by_end_use_domestic"`
	CoalByEndUseIndustry                        *float64 `json:"coal_by_end_use_industry"`
	CoalByEndUseMiscellaneous                   *float64 `json:"coal_by_end_use_miscellaneous"`
}

Data originally published by the UK's Department for Energy and Climate Change (DECC) in 2013. Updated in 2018 by the UK's Department for Business, Energy & Industrial Strategy (BEIS).Employment figures in the coal industry include contractors, and are sourced by DECC from The Coal Authority.Coal production per worker was calculated by dividing total UK coal output by the number employed in any given year.The share of the total workforce employed in the coal industry was calculated by dividing by total UK workforce figures available in 'A millennium of macroeconomic data' by the Bank of England. Available at: http://www.bankofengland.co.uk/research/Pages/datasets/default.aspx [accessed 5th October 2017].

type CoalProductionTheShiftProjectDataset 

type CoalProductionTheShiftProjectDataset struct {
	CoalProductionTheShiftProject *float64 `json:"coal_production_the_shift_project"`
}

Data from 1900-1980 is sourced from Bouda Etemad and Jean Luciani, World Energy Production 1800 – 1985, ISBN 2-600-56007-6.Data from 1980 onwards is source from U.S. Energy Information Administration, International Energy Statistics.

type CoefficientOfVariationCvInCaloricIntakeDataset 

type CoefficientOfVariationCvInCaloricIntakeDataset struct {
	CaloricCoefficientVariationCvFaoFoodSecurityIndicators2017 *float64 `json:"caloric_coefficient_variation_cv_fao_food_security_indicators_2017"`
}

The coefficient variation (CV) measures the inequality of caloric intake across a given population. It represents a statistical measure of the data spread around the mean caloric intake. Higher CV values represent larger levels of dietary inequality.Where data is sufficiently available, the FAO estimate the CV based on household survey data. Where unavailable, it is calculated based on regression analysis from Gini coefficient, income and food price data.The CV of caloric intake is reported only for developing countries within the Food Security Indicators.

type ConflictAndTerrorismDeathsOwidBasedOnIhmeAndGtdDataset 

type ConflictAndTerrorismDeathsOwidBasedOnIhmeAndGtdDataset struct {
	TerrorismDeaths  *float64 `json:"terrorism_deaths"`
	ConflictDeaths   *float64 `json:"conflict_deaths"`
	TerrorismPercent *float64 `json:"terrorism_percent"`
	ConflictPercent  *float64 `json:"conflict_percent"`
}

Data for conflict and terrorism deaths has been differentiated by Our World in Data based on published figures by the IHME, Global Burden of Disease (GBD), and Global Terrorism Database (GTD).The IHME, GBD report combined deaths from 'Conflict and Terrorism'. To differentiate 'conflict' deaths, we have subtracted number of terrorism deaths published by the GTD from this combined metric of 'Conflict and Terrorism'. We have also calculated both of these individual parameters in terms of the percentage of total deaths, by dividing by IHME figures of deaths for 'All causes'.Note that in some cases, estimates of homicide, conflict and terrorism can be challenging to differentiate, with different estimates from different sources and definitions. For terrorism, we have used data from the GTD; full data and definitions can be found at our entry: www.ourworldindata.org/terrorismSources:IHME, GBD: http://ghdx.healthdata.org/gbd-results-toolGTD: http://www.start.umd.edu/gtd/

type ConflictDeathsByCountryUcdp2019Dataset 

type ConflictDeathsByCountryUcdp2019Dataset struct {
	ViolentDeathsInConflictsAndOneSidedViolenceUcdp2019 *float64 `json:"violent_deaths_in_conflicts_and_one_sided_violence_ucdp_2019"`
}

Aggregation of the 'best' estimates for deaths listed for all incidents in UCDP GED 19.1 for each country and year. UCDP defines incidents as being "where armed force was used by an organised actor against another organized actor, or against civilians, resulting in at least 1 direct death at a specific location and a specific date".Note that currently the data excludes Syria.

type ConflictDeathsUcdpGeoreferencedEventData2019Dataset 

type ConflictDeathsUcdpGeoreferencedEventData2019Dataset struct {
	DirectConflictDeaths    *float64 `json:"direct_conflict_deaths"`
	DirectConflictDeathRate *float64 `json:"direct_conflict_death_rate"`
}

UCDP (GED) v19.1 provides estimates for direct conflict deaths (i.e. excluding indirect deaths from disease, malnutrition, exposure etc.) of both civilians and military personnel occurring in individual geo-referenced events. Taking the 'best' deaths estimates, we have aggregated these to country-year observations.UCDP documentation makes it clear that the dataset is intended to have global coverage since 1989, except for the case of Syria. As such, any country-year (as defined by the Gleditsch and Ward system of states) in which no events are recorded in the data were attributed zero deaths, except for Syria in this period.The World total figures are not an aggregation of the GED dataset (given the lack of coverage just mentioned). Instead it combines the three non-georeferenced datasets UCDP provides, covering three kinds of violence: One-sided violence, Non-state conflicts and State-based conflicts.Death rates are calculated using UN population figures.

type ConsumerExpenditureOnFoodUsda2017Dataset 

type ConsumerExpenditureOnFoodUsda2017Dataset struct {
	ShareOfConsumerExpenditureOnFoodUsda2017                         *float64 `json:"share_of_consumer_expenditure_on_food_usda_2017"`
	ShareOfConsumerExpenditureOnAlcoholicBeveragesAndTobaccoUsda2017 *float64 `json:"share_of_consumer_expenditure_on_alcoholic_beverages_and_tobacco_usda_2017"`
	ConsumerExpenditureUsda2017                                      *float64 `json:"consumer_expenditure_usda_2017"`
	FoodExpenditureUsda2017                                          *float64 `json:"food_expenditure_usda_2017"`
}

Data on the share of consumer expenditure spent on food, and on alcoholic beverages and tobacco are given as the percentage of total consumer expenditures per person.Data on overall annual consumer expenditure, and that spend on food is given in US$ per year.

type ConsumptionSharesInSelectedNonEssentialProductsWorldBankGlobalConsumptionDatabaseDataset 

type ConsumptionSharesInSelectedNonEssentialProductsWorldBankGlobalConsumptionDatabaseDataset struct {
	ShareOfAlcoholInTotalHouseholdConsumptionWorldBankGlobalConsumptionDatabase       *float64 `json:"share_of_alcohol_in_total_household_consumption_world_bank_global_consumption_database"`
	ShareOfGamesOfChanceInTotalHouseholdConsumptionWorldBankGlobalConsumptionDatabase *float64 `json:"share_of_games_of_chance_in_total_household_consumption_world_bank_global_consumption_database"`
	ShareOfTobaccoInTotalHouseholdConsumptionWorldBankGlobalConsumptionDatabase       *float64 `json:"share_of_tobacco_in_total_household_consumption_world_bank_global_consumption_database"`
	ShareOfNonEssentialProductsInTotalHouseholdWorldBankGlobalConsumptionDatabase     *float64 `json:"share_of_non_essential_products_in_total_household_world_bank_global_consumption_database"`
}

The Global Consumption Database is composed of individual country surveys to form a database on household consumption patterns in developing countries. The data has been standardized following a six-step process: <ul><li>Step 1: Annualizing consumption or expenditure data:In simple cases, this amounts to using a multiplying factor determined by the recall period (the period in which households are asked to recall their expenditure during that period). For example, food data collected for the last 7 days would be divided by 7, then multiplied by 365; monthly values by 12 etc. </li><li> Step 2: Detecting and fixing outliers:Expenditure values were flagged to be outliers if they exceeded the average amount consumed in the third quartile plus 5 times the interquartile range (the difference between the first and third quartiles of the data). Any flagged values need to be confirmed before imputations are made. If three or more non-food values are flagged as outliers for a household, it was assumed this indicates a rich household; hence the flags were removed. Households in the top two consumption quintiles were also assumed to spend unusually large shares of their income on education and jewellery. Outlier values that did not fit either of these criteria were replaced with the weighted mean of the non-extreme values for the consumption variable in question. </li><li> Step 3: Mapping commodities to the ICP/COICOP classification:Commodities found in each survey dataset were mapped to a standard classification of products and services, and then aggregate standard products and services into sectors and categories. This used the International Comparison Program (ICP) classification which is equivalent to the International Classification of Individual Consumption According to Purpose (COICOP). </li><li> Step 4: Extrapolation to 2010:Extrapolations were undertaken to convert all consumption and population data to a common reference year, 2010. For example, for the 2007 survey conducted in Guinea: final consumption expenditure per capita in LCU was 3,177,774 in 2010 and 1,547,012 in 2007 (the survey year). All survey values were therefore multiplied by 3,177,774/1,547,012=2.054137. Consumption data were converted from local currencies to international dollars adjusted for purchasing power parity (PPP$). </li><li> Step 5: Review and validation: Data was compared with other sources, notably the respective survey reports, and the World Bank’s poverty dataset, Povcalnet. </li><li> Step 6: Production of summary tables and metadata:The World Bank generated of a standard set of tables for each country showing consumption and demographic patterns across consumption segments. </li></ul>For more information on the Global Consumption Database methodology see: http://datatopics.worldbank.org/consumption/detail under the ‘Standardization of Data’ tab.As the World Bank’s Global Consumption Database draws on a variety of country surveys which differ in design, methodology, and timing, there are limits to the extent to which surveys can be standardized. Therefore, cross-country comparisons should be made with caution. For more information see http://datatopics.worldbank.org/consumption/detail under the ‘Note on comparability’ tab.All figures reported are based on national totals. The World Bank notes “each survey is composed of ordinary households only; “institutional households” (prisons, military barracks, hospitals, convents, and others) are not covered by household surveys. Homeless and nomadic populations and visitors present in a country during a survey are also excluded from the sample.” The surveys used in the database were conducted between 2000 and 2010. For more information see http://datatopics.worldbank.org/consumption/detail under the ‘Sources of Data’ tab.

type ConsumptionVsProductionBasedCo2EmissionsSharesBasedOnGcpAndUnDataset 

type ConsumptionVsProductionBasedCo2EmissionsSharesBasedOnGcpAndUnDataset struct {
	ShareOfGlobalPopulationPerc                   *float64 `json:"share_of_global_population_perc"`
	ConsumptionBasedCo2EmissionsPercOfGlobalTotal *float64 `json:"consumption_based_co2_emissions_perc_of_global_total"`
	ProductionBasedCo2EmissionsPercOfGlobalTotal  *float64 `json:"production_based_co2_emissions_perc_of_global_total"`
	ConsumptionBasedCo2PerCapita                  *float64 `json:"consumption_based_co2_per_capita"`
	ProductionBasedCo2PerCapita                   *float64 `json:"production_based_co2_per_capita"`
}

Variables include each country, region and World Bank income group's share of the global population; production-based (territorial); and consumption-based (trade-adjusted) carbon dioxide emissions. This was calculated by Our World in Data based on CO₂ figures produced by Le Quéré et al. (2017). Global Carbon Project. This is given as production (territorial) emissions in addition to trade-adjusted consumption-based emissions. Consumption-based emissions are national or regional emissions which have been adjusted for trade (i.e. territorial/production emissions minus emissions embedded in exports, plus emissions embedded in imports). If a country's consumption-based emissions are higher than its production emissions it is a net importer of carbon dioxide.Note that consumption-based emissions are not available for all countries; although those without complete data are a small fraction (3%) of the global total. Each country's share of world emissions are based on the share of the global total minus categories termed 'bunkers' and 'statistical differences' (which include cross-boundary emissions such as international travel and shipping.Calculation of each country's share of the global population is calculated using national and global population figures from the UN World Population Prospects (UNWPPP, 2018). Full reference for the Global Carbon Project:Full reference: Le Quéré, Corinne, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Cathy Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antoni Padin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven M. A. C. van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, Dan Zhu: Global Carbon Budget 2017, Earth Syst. Sci. Data Discussions, 2017. https://doi.org/10.5194/essdd-2017-123.

type CorporalPunishmentInSchoolsLongitudinalEvidenceFromEthiopiaIndiaPeruAndVietnamUnicef2015Dataset 

type CorporalPunishmentInSchoolsLongitudinalEvidenceFromEthiopiaIndiaPeruAndVietnamUnicef2015Dataset struct {
	ChildrenAge8WhoReportPhysicalPunishmentByTeachers  *float64 `json:"children_age_8_who_report_physical_punishment_by_teachers"`
	ChildrenAge15WhoReportPhysicalPunishmentByTeachers *float64 `json:"children_age_15_who_report_physical_punishment_by_teachers"`
}

The data in this chart comes from Figure 2 in the source paper, where there are also estimates of children's self-reports of teacher’s use of physical punishment on other children: https://ourworldindata.org/wp-content/uploads/2017/11/Teacher-violence-UNICEF-2015.png

type CorrelatesOfWarNationalMaterialCapabilitiesV40Dataset 

type CorrelatesOfWarNationalMaterialCapabilitiesV40Dataset struct {
	IronAndSteelProductionCorrelatesOfWarNationalMaterialCapabilitiesV40                    *float64 `json:"iron_and_steel_production_correlates_of_war_national_material_capabilities_v40"`
	MilitaryExpenditure1816_1913CurrentPricesCorrelatesOfWarNationalMaterialCapabilitiesV40 *float64 `json:"military_expenditure_1816_1913_current_prices_correlates_of_war_national_material_capabilities_v40"`
	MilitaryExpenditure1914_2007CurrentPricesCorrelatesOfWarNationalMaterialCapabilitiesV40 *float64 `json:"military_expenditure_1914_2007_current_prices_correlates_of_war_national_material_capabilities_v40"`
	MilitaryPersonnelCorrelatesOfWarNationalMaterialCapabilitiesV40                         *float64 `json:"military_personnel_correlates_of_war_national_material_capabilities_v40"`
}

The variable 'milex' has been split into two variables as the currency of expenditure changes pre and post-1914. Pre-1914, military expenditure is in thousands of UK pounds and post-1914 it is in thousands of US dollars. Both expenditure figures are measured in current prices.For values in real prices: Deflated the pre-1914 series to give the value in 1900 UK £s and  deflated the post-1914 series to give the value in 2000 US $s. Uses Quandl data.

type CorruptionPerceptionIndexTransparencyInternational2018Dataset 

type CorruptionPerceptionIndexTransparencyInternational2018Dataset struct {
	CorruptionPerceptionIndexTransparencyInternational2016 *float64 `json:"corruption_perception_index_transparency_international_2016"`
}

The index, which ranks 180 countries and territories by their perceived levels of public sector corruption according to experts and businesspeople, uses a scale of 0 to 100, where 0 is highly corrupt and 100 is very clean.

type CountriesContinentsDataset 

type CountriesContinentsDataset struct {
	CountriesContinents *float64 `json:"countries_continents"`
}

type CountryIncomeClassificationWorldBank2017Dataset 

type CountryIncomeClassificationWorldBank2017Dataset struct {
	IncomeClassificationWorldBank2017 *float64 `json:"income_classification_world_bank_2017"`
}

–The Atlas methodology is used to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of a country's exchange rate for that year and its exchange rates for the two preceding years, adjusted for the differences between the rate of inflation in the country and that in Japan, the United Kingdom, the United States, and the Euro area. A country's inflation rate is measured by the change in its GDP deflator. The inflation rate for the above countries, representing international inflation, is measured by the changes in the SDR deflator. (Special drawing rights, or SDRs, are the IMF's unit of account.)– Data on Serbia & Montenegro, prior to 2006 have been allocated to the sovereign states of Serbia, and Montenegro, respectively. Similarly, the 15 post-Soviet States have been allocated the USSR's classification for 1990. This includes Moldova, Estonia, Latvia, Lithuania, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Russia, Armenia, Azerbaijan, Georgia, Ukraine, and Belarus.

type CountryLevelLandPrecipitationDelawareDataset 

type CountryLevelLandPrecipitationDelawareDataset struct {
	AverageMonthlyPrecipitation       *float64 `json:"average_monthly_precipitation"`
	PrecipitationWeightedByPopulation *float64 `json:"precipitation_weighted_by_population"`
	PrecipitationWeightedByArea       *float64 `json:"precipitation_weighted_by_area"`
}

Country-level values were created by averaging all grid cells whose centroids were within the border of a country. Area weighted measures were weighted by the area of the grid cell when averaging the grid cells and population weighted averages used gridded population data from 2015 created by the Center for International Earth Science Information Network - CIESIN (http://dx.doi.org/10.7927/H4X63JVC).

type Covid2019Ecdc2020Dataset 

type Covid2019Ecdc2020Dataset struct {
	DailyNewConfirmedCasesOfCovid19                                                                      *float64 `json:"daily_new_confirmed_cases_of_covid_19"`
	DailyNewConfirmedDeathsDueToCovid19                                                                  *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19"`
	TotalConfirmedCasesOfCovid19                                                                         *float64 `json:"total_confirmed_cases_of_covid_19"`
	TotalConfirmedDeathsDueToCovid19                                                                     *float64 `json:"total_confirmed_deaths_due_to_covid_19"`
	DailyNewConfirmedCasesOfCovid19PerMillionPeople                                                      *float64 `json:"daily_new_confirmed_cases_of_covid_19_per_million_people"`
	DailyNewConfirmedDeathsDueToCovid19PerMillionPeople                                                  *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19_per_million_people"`
	TotalConfirmedCasesOfCovid19PerMillionPeople                                                         *float64 `json:"total_confirmed_cases_of_covid_19_per_million_people"`
	TotalConfirmedDeathsDueToCovid19PerMillionPeople                                                     *float64 `json:"total_confirmed_deaths_due_to_covid_19_per_million_people"`
	DaysSinceTheTotalConfirmedCasesOfCovid19Reached100                                                   *float64 `json:"days_since_the_total_confirmed_cases_of_covid_19_reached_100"`
	DaysSinceTheTotalConfirmedDeathsOfCovid19Reached5                                                    *float64 `json:"days_since_the_total_confirmed_deaths_of_covid_19_reached_5"`
	DaysSinceTheTotalConfirmedCasesOfCovid19PerMillionPeopleReached1                                     *float64 `json:"days_since_the_total_confirmed_cases_of_covid_19_per_million_people_reached_1"`
	DaysSinceTheTotalConfirmedDeathsOfCovid19PerMillionPeopleReached01                                   *float64 `json:"days_since_the_total_confirmed_deaths_of_covid_19_per_million_people_reached_01"`
	CaseFatalityRateOfCovid19Perc                                                                        *float64 `json:"case_fatality_rate_of_covid_19_perc"`
	CaseFatalityRateOfCovid19PercOnlyObservationsWithGreaterOrEqual100Cases                              *float64 `json:"case_fatality_rate_of_covid_19_perc_only_observations_with_greater_or_equal100_cases"`
	DaysSince30DailyNewConfirmedCasesRecorded                                                            *float64 `json:"days_since_30_daily_new_confirmed_cases_recorded"`
	DaysSince50DailyNewConfirmedCasesRecorded                                                            *float64 `json:"days_since_50_daily_new_confirmed_cases_recorded"`
	DaysSince10DailyNewConfirmedDeathsRecorded                                                           *float64 `json:"days_since_10_daily_new_confirmed_deaths_recorded"`
	DaysSince5DailyNewConfirmedDeathsRecorded                                                            *float64 `json:"days_since_5_daily_new_confirmed_deaths_recorded"`
	DaysSince3DailyNewConfirmedDeathsRecorded                                                            *float64 `json:"days_since_3_daily_new_confirmed_deaths_recorded"`
	DailyNewConfirmedDeathsDueToCovid19Rolling7DayAverageRightAligned                                    *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19_rolling_7_day_average_right_aligned"`
	DailyNewConfirmedCasesDueToCovid19Rolling7DayAverageRightAligned                                     *float64 `json:"daily_new_confirmed_cases_due_to_covid_19_rolling_7_day_average_right_aligned"`
	DaysSinceDailyNewConfirmedCasesOfCovid19PerMillionPeopleRolling7DayAverageRightAlignedReached1       *float64 `json:"days_since_daily_new_confirmed_cases_of_covid_19_per_million_people_rolling_7_day_average_right_aligned_reached_1"`
	DaysSinceDailyNewConfirmedDeathsDueToCovid19PerMillionPeopleRolling7DayAverageRightAlignedReached01  *float64 `` /* 126-byte string literal not displayed */
	DailyNewConfirmedCasesOfCovid19Rolling3DayAverageRightAligned                                        *float64 `json:"daily_new_confirmed_cases_of_covid_19_rolling_3_day_average_right_aligned"`
	DailyNewConfirmedDeathsDueToCovid19Rolling3DayAverageRightAligned                                    *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19_rolling_3_day_average_right_aligned"`
	DailyNewConfirmedCasesOfCovid19PerMillionPeopleRolling7DayAverageRightAligned                        *float64 `json:"daily_new_confirmed_cases_of_covid_19_per_million_people_rolling_7_day_average_right_aligned"`
	DailyNewConfirmedDeathsDueToCovid19PerMillionPeopleRolling7DayAverageRightAligned                    *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19_per_million_people_rolling_7_day_average_right_aligned"`
	DaysSinceDailyNewConfirmedCasesOfCovid19Rolling7DayAverageRightAlignedReached30                      *float64 `json:"days_since_daily_new_confirmed_cases_of_covid_19_rolling_7_day_average_right_aligned_reached_30"`
	DaysSinceDailyNewConfirmedDeathsDueToCovid19Rolling7DayAverageRightAlignedReached5                   *float64 `json:"days_since_daily_new_confirmed_deaths_due_to_covid_19_rolling_7_day_average_right_aligned_reached_5"`
	DaysSinceDailyNewConfirmedDeathsDueToCovid19PerMillionPeopleRolling7DayAverageRightAlignedReached001 *float64 `` /* 127-byte string literal not displayed */
	DailyNewConfirmedCasesOfCovid19PerMillionPeopleRolling3DayAverageRightAligned                        *float64 `json:"daily_new_confirmed_cases_of_covid_19_per_million_people_rolling_3_day_average_right_aligned"`
	DailyNewConfirmedDeathsDueToCovid19PerMillionPeopleRolling3DayAverageRightAligned                    *float64 `json:"daily_new_confirmed_deaths_due_to_covid_19_per_million_people_rolling_3_day_average_right_aligned"`
	DaysSinceTheTotalConfirmedCasesOfCovid19Reached100WithPopulationGreaterOrEqual5m                     *float64 `json:"days_since_the_total_confirmed_cases_of_covid_19_reached_100_with_population_greater_or_equal_5m"`
	HasPopulationGreaterOrEqual5mAndHadGreaterOrEqual100CasesGreaterOrEqual21DaysAgoAndHasTestingData    *float64 `json:"has_population_greater_or_equal_5m_and_had_greater_or_equal100_cases_greater_or_equal21_days_ago_and_has_testing_data"`
	DoublingDaysOfTotalConfirmedCases3DayPeriod                                                          *float64 `json:"doubling_days_of_total_confirmed_cases_3_day_period"`
	DoublingDaysOfTotalConfirmedCases7DayPeriod                                                          *float64 `json:"doubling_days_of_total_confirmed_cases_7_day_period"`
	DoublingDaysOfTotalConfirmedDeaths3DayPeriod                                                         *float64 `json:"doubling_days_of_total_confirmed_deaths_3_day_period"`
	DoublingDaysOfTotalConfirmedDeaths7DayPeriod                                                         *float64 `json:"doubling_days_of_total_confirmed_deaths_7_day_period"`
	WeeklyCases                                                                                          *float64 `json:"weekly_cases"`
	WeeklyDeaths                                                                                         *float64 `json:"weekly_deaths"`
	WeeklyCaseGrowthPerc                                                                                 *float64 `json:"weekly_case_growth_perc"`
	WeeklyDeathGrowthPerc                                                                                *float64 `json:"weekly_death_growth_perc"`
	BiweeklyCases                                                                                        *float64 `json:"biweekly_cases"`
	BiweeklyDeaths                                                                                       *float64 `json:"biweekly_deaths"`
	BiweeklyCaseGrowthPerc                                                                               *float64 `json:"biweekly_case_growth_perc"`
	BiweeklyDeathGrowthPerc                                                                              *float64 `json:"biweekly_death_growth_perc"`
	WeeklyCasesPerMillionPeople                                                                          *float64 `json:"weekly_cases_per_million_people"`
	WeeklyDeathsPerMillionPeople                                                                         *float64 `json:"weekly_deaths_per_million_people"`
	BiweeklyCasesPerMillionPeople                                                                        *float64 `json:"biweekly_cases_per_million_people"`
	BiweeklyDeathsPerMillionPeople                                                                       *float64 `json:"biweekly_deaths_per_million_people"`
	CaseFatalityRateOfCovid19PercShortTerm                                                               *float64 `json:"case_fatality_rate_of_covid_19_perc_short_term"`
}

Raw data on confirmed cases and deaths for all countries is sourced from the <a href="https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide">European Centre for Disease Prevention and Control (ECDC)</a>. 

Our complete COVID-19 dataset is a collection of the COVID-19 data maintained by <em>Our World in Data</em>.

<strong>It is updated daily</strong> and includes data on confirmed cases, deaths, and testing.We have created a new description of all our data sources. You find it at our GitHub repository <strong><a href="https://github.com/owid/covid-19-data/tree/master/public/data/">here</a></strong>. There you can download all of our data.

type Covid2019HospitalAndIcuDataset 

type Covid2019HospitalAndIcuDataset struct {
	DailyIcuOccupancy                     *float64 `json:"daily_icu_occupancy"`
	DailyIcuOccupancyPerMillion           *float64 `json:"daily_icu_occupancy_per_million"`
	DailyHospitalOccupancy                *float64 `json:"daily_hospital_occupancy"`
	DailyHospitalOccupancyPerMillion      *float64 `json:"daily_hospital_occupancy_per_million"`
	WeeklyNewIcuAdmissions                *float64 `json:"weekly_new_icu_admissions"`
	WeeklyNewIcuAdmissionsPerMillion      *float64 `json:"weekly_new_icu_admissions_per_million"`
	WeeklyNewHospitalAdmissions           *float64 `json:"weekly_new_hospital_admissions"`
	WeeklyNewHospitalAdmissionsPerMillion *float64 `json:"weekly_new_hospital_admissions_per_million"`
}

Our hospital & ICU data is collected from official sources and collated by Our World in Data. The complete list of country-by-country sources is available <a href="https://github.com/owid/covid-19-data/blob/master/public/data/hospitalizations/locations.csv">on GitHub</a>.Our complete COVID-19 dataset is a collection of the COVID-19 data maintained by <em>Our World in Data</em>. <strong>It is updated daily</strong> and includes data on confirmed cases, deaths, and testing.We have created a new description of all our data sources. You find it at our GitHub repository <strong><a href="https://github.com/owid/covid-19-data/tree/master/public/data/">here</a></strong>. There you can download all of our data.

type CovidGovernmentResponseOxbsgDataset 

type CovidGovernmentResponseOxbsgDataset struct {
	SchoolClosures                *float64 `json:"school_closures"`
	WorkplaceClosures             *float64 `json:"workplace_closures"`
	CancelPublicEvents            *float64 `json:"cancel_public_events"`
	ClosePublicTransport          *float64 `json:"close_public_transport"`
	PublicInformationCampaigns    *float64 `json:"public_information_campaigns"`
	RestrictionsInternalMovements *float64 `json:"restrictions_internal_movements"`
	InternationalTravelControls   *float64 `json:"international_travel_controls"`
	FiscalMeasures                *float64 `json:"fiscal_measures"`
	EmergencyInvestmentHealthcare *float64 `json:"emergency_investment_healthcare"`
	InvestmentVaccines            *float64 `json:"investment_vaccines"`
	ContactTracing                *float64 `json:"contact_tracing"`
	StringencyIndex               *float64 `json:"stringency_index"`
	RestrictionGatherings         *float64 `json:"restriction_gatherings"`
	StayHomeRequirements          *float64 `json:"stay_home_requirements"`
	IncomeSupport                 *float64 `json:"income_support"`
	DebtRelief                    *float64 `json:"debt_relief"`
	InternationalSupport          *float64 `json:"international_support"`
	TestingPolicy                 *float64 `json:"testing_policy"`
	ContainmentIndex              *float64 `json:"containment_index"`
	FacialCoverings               *float64 `json:"facial_coverings"`
	VaccinationPolicy             *float64 `json:"vaccination_policy"`
	VaccineEligibility            *float64 `json:"vaccine_eligibility"`
}

OxCGRT collects publicly available information on indicators of government response. These indicators take policies such as school closures, travel bans, etc. and record them on an ordinal scale. The remainder is financial indicators, such as fiscal or monetary measures.OxCGRT measures the variation in governments’ responses using its COVID-19 Government Response Stringency Index. This composite measure is a simple additive score of nine indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not be interpreted as a rating of the appropriateness or effectiveness of a country's response.It also includes a 'COVID-19 Containment and Health Response' index which is based on the metrics used in the 'Stringency Index' plus testing policy, contact tracing, face coverings and vaccine policy.The specific policy and response categories are coded as follows:School closures:0 - No measures1 - recommend closing2 - Require closing (only some levels or categories,e.g. just high school, or just public schools)3 - Require closing all levelsNo data - blankWorkplace closures:0 - No measures1 - recommend closing (or work from home)2 - require closing (or work from home) for somesectors or categories of workers3 - require closing (or work from home) all but essential workplaces (e.g. grocery stores, doctors)No data - blankCancel public events:0- No measures1 - Recommend cancelling2 - Require cancellingNo data - blankRestrictions on gatherings:0 - No restrictions1 - Restrictions on very large gatherings (the limit is above 1,000 people)2 - Restrictions on gatherings between 100-1,000 people3 - Restrictions on gatherings between 10-100 people4 - Restrictions on gatherings of less than 10 peopleNo data - blankClose public transport:0 - No measures1 - Recommend closing (or significantly reduce volume/route/means of transport available)2 - Require closing (or prohibit most citizens from using it)Public information campaigns:0 -No COVID-19 public information campaign1 - public officials urging caution about COVID-192 - coordinated public information campaign (e.g. across traditional and social media)No data - blankStay at home:0 - No measures1 - recommend not leaving house2 - require not leaving house with exceptions for daily exercise, grocery shopping, and ‘essential’ trips3 - Require not leaving house with minimal exceptions (e.g. allowed to leave only once every few days, or only one person can leave at a time, etc.)No data - blankRestrictions on internal movement:0 - No measures1 - Recommend movement restriction2 - Restrict movementInternational travel controls:0 - No measures1 - Screening2 - Quarantine arrivals from high-risk regions3 - Ban on high-risk regions4 - Total border closureNo data - blankTesting policy0 – No testing policy1 – Only those who both (a) have symptoms AND (b) meet specific criteria (eg key workers, admitted to hospital, came into contact with a known case, returned from overseas)2 – testing of anyone showing COVID-19 symptoms3 – open public testing (e.g. “drive through” testing available to asymptomatic people)No dataContract tracing0 - No contact tracing1 - Limited contact tracing - not done for all cases2 - Comprehensive contact tracing - done for all casesNo dataFace coverings0- No policy1- Recommended2- Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible3- Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible4- Required outside the home at all times, regardless of location or presence of other peopleVaccination policy0 - No availability1 - Availability for ONE of the following: key workers/ clinically vulnerable groups / elderly groups2 - Availability for TWO of the following: key workers/ clinically vulnerable groups / elderly groups3 - Availability for ALL the following: key workers/ clinically vulnerable groups / elderly groups4 - Availability for all three, plus partial additional availability (select broad groups/ages)5 - Universal availability

type CovidTestingTimeSeriesDataDataset 

type CovidTestingTimeSeriesDataDataset struct {
	TotalTests                      *float64 `json:"total_tests"`
	NewTests                        *float64 `json:"new_tests"`
	TotalTestsPerThousand           *float64 `json:"total_tests_per_thousand"`
	NewTestsPerThousand             *float64 `json:"new_tests_per_thousand"`
	Annotation                      *float64 `json:"annotation"`
	CumulativeTestsPerCase          *float64 `json:"cumulative_tests_per_case"`
	CumulativePositivityRate        *float64 `json:"cumulative_positivity_rate"`
	TestingObservations             *float64 `json:"testing_observations"`
	DaysSinceObservation            *float64 `json:"days_since_observation"`
	ObservationsFound               *float64 `json:"observations_found"`
	NewTests7daySmoothed            *float64 `json:"new_tests_7day_smoothed"`
	NewTestsPerThousand7daySmoothed *float64 `json:"new_tests_per_thousand_7day_smoothed"`
	ShortTermTestsPerCase           *float64 `json:"short_term_tests_per_case"`
	ShortTermPositivityRate         *float64 `json:"short_term_positivity_rate"`
}

Data on COVID-19 testing. Comparisons between countries are compromised for several reasons.You can download the full dataset, alongside detailed source descriptions here: https://github.com/owid/covid-19-data/tree/master/public/data/

type CrossCountryLiteracyRatesWorldBankCiaWorldFactbookAndOtherSourcesDataset 

type CrossCountryLiteracyRatesWorldBankCiaWorldFactbookAndOtherSourcesDataset struct {
	LiteracyRatesWorldBankCiaWorldFactbookAndOtherSources *float64 `json:"literacy_rates_world_bank_cia_world_factbook_and_other_sources"`
}

Additional Information</br>This long run cross-country dataset combines data from a number of sources. We took the estimates from the World Bank’s WDI as our base, and then extended coverage by adding literacy estimates from the CIA Factbook, as well as several other long-run series, as follows:<ul><li>Data before 1800: Buringh, E., & Van Zanden, J. L. (2009). Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, A long-term perspective from the sixth through eighteenth centuries. The Journal of Economic History. Online <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.553.9220&rep=rep1&type=pdf">here</a>. Observations before 1800 are plotted at the midpoint of the given time range (1475 refers to 1451–1500, 1550 refers to 1501-1600 etc.)</li><li>Data for 1820 and 1870 (except for the US): Broadberry and O'Rourke (2010) – The Cambridge Economic History of Modern Europe: Volume 1, 1700-1870 </li><li>Data for the US: National Center for Education Statistics, online <a href="nces.ed.gov/naal/lit_history.asp">here</a>. </li> <li>Global estimates for 1820-2000: van Zanden, J.L., et al. (eds.) (2014), How Was Life?: Global Well-being since 1820, OECD Publishing. Online <a href="http://www.oecd.org/statistics/how-was-life-9789264214262-en.htm">here</a>. </li><li>Historical estimates for Latin America: OxLAD – Oxford Latin American Economic History Data Base, online <a href="http://moxlad-staging.herokuapp.com/home/en">here</a>. </li><li>Most recent estimates for high-income countries, as well as any available estimates for 2016: <a href="https://www.cia.gov/library/publications/the-world-factbook/">CIA World Factbook</a>. </li></ul>Further notes: </br>- All sources rely on the same conceptual definition, but in many cases sources do not agree with one another. Because of this, year to year changes should be interpreted with caution. You can read more about literacy measurement here: https://ourworldindata.org/how-is-literacy-measured</br>- The World Bank's WDI estimates correspond to UNESCO Institute for Statistics. OxLAD estimates come from several underlying sources (see original documentation for more details).</br>- For Paraguay in 1982, the OxLAD data source was favoured over the World Bank (WDI) as the latter estimates literacy rates at 78.46%, a much lower estimate compared to neighbouring years for which there was data available.</br>- Sources for each country-year observation can be found in <a href="http://ourworldindata.org/wp-content/uploads/2018/06/cross-country-literacy-sources-final.csv" rel="noopener" target="_blank">this table.</a>

type CrudeBirthAndDeathRatesPer1000EnglandAndWales15412015WrigleyAndSchofieldMitchellUkOnsDataset 

type CrudeBirthAndDeathRatesPer1000EnglandAndWales15412015WrigleyAndSchofieldMitchellUkOnsDataset struct {
	CrudeBirthRateOwidBasedOnWrigleyAndSchofield1981Mitchell2010AndUkOns2016 *float64 `json:"crude_birth_rate_owid_based_on_wrigley_and_schofield_1981_mitchell_2010_and_uk_ons_2016"`
	CrudeDeathRateOwidBasedOnWrigleyAndSchofield1981Mitchell2010AndUkOns2016 *float64 `json:"crude_death_rate_owid_based_on_wrigley_and_schofield_1981_mitchell_2010_and_uk_ons_2016"`
}

*Note: Our World In Data compiled the data from the above sources as follows: for the years 1541-1861: Wrigley and Schofield (1981); for the years 1862-2003: Brian Mitchell (2010); for the years 2004-2015: UK Office for National Statistics (2016).

type CrudeMarriageRateOwidBasedOnUnOecdEurostatAndOtherSourcesDataset 

type CrudeMarriageRateOwidBasedOnUnOecdEurostatAndOtherSourcesDataset struct {
	CrudeMarriageRatePer1_000Inhabitants *float64 `json:"crude_marriage_rate_per_1_000_inhabitants"`
}

Estimates rely on data from multiple sources:- For European countries, the data comes from the <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Marriage_and_divorce_statistics#Fewer_marriages.2C_more_divorces">Eurostat dataset</a>.- For the US the series is composed of data taken from three sources: <a href="https://hsus.cambridge.org/HSUSWeb/HSUSEntryServlet">Carter et al. (2006)</a> for the period 1920 - 1995; the <a href="https://www.census.gov/library/publications/2006/compendia/statab/126ed/vital-statistics.html">US Census Bureau (2007)</a> for the period 1996 - 2004; and the <a href="https://www.cdc.gov/nchs/nvss/marriage-divorce.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fmardiv.htm">CDC</a> for the period 2005 to present.- For other non-European countries that are OECD members, the data comes from the <a href="http://www.oecd.org/els/family/database.htm">OECD Family Database</a>.- For all other countries the data comes from the <a href="https://www.un.org/en/development/desa/population/publications/dataset/marriage/data.asp">UN World Marriage Database</a> ,(<em>NB. The source for each observation can be found in the metadata spreadsheet <a href="https://owid.cloud/app/uploads/2020/01/marriage-rates-final-metadata-standard.xlsx">here</a></em>)Notes regarding comparability:1. The US Census Bureau (2007) figures include estimates for some States through 1965 and also for 1976 and 1977 and marriage licenses for some states for all years except 1973 and 1975. From 1978, estimates include nonlicensed marriages in California. Prior to 1960, figures exclude Alaska and Hawaii. 2. The CDC has revised the rate of marriages for 2016 due to revised figures for Illinois. 

Rates for 2001-2009 have also been revised and are based on intercensal population estimates from the 2000 and 2010 censuses.

Populations for 2010 rates are based on the 2010 census.3. For Canada, data include the legal union of two persons of the same sex in some provinces and territories from 2003 onwards, and in all of Canada from 2005 onwards. 4. Data for New Zealand include civil unions.5. Germany includes the German Democratic Republic. This series was favoured instead of 'Germany (until 1990 former territory of the FRG)' as when compared with the OECD series matched more closely.6. Footnote by Turkey. The information in this document with reference to « Cyprus » relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognizes the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.7. Footnote by all the European Union Member States of the OECD and the European Commission.. The Republic of Cyprus is recognized by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.8. Estimates from the UN World Marriage Database have a 10-year reference period. This means that estimates labeled as "1985", for example, correspond to data collected over the period 1980-1989.

type CrudeOilConsumptionVsPriceBpStatistics2016Dataset 

type CrudeOilConsumptionVsPriceBpStatistics2016Dataset struct {
	OilConsumptionBpStatistics2016 *float64 `json:"oil_consumption_bp_statistics_2016"`
	CrudeOilPriceBpStatistics2016  *float64 `json:"crude_oil_price_bp_statistics_2016"`
}

Oil consumption data is based on reported global consumption measured in barrels daily. This includes crude oil, shale oil, oil sands and Natural Gas Liquids (the gas component of which is treated separately and not included).

Oil prices are based on average crude oil prices as the Arabian Light series from 1965-1983 and Brent dated series from 1984-2015. This is reported in 2015 US$ per barrel (deflated by BP Energy Review using the Consumer Price Index for the US).

type CumulativeCo2EmissionsShareOwidBasedOnGcp2017Dataset 

type CumulativeCo2EmissionsShareOwidBasedOnGcp2017Dataset struct {
	CumulativeCo2EmissionsShareOwidBasedOnGcp2017 *float64 `json:"cumulative_co2_emissions_share_owid_based_on_gcp_2017"`
}

Cumulative carbon dioxide (CO₂) emissions have been calculated by Our World in Data based on annual CO₂ emissions published by the Global Carbon Project (GCP). By summing the annual emissions to a given year we have calculated the cumulative total. This is given as the share of global cumulative emissions by dividing each country or region's cumulative emissions by the cumulative world emissions for that given year. Data converted from carbon into carbon dioxide (using conversion factor of 3.67). Data for Australia appears to be negative from 1851-1858; for clarity, these figures have been renormalised to zero.Archived data is held at the Carbon Dioxide Information Analysis Centre (CDIAC). Reference: Tom Boden and Bob Andres (Oak Ridge National Laboratory); Gregg Marland (Appalachian State University). Available at: http://cdiac.ornl.gov/

type CumulativeShareOfMarriagesEndingInDivorceEnglandAndWalesUkOnsDataset 

type CumulativeShareOfMarriagesEndingInDivorceEnglandAndWalesUkOnsDataset struct {
	CumulativeShareOfMarriagesEndingInDivorceEnglandAndWalesUkOns *float64 `json:"cumulative_share_of_marriages_ending_in_divorce_england_and_wales_uk_ons"`
}

Data represents the cumulative share of marriages in England and Wales that had ended in divorce a given number of years following the year of marriage.Each line represents a given cohort, marked by the year in which they were married e.g. the line "1980" represents couples married in the year 1980.The UK Office for National Statistics notes the following points:– Cumulative percentages add a percentage from one period to the percentage of another period to show the total percentage over a given time period.– When calculating these percentages, it has been assumed that the couples who married each year have not moved out of England and Wales, couples who divorced each year have not moved into England and Wales since getting married, and couples marry in the country where they usually live.– The Divorce Reform Act 1969, which came into effect on 1 January 1971, made it easier for couples to divorce upon separation.

type CurrentGdpBritishPoundsFouquinAndHugotCepii2016Dataset 

type CurrentGdpBritishPoundsFouquinAndHugotCepii2016Dataset struct {
	GdpFouquinAndHugotCepii2016 *float64 `json:"gdp_fouquin_and_hugot_cepii_2016"`
}

Due to the long-run nature of Fouquin and Hugot (CEPII 2016) time series, GDP estimates are compiled using a variety of sources. The top five sources, which together make up more than a third of the dataset (approx 11,864 observations out of 31,541) are from the following sources: World Bank (2015) (8513 obs); Mitchell (2017a,b,c) (1714); Barbieri and Keshik (2012) (1037 obs); Smits et al (2014) (335 obs); and Dincecco and Prado (2013) (265 obs). A comprehensive list of GDP sources and full references can be found here: http://www.cepii.fr/PDF_PUB/wp/2016/wp2016-14.pdf, Table 7, page 19. Similarly, CEPII's exchange rate variable is set to the British pound value of one local currency unit. Again, it draws on a range of sources of which the top five are reported here: International Monetary Fund (2012) (4394 obs); extracted from Wikipedia (2448 obs); Barbieri and Keshk (2012) (2005 obs); Officer (2014) (1752 obs); and Denzel (2010) (1750 obs). A comprehensive list of exchange rate sources and full references can be found here: http://www.cepii.fr/PDF_PUB/wp/2016/wp2016-14.pdf, Table 8, page 21. Russia time series is comprised of Russia from 1992-2014 and the USSR from 1827-1991.

type D1VsD10D1IncomeconsumptionPovcal2018Dataset 

type D1VsD10D1IncomeconsumptionPovcal2018Dataset struct {
	BottomDecileIncomeconsumptionPerDay                        *float64 `json:"bottom_decile_incomeconsumption_per_day"`
	DifferenceBetweenTopAndBottomDecileIncomeconsumptionPerDay *float64 `json:"difference_between_top_and_bottom_decile_incomeconsumption_per_day"`
	WorldBankRegionIncludingAdvancedIndustrializedCategory     *float64 `json:"world_bank_region_including_advanced_industrialized_category"`
	Pop                                                        *float64 `json:"pop"`
}

type DailyFatSupplyFao2017Dataset 

type DailyFatSupplyFao2017Dataset struct {
	DailyFatSupplyFao2017 *float64 `json:"daily_fat_supply_fao_2017"`
}

Daily fat supply is defined as the average per capita fat availability. Note that this indicates the fat availability delivered to households but does not necessarily indicate the quantity of fat actually consumed (food may be wasted at the consumer level).Data from 1961-1991 for Post-Soviet states are assumed to be in line with reported data for the USSR over this period.

type DailyProteinSupplyFao2017Dataset 

type DailyProteinSupplyFao2017Dataset struct {
	DailyProteinSupplyFao2017               *float64 `json:"daily_protein_supply_fao_2017"`
	DailyProteinSupplyOfAnimalOriginFao2017 *float64 `json:"daily_protein_supply_of_animal_origin_fao_2017"`
	DailyProteinSupplyOfPlantOriginFao2017  *float64 `json:"daily_protein_supply_of_plant_origin_fao_2017"`
	ShareOfProteinOfAnimalOriginFao2017     *float64 `json:"share_of_protein_of_animal_origin_fao_2017"`
	ShareOfProteinOfPlantOriginFao2017      *float64 `json:"share_of_protein_of_plant_origin_fao_2017"`
}

Data from 1961 onwards is sourced from the UN Food and Agricultural Organization database. Available at: http://www.fao.org/faostat/en/#data/FBS [accessed 22nd August 2017].Data for the years 1948 and 1949 is derived from: FAO (1949) – The state of food and agriculture, 1949 - a survey of world conditions and prospects. The State of Food and Agriculture, 1949. Available at: http://www.fao.org/docrep/016/ap637e/ap637e.pdf.Data for the UK in 1837 and 1863 is derived from Clark, Gregory, Michael Huberman, and Peter Lindert. 1995. “A British Food Puzzle, 1770–1850.” Economic History Review 48(2): 215–237. Available at: https://www.jstor.org/stable/pdf/2598401.pdf?refreqid=excelsior%3A8924d3e19be34163046f26f10a92ee12Data for England in 1787 is sourced from Clark (2008) - A Farewell to Alms: A Brief Economic History of the World. Princeton University Press.Daily protein supply is defined as the average per capita protein availability. Note that this indicates the availability delivered to households but does not necessarily indicate the quantity of protein actually consumed (food may be wasted at the consumer level).Data from 1961-1991 for Post-Soviet states are assumed to be in line with reported data for the USSR over this period.Protein of animal origin includes protein supplied in the form of all meat commodities, eggs and dairy products, and fish & seafood.Protein of plant origin was dervied as the difference between total protein supply and that of animal origin.

type DailySupplyOfCaloriesPerPersonOwidBasedOnUnFaoAndHistoricalSourcesDataset 

type DailySupplyOfCaloriesPerPersonOwidBasedOnUnFaoAndHistoricalSourcesDataset struct {
	DailyCaloricSupplyOwidBasedOnUnFaoAndHistoricalSources *float64 `json:"daily_caloric_supply_owid_based_on_un_fao_and_historical_sources"`
}

This dataset has been constructed by Our World in Data from multiple sources.All data from 1961 onwards is sourced from the Food and Agriculture Organization of the United Nations. Available at: https://www.fao.org/faostat/en/#data/FBSPre-1961 data is available for some countries, based on a range of historical reconstructions.We provide further details of all of these sources, and how this dataset is constructed here: https://ourworldindata.org/calorie-supply-sources

type DaysAndHoursOfWorkInOldAndNewWorldsHubermanAndMinns2007Dataset 

type DaysAndHoursOfWorkInOldAndNewWorldsHubermanAndMinns2007Dataset struct {
	HoursOfWorkPerWeekTotalHubermanAndMinns2007  *float64 `json:"hours_of_work_per_week_total_huberman_and_minns_2007"`
	HoursOfWorkPerWeekMaleHubermanAndMinns2007   *float64 `json:"hours_of_work_per_week_male_huberman_and_minns_2007"`
	HoursOfWorkPerWeekFemaleHubermanAndMinns2007 *float64 `json:"hours_of_work_per_week_female_huberman_and_minns_2007"`
	NumbersOfDaysOffHubermanAndMinns2007         *float64 `json:"numbers_of_days_off_huberman_and_minns_2007"`
}

The New World includes Europe, the United States, Australia, and Canada while the Old World consists of all other countries included in Huberman and Minns's sample. The 'Old World (weighted)' and 'New World (weighted)' series are population weighted averages.See table 1 for further information regarding sources the authors consulted in producing hours of work per week estimates.See table 2 for more information about vacation and holidays estimates.

type DeathRateByAgeGroupInEnglandAndWalesOnsDataset 

type DeathRateByAgeGroupInEnglandAndWalesOnsDataset struct {
	Less1YearOld   *float64 `json:"less1_year_old"`
	O1_4YearsOld   *float64 `json:"o1_4_years_old"`
	O5_9YearsOld   *float64 `json:"o5_9_years_old"`
	O10_14YearsOld *float64 `json:"o10_14_years_old"`
	O15_19YearsOld *float64 `json:"o15_19_years_old"`
	O20_24YearsOld *float64 `json:"o20_24_years_old"`
	O25_29YearsOld *float64 `json:"o25_29_years_old"`
	O30_34YearsOld *float64 `json:"o30_34_years_old"`
	O35_39YearsOld *float64 `json:"o35_39_years_old"`
	O40_44YearsOld *float64 `json:"o40_44_years_old"`
	O45_49YearsOld *float64 `json:"o45_49_years_old"`
	O50_54YearsOld *float64 `json:"o50_54_years_old"`
	O55_59YearsOld *float64 `json:"o55_59_years_old"`
	O60_64YearsOld *float64 `json:"o60_64_years_old"`
	O65_69YearsOld *float64 `json:"o65_69_years_old"`
	O70_74YearsOld *float64 `json:"o70_74_years_old"`
	O75_79YearsOld *float64 `json:"o75_79_years_old"`
	O80YearsOld    *float64 `json:"o80_years_old"`
}

Death rates in England and Wales by age group, measured as the number of deaths from all causes per 1,000 individuals per age bracket.

type DeathsAttributedToAirPollutionLelieveldEtAl2019Dataset 

type DeathsAttributedToAirPollutionLelieveldEtAl2019Dataset struct {
	ExcessMortalityFromAirPollutionAllSources                               *float64 `json:"excess_mortality_from_air_pollution_all_sources"`
	ExcessMortalityFromFossilFuels                                          *float64 `json:"excess_mortality_from_fossil_fuels"`
	ExcessMortalityFromAllAnthropogenicPollution                            *float64 `json:"excess_mortality_from_all_anthropogenic_pollution"`
	TotalYearsLifeLostFromAirPollutionAllSources                            *float64 `json:"total_years_life_lost_from_air_pollution_all_sources"`
	TotalYearsLifeLostFromFossilFuels                                       *float64 `json:"total_years_life_lost_from_fossil_fuels"`
	TotalYearsLifeLostFromAllAnthropogenicPollution                         *float64 `json:"total_years_life_lost_from_all_anthropogenic_pollution"`
	DeathRatesFromAllAirPollutionPer100_000                                 *float64 `json:"death_rates_from_all_air_pollution_per_100_000"`
	DeathRatesFromAirPollutionFromFossilFuelsPer100_000                     *float64 `json:"death_rates_from_air_pollution_from_fossil_fuels_per_100_000"`
	DeathRatesFromAllAnthropogenicAirPollutionPer100_000                    *float64 `json:"death_rates_from_all_anthropogenic_air_pollution_per_100_000"`
	YllRatesFromAllAirPollutionPer100_000                                   *float64 `json:"yll_rates_from_all_air_pollution_per_100_000"`
	YllRatesFromAirPollutionFromFossilFuelsPer100_000                       *float64 `json:"yll_rates_from_air_pollution_from_fossil_fuels_per_100_000"`
	YllRatesFromAnthropogenicAirPollutionPer100_000                         *float64 `json:"yll_rates_from_anthropogenic_air_pollution_per_100_000"`
	DeathsFromFossilPollutionAsAShareOfTotalAirPollutionDeaths              *float64 `json:"deaths_from_fossil_pollution_as_a_share_of_total_air_pollution_deaths"`
	DeathsFromFossilPollutionAsAShareOfTotalAnthropogenicAirPollutionDeaths *float64 `json:"deaths_from_fossil_pollution_as_a_share_of_total_anthropogenic_air_pollution_deaths"`
	DeathsFromAnthropogenicPollutionAsAShareOfTotalAirPollutionDeaths       *float64 `json:"deaths_from_anthropogenic_pollution_as_a_share_of_total_air_pollution_deaths"`
}

Lelieveld et al. (2019) quantify excess mortality and years of life lost (YLL) from air pollution. They do this by applying an atmospheric chemistry–general circulation model to calculate the impacts of air pollution on climate and public health. This model includes concentrations of ozone (O3) and particulate matter, including PM2.5. These concentrations are then used to convert to health burden based on the Global Burden of Disease methodology.– Air pollution deaths from all sources includes pollution from all anthropogenic sources (fossil fuels, agriculture, residential energy use, and non-fossil industrial emissions) and natural emissions from sources such as desert dust.– Air pollution deaths from fossil fuels includes local air pollution generated from the burning of coal, oil and gas.– Air pollution deaths from all anthropogenic sources includes pollution from fossil fuels plus agriculture, residential energy use, and non-fossil industrial emissions.Our World in Data has also converted these mortality and YLL metrics into rates based on the population figures included in the study's Supplemental material.Our World in Data has also calculated fossil fuel and total anthropogenic pollution deaths as a share of total air pollution deaths based on the paper's original data.

type DeathsByWorldRegionWho2016Dataset 

type DeathsByWorldRegionWho2016Dataset struct {
	MalariaDeathsByWorldRegionWho2016 *float64 `json:"malaria_deaths_by_world_region_who_2016"`
}

The original dataset is published with confidence intervals.

type DeathsFromChernobylRangeOfLongTermEstimatesWho2005FairlieAndSumner2006CardisEtAl2006Dataset 

type DeathsFromChernobylRangeOfLongTermEstimatesWho2005FairlieAndSumner2006CardisEtAl2006Dataset struct {
	ChernobylWho2005FairlieAndSumner2006CardisEtAl2006 *float64 `json:"chernobyl_who_2005_fairlie_and_sumner_2006_cardis_et_al_2006"`
}

Estimates of the total number of deaths from the Chernobyl nuclear incidents remain contested: we have therefore included a series of published estimates to cover the range of considered values.

WHO (2005a) refers to the number of deaths (up to 4000) estimated in the populations with highest exposure to radioactive fallout from the incident. WHO (2005b) add this to further estimates by the WHO [not included in its report] on the potential death toll in individuals beyond proximate areas.

Fairlie & Sumner (a) and (b) are represent their published lower and upper estimates, respectively.

References:

IAEA, WHO (2005/06). Chernobyl’s Legacy: Health, Environmental and Socio-Economic Impacts. Press release available at: http://www.who.int/mediacentre/news/releases/2005/pr38/en/

Special Report: Counting the dead. Nature 440, 982-983 (20 April 2006) | doi:10.1038/440982a.

Cardis et al. (2006). Estimates of the cancer burden in Europe from radioactive fallout from the Chernobyl accident. International Journal of Cancer.

Fairlie and Sumner (2006). An independent scientific evaluation of health and environmental effects 20 years after the nuclear disaster providing critical analysis of a recent report by the International Atomic Energy Agency (IAEA) and the World Health Organisation (WHO). Available at: http://www.chernobylreport.org/?p=summary

type DeathsFromFukushimaWho20132016Dataset 

type DeathsFromFukushimaWho20132016Dataset struct {
	NumberOfDeathsFromFukushimaNuclearDisasterWho2013_2016 *float64 `json:"number_of_deaths_from_fukushima_nuclear_disaster_who_2013_2016"`
}

Estimates of the total number of deaths from the Fukushima nuclear incident.There were no direct deaths from the Fukushima Daiichi disaster. The official death toll was 573 people, all of which were premature deaths from evacuation and displacement of populations in the surrounding area: In 2018, the Japanese government reported that one worker has since died from lung cancer as a result of exposure from the event: https://www.bbc.co.uk/news/world-asia-45423575Estimates for Fukushima have been derived from the WHO (2013; 2016), and official death toll from the Government of Japan.The World Health Organization (WHO) Report published five years on, suggests very low risk of increased cancer deaths in Japan. In a review of the response and long-term health impacts of Fukushima, published by Michael Reich and Aya Goto in The Lancet (2015), the authors note that: “no one has died from radiation exposure, and the UN Scientific Committee on the Effects of Atomic Radiation report in 2013 stated that substantial changes in future cancer statistics attributed to radiation exposure are not expected to be observed".Sources:World Health Organisation (2013). Health risk assessment from the nuclear accident after the 2011 Great East Japan Earthquake and Tsunami based on a preliminary dose estimation. Available at: http://apps.who.int/iris/bitstream/10665/78218/1/9789241505130_eng.pdfThe Yomiuri Shimbun, 573 deaths ‘related to nuclear crisis’, The Yomiuri Shimbun, 5 February 2012, https://wayback.archive-it.org/all/20120204190315/http://www.yomiuri.co.jp/dy/national/T120204003191.htm.World Health Organization (2016). FAQs: Fukushima Five Years On. Available online at: https://www.who.int/ionizing_radiation/a_e/fukushima/faqs-fukushima/en/.Reich, M. R., & Goto, A. (2015). Towards long-term responses in Fukushima. The Lancet, 386(9992), 498-500.United Nations Scientific Committee on the Effects of Atomic Radiation. (2015). Report of the United Nations Scientific Committee on the effects of atomic radiation to the general assembly.

type DeathsFromSmalllpoxAndAllCausesInLondon16291902Dataset 

type DeathsFromSmalllpoxAndAllCausesInLondon16291902Dataset struct {
	DeathsFromSmallpoxInLondon1629_1902Owid2017  *float64 `json:"deaths_from_smallpox_in_london_1629_1902_owid_2017"`
	DeathsFromAllCausesInLondon1629_1902Owid2017 *float64 `json:"deaths_from_all_causes_in_london_1629_1902_owid_2017"`
	SmallpoxDeathRateInLondon1629_1902Owid2017   *float64 `json:"smallpox_death_rate_in_london_1629_1902_owid_2017"`
}

1629-1881: Guy, W. (1882). Two Hundred and Fifty Years of Small Pox in London. Journal Of The Statistical Society Of London, 45(3), 399.1882-1885: Registrar-general. (1886). Summary of Weekly Returns (Annual Summary) of (marriages) Births, Deaths and Causes of Death in London and other great towns. Eyre and Spottiswoode1886-1887: Registrar-general. (1888). Summary of Weekly Returns (Annual Summary) of (marriages) Births, Deaths and Causes of Death in London and other great towns. Eyre and Spottiswoode1888-1899: Registrar-general. (1900). Summary of Weekly Returns (Annual Summary) of (marriages) Births, Deaths and Causes of Death in London and other great towns. Eyre and Spottiswoode1900-1902: Registrar-general. (1903). Summary of Weekly Returns (Annual Summary) of (marriages) Births, Deaths and Causes of Death in London and other great towns. Eyre and Spottiswoode

type DeathsFromSmallpoxPerMillionPopulationEdwardes1902Dataset 

type DeathsFromSmallpoxPerMillionPopulationEdwardes1902Dataset struct {
	NumberOfDeathsFromSmallpoxPer1_000PopulationEdwardes1902 *float64 `json:"number_of_deaths_from_smallpox_per_1_000_population_edwardes_1902"`
}

type DeathsPerTwhEnergyProductionMarkandyaAndWilkinsonSovacoolEtAlDataset 

type DeathsPerTwhEnergyProductionMarkandyaAndWilkinsonSovacoolEtAlDataset struct {
	DeathsPerTwhMarkandyaAndWilkinsonSovacoolEtAl *float64 `json:"deaths_per_twh_markandya_and_wilkinson_sovacool_et_al"`
}

Death rates from energy production is measured as the number of deaths by energy source per terawatt-hour (TWh) of production. This data combines two sources: Markandya, A., & Wilkinson, P. (2007) assessed the death rates from accidents and air pollution major energy sources (fossil fuels, nuclear and biomass). Sovacool et al. (2016) assessed death rates from accidents from low-carbon energy sources (nuclear and renewables) based on historical records spanning the period 1990 to 2013.When we try to combine the two analyses referenced earlier, one issue we encounter is that neither study includes both of the major nuclear accidents in its death rate figure: Markandya and Wilkinson (2007) was published before the Fukushima disaster in 2011; and Sovacool et al. (2016) only look at death rates since 1990, and therefore do not include the 1986 Chernobyl accident. We have therefore reconstructed the death rate for nuclear to include both of these accidents.For Chernobyl, there are several death estimates. We rely on the estimate published by the World Health Organization (WHO) – the most-widely cited figure – although this is considered to be too high by several researchers, including a later report by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). The WHO estimates that 4000 people have, or will die, from the Chernobyl disaster. This includes the death of 31 people as a direct result of the disaster and those expected to die at a later date from cancers due to radiation exposure.The disaster in Fukushima killed 574 people. In 2018, the Japanese government reported that one worker has since died from lung cancer as a result of exposure from the event. No one died directly from the Fukushima disaster. Instead, most people died as a result of evacuation procedures. According to Japanese authorities 573 people died due to the impact of the evacuation and stress.To the death toll of history’s two nuclear disasters we have added the death rate that Markandya and Wilkinson (2007) estimated for occupational deaths, most from milling and mining. Their published rate is 0.022 deaths per TWh.The sum of these three data points gives us a death rate of 0.07 deaths per TWh.Full references to the underlying studies can be found here:Sovacool, B. K., Andersen, R., Sorensen, S., Sorensen, K., Tienda, V., Vainorius, A., ... & Bjørn-Thygesen, F. (2016). Balancing safety with sustainability: assessing the risk of accidents for modern low-carbon energy systems. Journal of cleaner production, 112, 3952-3965.Markandya, A., & Wilkinson, P. (2007). Electricity generation and health. The Lancet, 370(9591), 979-990. Available at: http://doi.org/10.1016/S0140-6736(07)61253-7

type DeathsPerTwhFromLowCarbonEnergySovacoolEtAl2016Dataset 

type DeathsPerTwhFromLowCarbonEnergySovacoolEtAl2016Dataset struct {
	DeathsPerTwhSovacoolEtAl *float64 `json:"deaths_per_twh_sovacool_et_al"`
}

Number of deaths attributed to energy-related accidents of low-carbon energy sources, measured as the number of deaths per terawatt-hour of production.Sovacool et al. (2016) developed a database of energy-related accidents over the period from 1950 to 2014. They define an accident as: "an unintentional incident or event at an energy facility that led to either one death (or more) or at least $50,000 in property damage."This database was developed based on a series of academic databases (including ScienceDirect and EBSCO host) as well as the internet (using Google and Safari).The normalized death rate data is presented by Sovacool et al. (2016) as the number of deaths per TWh over the period from 1990 to 2013.

type DecompositionOfGenderWageGap1980BlauAndKahn2017Dataset 

type DecompositionOfGenderWageGap1980BlauAndKahn2017Dataset struct {
	PercentOfGenderGap1980BlauAndKahn2017   *float64 `json:"percent_of_gender_gap_1980_blau_and_kahn_2017"`
	FemaleToMaleLogWageRatioBlauAndKahn2017 *float64 `json:"female_to_male_log_wage_ratio_blau_and_kahn_2017"`
}

See the authors' data appendix for more detail on how the data was prepared and analyzed.

type DecompositionOfGenderWageGap2010BlauAndKahn2017Dataset 

type DecompositionOfGenderWageGap2010BlauAndKahn2017Dataset struct {
	PercentOfGenderGap2010BlauAndKahn2017 *float64 `json:"percent_of_gender_gap_2010_blau_and_kahn_2017"`
}

See the authors' data appendix for more detail on how the data was prepared and analyzed.

type DecompositionTimesOfMarineDebrisDataset 

type DecompositionTimesOfMarineDebrisDataset struct {
	DecompositionRatesOfMarineDebrisYears *float64 `json:"decomposition_rates_of_marine_debris_years"`
}

Average estimated decomposition time of typical marine debris items.

type DeforestationByCommodityPendrillEtAl2019Dataset 

type DeforestationByCommodityPendrillEtAl2019Dataset struct {
	ForestLossHa                         *float64 `json:"forest_loss_ha"`
	ShareOfCommodityDrivenDeforestation  *float64 `json:"share_of_commodity_driven_deforestation"`
	DeforestationEmissions               *float64 `json:"deforestation_emissions"`
	ShareOfDeforestationEmissions        *float64 `json:"share_of_deforestation_emissions"`
	PeatEmissions                        *float64 `json:"peat_emissions"`
	ShareOfPeatEmissions                 *float64 `json:"share_of_peat_emissions"`
	TotalDeforestationAndPeatEmissions   *float64 `json:"total_deforestation_and_peat_emissions"`
	ShareOfDeforestationAndPeatEmissions *float64 `json:"share_of_deforestation_and_peat_emissions"`
}

Pendrill et al. (2019) developed a land-balance model which attributed detected forest loss across the world to the expansion of croplands, pasture and tree plantations. This is then linked to particular agricultural commodities based on national land use, crop and forest product statistics published in the UN Food and Agricultural Organization balance sheets.This study also maps deforestation and related CO2 emissions embedded in the international trade of these products using both a physical trade model, and a MRIO (multi-regional input-output) model. This allows for the quantification of deforestation and related emissions embedded in imported food and forestry products.

type DeforestationInTradePendrillDataset 

type DeforestationInTradePendrillDataset struct {
	ImportedDeforestationHectares             *float64 `json:"imported_deforestation_hectares"`
	ExportedDeforestationHectares             *float64 `json:"exported_deforestation_hectares"`
	NetDeforestationInTradeHectares           *float64 `json:"net_deforestation_in_trade_hectares"`
	EmbodiedDeforestation                     *float64 `json:"embodied_deforestation"`
	DeforestationDomesticConsumption          *float64 `json:"deforestation_domestic_consumption"`
	ShareDeforestationExported                *float64 `json:"share_deforestation_exported"`
	ShareDeforestationDomesticConsumption     *float64 `json:"share_deforestation_domestic_consumption"`
	ImportedDeforestationPerCapita            *float64 `json:"imported_deforestation_per_capita"`
	ExportedDeforestationPerCapita            *float64 `json:"exported_deforestation_per_capita"`
	DomesticConsumptionDeforestationPerCapita *float64 `json:"domestic_consumption_deforestation_per_capita"`
	NetTradedDeforestationPerCapita           *float64 `json:"net_traded_deforestation_per_capita"`
	DomesticAndImportedDeforestation          *float64 `json:"domestic_and_imported_deforestation"`
}

Pendrill et al. (2019) developed a land-balance model which attributed detected forest loss across the world to the expansion of croplands, pasture and tree plantations. This is then linked to particular agricultural commodities based on national land use, crop and forest product statistics published in the UN Food and Agricultural Organization balance sheets.This study maps deforestation embedded in the international trade of these products using both a physical trade model, and a MRIO (multi-regional input-output) model. This allows for the quantification of deforestation embedded in imported food and forestry products.Pendrill et al. (2019) provide data on exported and imported deforestation. Our World in Data have additionally calculated the net deforestation embedded in trade for each country by subtracting exports from imports.

type DeliveryPointsInTheUsUnitedStatesPostalService2018Dataset 

type DeliveryPointsInTheUsUnitedStatesPostalService2018Dataset struct {
	NumberOfDeliveryPointsToCityAddressesUsPostalService2018                 *float64 `json:"number_of_delivery_points_to_city_addresses_us_postal_service_2018"`
	NumberOfDeliveryPointsToRuralAddressesUsPostalService2018                *float64 `json:"number_of_delivery_points_to_rural_addresses_us_postal_service_2018"`
	NumberOfDeliveryPointsToPoBoxAddressesUsPostalService2018                *float64 `json:"number_of_delivery_points_to_po_box_addresses_us_postal_service_2018"`
	NumberOfDeliveryPointsToHighwayContractRouteAddressesUsPostalService2018 *float64 `json:"number_of_delivery_points_to_highway_contract_route_addresses_us_postal_service_2018"`
	NumberOfTotalDeliveryPointsUsPostalService2018                           *float64 `json:"number_of_total_delivery_points_us_postal_service_2018"`
}

Source notes: In 2004, the Postal Service refined its reporting of addresses on rural and highway contract routes by no longercounting vacant addresses (unoccupied for more than 90 days) and addresses of customers who received mail solelyvia Post Office box. Numbers may not add up due to rounding.

type DepressionPrevalenceByEducationOecdDataset 

type DepressionPrevalenceByEducationOecdDataset struct {
	AllLevelsOfEducationActive                                 *float64 `json:"all_levels_of_education_active"`
	AllLevelsOfEducationEmployed                               *float64 `json:"all_levels_of_education_employed"`
	AllLevelsOfEducationTotal                                  *float64 `json:"all_levels_of_education_total"`
	BelowUpperSecondaryEducationActive                         *float64 `json:"below_upper_secondary_education_active"`
	BelowUpperSecondaryEducationEmployed                       *float64 `json:"below_upper_secondary_education_employed"`
	BelowUpperSecondaryEducationTotal                          *float64 `json:"below_upper_secondary_education_total"`
	TertiaryEducationActive                                    *float64 `json:"tertiary_education_active"`
	TertiaryEducationEmployed                                  *float64 `json:"tertiary_education_employed"`
	TertiaryEducationTotal                                     *float64 `json:"tertiary_education_total"`
	UpperSecondaryAndPostSecondaryNonTertiaryEducationActive   *float64 `json:"upper_secondary_and_post_secondary_non_tertiary_education_active"`
	UpperSecondaryAndPostSecondaryNonTertiaryEducationEmployed *float64 `json:"upper_secondary_and_post_secondary_non_tertiary_education_employed"`
	UpperSecondaryAndPostSecondaryNonTertiaryEducationTotal    *float64 `json:"upper_secondary_and_post_secondary_non_tertiary_education_total"`
}

Share of adults (aged 25-64 years) who report having depression in data surveys, disaggregated by highest level of education. This is further differentiated based on those who are employed, active (those actively looking for work), and the total (which is the total population, including those unemployed).

type DietCompositionsByCommodityCategoriesFao2017Dataset 

type DietCompositionsByCommodityCategoriesFao2017Dataset struct {
	CerealsAndGrainsFao2017KilocaloriesPerPersonPerDay   *float64 `json:"cereals_and_grains_fao_2017_kilocalories_per_person_per_day"`
	PulsesFao2017KilocaloriesPerPersonPerDay             *float64 `json:"pulses_fao_2017_kilocalories_per_person_per_day"`
	StarchyRootsFao2017KilocaloriesPerPersonPerDay       *float64 `json:"starchy_roots_fao_2017_kilocalories_per_person_per_day"`
	SugarFao2017KilocaloriesPerPersonPerDay              *float64 `json:"sugar_fao_2017_kilocalories_per_person_per_day"`
	OilsAndFatsFao2017KilocaloriesPerPersonPerDay        *float64 `json:"oils_and_fats_fao_2017_kilocalories_per_person_per_day"`
	MeatFao2017KilocaloriesPerPersonPerDay               *float64 `json:"meat_fao_2017_kilocalories_per_person_per_day"`
	DairyAndEggsFao2017KilocaloriesPerPersonPerDay       *float64 `json:"dairy_and_eggs_fao_2017_kilocalories_per_person_per_day"`
	FruitAndVegetablesFao2017KilocaloriesPerPersonPerDay *float64 `json:"fruit_and_vegetables_fao_2017_kilocalories_per_person_per_day"`
	OtherFao2017KilocaloriesPerPersonPerDay              *float64 `json:"other_fao_2017_kilocalories_per_person_per_day"`
	AlcoholicBeveragesFao2017KilocaloriesPerPersonPerDay *float64 `json:"alcoholic_beverages_fao_2017_kilocalories_per_person_per_day"`
}

Data represents the average daily per capita supply of calories from the full range of commodities, grouped by food categories. Note that these figures do not correct for waste at the household/consumption level so may not directly reflect the quantity of food finally consumed by a given individual.Figures for Former Soviet Union states have been allocated the average food supply figures of the USSR for the period 1961-1991, then their respective national values from there onwards.Specific food commodities have been grouped into higher-level categories. The presented categories include the following FAO items:Cereals and grains: wheat, rice, maize, barley, oats, millet, sorghum, rye, cereals (other) and all derivative productsPulses: pulses, totalStarchy roots: starchy roots, totalFruits and vegetables: fruits - excluding wine, vegetablesOils & fats: vegetable oils, animal fats, oilcrops, treenutsSugar: sugar & sweeteners, sugar cropsMeat: bovine meat, poultry, pigmeat, mutton & goat meat, meat (other), fish and seafood (total)Dairy & eggs: Milk - excluding butterAlcoholic beverages: Alcohol, not including Alcohol (non-food)Other: additional categories including infant food, spices, and miscellaneous

type DietCompositionsBySpecificFoodCommoditiesFao2017Dataset 

type DietCompositionsBySpecificFoodCommoditiesFao2017Dataset struct {
	AlcoholicBeveragesFao2017 *float64 `json:"alcoholic_beverages_fao_2017"`
	AnimalFatsFao2017         *float64 `json:"animal_fats_fao_2017"`
	BarleyFao2017             *float64 `json:"barley_fao_2017"`
	BovineMeatFao2017         *float64 `json:"bovine_meat_fao_2017"`
	CerealsOtherFao2017       *float64 `json:"cereals_other_fao_2017"`
	EggsFao2017               *float64 `json:"eggs_fao_2017"`
	FishAndSeafoodFao2017     *float64 `json:"fish_and_seafood_fao_2017"`
	FruitFao2017              *float64 `json:"fruit_fao_2017"`
	MaizeFao2017              *float64 `json:"maize_fao_2017"`
	MeatOtherFao2017          *float64 `json:"meat_other_fao_2017"`
	MilkFao2017               *float64 `json:"milk_fao_2017"`
	MiscellaneousFao2017      *float64 `json:"miscellaneous_fao_2017"`
	MuttonAndGoatMeatFao2017  *float64 `json:"mutton_and_goat_meat_fao_2017"`
	OilcropsFao2017           *float64 `json:"oilcrops_fao_2017"`
	PigmeatFao2017            *float64 `json:"pigmeat_fao_2017"`
	PoultryMeatFao2017        *float64 `json:"poultry_meat_fao_2017"`
	PulsesFao2017             *float64 `json:"pulses_fao_2017"`
	RiceFao2017               *float64 `json:"rice_fao_2017"`
	StarchyRootsFao2017       *float64 `json:"starchy_roots_fao_2017"`
	SugarAndSweetenersFao2017 *float64 `json:"sugar_and_sweeteners_fao_2017"`
	SugarCropsFao2017         *float64 `json:"sugar_crops_fao_2017"`
	NutsAndSeedsFao2017       *float64 `json:"nuts_and_seeds_fao_2017"`
	VegetableOilsFao2017      *float64 `json:"vegetable_oils_fao_2017"`
	VegetablesFao2017         *float64 `json:"vegetables_fao_2017"`
	WheatFao2017              *float64 `json:"wheat_fao_2017"`
	TreenutsFao2017           *float64 `json:"treenuts_fao_2017"`
}

Data represents the average daily per capita supply of calories from the range of food commodities. Note that these figures do not correct for waste at the household/consumption level so may not directly reflect the quantity of food finally consumed by a given individual.Values given for commodities include supply provided in its raw form, as well as products derived from this commodity (for example, figures for 'wheat' are inclusive of wheat consumed in its raw form as well as derivative products such as bread).Figures for Former Soviet Union states have been allocated the average food supply figures of the USSR for the period 1961-1991, then their respective national values from there onwards.

type DietaryMacronutrientCompositionsFao2017Dataset 

type DietaryMacronutrientCompositionsFao2017Dataset struct {
	FatSupplyFao2017                        *float64 `json:"fat_supply_fao_2017"`
	ProteinSupplyFao2017                    *float64 `json:"protein_supply_fao_2017"`
	CarbohydratesSupplyFao2017              *float64 `json:"carbohydrates_supply_fao_2017"`
	CaloriesFromFatFao2017                  *float64 `json:"calories_from_fat_fao_2017"`
	CaloriesFromProteinFao2017              *float64 `json:"calories_from_protein_fao_2017"`
	CaloriesFromCarbohydratesFao2017        *float64 `json:"calories_from_carbohydrates_fao_2017"`
	ShareOfCaloriesFromFatFao2017           *float64 `json:"share_of_calories_from_fat_fao_2017"`
	ShareOfCaloriesFromProteinFao2017       *float64 `json:"share_of_calories_from_protein_fao_2017"`
	ShareOfCaloriesFromCarbohydratesFao2017 *float64 `json:"share_of_calories_from_carbohydrates_fao_2017"`
	ProteinSupplyOfAnimalOriginFao2017      *float64 `json:"protein_supply_of_animal_origin_fao_2017"`
	ProteinSupplyOfPlantOriginFao2017       *float64 `json:"protein_supply_of_plant_origin_fao_2017"`
	CaloriesFromAnimalProteinFao2017        *float64 `json:"calories_from_animal_protein_fao_2017"`
	CaloriesFromPlantProteinFao2017         *float64 `json:"calories_from_plant_protein_fao_2017"`
	ShareOfCaloriesFromAnimalProteinFao2017 *float64 `json:"share_of_calories_from_animal_protein_fao_2017"`
	ShareOfCaloriesFromPlantProteinFao2017  *float64 `json:"share_of_calories_from_plant_protein_fao_2017"`
}

Dietary compositions calculated by the OWID author (Hannah Ritchie) based on food supply statistics from the UN Food and Agricultural Organization database: FAOstat.The FAO provide annual figures from 1961 by country on daily caloric supply, fat supply (in grams), and protein supply (in grams). To calculate the daily per capita supply of carbohydrates, we assumed an energy density by macronutrient of 4 kcal per gram of both protein and carbohydrate and 9 kcal per gram of fat (based on established nutritional guidelines reported by the FAO). The daily supply of carbohydrates was therefore calculated as:((Daily supply of kcal)-(Daily supply of protein * 4 + Daily supply of fat * 9)) / 4The quantity of calories from each macronutrient was then calculated based on the energy density figures given above (e.g. calories from protein was calculated by multiplting the daily supply of protein in grams by 4).The share of calories derived from each macronutrient could then be calculated by dividing the number of calories derived from a given macronutrient by the total daily caloric supply.Protein of animal origin includes protein supplied in the form of all meat commodities, eggs and dairy products, and fish & seafood. Protein of plant origin was dervied as the difference between total protein supply and that of animal origin.References:UN Food and Agricultural Organization FAOstat food balance sheets. Available at: http://www.fao.org/faostat/en/#home [accessed 31st July 2017].Chapter 3: Calculation Of The Energy Content Of Foods - Energy Conversion Factors. Food and Agriculture Organization of the United Nations. Available at: http://www.fao.org/docrep/006/Y5022E/y5022e04.htm [accessed 31st July 2017].

type DifferenceInTheValueOfGoodsExportedToAndImportedByTheUsFor2016Dots2017Dataset 

type DifferenceInTheValueOfGoodsExportedToAndImportedByTheUsFor2016Dots2017Dataset struct {
	DifferenceInTheValueOfGoodsExportedToAndImportedByTheUsDots2017 *float64 `json:"difference_in_the_value_of_goods_exported_to_and_imported_by_the_us_dots_2017"`
}

This data set consists of differences between the value of goods that each country reports exporting to the US, and the value of goods that the US reports importing from the same countries. For example, for China, the figure in the chart corresponds to “Value of merchandise imports in US from China” minus “Value of merchandise exports from China to the US”. In all cases, values correspond to current US dollars. Asymmetries arise for different reasons, including the fact that exports are usually recorded in FOB prices, while imports are recorded in CIF prices.

type DifferencesInPopulationEstimatesOwidBasedOnUnVsUsCensusBureauDataset 

type DifferencesInPopulationEstimatesOwidBasedOnUnVsUsCensusBureauDataset struct {
	AbsoluteDifferenceInPopulationOwidBasedOnUnVsUsCensusBureau *float64 `json:"absolute_difference_in_population_owid_based_on_un_vs_us_census_bureau"`
	RelativeDifferenceInPopulationOwidBasedOnUnVsUsCensusBureau *float64 `json:"relative_difference_in_population_owid_based_on_un_vs_us_census_bureau"`
}

This dataset was calculated by OWID based on population estimates from the UN Population Division (2017) and US Census Bureau.

Absolute differences measure the absolute difference in regional and global population estimates from the UN Population Division and US Census Bureau. Figures were calculated as the annual total population estimate from the UN Population Division (2017) minus population estimates from the US Census Bureau.

Relative differences measure this difference in estimates as a percentage of UN figures (i.e. (UN population - US Census Bureau population) / UN population).

Negative figures therefore represent lower UN estimates vs. US Census Bureau estimates, and vice versa (positive values indicate higher UN estimates).

References:

United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/ [accessed 2nd October 2017].

United States Census Bureau International Database (IDB). Available at: https://www.census.gov/population/international/data/idb/informationGateway.php [accessed 2nd October 2017].

type DisabilityAdjustedLifeYearsWho2015Dataset 

type DisabilityAdjustedLifeYearsWho2015Dataset struct {
	AllCausesDisabilityAdjustedLifeYearsWho2015 *float64 `json:"all_causes_disability_adjusted_life_years_who_2015"`
}

Communicable diseases, as defined by the WHO in their Global Health Observatory data repository includes: HIV/AIDS, tuberculosis, malaria, Neglected tropical diseases, cholera, influenza, meningitis, other vaccine-preventable communicable diseases, and sexually transmitted infections. Data is available at the WHO's Global Health Observatory data repository: http://apps.who.int/gho/data/node.home

type DistributionOfBilateralAndUnilateralTradePartnershipsFouquinAndHugotCepii2016Dataset 

type DistributionOfBilateralAndUnilateralTradePartnershipsFouquinAndHugotCepii2016Dataset struct {
	NonTradingFouquinAndHugotCepii2016                  *float64 `json:"non_trading_fouquin_and_hugot_cepii_2016"`
	UnilateralTradePartnershipsFouquinAndHugotCepii2016 *float64 `json:"unilateral_trade_partnerships_fouquin_and_hugot_cepii_2016"`
	BilateralTradePartnershipsFouquinAndHugotCepii2016  *float64 `json:"bilateral_trade_partnerships_fouquin_and_hugot_cepii_2016"`
}

To construct this chart we started from a dataset with dyadic trade estimates. For each year we took all country pairs with data and classified them as follows: "Non-trading" (pairs in which countries do not trade with one another); "Bilateral" (pairs in which both countries export to one another); and "Unilateral" (pairs in which only one country exports to the other).

type DrinkingHabitsInGreatBritainUkOnsDataset 

type DrinkingHabitsInGreatBritainUkOnsDataset struct {
	DrankInLastWeek                                           *float64 `json:"drank_in_last_week"`
	DrankOn5OrMoreDaysInLastWeek                              *float64 `json:"drank_on_5_or_more_days_in_last_week"`
	DontDrink                                                 *float64 `json:"dont_drink"`
	ShareOfDrinkersWhoBingedOnHeaviestDayOfDrinkingInLastWeek *float64 `json:"share_of_drinkers_who_binged_on_heaviest_day_of_drinking_in_last_week"`
}

"Binge" drinking is defined by the UK Government as a man consuming more than 8 units of alcohol in a single day, or 6 units of alcohol in a single day in the case of a woman.

type DriversOfForestLossInBrazilLegalAmazonTyukavinaEtAl2017Dataset 

type DriversOfForestLossInBrazilLegalAmazonTyukavinaEtAl2017Dataset struct {
	CommercialCrops              *float64 `json:"commercial_crops"`
	TreePlantationsIncludingPalm *float64 `json:"tree_plantations_including_palm"`
	Pasture                      *float64 `json:"pasture"`
	SmallScaleClearing           *float64 `json:"small_scale_clearing"`
	Roads                        *float64 `json:"roads"`
	OtherInfrastructure          *float64 `json:"other_infrastructure"`
	FloodingDueToDams            *float64 `json:"flooding_due_to_dams"`
	Mining                       *float64 `json:"mining"`
	SelectiveLogging             *float64 `json:"selective_logging"`
	Fire                         *float64 `json:"fire"`
	NaturalDisturbances          *float64 `json:"natural_disturbances"`
	TotalForestLoss              *float64 `json:"total_forest_loss"`
}

The study quantifies types of forest disturbance and loss in the Brazilian Legal Amazon (BLA) using a sample-based approach from remotely sensed data.This quantifies not only complete deforestation drivers, but also temporary forest loss from natural disturbances, wildfires, flooding etc.

type DroughtSeverityIndexInUsNoaaDataset 

type DroughtSeverityIndexInUsNoaaDataset struct {
	PalmerDroughtSeverityIndexAnnualNoaa   *float64 `json:"palmer_drought_severity_index_annual_noaa"`
	PalmerDroughtSeverityIndex9YearAvgNoaa *float64 `json:"palmer_drought_severity_index_9_year_avg_noaa"`
}

The Palmer Drought Severity Index is the most widely used index to measure drought severity over time. is calculated from precipitation and temperature measurements at weather stations. An index value of zero represents the average moisture conditions observed between 1931 and 1990 at a given location. A positive value means conditions are wetter than average, while a negative value is drier than average. Index values from locations across US have been averaged together to produce the national or regional values.Positive values represent wetter-than-average conditions, while negative values represent drier-than-average conditions. A value between -2 and -3 indicates moderate drought, -3 to -4 is severe drought, and -4 or below indicates extreme drought.

type DurationOfMarriagesEndingInDivorceOwidBasedOnNationalStatisticsDataset 

type DurationOfMarriagesEndingInDivorceOwidBasedOnNationalStatisticsDataset struct {
	DurationOfMarriagesEndingInDivorce *float64 `json:"duration_of_marriages_ending_in_divorce"`
}

This dataset combines national published estimates of marriage length: some countries present this data as the median length of marriage; others the mean length of marriage.Due to skew in the distribution of marriage lengths, the median and mean are often notably different.We note for each time-series whether the median or mean is used. All countries are given as a median value with the exception of Ecuador, Germany, and Sweden where this is given as the mean.Estimates rely on multiple sources:- For the United States, the data comes from Table 2-12 in the <a href="https://www.cdc.gov/nchs/data/vsus/mgdv88_3.pdf">Vital Statistics of the United States, 1988 report</a> (1978-1988); Table 1 in <a href="https://www.cdc.gov/nchs/data/series/sr_21/sr21_038.pdf">The Duration of Marriage Before Divorce report</a> (1867 - 1977); Table 6 of the <a href="https://www.census.gov/prod/2005pubs/p70-97.pdf">Current Population Reports, Issued 2005</a> (2001); Table 8 of the <a href="https://www.census.gov/prod/2011pubs/p70-125.pdf">Current Population Reports, Issued 2011</a> (2009).- For the UK, data is taken from Figure 5 of the <a href="https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/divorce/bulletins/divorcesinenglandandwales/2018">ONS Statistical Bulletin</a>.- For Canada, the data is from <a href="https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3910003801#timeframe">Statistics Canada</a>, under Table 39-10-0038-01, Divorces by mean and median duration of marriage.- For Australia, the <a href="https://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3310.0Main%20Features42017?opendocument&tabname=Summary&prodno=3310.0&issue=2017&num=&view=">Australian Bureau of Statistics</a> under 'Length of Marriage of Divorcing Couples' sub-heading for 1997-2017. See the <a href="https://www.abs.gov.au/AUSSTATS/abs@.nsf/allprimarymainfeatures/893C1288678FD232CA2568A90013939C?opendocument">Australian Bureau of Statistics 2018 Release</a> under the 'Divorces' heading for 2018.- For New Zealand, <a href="http://archive.stats.govt.nz/infoshare/">NZ.Stat Infoshare</a> under the 'Population->Marriages Civil Unions and Divorces->Divorces by duration' subject category.- For Singapore, <a href="https://www.singstat.gov.sg/find-data/search-by-theme/population/marital-status-marriages-and-divorces/latest-data">Statistics Singapore</a>. The data can be found under the Divorces and Annulments sub-heading within the 'Key Indicators On Divorces, Annual' link.- For Ecuador, the data is available from slide 11 of the <a href="https://www.ecuadorencifras.gob.ec/documentos/web-inec/Poblacion_y_Demografia/Matrimonios_Divorcios/2018/Principales_resultados_MYD_2018.pdf">Instituto Nacional de Estadisticas y Censos, Registro Estadístico de Divorcios, 2018</a>.- For Germany, the <a href="https://www.destatis.de/EN/Themes/Society-Environment/Population/Marriages-Divorces-Life-Partnerships/Tables/statistical-parameters.html">Statistisches Bundesamt</a>.- For Sweden, <a href="http://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101L/AktenskapVaraktighet/">Statistics Sweden</a>.

type EarthquakeDeathsNgdcNoaaDataset 

type EarthquakeDeathsNgdcNoaaDataset struct {
	EarthquakeDeathsNgdcNoaa *float64 `json:"earthquake_deaths_ngdc_noaa"`
}

Estimated number of deaths from earthquake events. This is estimated as the total number from the earthquake event plus secondary impacts (such as a tsunami triggered by an earthquake).A significant earthquake is classified as one that meets at least one of the following criteria: caused deaths, caused moderate damage (approximately $1 million or more), magnitude 7.5 or greater, Modified Mercalli Intensity (MMI) X or greater, or the earthquake generated a tsunami.Our World in Data have aggregated significant earthquake deaths by country/location per year. Due to data availability, reporting and evidence, it's expected that more recent data will be more complete than the long historical record.

type EciCountryRankingsObservatoryOfEconomicComplexity2016AndTheAtlasOfEconomicComplexity2016Dataset 

type EciCountryRankingsObservatoryOfEconomicComplexity2016AndTheAtlasOfEconomicComplexity2016Dataset struct {
	EconomicComplexityIndexEciObservatoryOfEconomicComplexity2016    *float64 `json:"economic_complexity_index_eci_observatory_of_economic_complexity_2016"`
	EciObservatoryOfEconomicComplexity2016                           *float64 `json:"eci_observatory_of_economic_complexity_2016"`
	EconomicComplexityIndexEciAtlasOfEconomicComplexity2016          *float64 `json:"economic_complexity_index_eci_atlas_of_economic_complexity_2016"`
	OecEciRankingObservatoryOfEconomicComplexity2016                 *float64 `json:"oec_eci_ranking_observatory_of_economic_complexity_2016"`
	AtlasOfEconomicComplexityEciRankingAtlasOfEconomicComplexity2016 *float64 `json:"atlas_of_economic_complexity_eci_ranking_atlas_of_economic_complexity_2016"`
}

The Economic Complexity Index takes data on exports, and reduces a country’s economic system into two dimensions: (i) The number or ‘diversification’ of products in the export basket, and (ii) the quality, or ‘ubiquity’ of products in the export basket.

To measure these two dimensions, the ECI uses a cross-country export matrix. That is, a table with countries in the rows and product categories in the columns, so that each cell in the table shows the value of country-product exports.

From this matrix, ‘diversification’ is obtained from the distribution of country exports across products (i.e. the sum across columns for each row); while ‘ubiquity’ is given by the share of product exports contributed by each country (i.e. the sum across rows for each column).

To condense both dimensions into a single metric, the ECI further reduces the information in the matrix, such that countries with similar exports are close together in the ranking.

Loosely speaking, lower ECI scores correspond to countries that export very few different types of products (i.e. export baskets that are not diversified) and those products that they do export are produced in many other countries (i.e. export baskets that load heavily on just a few ubiquitous products).

By this logic, Germany has a high ECI score because it exports many different kinds of sophisticated things that are only produced by a handful of other countries with similarly diversified productive capacities.

The following visualization shows ECI rankings for all countries in the world. Link: https://ourworldindata.org/grapher/economic-complexity-rankings

As we can see, there is a clear pattern – richer countries tend to have similar economic structures, and these structures allow them to produce and export a varied basket of sophisticated products.

But what about countries endowed with rare natural resources, like petroleum, which would make a country’s export basket less ubiquitous, and hence misleadingly be assigned a more favourable ECI ranking? If a country exporting petroleum can only produce a few other products, then the low ubiquity for their export basket can be explained by these natural resources, and the ECI ranking is corrected by the country’s inability to produce different types of products. However, if this country can also produce a diverse range of other goods, then low ubiquity correctly indicates greater economic complexity which is reflected in the ranking. Hence, information on diversity can correct for the information of ubiquity, and vice versa.

The data here comes from the Economic Complexity Index scores published by the MIT Observatory of Economic Complexity. These are different to those published by the Harvard Atlas of Economic Complexity. As far as we are aware, the discrepancies stem from differences in the way each sources clean the underlying cross-country trade data. You can explore differences between these two data sources here: https://ourworldindata.org/grapher/eci-country-rankings-comparison?country=KOR.

type EconomicFreedomOfTheWorldFraserInstitute2018Dataset 

type EconomicFreedomOfTheWorldFraserInstitute2018Dataset struct {
	EconomicFreedomOfTheWorld *float64 `json:"economic_freedom_of_the_world"`
	EconomicFreedomRank       *float64 `json:"economic_freedom_rank"`
}

Economic Freedom of the World is calculated by the Fraser Institute, and measures the degree to which individuals are free to choose, trade, and cooperate with others, and compete as they see fit.The cornerstones of economic freedom are (1) personal choice, (2) voluntary exchange coordinated by markets, (3) freedom to enter and compete in markets, and (4) protection of persons and their property from aggression by others.Individuals have economic freedom when property they acquire without the use of force, fraud, or theft is protected from physical invasions by others and they are free to use, exchange, or give their property as long as their actions do not violate the identical rights of others.

type EconomicImpacts2vs15cPretisEtAlDataset 

type EconomicImpacts2vs15cPretisEtAlDataset struct {
	Median           *float64 `json:"median"`
	O975thPercentile *float64 `json:"o975th_percentile"`
	O83rdPercentile  *float64 `json:"o83rd_percentile"`
	O17thPercentile  *float64 `json:"o17th_percentile"`
	O25thPercentile  *float64 `json:"o25th_percentile"`
}

Please cite the data as:Pretis, Schwarz, Tang, Haustein, and Allen. 2018. "Uncertain Impacts on Economic Growth When Stabilizing Global Temperatures at 1.5°C or 2°C Warming". Philosophical Transactions of the Royal Society, A. DOI 10.1098/rsta.2016.0460.

type EconomicImpactsOf15cPretisEtAlDataset 

type EconomicImpactsOf15cPretisEtAlDataset struct {
	Median           *float64 `json:"median"`
	O17thPercentile  *float64 `json:"o17th_percentile"`
	O83rdPercentile  *float64 `json:"o83rd_percentile"`
	O975thPercentile *float64 `json:"o975th_percentile"`
	O25thPercentile  *float64 `json:"o25th_percentile"`
}

Please cite the data as:Pretis, Schwarz, Tang, Haustein, and Allen. 2018. "Uncertain Impacts on Economic Growth When Stabilizing Global Temperatures at 1.5°C or 2°C Warming". Philosophical Transactions of the Royal Society, A. DOI 10.1098/rsta.2016.0460.

type EconomicImpactsOf2cPretisEtAl2018Dataset 

type EconomicImpactsOf2cPretisEtAl2018Dataset struct {
	Median           *float64 `json:"median"`
	O17thPercentile  *float64 `json:"o17th_percentile"`
	O83rdPercentile  *float64 `json:"o83rd_percentile"`
	O975thPercentile *float64 `json:"o975th_percentile"`
	O25thPercentile  *float64 `json:"o25th_percentile"`
}

Please cite the data as:Pretis, Schwarz, Tang, Haustein, and Allen. 2018. "Uncertain Impacts on Economic Growth When Stabilizing Global Temperatures at 1.5°C or 2°C Warming". Philosophical Transactions of the Royal Society, A. DOI 10.1098/rsta.2016.0460.

type EconomicLossesFromDisastersAsAShareOfGdpPielke2018Dataset 

type EconomicLossesFromDisastersAsAShareOfGdpPielke2018Dataset struct {
	MunichReOverallDisasterLossesIn2017Money          *float64 `json:"munich_re_overall_disaster_losses_in_2017_money"`
	DisasterLossesAsPercOfGlobalGdpMunichRe           *float64 `json:"disaster_losses_as_perc_of_global_gdp_munich_re"`
	AonBenfieldOverallDisasterLossesIn2017Money       *float64 `json:"aon_benfield_overall_disaster_losses_in_2017_money"`
	DisasterLossesAsPercOfGlobalGdpAonBenfield        *float64 `json:"disaster_losses_as_perc_of_global_gdp_aon_benfield"`
	MunichReWeatherDisasterLossesIn2017Money          *float64 `json:"munich_re_weather_disaster_losses_in_2017_money"`
	WeatherDisasterLossesAsPercOfGlobalGdpMunichRe    *float64 `json:"weather_disaster_losses_as_perc_of_global_gdp_munich_re"`
	AonBenfieldWeatherDisasterLossesIn2017Money       *float64 `json:"aon_benfield_weather_disaster_losses_in_2017_money"`
	WeatherDisasterLossesAsPercOfGlobalGdpAonBenfield *float64 `json:"weather_disaster_losses_as_perc_of_global_gdp_aon_benfield"`
}

Data represents absolute global economic losses from disasters (total and weather-related) in 2017 US$ from two sources: Munich Re and Aon Benfield.This has been normalized to economic losses from disasters as a share of global gross domestic product (GDP) based on global GDP data derived from the World Bank.

type EducationDataDeprivationGemReport201718Uis2017Dataset 

type EducationDataDeprivationGemReport201718Uis2017Dataset struct {
	PrimaryEnrolmentDataAvailableAtLastCutoffGemReport201718                                           *float64 `json:"primary_enrolment_data_available_at_last_cutoff_gem_report_201718"`
	ExistenceOfNationallyRepresentativeLearningAssessmentInGrades2Or3OfPrimaryEducationGemReport201718 *float64 `json:"existence_of_nationally_representative_learning_assessment_in_grades_2_or_3_of_primary_education_gem_report_201718"`
	ExistenceOfNationallyRepresentativeLearningAssessmentAtTheEndOfPrimaryEducationGemReport201718     *float64 `json:"existence_of_nationally_representative_learning_assessment_at_the_end_of_primary_education_gem_report_201718"`
}

type EducationExpenditureUs19492013Nces2014Dataset 

type EducationExpenditureUs19492013Nces2014Dataset struct {
	PrivateEducationExpenditureInTheUsNces2014 *float64 `json:"private_education_expenditure_in_the_us_nces_2014"`
	PublicEducationExpenditureInTheUsNces2014  *float64 `json:"public_education_expenditure_in_the_us_nces_2014"`
}

type EducationalAttainmentBarroLeeEducationDataset2010Dataset 

type EducationalAttainmentBarroLeeEducationDataset2010Dataset struct {
	EducationalAttainmentAverageYearsOfTotalEducationBarroLeeEducationDataset2010 *float64 `json:"educational_attainment_average_years_of_total_education_barro_lee_education_dataset_2010"`
}

Christopher Farris kindly provided this dataset and shared it with Max Roser via email. It shows average years of schooling of the population aged 15 and over.

type EducationalOutcomesHanushekAndWoessmann2012Dataset 

type EducationalOutcomesHanushekAndWoessmann2012Dataset struct {
	AverageTestScoreInMathAndSciencePrimaryThroughEndOfSecondarySchool                                             *float64 `json:"average_test_score_in_math_and_science_primary_through_end_of_secondary_school"`
	AverageTestScoreInMathAndScienceOnlyLowerSecondary                                                             *float64 `json:"average_test_score_in_math_and_science_only_lower_secondary"`
	ShareOfStudentsReachingBasicLiteracyBasedOnAverageTestScoresInMathAndSciencePrimaryThroughEndOfSecondarySchool *float64 `` /* 137-byte string literal not displayed */
	ShareOfTopPerformingStudentsBasedOnAverageTestScoresInMathAndSciencePrimaryThroughEndOfSecondarySchool         *float64 `` /* 128-byte string literal not displayed */
}

Hanushek, E. A. and Woessmann, L. (2012) – Do better school lead to more growth? Cognitive skills, economic outcomes, and causation. In Journal of Economic Growth, 17, 267–321. The paper is available on Eric Hanushek's website and at the journal's site.

The authors standardized the scores to the PISA test scale but divided the score then by 100. The PISA test scale has a mean of 500 and a standard deviation of 100.

The test scores are not given for a particular year, but instead are the average of all standardized math and science test scores of the international student achievement tests in which that country participated. The first of which is from 1964 and the last is from 2003 – but in general most results come from the later period.

type ElectricityMixFromBpAndEmber2022ArchiveDataset 

type ElectricityMixFromBpAndEmber2022ArchiveDataset struct {
	ElectricityFromBioenergyTwh                             *float64 `json:"electricity_from_bioenergy_twh"`
	ElectricityFromCoalTwh                                  *float64 `json:"electricity_from_coal_twh"`
	ElectricityGenerationTwh                                *float64 `json:"electricity_generation_twh"`
	ElectricityFromFossilFuelsTwh                           *float64 `json:"electricity_from_fossil_fuels_twh"`
	ElectricityFromGasTwh                                   *float64 `json:"electricity_from_gas_twh"`
	ElectricityFromHydroTwh                                 *float64 `json:"electricity_from_hydro_twh"`
	LowCarbonElectricityTwh                                 *float64 `json:"low_carbon_electricity_twh"`
	ElectricityFromNuclearTwh                               *float64 `json:"electricity_from_nuclear_twh"`
	ElectricityFromOilTwh                                   *float64 `json:"electricity_from_oil_twh"`
	ElectricityFromOtherRenewablesExcludingBioenergyTwh     *float64 `json:"electricity_from_other_renewables_excluding_bioenergy_twh"`
	ElectricityFromOtherRenewablesIncludingBioenergyTwh     *float64 `json:"electricity_from_other_renewables_including_bioenergy_twh"`
	ElectricityFromRenewablesTwh                            *float64 `json:"electricity_from_renewables_twh"`
	ElectricityFromSolarTwh                                 *float64 `json:"electricity_from_solar_twh"`
	ElectricityFromWindTwh                                  *float64 `json:"electricity_from_wind_twh"`
	PerCapitaElectricityKwh                                 *float64 `json:"per_capita_electricity_kwh"`
	CoalElectricityPerCapitaKwh                             *float64 `json:"coal_electricity_per_capita_kwh"`
	OilElectricityPerCapitaKwh                              *float64 `json:"oil_electricity_per_capita_kwh"`
	GasElectricityPerCapitaKwh                              *float64 `json:"gas_electricity_per_capita_kwh"`
	FossilFuelElectricityPerCapitaKwh                       *float64 `json:"fossil_fuel_electricity_per_capita_kwh"`
	RenewableElectricityPerCapitaKwh                        *float64 `json:"renewable_electricity_per_capita_kwh"`
	LowCarbonElectricityPerCapitaKwh                        *float64 `json:"low_carbon_electricity_per_capita_kwh"`
	NuclearElectricityPerCapitaKwh                          *float64 `json:"nuclear_electricity_per_capita_kwh"`
	SolarElectricityPerCapitaKwh                            *float64 `json:"solar_electricity_per_capita_kwh"`
	WindElectricityPerCapitaKwh                             *float64 `json:"wind_electricity_per_capita_kwh"`
	HydroElectricityPerCapitaKwh                            *float64 `json:"hydro_electricity_per_capita_kwh"`
	BioenergyElectricityPerCapitaKwh                        *float64 `json:"bioenergy_electricity_per_capita_kwh"`
	OtherRenewableElectricityExcludingBioenergyPerCapitaKwh *float64 `json:"other_renewable_electricity_excluding_bioenergy_per_capita_kwh"`
	OtherRenewableElectricityIncludingBioenergyPerCapitaKwh *float64 `json:"other_renewable_electricity_including_bioenergy_per_capita_kwh"`
	ElectricityAsShareOfPrimaryEnergy                       *float64 `json:"electricity_as_share_of_primary_energy"`
	CoalPercElectricity                                     *float64 `json:"coal_perc_electricity"`
	OilPercElectricity                                      *float64 `json:"oil_perc_electricity"`
	GasPercElectricity                                      *float64 `json:"gas_perc_electricity"`
	FossilFuelsPercElectricity                              *float64 `json:"fossil_fuels_perc_electricity"`
	RenewablesPercElectricity                               *float64 `json:"renewables_perc_electricity"`
	LowCarbonElectricityPercElectricity                     *float64 `json:"low_carbon_electricity_perc_electricity"`
	NuclearPercElectricity                                  *float64 `json:"nuclear_perc_electricity"`
	SolarPercElectricity                                    *float64 `json:"solar_perc_electricity"`
	WindPercElectricity                                     *float64 `json:"wind_perc_electricity"`
	HydroPercElectricity                                    *float64 `json:"hydro_perc_electricity"`
	BioenergyPercElectricity                                *float64 `json:"bioenergy_perc_electricity"`
	OtherRenewablesExcludingBioenergyPercElectricity        *float64 `json:"other_renewables_excluding_bioenergy_perc_electricity"`
	OtherRenewablesIncludingBioenergyPercElectricity        *float64 `json:"other_renewables_including_bioenergy_perc_electricity"`
	CarbonIntensityOfElectricityGco2kwh                     *float64 `json:"carbon_intensity_of_electricity_gco2kwh"`
	ElectricityDemandTwh                                    *float64 `json:"electricity_demand_twh"`
	NetImportsTwh                                           *float64 `json:"net_imports_twh"`
	NetElectricityImportsAsAShareOfDemand                   *float64 `json:"net_electricity_imports_as_a_share_of_demand"`
}

Data is compiled by Our World in Data based on three main sources: – BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html– Ember: https://ember-climate.org/data/- Ember European Electricity Review (2022): https://ember-climate.org/project/european-electricity-review-2022/We also include European carbon intensities (gCO2/kWh) from Ember: https://ember-climate.org/project/eu-power-sector-2020/. The underlying source for much of this data is the European Environment Agency: https://www.eea.europa.eu/ims/greenhouse-gas-emission-intensity-of-1 Electricity mix data from BP provides primary energy (not just electricity) consumption data, meaning energy and electricity data is consistent from the same source. It provides a longer time-series (dating back to 1965) versus only 2000 from Ember. However, BP does not provide data for all countries and is not as up-to-date as Ember data.So, where data from Ember is available for a given country or year, we rely on it as the primary source. We then supplement this with data from BP where it's not available.2021 electricity data is currently only available for European countries based on the latest European Electricity Review.Our World in Data has converted absolute electricity production by source to the share in the mix by dividing each by total electricity production.

type ElectricityMixFromBpAndEmber2022Dataset 

type ElectricityMixFromBpAndEmber2022Dataset struct {
	ElectricityFromBioenergyTwh                             *float64 `json:"electricity_from_bioenergy_twh"`
	ElectricityFromCoalTwh                                  *float64 `json:"electricity_from_coal_twh"`
	ElectricityDemandTwh                                    *float64 `json:"electricity_demand_twh"`
	ElectricityGenerationTwh                                *float64 `json:"electricity_generation_twh"`
	EmissionsMtco2                                          *float64 `json:"emissions_mtco2"`
	ElectricityFromFossilFuelsTwh                           *float64 `json:"electricity_from_fossil_fuels_twh"`
	ElectricityFromGasTwh                                   *float64 `json:"electricity_from_gas_twh"`
	ElectricityFromHydroTwh                                 *float64 `json:"electricity_from_hydro_twh"`
	LowCarbonElectricityTwh                                 *float64 `json:"low_carbon_electricity_twh"`
	NetImportsTwh                                           *float64 `json:"net_imports_twh"`
	ElectricityFromNuclearTwh                               *float64 `json:"electricity_from_nuclear_twh"`
	ElectricityFromOilTwh                                   *float64 `json:"electricity_from_oil_twh"`
	ElectricityFromOtherRenewablesExcludingBioenergyTwh     *float64 `json:"electricity_from_other_renewables_excluding_bioenergy_twh"`
	ElectricityFromOtherRenewablesIncludingBioenergyTwh     *float64 `json:"electricity_from_other_renewables_including_bioenergy_twh"`
	ElectricityFromRenewablesTwh                            *float64 `json:"electricity_from_renewables_twh"`
	ElectricityFromSolarTwh                                 *float64 `json:"electricity_from_solar_twh"`
	ElectricityFromWindTwh                                  *float64 `json:"electricity_from_wind_twh"`
	CarbonIntensityOfElectricityGco2kwh                     *float64 `json:"carbon_intensity_of_electricity_gco2kwh"`
	PerCapitaElectricityKwh                                 *float64 `json:"per_capita_electricity_kwh"`
	CoalElectricityPerCapitaKwh                             *float64 `json:"coal_electricity_per_capita_kwh"`
	OilElectricityPerCapitaKwh                              *float64 `json:"oil_electricity_per_capita_kwh"`
	GasElectricityPerCapitaKwh                              *float64 `json:"gas_electricity_per_capita_kwh"`
	FossilFuelElectricityPerCapitaKwh                       *float64 `json:"fossil_fuel_electricity_per_capita_kwh"`
	RenewableElectricityPerCapitaKwh                        *float64 `json:"renewable_electricity_per_capita_kwh"`
	LowCarbonElectricityPerCapitaKwh                        *float64 `json:"low_carbon_electricity_per_capita_kwh"`
	NuclearElectricityPerCapitaKwh                          *float64 `json:"nuclear_electricity_per_capita_kwh"`
	SolarElectricityPerCapitaKwh                            *float64 `json:"solar_electricity_per_capita_kwh"`
	WindElectricityPerCapitaKwh                             *float64 `json:"wind_electricity_per_capita_kwh"`
	HydroElectricityPerCapitaKwh                            *float64 `json:"hydro_electricity_per_capita_kwh"`
	BioenergyElectricityPerCapitaKwh                        *float64 `json:"bioenergy_electricity_per_capita_kwh"`
	OtherRenewableElectricityExcludingBioenergyPerCapitaKwh *float64 `json:"other_renewable_electricity_excluding_bioenergy_per_capita_kwh"`
	OtherRenewableElectricityIncludingBioenergyPerCapitaKwh *float64 `json:"other_renewable_electricity_including_bioenergy_per_capita_kwh"`
	ElectricityAsShareOfPrimaryEnergy                       *float64 `json:"electricity_as_share_of_primary_energy"`
	NetElectricityImportsAsAShareOfDemand                   *float64 `json:"net_electricity_imports_as_a_share_of_demand"`
	CoalPercElectricity                                     *float64 `json:"coal_perc_electricity"`
	OilPercElectricity                                      *float64 `json:"oil_perc_electricity"`
	GasPercElectricity                                      *float64 `json:"gas_perc_electricity"`
	FossilFuelsPercElectricity                              *float64 `json:"fossil_fuels_perc_electricity"`
	RenewablesPercElectricity                               *float64 `json:"renewables_perc_electricity"`
	LowCarbonElectricityPercElectricity                     *float64 `json:"low_carbon_electricity_perc_electricity"`
	NuclearPercElectricity                                  *float64 `json:"nuclear_perc_electricity"`
	SolarPercElectricity                                    *float64 `json:"solar_perc_electricity"`
	WindPercElectricity                                     *float64 `json:"wind_perc_electricity"`
	HydroPercElectricity                                    *float64 `json:"hydro_perc_electricity"`
	BioenergyPercElectricity                                *float64 `json:"bioenergy_perc_electricity"`
	OtherRenewablesExcludingBioenergyPercElectricity        *float64 `json:"other_renewables_excluding_bioenergy_perc_electricity"`
	OtherRenewablesIncludingBioenergyPercElectricity        *float64 `json:"other_renewables_including_bioenergy_perc_electricity"`
}

Data is compiled by Our World in Data based on three main sources: – BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html– Ember Global Electricity Review (2022): https://ember-climate.org/insights/research/global-electricity-review-2022/- Ember European Electricity Review (2022): https://ember-climate.org/insights/research/european-electricity-review-2022/Ember compile their global dataset from various sources including:– Eurostat: Annual European generation and import data, and monthly data in some cases where better sources are not available.– ENTSO-E: Monthly European generation and import data.– EIA: Annual global generation and import data.– UN: Monthly global generation data in some cases.– GEM: Annual global coal and gas capacity data.– IRENA: Annual global capacity data for all non-fossil fuel types, and for Other Fossil where available.– WRI: Annual global capacity data for Other Fossil where other sources are not available.– European carbon intensities rely on data from the European Environment Agency (EEA).– A complete list of data sources for each individual country in Ember's Global Electricity Review can be found here: https://ember-climate.org/app/uploads/2022/03/GER22-Methodology.pdf– A complete list of data sources for each individual country in Ember's European Electricity Review can be found here: https://ember-climate.org/app/uploads/2022/02/EER-Methodology.pdfElectricity mix data from BP provides primary energy (not just electricity) consumption data. BP provides a longer time-series (dating back to 1965) versus only 1990 from Ember. However, BP does not provide data for all countries and is not as up-to-date as Ember data. So, where data from Ember is available for a given country or year, we rely on it as the primary source. We then supplement this with data from BP where it's not available.Our World in Data has converted absolute electricity production by source to the share in the mix by dividing each by total electricity production.

type ElephantPopulationAfesgAndasesg2019Dataset 

type ElephantPopulationAfesgAndasesg2019Dataset struct {
	AfricanElephantPopulationAfesg2019   *float64 `json:"african_elephant_population_afesg_2019"`
	AfricanElephantCarcassRatioAfesg2019 *float64 `json:"african_elephant_carcass_ratio_afesg_2019"`
	AsianElephantPopulationAsesg2019     *float64 `json:"asian_elephant_population_asesg_2019"`
}

Data on elephant populations was gathered on two species: the African elephant, and the Asian elephant.African elephant population data was primarily gathered from the African Elephant Database. Available at: https://www.iucn.org/ssc-groups/mammals/african-elephant-specialist-group/african-elephant-database. This database is maintained by the IUCN SSC African Elephant Specialist Group (AfESG) and Great Elephant Census. National data is available in its African Elephant Status reports: https://www.dropbox.com/s/7a8w3kk6r9hzm0r/AfESG%20African%20Elephant%20Status%20Report%202016.pdf?dl=1.Prior to 2013, this data was collected and reported on the basis of the DPPS method, where estimates were given as 'Definite', 'Probable', 'Possible' and 'Speculative'. In its 2013 and 2015 estimates, this method was simplified and changed to 'Estimates' and 'Guesses'. To provide comparative statistics over time, here we have taken historical data coded as 'Definite' to be equivalent to estimated elephant populations. In 2013 and 2015 data (which is given by both methods for comparison), these figures are comparable; this therefore seems like a reasonable assumption. It may however introduce some additional uncertainty.Long historical estimates for Africa are taken from the Great Elephant Census: http://www.greatelephantcensus.com/background-on-conservation.Figures for some countries on 'carcass ratio' are also provided. The carcass ratio is the number of dead elephants observed during the count, as a percentage of the total population. Carcass ratios of more than 8 percent are considered to indicate poaching at a high enough level to cause a declining population.Asian elephant population data is sourced primarily from the Asian Elephant Specialist Group (AsESG); UN FAO and national records. Latest estimates are available here: https://www.asesg.org/PDFfiles/2017/AsERSM%202017_Final%20Report.pdfThese have been supplemented with longer records for Nepal (http://www.fao.org/3/ad031e/ad031e0e.htm); China (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124834); Vietnam (http://www.fao.org/3/ad031e/ad031e0f.htm); and India (https://shodhganga.inflibnet.ac.in/bitstream/10603/23637/7/07_chapter%201.pdf).Time-series estimates of total Asian elephant populations are not widely available. The IUCN estimates there were around 100,000 at the start of the 20th century. Today that figure is around 45,000. Source: http://wwf.panda.org/knowledge_hub/endangered_species/elephants/asian_elephants/.

type EmissionsAirPollutantsOverLongTermDefraAndEpaDataset 

type EmissionsAirPollutantsOverLongTermDefraAndEpaDataset struct {
	SulphurDioxideSo2                       *float64 `json:"sulphur_dioxide_so2"`
	NitrogenOxidesNox                       *float64 `json:"nitrogen_oxides_nox"`
	NonMethaneVolatileOrganicCompoundsVocs  *float64 `json:"non_methane_volatile_organic_compounds_vocs"`
	Ammonia                                 *float64 `json:"ammonia"`
	Pm10                                    *float64 `json:"pm10"`
	Pm25                                    *float64 `json:"pm25"`
	SulphurDioxideIndex                     *float64 `json:"sulphur_dioxide_index"`
	NitrogenOxidesIndex                     *float64 `json:"nitrogen_oxides_index"`
	NonMethaneVolatileOrganicCompoundsIndex *float64 `json:"non_methane_volatile_organic_compounds_index"`
	AmmoniaIndex                            *float64 `json:"ammonia_index"`
	Pm10Index                               *float64 `json:"pm10_index"`
	Pm25Index                               *float64 `json:"pm25_index"`
}

Data is presented as total UK and US emissions of air pollutants from all anthropogenic/human sources measured in tonnes per year. Also provided is an index of emissions, normalised to emissions in the first year of data (1970/1980 or 1990) 1970. In other words, annual emissions in 1970 are denoted as equal to 100; values below 100 therefore indicate a fall in emissions; values above 100 indicate an increase in emissions.PM10 and PM2.5 are used to denote emissions of particulate matter (PM) measuring less than 10 microns in diameter (PM10) and 2.5 microns in diameter (PM2.5).UK DEFRA Source: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/681445/Emissions_of_air_pollutants_statistical_release_FINALv4.pdfUS EPA Source: https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data

type EmissionsIntensityAndValueAddedBySectorLinusEtAlDataset 

type EmissionsIntensityAndValueAddedBySectorLinusEtAlDataset struct {
	ShareOfCo2EmissionsPercChina   *float64 `json:"share_of_co2_emissions_perc_china"`
	ValueAddedToGdpPercChina       *float64 `json:"value_added_to_gdp_perc_china"`
	EmissionsGrowthPercChina       *float64 `json:"emissions_growth_perc_china"`
	ValueAddedGrowthPercChina      *float64 `json:"value_added_growth_perc_china"`
	ShareOfCo2EmissionsPercUsa     *float64 `json:"share_of_co2_emissions_perc_usa"`
	ValueAddedToGdpPercUsa         *float64 `json:"value_added_to_gdp_perc_usa"`
	EmissionsGrowthPercUsa         *float64 `json:"emissions_growth_perc_usa"`
	ValueAddedGrowthPercUsa        *float64 `json:"value_added_growth_perc_usa"`
	ShareOfCo2EmissionsPercGermany *float64 `json:"share_of_co2_emissions_perc_germany"`
	ValueAddedToGdpPercGermany     *float64 `json:"value_added_to_gdp_perc_germany"`
	EmissionsGrowthPercGermany     *float64 `json:"emissions_growth_perc_germany"`
	ValueAddedGrowthPercGermany    *float64 `json:"value_added_growth_perc_germany"`
}

Figures are derived from authors based on data from the (WIOD) World Input-Output Dataset. World Input-Output Database 2013 release [Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015),"An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production", Review of International Economics., 23: 575–605]. Year 2009 in basic prices, value added computed as net sectoral output (gross sectoral output less sectoral input).

type EmploymentAndGenderAttitudesPewResearchCentre2012Dataset 

type EmploymentAndGenderAttitudesPewResearchCentre2012Dataset struct {
	ShareWhoAgreePewResearchCentre2012    *float64 `json:"share_who_agree_pew_research_centre_2012"`
	ShareWhoDisagreePewResearchCentre2012 *float64 `json:"share_who_disagree_pew_research_centre_2012"`
}

Figures for China and India are non-national samples. Country figures are taken from the Spring 2010 Survey conducted by the Pew Research Centre with the exception of Turkey, Pakistan, Lebanon, Jordan, and Egypt, for which figures are taken from the Spring 2012 Survey.

type EmploymentRateAges2534ByEducationEducationAtAGlanceOecdIndicators2017Dataset 

type EmploymentRateAges2534ByEducationEducationAtAGlanceOecdIndicators2017Dataset struct {
	EmploymentRateAges25_34BelowUpperSecondaryEducationOecd2017                                            *float64 `json:"employment_rate_ages_25_34_below_upper_secondary_education_oecd_2017"`
	EmploymentRateAges25_34UpperSecondaryOrPostSecondaryNonTertiaryVocationalEducationOecd2017             *float64 `json:"employment_rate_ages_25_34_upper_secondary_or_post_secondary_non_tertiary_vocational_education_oecd_2017"`
	EmploymentRateAges25_34UpperSecondaryOrPostSecondaryNonTertiaryGeneralOrNoDistinctionEducationOecd2017 *float64 `` /* 126-byte string literal not displayed */
	EmploymentRateAges25_34TertiaryEducationOecd2017                                                       *float64 `json:"employment_rate_ages_25_34_tertiary_education_oecd_2017"`
}

The year of reference is 2016 for all countries except for: Argentina (2014), Russia (2015), Brazil (2015), Chile (2015), Ireland (2015), Indonesia (2015), South Africa (2015), and Saudi Arabia (2014). Data on Argentina should be used with caution. Data below 30 persons in the denominator are generally considered unreliable. Data below 5 persons in the numerator have been omitted due to confidentiality reasons. For further details on national data sources and reliability thresholds, see Table 3 in <a href="http://www.oecd.org/education/skills-beyond-school/EAG2017-Annex-3.pdf" rel="noopener" target="_blank">Annex 3: Sources, methods and technical notes</a>Note: for the United Kingdom the OECD specifies "Data for upper secondary attainment include completion of a sufficient volume and standard of programmes that would be classified individually as completion of intermediate upper secondary programmes (16% of the adults aged 25-64 are in this group)."

type EndemicAndThreatenedInvertebrateSpeciesByCountryIucn2020Dataset 

type EndemicAndThreatenedInvertebrateSpeciesByCountryIucn2020Dataset struct {
	FreshwaterCrabsTotalEndemics         *float64 `json:"freshwater_crabs_total_endemics"`
	FreshwaterCrabsThreatenedEndemics    *float64 `json:"freshwater_crabs_threatened_endemics"`
	FreshwaterCrayfishTotalEndemics      *float64 `json:"freshwater_crayfish_total_endemics"`
	FreshwaterCrayfishThreatenedEndemics *float64 `json:"freshwater_crayfish_threatened_endemics"`
	LobstersTotalEndemics                *float64 `json:"lobsters_total_endemics"`
	LobstersThreatenedEndemics           *float64 `json:"lobsters_threatened_endemics"`
	ConeSnailsTotalEndemics              *float64 `json:"cone_snails_total_endemics"`
	ConeSnailsThreatenedEndemics         *float64 `json:"cone_snails_threatened_endemics"`
	ReefFormingCoralsTotalEndemics       *float64 `json:"reef_forming_corals_total_endemics"`
	ReefFormingCoralsThreatenedEndemics  *float64 `json:"reef_forming_corals_threatened_endemics"`
}

Data denotes the number of endemic invertebrate species within a given organism group by country. Endemic species are those which are known to naturally occur in only one country. Also available is the number of these endemic species which are categorised as 'threatened'. 'Threatened' species are those in any of the three Red List categories: Critically Endangered, Endangered, or Vulnerable. They are considered high or greater risk of extinction in the wild.This data is only given for the more comprehensively assessed species groups (i.e., where >80% of the species in the group have been assessed).

type EndemicVertebrateSpeciesByCountryIucn2020Dataset 

type EndemicVertebrateSpeciesByCountryIucn2020Dataset struct {
	MammalsTotalEndemic                      *float64 `json:"mammals_total_endemic"`
	MammalsThreatenedEndemic                 *float64 `json:"mammals_threatened_endemic"`
	BirdsTotalEndemic                        *float64 `json:"birds_total_endemic"`
	BirdsThreatenedEndemic                   *float64 `json:"birds_threatened_endemic"`
	CrocodilesAndAlligatorsTotalEndemic      *float64 `json:"crocodiles_and_alligators_total_endemic"`
	CrocodilesAndAlligatorsThreatenedEndemic *float64 `json:"crocodiles_and_alligators_threatened_endemic"`
	ChameleonsTotalEndemic                   *float64 `json:"chameleons_total_endemic"`
	ChameleonsThreatenedEndemic              *float64 `json:"chameleons_threatened_endemic"`
	AmphibiansTotalEndemic                   *float64 `json:"amphibians_total_endemic"`
	AmphibiansThreatenedEndemic              *float64 `json:"amphibians_threatened_endemic"`
	GroupersTotalEndemic                     *float64 `json:"groupers_total_endemic"`
	GroupersThreatenedEndemic                *float64 `json:"groupers_threatened_endemic"`
	HerringsAnchoviesEtcTotalEndemic         *float64 `json:"herrings_anchovies_etc_total_endemic"`
	HerringsAnchoviesEtcThreatenedEndemic    *float64 `json:"herrings_anchovies_etc_threatened_endemic"`
	SeahorsesAndPipefishesTotalEndemic       *float64 `json:"seahorses_and_pipefishes_total_endemic"`
	SeahorsesAndPipefishesThreatenedEndemic  *float64 `json:"seahorses_and_pipefishes_threatened_endemic"`
	SturgeonsTotalEndemic                    *float64 `json:"sturgeons_total_endemic"`
	SturgeonsThreatenedEndemic               *float64 `json:"sturgeons_threatened_endemic"`
	WrassesAndParrotfishesTotalEndemic       *float64 `json:"wrasses_and_parrotfishes_total_endemic"`
	WrassesAndParrotfishesThreatenedEndemic  *float64 `json:"wrasses_and_parrotfishes_threatened_endemic"`
	SharksAndRaysTotalEndemic                *float64 `json:"sharks_and_rays_total_endemic"`
	SharksAndRaysThreatenedEndemic           *float64 `json:"sharks_and_rays_threatened_endemic"`
}

Data denotes the number of endemic vertebrate species within a given organism group by country. Endemic species are those which are known to naturally occur in only one country. Also available is the number of these endemic species which are categorised as 'threatened'. 

'Threatened' species are those in any of the three Red List categories: Critically Endangered, Endangered, or Vulnerable.

They are considered high or greater risk of extinction in the wild.This data is only given for the more comprehensively assessed species groups (i.e., where >80% of the species in the group have been assessed).

type EnergyEfficiencyByPassengerModeInUsaBtsDataset 

type EnergyEfficiencyByPassengerModeInUsaBtsDataset struct {
	EnergyIntensityKwhPerPassengerKm *float64 `json:"energy_intensity_kwh_per_passenger_km"`
}

Energy intensity of transport is measured by the US Bureau of Transport Statistics (BTS) in btu per passenger-mile. We have converted this data to kilowatt-hours per passenger-kilometer using the following conversion factors:– BTU to kWH = 0.000293071– mile to km = 1.60934The US BTS notes the following about transport categories:"Data from 2007 were calculated using a new methodology developed by FHWA. Data for these years are based on new categories and are not comparable to previous years. The new category [Small passenger vehicles] replaces the old category Passenger car and includes passenger cars, light trucks, vans and sport utility vehicles with a wheelbase (WB) equal to or less than 121 inches. The new category [Large road vehicles] replaces Other 2-axle, 4-tire vehicle and includes large passenger cars, vans, pickup trucks, and sport/utility vehicles with wheelbases (WB) larger than 121 inches. This edition of 4-20 is not comparable to those before the 2019 edition."

type EnergyImportsPercEnergyUseWorldBank2014Dataset 

type EnergyImportsPercEnergyUseWorldBank2014Dataset struct {
	EnergyImportsWorldBank2014 *float64 `json:"energy_imports_world_bank_2014"`
}

"Net energy imports are estimated as energy use less production, both measured in oil equivalents. A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport." (World Bank)

type EnergyLandUseScenarioAnalysisOwidBasedOnUneceAndEmberDataset 

type EnergyLandUseScenarioAnalysisOwidBasedOnUneceAndEmberDataset struct {
	LandAllCoal                    *float64 `json:"land_all_coal"`
	LandAllGas                     *float64 `json:"land_all_gas"`
	LandAllCoalCcs                 *float64 `json:"land_all_coal_ccs"`
	LandAllGasCcs                  *float64 `json:"land_all_gas_ccs"`
	LandAllHydro                   *float64 `json:"land_all_hydro"`
	LandAllNuclear                 *float64 `json:"land_all_nuclear"`
	LandAllSolarSilicon            *float64 `json:"land_all_solar_silicon"`
	LandAllSolarCadmium            *float64 `json:"land_all_solar_cadmium"`
	LandAllConcentratingSolar      *float64 `json:"land_all_concentrating_solar"`
	LandAllWind                    *float64 `json:"land_all_wind"`
	LandAllCoalShare               *float64 `json:"land_all_coal_share"`
	LandAllGasShare                *float64 `json:"land_all_gas_share"`
	LandAllCoalCcsShare            *float64 `json:"land_all_coal_ccs_share"`
	LandAllGasCcsShare             *float64 `json:"land_all_gas_ccs_share"`
	LandAllHydroShare              *float64 `json:"land_all_hydro_share"`
	LandAllNuclearShare            *float64 `json:"land_all_nuclear_share"`
	LandAllSolarSiliconShare       *float64 `json:"land_all_solar_silicon_share"`
	LandAllSolarCadmiumShare       *float64 `json:"land_all_solar_cadmium_share"`
	LandAllConcentratingSolarShare *float64 `json:"land_all_concentrating_solar_share"`
	LandAllWindShare               *float64 `json:"land_all_wind_share"`
	LandActualCoal                 *float64 `json:"land_actual_coal"`
	LandActualGas                  *float64 `json:"land_actual_gas"`
	LandActualHydro                *float64 `json:"land_actual_hydro"`
	LandActualNuclear              *float64 `json:"land_actual_nuclear"`
	LandActualSolar                *float64 `json:"land_actual_solar"`
	LandActualWind                 *float64 `json:"land_actual_wind"`
	LandActualTotal                *float64 `json:"land_actual_total"`
	LandEnergyIntensity            *float64 `json:"land_energy_intensity"`
	LandActualCoalShare            *float64 `json:"land_actual_coal_share"`
	LandActualGasShare             *float64 `json:"land_actual_gas_share"`
	LandActualHydroShare           *float64 `json:"land_actual_hydro_share"`
	LandActualNuclearShare         *float64 `json:"land_actual_nuclear_share"`
	LandActualSolarShare           *float64 `json:"land_actual_solar_share"`
	LandActualWindShare            *float64 `json:"land_actual_wind_share"`
	LandActualTotalShare           *float64 `json:"land_actual_total_share"`
}

This dataset was calculated by Our World in Data based on:1. 

Land use intensity data sourced from the UNECE's Lifecycle Assessment of Electricity Generation Options assessment.

This provides data on the amount of land needed to produce one unit of electricity across different energy sources. This is based on literature review of life-cycle assessments of electricity sources: these not only include the land used for individual power plants, but also include any land use upstream in supply chains, such as mining for fuel or raw materials.2. Electricity consumption data sourced from Ember Climate.By multiplying annual electricity consumption for each country by the average land use of different energy sources, we have calculated the amount of land that would be needed if a country got all of its electricity from a single source. This is an unrealistic and simplistic scenario, but serves to provide a magnitude estimate of how much would be needed for energy production under different circumstances.References:UNECE (2021). Lifecycle Assessment of Electricity Generation Options. United Nations Economic Commission for Europe. Available at: https://unece.org/sed/documents/2021/10/reports/life-cycle-assessment-electricity-generation-optionsEmber Climate. Available at: https://ember-climate.org/

type EnergyMixFromBp2020Dataset 

type EnergyMixFromBp2020Dataset struct {
	PrimaryEnergyTwh                       *float64 `json:"primary_energy_twh"`
	FossilFuelsTwh                         *float64 `json:"fossil_fuels_twh"`
	LowCarbonEnergyTwh                     *float64 `json:"low_carbon_energy_twh"`
	CoalPercPrimaryDirectEnergy            *float64 `json:"coal_perc_primary_direct_energy"`
	GasPercPrimaryDirectEnergy             *float64 `json:"gas_perc_primary_direct_energy"`
	OilPercPrimaryDirectEnergy             *float64 `json:"oil_perc_primary_direct_energy"`
	NuclearPercPrimaryDirectEnergy         *float64 `json:"nuclear_perc_primary_direct_energy"`
	HydroPercPrimaryDirectEnergy           *float64 `json:"hydro_perc_primary_direct_energy"`
	RenewablesPercPrimaryDirectEnergy      *float64 `json:"renewables_perc_primary_direct_energy"`
	SolarPercPrimaryDirectEnergy           *float64 `json:"solar_perc_primary_direct_energy"`
	WindPercPrimaryDirectEnergy            *float64 `json:"wind_perc_primary_direct_energy"`
	OtherRenewablesPercPrimaryDirectEnergy *float64 `json:"other_renewables_perc_primary_direct_energy"`
	CoalPercSubEnergy                      *float64 `json:"coal_perc_sub_energy"`
	GasPercSubEnergy                       *float64 `json:"gas_perc_sub_energy"`
	OilPercSubEnergy                       *float64 `json:"oil_perc_sub_energy"`
	NuclearPercSubEnergy                   *float64 `json:"nuclear_perc_sub_energy"`
	HydroPercSubEnergy                     *float64 `json:"hydro_perc_sub_energy"`
	RenewablesPercSubEnergy                *float64 `json:"renewables_perc_sub_energy"`
	SolarPercSubEnergy                     *float64 `json:"solar_perc_sub_energy"`
	WindPercSubEnergy                      *float64 `json:"wind_perc_sub_energy"`
	OtherRenewablesPercSubEnergy           *float64 `json:"other_renewables_perc_sub_energy"`
	FossilFuelsPercSubEnergy               *float64 `json:"fossil_fuels_perc_sub_energy"`
	LowCarbonEnergyPercSubEnergy           *float64 `json:"low_carbon_energy_perc_sub_energy"`
	CoalPercGrowth                         *float64 `json:"coal_perc_growth"`
	CoalTwhGrowthSubMethod                 *float64 `json:"coal_twh_growth_sub_method"`
	GasPercGrowth                          *float64 `json:"gas_perc_growth"`
	GasTwhGrowthSubMethod                  *float64 `json:"gas_twh_growth_sub_method"`
	OilPercGrowth                          *float64 `json:"oil_perc_growth"`
	OilTwhGrowthSubMethod                  *float64 `json:"oil_twh_growth_sub_method"`
	NuclearPercGrowth                      *float64 `json:"nuclear_perc_growth"`
	NuclearTwhGrowthSubMethod              *float64 `json:"nuclear_twh_growth_sub_method"`
	HydroPercGrowth                        *float64 `json:"hydro_perc_growth"`
	HydroTwhGrowthSubMethod                *float64 `json:"hydro_twh_growth_sub_method"`
	SolarPercGrowth                        *float64 `json:"solar_perc_growth"`
	SolarTwhGrowthSubMethod                *float64 `json:"solar_twh_growth_sub_method"`
	WindPercGrowth                         *float64 `json:"wind_perc_growth"`
	WindTwhGrowthSubMethod                 *float64 `json:"wind_twh_growth_sub_method"`
	OtherRenewablesPercGrowth              *float64 `json:"other_renewables_perc_growth"`
	OtherRenewablesTwhGrowthSubMethod      *float64 `json:"other_renewables_twh_growth_sub_method"`
	RenewablesPercGrowth                   *float64 `json:"renewables_perc_growth"`
	RenewablesTwhGrowthSubMethod           *float64 `json:"renewables_twh_growth_sub_method"`
	FossilFuelsPercGrowth                  *float64 `json:"fossil_fuels_perc_growth"`
	FossilFuelsTwhGrowthSubMethod          *float64 `json:"fossil_fuels_twh_growth_sub_method"`
	LowCarbonEnergyPercGrowth              *float64 `json:"low_carbon_energy_perc_growth"`
	LowCarbonEnergyTwhGrowthSubMethod      *float64 `json:"low_carbon_energy_twh_growth_sub_method"`
	FossilFuelsPercPrimaryDirectEnergy     *float64 `json:"fossil_fuels_perc_primary_direct_energy"`
	LowCarbonEnergyPercPrimaryDirectEnergy *float64 `json:"low_carbon_energy_perc_primary_direct_energy"`
	CoalPerCapitaKwh                       *float64 `json:"coal_per_capita_kwh"`
	OilPerCapitaKwh                        *float64 `json:"oil_per_capita_kwh"`
	GasPerCapitaKwh                        *float64 `json:"gas_per_capita_kwh"`
	NuclearPerCapitaKwh                    *float64 `json:"nuclear_per_capita_kwh"`
	SolarPerCapitaKwh                      *float64 `json:"solar_per_capita_kwh"`
	WindPerCapitaKwh                       *float64 `json:"wind_per_capita_kwh"`
	HydroPerCapitaKwh                      *float64 `json:"hydro_per_capita_kwh"`
	RenewablesPerCapitaKwh                 *float64 `json:"renewables_per_capita_kwh"`
	FossilFuelsPerCapitaKwh                *float64 `json:"fossil_fuels_per_capita_kwh"`
	LowCarbonEnergyPerCapitakwh            *float64 `json:"low_carbon_energy_per_capitakwh"`
	OtherRenewablesPerCapitaKwh            *float64 `json:"other_renewables_per_capita_kwh"`
	HydroTwhSubMethod                      *float64 `json:"hydro_twh_sub_method"`
	NuclearTwhSubMethod                    *float64 `json:"nuclear_twh_sub_method"`
	RenewablesTwhSubMethod                 *float64 `json:"renewables_twh_sub_method"`
	SolarTwhSubMethod                      *float64 `json:"solar_twh_sub_method"`
	WindTwhSubMethod                       *float64 `json:"wind_twh_sub_method"`
	OtherRenewablesTwhSubMethod            *float64 `json:"other_renewables_twh_sub_method"`
	LowCarbonEnergyTwhSubMethod            *float64 `json:"low_carbon_energy_twh_sub_method"`
	PrimaryEnergyDirectTwh                 *float64 `json:"primary_energy_direct_twh"`
	BiofuelsTwh                            *float64 `json:"biofuels_twh"`
	BiofuelsPercPrimaryDirectEnergy        *float64 `json:"biofuels_perc_primary_direct_energy"`
	BiofuelsPercSubEnergy                  *float64 `json:"biofuels_perc_sub_energy"`
	BiofuelsPercGrowth                     *float64 `json:"biofuels_perc_growth"`
	BiofuelsTwhGrowthSubMethod             *float64 `json:"biofuels_twh_growth_sub_method"`
	BiofuelsPerCapitaKwh                   *float64 `json:"biofuels_per_capita_kwh"`
}

Raw data on energy consumption is sourced from the BP Statistical Review of World Energy. Available at: http://www.bp.com/statisticalreviewPrimary energy in exajoules (EJ) has been converted to TWh by Our World in Data based on a conversion factor of 277.778.Each source's share of energy based on the "substitution method" were calculated by Our World in Data by taking all energy sources' energy normalised to EJ – this takes account of the inefficiencies in fossil fuel production and is a better approximation of "final energy" consumption.Additional metrics have been calculated by Our World in Data:– Annual change in energy consumption by source: this is calculated as the difference from the previous year– % of total primary energy: calculated as each source's share of primary energy from all sources– Per capita energy by source: calculated as primary energy consumption by source, divided by population (source from the UN Population Division)

type EnergyMixFromBp2021Dataset 

type EnergyMixFromBp2021Dataset struct {
	BiofuelsTwh                            *float64 `json:"biofuels_twh"`
	HydroTwhSubMethod                      *float64 `json:"hydro_twh_sub_method"`
	NuclearTwhSubMethod                    *float64 `json:"nuclear_twh_sub_method"`
	RenewablesTwhSubMethod                 *float64 `json:"renewables_twh_sub_method"`
	SolarTwhSubMethod                      *float64 `json:"solar_twh_sub_method"`
	WindTwhSubMethod                       *float64 `json:"wind_twh_sub_method"`
	OtherRenewablesTwhSubMethod            *float64 `json:"other_renewables_twh_sub_method"`
	LowCarbonEnergyTwhSubMethod            *float64 `json:"low_carbon_energy_twh_sub_method"`
	LowCarbonEnergyTwh                     *float64 `json:"low_carbon_energy_twh"`
	FossilFuelsTwh                         *float64 `json:"fossil_fuels_twh"`
	PrimaryEnergyTwh                       *float64 `json:"primary_energy_twh"`
	PrimaryEnergyDirectTwh                 *float64 `json:"primary_energy_direct_twh"`
	CoalPercPrimaryDirectEnergy            *float64 `json:"coal_perc_primary_direct_energy"`
	GasPercPrimaryDirectEnergy             *float64 `json:"gas_perc_primary_direct_energy"`
	OilPercPrimaryDirectEnergy             *float64 `json:"oil_perc_primary_direct_energy"`
	BiofuelsPercPrimaryDirectEnergy        *float64 `json:"biofuels_perc_primary_direct_energy"`
	NuclearPercPrimaryDirectEnergy         *float64 `json:"nuclear_perc_primary_direct_energy"`
	HydroPercPrimaryDirectEnergy           *float64 `json:"hydro_perc_primary_direct_energy"`
	RenewablesPercPrimaryDirectEnergy      *float64 `json:"renewables_perc_primary_direct_energy"`
	SolarPercPrimaryDirectEnergy           *float64 `json:"solar_perc_primary_direct_energy"`
	WindPercPrimaryDirectEnergy            *float64 `json:"wind_perc_primary_direct_energy"`
	OtherRenewablesPercPrimaryDirectEnergy *float64 `json:"other_renewables_perc_primary_direct_energy"`
	FossilFuelsPercPrimaryDirectEnergy     *float64 `json:"fossil_fuels_perc_primary_direct_energy"`
	LowCarbonEnergyPercPrimaryDirectEnergy *float64 `json:"low_carbon_energy_perc_primary_direct_energy"`
	CoalPercSubEnergy                      *float64 `json:"coal_perc_sub_energy"`
	GasPercSubEnergy                       *float64 `json:"gas_perc_sub_energy"`
	OilPercSubEnergy                       *float64 `json:"oil_perc_sub_energy"`
	BiofuelsPercSubEnergy                  *float64 `json:"biofuels_perc_sub_energy"`
	NuclearPercSubEnergy                   *float64 `json:"nuclear_perc_sub_energy"`
	HydroPercSubEnergy                     *float64 `json:"hydro_perc_sub_energy"`
	RenewablesPercSubEnergy                *float64 `json:"renewables_perc_sub_energy"`
	SolarPercSubEnergy                     *float64 `json:"solar_perc_sub_energy"`
	WindPercSubEnergy                      *float64 `json:"wind_perc_sub_energy"`
	OtherRenewablesPercSubEnergy           *float64 `json:"other_renewables_perc_sub_energy"`
	FossilFuelsPercSubEnergy               *float64 `json:"fossil_fuels_perc_sub_energy"`
	LowCarbonEnergyPercSubEnergy           *float64 `json:"low_carbon_energy_perc_sub_energy"`
	CoalPercGrowth                         *float64 `json:"coal_perc_growth"`
	CoalTwhGrowthSubMethod                 *float64 `json:"coal_twh_growth_sub_method"`
	OilPercGrowth                          *float64 `json:"oil_perc_growth"`
	OilTwhGrowthSubMethod                  *float64 `json:"oil_twh_growth_sub_method"`
	GasPercGrowth                          *float64 `json:"gas_perc_growth"`
	GasTwhGrowthSubMethod                  *float64 `json:"gas_twh_growth_sub_method"`
	BiofuelsPercGrowth                     *float64 `json:"biofuels_perc_growth"`
	BiofuelsTwhGrowthSubMethod             *float64 `json:"biofuels_twh_growth_sub_method"`
	FossilFuelsPercGrowth                  *float64 `json:"fossil_fuels_perc_growth"`
	FossilFuelsTwhGrowthSubMethod          *float64 `json:"fossil_fuels_twh_growth_sub_method"`
	HydroPercGrowth                        *float64 `json:"hydro_perc_growth"`
	HydroTwhGrowthSubMethod                *float64 `json:"hydro_twh_growth_sub_method"`
	NuclearPercGrowth                      *float64 `json:"nuclear_perc_growth"`
	NuclearTwhGrowthSubMethod              *float64 `json:"nuclear_twh_growth_sub_method"`
	RenewablesPercGrowth                   *float64 `json:"renewables_perc_growth"`
	RenewablesTwhGrowthSubMethod           *float64 `json:"renewables_twh_growth_sub_method"`
	SolarPercGrowth                        *float64 `json:"solar_perc_growth"`
	SolarTwhGrowthSubMethod                *float64 `json:"solar_twh_growth_sub_method"`
	WindPercGrowth                         *float64 `json:"wind_perc_growth"`
	WindTwhGrowthSubMethod                 *float64 `json:"wind_twh_growth_sub_method"`
	OtherRenewablesPercGrowth              *float64 `json:"other_renewables_perc_growth"`
	OtherRenewablesTwhGrowthSubMethod      *float64 `json:"other_renewables_twh_growth_sub_method"`
	LowCarbonEnergyPercGrowth              *float64 `json:"low_carbon_energy_perc_growth"`
	LowCarbonEnergyTwhGrowthSubMethod      *float64 `json:"low_carbon_energy_twh_growth_sub_method"`
	CoalPerCapitaKwh                       *float64 `json:"coal_per_capita_kwh"`
	OilPerCapitaKwh                        *float64 `json:"oil_per_capita_kwh"`
	GasPerCapitaKwh                        *float64 `json:"gas_per_capita_kwh"`
	BiofuelsPerCapitaKwh                   *float64 `json:"biofuels_per_capita_kwh"`
	FossilFuelsPerCapitaKwh                *float64 `json:"fossil_fuels_per_capita_kwh"`
	HydroPerCapitaKwh                      *float64 `json:"hydro_per_capita_kwh"`
	NuclearPerCapitaKwh                    *float64 `json:"nuclear_per_capita_kwh"`
	RenewablesPerCapitaKwh                 *float64 `json:"renewables_per_capita_kwh"`
	SolarPerCapitaKwh                      *float64 `json:"solar_per_capita_kwh"`
	WindPerCapitaKwh                       *float64 `json:"wind_per_capita_kwh"`
	OtherRenewablesPerCapitaKwh            *float64 `json:"other_renewables_per_capita_kwh"`
	LowCarbonEnergyPerCapitaKwh            *float64 `json:"low_carbon_energy_per_capita_kwh"`
}

Raw data on energy consumption is sourced from the BP Statistical Review of World Energy. Available at: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.htmlPrimary energy in exajoules (EJ) has been converted to TWh by Our World in Data based on a conversion factor of 277.778.Each source's share of energy based on the "substitution method" were calculated by Our World in Data by taking all energy sources' energy normalised to EJ – this takes account of the inefficiencies in fossil fuel production and is a better approximation of "final energy" consumption.Additional metrics have been calculated by Our World in Data:– Annual change in energy consumption by source: this is calculated as the difference from the previous year– % of total primary energy: calculated as each source's share of primary energy from all sources– Per capita energy by source: calculated as primary energy consumption by source, divided by population.Per capita figures have been calculated using a population dataset that is built and maintained by Our World in Data, based on different sources:https://ourworldindata.org/population-sources

type EnergyMixInTheUkDukes2018Dataset 

type EnergyMixInTheUkDukes2018Dataset struct {
	CoalMtoe              *float64 `json:"coal_mtoe"`
	OilMtoe               *float64 `json:"oil_mtoe"`
	NaturalGasMtoe        *float64 `json:"natural_gas_mtoe"`
	NuclearMtoe           *float64 `json:"nuclear_mtoe"`
	HydroWindAndSolarMtoe *float64 `json:"hydro_wind_and_solar_mtoe"`
	ImportsMtoe           *float64 `json:"imports_mtoe"`
	BioenergyMtoe         *float64 `json:"bioenergy_mtoe"`
	CoalPerc              *float64 `json:"coal_perc"`
	OilPerc               *float64 `json:"oil_perc"`
	NaturalGasPerc        *float64 `json:"natural_gas_perc"`
	NuclearPerc           *float64 `json:"nuclear_perc"`
	HydroWindAndSolarPerc *float64 `json:"hydro_wind_and_solar_perc"`
	ImportsPerc           *float64 `json:"imports_perc"`
	BioenergyPerc         *float64 `json:"bioenergy_perc"`
	FossilFuelsPerc       *float64 `json:"fossil_fuels_perc"`
	LowCarbonPerc         *float64 `json:"low_carbon_perc"`
	RenewablesPerc        *float64 `json:"renewables_perc"`
}

Primary energy mix in the United Kingdom by source. This is available in absolute terms of million tonnes of oil equivalents (Mtoe) and as a relative share of the total energy mix. Primary energy mix relates to the quantity of energy inputs to the system; thermal losses are not taken into account, meaning this does not represent the final energy share.

type EnergyPricesBpStatistics2016Dataset 

type EnergyPricesBpStatistics2016Dataset struct {
	OilBpStatistics2016        *float64 `json:"oil_bp_statistics_2016"`
	NaturalGasBpStatistics2016 *float64 `json:"natural_gas_bp_statistics_2016"`
	CoalBpStatistics2016       *float64 `json:"coal_bp_statistics_2016"`
}

Prices corrected to 2015 US$ using the Consumer Price Index for the US

Prices have been normalised to a commodity-specific index by the author where prices in 2000=100

type EnvironmentalImpactsOfFoodPooreAndNemecek2018Dataset 

type EnvironmentalImpactsOfFoodPooreAndNemecek2018Dataset struct {
	LandUsePerKilogramPooreAndNemecek2018                     *float64 `json:"land_use_per_kilogram_poore_and_nemecek_2018"`
	GhgEmissionsPerKilogramPooreAndNemecek2018                *float64 `json:"ghg_emissions_per_kilogram_poore_and_nemecek_2018"`
	EutrophyingEmissionsPerKilogramPooreAndNemecek2018        *float64 `json:"eutrophying_emissions_per_kilogram_poore_and_nemecek_2018"`
	FreshwaterWithdrawalsPerKilogramPooreAndNemecek2018       *float64 `json:"freshwater_withdrawals_per_kilogram_poore_and_nemecek_2018"`
	ScarcityWeightedWaterUsePerKilogramPooreAndNemecek2018    *float64 `json:"scarcity_weighted_water_use_per_kilogram_poore_and_nemecek_2018"`
	LandUsePer100gProteinPooreAndNemecek2018                  *float64 `json:"land_use_per_100g_protein_poore_and_nemecek_2018"`
	GhgEmissionsPer100gProteinPooreAndNemecek2018             *float64 `json:"ghg_emissions_per_100g_protein_poore_and_nemecek_2018"`
	EutrophyingEmissionsPer100gProteinPooreAndNemecek2018     *float64 `json:"eutrophying_emissions_per_100g_protein_poore_and_nemecek_2018"`
	FreshwaterWithdrawalsPer100gProteinPooreAndNemecek2018    *float64 `json:"freshwater_withdrawals_per_100g_protein_poore_and_nemecek_2018"`
	ScarcityWeightedWaterUsePer100gProteinPooreAndNemecek2018 *float64 `json:"scarcity_weighted_water_use_per_100g_protein_poore_and_nemecek_2018"`
	LandUsePer1000kcalPooreAndNemecek2018                     *float64 `json:"land_use_per_1000kcal_poore_and_nemecek_2018"`
	GhgEmissionsPer1000kcalPooreAndNemecek2018                *float64 `json:"ghg_emissions_per_1000kcal_poore_and_nemecek_2018"`
	EutrophyingEmissionsPer1000kcalPooreAndNemecek2018        *float64 `json:"eutrophying_emissions_per_1000kcal_poore_and_nemecek_2018"`
	FreshwaterWithdrawalsPer1000kcalPooreAndNemecek2018       *float64 `json:"freshwater_withdrawals_per_1000kcal_poore_and_nemecek_2018"`
	ScarcityWeightedWaterUsePer1000kcalPooreAndNemecek2018    *float64 `json:"scarcity_weighted_water_use_per_1000kcal_poore_and_nemecek_2018"`
}

Data is based on the largest meta-analysis of food system impact studies to date, from Poore & Nemecek's 2018 study.The authors note the following about the scope of the studies included in this meta-analysis:"We derived data from a comprehensive meta-analysis, identifying 1530 studies for potential inclusion, which were supplemented with additional data received from 139 authors. Studies were assessed against 11 criteria designed to standardize methodology, resulting in 570 suitable studies with a median reference year of 2010. The data set covers ~38,700 commercially viable farms in 119 countries and 40 products representing ~90% of global protein and calorie consumption'.Environmental impacts are compared across several metrics: land use (m2), greenhouse gas emissions (tonnes of CO2-equivalents), eutrophying emissions (grams of PO4-equivalents), freshwater withdrawals (liters), and scarcity-weighted water (liters) which are freshwater withdrawals weighted for local water scarcity.All comparisons here are based on the global mean value per food product across all studies.Comparisons can be made in functional units: here all comparisons are made as impacts per kilogram of product.Comparisons are also made on the basis of nutritional units in two categories: per 100 grams of protein and per 1000 kilocalories.Poore & Nemecek (2018) quantified a range of footprints in nutritional units:(1) protein products, which are compared per 100 grams of protein. Protein products include all meats, seafood, dairy, nuts, tofu and pulses. Grains are also compared here – despite being a low-quality source of protein – since a large share of global protein is derived from cereals.(2) grains and staples, which are compared per 1000 kilocalories.Poore & Nemecek (2018) do not provide data per 100g protein for food products which are not protein-rich, or kilocalorie measures for non-stale crops. To provide footprints for all products Our World in Data have filled these gaps by calculating footprints per nutritional unit using food composition factors from the FAO INFOODS International Database and Food Balance Sheets:http://www.fao.org/3/X9892E/X9892e05.htm#P8217_125315http://www.fao.org/infoods/infoods/tables-and-databases/international-databases/en/Footprints expressed per kilogram of food product can be converted to per unit protein or kilocalorie using data on the nutrient density of food products.Where nutritional footprints are available from Poore & Nemecek (2018), this data has been used. Where there were gaps, this data has been calculated by Our World in Data.

type EstimatedAverageAgeAtMarriageByGenderUnAndOecdDataset 

type EstimatedAverageAgeAtMarriageByGenderUnAndOecdDataset struct {
	EstimatedAverageAgeAtMarriageWomen *float64 `json:"estimated_average_age_at_marriage_women"`
	EstimatedAverageAgeAtMarriageMen   *float64 `json:"estimated_average_age_at_marriage_men"`
}

<strong>Definition and sources by country</strong></br>For OECD countries, figures correspond to mean age at first marriage from the OECD Family Database. For other countries, figures correspond to Singulate Mean Age at Marriage from the UN World Marriage Data 2019. 

The Singulate Mean Age at Marriage (SMAM) is an indirect estimate of the average age at first marriage, and is calculated from survey data on marital status by age.To calculate the SMAM, the UN World Marriage Database combines multiple sources for each country, including both census and survey estimates.

For comparability, we have chosen to report only one source per country, favouring the source that provides the longest time series for each country. The following is a list of sources underlying our UN estimates, from most to least coverage: UNSD, DHS_STATcompiler, National Statistics, MICS, the US Census Bureau, INED, Eurostat, and IPUMS. A full breakdown of the source used for each observation in this dataset can be found <a href="https://owid.cloud/app/uploads/2020/01/oecd-un-mix-final-metadata-stand.csv">here</a>.<strong>Further notes on Singulate Mean Age at Marriage</strong></br>The Singulate Mean Age at Marriage is derived from the proportion of single persons of each sex in successive age groups. The main assumption involved in this computation is that change in the proportion single from age x to x+1 is a measure of the proportion of a birth cohort who married at age x. The methodology enables computation of mean age at marriage of persons (male or female) aged 15 years and above before they attain the age of 50. In other words, it provides an estimate of the average number of years lived in the never married status by those who marry before the age of 50.<strong>OECD country series notes</strong></br>- Estimates for Australia, New Zealand and the United States provide the median, rather than mean age at first marriage- For Mexico, data refer to all marriages and not only first marriage- For Canada, data include the legal union of two persons of the same sex in some provinces and territories from 2003 onwards, and in all of Canada from 2005 onwards.- For New Zealand data include civil unions. From 2007 onwards, data include those who transferred their civil union to a marriage. - From 2014 onwards, data for the United Kingdom include marriages between same-sex partners.

type EstimatedAverageAgeAtMarriageByGenderUnDataset 

type EstimatedAverageAgeAtMarriageByGenderUnDataset struct {
	EstimatedAverageAgeAtMarriageMenUn   *float64 `json:"estimated_average_age_at_marriage_men_un"`
	EstimatedAverageAgeAtMarriageWomenUn *float64 `json:"estimated_average_age_at_marriage_women_un"`
}

<strong>Definition and sources by country.</strong></br>The estimates of average age at marriage in this dataset correspond to what is known as "singulate mean age at marriage". This is an estimate of the average number of years lived by a cohort of women or men before their first marriage. It is an indirect estimate, obtained from survey data on marital status by age.The source of the estimates is the UN World Marriage Database (2019). The UN World Marriage Database combines multiple sources for each country, including both census and survey estimates. For comparability, we have chosen to use only one source per country, favouring the source that provides the longest time series for each country. The following is a list of underlying sources, from most to least coverage: UNSD, DHS_STATcompiler, National Statistics, MICS, the US Census Bureau, INED, Eurostat, and IPUMS. A full breakdown of the source used for each observation in the UN dataset can be found <a href="https://owid.cloud/app/uploads/2020/01/un-smam-19-metadata_country_stan.csv">here</a>.<strong>Further notes on Singulate Mean Age at Marriage: </strong></br><em>The Singulate Mean Age at Marriage is derived from the proportion of single persons of each sex in successive age groups. The main assumption involved in this computation is that change in the proportion single from age x to x+1 is a measure of the proportion of a birth cohort who married at age x. The methodology enables computation of mean age at marriage of persons (male or female) aged 15 years and above before they attain the age of 50. In other words, it provides an estimate of the average number of years lived in the never married status by those who marry before the age of 50.</em>

type EstimatedFundingAndFutureNeedsForHivInLowAndMiddleIncomeCountriesUnaidsDataset 

type EstimatedFundingAndFutureNeedsForHivInLowAndMiddleIncomeCountriesUnaidsDataset struct {
	EstimatedHivResourceAvailabilityForLowAndMiddleIncomeCountriesMillionUsmoney *float64 `json:"estimated_hiv_resource_availability_for_low_and_middle_income_countries_million_usmoney"`
	EstimatedResourceNeedsForFastTrackTargetsMillionUsmoney                      *float64 `json:"estimated_resource_needs_for_fast_track_targets_million_usmoney"`
}

Estimated funding to addressing HIV in low-to-middle income countries by region until the year 2016. Also provided by UNAIDS are estimates of resource requirements from 2018-2030 in low-to-middle income countries to meet fast-track HIV targets by 2030.

type EstimatedHistoricalLiteracyRatesBuringhAndVanZanden2009Dataset 

type EstimatedHistoricalLiteracyRatesBuringhAndVanZanden2009Dataset struct {
	EstimatedLiteracyRatesBuringhAndVanZanden2009 *float64 `json:"estimated_literacy_rates_buringh_and_van_zanden_2009"`
}

See Table 9 (pg 434) for the original estimates.

type EstimatedPercentOfWomenWhoAreMarriedOrInAUnionUnDataset 

type EstimatedPercentOfWomenWhoAreMarriedOrInAUnionUnDataset struct {
	EstimatedPercentOfWomenWhoAreMarriedOrInAUnion *float64 `json:"estimated_percent_of_women_who_are_married_or_in_a_union"`
}

The estimates and projections of the number of women of reproductive age (15 to 49 years) who are married or in a union were prepared based on data from individual countries on an age-specific basis (for five-year age groups 15-19 to 45-49).For the purpose of this data, women who are currently married or in a union are either (i) women who have been married and are not divorced, widowed or separated; or (ii) women who are living in a cohabiting union.The question usually asked in censuses and surveys is on the marital status or union status of an individual.The definitions of marital status and union status of individuals differ by the source of data, country and time period. For example, in the United Nations principles and recommendations for census taking, only registered partnerships and consensual unions that are legal and binding under law should be reported. However, there is considerable variation among the definitions and terminology used in surveys. The Demographic and Health Surveys, one of the major sources of data on marital and union status, ask individuals whether they are married or ‘living together’ with a partner; however, in countries with low prevalence of cohabiting unions, the survey question is normally limited only to marriage.For a breakdown of countries by region see the UN Methodology page <a href="https://unstats.un.org/unsd/methodology/m49/overview/">here</a>.<strong>Data is estimated until 2010, and projected for 2020 and 2030. </strong>

type EthnographicAndArchaeologicalEvidenceOnViolentDeathsDataset 

type EthnographicAndArchaeologicalEvidenceOnViolentDeathsDataset struct {
	ShareOfViolentDeathsPrehistoricArcheologicalSites *float64 `json:"share_of_violent_deaths_prehistoric_archeological_sites"`
	ShareOfViolentDeathsNonStateSocieties             *float64 `json:"share_of_violent_deaths_non_state_societies"`
	ShareOfViolentDeathsStateSocieties                *float64 `json:"share_of_violent_deaths_state_societies"`
	RateOfViolentDeathsStateSocieties                 *float64 `json:"rate_of_violent_deaths_state_societies"`
	RateOfViolentDeathsNonStateSocieties              *float64 `json:"rate_of_violent_deaths_non_state_societies"`
}

This dataset contains estimates of the frequency of violent deaths due to murder or war in modern and prehistoric state and non-state societies, based on archaeological and ethnographic evidence.For modern state societies, homicide rates are routinely published by statistical offices or other state agencies, and reliable data on war deaths are published by research institutes. For non-state societies, we generally have two different sources of information: for the more recent past (since the late 19th century), abundant ethnographic evidence is available; for the more distant past, we have evidence from archaeological sites and skeletal remains.The main sources for this dataset are as follows:- Bowles (2009) – Did Warfare Among Ancestral Hunter-Gatherers Affect the Evolution of Human Social Behaviors?. In Science, 324, 5932, 1293–1298.- Gat (2006) – War in Human Civilization. Oxford University Press, USA.- Knauft, Bruce M. et al (1987) – Reconsidering Violence in Simple Human Societies: Homicide among the Gebusi of New Guinea. In Current Anthropology, 28, 4, 457-500.- Keeley (1997) – War Before Civilization: The Myth of the Peaceful Savage. Oxford University Press, USA.- Pinker (2011) – The Better Angels of Our Nature: Why Violence Has Declined. Viking.- Walker and Bailey (2013) – Body counts in lowland South American violence. In Evolution and Human Behavior, 34, 1, 29–34.

type EuropeanVehiclePassengerSalesIcct2021Dataset 

type EuropeanVehiclePassengerSalesIcct2021Dataset struct {
	TotalPassengerVehicleSales  *float64 `json:"total_passenger_vehicle_sales"`
	ShareEuropeanCarSales       *float64 `json:"share_european_car_sales"`
	VehiclePriceEur             *float64 `json:"vehicle_price_eur"`
	DieselShare                 *float64 `json:"diesel_share"`
	GasShare                    *float64 `json:"gas_share"`
	FullHybridShare             *float64 `json:"full_hybrid_share"`
	PluginHybridShare           *float64 `json:"plugin_hybrid_share"`
	MildHybridShare             *float64 `json:"mild_hybrid_share"`
	BatteryElectricShare        *float64 `json:"battery_electric_share"`
	PetrolShare                 *float64 `json:"petrol_share"`
	Co2PerKm                    *float64 `json:"co2_per_km"`
	FuelEfficiencyPerKm         *float64 `json:"fuel_efficiency_per_km"`
	DieselNumber                *float64 `json:"diesel_number"`
	GasNumber                   *float64 `json:"gas_number"`
	FullHybridNumber            *float64 `json:"full_hybrid_number"`
	PluginHybridNumber          *float64 `json:"plugin_hybrid_number"`
	MildHybridNumber            *float64 `json:"mild_hybrid_number"`
	BatteryElectricNumber       *float64 `json:"battery_electric_number"`
	PetrolNumber                *float64 `json:"petrol_number"`
	AllHybridShare              *float64 `json:"all_hybrid_share"`
	AllHybridNumber             *float64 `json:"all_hybrid_number"`
	FullMildHybridShare         *float64 `json:"full_mild_hybrid_share"`
	DieselGasShare              *float64 `json:"diesel_gas_share"`
	FullMildHybridNumber        *float64 `json:"full_mild_hybrid_number"`
	DieselGasNumber             *float64 `json:"diesel_gas_number"`
	BatteryPluginElectricShare  *float64 `json:"battery_plugin_electric_share"`
	BatteryPluginElectricNumber *float64 `json:"battery_plugin_electric_number"`
}

Vehicle registrations data by type across specific European countries is sourced from the International Council on Clean Transport (ICCT): http://eupocketbook.org/European aggregate figures is sourced from the European Environment Agency: https://www.eea.europa.eu/data-and-maps/indicators/proportion-of-vehicle-fleet-meeting-5/assessment. Here 'Europe' refers to the sum of the EU-27, Iceland, Norway and United Kingdom.

type ExcessMortalityDataOwid2022Dataset 

type ExcessMortalityDataOwid2022Dataset struct {
	PAvgAllAges                     *float64 `json:"p_avg_all_ages"`
	PAvg15_64                       *float64 `json:"p_avg_15_64"`
	PAvg65_74                       *float64 `json:"p_avg_65_74"`
	PAvg75_84                       *float64 `json:"p_avg_75_84"`
	PAvg85p                         *float64 `json:"p_avg_85p"`
	Deaths2020AllAges               *float64 `json:"deaths_2020_all_ages"`
	AverageDeaths2015_2019AllAges   *float64 `json:"average_deaths_2015_2019_all_ages"`
	Deaths2015AllAges               *float64 `json:"deaths_2015_all_ages"`
	Deaths2016AllAges               *float64 `json:"deaths_2016_all_ages"`
	Deaths2017AllAges               *float64 `json:"deaths_2017_all_ages"`
	Deaths2018AllAges               *float64 `json:"deaths_2018_all_ages"`
	Deaths2019AllAges               *float64 `json:"deaths_2019_all_ages"`
	Deaths2010AllAges               *float64 `json:"deaths_2010_all_ages"`
	Deaths2011AllAges               *float64 `json:"deaths_2011_all_ages"`
	Deaths2012AllAges               *float64 `json:"deaths_2012_all_ages"`
	Deaths2013AllAges               *float64 `json:"deaths_2013_all_ages"`
	Deaths2014AllAges               *float64 `json:"deaths_2014_all_ages"`
	Deaths2021AllAges               *float64 `json:"deaths_2021_all_ages"`
	Time                            *float64 `json:"time"`
	TimeUnit                        *float64 `json:"time_unit"`
	PAvg0_14                        *float64 `json:"p_avg_0_14"`
	ProjectedDeaths2020_2022AllAges *float64 `json:"projected_deaths_2020_2022_all_ages"`
	ExcessProjAllAges               *float64 `json:"excess_proj_all_ages"`
	CumExcessProjAllAges            *float64 `json:"cum_excess_proj_all_ages"`
	CumProjDeathsAllAges            *float64 `json:"cum_proj_deaths_all_ages"`
	CumPProjAllAges                 *float64 `json:"cum_p_proj_all_ages"`
	PProjAllAges                    *float64 `json:"p_proj_all_ages"`
	PProj0_14                       *float64 `json:"p_proj_0_14"`
	PProj15_64                      *float64 `json:"p_proj_15_64"`
	PProj65_74                      *float64 `json:"p_proj_65_74"`
	PProj75_84                      *float64 `json:"p_proj_75_84"`
	PProj85p                        *float64 `json:"p_proj_85p"`
	CumExcessPerMillionProjAllAges  *float64 `json:"cum_excess_per_million_proj_all_ages"`
	ExcessPerMillionProjAllAges     *float64 `json:"excess_per_million_proj_all_ages"`
	Deaths2022AllAges               *float64 `json:"deaths_2022_all_ages"`
	Deaths2020_2022AllAges          *float64 `json:"deaths_2020_2022_all_ages"`
}

All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.We do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in this spreadsheet: https://docs.google.com/spreadsheets/d/1JPMtzsx-smO3_K4ReK_HMeuVLEzVZ71qHghSuAfG788/edit?usp=sharing.For a full list of source information (i.e., HMD or WMD) country by country, see: https://ourworldindata.org/excess-mortality-covid#source-information-country-by-country.We calculate P-scores using the reported deaths data from HMD and WMD and the projected deaths for 2020–2022 from WMD (which we use for all countries and regions, including for deaths broken down by age group). The P-score is the percentage difference between the reported number of weekly or monthly deaths in 2020–2022 and the projected number of deaths for the same period based on previous years.We calculate the number of weekly deaths for the United Kingdom by summing the weekly deaths from England & Wales, Scotland, and Northern Ireland.For important issues and caveats to understand when interpreting excess mortality data, see our excess mortality page at https://ourworldindata.org/excess-mortality-covid.For a more detailed description of the HMD data, including week date definitions, the coverage (of individuals, locations, and time), whether dates are for death occurrence or registration, the original national source information, and important caveats, see the HMD metadata file at https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf.For a more detailed description of the WMD data, including original source information, see their GitHub page at https://github.com/akarlinsky/world_mortality.

type ExecutionsByCountryAmnestyInternationalDataset 

type ExecutionsByCountryAmnestyInternationalDataset struct {
	NumberOfExecutionsAmnestyInternational *float64 `json:"number_of_executions_amnesty_international"`
}
Amnesty Interntional note:

"Amnesty International reports only on executions, death sentences and other aspects of the use of the death penalty, such as commutations and exonerations, where there is reasonable confirmation. In many countries governments do not publish information on their use of the death penalty. In Belarus, China and Viet Nam, data on the use of the death penalty is classified as a state secret. During 2016 little or no information was available on some countries – in particular Laos, North Korea (the Democratic People’s Republic of Korea), Syria and Yemen – due to restrictive state practice and/or armed conflict. Therefore, with only a few exceptions, Amnesty International’s figures on the use of the death penalty are minimum figures. The true figures are likely to be higher. Where the organization obtains fuller information on a specific country in a given year this is noted in Amnesty International's report. In 2009 Amnesty International stopped publishing its estimated figures on the use of the death penalty in China. Amnesty International always made clear that the figures it was able to publish on China were significantly lower than the reality, because of the restrictions on access to information. Amnesty International’s decision to stop publishing data reflected concerns about how the Chinese authorities misrepresented Amnesty International’s numbers. Since 2009 the organization challenged China to publish information on the use of the death penalty. China has yet to publish any figures on the death penalty. However, available information indicates that thousands of people are executed and sentenced to death in China each year."

type ExpectedYearsOfSchoolingUndp2018Dataset 

type ExpectedYearsOfSchoolingUndp2018Dataset struct {
	ExpectedYearsOfSchoolingYears *float64 `json:"expected_years_of_schooling_years"`
}

This series was published by the United Nations Development Programme (UNDP) Human Development Reports (HDR), which combines data from: UNESCO Institute for Statistics (2018); ICF Macro Demographic and Health Surveys; UNICEF Multiple Indicator Cluster Surveys; and OECD (2017a).

type ExperienceCurvesLafond2017Dataset 

type ExperienceCurvesLafond2017Dataset struct {
	CumulativeProductionLafond2017 *float64 `json:"cumulative_production_lafond_2017"`
	UnitCostLafond2017             *float64 `json:"unit_cost_lafond_2017"`
}

type ExtensionsInLifeExpectancyOwidCalculationsBasedOnUnPopulationDivision2017RevisionDataset 

type ExtensionsInLifeExpectancyOwidCalculationsBasedOnUnPopulationDivision2017RevisionDataset struct {
	IncreaseInLifeExpectancyByHoursPerDayOwidCalculationsBasedOnUnPopulationDivision2017Revision               *float64 `json:"increase_in_life_expectancy_by_hours_per_day_owid_calculations_based_on_un_population_division_2017_revision"`
	IncreaseInLifeExpectancyByMonthsPerYearOwidCalculationsBasedOnUnPopulationDivision2017Revision             *float64 `json:"increase_in_life_expectancy_by_months_per_year_owid_calculations_based_on_un_population_division_2017_revision"`
	IncreaseInLifeExpectancyHoursPerDayBetween1950_2015OwidCalculationsBasedOnUnPopulationDivision2017Revision *float64 `` /* 130-byte string literal not displayed */
}

The increase in life expectancy in 1955 was calculated as follows: Life expectancy in 1955 minus life expectancy in 1950 provides the total increase in life expectancy across this five year interval (x). The increase in life expectancy in hours per day is given by x multiplied by 365 divided by 5x365. This total is then multiplied by 24. The increase in life expectancy in months per year is given by x divided by 5, multiplied by 12.

The increase in life expectancy between 1950-2015 has been calculated as follows: Life expectancy in 2015 minus life expectancy in 1950 provides the total increase in years over the 65 year interval (y). The increase in life expectancy in hours per day is given by y multiplied by 365 divided by 65x365. This total is then multiplied by 24.

type ExtremeIncomePovertyInEuropeBradshawAndMayhew2011Dataset 

type ExtremeIncomePovertyInEuropeBradshawAndMayhew2011Dataset struct {
	Money1PovertyRateBradshawAndMayhew2011   *float64 `json:"money1_poverty_rate_bradshaw_and_mayhew_2011"`
	Money215PovertyRateBradshawAndMayhew2011 *float64 `json:"money215_poverty_rate_bradshaw_and_mayhew_2011"`
}

Estimates correspond to individuals living in households with per capita disposable income below the corresponding poverty line.

type ExtremePoverty2030ProjectionsBySspCrespoEtAl2018Dataset 

type ExtremePoverty2030ProjectionsBySspCrespoEtAl2018Dataset struct {
	Ssp1NumberOfPeopleInExtremePoverty               *float64 `json:"ssp1_number_of_people_in_extreme_poverty"`
	Ssp2NumberOfPeopleInExtremePoverty               *float64 `json:"ssp2_number_of_people_in_extreme_poverty"`
	Ssp3NumberOfPeopleInExtremePoverty               *float64 `json:"ssp3_number_of_people_in_extreme_poverty"`
	Ssp4NumberOfPeopleInExtremePoverty               *float64 `json:"ssp4_number_of_people_in_extreme_poverty"`
	Ssp5NumberOfPeopleInExtremePoverty               *float64 `json:"ssp5_number_of_people_in_extreme_poverty"`
	Ssp1DifferenceInNumberInExtremePovertyToBaseline *float64 `json:"ssp1_difference_in_number_in_extreme_poverty_to_baseline"`
	Ssp3DifferenceInNumberInExtremePovertyToBaseline *float64 `json:"ssp3_difference_in_number_in_extreme_poverty_to_baseline"`
	Ssp4DifferenceInNumberInExtremePovertyToBaseline *float64 `json:"ssp4_difference_in_number_in_extreme_poverty_to_baseline"`
	Ssp5DifferenceInNumberInExtremePovertyToBaseline *float64 `json:"ssp5_difference_in_number_in_extreme_poverty_to_baseline"`
}

Data on extreme poverty projections by country and region are sourced from the Nature publication by Crespo-Cuaresma et al. (2018). This data is also presented visually at the World Poverty Clock: http://worldpoverty.io/index.htmlExtreme poverty is defined by the international poverty line, set at a threshold of $1.90 per person per day, which is adjusted for inflation (by normalising to 2011 dollars), and corrected for cross-country price differences (PPP).The UN's Sustainable Development Goal (SDG) Target 1.1 is to end extreme poverty by 2030. 'Ending extreme poverty' is assumed when a country reaches an extreme poverty level below 3% of the total population.Scenarios of future extreme poverty rates were assessed and modelled by Crespo-Cuaresma et al. (2018) using a combination of the IMF World Economic Outlook, and multiple Shared Socioeconomic Pathways (SSP). The SSPs are defined broadly as follows:- SSP1: low challenges for both climate change adaptation and mitigation resulting from income growth which does not rely heavily on natural resources and technological change, coupled with low fertility rate and high educational attainment.- SSP2: the benchmark scenario and assumes the continuation of current global socioeconomic trends at the global level. - SSP3: low economic growth coupled with low educational attainment levels and high population growth at the global level are the main elements of the narrative, which is characterized by high mitigation and adaptation challenges.- SSP4: presents a narrative of worldwide polarization, with high income countries exhibiting relatively high growth rates of income, while developing economies present low levels of education, high fertility and economic stagnation.- SSP5: presents a scenario with high economic growth (and therefore low adaptation challenges) coupled with high demand for fossil energy from developing economies, but with high global CO2 emissions.SSP2 is defined as the baseline 'business-as-usual' scenario. The categories of 'extremely fragile', 'fragile' and 'non-fragile' have been added for reference based on the OECD (2018) States of Fragility framework. This framework measures the fragility of countries based on multiple political and socioeconomic indicators. More information on this fragility measure can be found at the OECD (2018) report.Reference: OECD (2018), States of Fragility. OECD Publishing, Paris. Available at: https://www.oecd-ilibrary.org/development/states-of-fragility-2018_9789264302075-en

type ExtremePovertyInAbsoluteNumbersRavallion2016UpdatedWithWorldBank2019Dataset 

type ExtremePovertyInAbsoluteNumbersRavallion2016UpdatedWithWorldBank2019Dataset struct {
	NumberOfPeopleLivingInExtremePovertyOwidBasedOnWorldBank2016AndBourguignonAndMorrisson2002 *float64 `json:"number_of_people_living_in_extreme_poverty_owid_based_on_world_bank_2016_and_bourguignon_and_morrisson_2002"`
	NumberOfPeopleNotInExtremePovertyOwidBasedOnWorldBank2016AndBourguignonAndMorrisson2002    *float64 `json:"number_of_people_not_in_extreme_poverty_owid_based_on_world_bank_2016_and_bourguignon_and_morrisson_2002"`
}

According to the 'International Poverty Line' set by the United Nations people are considered to live in extreme poverty when living on less than 1.90 international-$ per day. International $ are adjusted for price differences between countries and for price changes over time (inflation).This visualization can be found in Ravallion (2016) – The Economics of Poverty: History, Measurement, and Policy. Oxford University Press. 28 January 2016. 736 Pages.We have updated the visualization from Ravallion (2016) using the same sources the author relies on: Bourguignon and Morrison (2002) for the historical estimates and the World Bank for data from 1981 onwards. All poverty estimates 1981 and later are taken from the World Bank's Povcal Net: iresearch.worldbank.org/PovcalNet/ (downloaded in February 2019).All data from 1980 and earlier is taken from Bourguignon and Morrisson (2002) – Inequality Among World Citizens: 1820–1992. In American Economic Review, 92, 4, 727–748. Bourguignon and Morrisson (2002) state that 'the poverty lines were calibrated so that poverty and extreme poverty headcounts in 1992 coincided roughly with estimates from other sources’; here we rely on the midpoint of the two series published by Bourguignon and Morrison. The absolute number of the world population is taken from the OurWorldInData world population data set: https://ourworldindata.org/world-population-growth.

type ExtremePovertyMichailMoatsosOecdDataset 

type ExtremePovertyMichailMoatsosOecdDataset struct {
	ShareInExtremePoverty    *float64 `json:"share_in_extreme_poverty"`
	ShareNotInExtremePoverty *float64 `json:"share_not_in_extreme_poverty"`
}

The share in extreme poverty, estimated by Michail Moatsos based on the 'cost of basic needs'-approach.The ‘cost of basic needs’-approach was recommended by the ‘World Bank Commission on Global Poverty’, headed by Tony Atkinson, as a complementary method in measuring poverty.Tony Atkinson – and after his death his colleagues – turned this report into a book that was published as Anthony B. Atkinson (2019) – Measuring Poverty around the World. You find more information on Atkinson’s website.The CBN-approach Moatsos’ work is based on was suggested by Allen in Robert Allen (2017) – Absolute poverty: When necessity displaces desire. In American Economic Review, Vol. 107/12, pp. 3690-3721, https://doi.org/10.1257/aer.20161080 Moatsos describes the methodology as follows: “In this approach, poverty lines are calculated for every year and country separately, rather than using a single global line. The second step is to gather the necessary data to operationalise this approach, alongside imputation methods in cases where not all the necessary data are available. The third step is to devise a method for aggregating countries’ poverty estimates on a global scale to account for countries that lack some of the relevant data.” In his publication – linked above – you find much more detail on all of the shown poverty data.

type ExtremePrecipitationInUsNoaaDataset 

type ExtremePrecipitationInUsNoaaDataset struct {
	ShareOfLandAreaExperiencedUnusuallyHighAnnualPrecipitationNoaa       *float64 `json:"share_of_land_area_experienced_unusually_high_annual_precipitation_noaa"`
	ShareOfLandAreaExperiencedUnusuallyHighAnnualPrecipitation9yrAvgNoaa *float64 `json:"share_of_land_area_experienced_unusually_high_annual_precipitation_9yr_avg_noaa"`
	ShareOfLandAreaExperiencedExtremeOneDayPrecipitationNoaa             *float64 `json:"share_of_land_area_experienced_extreme_one_day_precipitation_noaa"`
	ShareOfLandAreaExperiencedExtremeOneDayPrecipitation9yrAvgNoaa       *float64 `json:"share_of_land_area_experienced_extreme_one_day_precipitation_9yr_avg_noaa"`
}

The metric of extreme single-day precipitation events measures the share of land area which experienced over a threshold percentage of precipitation in an extreme single-day burst. The metric of an unusually high precipitation year is measured on the basis of the standardized precipitation index (SPI). which compares actual yearly precipitation totals with the range of precipitation totals that one would typically expect at a specific location, based on historical data. If a location experiences less precipitation than normal during a particular period, it will receive a negative SPI score, while a period with more precipitation than normal will receive a positive score. The more precipitation (compared with normal), the higher the SPI score. The SPI is a useful way to look at precipitation totals because it allows comparison of different locations and different seasons on a standard scale. The data on unusually high precipitation shows the share of land area in the US which experienced an SPI value of 2.0 or above (well above normal) in a given year.Nine-year smoothed average trends for these metrics are also available.

type ExtremeTemperaturesInUsNoaaDataset 

type ExtremeTemperaturesInUsNoaaDataset struct {
	HeatWaveIndexNoaa           *float64 `json:"heat_wave_index_noaa"`
	ColdDailyHighsNoaa          *float64 `json:"cold_daily_highs_noaa"`
	O9YearAverageColdDailyHighs *float64 `json:"o9_year_average_cold_daily_highs"`
	ColdDailyLowsNoaa           *float64 `json:"cold_daily_lows_noaa"`
	O9YearAverageColdDailyLows  *float64 `json:"o9_year_average_cold_daily_lows"`
	HotDailyHighsNoaa           *float64 `json:"hot_daily_highs_noaa"`
	O9YearAverageHotDailyHighs  *float64 `json:"o9_year_average_hot_daily_highs"`
	HotDailyLowsNoaa            *float64 `json:"hot_daily_lows_noaa"`
	O9YearAverageHotDailyLows   *float64 `json:"o9_year_average_hot_daily_lows"`
}

The U.S. Annual Heat Wave Index tracks the occurrence of heat wave conditions across the United States. While there is no universal definition of a heat wave, this index defines a heat wave as a period lasting at least four days with an average temperature that would only be expected to occur once every 10 years, based on the historical record. The index value for a given year depends on how often heat waves occur and how widespread they are.Unusually high and low temperatures measure the percentage of the country’s area experiencing unusually hot temperatures in the summer and unusually cold temperatures in the winter. These graphs are based on daily maximum temperatures, which usually occur during the day, and daily minimum temperatures, which usually occur at night. At each station, the recorded highs and lows are compared with the full set of historical records. After averaging over a particular month or season of interest, the coldest 10 percent of years are considered “unusually cold” and the warmest 10 percent are “unusually hot.” For example, if last year’s summer highs were the 10th warmest on record for a particular location with more than 100 years of data, that year’s summer highs would be considered unusually warm. Data are available from 1910 to 2015 for summer (June through August) and from 1911 to 2016 for winter (December of the previous year through February).

type FamilyBenefitsPublicSpendingOecd2016Dataset 

type FamilyBenefitsPublicSpendingOecd2016Dataset struct {
	FamilyBenefitsPublicSpendingPercGdpOecd2016 *float64 `json:"family_benefits_public_spending_perc_gdp_oecd_2016"`
}

type Fao203050ProjectionsOfArableLandFao2017Dataset 

type Fao203050ProjectionsOfArableLandFao2017Dataset struct {
	ArableLandAndPermanentCropsFao2017 *float64 `json:"arable_land_and_permanent_crops_fao_2017"`
	FaoArableLandProjectionsFao2017    *float64 `json:"fao_arable_land_projections_fao_2017"`
}

This series is based on the combination of two datasets--both produced by the UN Food and Agricultural Organization (FAO).Data from 1961-2014 is derived from the UN FAO statistical database, and is given as the variable "Arable land and permanent crops". Available at: http://www.fao.org/faostat/en/#data [accessed 17th August 2017].Data from 2020-2050 is based on UN FAO projections of arable land and permanent crop area from its report: Alexandratos, N. and J. Bruinsma. 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12-03. Rome, FAO. Available at: http://www.fao.org/docrep/016/ap106e/ap106e.pdf [accessed 17th August 2017].

type FaoUndernourishmentComparison2010Vs2012Dataset 

type FaoUndernourishmentComparison2010Vs2012Dataset struct {
	O2012Report *float64 `json:"o2012_report"`
	O2010Report *float64 `json:"o2010_report"`
}

The original sources:FAO, IFAD & WFP (2010) – The State of Food Insecurity in the World 2013 – Addressing food insecurity in protracted crises, Food and Agriculture Organization of the United Nations (FAO), the International Fund for Agricultural Development (IFAD) or of the World Food Programme (WFP), FAO, Rome, 2013. http://www.fao.org/docrep/013/i1683e/i1683e.pdfFAO, IFAD & WFP (2013) – The State of Food Insecurity in the World 2013 – The multiple dimensions of food security, Food and Agriculture Organization of the United Nations (FAO), the International Fund for Agricultural Development (IFAD) or of the World Food Programme (WFP), FAO, Rome, 2013. http://www.fao.org/docrep/018/i3434e/i3434e.pdf

type FatalAviationAccidentsAndFataltiesAviationSafetyNetworkAsnDataset 

type FatalAviationAccidentsAndFataltiesAviationSafetyNetworkAsnDataset struct {
	FatalAccidentsFromCommercialAndNonCommercialAirlinersAsn *float64 `json:"fatal_accidents_from_commercial_and_non_commercial_airliners_asn"`
	FatalitiesFromCommercialAndNonCommercialAirlinersAsn     *float64 `json:"fatalities_from_commercial_and_non_commercial_airliners_asn"`
	HijackingIncidentsAsn                                    *float64 `json:"hijacking_incidents_asn"`
	FataltiesFromHijakingIncidentsAsn                        *float64 `json:"fatalties_from_hijaking_incidents_asn"`
	FatalAccidentsFromCommercialAirlinersAsn                 *float64 `json:"fatal_accidents_from_commercial_airliners_asn"`
	FatalitiesFromCommercialAirlinersAsn                     *float64 `json:"fatalities_from_commercial_airliners_asn"`
	NumberOfFlightDeparturesAsn                              *float64 `json:"number_of_flight_departures_asn"`
	FatalAccidentsFromPassengerOnlyCommercialAirlinersAsn    *float64 `json:"fatal_accidents_from_passenger_only_commercial_airliners_asn"`
	FatalitiesFromPassengerOnlyCommercialAirlinersAsn        *float64 `json:"fatalities_from_passenger_only_commercial_airliners_asn"`
	FatalitiesPerMillionFlightsAsn                           *float64 `json:"fatalities_per_million_flights_asn"`
	FatalAccidentsPerMillionCommercialFlightsAsn             *float64 `json:"fatal_accidents_per_million_commercial_flights_asn"`
	MillionFlightsPerFatalAccidentAsn                        *float64 `json:"million_flights_per_fatal_accident_asn"`
	PassengersPerYearWorldBank                               *float64 `json:"passengers_per_year_world_bank"`
	FatalitiesPerMillionPassengersAsn                        *float64 `json:"fatalities_per_million_passengers_asn"`
	MillionPassengersPerFatalityAsn                          *float64 `json:"million_passengers_per_fatality_asn"`
	NumberOfFlightDepartures                                 *float64 `json:"number_of_flight_departures"`
}

Data on fatal accidents, hijacking incidents and numbers of fatalities are based on airliners of 14+ passengers, and do not include corporate jet and military transport accidents.

Air traffic figures are based on the number of airliner flight departures (not the number of passengers) per year.

type FemaleAndMaleLifeExpectancyAtBirthOwidBasedOnUnPopulationDivision2017Dataset 

type FemaleAndMaleLifeExpectancyAtBirthOwidBasedOnUnPopulationDivision2017Dataset struct {
	FemaleToMaleLifeExpectancyRatioAtBirthOwid *float64 `json:"female_to_male_life_expectancy_ratio_at_birth_owid"`
	FemaleMinusMaleLifeExpectancyAtBirthOwid   *float64 `json:"female_minus_male_life_expectancy_at_birth_owid"`
}

Annually interpolated demographic indicators.Note: Guadeloupe includes Saint-Barthelemy and Saint-Martin.

type FemaleLaborForceParticipationRateOwid2017Dataset 

type FemaleLaborForceParticipationRateOwid2017Dataset struct {
	FemaleLaborForceParticipationRateOwidBasedOnOecd2017AndOthers *float64 `json:"female_labor_force_participation_rate_owid_based_on_oecd_2017_and_others"`
}

Estimates in this series come primarily from OECD.stat. However, for Canada, Germany, Great Britain and the US, we combined estimates from Long (1958) and Heckman and Killingsworth (1986), in order to provide a longer perspective. Details on how we combined these sources can be found in this online table (https://ourworldindata.org/wp-content/uploads/2017/07/OECD-historical-female-participation-OWID.xlsx).It is important to bear in mind that OECD estimates correspond to population ages 15+, while other estimates correspond to population 14+. Also, for France 1962-1966 we assigned missing values where the OECD.stat file reports zero.

type FemaleWeeklyHoursWorkedOecd2017Dataset 

type FemaleWeeklyHoursWorkedOecd2017Dataset struct {
	AverageWeeklyHoursWorkedWomen15OecdLaborForceStatistics2017 *float64 `json:"average_weekly_hours_worked_women_15_oecd_labor_force_statistics_2017"`
}

Original source notes: Part-time employment is defined according to a common definition of less than 30-weekly-usual hours worked in the main job. Full-time employment is defined according to a common definition of more than 30-weekly-usual hours worked in the main job. Information for all countries and all LFS subjects may be found in the attached file : www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf.

type FertilityRateCompleteGapminderV122017Dataset 

type FertilityRateCompleteGapminderV122017Dataset struct {
	FertilityRateCompleteGapminderV12_2017 *float64 `json:"fertility_rate_complete_gapminder_v12_2017"`
}

Data is that of version 12 of Gapminder, the latest version as of 2019. This is the full fertility rate dataset published by Gapminder.Gapminder's sources and methodology if well-documented in its dataset at: https://www.gapminder.org/data/It notes its data sources during three key periods of time:— 1800 to 1950 (and in some cases also years after 1950): Gapminder v6 which were compiled and documented by Mattias Lindgren.— 1950 to 2014: In most cases we use the latest UN estimates from World Population Prospects 2017 published in the file with Annually interpolated demographic indicators, called WPP2017_INT_F01_ANNUAL_DEMOGRAPHIC_INDICATORS.xlsx , accessed on September 2, 2017.— 2015 – 2099: We use the UN forecast of future fertility rate in all countries, called median fertility variant.Version 12 of the dataset extends back to the year 1800. Version 6 of Gapminder's fertility series includes data for a few countries further than 1800. We have included more historic data from Version 6 for Finland, the United Kingdom and Sweden. All data from 1800 onwards is from Version 12; data from pre-1800 is from Version 6.There are significant uncertainties in data for many countries pre-1950. To develop full series back to 1800 for all countries, Gapminder combines published estimates within the academic literature and national statistics, with their own guesstimates and extrapolations for countries without published estimates. This series presents the full Gapminder dataset: both those from published estimates and estimates made by Gapminder with high uncertainty. This is provided so users have access to the full dataset.However, for our main long-term series on fertility rates at Our World in Data we exclude the highly uncertain data points which are not backed up with published estimates within the literature. Users looking for a series with less uncertainty should refer to that instead.

type FertilityRateSelectedGapminderV122017Dataset 

type FertilityRateSelectedGapminderV122017Dataset struct {
	FertilityRateSelectGapminderV12_2017 *float64 `json:"fertility_rate_select_gapminder_v12_2017"`
}

Data is that of version 12 of Gapminder, the latest version as of 2019. This is the full fertility rate dataset published by Gapminder.Gapminder's sources and methodology if well-documented in its dataset at: https://www.gapminder.org/data/It notes its data sources during three key periods of time:— 1800 to 1950 (and in some cases also years after 1950): Gapminder v6 which were compiled and documented by Mattias Lindgren.— 1950 to 2014: In most cases we use the latest UN estimates from World Population Prospects 2017 published in the file with Annually interpolated demographic indicators, called WPP2017_INT_F01_ANNUAL_DEMOGRAPHIC_INDICATORS.xlsx , accessed on September 2, 2017.— 2015 – 2099: We use the UN forecast of future fertility rate in all countries, called median fertility variant.Version 12 of the dataset extends back to the year 1800. Version 6 of Gapminder's fertility series includes data for a few countries further than 1800. We have included more historic data from Version 6 for Finland, the United Kingdom and Sweden. All data from 1800 onwards is from Version 12; data from pre-1800 is from Version 6.There are significant uncertainties in data for many countries pre-1950. To develop full series back to 1800 for all countries, Gapminder combines published estimates within the academic literature and national statistics, with their own guesstimates and extrapolations for countries without published estimates. This series presents the selective Gapminder dataset: we have removed data points which were estimated by Gapminder with high uncertainty and instead only include those from published sources or the United Nations dataset. We also publish the full dataset from Gapminder for users looking for a complete series. However, we should highlight that some of these estimates have a high degree of uncertainty. This dataset can be accessed here: https://ourworldindata.org/grapher/fertility-rate-complete-gapminder

type FertilityRateWcIiasa2017Dataset 

type FertilityRateWcIiasa2017Dataset struct {
	FertilityRateSsp2GetScenarioWcIiasa *float64 `json:"fertility_rate_ssp2_get_scenario_wc_iiasa"`
	FertilityRateSsp2CerScenarioWcIiasa *float64 `json:"fertility_rate_ssp2_cer_scenario_wc_iiasa"`
	FertilityRateSsp2FtScenarioWcIiasa  *float64 `json:"fertility_rate_ssp2_ft_scenario_wc_iiasa"`
}

In the original data the fertility rate refer to 5 year windows. Here the mid year for each window is taken. For example 1978 here refers to the period 1975-1980.

type FertilityUnPopulationDivision2015RevisionDataset 

type FertilityUnPopulationDivision2015RevisionDataset struct {
	FertilityUnPopulationDivision2015Revision *float64 `json:"fertility_un_population_division_2015_revision"`
}

The data for a particular refers to the 5 years preceding that year. E.g. 2015 refers to 2010-2015 in the original data.

type FertilizerPricesWorldBank2017Dataset 

type FertilizerPricesWorldBank2017Dataset struct {
	UreaWorldBank2017                           *float64 `json:"urea_world_bank_2017"`
	FertilizerPriceIndex2010_100WorldBank2017   *float64 `json:"fertilizer_price_index_2010_100_world_bank_2017"`
	UreaPriceProjectionWorldBank2017            *float64 `json:"urea_price_projection_world_bank_2017"`
	FertilizerPriceIndexProjectionWorldBank2017 *float64 `json:"fertilizer_price_index_projection_world_bank_2017"`
	FertilizerPriceIndexWorldBank2017           *float64 `json:"fertilizer_price_index_world_bank_2017"`
}

Fertilizer prices are measured as a real price index, where prices in 2010 are equal to 100 (2010 = 100).Urea prices are reported in real US$ per metric ton.

type FertilizerUsePerHectareOfLandFaoAndFedericoDataset 

type FertilizerUsePerHectareOfLandFaoAndFedericoDataset struct {
	FertilizerConsumptionFao2017AndFederico2008 *float64 `json:"fertilizer_consumption_fao_2017_and_federico_2008"`
}

This data on fertilizer consumption rates combines two data sources. Data for all countries from 2002 onwards is sourced from the World Bank World Development Indicators (WDI). Available at: https://data.worldbank.org/ [accessed 13th September 2017]. Long-term data for select countries from 1880 is taken from Table 6.3 in Giovanni Federico (2008) – Feeding the World: An Economic History of Agriculture, 1800-2000. Princeton University Press. The original data sources cited by the author are: 1890-1957: Germany: (1880, 1910, 1920, 1940, and I960) Historical Statistics 1975, series K193 and J53 (assuming an average content of nutrients 17.5%); Japan: (1898-1902, 1908-12, 1918-22, 1933-37, and 1958-62) Hayami and Yamada 1991, table A5. Belgium: (1895,1910, 1929, and 1960-61) Blomme 1992, table 48. Year 1937 is referred to as 1937-38 in the original source; likewise 1957 refers to 1957-58, and 1999 to 1998-00.All data is measured in kilograms of nutrient per hecatre of arable land.

type FirmsWithFinancialConstraintsWorldBankEnterpriseSurvey2019Dataset 

type FirmsWithFinancialConstraintsWorldBankEnterpriseSurvey2019Dataset struct {
	ShareOfFirmsForWhichAccessToFinanceIsAMajorConstraintInPerc *float64 `json:"share_of_firms_for_which_access_to_finance_is_a_major_constraint_in_perc"`
}

Surveys were conducted in different years for different countries, but all during the time period of 2009 to 2018. The date displayed is standardized to 2018.

type FishStocksRamlegacyDataset 

type FishStocksRamlegacyDataset struct {
	GeneralTotalBiomass                        *float64 `json:"general_total_biomass"`
	GeneralTotalCatch                          *float64 `json:"general_total_catch"`
	GeneralExploitationRate                    *float64 `json:"general_exploitation_rate"`
	BiomassRelativeToMsy                       *float64 `json:"biomass_relative_to_msy"`
	HarvestRelativeToPreferredRate             *float64 `json:"harvest_relative_to_preferred_rate"`
	BiomassRelativeToPreferredManagementRate   *float64 `json:"biomass_relative_to_preferred_management_rate"`
	HarvestRateRelativeToManagementTarget      *float64 `json:"harvest_rate_relative_to_management_target"`
	TotalBiomass                               *float64 `json:"total_biomass"`
	SpawningStockBiomass                       *float64 `json:"spawning_stock_biomass"`
	TotalNumber                                *float64 `json:"total_number"`
	Recruits                                   *float64 `json:"recruits"`
	TotalCatch                                 *float64 `json:"total_catch"`
	TotalLandings                              *float64 `json:"total_landings"`
	RecreationalCatch                          *float64 `json:"recreational_catch"`
	FishingMortality                           *float64 `json:"fishing_mortality"`
	ExploitationRate                           *float64 `json:"exploitation_rate"`
	TotalBiomassRelativeToMsy                  *float64 `json:"total_biomass_relative_to_msy"`
	SpawningStockRelativeToMsy                 *float64 `json:"spawning_stock_relative_to_msy"`
	NumberRelativeToMsy                        *float64 `json:"number_relative_to_msy"`
	FishingMortalityRelativeToMsy              *float64 `json:"fishing_mortality_relative_to_msy"`
	ExploitationRateRelativeToMsy              *float64 `json:"exploitation_rate_relative_to_msy"`
	CatchRelativeToMsy                         *float64 `json:"catch_relative_to_msy"`
	CatchRelativeToMeanCatch                   *float64 `json:"catch_relative_to_mean_catch"`
	TotalBiomassRelativeToManagementTarget     *float64 `json:"total_biomass_relative_to_management_target"`
	SpawningStockRelativeToManagementTarget    *float64 `json:"spawning_stock_relative_to_management_target"`
	NumberRelativeToManagementTarget           *float64 `json:"number_relative_to_management_target"`
	FishingMortalityRelativeToManagementTarget *float64 `json:"fishing_mortality_relative_to_management_target"`
	ExploitationRateRelativeToManagementTarget *float64 `json:"exploitation_rate_relative_to_management_target"`
	CatchCorrespondingToTotalAllowableAdvised  *float64 `json:"catch_corresponding_to_total_allowable_advised"`
	TotalAllowableCatch                        *float64 `json:"total_allowable_catch"`
	ScientificAdviceForCatchLimit              *float64 `json:"scientific_advice_for_catch_limit"`
	SurveyBiomass                              *float64 `json:"survey_biomass"`
	CatchPerUnitEffort                         *float64 `json:"catch_per_unit_effort"`
	FishingEffort                              *float64 `json:"fishing_effort"`
}

This data is sourced from the RAM Legacy Stock Assessment Database, which can be accessed here: https://www.ramlegacy.org/.The primary publication describing the RAM Legacy Stock Assessment Database, and suggested citation for general use is:Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) Evaluating the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries 13 (4) 380-398. DOI: 10.1111/j.1467-2979.2011.00435.x

type FisheryCatchBreakdownPaulyAndZeller2016Dataset 

type FisheryCatchBreakdownPaulyAndZeller2016Dataset struct {
	IndustrialLargeScaleCommercial *float64 `json:"industrial_large_scale_commercial"`
	ArtisanalSmallScaleCommercial  *float64 `json:"artisanal_small_scale_commercial"`
	Discards                       *float64 `json:"discards"`
	Subsistence                    *float64 `json:"subsistence"`
	Recreational                   *float64 `json:"recreational"`
}

Data from the Pauly and Zeller (2016) paper is available at the Dryad Digital Repository:Pauly D, Zeller D (2016) Data from: Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Dryad Digital Repository. https://doi.org/10.5061/dryad.4s4t1

type FiveYearCancerSurvivalRatesAllemaniEtAlDataset 

type FiveYearCancerSurvivalRatesAllemaniEtAlDataset struct {
	Stomach   *float64 `json:"stomach"`
	Colon     *float64 `json:"colon"`
	Rectum    *float64 `json:"rectum"`
	Liver     *float64 `json:"liver"`
	Lung      *float64 `json:"lung"`
	Breast    *float64 `json:"breast"`
	Cervix    *float64 `json:"cervix"`
	Ovary     *float64 `json:"ovary"`
	Prostate  *float64 `json:"prostate"`
	Leukaemia *float64 `json:"leukaemia"`
}

Data is based on the percentage of those diagnosed with cancer who survive at least five years after the date of diagnosis. This is measured across the top 10 common malignancies in adults (aged 15-99), and leukaemia in children (aged 0-14). This share is age-standardized assuming a constant age distribution of the population to compare between countries and with time.The dates presented here are given as single-year but represent average net survival rates over a multi-year diagnosis period. Data here labelled '1999' represents average survival rates over the period 1995-1999; '2004' for 2000-2004; '2009' for 2005-2009.

type FiveYearCancerSurvivalRatesNationalCancerInstituteDataset 

type FiveYearCancerSurvivalRatesNationalCancerInstituteDataset struct {
	AllCancers            *float64 `json:"all_cancers"`
	BrainAndNervousSystem *float64 `json:"brain_and_nervous_system"`
	BreastCancer          *float64 `json:"breast_cancer"`
	CervixUteriCancer     *float64 `json:"cervix_uteri_cancer"`
	ColonAndRectumCancer  *float64 `json:"colon_and_rectum_cancer"`
	EsophagalCancer       *float64 `json:"esophagal_cancer"`
	Leukemia              *float64 `json:"leukemia"`
	LungAndBronchusCancer *float64 `json:"lung_and_bronchus_cancer"`
	LiverCancer           *float64 `json:"liver_cancer"`
	Myeloma               *float64 `json:"myeloma"`
	PancreaticCancer      *float64 `json:"pancreatic_cancer"`
	ProstateCancer        *float64 `json:"prostate_cancer"`
	StomachCancer         *float64 `json:"stomach_cancer"`
	ThyroidCancer         *float64 `json:"thyroid_cancer"`
	BladderCancer         *float64 `json:"bladder_cancer"`
	OvarianCancer         *float64 `json:"ovarian_cancer"`
	SkinCancer            *float64 `json:"skin_cancer"`
}

Five-year cancer survival rate, measured as the percentage of people who survive at least five years since diagnosis. Data is given as a single year, but denotes the average over a given period as follows:1963: 1960-19631973: 1970-19731977: 1975-19771980: 1978-19801983: 1981-19831986: 1984-19861989: 1987-19891992: 1989-19921995: 1992-19951998: 1996-19982001: 1999-20012006: 2002-20062013: 2007-2013

type FoodExpenditureInTheUsaUsdaDataset 

type FoodExpenditureInTheUsaUsdaDataset struct {
	FoodConsumedAtHomeAsShareOfDisposableIncome          *float64 `json:"food_consumed_at_home_as_share_of_disposable_income"`
	FoodConsumedAwayFromHomeAsShareOfDisposableIncome    *float64 `json:"food_consumed_away_from_home_as_share_of_disposable_income"`
	FoodAsShareOfDisposableIncomeAtAndAwayFromHome       *float64 `json:"food_as_share_of_disposable_income_at_and_away_from_home"`
	RestaurantPricesRelativeToRetailPrices               *float64 `json:"restaurant_prices_relative_to_retail_prices"`
	ManufacturersAndShippersPricesRelativeToRetailPrices *float64 `json:"manufacturers_and_shippers_prices_relative_to_retail_prices"`
	FoodEatenAwayFromHomeAsShareOfTotalFoodExpenditure   *float64 `json:"food_eaten_away_from_home_as_share_of_total_food_expenditure"`
	FoodExpenditureAtHomeCurrentPrices                   *float64 `json:"food_expenditure_at_home_current_prices"`
	FoodExpenditureAwayFromHomeCurrentPrices             *float64 `json:"food_expenditure_away_from_home_current_prices"`
	FoodExpenditureTotalCurrentPrices                    *float64 `json:"food_expenditure_total_current_prices"`
	FoodExpenditureAtHomeConstant1988Prices              *float64 `json:"food_expenditure_at_home_constant_1988_prices"`
	FoodExpenditureAwayFromHomeConstant1988Prices        *float64 `json:"food_expenditure_away_from_home_constant_1988_prices"`
	FoodExpenditureTotalConstant1988Prices               *float64 `json:"food_expenditure_total_constant_1988_prices"`
}

Data on food expenditure does not include alcoholic beverages or tobacco.

type FoodMilesByTransportMethodPooreAndNemecek2018Dataset 

type FoodMilesByTransportMethodPooreAndNemecek2018Dataset struct {
	FoodMilesMillionTonneKilometersPooreAndNemecek2018                       *float64 `json:"food_miles_million_tonne_kilometers_poore_and_nemecek_2018"`
	ShareOfFoodMilesPooreAndNemecek2018                                      *float64 `json:"share_of_food_miles_poore_and_nemecek_2018"`
	EmissionsFactorForAmbientTransportKgCo2eqPerTonneKilometer               *float64 `json:"emissions_factor_for_ambient_transport_kg_co2eq_per_tonne_kilometer"`
	EmissionsFactorForTemperatureControlledTransportKgCo2eqPerTonneKilometer *float64 `json:"emissions_factor_for_temperature_controlled_transport_kg_co2eq_per_tonne_kilometer"`
}

Data is based on an the largest meta-analysis of food system impact studies to date, from Poore & Nemecek's 2018 study.The authors note the following about the scope of the studies included in this meta-analysis:"We derived data from a comprehensive meta-analysis, identifying 1530 studies for potential inclusion, which were supplemented with additional data received from 139 authors. Studies were assessed against 11 criteria designed to standardize methodology, resulting in 570 suitable studies with a median reference year of 2010. The data set covers ~38,700 commercially viable farms in 119 countries and 40 products representing ~90% of global protein and calorie consumption'.A tonne-kilometre, abbreviated as tkm, is a unit of measure of freight transport which represents the transport of one tonne of goods (including packaging and tare weights of intermodal transport units) by a given transport mode (road, rail, air, sea, inland waterways, pipeline etc.) over a distance of one kilometre.These emissions factors by transport mode are those applied in the analysis by Joseph Poore and Thomas Nemecek (2018), published in Science. These emission factors are sourced from Ecoinvent v3.3, a comprehensive database which is commonly used in international life-cycle analyses (LCA). Emission factors can span a range of values depending on factors such as the efficiency of vehicle used; packing/loading density of freight; distribution between passenger and freight allocation in shared transport; amongst other factors.

type FoodPricesExpressedInHourlyWagesUsBureauOfLaborStatistics2015Dataset 

type FoodPricesExpressedInHourlyWagesUsBureauOfLaborStatistics2015Dataset struct {
	FoodPricesExpressedInHourlyWagesUsBureauOfLaborStatistics2015 *float64 `json:"food_prices_expressed_in_hourly_wages_us_bureau_of_labor_statistics_2015"`
}

type FoodSupplyFao2020Dataset 

type FoodSupplyFao2020Dataset struct {
	FoodSupplyKcalcapitadayGrandTotal               *float64 `json:"food_supply_kcalcapitaday_grand_total"`
	FatSupplyQuantityGcapitadayGrandTotal           *float64 `json:"fat_supply_quantity_gcapitaday_grand_total"`
	ProteinSupplyQuantityGcapitadayGrandTotal       *float64 `json:"protein_supply_quantity_gcapitaday_grand_total"`
	MeatFoodSupplyQuantityKgcapitayr                *float64 `json:"meat_food_supply_quantity_kgcapitayr"`
	BovineMeatFoodSupplyQuantityKgcapitayr          *float64 `json:"bovine_meat_food_supply_quantity_kgcapitayr"`
	PoultryMeatFoodSupplyQuantityKgcapitayr         *float64 `json:"poultry_meat_food_supply_quantity_kgcapitayr"`
	PigmeatFoodSupplyQuantityKgcapitayr             *float64 `json:"pigmeat_food_supply_quantity_kgcapitayr"`
	MuttonAndGoatMeatFoodSupplyQuantityKgcapitayr   *float64 `json:"mutton_and_goat_meat_food_supply_quantity_kgcapitayr"`
	MeatOtherFoodSupplyQuantityKgcapitayr           *float64 `json:"meat_other_food_supply_quantity_kgcapitayr"`
	EggsFoodSupplyQuantityKgcapitayr                *float64 `json:"eggs_food_supply_quantity_kgcapitayr"`
	MilkExcludingButterFoodSupplyQuantityKgcapitayr *float64 `json:"milk_excluding_butter_food_supply_quantity_kgcapitayr"`
	FishSeafoodFoodSupplyQuantityKgcapitayr         *float64 `json:"fish_seafood_food_supply_quantity_kgcapitayr"`
	AnimalProductsProteinSupplyQuantityGcapitaday   *float64 `json:"animal_products_protein_supply_quantity_gcapitaday"`
	VegetalProductsProteinSupplyQuantityGcapitaday  *float64 `json:"vegetal_products_protein_supply_quantity_gcapitaday"`
	FruitsExcludingWineFoodSupplyQuantityKgcapitayr *float64 `json:"fruits_excluding_wine_food_supply_quantity_kgcapitayr"`
	VegetablesFoodSupplyQuantityKgcapitayr          *float64 `json:"vegetables_food_supply_quantity_kgcapitayr"`
}

This dataset is sourced from the UN Food and Agriculture Organization (FAO) and combines data from its Food Balance Sheets into a complete series from 1961 to 2017. In the original FAO dataset, food supply data from 1961 to 2013 is stored under its 'old methodology' variable set. Data from 2014 to 2017 was stored under its 'new methodology' for food balance sheets.Our World in Data has combined this data to give a complete series from 1961 onwards. No transformations have been made to the original data.Food supply is defined as food available for human consumption. At country level, it is calculated as the food remaining for human use after deduction of all non-food utilizations (i.e. food = production + imports + stock withdrawals − exports − industrial use − animal feed – seed – wastage − additions to stock). Wastage includes losses of usable products occurring along distribution chains from farm gate (or port of import) up to the retail level. However, such values do not include consumption-level waste (i.e. retail, restaurant and household waste) and therefore overestimates the average amount of food actually consumed.

type FoodWasteInTheEuropeanUnionEuropa2010Dataset 

type FoodWasteInTheEuropeanUnionEuropa2010Dataset struct {
	ManufacturingEuropa2010  *float64 `json:"manufacturing_europa_2010"`
	HouseholdsEuropa2010     *float64 `json:"households_europa_2010"`
	OtherEuropa2010          *float64 `json:"other_europa_2010"`
	TotalFoodWasteEuropa2010 *float64 `json:"total_food_waste_europa_2010"`
}

Data is measured as total food waste per year, measured in mass terms (tonnes per year).Food waste is disaggregated by wastage from manufacturing, households and 'other' categories. 'Other' is inclusive of all other sectors where food waste occurs, including agricultural production, distribution, retail and catering.

type FoodWasteInTheSupplyChainTWrap2015AndEuropa2015Dataset 

type FoodWasteInTheSupplyChainTWrap2015AndEuropa2015Dataset struct {
	ManufacturingWrap2015AndEuropa2015          *float64 `json:"manufacturing_wrap_2015_and_europa_2015"`
	RetailAndWholesaleWrap2015AndEuropa2015     *float64 `json:"retail_and_wholesale_wrap_2015_and_europa_2015"`
	CateringAndHospitalityWrap2015AndEuropa2015 *float64 `json:"catering_and_hospitality_wrap_2015_and_europa_2015"`
	HouseholdWrap2015AndEuropa2015              *float64 `json:"household_wrap_2015_and_europa_2015"`
	ProductionWrap2015AndEuropa2015             *float64 `json:"production_wrap_2015_and_europa_2015"`
}

Food waste data for the UK is sourced from: WRAP (2015). Estimates of Food and Packaging Waste in the UK Grocery Retail and Hospitality Supply Chains. Available at: http://www.wrap.org.uk/sites/files/wrap/UK%20Estimates%20October%2015%20%28FINAL%29_0.pdf [accessed 12th October 2017].Food waste data for the EU is sourced from: FUSIONS (2015). Estimates of European food waste levels. Available at: http://www.eu-fusions.org/phocadownload/Publications/Estimates%20of%20European%20food%20waste%20levels.pdf [accessed 12th October 2017].Food waste data includes all food and drink categories and is based on waste by mass (i.e. measured in tonnes).

type ForestLandDeforestationAndChangeFao2020Dataset 

type ForestLandDeforestationAndChangeFao2020Dataset struct {
	Afforestation                    *float64 `json:"afforestation"`
	ConversionAsShareOfForestArea    *float64 `json:"conversion_as_share_of_forest_area"`
	Deforestation                    *float64 `json:"deforestation"`
	DeforestationAsShareOfForestArea *float64 `json:"deforestation_as_share_of_forest_area"`
	ExpansionAsShareOfForestArea     *float64 `json:"expansion_as_share_of_forest_area"`
	ForestArea                       *float64 `json:"forest_area"`
	ForestExpansion                  *float64 `json:"forest_expansion"`
	NaturalForestExpansion           *float64 `json:"natural_forest_expansion"`
	AnnualNetChangeInForestArea      *float64 `json:"annual_net_change_in_forest_area"`
	ShareOfGlobalDeforestation       *float64 `json:"share_of_global_deforestation"`
	ShareOfGlobalForestArea          *float64 `json:"share_of_global_forest_area"`
	ShareOfGlobalForestExpansion     *float64 `json:"share_of_global_forest_expansion"`
	ShareOfRegionalDeforestation     *float64 `json:"share_of_regional_deforestation"`
	ShareOfRegionalForestArea        *float64 `json:"share_of_regional_forest_area"`
	ForestCover                      *float64 `json:"forest_cover"`
}

Raw data for forest area, deforestation, afforestation and expansion is sourced from the UN FAO Forest Resources Assessment.Our World in Data have calculated several metrics based on this raw data including:– Net change in forest area (afforestation minus deforestation)– Net conversion rate (net change as a percentage of forest area)– Each country's share of global forest area, deforestation, afforestation, and net change in forests– Each country's share of regional forest area, deforestation, afforestation, and net change in forestsThe UN FAO publish forest area and forest change data as the annual average on 10- or 5-year timescales. Therefore the following year allocation applies:– 1990: the annual average over the period from 1990 to 2000.– 2000: the annual average over the period from 2000 to 2010.– 2010: the annual average over the period from 2010 to 2015.– 2015: the annual average over the period from 2015 to 2020.Data on forest cover by country pre-1990 is sourced from a variety sources which are documented here: https://docs.google.com/spreadsheets/d/1nYpao4e8Ai-P86jIUZ3r7X6-5MjZ7ZbG7TJQSO729Bg/edit?usp=sharing

type ForestTransitionPhasePendrillEtAl2019Dataset 

type ForestTransitionPhasePendrillEtAl2019Dataset struct {
	ForestTransitionPhase *float64 `json:"forest_transition_phase"`
}

The study categorized countries into four categories based on their stage in the forest transition.As, detailed, they follow a similar framework to Hosonuma et al. (2012)."Countries exhibiting low deforestation rates are classified as pre- or post-transition depending on whether forest cover is high or low (or if net reforestation is occurring); countries with high deforestation rates are classified as early-transition if gross deforestation is increasing and remaining forest cover is not too low, and late-transition otherwise. We decided to use a slightly lower threshold than Hosonuma et al (2012). We also manually adjusted the classification for a few post-transition countries that were not classified as such and excluded countries with less than 5% forest cover."Hosonuma describe the Forest Transition Model as:"The four FT phases are pre-transition, early transition, late transition and post-transition, which generally represent a time sequence of national development. Pre-transition countries have high forest cover and low deforestation rates. In early-transition countries, forest cover is lost at an increasingly rapid rate. Late-transition countries with a rather small fraction of remaining forests exhibit a slowing of the deforestation rate and eventually come into the post-transition phase, where the forest area change rate becomes positive and forest cover increases through reforestation. The FT model reflects a broad-scale typology of tropical developing countries, applicable as a proxy for analyzing the temporal variability of drivers of deforestation and forest degradation."Additional references:Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009.

type ForestryAreaFao2017Dataset 

type ForestryAreaFao2017Dataset struct {
	ForestAreaFao2017 *float64 `json:"forest_area_fao_2017"`
}

Data on global forestry area from 1990 onwards is based on data from the UN Food and Agricultural Organization (FAO) database.Data on global forestry prior to 1990 is less certain, and often prone to changes in definitions concerning forestry cover and density. We have extended this dataset to 1958 using reported data from the FAO World Forestry Inventory 1958 (published in 1960). Available at: http://www.fao.org/3/a-ad906t.pdf [accessed 18th August 2017].

type FossilFuelConsumptionPerCapitaBpAndUn2017RevisionDataset 

type FossilFuelConsumptionPerCapitaBpAndUn2017RevisionDataset struct {
	OilConsumptionPerCapitaBpAndUn2017Revision  *float64 `json:"oil_consumption_per_capita_bp_and_un_2017_revision"`
	CoalConsumptionPerCapitaBpAndUn2017Revision *float64 `json:"coal_consumption_per_capita_bp_and_un_2017_revision"`
	GasConsumptionPerCapitaBpAndUn2017Revision  *float64 `json:"gas_consumption_per_capita_bp_and_un_2017_revision"`
}

Per capita trends in fossil fuel consumption were calculated by OWID based on population data from the UN Population Division, and national fossil fuel data - coal, oil, and natural gas - from BP's Statistical Review of Global Energy.

Per capita consumption was calculated by dividing total population in any given year by a country or region's coal, oil or gas consumption.

Population data was sourced from the UN Population Division (2017 Revision). Available at: https://esa.un.org/unpd/wpp/DataQuery/ [accessed 17th October 2017].

Fossil fuel consumption data was sourced from the BP's Statistical Review of Global Energy. Available at: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html [accessed 17th October 2017].

type FossilFuelProductionBpAndShift2020Dataset 

type FossilFuelProductionBpAndShift2020Dataset struct {
	CoalProductionTwh               *float64 `json:"coal_production_twh"`
	OilProductionTwh                *float64 `json:"oil_production_twh"`
	GasProductionTwh                *float64 `json:"gas_production_twh"`
	AnnualChangeCoalProductionPerc  *float64 `json:"annual_change_coal_production_perc"`
	AnnualChangeInCoalProductionTwh *float64 `json:"annual_change_in_coal_production_twh"`
	AnnualChangeOilProductionPerc   *float64 `json:"annual_change_oil_production_perc"`
	AnnualChangeInOilProductionTwh  *float64 `json:"annual_change_in_oil_production_twh"`
	AnnualChangeGasProductionPerc   *float64 `json:"annual_change_gas_production_perc"`
	AnnualChangeInGasProductionTwh  *float64 `json:"annual_change_in_gas_production_twh"`
	CoalProductionPerCapitaKwh      *float64 `json:"coal_production_per_capita_kwh"`
	OilProductionPerCapitaKwh       *float64 `json:"oil_production_per_capita_kwh"`
	GasProductionPerCapitaKwh       *float64 `json:"gas_production_per_capita_kwh"`
}

This dataset on fossil fuel production is produced by combining the latest data from the BP Statistical Review of World Energy and the Shift Project Data Portal.BP provide fossil fuel production data from 1965 onwards. The Shift Data Portal provides long-term data to 1900, but only extends to 2016.To maintain consistency with the energy datasets on Our World in Data, we have taken BP data as preference – meaning if BP provides data for the given country and year, this is used. Where data is not available from BP for a given country, or pre-1965 we rely on the Shift Project.We have converted primary production in exajoules to terawatt-hours using the conversion factor: 278.BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.htmlShift Data Project: https://www.theshiftdataportal.org/energyProduction per capita has been calculated using population data from the UN World Population Prospects: https://population.un.org/wpp/.

type FossilFuelProductionBpAndShift2022Dataset 

type FossilFuelProductionBpAndShift2022Dataset struct {
	CoalProductionTwh                *float64 `json:"coal_production_twh"`
	GasProductionTwh                 *float64 `json:"gas_production_twh"`
	OilProductionTwh                 *float64 `json:"oil_production_twh"`
	AnnualChangeInCoalProductionPerc *float64 `json:"annual_change_in_coal_production_perc"`
	AnnualChangeInCoalProductionTwh  *float64 `json:"annual_change_in_coal_production_twh"`
	AnnualChangeInOilProductionPerc  *float64 `json:"annual_change_in_oil_production_perc"`
	AnnualChangeInOilProductionTwh   *float64 `json:"annual_change_in_oil_production_twh"`
	AnnualChangeInGasProductionPerc  *float64 `json:"annual_change_in_gas_production_perc"`
	AnnualChangeInGasProductionTwh   *float64 `json:"annual_change_in_gas_production_twh"`
	CoalProductionPerCapitaKwh       *float64 `json:"coal_production_per_capita_kwh"`
	OilProductionPerCapitaKwh        *float64 `json:"oil_production_per_capita_kwh"`
	GasProductionPerCapitaKwh        *float64 `json:"gas_production_per_capita_kwh"`
}

This dataset on fossil fuel production is produced by combining the latest data from the BP Statistical Review of World Energy and the Shift Project Data Portal.BP provide fossil fuel production data from 1965 onwards (and crude prices from 1861 onwards). The Shift Data Portal provides long-term data from 1900, but only extends to 2016.To maintain consistency with the energy datasets on Our World in Data, we have taken BP data as preference – meaning if BP provides data for the given country and year, this is used. Where data is not available from BP for a given country, or pre-1965 we rely on the Shift Project.We have converted primary production in exajoules to terawatt-hours using the conversion factor: 278.BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.htmlShift Data Project: https://www.theshiftdataportal.org/energyProduction per capita has been calculated using a population dataset that is built and maintained by Our World in Data, based on different sources: https://ourworldindata.org/population-sources

type FriendsAndFamilySupportOecdBasedOnGallup2016Dataset 

type FriendsAndFamilySupportOecdBasedOnGallup2016Dataset struct {
	PeopleWhoReportHavingFriendsOrRelativesTheyCanCountOn *float64 `json:"people_who_report_having_friends_or_relatives_they_can_count_on"`
}

type GapminderIgnoranceTestResultsGapminderDataset 

type GapminderIgnoranceTestResultsGapminderDataset struct {
	Question1EducationOfGirls          *float64 `json:"question_1_education_of_girls"`
	Question2MajorityIncomeLevel       *float64 `json:"question_2_majority_income_level"`
	Question3ExtremePoverty            *float64 `json:"question_3_extreme_poverty"`
	Question4LifeExpectancy            *float64 `json:"question_4_life_expectancy"`
	Question5FutureNumberOfChildren    *float64 `json:"question_5_future_number_of_children"`
	Question6ReasonForPopulationGrowth *float64 `json:"question_6_reason_for_population_growth"`
	Question7NaturalDisasters          *float64 `json:"question_7_natural_disasters"`
	Question8WherePeopleLive           *float64 `json:"question_8_where_people_live"`
	Question9Vaccination               *float64 `json:"question_9_vaccination"`
	Question10WomensEducation          *float64 `json:"question_10_womens_education"`
	Question11EndangeredAnimals        *float64 `json:"question_11_endangered_animals"`
	Question12ElectricityAccess        *float64 `json:"question_12_electricity_access"`
	Question13ClimateChange            *float64 `json:"question_13_climate_change"`
}

This data presents the results of the Gapminder Test launched in 2017. This was developed with Ipsos MORI and Novus. The results are presented in the book 'Factufulness: Ten reasons we're wrong about the world - and why things are better than you think' by Gapminder founders, Hans Rosling, Ola Rosling and Anna Rosling Ronnlund. The Gapminder Test consists of 13 questions, all with simple A, B, C alternatives. 12,000 people in 14 countries were tested, using weighted to be representative of the adult populations.The questions were as follows:(1) In all low-income countries across the world today, how many girls finish primary school? A: 20 percent B: 40 percent C: 60 percent (correct)(2) Where does the majority of the world population live?A: Low-income countries B: Middle-income countries (correct) C: High-income countries(3) In the last 20 years, the proportion of the world population living in extreme poverty has...?A: almost doubled B: stayed the same C: almost halved (correct)(4) What is the life expectancy of the world today?A: 50 years B: 60 years C: 70 years(5) There are two billion children in the world today, aged 0 to 15 years old. How any children will there be in the year 2100 according to the United Nations? A: 4 billion B: 3 billion C: 2 billion (correct)(6) The UN predicts that by 2100 the world population will have increased by another 4 billion people. What is the main reason?A: There will be more children (age below 15) B: There will be more adults (age 15 to 74) (correct) C: There will be more very old people (age 75 and older)(7) How did the number of deaths per year from natural disasters change over the last hundred years?A: More than doubled B: Remained about the same C: Decreased to less than half (correct)(8) There are roughly 7 billion people in the world today. Which map shows best where they live? A: 1 Europe; 1 Americas; 1 Africa; 4 Asia (correct) B: 1 Europe; 1 Americas; 2 Africa; 3 Asia C: 1 Europe; 2 Americas; 1 Africa; 3 Asia(9) How many of the world’s 1-year-old children today have been vaccinated against some disease?A: 20 percent B: 50 percent C: 80 percent (correct)(10) Worldwide, 30-year-old men have spent 10 years in school, on average. How many years have women of the same age spent in school?A: 9 years (correct) B: 6 years C: 3 years(11) In 1996, tigers, giant pandas, and black rhinos were all listed as endangered. How many of these three species are more critically endangered today?A: Two of them B: One of them C: None of them (correct)(12) How many people in the world have some access to electricity?A: 20 percent B: 50 percent C: 80 percent (correct)(13) Global climate experts believe that, over the next 100 years, the average temperature will . . .A: get warmer (correct) B: remain the same C: get colder

type GasProductionEtemadAndLucianaDataset 

type GasProductionEtemadAndLucianaDataset struct {
	NaturalGasProductionEtemadAndLuciana *float64 `json:"natural_gas_production_etemad_and_luciana"`
}

Data from 1900-1980 is sourced from Bouda Etemad and Jean Luciani, World Energy Production 1800 – 1985, ISBN 2-600-56007-6.Data from 1980 onwards is source from U.S. Energy Information Administration, International Energy Statistics.

type GdpGrowthFromPreviousYear2020Q2EurostatOecdNationalSourcesDataset 

type GdpGrowthFromPreviousYear2020Q2EurostatOecdNationalSourcesDataset struct {
	GdpGrowthFromPreviousYear2020Q2 *float64 `json:"gdp_growth_from_previous_year_2020_q2"`
}

We combined data on year on year GDP growth by quarter from different sources:1) The Eurostat 'flash estimates' for GDP, as released in the euroindicators news release (125/2020) on 14 August 20202) OECD's quarterly national accounts data, available at OECD.stat.In both cases, the data relates to the percentage change in GDP compared with the same quarter of the previous year (Q2 2019). This is calculated using a volume measure of GDP and as such, is adjusted to account for inflation between the years. The data is also seasonally adjusted.3) Data or press releases from national statistical agencies for Taiwan, Nigeria, Singapore, Colombia, Philippines, Malaysia, Tunisia and Peru.Note that estimates of GDP are often subject to revision as more data becomes available to national statistical agencies. The pandemic has impacted agencies' ability to collect information that inform their GDP estimates. Eurostat note that this is likely to have impacted the quality of the data in some cases (see: https://ec.europa.eu/eurostat/documents/24987/725066/Country_specific_metadata_associated_with_national_estimates_2020Q2)

type GdpInEnglandUsingBoe2017Dataset 

type GdpInEnglandUsingBoe2017Dataset struct {
	PopulationBoe2017            *float64 `json:"population_boe_2017"`
	RealGdpPerCapitaBoe2017      *float64 `json:"real_gdp_per_capita_boe_2017"`
	RealGdpAtMarketPricesBoe2017 *float64 `json:"real_gdp_at_market_prices_boe_2017"`
}

This dataset is produced using ‘A1. Headline Series’ sheet of the BoE dataset. The variables Real GDP (at market prices) and Population are taken directly from BoE. Real GDP per capita is, however, calculated by dividing Real GDP with Population.

type GdpPerCapitaIndexedAt1950MaddisonProjectData2018Dataset 

type GdpPerCapitaIndexedAt1950MaddisonProjectData2018Dataset struct {
	GdpPerCapitaIndexedAt1950MaddisonProjectData2018 *float64 `json:"gdp_per_capita_indexed_at_1950_maddison_project_data_2018"`
}

Full citation: Maddison Project Database, version 2018. Bolt, Jutta, Robert Inklaar, Herman de Jong and Jan Luiten van Zanden (2018), “Rebasing ‘Maddison’: new income comparisons and the shape of long-run economic development”, Maddison Project Working paper 10The original Maddison Project dataset expresses the population "in thousands". In our dataset we have multiplied it by 1000 to avoid expressing it "in thousands".The Maddison region definitions were adapted from the source from Maddison (2010)'s homepage at: http://www.ggdc.net/maddison/oriindex.htm under the heading "Historical Statistics" and the file titled: Statistics on World Population, GDP and Per Capita GDP, 1-2008 AD (Horizontal file, copyright Angus Maddison, University of Groningen) To calculate average real GDP per capita across regions, we have calculated a population weight for each country per year by dividing country population in year x by the total regional population. This weight has been used to multiply real GDP per capita (in 2011US$, with multiple benchmarks) to give real regional GDP per capita weighted by population.

type GenderInequalityIndexHumanDevelopmentReport2015Dataset 

type GenderInequalityIndexHumanDevelopmentReport2015Dataset struct {
	GenderInequalityIndexHumanDevelopmentReport2015 *float64 `json:"gender_inequality_index_human_development_report_2015"`
	Hdi2015RankingHumanDevelopmentReport2015        *float64 `json:"hdi_2015_ranking_human_development_report_2015"`
}

All observations for 1995 use 'share of seats in parliament' that refers to 1997.

type GenderPreferenceForBossGallup2017Dataset 

type GenderPreferenceForBossGallup2017Dataset struct {
	ShareOfPeopleWhoPreferMaleBossGallup2017   *float64 `json:"share_of_people_who_prefer_male_boss_gallup_2017"`
	ShareOfPeopleWhoPreferFemaleBossGallup2017 *float64 `json:"share_of_people_who_prefer_female_boss_gallup_2017"`
	ShareOfWomenWhoPreferAMaleBossGallup2017   *float64 `json:"share_of_women_who_prefer_a_male_boss_gallup_2017"`
	ShareOfWomenWhoPreferAFemaleBossGallup2017 *float64 `json:"share_of_women_who_prefer_a_female_boss_gallup_2017"`
	ShareOfMenWhoPreferAMaleBossGallup2017     *float64 `json:"share_of_men_who_prefer_a_male_boss_gallup_2017"`
	ShareOfMenWhoPreferAFemaleBossGallup2017   *float64 `json:"share_of_men_who_prefer_a_female_boss_gallup_2017"`
}

Question posed to respondents: If you were taking a new job and had your choice of a boss would you prefer to work for a man or a woman?Possible answers included: prefer man boss, prefer woman boss, no difference, and no opinion. We report the first two responses to this question.For more information on the Gallup Poll see the link above.

type GenderWageGapAssigningZerosForNoWorkDataset 

type GenderWageGapAssigningZerosForNoWorkDataset struct {
	WomensIncomeAsAPercentOfMens *float64 `json:"womens_income_as_a_percent_of_mens"`
}

type GenderWageGapOecd2017Dataset 

type GenderWageGapOecd2017Dataset struct {
	GenderWageGapOecd2017 *float64 `json:"gender_wage_gap_oecd_2017"`
}

type GenuineSavingEstimatesByVariousMeasuresBlumDucoingMclaughlin2017Dataset 

type GenuineSavingEstimatesByVariousMeasuresBlumDucoingMclaughlin2017Dataset struct {
	NetInvestmentBlumDucoingMclaughlin2017   *float64 `json:"net_investment_blum_ducoing_mclaughlin_2017"`
	GreenInvestmentBlumDucoingMclaughlin2017 *float64 `json:"green_investment_blum_ducoing_mclaughlin_2017"`
	GenuineSavingBlumDucoingMclaughlin2017   *float64 `json:"genuine_saving_blum_ducoing_mclaughlin_2017"`
	GstfpBlumDucoingMclaughlin2017           *float64 `json:"gstfp_blum_ducoing_mclaughlin_2017"`
	GdpBlumDucoingMclaughlin2017             *float64 `json:"gdp_blum_ducoing_mclaughlin_2017"`
	GreenCarbonBlumDucoingMclaughlin2017     *float64 `json:"green_carbon_blum_ducoing_mclaughlin_2017"`
}

type GermanRoadDeathsAndAccidentsDestatisDataset 

type GermanRoadDeathsAndAccidentsDestatisDataset struct {
	AccidentsReportedToPolice           *float64 `json:"accidents_reported_to_police"`
	AccidentsReportedWithInjury         *float64 `json:"accidents_reported_with_injury"`
	AccidentsReportedWithMaterialDamage *float64 `json:"accidents_reported_with_material_damage"`
	RoadCasualties                      *float64 `json:"road_casualties"`
	RoadDeaths                          *float64 `json:"road_deaths"`
	RoadInjuries                        *float64 `json:"road_injuries"`
}

type GhgEmissionsByCountryAndSectorCait2020Dataset 

type GhgEmissionsByCountryAndSectorCait2020Dataset struct {
	AgricultureGhgEmissionsCait                               *float64 `json:"agriculture_ghg_emissions_cait"`
	BunkerFuelsGhgEmissionsCait                               *float64 `json:"bunker_fuels_ghg_emissions_cait"`
	EnergyGhgEmissionsCait                                    *float64 `json:"energy_ghg_emissions_cait"`
	IndustryGhgEmissionsCait                                  *float64 `json:"industry_ghg_emissions_cait"`
	LandUseChangeAndForestryGhgEmissionsCait                  *float64 `json:"land_use_change_and_forestry_ghg_emissions_cait"`
	TotalGhgEmissionsIncludingLucfCait                        *float64 `json:"total_ghg_emissions_including_lucf_cait"`
	WasteGhgEmissionsCait                                     *float64 `json:"waste_ghg_emissions_cait"`
	TotalGhgEmissionsExcludingLucfCait                        *float64 `json:"total_ghg_emissions_excluding_lucf_cait"`
	BuildingsGhgEmissionsCait                                 *float64 `json:"buildings_ghg_emissions_cait"`
	ElectricityAndHeatGhgEmissionsCait                        *float64 `json:"electricity_and_heat_ghg_emissions_cait"`
	FugitiveFromEnergyProductionGhgEmissionsCait              *float64 `json:"fugitive_from_energy_production_ghg_emissions_cait"`
	ManufacturingconstructionEnergyGhgEmissionsCait           *float64 `json:"manufacturingconstruction_energy_ghg_emissions_cait"`
	OtherFuelCombustionGhgEmissionsCait                       *float64 `json:"other_fuel_combustion_ghg_emissions_cait"`
	TransportGhgEmissionsCait                                 *float64 `json:"transport_ghg_emissions_cait"`
	AgriculturePerCapitaGhgEmissionsCait                      *float64 `json:"agriculture_per_capita_ghg_emissions_cait"`
	BuildingsPerCapitaGhgEmissionsCait                        *float64 `json:"buildings_per_capita_ghg_emissions_cait"`
	ElectricityAndHeatPerCapitaGhgEmissionsCait               *float64 `json:"electricity_and_heat_per_capita_ghg_emissions_cait"`
	FugitiveEmissionsPerCapitaGhgEmissionsCait                *float64 `json:"fugitive_emissions_per_capita_ghg_emissions_cait"`
	IndustryPerCapitaGhgEmissionsCait                         *float64 `json:"industry_per_capita_ghg_emissions_cait"`
	InternationalAviationAndShippingPerCapitaGhgEmissionsCait *float64 `json:"international_aviation_and_shipping_per_capita_ghg_emissions_cait"`
	LandUseChangeAndForestryPerCapitaGhgEmissionsCait         *float64 `json:"land_use_change_and_forestry_per_capita_ghg_emissions_cait"`
	ManufacturingAndConstructionPerCapitaGhgEmissionsCait     *float64 `json:"manufacturing_and_construction_per_capita_ghg_emissions_cait"`
	TotalExcludingLucfPerCapitaGhgEmissionsCait               *float64 `json:"total_excluding_lucf_per_capita_ghg_emissions_cait"`
	TotalIncludingLucfPerCapitaGhgEmissionsCait               *float64 `json:"total_including_lucf_per_capita_ghg_emissions_cait"`
	TransportPerCapitaGhgEmissionsCait                        *float64 `json:"transport_per_capita_ghg_emissions_cait"`
	WastePerCapitaGhgEmissionsCait                            *float64 `json:"waste_per_capita_ghg_emissions_cait"`
}

Total greenhouse gas emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type GhgEmissionsByCountryAndSectorCait2021Dataset 

type GhgEmissionsByCountryAndSectorCait2021Dataset struct {
	Agriculture                           *float64 `json:"agriculture"`
	AgriculturePerCapita                  *float64 `json:"agriculture_per_capita"`
	AviationAndShipping                   *float64 `json:"aviation_and_shipping"`
	AviationAndShippingPerCapita          *float64 `json:"aviation_and_shipping_per_capita"`
	Buildings                             *float64 `json:"buildings"`
	BuildingsPerCapita                    *float64 `json:"buildings_per_capita"`
	ElectricityAndHeat                    *float64 `json:"electricity_and_heat"`
	ElectricityAndHeatPerCapita           *float64 `json:"electricity_and_heat_per_capita"`
	Energy                                *float64 `json:"energy"`
	EnergyPerCapita                       *float64 `json:"energy_per_capita"`
	FugitiveEmissions                     *float64 `json:"fugitive_emissions"`
	FugitiveEmissionsPerCapita            *float64 `json:"fugitive_emissions_per_capita"`
	Industry                              *float64 `json:"industry"`
	IndustryPerCapita                     *float64 `json:"industry_per_capita"`
	LandUseChangeAndForestry              *float64 `json:"land_use_change_and_forestry"`
	LandUseChangeAndForestryPerCapita     *float64 `json:"land_use_change_and_forestry_per_capita"`
	ManufacturingAndConstruction          *float64 `json:"manufacturing_and_construction"`
	ManufacturingAndConstructionPerCapita *float64 `json:"manufacturing_and_construction_per_capita"`
	OtherFuelCombustion                   *float64 `json:"other_fuel_combustion"`
	OtherFuelCombustionPerCapita          *float64 `json:"other_fuel_combustion_per_capita"`
	TotalExcludingLucf                    *float64 `json:"total_excluding_lucf"`
	TotalExcludingLucfPerCapita           *float64 `json:"total_excluding_lucf_per_capita"`
	TotalIncludingLucf                    *float64 `json:"total_including_lucf"`
	TotalIncludingLucfPerCapita           *float64 `json:"total_including_lucf_per_capita"`
	Transport                             *float64 `json:"transport"`
	TransportPerCapita                    *float64 `json:"transport_per_capita"`
	Waste                                 *float64 `json:"waste"`
	WastePerCapita                        *float64 `json:"waste_per_capita"`
}

Total greenhouse gas emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type GhgEmissionsPerCapitaEdgar2019Dataset 

type GhgEmissionsPerCapitaEdgar2019Dataset struct {
	GhgEmissionsPerCapitaTonnesCo2eEdgar2019   *float64 `json:"ghg_emissions_per_capita_tonnes_co2e_edgar_2019"`
	GhgEmissionsPerCapitaPerDayKgco2eEdgar2019 *float64 `json:"ghg_emissions_per_capita_per_day_kgco2e_edgar_2019"`
}

Total greenhouse gas emissions in CO2 equivalent are composed of CO2 totals excluding short-cycle biomass burning (such as agricultural waste burning and Savannah burning) but including other biomass burning (such as forest fires, post-burn decay, peat fires and decay of drained peatlands), all anthropogenic CH4 sources, N2O sources and F-gases (HFCs, PFCs and SF6).Figures on per capita greenhouse gas (GHG) emissions were calculated by Our World in Data based on total GHG emissions, and population data published in the World Bank, World Development Indicators. The World Bank (WDI) publishes figures on annual GHG emissions by country, sourced from the European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). And population data sourced from the UN World Population Prospects.Per capita GHG emissions per year was calculated by dividing total annual emissions by country by its population in the given year.Per capita GHG emissions per day was calculated by diving annual per capita figures by 365 (and converting from tonnes to kilograms by multiplying by 1000).The World Bank, World Development Indicators can be sourced at: http://data.worldbank.org/data-catalog/world-development-indicatorsThe original data from EDGAR can be sourced at: http://edgar.jrc.ec.europa.eu/

type GiniCoefficientEquivalizedIncomeAfterTaxAndTransfersChartbookOfEconomicInequality2017Dataset 

type GiniCoefficientEquivalizedIncomeAfterTaxAndTransfersChartbookOfEconomicInequality2017Dataset struct {
	GiniCoefficientEquivalizedIncomeAfterTaxAndTransfers *float64 `json:"gini_coefficient_equivalized_income_after_tax_and_transfers"`
}

type GiniCoefficientsForLifetimeInequalityPeltzman2009Dataset 

type GiniCoefficientsForLifetimeInequalityPeltzman2009Dataset struct {
	GiniCoefficientsForLifetimeInequalityPeltzman2009 *float64 `json:"gini_coefficients_for_lifetime_inequality_peltzman_2009"`
}

Sent in by Sam Peltzman, who uses this data in his paper “Mortality Inequality”, Journal of Economic Perspectives 23(4), Fall 2009: 175-190.

Life tables defined by Peltzman (2009) "lists the number of survivors at each age from a hypothetical birth (age=0) cohort of 100,000." The life table data is compiled from two online databases: www.mortality.org developed by researchers at the University of California Berkeley and the Data Laboratory of the Max Planck Institute for Demographic Research in Rostock, Germany. The http://www.lifetable.de/ data was put together by researchers from these two institutions and the researchers from the Institut national d'etudes demographiques in Paris, France.

type GiniCoefficientsOecd2016Dataset 

type GiniCoefficientsOecd2016Dataset struct {
	GiniCoefficientIncomeAfterTaxesAndTransfersOecdIncomeDistributionDatabase2016 *float64 `json:"gini_coefficient_income_after_taxes_and_transfers_oecd_income_distribution_database_2016"`
	GiniCoefficientBeforeTaxesAndTransfersOecdIncomeDistributionDatabase2016      *float64 `json:"gini_coefficient_before_taxes_and_transfers_oecd_income_distribution_database_2016"`
}

All estimates use the new OECD methodology for calculating incomes, introduced in 2012. The source notes: "Data calculated according to the new OECD Terms of reference. Compared to previous terms of reference, these include a more detail breakdown of current transfers received and paid by households as well as a revised definition of household income, including the value of goods produced for own consumption as an element of self-employed income."

type GistempTemperatureAnomalyDataset 

type GistempTemperatureAnomalyDataset struct {
	SurfaceTemperatureAnomaly                     *float64 `json:"surface_temperature_anomaly"`
	SurfaceTemperatureAnomalyWeightedByPopulation *float64 `json:"surface_temperature_anomaly_weighted_by_population"`
	SurfaceTemperatureAnomalyWeightedByArea       *float64 `json:"surface_temperature_anomaly_weighted_by_area"`
}

The temperature anomaly is measured relative to the 1951-1980 global average temperature.The GISTEMP analysis recalculates consistent temperature anomaly series from 1880 to the present for a regularly spaced array of virtual stations covering the whole globe. Those data are used to investigate regional and global patterns and trends. Country-level values were created by averaging all grid cells whose centroids were within the border of a country. Area weighted measures were weighted by the area of the grid cell when averaging the grid cells and population weighted averages used gridded population data from 2015 created by the Center for International Earth Science Information Network - CIESIN (http://dx.doi.org/10.7927/H4X63JVC).

type GlobalAgriculturalLandByCropFao2017Dataset 

type GlobalAgriculturalLandByCropFao2017Dataset struct {
	AreaUsedForProductionFao2017 *float64 `json:"area_used_for_production_fao_2017"`
}

Data for crops refers to the total global area under production, measured in hectares.

type GlobalAirlineTrafficAndCapacityIcao2020Dataset 

type GlobalAirlineTrafficAndCapacityIcao2020Dataset struct {
	AircraftDepartures                          *float64 `json:"aircraft_departures"`
	AircraftTravelDistance                      *float64 `json:"aircraft_travel_distance"`
	AvailableSeatKilometersAsks                 *float64 `json:"available_seat_kilometers_asks"`
	CargoRevenueTonneKilometresRtks             *float64 `json:"cargo_revenue_tonne_kilometres_rtks"`
	FreightRevenueTonneKilometresRtks           *float64 `json:"freight_revenue_tonne_kilometres_rtks"`
	FreightTonnes                               *float64 `json:"freight_tonnes"`
	MailRevenueTonneKilometersRtks              *float64 `json:"mail_revenue_tonne_kilometers_rtks"`
	NumberOfPassengers                          *float64 `json:"number_of_passengers"`
	PassengerLoadFactorPassengersAvailableSeats *float64 `json:"passenger_load_factor_passengers_available_seats"`
	RevenuePassengerKilometers                  *float64 `json:"revenue_passenger_kilometers"`
}

Global airline traffic and capacity data is sourced from 'Airlines for America': https://www.airlines.org/dataset/world-airlines-traffic-and-capacityThe source notes that:"Traffic and operations data below reflects the systemwide scheduled activity of passenger and cargo airlines operating worldwide, as recorded by ICAO; domestic operations within the former USSR are excluded prior to 1970."

type GlobalAverageTemperatureAnomalyHadleyCentreDataset 

type GlobalAverageTemperatureAnomalyHadleyCentreDataset struct {
	GlobalAverageTemperatureAnomalyHadleyCentre *float64 `json:"global_average_temperature_anomaly_hadley_centre"`
}

The temperature anomaly is measured relative to the 1961-1990 global average temperature. Temperatures at this baseline represent an increase of 0.3-0.4 degrees celcius since pre-Industrial (1850) global average temperatures.

The data presented here is a combined land-surface air temperature and sea-surface temperature series. Upper and lower bounds represent the 95% confidence intervals.

type GlobalBmiInFemalesNcdrisc2017Dataset 

type GlobalBmiInFemalesNcdrisc2017Dataset struct {
	MeanBmiNcdrisc2017 *float64 `json:"mean_bmi_ncdrisc_2017"`
}

This dataset presents the mean female Body Mass Index (BMI) by country, region, and globally.Body Mass Index (BMI) is a person's weight in kilograms (kg) divided by his or her height in meters squared (m2). The WHO define a BMI <=18.5 as 'underweight'; 18.5 to <25 as 'normal/healthy'; 25.0 to <30 as 'overweight'; and >30.0 as 'obese'.NCD Risk Factor Collaboration (NCD-RisC) is a network of health scientists around the world that provides rigorous and timely data on risk factors for non-communicable diseases (NCDs) for 200 countries and territories. The group works closely with the World Health Organisation (WHO), through the WHO Collaborating Centre on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-quality population-based data using advanced statistical methods, designed specifically for analysing NCD risk factors. The Collaboration currently has data from over 2,000 population-based surveys from 189 countries since 1957, with nearly 25 million participants whose risk factor levels have been measured.

type GlobalBmiInMalesNcdRisc2017Dataset 

type GlobalBmiInMalesNcdRisc2017Dataset struct {
	MeanBmiNcdrisc2017 *float64 `json:"mean_bmi_ncdrisc_2017"`
}

This dataset presents the mean male Body Mass Index (BMI) by country, region, and globally.BMI is a person's weight in kilograms (kg) divided by his or her height in meters squared (m^2). The WHO define a BMI <=18.5 as 'underweight'; 18.5 to <25 as 'normal/healthy'; 25.0 to <30 as 'overweight'; and >30.0 as 'obese'.NCD Risk Factor Collaboration (NCD-RisC) is a network of health scientists around the world that provides rigorous and timely data on risk factors for non-communicable diseases (NCDs) for 200 countries and territories. The group works closely with the World Health Organisation (WHO), through the WHO Collaborating Centre on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-quality population-based data using advanced statistical methods, designed specifically for analysing NCD risk factors. The Collaboration currently has data from over 2,000 population-based surveys from 189 countries since 1957, with nearly 25 million participants whose risk factor levels have been measured.

type GlobalCarbonBudgetFor2cIpcc2013Dataset 

type GlobalCarbonBudgetFor2cIpcc2013Dataset struct {
	GlobalCarbonBudgetIpcc2013 *float64 `json:"global_carbon_budget_ipcc_2013"`
}

The carbon budget refers to the maximum quantity of carbon which can be released to maintain a 50 percent probability of global average temperature rise remaining below two-degrees celcius (the target set within the UN Paris climate agreement).

This has been measured relative to the quantity of carbon which would be released if all fossil fuel reserves were burned without the use of carbon capture and storage (CCS) technology. The difference between the two is defined as 'unburnable carbon'.

References:

Intergovernmental Panel on Climate Change Climate Change 2013: The Physical Science Basis, Summary for Policy Makers, WG1 Contribution to IPCC AR5. Available at: http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_SPM_FINAL.pdf

type GlobalCarbonBudgetGcp2021Dataset 

type GlobalCarbonBudgetGcp2021Dataset struct {
	FossilFuelAndIndustryEmissionsGtco2 *float64 `json:"fossil_fuel_and_industry_emissions_gtco2"`
	LandUseEmissionsGtco2               *float64 `json:"land_use_emissions_gtco2"`
	FossilFuelLandUseEmissionsGtco2     *float64 `json:"fossil_fuel_land_use_emissions_gtco2"`
}

The Global Carbon Budget provides global CO2 emissions data separated into two categories:– emissions from fossil fuels and industry (which includes coal, oil, gas, flaring, and cement production)– land use change emissions.Data has been converted by Our World in Data from tonnes of carbon to tonnes of carbon dioxide (CO₂) using a conversion factor of 3.664.Full reference of the Global Carbon Project Dataset is as follows:Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee, C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E.M.S Nabel Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido van der Werf, Nicolas Vuichard, Chisato Wada Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng. Global Carbon Budget 2021, Earth Syst. Sci. Data, 2021.

type GlobalChildMortalitySince1800BasedOnGapminderAndWorldBank2019Dataset 

type GlobalChildMortalitySince1800BasedOnGapminderAndWorldBank2019Dataset struct {
	ShareDyingInFirst5YearsBasedOnGapminderAndWorldBank2019         *float64 `json:"share_dying_in_first_5_years_based_on_gapminder_and_world_bank_2019"`
	ShareSurvivingFirst5YearsOfLifeBasedOnGapminderAndWorldBank2019 *float64 `json:"share_surviving_first_5_years_of_life_based_on_gapminder_and_world_bank_2019"`
}

More about the Gapminder estimates can be found here: http://www.gapminder.org/data/documentation/gd005/.Due to data inconsistencies between previous (2016) World Bank child mortality series (which extended from 1960 to 2015) to most recent World Bank series (2019) extending only from 1990 to 2017, data for the year 1989 has been excluded as an observation.

type GlobalCo2EmissionsCdiacAndUnPopulationDataset 

type GlobalCo2EmissionsCdiacAndUnPopulationDataset struct {
	Co2EmissionsCdiacAndUnPopulation          *float64 `json:"co2_emissions_cdiac_and_un_population"`
	PerCapitaCo2EmissionsCdiacAndUnPopulation *float64 `json:"per_capita_co2_emissions_cdiac_and_un_population"`
}

Per capita CO2 emissions have been calculated based on the combination of global emissions data from CDIAC (described below), and UN Population Prospects data.

Population data was derived from: The History Database of the Global Environment (HYDE) collected the data by earlier publications. For the 'OurWorldInData'-series we used various sources: The data for the period before 1900 are taken from the History Database of the Global Environment (HYDE). The data for the World Population between 1900 and 1940 is taken from the UN puplication 'The World at Six Billion'. The annual data for the World Population between 1950 and 2015 is taken from the UN's World Population Prospects: The 2015 Revision. It is the series 'Total Population - Both Sexes' online available at: https://esa.un.org/unpd/wpp/.

Emissions data have been sourced from the Carbon Dioxide Information Analysis Centre (CDIAC) database. Emissions data have been converted from units of carbon to carbon dioxide (CO2) using a conversion factor of 3.67.

CDIAC denote a "statistical difference" component which has been included in this data. This statistical difference represents the difference between estimated global CO2 emissions and the sum of national totals. Estimates of CO2 emissions show that the global total of emissions is not equal to the sum of emissions from all countries. This is introduced in several cases: emissions within international territories, which are included in global totals but not attributed to individual countries; inconsistent national reporting where global import and export data is imbalanced; and differing treatment of non-fuel uses of hydrocarbons.

Full methodology on global, regional, national and statistical difference estimations can be found in Le Quere et al. (2016): Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., ... & Keeling, R. F. (2016). Global carbon budget 2016. Earth System Science Data, 8(2), 605. Available at: doi:10.5194/essd-8-605-2016.

type GlobalDataSetOnEducationQuality19652015AltinokAngristAndPatrinos2018Dataset 

type GlobalDataSetOnEducationQuality19652015AltinokAngristAndPatrinos2018Dataset struct {
	AverageHarmonisedLearningOutcomeScoreAltinokAngristAndPatrinos2018            *float64 `json:"average_harmonised_learning_outcome_score_altinok_angrist_and_patrinos_2018"`
	ShareOfStudentsAchievingTheMinimumThresholdAltinokAngristAndPatrinos2018      *float64 `json:"share_of_students_achieving_the_minimum_threshold_altinok_angrist_and_patrinos_2018"`
	ShareOfStudentsAchievingTheIntermediateThresholdAltinokAngristAndPatrinos2018 *float64 `json:"share_of_students_achieving_the_intermediate_threshold_altinok_angrist_and_patrinos_2018"`
	ShareOfStudentsAchievingTheAdvancedThresholdAltinokAngristAndPatrinos2018     *float64 `json:"share_of_students_achieving_the_advanced_threshold_altinok_angrist_and_patrinos_2018"`
}

This dataset covers 163 countries and regions over 1965–2015. The globally comparable achievement outcomes were constructed by linking standardized, psychometrically-robust international and regional achievement tests, including: <a href="https://nces.ed.gov/timss/" rel="noopener" target="_blank">TIMSS</a>, <a href="https://www.iea.nl/pirls" rel="noopener" target="_blank">PIRLS</a>, <a href="http://www.oecd.org/pisa/" rel="noopener" target="_blank">PISA</a>, <a href="https://www.iea.nl/fims" rel="noopener" target="_blank">FIMS</a>, <a href="https://www.iea.nl/fiss" rel="noopener" target="_blank">FISS</a>, <a href="https://www.capita-sims.co.uk/products-and-services/sims-assessment" rel="noopener" target="_blank">SIMS</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/SISS" rel="noopener" target="_blank">SISS</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/Six_Subject_Survey/SSS_Reading" rel="noopener" target="_blank">SRC</a>, <a href="https://ips.gu.se/english/research/research_databases/compeat/Before_1995/RLS" rel="noopener" target="_blank">RLS</a>, <a href="https://www.unicef.org/education/index_achievement.html" rel="noopener" target="_blank">MLA</a>, <a href="https://www.nap.edu/read/9174/chapter/9" rel="noopener" target"_blank">IAEP</a>, <a href="http://www.sacmeq.org/" rel="noopener" target="_blank" >SACMEQ</a>, <a href="https://www.epdc.org/data-about-epdc-data-epdc-learning-outcomes-data/sacmeq-and-pasec" rel="noopener" target="_blank">PASEC</a>, and <a href="http://www.unesco.org/new/en/santiago/education/education-assessment-llece/" rel="noopener" target="_blank">LLECE</a>.For the purpose of comparing outcomes, the authors construct minimum, intermediate, and advanced thresholds of educational proficiency. These thresholds are defined by level of education (primary or secondary) and by subject (maths, science, and reading). Primary education thresholds are defined by PIRLS and TIMSS at scores of 400 (minimum threshold) , 475 (intermediate threshold), and 625 (advanced threshold) across all subjects. Secondary education thresholds are defined using PISA at scores of roughly 400, 475, and over 600, varying by subject. 

For more information on thresholds used by level of education and subject, see Table 3.1, 3.2, 3.3 in the original paper.

type GlobalDeathRatesFromDisastersEmdatUnAndHydeDataset 

type GlobalDeathRatesFromDisastersEmdatUnAndHydeDataset struct {
	GlobalDeathRatesFromNaturalDisasters *float64 `json:"global_death_rates_from_natural_disasters"`
}

Disaster-related deaths from EMDAT (OFDA/CRED International Disaster Database) have been normalised by OurWorldinData to global population size based on HYDE (1900-1949) & UN (1950 onwards) population estimates. This provides data in terms of deaths per 100,000 people.The data presented here includes all categories classified as "natural disasters" (distinguished from technological disasters, such as oil spills and industrial accidents). This includes those from drought, floods, extreme weather, extreme temperature, landslides, dry mass movements, wildfires, volcanic activity and earthquakes.The combined set of "all natural disasters" also includes disasters defined as "insect infestations", however this has not been included as a discrete dataset due to low impact numbers.UN Population figures can found at: https://esa.un.org/unpd/wpp/Download/Standard/Population/HYDE Population figures can be found at: https://themasites.pbl.nl/tridion/en/themasites/hyde/basicdrivingfactors/population/index-2.html

type GlobalDeathsByCauseAndRiskGlobalBurdenOfDisease2017Dataset 

type GlobalDeathsByCauseAndRiskGlobalBurdenOfDisease2017Dataset struct {
	AlcoholUse                         *float64 `json:"alcohol_use"`
	OutdoorAirPollution                *float64 `json:"outdoor_air_pollution"`
	MalnutritionChildAndMaternal       *float64 `json:"malnutrition_child_and_maternal"`
	Diet                               *float64 `json:"diet"`
	DrugUse                            *float64 `json:"drug_use"`
	ObesityHighBmi                     *float64 `json:"obesity_high_bmi"`
	HighBloodSugar                     *float64 `json:"high_blood_sugar"`
	HighBloodPressure                  *float64 `json:"high_blood_pressure"`
	HighCholesterol                    *float64 `json:"high_cholesterol"`
	HouseholdAirPollution              *float64 `json:"household_air_pollution"`
	ImpairedKidneyFunction             *float64 `json:"impaired_kidney_function"`
	LowBoneMineralDensity              *float64 `json:"low_bone_mineral_density"`
	SecondhandSmoke                    *float64 `json:"secondhand_smoke"`
	TobaccoSmoking                     *float64 `json:"tobacco_smoking"`
	Under5Mortality                    *float64 `json:"under_5_mortality"`
	AlcoholDisorder                    *float64 `json:"alcohol_disorder"`
	DiarrhealDiseases                  *float64 `json:"diarrheal_diseases"`
	Drowning                           *float64 `json:"drowning"`
	DrugDisorder                       *float64 `json:"drug_disorder"`
	NaturalDisasters                   *float64 `json:"natural_disasters"`
	Ebola                              *float64 `json:"ebola"`
	HeatRelatedDeathsHotOrColdExposure *float64 `json:"heat_related_deaths_hot_or_cold_exposure"`
	Fire                               *float64 `json:"fire"`
	Hivaids                            *float64 `json:"hivaids"`
	Homicide                           *float64 `json:"homicide"`
	Malaria                            *float64 `json:"malaria"`
	MaternalDeaths                     *float64 `json:"maternal_deaths"`
	NeonatalDeaths                     *float64 `json:"neonatal_deaths"`
	NutritionalDeficiencies            *float64 `json:"nutritional_deficiencies"`
	RoadAccidents                      *float64 `json:"road_accidents"`
	Suicide                            *float64 `json:"suicide"`
	Tuberculosis                       *float64 `json:"tuberculosis"`
	PhysicalInactivity                 *float64 `json:"physical_inactivity"`
	LowFruitAndVegIntake               *float64 `json:"low_fruit_and_veg_intake"`
}

The Global Burden of Disease (GBD) database reports the absolute number of deaths across mortality cause and risk factors for any given year. It is important to understand the distinction between risk factors and causes of deaths. Causes of deaths are defined by the OECD as: "the underlying cause of death refers to the disease or injury that initiated the train of morbid events leading directly to death or the circumstances of the accident or violence that produced the injury." Causes of deaths therefore include particular events such as road accidents, natural disasters, drowning, HIV/AIDS, and non-communicable diseases such as diabetes, and various cancers. Risk factors are particular behaviors or lifestyle factors which affect the probability of incidence of particular diseases and causes of death. For example, tobacco smoking is a risk factor which increases the probability of lung cancer. In this case, it is not tobacco smoking in itself which is the cause of death, but the increased incidence of lung cancer. In order to identify and highlight the specific behaviours, lifestyle factors - and thereby the potential intervention points for prevention of mortality - this data presents a mix of causes of death and risk factors. Many of the variables are straightforward causes of death - such as road accidents, drowning, fire, malaria, terrorism fatalities. However, for non-communicable diseases such as stroke, cancer, diabetes, which can form a complex combination of preceding behaviour and lifestyle factors we have instead presented the IHME's attributed figures by risk factors, including high blood pressure, obesity, high blood sugar & tobacco smoking. Identification of these factors is more conducive to highlighting the specific behaviours or preventative factors which can be addressed in order to improve health outcomes and reduce mortality rates. Causes and risk factors are further clarified as follows: 'Outdoor air pollution' refers to outdoor/ambient exposure to particulate matter (PM) and ozone. 'Drug use' refers to the use of cannabis, opioids, or amphetamines, or use of injecting drugs [does not include tobacco or alcohol]. This is distinguished from 'drug disorders' which refers to direct death as a result of drug dependence and drug abuse. 'Alcohol disorders' refers to death as a result of alcohol dependence and alcohol abuse; this is distinguishable from 'alcohol use', which is premature death as a result of general alcohol consumption which is linked to increased risk of certain diseases. 'Road accidents' includes deaths from all road vehicles, including drivers, passengers, pedestrians and cyclists.

'Nutritional deficiencies' are defined as protein-energy malnutrition, iodine, vitamin-A, and iron deficiency.

type GlobalEducationOecdIiasa2016Dataset 

type GlobalEducationOecdIiasa2016Dataset struct {
	ShareThatHasAtLeastSomeFormalBasicEducationOecdIiasa2016 *float64 `json:"share_that_has_at_least_some_formal_basic_education_oecd_iiasa_2016"`
	ShareWithoutAnyFormalEducationOecdIiasa2016              *float64 `json:"share_without_any_formal_education_oecd_iiasa_2016"`
}

The series shows the share of the global population (older than 15) with at least basic education. The data for 1820 to 1960 is taken from the OECD (2014). The series measures the percentage of population aged over 15 enrolled in formal education. The estimate for 1820 is labelled a ’Best Guess' in the OECD publication and should be considered as such.The data for 1970 and later is taken from the Wittgenstein Centre for Demography and Global Human Capital (2015). It shows the share of the population (older than 15 years) that has attained at least some basic education.

type GlobalFishCatchByEndUseFishstatViaSeaaroundusDataset 

type GlobalFishCatchByEndUseFishstatViaSeaaroundusDataset struct {
	FishCatchDirectConsumption *float64 `json:"fish_catch_direct_consumption"`
	FishmealAndFishOil         *float64 `json:"fishmeal_and_fish_oil"`
	FishOtherUses              *float64 `json:"fish_other_uses"`
	TotalFishCatch             *float64 `json:"total_fish_catch"`
	BottomTrawl                *float64 `json:"bottom_trawl"`
	Gillnet                    *float64 `json:"gillnet"`
	Longline                   *float64 `json:"longline"`
	OtherGear                  *float64 `json:"other_gear"`
	PelagicTrawl               *float64 `json:"pelagic_trawl"`
	PurseSeine                 *float64 `json:"purse_seine"`
	SmallScale                 *float64 `json:"small_scale"`
	UnknownGear                *float64 `json:"unknown_gear"`
	Artisanal                  *float64 `json:"artisanal"`
	Industrial                 *float64 `json:"industrial"`
	Recreational               *float64 `json:"recreational"`
	Subsistence                *float64 `json:"subsistence"`
	Discards                   *float64 `json:"discards"`
	Landings                   *float64 `json:"landings"`
	ReportedLandings           *float64 `json:"reported_landings"`
	UnreportedLandings         *float64 `json:"unreported_landings"`
}

This data is sourced from the Sea Around Us database published by Pauly, Zeller and Palomares, available at: http://www.seaaroundus.org/.This project relies on multiple sources but primarily the FishStat database published by the Food and Agriculture Organization of the United Nations. This is available here: http://www.fao.org/fishery/statistics/software/fishstatj/en.

type GlobalFreshwaterUseSince1900IgbDataset 

type GlobalFreshwaterUseSince1900IgbDataset struct {
	FreshwaterUse *float64 `json:"freshwater_use"`
}

Data measures global freshwater use which is the sum of water withdrawals for agriculture, industrial and domestic uses. Data from 1900-2010 is sourced from the IGB Programme (full reference below). Global data has been extended to 2014 by combining with 2014 'World' figures as reported in the World Bank - World Development Indicators, under the variable "Annual Freshwater Withdrawals, Total (billion cubic meters)". Available at: http://data.worldbank.org/data-catalog/world-development-indicators [accessed 2017-11-08].Data from 1900-2010 is sourced from the IGB Database. IGB's data is estimated using the WaterGAP model from Flörke et al. 2013 (full reference below). Data is available at aggregates in OECD, BRICS and Rest of the World (ROW). OECD members are defined as countries who were members in 2010 and their membership was carried back in time. BRICS countries are Brazil, Russia, India, China and South Africa.Full references:Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., Siebert, S., 2003. Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal 48:317–337.aus der Beek, T., Flörke, M., Lapola, D. M., Schaldach, R., Voß, F., and Teichert, E. 2010. Modelling historical and current irrigation water demand on the continental scale: Europe. Advances in Geoscience 27:79-85 doi:10.5194/adgeo-27-79-2010Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., Alcamo, J. 2013. Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study. Global Environmental Change 23: 144-156

type GlobalHungerIndex2021Dataset 

type GlobalHungerIndex2021Dataset struct {
	GlobalHungerIndex2021 *float64 `json:"global_hunger_index_2021"`
}

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. To reflect the multidimensional nature of hunger, the GHI combines four component indicators into one index score. An increase in a country's GHI score indicates that the hunger situation is worsening, while a decrease in the score indicates an improvement in the hunger situation.The four indicators used to calculate the GHI are:- Undernourishment: the proportion of undernourished people as a percentage of the population;- Child wasting: the proportion of children under the age of five who suffer from wasting (low weight for their height, reflecting acute undernutrition);- Child stunting: the proportion of children under the age of five who suffer from stunting (low height for their age, reflecting chronic undernutrition); and- Child mortality: the mortality rate of children under the age of five (partially reflecting the fatal synergy of inadequate nutrition and unhealthy environments).The formula and weighting of these four indicators in relation to the final index score can be found at: https://www.globalhungerindex.org/pdf/en/2021.pdfThe 2021 GHI has been calculated for 135 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher-income countries where the prevalence of hunger is very low.Where original source data were unavailable, the GHI was estimated based on the most recent data available. For 19 countries, individual scores could not be calculated owing to lack of data. 12 of those countries (Burundi, Comoros, Guinea, Guinea-Bissau, Moldova, Niger, South Sudan, Syria, Tajikistan, Uganda, Zambia, and Zimbabwe) were provisionally designated by severity. In OWID's dataset, the GHI of these 12 countries corresponds to the mid-point of their group in the severity scale. For example, for the 'moderate' group, with GHI between 10 and 20, we assign 15.

type GlobalHungerIndexIfpri2018Dataset 

type GlobalHungerIndexIfpri2018Dataset struct {
	GlobalHungerIndexIfpri2016     *float64 `json:"global_hunger_index_ifpri_2016"`
	GlobalHungerIndexRankIfpri2018 *float64 `json:"global_hunger_index_rank_ifpri_2018"`
}

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. To reflect the multidimensional nature of hunger, the GHI combines four component indicators into one index score. An increase in a country's GHI score indicates that the hunger situation is worsening, while a decrease in the score indicates an improvement in the hunger situation.The four indicators used to calculate the GHI are:- Undernourishment: the proportion of undernourished people as a percentage of the population- Child wasting: the proportion of children under the age of five who suffer from wasting (low weight for their height, reflecting acute undernutrition);- Child stunting: the proportion of children under the age of five who suffer from stunting (low height for their age, reflecting chronic undernutrition); and- Child mortality: the mortality rate of children under the age of five (partially reflecting the fatal synergy of inadequate nutrition and unhealthy environments)The formula and weighting of these four indicators in relation to the final index score can be found at: http://library.ifpri.info/files/2016/09/BK_2016_GHI_appendix_a_w.pdf [accessed 24th July 2017]The 2016 GHI has been calculated for 118 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher-income countries where the prevalence of hunger is very low.

type GlobalHungerIndexIn1992Listed2017GlobalHungerIndex2017Dataset 

type GlobalHungerIndexIn1992Listed2017GlobalHungerIndex2017Dataset struct {
	GlobalHungerIndexIn1992 *float64 `json:"global_hunger_index_in_1992"`
}

type GlobalHungerIndexIn2017Listed2017GlobalHungerIndex2017Dataset 

type GlobalHungerIndexIn2017Listed2017GlobalHungerIndex2017Dataset struct {
	GlobalHungerIndexIn2017GlobalHungerIndex2017 *float64 `json:"global_hunger_index_in_2017_global_hunger_index_2017"`
}

type GlobalLiteracySince1800OwidBasedOnOecdAndUnesco2019Dataset 

type GlobalLiteracySince1800OwidBasedOnOecdAndUnesco2019Dataset struct {
	LiterateWorldPopulationOwidBasedOnOecdAndUnesco2019   *float64 `json:"literate_world_population_owid_based_on_oecd_and_unesco_2019"`
	IlliterateWorldPopulationOwidBasedOnOecdAndUnesco2019 *float64 `json:"illiterate_world_population_owid_based_on_oecd_and_unesco_2019"`
}

Global literacy series was compiled by Our World in Data from 1800 based on multiple sources. This is based on adult literacy rates (of those aged 15+ years old).Data from 1820 to 1940 was based on estimates from the "OECD (2014) – How Was Life? – Global Well-being since 1820." Available at: https://www.oecd-ilibrary.org/economics/how-was-life_9789264214262-en. The data point for 1820 has also been assumed for the year 1800.1950-1970 estimates sourced from: UNESCO. 1972. Literacy 1969-1971. Progress Achieved in Literacy throughout the World. Paris: UNESCO (https://unesdoc.unesco.org/in/rest/annotationSVC/DownloadWatermarkedAttachment/attach_import_c0206949-c3f1-4eac-a189-9c5bcfdac220?_=001736engo.pdf)1980-2000 estimates sourced from: Carr-Hill, R., & Pessoa, J. (2008). International literacy statistics: A review of concepts, methodology and current data. Montreal: UNESCO Institute for Statistics. Available at: http://uis.unesco.org/sites/default/files/documents/international-literacy-statistics-a-review-of-concepts-methodology-and-current-data-en_0.pdf And: UNESCO Institute for Statistics. (2013). Adult and youth literacy: National, regional and global trends, 1985–2015. Available at: http://uis.unesco.org/sites/default/files/documents/adult-and-youth-literacy-national-regional-and-global-trends-1985-2015-en_0.pdf2012-2016 estimates sourced from: UNESCO Statistics (http://data.uis.unesco.org/index.aspx?queryid=166&lang=en).

type GlobalMeatProjectionsTo2050FaoDataset 

type GlobalMeatProjectionsTo2050FaoDataset struct {
	SheepAndGoat   *float64 `json:"sheep_and_goat"`
	BeefAndBuffalo *float64 `json:"beef_and_buffalo"`
	Pigmeat        *float64 `json:"pigmeat"`
	Poultry        *float64 `json:"poultry"`
	Eggs           *float64 `json:"eggs"`
}

Data extending from 1961-2013 is based on the UN Food and Agriculture (FAO) Statistics database: http://www.fao.org/faostat/en/Projections to 2050 are based on UN FAO projections under business-as-usual population, and forecasted economic growth models. This data is sourced from:Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision (Vol. 12, No. 3). FAO, Rome: ESA Working paper. Available at: http://www.fao.org/docrep/016/ap106e/ap106e.pdf

type GlobalPlasticProductionGeyerEtAl2017Dataset 

type GlobalPlasticProductionGeyerEtAl2017Dataset struct {
	GlobalPlasticsProductionMillionTonnes           *float64 `json:"global_plastics_production_million_tonnes"`
	CumulativeGlobalPlasticsProductionMillionTonnes *float64 `json:"cumulative_global_plastics_production_million_tonnes"`
}

Data denotes annual global polymer resin and fiber production (plastic production), measured in metric tonnes.

type GlobalPopulationByRegionWithProjectionsHyde2016AndUn2017Dataset 

type GlobalPopulationByRegionWithProjectionsHyde2016AndUn2017Dataset struct {
	GlobalPopulationByRegionHyde2016AndUn2017                *float64 `json:"global_population_by_region_hyde_2016_and_un_2017"`
	ProjectedPopulationTo2100Un2017                          *float64 `json:"projected_population_to_2100_un_2017"`
	CombinedHistorialAndProjectedPopulationByRegionHydeAndUn *float64 `json:"combined_historial_and_projected_population_by_region_hyde_and_un"`
}

This long-run population series by region was constructed by Our World in Data based on two key sources: the HYDE database (2016) and UN World Population Prospects (2017). - HYDE (2016) data is used from the year -10,000 to 1940- UN World Population Population Prospects (2017) from 1950 to 2015; - Projections to 2100 from the UN World Population Population Prospects (2017)HYDE Database, available at: ftp://ftp.pbl.nl/hyde/supplementary/Klein_Goldewijk_et_al_2016_HYDE32_paper/United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/

type GlobalPopulationTrendsUsCensusBureau2017Dataset 

type GlobalPopulationTrendsUsCensusBureau2017Dataset struct {
	PopulationUsCensusBureau2017 *float64 `json:"population_us_census_bureau_2017"`
}

Population data is reported annually based on review of census data, surveys, vital registration and administrative records from a variety of sources.

Population is recorded as 'de facto' meaning it includes all persons who are physically present in the country at the reference date, whether or not they are usual and/or legal residents.

type GlobalPrecipitationAnomalyNoaaDataset 

type GlobalPrecipitationAnomalyNoaaDataset struct {
	GlobalPrecipitationAnomalyInches *float64 `json:"global_precipitation_anomaly_inches"`
}

Data shows global precipitation patterns, based on rainfall and snowfall measurements from land-based weather stations worldwide. This indicator shows annual anomalies, or differences, compared with the average precipitation from 1901 to 2000. At each weather station, annual precipitation anomalies were calculated from total annual precipitation in inches. Global anomalies have been determined by dividing the world into a grid, averaging the data for each cell of the grid, and then averaging the grid cells together.

type GlobalPrimaryEnergyConsumptionVaclavSmil2017AndBpStatistics2020Dataset 

type GlobalPrimaryEnergyConsumptionVaclavSmil2017AndBpStatistics2020Dataset struct {
	CoalTerrawattHoursSmilAndBp2019                 *float64 `json:"coal_terrawatt_hours_smil_and_bp_2019"`
	CrudeOilTerrawattHoursSmilAndBp2019             *float64 `json:"crude_oil_terrawatt_hours_smil_and_bp_2019"`
	NaturalGasTerrawattHoursSmilAndBp2019           *float64 `json:"natural_gas_terrawatt_hours_smil_and_bp_2019"`
	HydropowerTerrawattHoursSmilAndBp2019           *float64 `json:"hydropower_terrawatt_hours_smil_and_bp_2019"`
	NuclearTerrawattHoursSmilAndBp2019              *float64 `json:"nuclear_terrawatt_hours_smil_and_bp_2019"`
	SolarTerrawattHoursSmilAndBp2019                *float64 `json:"solar_terrawatt_hours_smil_and_bp_2019"`
	OtherRenewablesTerrawattHoursSmilAndBp2019      *float64 `json:"other_renewables_terrawatt_hours_smil_and_bp_2019"`
	TraditionalBiofuelsTerrawattHoursVaclavSmil2017 *float64 `json:"traditional_biofuels_terrawatt_hours_vaclav_smil_2017"`
	WindTerrawattHoursSmilAndBp2019                 *float64 `json:"wind_terrawatt_hours_smil_and_bp_2019"`
	BiofuelsTwhDirectEnergy                         *float64 `json:"biofuels_twh_direct_energy"`
	CoalTwhSubstitutedEnergy                        *float64 `json:"coal_twh_substituted_energy"`
	SolarTwhSubstitutedEnergy                       *float64 `json:"solar_twh_substituted_energy"`
	OilTwhSubstitutedEnergy                         *float64 `json:"oil_twh_substituted_energy"`
	GasTwhSubstitutedEnergy                         *float64 `json:"gas_twh_substituted_energy"`
	TraditionalBimassTwhSubstitutedEnergy           *float64 `json:"traditional_bimass_twh_substituted_energy"`
	OtherRenewablesTwhSubstitutedEnergy             *float64 `json:"other_renewables_twh_substituted_energy"`
	HydropowerTwhSubstitutedEnergy                  *float64 `json:"hydropower_twh_substituted_energy"`
	NuclearTwhSubstitutedEnergy                     *float64 `json:"nuclear_twh_substituted_energy"`
	WindTwhSubstitutedEnergy                        *float64 `json:"wind_twh_substituted_energy"`
	BiofuelsTwhSubstitutedEnergy                    *float64 `json:"biofuels_twh_substituted_energy"`
}

This data comprises of a combination of data from Appendix A of Vaclav Smil's Updated and Revised Edition of his book, 'Energy Transitions: Global and National Perspectives' (2017). & BP Statistical Review of World Energy.All data prior to the year 1965 is sourced from Smil (2017). All data from 1965 onwards, with the exception of traditional biomass is sourced from BP Statistical Review. Smil's estimates of traditional biomass are only available until 2015. For the years 2016 onwards, we have assumed a similar level of traditional biomass consumption. This is approximately in line with recent trends in traditional biomass from Smil's data.Our World in Data has normalised all BP fossil fuels data to terawatt-hours (TWh) using a conversion factor of 277.778 to convert from exajoules (EJ) to TWh.This dataset includes primary energy data using two methodologies.(1) 'direct' primary energy, which does not take account of the inefficiencies in fossil fuel production. Fossil fuel data in its input equivalents (in exajoules) is compared to electricity generation (not in input equivalents) of nuclear and renewables.(2) 'substitution' primary energy, which does take account of inefficiencies in fossil fuel production. This converts non-fossil energy to their 'input equivalents' – the amount of primary energy that would be needed if they had the same inefficiencies as fossil fuels. This is the methodology adopted in the BP statistics when all data is compared in exajoules.

type GlobalPrimaryEnergyShareSmilAndBpDataset 

type GlobalPrimaryEnergyShareSmilAndBpDataset struct {
	CoalPerc                *float64 `json:"coal_perc"`
	SolarPerc               *float64 `json:"solar_perc"`
	CrudeOilPerc            *float64 `json:"crude_oil_perc"`
	NaturalGasPerc          *float64 `json:"natural_gas_perc"`
	TraditionalBiofuelsPerc *float64 `json:"traditional_biofuels_perc"`
	OtherRenewablesPerc     *float64 `json:"other_renewables_perc"`
	HydropowerPerc          *float64 `json:"hydropower_perc"`
	NuclearPerc             *float64 `json:"nuclear_perc"`
	WindPerc                *float64 `json:"wind_perc"`
}

Global share of primary energy consumption by source, calculated by Our World in Data based on absolute energy consumption figures from Smil (2017) and BP Statistical Review of World Energy (2018).This data comprises of a combination of data from Appendix A of Vaclav Smil's Updated and Revised Edition of his book, 'Energy Transitions: Global and National Perspectives' (2017). & BP Statistical Review of World Energy.All data prior to the year 1965 is sourced from Smil (2017). All data from 1965 onwards, with the exception of traditional biomass is sourced from BP Statistical Review. Smil's estimates of traditional biomass have been used for the full series, with interpolation of annual changes by Our World in Data between reported 5-year increments by Smil. Traditional biomass for the years 2016 and 2017 have been estimated based on the approximate rate of change in the previous 5 years from Smil data.Data represents primary energy (rather than final energy) consumption. 'Other renewables' represents all renewable sources minus solar, wind, and hydropower (e.g. geothermal, wave and tidal, and modern biofuels).

type GlobalProjectionMediumSsp2Iiasa2016Dataset 

type GlobalProjectionMediumSsp2Iiasa2016Dataset struct {
	Under15Iiasa2016           *float64 `json:"under_15_iiasa_2016"`
	NoEducationIiasa2016       *float64 `json:"no_education_iiasa_2016"`
	IncompletePrimaryIiasa2016 *float64 `json:"incomplete_primary_iiasa_2016"`
	PrimaryIiasa2016           *float64 `json:"primary_iiasa_2016"`
	LowerSecondaryIiasa2016    *float64 `json:"lower_secondary_iiasa_2016"`
	UpperSecondaryIiasa2016    *float64 `json:"upper_secondary_iiasa_2016"`
	PostSecondaryIiasa2016     *float64 `json:"post_secondary_iiasa_2016"`
}

This is Medium (SSP2) scenario.

type GlobalRevenueStatisticsDatabaseOecd2018Dataset 

type GlobalRevenueStatisticsDatabaseOecd2018Dataset struct {
	TotalTaxPercOfGdpOecd2018                            *float64 `json:"total_tax_perc_of_gdp_oecd_2018"`
	TaxesOnIncomeProfitsAndCapitalGainsPercOfGdpOecd2018 *float64 `json:"taxes_on_income_profits_and_capital_gains_perc_of_gdp_oecd_2018"`
	SocialSecurityContributionsSscPercOfGdpOecd2018      *float64 `json:"social_security_contributions_ssc_perc_of_gdp_oecd_2018"`
	TaxesOnPayrollAndWorkforcePercOfGdpOecd2018          *float64 `json:"taxes_on_payroll_and_workforce_perc_of_gdp_oecd_2018"`
	TaxesOnPropertyPercOfGdpOecd2018                     *float64 `json:"taxes_on_property_perc_of_gdp_oecd_2018"`
	TaxesOnGoodsAndServicesPercOfGdpOecd2018             *float64 `json:"taxes_on_goods_and_services_perc_of_gdp_oecd_2018"`
	OtherTaxPercOfGdpOecd2018                            *float64 `json:"other_tax_perc_of_gdp_oecd_2018"`
}

In 69 countries, the reporting year of tax revenue coincides with the calendar year, whereas 11 have different reporting years. 

Where the GDP reporting year differs from the calendar year, annual GDP estimates are calculated by aggregating quarterly GDP estimates corresponding to each country’s fiscal (tax) year.For further information on the OECD classification of taxes, see the OECD's Revenue Statistics Interpretative Guide (2017) available at: https://www.oecd.org/tax/tax-policy/oecd-classification-taxes-interpretative-guide.pdfFor a more extensive discussion of revenue statistics, see the full <a href="http://www.oecd.org/tax/tax-policy/revenue-statistics-2522770x.htm">OECD Revenue Statistics 2018</a> report.

type GlobalSmallpoxCasesDataset 

type GlobalSmallpoxCasesDataset struct {
	GlobalSmallpoxCasesWho2011   *float64 `json:"global_smallpox_cases_who_2011"`
	ReportedSmallpoxCasesWho2011 *float64 `json:"reported_smallpox_cases_who_2011"`
}

Because smallpox was eradicated in 1977, there were no new cases since 2010 so we extended the time series until 2016.

type GlobalTemperatureAnomalyMetOfficeHadcrut4Dataset 

type GlobalTemperatureAnomalyMetOfficeHadcrut4Dataset struct {
	MedianTemperatureAnomalyFrom1961_1990Average *float64 `json:"median_temperature_anomaly_from_1961_1990_average"`
	UpperBound95percCi                           *float64 `json:"upper_bound_95perc_ci"`
	LowerBound95percCi                           *float64 `json:"lower_bound_95perc_ci"`
}

Temperature anomalies are based on the HadCRUT4 land-sea dataset as published by the Met Office Hadley Centre. Temperature anomalies are given in degrees celcius relative to the average temperature over the period 1961-1990. These are available at the global level, for the Northern Hemisphere, South Hemisphere, and Tropics (defined as 30 degree north and south of the equator).The median temperature anomaly, as well as the upper and lower bound anomalies (with a 95% confidence interval) are provided.Full details of the source of this dataset is available in the following paper:Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones (2012), Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset, J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187.

type GlobalTuberculosisReportCaseNotificationsWho2019Dataset 

type GlobalTuberculosisReportCaseNotificationsWho2019Dataset struct {
	NumberOfLaboratoryConfirmedXdrTbCasesIdentifiedInTheCurrentYearIncludingInMdrCasesDiagnosedInPreviousYearsAllConfXdr                                                                                                                                                                                 *float64 `` /* 145-byte string literal not displayed */
	TotalOfNewAndRelapseCasesAndCasesWithUnknownPreviousTbTreatmentHistoryCNewinc                                                                                                                                                                                                                        *float64 `json:"total_of_new_and_relapse_cases_and_cases_with_unknown_previous_tb_treatment_history_c_newinc"`
	NumberOfLaboratoryConfirmedMdrTbCasesIdentifiedConfMdr                                                                                                                                                                                                                                               *float64 `json:"number_of_laboratory_confirmed_mdr_tb_cases_identified_conf_mdr"`
	NumberOfLaboratoryConfirmedMdrTbPatientsWhoStartedTreatmentForMdrTbConfMdrTx                                                                                                                                                                                                                         *float64 `json:"number_of_laboratory_confirmed_mdr_tb_patients_who_started_treatment_for_mdr_tb_conf_mdr_tx"`
	NumberOfLaboratoryConfirmedRrTbOrMdrTbCasesIdentifiedConfRrmdr                                                                                                                                                                                                                                       *float64 `json:"number_of_laboratory_confirmed_rr_tb_or_mdr_tb_cases_identified_conf_rrmdr"`
	NumberOfLaboratoryConfirmedRifampicinResistantRrTbOrMultidrugResistantTbMdrTbPatientsWhoStartedTreatmentForMdrTbConfRrmdrTx                                                                                                                                                                          *float64 `` /* 153-byte string literal not displayed */
	NumberOfLaboratoryConfirmedXdrTbPatientsWhoStartedTreatmentForXdrTbConfXdrTx                                                                                                                                                                                                                         *float64 `json:"number_of_laboratory_confirmed_xdr_tb_patients_who_started_treatment_for_xdr_tb_conf_xdr_tx"`
	HivPositiveTbPatientsStartedOrContinuedOnAntiretroviralTherapyArtHivArt                                                                                                                                                                                                                              *float64 `json:"hiv_positive_tb_patients_started_or_continued_on_antiretroviral_therapy_art_hiv_art"`
	HivPositiveTbPatientsStartedOrContinuedOnCoTrimoxazolePreventiveTherapyCptHivCpt                                                                                                                                                                                                                     *float64 `json:"hiv_positive_tb_patients_started_or_continued_on_co_trimoxazole_preventive_therapy_cpt_hiv_cpt"`
	PeopleLivingWithHivNewlyEnrolledInHivCareWhoStartedTreatmentForLatentTbInfectionHivIpt                                                                                                                                                                                                               *float64 `json:"people_living_with_hiv_newly_enrolled_in_hiv_care_who_started_treatment_for_latent_tb_infection_hiv_ipt"`
	PeopleLivingWithHivCurrentlyEnrolledInHivCareWhoStartedTreatmentForLatentTbInfectionHivIptRegAll                                                                                                                                                                                                     *float64 `json:"people_living_with_hiv_currently_enrolled_in_hiv_care_who_started_treatment_for_latent_tb_infection_hiv_ipt_reg_all"`
	TotalNumberOfPeopleRegisteredAsHivPositiveRegardlessOfYearOfDiagnosisTotalNumberOfAdultsAndChildrenEnrolledInHivCareIncludesEveryoneInTheHivCareAndorArtRegisterHivReg                                                                                                                               *float64 `` /* 206-byte string literal not displayed */
	NumberOfAdultsAndChildrenNewlyEnrolledInHivCareDuringTheYearHivRegNew                                                                                                                                                                                                                                *float64 `json:"number_of_adults_and_children_newly_enrolled_in_hiv_care_during_the_year_hiv_reg_new"`
	TotalNumberOfAdultsAndChildrenNewlyEnrolledInPreArtCareOrOnArtDuringTheReportingPeriodHivRegNew2                                                                                                                                                                                                     *float64 `json:"total_number_of_adults_and_children_newly_enrolled_in_pre_art_care_or_on_art_during_the_reporting_period_hiv_reg_new2"`
	TotalNumberOfAdultsAndChildrenNewlyEnrolledInHivCareWhoAreDiagnosedAsHavingActiveTbDiseaseDuringTheReportingPeriodHivTbdetect                                                                                                                                                                        *float64 `` /* 156-byte string literal not displayed */
	NumberOfAdultsAndChildrenEnrolledInHivCareWhoHadTheirTbStatusAssessedAndRecordedDuringTheirLastVisitHivTbscr                                                                                                                                                                                         *float64 `` /* 137-byte string literal not displayed */
	TbPatientsNewAndReTreatmentWithAnHivTestResultRecordedInTheTbRegisterHivtest                                                                                                                                                                                                                         *float64 `json:"tb_patients_new_and_re_treatment_with_an_hiv_test_result_recorded_in_the_tb_register_hivtest"`
	TbPatientsNewAndReTreatmentRecordedAsHivPositiveHivtestPos                                                                                                                                                                                                                                           *float64 `json:"tb_patients_new_and_re_treatment_recorded_as_hiv_positive_hivtest_pos"`
	NumberOfPatientsStartedOnShorterMdrTbTreatmentRegimentsDuringTheReportingYearMdrShortregTx                                                                                                                                                                                                           *float64 `json:"number_of_patients_started_on_shorter_mdr_tb_treatment_regiments_during_the_reporting_year_mdr_shortreg_tx"`
	HadAnyPatientsBeenStartedOnShorterMdrTbTreatmentRegimensByTheEndOfTheReportingYearMdrShortregUsed                                                                                                                                                                                                    *float64 `json:"had_any_patients_been_started_on_shorter_mdr_tb_treatment_regimens_by_the_end_of_the_reporting_year_mdr_shortreg_used"`
	NumberOfPatientsActivelyMonitoredForAdverseEventsWhileOnMdrTbTreatmentInTheReportingYearMdrTxAdsm                                                                                                                                                                                                    *float64 `json:"number_of_patients_actively_monitored_for_adverse_events_while_on_mdr_tb_treatment_in_the_reporting_year_mdr_tx_adsm"`
	NumberOfPatientsOnMdrTbTreatmentWhoHadAdverseEventsRegisteredInTheReportingYearMdrTxAdverseEvents                                                                                                                                                                                                    *float64 `json:"number_of_patients_on_mdr_tb_treatment_who_had_adverse_events_registered_in_the_reporting_year_mdr_tx_adverse_events"`
	NumberOfPatientsStartedOnBedaquilineDuringTheReportingYearMdrxdrBdqTx                                                                                                                                                                                                                                *float64 `json:"number_of_patients_started_on_bedaquiline_during_the_reporting_year_mdrxdr_bdq_tx"`
	HadAnyTbPatientsBeenStartedOnBedaquilineForTheTreatmentOfMdrXdrTbByTheEndOfReportingYearAsPartOfExpandedAccessCompassionateUseOrUnderNormalProgrammaticUseWhetherInThePublicOrPrivateSectorMdrxdrBdqUsed                                                                                             *float64 `` /* 249-byte string literal not displayed */
	NumberOfPatientsStartedOnDelamanidDuringTheReportingYearMdrxdrDlmTx                                                                                                                                                                                                                                  *float64 `json:"number_of_patients_started_on_delamanid_during_the_reporting_year_mdrxdr_dlm_tx"`
	HadAnyTbPatientsBeenStartedOnDelamanidForTheTreatmentOfMdrXdrTbByTheEndOfReportingAsPartOfExpandedAccessCompassionateUseOrUnderNormalProgrammaticUseWhetherInThePublicOrPrivateSectorMdrxdrDlmUsed                                                                                                   *float64 `` /* 242-byte string literal not displayed */
	NewPulmonaryClinicallyDiagnosedTbCasesNotBacteriologicallyConfirmedAsPositiveForTbButDiagnosedWithActiveTbByAClinicianOrAnotherMedicalPractitionerWhoHasDecidedToGiveThePatientAFullCourseOfTbTreatmentItAlsoIncludesPulmonaryClinicallyDiagnosedCasesWithUnknownPreviousTbTreatmentHistoryNewClindx *float64 `` /* 351-byte string literal not displayed */
	NewExtrapulmonaryCasesBacteriologicallyConfirmedOrClinicallyDiagnosedAsOf2013ThisAlsoIncludesExtrapulmonaryCasesWithUnknownPreviousTbTreatmentHistoryNewEp                                                                                                                                           *float64 `` /* 184-byte string literal not displayed */
	NewExtrapulmonaryCasesFemalesAged0_14YearsNotUsedAfter2012NewEpF014                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_females_aged_0_14_years_not_used_after_2012_new_ep_f014"`
	NewExtrapulmonaryCasesFemalesAged0_4YearsNotUsedAfter2012NewEpF04                                                                                                                                                                                                                                    *float64 `json:"new_extrapulmonary_cases_females_aged_0_4_years_not_used_after_2012_new_ep_f04"`
	NewExtrapulmonaryCasesFemalesAged15_24YearsNotUsedAfter2012NewEpF1524                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_females_aged_15_24_years_not_used_after_2012_new_ep_f1524"`
	NewExtrapulmonaryCasesFemalesAged15YearsAndOverNotUsedAfter2012NewEpF15plus                                                                                                                                                                                                                          *float64 `json:"new_extrapulmonary_cases_females_aged_15_years_and_over_not_used_after_2012_new_ep_f15plus"`
	NewExtrapulmonaryCasesFemalesAged25_34YearsNotUsedAfter2012NewEpF2534                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_females_aged_25_34_years_not_used_after_2012_new_ep_f2534"`
	NewExtrapulmonaryCasesFemalesAged35_44YearsNotUsedAfter2012NewEpF3544                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_females_aged_35_44_years_not_used_after_2012_new_ep_f3544"`
	NewExtrapulmonaryCasesFemalesAged45_54YearsNotUsedAfter2012NewEpF4554                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_females_aged_45_54_years_not_used_after_2012_new_ep_f4554"`
	NewExtrapulmonaryCasesFemalesAged5_14YearsNotUsedAfter2012NewEpF514                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_females_aged_5_14_years_not_used_after_2012_new_ep_f514"`
	NewExtrapulmonaryCasesFemalesAged55_64YearsNotUsedAfter2012NewEpF5564                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_females_aged_55_64_years_not_used_after_2012_new_ep_f5564"`
	NewExtrapulmonaryCasesFemalesAged65YearsAndOverNotUsedAfter2012NewEpF65                                                                                                                                                                                                                              *float64 `json:"new_extrapulmonary_cases_females_aged_65_years_and_over_not_used_after_2012_new_ep_f65"`
	NewExtrapulmonaryCasesFemalesAgeUnknownNotUsedAfter2012NewEpFu                                                                                                                                                                                                                                       *float64 `json:"new_extrapulmonary_cases_females_age_unknown_not_used_after_2012_new_ep_fu"`
	NewExtrapulmonaryCasesMalesAged0_14YearsNotUsedAfter2012NewEpM014                                                                                                                                                                                                                                    *float64 `json:"new_extrapulmonary_cases_males_aged_0_14_years_not_used_after_2012_new_ep_m014"`
	NewExtrapulmonaryCasesMalesAged0_4YearsNotUsedAfter2012NewEpM04                                                                                                                                                                                                                                      *float64 `json:"new_extrapulmonary_cases_males_aged_0_4_years_not_used_after_2012_new_ep_m04"`
	NewExtrapulmonaryCasesMalesAged15_24YearsNotUsedAfter2012NewEpM1524                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_males_aged_15_24_years_not_used_after_2012_new_ep_m1524"`
	NewExtrapulmonaryCasesMalesAged15YearsAndOverNotUsedAfter2012NewEpM15plus                                                                                                                                                                                                                            *float64 `json:"new_extrapulmonary_cases_males_aged_15_years_and_over_not_used_after_2012_new_ep_m15plus"`
	NewExtrapulmonaryCasesMalesAged25_34YearsNotUsedAfter2012NewEpM2534                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_males_aged_25_34_years_not_used_after_2012_new_ep_m2534"`
	NewExtrapulmonaryCasesMalesAged35_44YearsNotUsedAfter2012NewEpM3544                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_males_aged_35_44_years_not_used_after_2012_new_ep_m3544"`
	NewExtrapulmonaryCasesMalesAged45_54YearsNotUsedAfter2012NewEpM4554                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_males_aged_45_54_years_not_used_after_2012_new_ep_m4554"`
	NewExtrapulmonaryCasesMalesAged5_14YearsNotUsedAfter2012NewEpM514                                                                                                                                                                                                                                    *float64 `json:"new_extrapulmonary_cases_males_aged_5_14_years_not_used_after_2012_new_ep_m514"`
	NewExtrapulmonaryCasesMalesAged55_64YearsNotUsedAfter2012NewEpM5564                                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_males_aged_55_64_years_not_used_after_2012_new_ep_m5564"`
	NewExtrapulmonaryCasesMalesAged65YearsAndOverNotUsedAfter2012NewEpM65                                                                                                                                                                                                                                *float64 `json:"new_extrapulmonary_cases_males_aged_65_years_and_over_not_used_after_2012_new_ep_m65"`
	NewExtrapulmonaryCasesMalesAgeUnknownNotUsedAfter2012NewEpMu                                                                                                                                                                                                                                         *float64 `json:"new_extrapulmonary_cases_males_age_unknown_not_used_after_2012_new_ep_mu"`
	NewExtrapulmonaryCasesSexUnknownAged0_14YearsNotUsedAfter2012NewEpSexunk014                                                                                                                                                                                                                          *float64 `json:"new_extrapulmonary_cases_sex_unknown_aged_0_14_years_not_used_after_2012_new_ep_sexunk014"`
	NewExtrapulmonaryCasesSexUnknownAged0_4YearsNotUsedAfter2012NewEpSexunk04                                                                                                                                                                                                                            *float64 `json:"new_extrapulmonary_cases_sex_unknown_aged_0_4_years_not_used_after_2012_new_ep_sexunk04"`
	NewExtrapulmonaryCasesSexUnknownAged15YearsAndOverNotUsedAfter2012NewEpSexunk15plus                                                                                                                                                                                                                  *float64 `json:"new_extrapulmonary_cases_sex_unknown_aged_15_years_and_over_not_used_after_2012_new_ep_sexunk15plus"`
	NewExtrapulmonaryCasesSexUnknownAged5_14YearsNotUsedAfter2012NewEpSexunk514                                                                                                                                                                                                                          *float64 `json:"new_extrapulmonary_cases_sex_unknown_aged_5_14_years_not_used_after_2012_new_ep_sexunk514"`
	NewExtrapulmonaryCasesSexUnknownAgeUnknownNotUsedAfter2012NewEpSexunkageunk                                                                                                                                                                                                                          *float64 `json:"new_extrapulmonary_cases_sex_unknown_age_unknown_not_used_after_2012_new_ep_sexunkageunk"`
	NewPulmonaryBacteriologicallyConfirmedTbCasesSmearPositiveOrCulturePositiveOrPositiveByWhoRecommendedRapidDiagnosticsSuchAsXpertMtbrifAsOf2013ThisAlsoIncludesPulmonaryBacteriologicallyConfirmedCasesWithUnknownPreviousTbTreatmentHistoryNewLabconf                                                *float64 `` /* 291-byte string literal not displayed */
	OtherNewCasesNotUsedAfter2012NewOth                                                                                                                                                                                                                                                                  *float64 `json:"other_new_cases_not_used_after_2012_new_oth"`
	NewPulmonarySmearNegativeCasesNotUsedAfter2012NewSn                                                                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negative_cases_not_used_after_2012_new_sn"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged0_14YearsNotUsedAfter2012NewSnF014                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_females_aged_0_14_years_not_used_after_2012_new_sn_f014"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged0_4YearsNotUsedAfter2012NewSnF04                                                                                                                                                                                                    *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_females_aged_0_4_years_not_used_after_2012_new_sn_f04"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged15_24YearsNotUsedAfter2012NewSnF1524                                                                                                                                                                                                *float64 `` /* 126-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged15YearsAndOverNotUsedAfter2012NewSnF15plus                                                                                                                                                                                          *float64 `` /* 134-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged25_34YearsNotUsedAfter2012NewSnF2534                                                                                                                                                                                                *float64 `` /* 126-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged35_44YearsNotUsedAfter2012NewSnF3544                                                                                                                                                                                                *float64 `` /* 126-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged45_54YearsNotUsedAfter2012NewSnF4554                                                                                                                                                                                                *float64 `` /* 126-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged5_14YearsNotUsedAfter2012NewSnF514                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_females_aged_5_14_years_not_used_after_2012_new_sn_f514"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged55_64YearsNotUsedAfter2012NewSnF5564                                                                                                                                                                                                *float64 `` /* 126-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAged65YearsAndOverNotUsedAfter2012NewSnF65                                                                                                                                                                                              *float64 `` /* 130-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesFemalesAgeUnknownNotUsedAfter2012NewSnFu                                                                                                                                                                                                       *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_females_age_unknown_not_used_after_2012_new_sn_fu"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged0_14YearsNotUsedAfter2012NewSnM014                                                                                                                                                                                                    *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_0_14_years_not_used_after_2012_new_sn_m014"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged0_4YearsNotUsedAfter2012NewSnM04                                                                                                                                                                                                      *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_0_4_years_not_used_after_2012_new_sn_m04"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged15_24YearsNotUsedAfter2012NewSnM1524                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_15_24_years_not_used_after_2012_new_sn_m1524"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged15YearsAndAboveNotUsedAfter2012NewSnM15plus                                                                                                                                                                                           *float64 `` /* 133-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged25_34YearsNotUsedAfter2012NewSnM2534                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_25_34_years_not_used_after_2012_new_sn_m2534"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged35_44YearsNotUsedAfter2012NewSnM3544                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_35_44_years_not_used_after_2012_new_sn_m3544"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged45_54YearsNotUsedAfter2012NewSnM4554                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_45_54_years_not_used_after_2012_new_sn_m4554"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged5_14YearsNotUsedAfter2012NewSnM514                                                                                                                                                                                                    *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_5_14_years_not_used_after_2012_new_sn_m514"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged55_64YearsNotUsedAfter2012NewSnM5564                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_aged_55_64_years_not_used_after_2012_new_sn_m5564"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAged65YearsAndOverNotUsedAfter2012NewSnM65                                                                                                                                                                                                *float64 `` /* 128-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesMalesAgeUnknownNotUsedAfter2012NewSnMu                                                                                                                                                                                                         *float64 `json:"new_pulmonary_smear_negativesmear_unknownsmear_not_done_cases_males_age_unknown_not_used_after_2012_new_sn_mu"`
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesSexUnknownAged0_14YearsNotUsedAfter2012NewSnSexunk014                                                                                                                                                                                          *float64 `` /* 133-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesSexUnknownAged0_4YearsNotUsedAfter2012NewSnSexunk04                                                                                                                                                                                            *float64 `` /* 131-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesSexUnknownAged15YearsAndOverNotUsedAfter2012NewSnSexunk15plus                                                                                                                                                                                  *float64 `` /* 143-byte string literal not displayed */
	NewPulmonarySmearNegativesmearUnknownsmearNotDoneCasesSexUnknownAged5_14YearsNotUsedAfter2012NewSnSexunk514                                                                                                                                                                                          *float64 `` /* 133-byte string literal not displayed */
	NewPulmonarySmearPositiveCasesNotUsedAfter2012NewSp                                                                                                                                                                                                                                                  *float64 `json:"new_pulmonary_smear_positive_cases_not_used_after_2012_new_sp"`
	NewPulmonarySmearPositiveCasesFemalesAged0_14YearsNotUsedAfter2012NewSpF014                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_0_14_years_not_used_after_2012_new_sp_f014"`
	NewPulmonarySmearPositiveCasesFemalesAged0_4YearsNotUsedAfter2012NewSpF04                                                                                                                                                                                                                            *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_0_4_years_not_used_after_2012_new_sp_f04"`
	NewPulmonarySmearPositiveCasesFemalesAged15_24YearsNotUsedAfter2012NewSpF1524                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_15_24_years_not_used_after_2012_new_sp_f1524"`
	NewPulmonarySmearPositiveCasesFemalesAged25_34YearsNotUsedAfter2012NewSpF2534                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_25_34_years_not_used_after_2012_new_sp_f2534"`
	NewPulmonarySmearPositiveCasesFemalesAged35_44YearsNotUsedAfter2012NewSpF3544                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_35_44_years_not_used_after_2012_new_sp_f3544"`
	NewPulmonarySmearPositiveCasesFemalesAged45_54YearsNotUsedAfter2012NewSpF4554                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_45_54_years_not_used_after_2012_new_sp_f4554"`
	NewPulmonarySmearPositiveCasesFemalesAged5_14YearsNotUsedAfter2012NewSpF514                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_5_14_years_not_used_after_2012_new_sp_f514"`
	NewPulmonarySmearPositiveCasesFemalesAged55_64YearsNotUsedAfter2012NewSpF5564                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_55_64_years_not_used_after_2012_new_sp_f5564"`
	NewPulmonarySmearPositiveCasesFemalesAged65AndOverNotUsedAfter2012NewSpF65                                                                                                                                                                                                                           *float64 `json:"new_pulmonary_smear_positive_cases_females_aged_65_and_over_not_used_after_2012_new_sp_f65"`
	NewPulmonarySmearPositiveCasesFemalesAgeUnknownNotUsedAfter2012NewSpFu                                                                                                                                                                                                                               *float64 `json:"new_pulmonary_smear_positive_cases_females_age_unknown_not_used_after_2012_new_sp_fu"`
	NewPulmonarySmearPositiveCasesMalesAged0_14YearsNotUsedAfter2012NewSpM014                                                                                                                                                                                                                            *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_0_14_years_not_used_after_2012_new_sp_m014"`
	NewPulmonarySmearPositiveCasesMalesAged0_4YearsNotUsedAfter2012NewSpM04                                                                                                                                                                                                                              *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_0_4_years_not_used_after_2012_new_sp_m04"`
	NewPulmonarySmearPositiveCasesMalesAged15_24YearsNotUsedAfter2012NewSpM1524                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_15_24_years_not_used_after_2012_new_sp_m1524"`
	NewPulmonarySmearPositiveCasesMalesAged25_34YearsNotUsedAfter2012NewSpM2534                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_25_34_years_not_used_after_2012_new_sp_m2534"`
	NewPulmonarySmearPositiveCasesMalesAged35_44YearsNotUsedAfter2012NewSpM3544                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_35_44_years_not_used_after_2012_new_sp_m3544"`
	NewPulmonarySmearPositiveCasesMalesAged45_54YearsNotUsedAfter2012NewSpM4554                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_45_54_years_not_used_after_2012_new_sp_m4554"`
	NewPulmonarySmearPositiveCasesMalesAged5_14YearsNotUsedAfter2012NewSpM514                                                                                                                                                                                                                            *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_5_14_years_not_used_after_2012_new_sp_m514"`
	NewPulmonarySmearPositiveCasesMalesAged55_64YearsNotUsedAfter2012NewSpM5564                                                                                                                                                                                                                          *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_55_64_years_not_used_after_2012_new_sp_m5564"`
	NewPulmonarySmearPositiveCasesMalesAged65YearsAndOverNotUsedAfter2012NewSpM65                                                                                                                                                                                                                        *float64 `json:"new_pulmonary_smear_positive_cases_males_aged_65_years_and_over_not_used_after_2012_new_sp_m65"`
	NewPulmonarySmearPositiveCasesMalesAgeUnknownNotUsedAfter2012NewSpMu                                                                                                                                                                                                                                 *float64 `json:"new_pulmonary_smear_positive_cases_males_age_unknown_not_used_after_2012_new_sp_mu"`
	NewPulmonarySmearUnknownnotDoneCasesNewSu                                                                                                                                                                                                                                                            *float64 `json:"new_pulmonary_smear_unknownnot_done_cases_new_su"`
	IfRdxDataAvailable60NumberOfNewAndRelapseCasesNotifiedAndTestedUsingAWhoRecommendedRapidDiagnosticForExampleXpertMtbrifAtTheTimeOfTbDiagnosisRegardlessOfTestResultNewincRdx                                                                                                                         *float64 `` /* 213-byte string literal not displayed */
	HivPositiveNewAndRelapseOrAllIfNewrelTbhivFlg0AndYearGreater2015TbPatientsStartedOrContinuedOnAntiretroviralTherapyNewrelArt                                                                                                                                                                         *float64 `` /* 156-byte string literal not displayed */
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged0_14YearsNewrelF014                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_0_14_years_newrel_f014"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged0_4YearsNewrelF04                                                                                                                                                                                                                       *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_0_4_years_newrel_f04"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged15_24YearsNewrelF1524                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_15_24_years_newrel_f1524"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged15YearsAndOverNewrelF15plus                                                                                                                                                                                                             *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_15_years_and_over_newrel_f15plus"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged25_34YearsNewrelF2534                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_25_34_years_newrel_f2534"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged35_44YearsNewrelF3544                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_35_44_years_newrel_f3544"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged45_54YearsNewrelF4554                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_45_54_years_newrel_f4554"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged5_14YearsNewrelF514                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_5_14_years_newrel_f514"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged55_64YearsNewrelF5564                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_55_64_years_newrel_f5564"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAged65YearsAndOverNewrelF65                                                                                                                                                                                                                 *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_aged_65_years_and_over_newrel_f65"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0FemalesAgeUnknownNewrelFu                                                                                                                                                                                                                          *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_females_age_unknown_newrel_fu"`
	NumberOfNewAndRelapseOrAllIfNewrelTbhivFlg0AndYearGreater2015TbPatientsRecordedAsHivPositiveNewrelHivpos                                                                                                                                                                                             *float64 `` /* 134-byte string literal not displayed */
	NumberOfNewAndRelapseOrAllIfNewrelTbhivFlg0AndYearGreater2015TbPatientsTestedForHivAtTheTimeOfTbDiagnosisOrWithKnownHivStatusAtTheTimeOfTbDiagnosisNewrelHivtest                                                                                                                                     *float64 `` /* 206-byte string literal not displayed */
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged0_14YearsNewrelM014                                                                                                                                                                                                                       *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_0_14_years_newrel_m014"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged0_4YearsNewrelM04                                                                                                                                                                                                                         *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_0_4_years_newrel_m04"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged15_24YearsNewrelM1524                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_15_24_years_newrel_m1524"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged15YearsAndOverNewrelM15plus                                                                                                                                                                                                               *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_15_years_and_over_newrel_m15plus"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged25_34YearsNewrelM2534                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_25_34_years_newrel_m2534"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged35_44YearsNewrelM3544                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_35_44_years_newrel_m3544"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged45_54YearsNewrelM4554                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_45_54_years_newrel_m4554"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged5_14YearsNewrelM514                                                                                                                                                                                                                       *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_5_14_years_newrel_m514"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged55_64YearsNewrelM5564                                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_55_64_years_newrel_m5564"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAged65YearsAndOverNewrelM65                                                                                                                                                                                                                   *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_aged_65_years_and_over_newrel_m65"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0MalesAgeUnknownNewrelMu                                                                                                                                                                                                                            *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_males_age_unknown_newrel_mu"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0SexUnknownAged0_14YearsNewrelSexunk014                                                                                                                                                                                                             *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_sex_unknown_aged_0_14_years_newrel_sexunk014"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0SexUnknownAged0_4YearsNewrelSexunk04                                                                                                                                                                                                               *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_sex_unknown_aged_0_4_years_newrel_sexunk04"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0SexUnknownAged15YearsAndOverNewrelSexunk15plus                                                                                                                                                                                                     *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_sex_unknown_aged_15_years_and_over_newrel_sexunk15plus"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0SexUnknownAged5_14YearsNewrelSexunk514                                                                                                                                                                                                             *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_sex_unknown_aged_5_14_years_newrel_sexunk514"`
	NewAndRelapseCasesButOnlyNewCasesIfRelInAgesexFlg0SexUnknownAgeUnknownNewrelSexunkageunk                                                                                                                                                                                                             *float64 `json:"new_and_relapse_cases_but_only_new_cases_if_rel_in_agesex_flg_0_sex_unknown_age_unknown_newrel_sexunkageunk"`
	AreDataOnHivTestingHivPositivityAndProvisionOfAntiretroviralTherapyArtRestrictedToNewAndRelapseTbCasesOnlyNewrelTbhivFlg                                                                                                                                                                             *float64 `` /* 150-byte string literal not displayed */
	OtherCasesNotIncludedInNewAndReTreatmentCaseNumbersNewretOth                                                                                                                                                                                                                                         *float64 `json:"other_cases_not_included_in_new_and_re_treatment_case_numbers_newret_oth"`
	NewAndReTreatmentTbCasesAmongForeignBornIndividualsNotifForeign                                                                                                                                                                                                                                      *float64 `json:"new_and_re_treatment_tb_cases_among_foreign_born_individuals_notif_foreign"`
	NumberOfTbCasesPulmonaryOrExtrapulmonaryTestedForSusceptibilityToRifampicinUsingPhenotypicDstOrWhoRecommendedRapidMolecularDiagnosticsEgXpertMtbrifBeforeOrAfterStartingTreatmentNewCasesRdstNew                                                                                                     *float64 `` /* 231-byte string literal not displayed */
	NumberOfTbCasesPulmonaryOrExtrapulmonaryTestedForSusceptibilityToRifampicinUsingPhenotypicDstOrWhoRecommendedRapidMolecularDiagnosticsEgXpertMtbrifBeforeOrAfterStartingTreatmentPreviouslyTreatedCasesRdstRet                                                                                       *float64 `` /* 246-byte string literal not displayed */
	NumberOfTbCasesPulmonaryOrExtrapulmonaryTestedForSusceptibilityToRifampicinUsingPhenotypicDstOrWhoRecommendedRapidMolecularDiagnosticsEgXpertMtbrifBeforeOrAfterStartingTreatmentCasesWithUnknownPreviousTbTreatmentHistoryRdstUnk                                                                   *float64 `` /* 270-byte string literal not displayed */
	AreDataAvailableOnTheNumberOfNewAndRelapseCasesTestedUsingAWhoRecommendedRapidDiagnosticDuringTheReportingYearRdxDataAvailable                                                                                                                                                                       *float64 `` /* 157-byte string literal not displayed */
	IfRdxDataAvailable61NumberOfNewAndRelapseCasesWhoseMedicalRecordsOrTreatmentCardsWereIncludedInTheSurveyRdxsurveyNewinc                                                                                                                                                                              *float64 `` /* 148-byte string literal not displayed */
	IfRdxDataAvailable61AmongTheCasesReportedInRdxsurveyNewincTheNumberTestedUsingAWhoRecommendedRapidDiagnosticSuchAsXpertMtbrifAtTheTimeOfTbDiagnosisRegardlessOfTestResultRdxsurveyNewincRdx                                                                                                          *float64 `` /* 230-byte string literal not displayed */
	AreAllRelapseCasesIncludedWithNewCasesInTheDisaggregationsByAgeAndSexRelInAgesexFlg                                                                                                                                                                                                                  *float64 `json:"are_all_relapse_cases_included_with_new_cases_in_the_disaggregations_by_age_and_sex_rel_in_agesex_flg"`
	PreviouslyTreatedPatientsExcludingRelapseCasesPulmonaryOrExtrapulmonaryBacteriologicallyConfirmedOrClinicallyDiagnosedRetNrel                                                                                                                                                                        *float64 `` /* 147-byte string literal not displayed */
	OtherReTreatmentCasesRetOth                                                                                                                                                                                                                                                                          *float64 `json:"other_re_treatment_cases_ret_oth"`
	RelapseCasesRetRel                                                                                                                                                                                                                                                                                   *float64 `json:"relapse_cases_ret_rel"`
	RelapsePulmonaryClinicallyDiagnosedTbCasesNotBacteriologicallyConfirmedAsPositiveForTbButDiagnosedWithActiveTbByAClinicianOrAnotherMedicalPractitionerWhoHasDecidedToGiveThePatientAFullCourseOfTbTreatmentRetRelClindx                                                                              *float64 `` /* 262-byte string literal not displayed */
	RelapseExtrapulmonaryCasesBacteriologicallyConfirmedOrClinicallyDiagnosedRetRelEp                                                                                                                                                                                                                    *float64 `json:"relapse_extrapulmonary_cases_bacteriologically_confirmed_or_clinically_diagnosed_ret_rel_ep"`
	RelapsePulmonaryBacteriologicallyConfirmedTbCasesSmearPositiveOrCulturePositiveOrPositiveByWhoRecommendedRapidDiagnosticsSuchAsXpertMtbrifRetRelLabconf                                                                                                                                              *float64 `` /* 182-byte string literal not displayed */
	TreatmentAfterDefaultCasesRetTad                                                                                                                                                                                                                                                                     *float64 `json:"treatment_after_default_cases_ret_tad"`
	TreatmentAfterFailureCasesRetTaf                                                                                                                                                                                                                                                                     *float64 `json:"treatment_after_failure_cases_ret_taf"`
	NumberOfLaboratoryConfirmedRrTbOrMdrTbCasesTestedForSusceptibilityToSecondLineDrugsFluoroquinolonesAndSecondLineInjectableAgentsRrSldst                                                                                                                                                              *float64 `` /* 166-byte string literal not displayed */
	NumberOfMdrTbNotLaboratoryConfirmedPatientsWhoStartedTreatmentForMdrTbUnconfMdrTx                                                                                                                                                                                                                    *float64 `json:"number_of_mdr_tb_not_laboratory_confirmed_patients_who_started_treatment_for_mdr_tb_unconf_mdr_tx"`
	NumberOfPatientsNotLaboratoryConfirmedAsHavingRifampicinResistantRrTbOrMultidrgResistantTbMdrTbWhoStartedTreatmentForMdrTbUnconfRrmdrTx                                                                                                                                                              *float64 `` /* 168-byte string literal not displayed */
}

WHO has published a global TB report every year since 1997. The main aim of the report is to provide a comprehensive and up-to-date assessment of the TB epidemic, and of progress in prevention, diagnosis and treatment of the disease, at global, regional and country levels. This is done in the context of recommended global TB strategies and targets endorsed by WHO’s Member States, broader development goals set by the United Nations (UN) and targets set in the political declaration at the first UN high-level meeting on TB (held in September 2018) .The 2019 edition of the global TB report was released on 17 October 2019. The report can be found at https://www.who.int/tb/publications/global_report/en/

type GlobalTuberculosisReportTbBurdenEstimatesWho2019Dataset 

type GlobalTuberculosisReportTbBurdenEstimatesWho2019Dataset struct {
	CaseDetectionRateAllFormsAlsoKnownAsTbTreatmentCoveragePercentCCdr                                                                            *float64 `json:"case_detection_rate_all_forms_also_known_as_tb_treatment_coverage_percent_c_cdr"`
	CaseDetectionRateAllFormsAlsoKnownAsTbTreatmentCoveragePercentHighBoundCCdrHi                                                                 *float64 `json:"case_detection_rate_all_forms_also_known_as_tb_treatment_coverage_percent_high_bound_c_cdr_hi"`
	CaseDetectionRateAllFormsAlsoKnownAsTbTreatmentCoveragePercentLowBoundCCdrLo                                                                  *float64 `json:"case_detection_rate_all_forms_also_known_as_tb_treatment_coverage_percent_low_bound_c_cdr_lo"`
	CaseNotificationRateWhichIsTheTotalOfNewAndRelapseCasesAndCasesWithUnknownPreviousTbTreatmentHistoryPer100_000PopulationCalculatedCNewinc100k *float64 `` /* 174-byte string literal not displayed */
	EstimatedTbCaseFatalityRatioCfr                                                                                                               *float64 `json:"estimated_tb_case_fatality_ratio_cfr"`
	EstimatedTbCaseFatalityRatioHighBoundCfrHi                                                                                                    *float64 `json:"estimated_tb_case_fatality_ratio_high_bound_cfr_hi"`
	EstimatedTbCaseFatalityRatioLowBoundCfrLo                                                                                                     *float64 `json:"estimated_tb_case_fatality_ratio_low_bound_cfr_lo"`
	EstimatedTbCaseFatalityRatioExpressedAsAPercentageCfrPct                                                                                      *float64 `json:"estimated_tb_case_fatality_ratio_expressed_as_a_percentage_cfr_pct"`
	EstimatedTbCaseFatalityRatioHighBoundExpressedAsAPercentageCfrPctHi                                                                           *float64 `json:"estimated_tb_case_fatality_ratio_high_bound_expressed_as_a_percentage_cfr_pct_hi"`
	EstimatedTbCaseFatalityRatioLowBoundExpressedAsAPercentageCfrPctLo                                                                            *float64 `json:"estimated_tb_case_fatality_ratio_low_bound_expressed_as_a_percentage_cfr_pct_lo"`
	EstimatedIncidenceAllFormsPer100_000PopulationEInc100k                                                                                        *float64 `json:"estimated_incidence_all_forms_per_100_000_population_e_inc_100k"`
	EstimatedIncidenceAllFormsPer100_000PopulationHighBoundEInc100kHi                                                                             *float64 `json:"estimated_incidence_all_forms_per_100_000_population_high_bound_e_inc_100k_hi"`
	EstimatedIncidenceAllFormsPer100_000PopulationLowBoundEInc100kLo                                                                              *float64 `json:"estimated_incidence_all_forms_per_100_000_population_low_bound_e_inc_100k_lo"`
	EstimatedNumberOfIncidentCasesAllFormsEIncNum                                                                                                 *float64 `json:"estimated_number_of_incident_cases_all_forms_e_inc_num"`
	EstimatedNumberOfIncidentCasesAllFormsHighBoundEIncNumHi                                                                                      *float64 `json:"estimated_number_of_incident_cases_all_forms_high_bound_e_inc_num_hi"`
	EstimatedNumberOfIncidentCasesAllFormsEIncNumLo                                                                                               *float64 `json:"estimated_number_of_incident_cases_all_forms_e_inc_num_lo"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositivePer100_000PopulationEIncTbhiv100k                                                                 *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_per_100_000_population_e_inc_tbhiv_100k"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositivePer100_000PopulationHighBoundEIncTbhiv100kHi                                                      *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_per_100_000_population_high_bound_e_inc_tbhiv_100k_hi"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositivePer100_000PopulationLowBoundEIncTbhiv100kLo                                                       *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_per_100_000_population_low_bound_e_inc_tbhiv_100k_lo"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositiveEIncTbhivNum                                                                                      *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_e_inc_tbhiv_num"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositiveHighBoundEIncTbhivNumHi                                                                           *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_high_bound_e_inc_tbhiv_num_hi"`
	EstimatedIncidenceOfTbCasesWhoAreHivPositiveLowBoundEIncTbhivNumLo                                                                            *float64 `json:"estimated_incidence_of_tb_cases_who_are_hiv_positive_low_bound_e_inc_tbhiv_num_lo"`
	EstimatedMortalityOfTbCasesAllFormsPer100_000PopulationEMort100k                                                                              *float64 `json:"estimated_mortality_of_tb_cases_all_forms_per_100_000_population_e_mort_100k"`
	EstimatedMortalityOfTbCasesAllFormsPer100_000PopulationHighBoundEMort100kHi                                                                   *float64 `json:"estimated_mortality_of_tb_cases_all_forms_per_100_000_population_high_bound_e_mort_100k_hi"`
	EstimatedMortalityOfTbCasesAllFormsPer100_000PopulationLowBoundEMort100kLo                                                                    *float64 `json:"estimated_mortality_of_tb_cases_all_forms_per_100_000_population_low_bound_e_mort_100k_lo"`
	EstimatedMortalityOfTbCasesAllFormsExcludingHivPer100_000PopulationEMortExcTbhiv100k                                                          *float64 `json:"estimated_mortality_of_tb_cases_all_forms_excluding_hiv_per_100_000_population_e_mort_exc_tbhiv_100k"`
	EstimatedMortalityOfTbCasesAllFormsExcludingHivPer100_000PopulationHighBoundEMortExcTbhiv100kHi                                               *float64 `json:"estimated_mortality_of_tb_cases_all_forms_excluding_hiv_per_100_000_population_high_bound_e_mort_exc_tbhiv_100k_hi"`
	EstimatedMortalityOfTbCasesAllFormsExcludingHivPer100_000PopulationLowBoundEMortExcTbhiv100kLo                                                *float64 `json:"estimated_mortality_of_tb_cases_all_forms_excluding_hiv_per_100_000_population_low_bound_e_mort_exc_tbhiv_100k_lo"`
	EstimatedNumberOfDeathsFromTbAllFormsExcludingHivEMortExcTbhivNum                                                                             *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_excluding_hiv_e_mort_exc_tbhiv_num"`
	EstimatedNumberOfDeathsFromTbAllFormsExcludingHivHighBoundEMortExcTbhivNumHi                                                                  *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_excluding_hiv_high_bound_e_mort_exc_tbhiv_num_hi"`
	EstimatedNumberOfDeathsFromTbAllFormsExcludingHivLowBoundEMortExcTbhivNumLo                                                                   *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_excluding_hiv_low_bound_e_mort_exc_tbhiv_num_lo"`
	EstimatedNumberOfDeathsFromTbAllFormsEMortNum                                                                                                 *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_e_mort_num"`
	EstimatedNumberOfDeathsFromTbAllFormsHighBoundEMortNumHi                                                                                      *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_high_bound_e_mort_num_hi"`
	EstimatedNumberOfDeathsFromTbAllFormsLowBoundEMortNumLo                                                                                       *float64 `json:"estimated_number_of_deaths_from_tb_all_forms_low_bound_e_mort_num_lo"`
	EstimatedMortalityOfTbCasesWhoAreHivPositivePer100_000PopulationEMortTbhiv100k                                                                *float64 `json:"estimated_mortality_of_tb_cases_who_are_hiv_positive_per_100_000_population_e_mort_tbhiv_100k"`
	EstimatedMortalityOfTbCasesWhoAreHivPositivePer100_000PopulationHighBoundEMortTbhiv100kHi                                                     *float64 `json:"estimated_mortality_of_tb_cases_who_are_hiv_positive_per_100_000_population_high_bound_e_mort_tbhiv_100k_hi"`
	EstimatedMortalityOfTbCasesWhoAreHivPositivePer100_000PopulationLowBoundEMortTbhiv100kLo                                                      *float64 `json:"estimated_mortality_of_tb_cases_who_are_hiv_positive_per_100_000_population_low_bound_e_mort_tbhiv_100k_lo"`
	EstimatedNumberOfDeathsFromTbInPeopleWhoAreHivPositiveEMortTbhivNum                                                                           *float64 `json:"estimated_number_of_deaths_from_tb_in_people_who_are_hiv_positive_e_mort_tbhiv_num"`
	EstimatedNumberOfDeathsFromTbInPeopleWhoAreHivPositiveHighBoundEMortTbhivNumHi                                                                *float64 `json:"estimated_number_of_deaths_from_tb_in_people_who_are_hiv_positive_high_bound_e_mort_tbhiv_num_hi"`
	EstimatedNumberOfDeathsFromTbInPeopleWhoAreHivPositiveLowBoundEMortTbhivNumLo                                                                 *float64 `json:"estimated_number_of_deaths_from_tb_in_people_who_are_hiv_positive_low_bound_e_mort_tbhiv_num_lo"`
	EstimatedTotalPopulationNumberEPopNum                                                                                                         *float64 `json:"estimated_total_population_number_e_pop_num"`
	EstimatedHivInIncidentTbPercentETbhivPrct                                                                                                     *float64 `json:"estimated_hiv_in_incident_tb_percent_e_tbhiv_prct"`
	EstimatedHivInIncidentTbPercentHighBoundETbhivPrctHi                                                                                          *float64 `json:"estimated_hiv_in_incident_tb_percent_high_bound_e_tbhiv_prct_hi"`
	EstimatedHivInIncidentTbPercentLowBoundETbhivPrctLo                                                                                           *float64 `json:"estimated_hiv_in_incident_tb_percent_low_bound_e_tbhiv_prct_lo"`
}

WHO has published a global TB report every year since 1997. The main aim of the report is to provide a comprehensive and up-to-date assessment of the TB epidemic, and of progress in prevention, diagnosis and treatment of the disease, at global, regional and country levels. This is done in the context of recommended global TB strategies and targets endorsed by WHO’s Member States, broader development goals set by the United Nations (UN) and targets set in the political declaration at the first UN high-level meeting on TB (held in September 2018) .The 2019 edition of the global TB report was released on 17 October 2019. The report can be found at https://www.who.int/tb/publications/global_report/en/

type GlobalWarmingPotentialFactorsGwp100Ipcc2014Dataset 

type GlobalWarmingPotentialFactorsGwp100Ipcc2014Dataset struct {
	Co2Ipcc2014     *float64 `json:"co2_ipcc_2014"`
	Ch4Ipcc2014     *float64 `json:"ch4_ipcc_2014"`
	N2oIpcc2014     *float64 `json:"n2o_ipcc_2014"`
	Cf4Ipcc2014     *float64 `json:"cf4_ipcc_2014"`
	Hfc152aIpcc2014 *float64 `json:"hfc_152a_ipcc_2014"`
	Sf6Ipcc2014     *float64 `json:"sf6_ipcc_2014"`
	Pfc14Ipcc2014   *float64 `json:"pfc_14_ipcc_2014"`
}

Data denotes the global warming potential (GWP) over a 100-year timescale for greenhouse gases relative to the GWP of carbon dioxide (which is denoted as 1). Global warming potential measures the relative warming impact of one unit mass of gas relative to one unit of carbon dioxide. For example, a GWP value for gas 'x' of 25 would mean one tonne of 'x' would have 25 times the warming impact of one tonne of carbon dioixde.GWP100 values are used to convert greenhouse gases into a carbon dioxide equivalent (CO2e) metric by multiplying emissions in mass terms by their respective GWP100 factors.In the IPCC's 5th Assessment Report (AR5), it presents GWP both with and without climate change feedback effects. For some gases, for example methane, this can introduce significant uncertainty. In the case of methane, the GWP100 value without feedbacks is 28; with feedbacks this increases to 34. In its official figures the IPCC adopts GWP100 factors without climate change feedbacks. Here we present the same figures without climate change feedbacks to maintain consistency with the IPCC.

type GlobalYearOfLastPolioCasePlusCertificationStatusGpei2017Dataset 

type GlobalYearOfLastPolioCasePlusCertificationStatusGpei2017Dataset struct {
	WhoRegionGpei2017   *float64 `json:"who_region_gpei_2017"`
	PolioStatusGpei2017 *float64 `json:"polio_status_gpei_2017"`
}

The definition of WHO Regions can be found here: http://www.who.int/about/regions/en/ Because the certification of being polio-free is done by the WHO by region and not by country (and only three years after the last case of polio was recorded), you see three polio statuses: endemic, polio-free (WHO Region not yet certified) and polio-free with the WHO Region of the country also certified polio-free.

type GlobalizationOver5CenturiesPwt90KlasingAndMilionis2014AndEstevadeordalFrantzAndTaylor2003Dataset 

type GlobalizationOver5CenturiesPwt90KlasingAndMilionis2014AndEstevadeordalFrantzAndTaylor2003Dataset struct {
	WorldTradePercOfGdpPwt90 *float64 `json:"world_trade_perc_of_gdp_pwt_90"`
}

type GoldPricesLaurenceAndWilliamson2017Dataset 

type GoldPricesLaurenceAndWilliamson2017Dataset struct {
	GoldNewYorkMarketPriceLaurenceAndWilliamson2017 *float64 `json:"gold_new_york_market_price_laurence_and_williamson_2017"`
}

Gold prices are based on New York Market Prices, and are measured in US$ per fine ounce.

type GoogleMobilityTrends2020Dataset 

type GoogleMobilityTrends2020Dataset struct {
	RetailAndRecreation *float64 `json:"retail_and_recreation"`
	GroceryAndPharmacy  *float64 `json:"grocery_and_pharmacy"`
	Parks               *float64 `json:"parks"`
	TransitStations     *float64 `json:"transit_stations"`
	Workplaces          *float64 `json:"workplaces"`
	Residential         *float64 `json:"residential"`
}

Google provide an overview of what its mobility trends represent and how it's measured here: https://support.google.com/covid19-mobility/answer/9824897?hl=en&ref_topic=9822927As it describes:"The data shows how visitors to (or time spent in) categorized places change compared to our baseline days. A baseline day represents a normal value for that day of the week. The baseline day is the median value from the 5‑week period Jan 3 – Feb 6, 2020.For each region-category, the baseline isn’t a single value—it’s 7 individual values. The same number of visitors on 2 different days of the week, result in different percentage changes. So, we recommend the following:– Don’t infer that larger changes mean more visitors or smaller changes mean less visitors.– Avoid comparing day-to-day changes. Especially weekends with weekdays."Mobility trends are measured across six broad categories:(1) Residential: places of residence.(2) Grocery & Pharmacy stores: places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.(3) Workplaces: places of work.(4) Parks: places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.(5) Transit stations: places like public transport hubs such as subway, bus, and train stations.(6) Retail & Recreation: places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.The 'Residential' category shows a change in duration—the other categories measure a change in total visitors.This index is smoothed to the rolling 7-day average.

type GovernmentEducationExpenditure19602010Szirmai2015Dataset 

type GovernmentEducationExpenditure19602010Szirmai2015Dataset struct {
	GovernmentEducationExpenditure1960_2010Szirmai2015 *float64 `json:"government_education_expenditure_1960_2010_szirmai_2015"`
}

type GovernmentExpenditureAndLearningOutcomesDataset 

type GovernmentExpenditureAndLearningOutcomesDataset struct {
	GovernmentExpenditurePerPrimaryStudentPppmoney2006_2014    *float64 `json:"government_expenditure_per_primary_student_pppmoney_2006_2014"`
	ShareOfStudentsReachingNoOrMinimumAchievementOnly2005_2015 *float64 `json:"share_of_students_reaching_no_or_minimum_achievement_only_2005_2015"`
}

This dataset was compiled to compare the government expenditure per primary student ($PPP) with learning outcomes. Because of the patchy nature of the government expenditure data as obtained from the World Bank EdStats Dataset, the most recent available expenditure data were used while 2006 was used as a cut-off point. The measure of learning outcome used is the share of students either achieving or not achieving a minimum proficiency benchmark. Achievement outcomes come from standardized, psychometrically-robust international and regional tests. In order to maximize coverage by country, tests have been harmonized and pooled across subjects (math, reading, science) and levels (primary and secondary education). Higher proficiency benchmarks that represent the missing share of 100% are students that reached intermediate or advanced proficiency levels. See source for details on proficiency benchmarks.

type GovernmentExpenditureImfBasedOnMauroEtAl2015Dataset 

type GovernmentExpenditureImfBasedOnMauroEtAl2015Dataset struct {
	GovernmentExpenditureImfBasedOnMauroEtAl2015 *float64 `json:"government_expenditure_imf_based_on_mauro_et_al_2015"`
}

Government expenditure estimates correspond to non-interest government expenditures. The authors als note: "The database covers an unbalanced panel of 55 countries (24 advanced economies—by present day definition from the IMF’s World Economic Outlook classification—and 31 nonadvanced) over 1800–2011. The data consist of government revenue, non-interest government expenditure, and the interest bill (and thus also the overall fiscal balance and the primary balance), as well as gross public debt, all expressed as a share of GDP...About half of the observations for the fiscal variables in our dataset are drawn from various cross-country sources, including the IMF’s World Economic Outlook (WEO) and International Financial Statistics (IFS) and the OECD Analytical Database...We hand-collected the other half of the data from country-specific sources, such as official government publications or economic histories that included public finance statistics."IMF data on government expenditure does not include interest paid on debt payments. In order to derive government expenditure with interest paid on debt included, the datasets "government expenditure, percent of GDP" and "interest paid on public debt, percent of GDP" from the IMF database.This data is therefore inclusive of interest on debt payments.

type GovernmentRevenueWallis2000Dataset 

type GovernmentRevenueWallis2000Dataset struct {
	NationalGovRevenuesWallis2000 *float64 `json:"national_gov_revenues_wallis_2000"`
	StateGovRevenuesWallis2000    *float64 `json:"state_gov_revenues_wallis_2000"`
	LocalGovRevenuesWallis2000    *float64 `json:"local_gov_revenues_wallis_2000"`
}

Source notes: Data after 1902 taken from Department of Commerce (1975, 1985, 1997) and Advisory on Intergovernmental Relations (1994). State revenues 1800 to 1900, data collected by Sylla, Legler, and Wallis. Local revenues 1840 to 1890, Legler, Sylla, and Wallis (1988). GNP from Gallman (1966), up to 1860; remaining years up to 1929 from Balke and Gordon (1989).

type GovernmentSpendingOecd2017Dataset 

type GovernmentSpendingOecd2017Dataset struct {
	TotalGovernmentSpendingPerCapita                                  *float64 `json:"total_government_spending_per_capita"`
	ExpendituresOnGeneralGovernmentOutsourcingPercOfGdp               *float64 `json:"expenditures_on_general_government_outsourcing_perc_of_gdp"`
	GeneralGovernmentProcurementExpenditurePercOfGdp                  *float64 `json:"general_government_procurement_expenditure_perc_of_gdp"`
	GeneralGovernmentProcurementExpenditurePercGovernmentExpenditures *float64 `json:"general_government_procurement_expenditure_perc_government_expenditures"`
}

type GovernmentSpendingRoineVlachosWaldenstrom2009AndUsHistoricalTables2016Dataset 

type GovernmentSpendingRoineVlachosWaldenstrom2009AndUsHistoricalTables2016Dataset struct {
	CentralGovExpenditureRoineVlachosWaldenstrom2009AndUsHistoricalTables2016 *float64 `json:"central_gov_expenditure_roine_vlachos_waldenstrom_2009_and_us_historical_tables_2016"`
}

type GovernmentTransparencyIndexHollyerEtAl2014Dataset 

type GovernmentTransparencyIndexHollyerEtAl2014Dataset struct {
	GovTransparency      *float64 `json:"gov_transparency"`
	GovTransparencyUpper *float64 `json:"gov_transparency_upper"`
	GovTransparencyLower *float64 `json:"gov_transparency_lower"`
}

This dataset provides information on government transparency, as per the index by Hollyer et al. (2014).A government is considered more transparent if it provides data on many measures included in the World Governance Indicators to the World Bank, even if other countries do not.

type GuineaWormCasesTheCarterCenter2022Dataset 

type GuineaWormCasesTheCarterCenter2022Dataset struct {
	GuineaWormCasesTheCarterCenter2022 *float64 `json:"guinea_worm_cases_the_carter_center_2022"`
}

Up to date data is available at the Carter Center:http://www.cartercenter.org/resources/gallery/images/highres/guinea-worm-current-case-count-chart.pdfFor 1986-2018: http://www.cartercenter.org/resources/pdfs/news/health_publications/guinea_worm/guinea-worm-cases-by-year-from-1989.pdfFor 2019-2021: - 2019 (second half table 4) -https://www.cartercenter.org/resources/pdfs/news/health_publications/guinea_worm/wrap-up/273.pdf - 2020 (second table on page 9) - https://www.cartercenter.org/resources/pdfs/news/health_publications/guinea_worm/wrap-up/283.pdf - 2021 (second table on page 9) - https://www.cartercenter.org/resources/pdfs/news/health_publications/guinea_worm/wrap-up/284.pdfThis dataset should be next updated by the source in March 2023. We will update it on Our World in Data soon after the new version is published. At the link above you can directly access the source page and see the latest available data.

type GuineaWormCasesWho2018Dataset 

type GuineaWormCasesWho2018Dataset struct {
	GuineaWormCasesWho2018 *float64 `json:"guinea_worm_cases_who_2018"`
}

We excluded all countries that the WHO labelled as "guinea worm not endemic in the 1980s" from this dataset as these countries never documented any cases of guinea worm disease.

type GuineaWormWhoCertificationStatusWho2018Dataset 

type GuineaWormWhoCertificationStatusWho2018Dataset struct {
	GuineaWormCertificationStatusWho2018 *float64 `json:"guinea_worm_certification_status_who_2018"`
}

type HadcrutTemperatureAnomalyDataset 

type HadcrutTemperatureAnomalyDataset struct {
	SurfaceTemperatureAnomaly                     *float64 `json:"surface_temperature_anomaly"`
	SurfaceTemperatureAnomalyWeightedByPopulation *float64 `json:"surface_temperature_anomaly_weighted_by_population"`
	SurfaceTemperatureAnomalyWeightedByArea       *float64 `json:"surface_temperature_anomaly_weighted_by_area"`
}

For land regions of the world over 4800 monthly station temperature time series are used and CRUTEM4 (land) is created. For marine regions, sea surface temperature (SST) measurements taken on board merchant and some naval vessels are used and HadSST3 (ocean) is created. HadCRUT4 combines both datasets.The temperature anomaly is measured relative to the 1961-1990 global average temperature. Country-level values were created by averaging all grid cells whose centroids were within the border of a country. Area weighted measures were weighted by the area of the grid cell when averaging the gridd cells and population weighted averages used gridded population data from 2015 created by the Center for International Earth Science Information Network - CIESIN (http://dx.doi.org/10.7927/H4X63JVC).

type HalfIndexLandUseAlexanderEtAl2016Dataset 

type HalfIndexLandUseAlexanderEtAl2016Dataset struct {
	HalfIndexTotalLandAreaAlexanderEtAl2016     *float64 `json:"half_index_total_land_area_alexander_et_al_2016"`
	HalfIndexHabitableLandAreaAlexanderEtAl2016 *float64 `json:"half_index_habitable_land_area_alexander_et_al_2016"`
}

This data is based on the published work of Alexander et al. (2016). Human appropriation of land for food: The role of diet. Full reference below.The authors calculate the HALF (Human appropriation of land for food) index, which measures the percentage of total land area we would need for the global population to consume the average diet of any given country. This is based on population and dietary figures from FAO 2011 data.OWID have re-calculated these figures as the percentage of habitable land area (rather than total land area). This corrects for land which is either barren or glacial land and could not be used for agriculture or other land uses. To correct for this factor, we have assumed that 71% of total land area as habitable (with 10% glaciers and 19% barren land); figures based on WWF (2016). Living Planet Report 2016.A HALF Index value of 100% means that 100% of global habitable land area would be needed as agricultural land to provide for global diets. Values >100% are not physically possible within global land constraints.References:Alexander, P., Brown, C., Arneth, A., Finnigan, J., & Rounsevell, M. D. (2016). Human appropriation of land for food: the role of diet. Global Environmental Change, 41, 88-98. Available at: http://www.sciencedirect.com/science/article/pii/S0959378016302370WWF. 2016. Living Planet Report 2016. Risk and resilience in a new era. WWF International, Gland, Switzerland. Available at: http://awsassets.panda.org/downloads/lpr_living_planet_report_2016.pdf

type HappinessPredictorsWorldHappinessReport2017Dataset 

type HappinessPredictorsWorldHappinessReport2017Dataset struct {
	HappinessAndLogIncomePartialCorrelationClarkEtAlInWorldHappinessReport2017        *float64 `json:"happiness_and_log_income_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndYearsOfEducationPartialCorrelationClarkEtAlInWorldHappinessReport2017 *float64 `json:"happiness_and_years_of_education_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndNotUnemployedPartialCorrelationClarkEtAlInWorldHappinessReport2017    *float64 `json:"happiness_and_not_unemployed_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndPartneredPartialCorrelationClarkEtAlInWorldHappinessReport2017        *float64 `json:"happiness_and_partnered_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndPhysicalIllnessPartialCorrelationClarkEtAlInWorldHappinessReport2017  *float64 `json:"happiness_and_physical_illness_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndMentalIllnessPartialCorrelationClarkEtAlInWorldHappinessReport2017    *float64 `json:"happiness_and_mental_illness_partial_correlation_clark_et_al_in_world_happiness_report_2017"`
	HappinessAndGenderPartialCorrelationWithFemaleClarkEtAlInWorldHappinessReport2017 *float64 `json:"happiness_and_gender_partial_correlation_with_female_clark_et_al_in_world_happiness_report_2017"`
}

The variables have been dated 2017 in our dataset, in order to be consistent with the date of publication. However, the underlying data comes from various survey waves, varying by country. The online appendix in the original article provides details regarding actual dates of data collection.

type HealthCoverageIlo2014Dataset 

type HealthCoverageIlo2014Dataset struct {
	ShareOfPopulationCoveredByHealthInsuranceIlo2014 *float64 `json:"share_of_population_covered_by_health_insurance_ilo_2014"`
}

Share of population covered by health insurance:Estimate of health coverage as a percentage of total population. Coverage includes affiliated members of health insurance or estimation of the population having free access to health care services provided by the State. Consult detailed data and sources available from the original tables (http://www.social-protection.org/gimi/gess/RessourceDownload.action?ressource.ressourceId=37218) and the discussion paper (https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---soc_sec/documents/publication/wcms_305947.pdf)---Total health care coverage:This dataset combines observations for OECD countries from the 1960s, with observations for non-OECD countries from the 2000s. If you are interested in complete series please visit the underlying sources.Estimate of health coverage as a percentage of total population. Coverage includes affiliated members of health insurance or estimation of the population having free access to health care services provided by the State. Consult detailed data and sources available from the original tables (http://www.social-protection.org/gimi/gess/RessourceDownload.action?ressource.ressourceId=37218) and the discussion paper (https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---soc_sec/documents/publication/wcms_305947.pdf)

type HealthExpenditureAndFinancingOecdstat2017Dataset 

type HealthExpenditureAndFinancingOecdstat2017Dataset struct {
	HealthExpenditureAndFinancingPerCapitaOecdstat2017 *float64 `json:"health_expenditure_and_financing_per_capita_oecdstat_2017"`
}

Per capita health expenditure here is measured in 2010 international dollars.

The definition of health spending given by the OECD is the following: "Health spending measures the final consumption of health care goods and services (i.e. current health expenditure) including personal health care (curative care, rehabilitative care, long-term care, ancillary services and medical goods) and collective services (prevention and public health services as well as health administration), but excluding spending on investments. Health care is financed through a mix of financing arrangements including government spending and compulsory health insurance (“public”) as well as voluntary health insurance and private funds such as households’ out-of-pocket payments, NGOs and private corporations (“private”). This indicator is presented as a total and by type of financing (“public”, “private”, “out-of-pocket”) and is measured as a share of GDP, as a share of total health spending and in USD per capita (using economy-wide PPPs)."

type HealthExpenditurePerCapitaWorldBankWdi2018Dataset 

type HealthExpenditurePerCapitaWorldBankWdi2018Dataset struct {
	CurrentHealthExpenditurePerCapitaPppCurrentInternationalMoney *float64 `json:"current_health_expenditure_per_capita_ppp_current_international_money"`
}

type HealthExpenditureUk19502012OfficeOfHealthEconomics2012Dataset 

type HealthExpenditureUk19502012OfficeOfHealthEconomics2012Dataset struct {
	NhsExpenditurePercgdp1950_2012OfficeOfHealthEconomics2012 *float64 `json:"nhs_expenditure_percgdp_1950_2012_office_of_health_economics_2012"`
	NhsExpenditurePerCapitaOfficeOfHealthEconomics2012        *float64 `json:"nhs_expenditure_per_capita_office_of_health_economics_2012"`
}

type HealthExpenditureUs19292013PrivateUsCensusAndWdi2013Dataset 

type HealthExpenditureUs19292013PrivateUsCensusAndWdi2013Dataset struct {
	UsHealthExpenditurePrivateUsCensusAndWdi2013 *float64 `json:"us_health_expenditure_private_us_census_and_wdi_2013"`
}

type HealthExpenditureUs19292013PublicUsCensusAndWdi2013Dataset 

type HealthExpenditureUs19292013PublicUsCensusAndWdi2013Dataset struct {
	UsHealthExpenditurePublicUsCensusAndWdi2013 *float64 `json:"us_health_expenditure_public_us_census_and_wdi_2013"`
}

type HealthInsuranceCoverageUsUsCurrentPopulationSurvey2014Dataset 

type HealthInsuranceCoverageUsUsCurrentPopulationSurvey2014Dataset struct {
	HealthInsuranceCoverageUsAnyPlanUsCurrentPopulationSurvey2014     *float64 `json:"health_insurance_coverage_us_any_plan_us_current_population_survey_2014"`
	HealthInsuranceCoverageUsGovtPlanUsCurrentPopulationSurvey2014    *float64 `json:"health_insurance_coverage_us_govt_plan_us_current_population_survey_2014"`
	HealthInsuranceCoverageUsPrivatePlanUsCurrentPopulationSurvey2014 *float64 `json:"health_insurance_coverage_us_private_plan_us_current_population_survey_2014"`
}

Health Insurance plans are not mutually exclusive – individuals may be simultaneously covered by private and government programs.

Measurement methodology of insurance coverage changed in 1999. For more details see the report Income, Poverty, and Health Insurance Coverage in the United States: 2008, available online at https://www.census.gov/prod/2009pubs/p60-236.pdf

Data for the period 1987-2008 was taken from the report Income, Poverty, and Health Insurance Coverage in the United States: 2008. Data thereafter taken from the Current Population Survey's Annual Social and Economic Supplement available at https://www.census.gov/hhes/www/hlthins/data/historical/HIB_tables.html

type HealthProviderAbsenceRatesChaudhuryHammerKremerMuralidharanAndRogers2006Dataset 

type HealthProviderAbsenceRatesChaudhuryHammerKremerMuralidharanAndRogers2006Dataset struct {
	HealthProviderAbsenceRatePercChaudhuryHammerKremerMuralidharanAndRogers2006 *float64 `json:"health_provider_absence_rate_perc_chaudhury_hammer_kremer_muralidharan_and_rogers_2006"`
}

Authors' note: "Absence data are based on direct physical verification of the provider's presence, rather than attendance logbooks or interviews with the faculty head." For the original data, see table 1 in the linked paper.The majority of this fieldwork was carried out between October 2002 and April 2003. See the source link for further information on the collection of absence data.

type HealthcareAccessAndQualityIndexIhme2017Dataset 

type HealthcareAccessAndQualityIndexIhme2017Dataset struct {
	HaqIndexIhme2017 *float64 `json:"haq_index_ihme_2017"`
}

Global Burden of Disease Study 2015 (GBD 2015) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2015 Global Burden of Disease Study 2015 (GBD 2015) estimates were used in an analysis of national levels of personal healthcare access and quality based on 32 causes considered amenable to healthcare over time.

This dataset includes the the Healthcare Quality and Access (HAQ) Index for global, regional, and national or territory-level estimates for 1990-2015.

type HealthyLifeExpectancyIhmeDataset 

type HealthyLifeExpectancyIhmeDataset struct {
	HealthyLifeExpectancyIhme    *float64 `json:"healthy_life_expectancy_ihme"`
	LifeExpectancyIhme           *float64 `json:"life_expectancy_ihme"`
	YearsLivedWithDisabilityIhme *float64 `json:"years_lived_with_disability_ihme"`
}

Data on 'Healthy Life Expectancy' and 'Life Expectancy' are provided by the Institute of Health Metrics and Evaluation (IHME), Global Burden of Disease. This is measured based on expectancy of a newborn born in the given year.Our World in Data have calculated the number of years of living with a disability as the difference between total and healthy life expectancy.

type HeightsOfEarlyEuropeansBasedOnHermanussen2003AndTheNcdRisc2017Dataset 

type HeightsOfEarlyEuropeansBasedOnHermanussen2003AndTheNcdRisc2017Dataset struct {
	MalesHermanussen2003AndNcdRisc2017   *float64 `json:"males_hermanussen_2003_and_ncd_risc_2017"`
	FemalesHermanussen2003AndNcdRisc2017 *float64 `json:"females_hermanussen_2003_and_ncd_risc_2017"`
}

This series combines figures from two published datasets.Data on human heights in Early Europeans in the Eastern Mediterranean region was sourced from Stature of Early Europeans - Hermanussen (2013). These heights are provided for specific historical periods: where values are provided over a time range (for example, 10000-8000 BC), we have have allocated a date in the middle of this range (9000 BC, in this example).To compare how historical heights have evolved into the 19th and 20th century, we have combined this series with data for Europe and Central Asia in the period 1896-1996 from the NCD RisC.References:Hermanussen, M. (2003). Stature of early Europeans. HORMONES-ATHENS-, 2, 175-178.

type HiddenHungerIndexInPreSchoolChildrenMuthayyaEtAl2013Dataset 

type HiddenHungerIndexInPreSchoolChildrenMuthayyaEtAl2013Dataset struct {
	HiddenHungerIndexInPreSchoolChildrenMuthayyaEtAl2013 *float64 `json:"hidden_hunger_index_in_pre_school_children_muthayya_et_al_2013"`
}

The authors note: "Hidden Hunger Index (HHI-PD) for preschool-age children is calculated as the average of three deficiency prevalence estimates: preschool children affected by stunting, anemia due to iron deficiency, and vitamin-A deficiency. The three components were equally weighted (HHI-PD score = [stunting (%) + anemia (%) + low serum retinol (%)]/3).The HHI-PD score ranged between the best and worst possible scores of 0 and 100, respectively. Applying arbitrary cut-offs, HHI-PD scores between 0 and 19.9 were considered mild, 20-34.9 as moderate, 35-44.9 as severe, and 45-100 as alarmingly high. Highly developed countries with a 2007 Human Development Index (HDI) score above 0.9 (n=41) were assumed to have a low prevalence of micronutrient deficiencies, and were therefore excluded from this analysis.Data on micronutrient deficiencies used in this index relate to national-level analysis and surveys over the period 1999-2009.

type HistoricalCostOfComputerMemoryAndStorageJohnCMccallumDataset 

type HistoricalCostOfComputerMemoryAndStorageJohnCMccallumDataset struct {
	Memory     *float64 `json:"memory"`
	DiskDrives *float64 `json:"disk_drives"`
	Ssd        *float64 `json:"ssd"`
	Flash      *float64 `json:"flash"`
}

type HistoricalEmploymentAndOutputBySectorOwid2017Dataset 

type HistoricalEmploymentAndOutputBySectorOwid2017Dataset struct {
	ShareOfAgricultureInGdpAtCurrentPricesHerrendorfEtAlData *float64 `json:"share_of_agriculture_in_gdp_at_current_prices_herrendorf_et_al_data"`
	ShareOfIndustryInGdpAtCurrentPricesHerrendorfEtAlData    *float64 `json:"share_of_industry_in_gdp_at_current_prices_herrendorf_et_al_data"`
	ShareOfServicesInGdpAtCurrentPricesHerrendorfEtAlData    *float64 `json:"share_of_services_in_gdp_at_current_prices_herrendorf_et_al_data"`
	NumberOfPeopleEmployedInAgricultureHerrendorfEtAlData    *float64 `json:"number_of_people_employed_in_agriculture_herrendorf_et_al_data"`
	NumberOfPeopleEmployedInIndustryHerrendorfEtAlData       *float64 `json:"number_of_people_employed_in_industry_herrendorf_et_al_data"`
	NumberOfPeopleEmployedInServicesHerrendorfEtAlData       *float64 `json:"number_of_people_employed_in_services_herrendorf_et_al_data"`
}

Observations in this dataset correspond to the data published by Herrendorf, Rogerson, and Valentinyi (2014), except in some cases where we have updated observation using new releases of the same underlying data sources. The most important update corresponds to the 2015 release of the Groningen Growth and Development Centre’s (GGDC) 10-sector database. However, some other country-specific updates were also considered (e.g. US data published by the Bureau of Economic Analysis). In the attached documentation (https://ourworldindata.org/wp-content/uploads/2017/05/Documentation-for-Historical-employment-and-output-by-sector-%E2%80%93-OWID-2017.pdf) we describe sources and updates, country by country.

type HistoricalGenderEqualityIndexHowWasLife2014Dataset 

type HistoricalGenderEqualityIndexHowWasLife2014Dataset struct {
	RegionalAveragesOfTheCompositeGenderEqualityIndex *float64 `json:"regional_averages_of_the_composite_gender_equality_index"`
}

The HGI is constructed by following Hausmann et al. (2012) who created the Global Gender Gap index (GGG). The composite index includes the gender differences in four dimensions, health, socio-economic resources, household and politics. Health is measured by life expectancy and sex ratios whereas socio-economic resources are captured by average years of education and labour force participation. The gender disparities in the household are captured by the marriage ages and the data on distribution of parliamentary seats between men and women is used as an indication of gender disparities in the politics. Each of these variables is presented in female/male ratio. Before creating the composite index, values above 1 were truncated to be 1 except for sex ratio where the equality benchmark is set to be 0.944. For health and socio-economic resources, we have two indicators capturing these dimensions. We have given a weight to each of these indicators, so that the variable with higher standard deviation would not get a higher weight in the sub-index. Thus we normalize the variables in each sub-index by first determining what a 1% point change would translate into in the standard deviations (calculated by dividing .01 by the standard deviation of each variable), then determining the weight to each variable. As a final step, the total of the four sub-indexes was taken, divided by four and multiplied by 100 for the ease of interpretation. A higher score in our index thus highlights less gender inequality in favour of women. A more detailed discussion of the composite index is provided in Dilli et al. (2014).Note: this is an expanded version compared to the one released in 2014. In line with the procedure required for the How was life report (Carmichael et al. 2014), only decennial averages for the 25 clio-infra countries were reported. This dataset contains all our observations.

type HistoricalIndexOfHumanDevelopmentPradosDeLaEscosuraDataset 

type HistoricalIndexOfHumanDevelopmentPradosDeLaEscosuraDataset struct {
	HistoricalIndexOfHumanDevelopmentPradosDeLaEscosura *float64 `json:"historical_index_of_human_development_prados_de_la_escosura"`
}

The Historical Index of Human Development (HIHD) is a composite statistic (index) that measures key dimensions of human development:- life expectancy- literacy- educational enrolment– and per capita gross domestic product (GDP)Sources on the following dimensions can be found at: https://espacioinvestiga.org/home-hihd/about-indices-hihd/hihd-sources-and-procedures/?lang=en

type HistoricalIndexOfHumanDevelopmentWithoutGdpPradosDeLaEscosuraDataset 

type HistoricalIndexOfHumanDevelopmentWithoutGdpPradosDeLaEscosuraDataset struct {
	HumanDevelopmentIndexWithoutGdpMetric *float64 `json:"human_development_index_without_gdp_metric"`
}

The Historical Index of Human Development (HIHD) is a composite statistic (index) that measures key dimensions of human development:- life expectancy- literacy- educational enrolment– and per capita gross domestic product (GDP)This particular metric presents the assessed HDI normalised without the variable of per capita gross domestic product (GDP), so includes life expectancy, literacy and schooling rates only.Sources on the following dimensions can be found at: https://espacioinvestiga.org/home-hihd/about-indices-hihd/hihd-sources-and-procedures/?lang=en

type HistoricalUnPopulationProjectionsDataset 

type HistoricalUnPopulationProjectionsDataset struct {
	Un1998Revision   *float64 `json:"un_1998_revision"`
	Un1982Revision   *float64 `json:"un_1982_revision"`
	Un1984Revision   *float64 `json:"un_1984_revision"`
	Un1990Revision   *float64 `json:"un_1990_revision"`
	Un2017Revision   *float64 `json:"un_2017_revision"`
	Un2017Projection *float64 `json:"un_2017_projection"`
	Un1968Revision   *float64 `json:"un_1968_revision"`
	Un1973Revision   *float64 `json:"un_1973_revision"`
	Un1980Revision   *float64 `json:"un_1980_revision"`
}

Global population projections are based on historical World Population Prospects Editions published by the United Nations (UN) across a number of periodic publications.

The following UN Population Prospects have been included:

United Nations (1973). World Population Prospects as Assessed in 1968. United Nations, New York.

United Nations. World Population Prospects as Assessed in 1973. United Nations, New York.

United Nations. World Population Prospects as Assessed in 1980. United Nations, New York.

United Nations (1985). World Population Prospects: estimates and projections as assessed in 1982. United Nations, New York.

United Nations (1986). World Population Prospects: estimates and projections as assessed in 1984. United Nations, New York.

United Nations (1991). World Population Prospects 1990. United Nations, New York.

United Nations (1998). World Population Prospects: the 1998 Revision. Volume I: Comprehensive Tables. United Nations, New York.

United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision.

type HistoricalUrbanFractionEstimatesAndTotalComputedUrbanAreasHyde312010Dataset 

type HistoricalUrbanFractionEstimatesAndTotalComputedUrbanAreasHyde312010Dataset struct {
	HistoricalUrbanFractionEstimatePerRegionHyde31_2010 *float64 `json:"historical_urban_fraction_estimate_per_region_hyde_31_2010"`
	TotalUrbanAreaPerWorldRegionHyde31_2010             *float64 `json:"total_urban_area_per_world_region_hyde_31_2010"`
	HistoricalRuralFractionEstimatePerRegionHyde31_2010 *float64 `json:"historical_rural_fraction_estimate_per_region_hyde_31_2010"`
}

Authors' note on the data sources used: "Urban/rural fractions for all countries were derived from the UN after ad 1950 (UN, 2008b). Earlier historical urbanization estimates for Europe were derived from De Vries (1984), Bairoch et al. (1988), Chandler (1987), for Canada after ad 1890 from Urquhart and Buckley (1965), for China from Rozman (1973) and Maddison (1995), for Colombia from Etter et al. (2006), USA from Dodd (1993), all other countries were estimated similar to De Vries (1984), which data yielded roughly a factor 10 lower in ad 1700 compared with the 1950 value of the UN."Full citation: Klein Goldewijk, K. , A. Beusen, M. de Vos and G. van Drecht (2011). The HYDE 3.1 spatially explicit database of human induced land use change over the past 12,000 years, Global Ecology and Biogeography20(1): 73-86. DOI: 10.1111/j.1466-8238.2010.00587.x.Klein Goldewijk, K. , A. Beusen, and P. Janssen (2010). Long term dynamic modeling of global population and built-up area in a spatially explicit way, HYDE 3 .1. The Holocene20(4):565-573. http://dx.doi.org/10.1177/0959683609356587

type HistoricalWorldPopulationComparisonOfDifferentSourcesDataset 

type HistoricalWorldPopulationComparisonOfDifferentSourcesDataset struct {
	HistoricalWorldPopulationComparisonOfDifferentSources *float64 `json:"historical_world_population_comparison_of_different_sources"`
}

Among others these are the original source:

McEvedy, Colin and Richard Jones, 1978, “Atlas of World Population History,” Facts on File, New York, pp. 342-351.

Biraben, Jean-Noel, 1980, An Essay Concerning Mankind’s Evolution, Population, Selected Papers, December, table 2.

Durand, John D., 1974, “Historical Estimates of World Population: An Evaluation,” University of Pennsylvania, Population Center, Analytical and Technical Reports, Number 10, table 2.

Haub, Carl, 1995, “How Many People Have Ever Lived on Earth?” Population Today, February, p. 5.

Thomlinson, Ralph, 1975, “Demographic Problems, Controversy Over Population Control,” Second Edition, Table 1.

United Nations, 1999, The World at Six Billion, Table 1, “World Population From” Year 0 to Stabilization, p. 5, U.S. Census Bureau (USCB), 2012, Total Midyear Population for the World: 1950-2050.

Michael Kremer (1993) “Population Growth and Technological Change: One Million B.C. to 1990”, Quarterly Journal of Economics., August 1993, pp.681-716.

type HomelessnessAndPrecariousHousingOecd2016Dataset 

type HomelessnessAndPrecariousHousingOecd2016Dataset struct {
	PrecariousHousingPer100_000Oecd2016 *float64 `json:"precarious_housing_per_100_000_oecd_2016"`
	HomelessnessPer100_000Oecd2016      *float64 `json:"homelessness_per_100_000_oecd_2016"`
}

Different OECD countries adopt different definitions of homelessness. A full table of definitions used for the purpose of data collection in the OECD can be found in Annex 1 of OECD Affordable Housing Database (2016) (https://www.oecd.org/els/family/HC3-1-Homeless-population.pdf)The variable Homelessness – OECD corresponds to homelessness estimates including exclusively (i) people living in the streets or public spaces without a shelter that can be defined as living quarters; (ii) people in emergency accommodation with no place of usual residence, who move frequently between various types of accommodation; and (iii) people living in accommodation for the homeless, including homeless hostels, temporary accommodation and other types of shelters for the homeless.The variable Precarious housing – OECD corresponds to homelessness estimates including the three categories mentioned above, plus (iv) people living in institutions and (v) people living temporarily in conventional housing with family and friends.

type HomelessnessPrevalenceToroEtAl2007Dataset 

type HomelessnessPrevalenceToroEtAl2007Dataset struct {
	ShareOfPopulationWithAnEpisodeOfHomelessnessToroEtAl2007 *float64 `json:"share_of_population_with_an_episode_of_homelessness_toro_et_al_2007"`
}

Interviews took place at different points in time within and across countries. All interviews took place in the period 1999-2003.

The source provides the following information regarding the data-collecting process:

"Random samples of 250–435 adults were interviewed by telephone in five different nations (N = 1,546): Belgium, Germany, Italy, the UK, and the United States. The interview included questions on respondent attitudes, knowledge, and opinions regarding homelessness; respondents’ own personal experiences with homelessness and homeless people; and demographic characteristics of the respondents.

Respondents’ personal experiences with homelessness were assessed by querying whether they had ever considered themselves homeless or in another precarious housing situation, following up with items directed at ascertaining the age at which they experienced homelessness, the duration of the episode of homelessness, and whether they were literally homeless (slept in a shelter, in a park) or 'precariously housed' (slept at a friend’s house because they had no other place to go)."

We report estimates only for literal homelessness.

type HomicideRatesInEuropeOverLongTermEisnerAndIhmeDataset 

type HomicideRatesInEuropeOverLongTermEisnerAndIhmeDataset struct {
	HomicideRateInEuropeOverLongTermPer100_000 *float64 `json:"homicide_rate_in_europe_over_long_term_per_100_000"`
}

Data for all observations up to 1985 is taken from Table 1 in Eisner (2003) - Long-Term Historical Trends in Violent Crime. In Crime and Justice, 30, 83--142. Available at: https://www.jstor.org/stable/1147697There, we assume and allocate the homicide rate at the midpoint of the given period.Data from 1990 onwards is taken from the Institute of Health Metrics and Evaluation (IHME), Global Burden of Disease study. For categories with more than one country we take the average of all countries in that region. For example, the average of Germany and Switzerland, and for Scandinavia, the average of Sweden, Norway, Finland, Denmark and Iceland.IHME data is sourced from: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2017. Available at: http://ghdx.healthdata.org/gbd-results-tool

type HomosexualityLawsOwidBasedOnKennyAndPatel2017Dataset 

type HomosexualityLawsOwidBasedOnKennyAndPatel2017Dataset struct {
	YearWhenHomosexualityBecameLegal *float64 `json:"year_when_homosexuality_became_legal"`
}

Estimates are based on Figure 2 in <em>Kenny, C., & Patel, D. (2017). Norms and Reform: Legalizing Homosexuality Improves Attitudes. Center for Global Development Working Paper, (465).</em>We updated the classification for those countries where same-sex sexual activity was decriminalized after 2017, namely: Angola (2019), Botswana (2019), Micronesia (2018), India (2018) and Trinidad & Tobago (2018).

type HomosexualityOpinionsWvs19812016Dataset 

type HomosexualityOpinionsWvs19812016Dataset struct {
	PercentageReportingHomosexualityIsNeverJustified        *float64 `json:"percentage_reporting_homosexuality_is_never_justified"`
	PercentageReportingThatTheyDoNotWantHomosexualNeighbors *float64 `json:"percentage_reporting_that_they_do_not_want_homosexual_neighbors"`
}

Percentages are taken only with respect to survey respondents who provide an answer. Those who fail to provide an answer, as well as those who respond "Don't know", are excluded from the total.

type HomosexualityPublicOpinionPewResearch2013Dataset 

type HomosexualityPublicOpinionPewResearch2013Dataset struct {
	ShareWhoThinkHomosexualityShouldNotBeAcceptedBySociety *float64 `json:"share_who_think_homosexuality_should_not_be_accepted_by_society"`
}

<strong>Notes </strong>The full question asked: <em>Which one of these comes closest to your opinion, number 1 or number 2?...</br> #1 - Homosexuality should be accepted by society; or</br> #2 - Homosexuality should not be accepted by society </em>For some countries the survey is not representative at the national level.

type HouseholdExpenditureOnHousingWaterElectricityGasAndOtherFuelsAsAShareOfGdpUnDataset 

type HouseholdExpenditureOnHousingWaterElectricityGasAndOtherFuelsAsAShareOfGdpUnDataset struct {
	HouseholdExpenditureOnHousingWaterElectricityGasAndOtherFuelsAsAShareOfGdp *float64 `json:"household_expenditure_on_housing_water_electricity_gas_and_other_fuels_as_a_share_of_gdp"`
}

The UN collects National Accounts data from individual countries. Within the 'Individual consumption expenditure of households, NPISHs, and general government at current prices' tables, it provides a breakdown of household final expenditure by purpose, according to the COICOP classification (Classification of Individual Consumption According to Purpose). Category 4 relates to Housing, water, electricity, gas and other fuels, which includes actual and imputed rent, as well expenditure on the utilities indicated.

type HouseholdsActualAndImputedRentAsShareOfGdpOecdDataset 

type HouseholdsActualAndImputedRentAsShareOfGdpOecdDataset struct {
	ActualRentsAsShareOfGdp           *float64 `json:"actual_rents_as_share_of_gdp"`
	ImputedRentsAsShareOfGdp          *float64 `json:"imputed_rents_as_share_of_gdp"`
	ActualAndImputedRentsAsShareOfGdp *float64 `json:"actual_and_imputed_rents_as_share_of_gdp"`
}

The OECD collects National Accounts data from member states and a limited number of non-member states. Within the 'Final consumption expenditure of households' tables, it provides a breakdown of household final expenditure by purpose, according to the COICOP classification (Classification of Individual Consumption According to Purpose). This includes actual and imputed rent. Imputed rent is an estimate of the rent foregone by home owners in living in their own property, rather than renting it out.

type HouseholdsUsingSolidFuelsForCookingUrbanVsRuralUnDataset 

type HouseholdsUsingSolidFuelsForCookingUrbanVsRuralUnDataset struct {
	Urban *float64 `json:"urban"`
	Rural *float64 `json:"rural"`
}
Data presents the percentage of households using solid fuels (wood, crop residues, dung, charcoal, and coal) for cooking by region.

This data has been aggregated by urban vs. rural households.

type HowEuropeansSpendTheirTimeEuropeanCommission2004Dataset 

type HowEuropeansSpendTheirTimeEuropeanCommission2004Dataset struct {
	FreeTimeWomenEuropeanCommission2004          *float64 `json:"free_time_women_european_commission_2004"`
	MealsPersonalCareWomenEuropeanCommission2004 *float64 `json:"meals_personal_care_women_european_commission_2004"`
	SleepWomenEuropeanCommission2004             *float64 `json:"sleep_women_european_commission_2004"`
	TravelWomenEuropeanCommission2004            *float64 `json:"travel_women_european_commission_2004"`
	DomesticWorkWomenEuropeanCommission2004      *float64 `json:"domestic_work_women_european_commission_2004"`
	GainfulWorkStudyWomenEuropeanCommission2004  *float64 `json:"gainful_work_study_women_european_commission_2004"`
	FreeTimeMenEuropeanCommission2004            *float64 `json:"free_time_men_european_commission_2004"`
	MealsPersonalCareMenEuropeanCommission2004   *float64 `json:"meals_personal_care_men_european_commission_2004"`
	SleepMenEuropeanCommission2004               *float64 `json:"sleep_men_european_commission_2004"`
	TravelMenEuropeanCommission2004              *float64 `json:"travel_men_european_commission_2004"`
	DomesticWorkMenEuropeanCommission2004        *float64 `json:"domestic_work_men_european_commission_2004"`
	GainfulWorkStudyMenEuropeanCommission2004    *float64 `json:"gainful_work_study_men_european_commission_2004"`
}

Ten European countries, Belgium, Germany, Estonia, France, Hungary, Slovenia, Finland, Sweden, the United Kingdom, and Norway are included because the survey methods used closely followed the <a href="http://ec.europa.eu/eurostat/ramon/statmanuals/files/KS-RA-08-014-EN.pdf" rel="noopener" target="_blank">Guidelines on Harmonised European Time Use Surveys</a> published in September 2000. Therefore, results are considered to be comparable. A representative sample of individuals completed a diary during one weekday and one weekend day distributed over the whole year.

type HubbertsPeakCavalloAndEiaDataset 

type HubbertsPeakCavalloAndEiaDataset struct {
	ActualProductionCavalloAndEia *float64 `json:"actual_production_cavallo_and_eia"`
	HubbertsCurveCavalloAndEia    *float64 `json:"hubberts_curve_cavallo_and_eia"`
}

Data on actual US oil production is based on reported historical figures from the US Energy Information Administration (EIA). This has been plotted based on its dataset on annual 'US Field Production of Crude Oil'. Available online at: https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=pet&s=mcrfpus1&f=a [accessed 30th July 2017].

Data on Hubbert's hypothesised peak oil production in the US is based on distribution analysis discussed and reported in: Cavallo, A. J. (2004). Hubbert’s petroleum production model: an evaluation and implications for world oil production forecasts. Natural Resources Research, 13(4), 211-221.

type HumanCapitalInLongRunLeeLee2016Dataset 

type HumanCapitalInLongRunLeeLee2016Dataset struct {
	PrimaryAdjustedEnrolmentRatioLeeLee2016      *float64 `json:"primary_adjusted_enrolment_ratio_lee_lee_2016"`
	SecondaryAdjustedEnrolmentRatioLeeLee2016    *float64 `json:"secondary_adjusted_enrolment_ratio_lee_lee_2016"`
	TertiaryAdjustedEnrolmentRatioLeeLee2016     *float64 `json:"tertiary_adjusted_enrolment_ratio_lee_lee_2016"`
	ShareOfNoSchoolingLeeLee2016                 *float64 `json:"share_of_no_schooling_lee_lee_2016"`
	ShareOfPrimarySchoolingLeeLee2016            *float64 `json:"share_of_primary_schooling_lee_lee_2016"`
	ShareOfSecondarySchoolingLeeLee2016          *float64 `json:"share_of_secondary_schooling_lee_lee_2016"`
	ShareOfTertiarySchoolingLeeLee2016           *float64 `json:"share_of_tertiary_schooling_lee_lee_2016"`
	TotalYearsOfSchoolingLeeLee2016              *float64 `json:"total_years_of_schooling_lee_lee_2016"`
	YearsOfPrimarySchoolingLeeLee2016            *float64 `json:"years_of_primary_schooling_lee_lee_2016"`
	YearsOfSecondarySchoolingLeeLee2016          *float64 `json:"years_of_secondary_schooling_lee_lee_2016"`
	YearsOfTertiarySchoolingLeeLee2016           *float64 `json:"years_of_tertiary_schooling_lee_lee_2016"`
	MalePrimaryAdjustedEnrolmentLeeLee2016       *float64 `json:"male_primary_adjusted_enrolment_lee_lee_2016"`
	MaleSecondaryAdjustedEnrolmentLeeLee2016     *float64 `json:"male_secondary_adjusted_enrolment_lee_lee_2016"`
	MaleTertiaryAdjustedEnrolmentLeeLee2016      *float64 `json:"male_tertiary_adjusted_enrolment_lee_lee_2016"`
	FemaleToMaleYearsSchoolingLeeLee2016         *float64 `json:"female_to_male_years_schooling_lee_lee_2016"`
	RegionalFemaleToMaleYearsSchoolingLeeLee2016 *float64 `json:"regional_female_to_male_years_schooling_lee_lee_2016"`
}

Author's note: "We construct a complete data set of historical enrollment ratios, subdivided by education level and gender, for 111 countries from 1820 to 1945 (at five-year intervals) by using newly compiled census observations and information on the year of establishment of the oldest school in individual countries. Then, by utilizing these enrollment ratios, as well as available census data from 1945 onward on different age groups' educational attainment, we construct a data set of estimated educational attainment, disaggregated by gender and age group, and aggregate human capital stock that spans from 1870 to 2010."For regional estimates, the authors classify the following countries as 'Advanced Economies': Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, USA, United Kingdom. Further details on regional classifications are available in the source paper.

type HumanCapitalIndexWorldBank2018Dataset 

type HumanCapitalIndexWorldBank2018Dataset struct {
	HumanCapitalIndexHci     *float64 `json:"human_capital_index_hci"`
	HumanCapitalIndexMales   *float64 `json:"human_capital_index_males"`
	HumanCapitalIndexFemales *float64 `json:"human_capital_index_females"`
}

The Human Capital Index (HCI) combines indicators of health and education into a measure of the human capital that a child born today can expect to obtain by her 18th birthday, given the risks of poor education and health that prevail in the country where she lives. The HCI is measured in units of productivity relative to a benchmark of complete education and full health, and ranges from 0 to 1. A value of x on the HCI indicates that a child born today can expect to be only x x100 percent as productive as a future worker as she would be if she enjoyed complete education and full health. Ranging between 0 and 1, the index takes the value 1 only if a child born today can expect to achieve fullhealth (defined as no stunting and survival up to at least age 60) and complete her education potential(defined as 14 years of high-quality school by age 18). HCI is calculated based on the metrics of probability of survival to age 5, expected years of school, harmonized test scores, learning-adjusted years of schooling, stunting and adult survival rates.

type HumanDevelopmentIndexUndpDataset 

type HumanDevelopmentIndexUndpDataset struct {
	HumanDevelopmentIndexUndp *float64 `json:"human_development_index_undp"`
}

type HumanHeightNcdRisc2017Dataset 

type HumanHeightNcdRisc2017Dataset struct {
	MeanMaleHeightCm         *float64 `json:"mean_male_height_cm"`
	MeanFemaleHeightCm       *float64 `json:"mean_female_height_cm"`
	MaleToFemaleHeightRatio  *float64 `json:"male_to_female_height_ratio"`
	ChangeInMaleHeightCm     *float64 `json:"change_in_male_height_cm"`
	ChangeInFemaleHeightCm   *float64 `json:"change_in_female_height_cm"`
	ChangeInMaleHeightPerc   *float64 `json:"change_in_male_height_perc"`
	ChangeInFemaleHeightPerc *float64 `json:"change_in_female_height_perc"`
}

Mean heights of men and women aged 18 or older by birth year, extending from 1896 to 1996.

type HumanHeightUniversityOfTuebingen2015Dataset 

type HumanHeightUniversityOfTuebingen2015Dataset struct {
	HumanHeightUniversityOfTuebingen2015 *float64 `json:"human_height_university_of_tuebingen_2015"`
}

Historical heights of soldiers, conscripts, prisoners and others. Please consult the sources for details for each country.

type HumanRightsProtectionFarissEtAl2020Dataset 

type HumanRightsProtectionFarissEtAl2020Dataset struct {
	HumanRightsProtection *float64 `json:"human_rights_protection"`
}

This dataset provides the human rights protection scores by Fariss et al. (2020), first developed by Schnakenberg and Fariss (2014).

type HumanRightsProtectionScoreChristopherFarris2014AndKeithSchnakenbergDataset 

type HumanRightsProtectionScoreChristopherFarris2014AndKeithSchnakenbergDataset struct {
	CanBeDeletedHumanRightsProtectionScoreByChristopherFarrisAndKeithSchnakenberg *float64 `json:"can_be_deleted_human_rights_protection_score_by_christopher_farris_and_keith_schnakenberg"`
}

The original dataset includes the country ids 666.001, 666.002, and 666.003. These correspond to different human rights reports which were produced for distinct areas in Israel (Israel, pre-1967 borders Israel, occupied territories Palestinian Authority). Because of technical limitations they cannot be shown in these visualisations and shown is only the 'Israel' series.

type HurricaneForecastingErrorNhc2019Dataset 

type HurricaneForecastingErrorNhc2019Dataset struct {
	TrackError0hNMi      *float64 `json:"track_error_0h_n_mi"`
	TrackError12hNMi     *float64 `json:"track_error_12h_n_mi"`
	TrackError24hNMi     *float64 `json:"track_error_24h_n_mi"`
	TrackError36hNMi     *float64 `json:"track_error_36h_n_mi"`
	TrackError48hNMi     *float64 `json:"track_error_48h_n_mi"`
	TrackError72hNMi     *float64 `json:"track_error_72h_n_mi"`
	TrackError96hNMi     *float64 `json:"track_error_96h_n_mi"`
	TrackError120hNMi    *float64 `json:"track_error_120h_n_mi"`
	IntensityError0hKt   *float64 `json:"intensity_error_0h_kt"`
	IntensityError12hKt  *float64 `json:"intensity_error_12h_kt"`
	IntensityError24hKt  *float64 `json:"intensity_error_24h_kt"`
	IntensityError36hKt  *float64 `json:"intensity_error_36h_kt"`
	IntensityError48hKt  *float64 `json:"intensity_error_48h_kt"`
	IntensityError72hKt  *float64 `json:"intensity_error_72h_kt"`
	IntensityError96hKt  *float64 `json:"intensity_error_96h_kt"`
	IntensityError120hKt *float64 `json:"intensity_error_120h_kt"`
}

Data on intensity and track forecast errors for tropical cyclones in the Atlantic basin and the Eastern North Pacific Basin, published by the National Hurricane Centre to show changes in the accuracy and skill of its forecasts.

type HurricaneLandfallsContinentalUsHurdatNoaaDataset 

type HurricaneLandfallsContinentalUsHurdatNoaaDataset struct {
	Category1 *float64 `json:"category_1"`
	Category2 *float64 `json:"category_2"`
	Category3 *float64 `json:"category_3"`
	Category4 *float64 `json:"category_4"`
	Category5 *float64 `json:"category_5"`
}

Data on Continental US Hurricane Impacts/Landfalls as published in the HURDAT (Hurricane Database) of the National Oceanic & Atmospheric Administration (NOAA).This data runs from 1851 through to the latest annual data.Hurricanes are categorised by the Saffir–Simpson hurricane wind scale (SSHWS) which classifies by five categories (1 being the lowest; 5 the highest) based on the intensity of sustained winds. This scale estimates potential property damage. Hurricanes reaching Category 3 and higher are considered major hurricanes because of their potential for significant loss of life and damage.The NOAA notes that because of the sparseness of towns and cities before 1900 in some coastal locations along the United States, the data prior to 1900 may not be complete for all states.

type HypotheticalGlobalCo2EmissionsCdiac2014Dataset 

type HypotheticalGlobalCo2EmissionsCdiac2014Dataset struct {
	HypotheticalGlobalEmissionsCdiac2014 *float64 `json:"hypothetical_global_emissions_cdiac_2014"`
}

Data is based on the hypothetical scenario where the total global population had the same carbon dioxide production footprint as the average citizen of a given country.

Figures were derived by multiplying the average per capita CO2 footprint (sourced from CDIAC, referenced below) in 2014, by the UN estimated global population in 2014 of 7,265,786,000.

References:

Carbon Dioxide Information Analysis Center (CDIAC). Available at: http://cdiac.ornl.gov/CO2_Emission/ (accessed 2017-05-06)

World Population Prospects, the 2015 Revision. United Nations Population Division. Available at: https://esa.un.org/unpd/wpp/ (accessed 2017-05-06)

type HypotheticalMeatConsumptionOwidBasedOnFaoAndUnDataset 

type HypotheticalMeatConsumptionOwidBasedOnFaoAndUnDataset struct {
	HypotheticalGlobalMeatConsumptionOwidBasedOnFaoAndUn *float64 `json:"hypothetical_global_meat_consumption_owid_based_on_fao_and_un"`
}

This is a hypothetical variable derived by Our World in Data which asks the question: "what would global meat production have to be if everyone in the world was to consume the average per capita amount of a given country?" e.g. "how much meat would we need if everyone in the world consumed the same amount of meat as the average UK citizen?".This was derived by multiplying global population figures from the UN Population Division (2017 Revision) by per capita meat supply of a given, as published by the UN FAO. Sources: UN FAO: http://www.fao.org/faostat/en/UN Population Division (2017 Revision): United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/

type IncidenceOfChildLaborEnglandItalyUsWorldCunninghamAndViazzo1996AndOthersDataset 

type IncidenceOfChildLaborEnglandItalyUsWorldCunninghamAndViazzo1996AndOthersDataset struct {
	ChildLaborPercChildrenRecordedAsWorkingCunninghamAndViazzo1996AndOthers *float64 `json:"child_labor_perc_children_recorded_as_working_cunningham_and_viazzo_1996_and_others"`
}

Incidence of child labour in datasets for England and Italy is based on the percentage of children (aged 10-14) recorded as working at least one hour of work per week (thus defined as "children in employment"). Data for the United States is for children aged 10-13 only. Figures for the US have been calculated as the weighted-average of gender-specific incidences of labour, based on the male:female ratio (10-13 year-olds) from reported population figures.Data for the global level is based on the definition of full-time child labour (which excludes children participating in light part-time work). World ILO-EPEAP data from 1950-1995 is based on children aged 10-14. World ILO-IPEC data from 2000-2012 broadens this definition to those aged 5-17. Estimates are based on census and national survey data.

type IncidenceOfManagerialOrProfessionalJobsAndCollectiveBargainingByGenderBlauAndKahn2017Dataset 

type IncidenceOfManagerialOrProfessionalJobsAndCollectiveBargainingByGenderBlauAndKahn2017Dataset struct {
	GapBetweenMenAndWomenInManagerialJobsBlauAndKahn2017               *float64 `json:"gap_between_men_and_women_in_managerial_jobs_blau_and_kahn_2017"`
	GapBetweenMenAndWomenInProfessionalJobsBlauAndKahn2017             *float64 `json:"gap_between_men_and_women_in_professional_jobs_blau_and_kahn_2017"`
	GapBetweenMenAndWomenInMaleProfessionalJobsBlauAndKahn2017         *float64 `json:"gap_between_men_and_women_in_male_professional_jobs_blau_and_kahn_2017"`
	GapBetweenMenAndWomenInCollectiveBargainingCoverageBlauAndKahn2017 *float64 `json:"gap_between_men_and_women_in_collective_bargaining_coverage_blau_and_kahn_2017"`
}

See the authors' data appendix for more detail on how the data was prepared and analyzed.

type IncomeClassificationWorldBank2017Dataset 

type IncomeClassificationWorldBank2017Dataset struct {
	IncomeClassificationsWorldBank2017 *float64 `json:"income_classifications_world_bank_2017"`
}

–The Atlas methodology is used to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of a country's exchange rate for that year and its exchange rates for the two preceding years, adjusted for the differences between the rate of inflation in the country and that in Japan, the United Kingdom, the United States, and the Euro area. A country's inflation rate is measured by the change in its GDP deflator. The inflation rate for the above countries, representing international inflation, is measured by the changes in the SDR deflator. (Special drawing rights, or SDRs, are the IMF's unit of account.)– Data on Serbia & Montenegro, prior to 2006 have been allocated to the sovereign states of Serbia, and Montenegro, respectively. Similarly, the 15 post-Soviet States have been allocated the USSR's classification for 1990. This includes Moldova, Estonia, Latvia, Lithuania, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Russia, Armenia, Azerbaijan, Georgia, Ukraine, and Belarus.

type IncomesAcrossTheDistributionDatabaseAuthoredByNolanThewissenRoserBasedOnLisIndexedToTheFirstYear2016Dataset 

type IncomesAcrossTheDistributionDatabaseAuthoredByNolanThewissenRoserBasedOnLisIndexedToTheFirstYear2016Dataset struct {
	O1stIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o1st_incomes_across_the_distribution_database_2016"`
	O2ndIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o2nd_incomes_across_the_distribution_database_2016"`
	O3rdIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o3rd_incomes_across_the_distribution_database_2016"`
	O4thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o4th_incomes_across_the_distribution_database_2016"`
	O5thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o5th_incomes_across_the_distribution_database_2016"`
	O6thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o6th_incomes_across_the_distribution_database_2016"`
	O7thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o7th_incomes_across_the_distribution_database_2016"`
	O8thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o8th_incomes_across_the_distribution_database_2016"`
	O9thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o9th_incomes_across_the_distribution_database_2016"`
	MeanIncomesAcrossTheDistributionDatabase2016 *float64 `json:"mean_incomes_across_the_distribution_database_2016"`
}

The decile cut-offs  are calculated on the basis of disposable household income. The measure of disposable household income employed in the Luxembourg Income Study is paid employment and self-employment income, capital income, transfer income, which includes social security transfers (work-related insurance transfers, universal benefits, and assistance benefits) and private transfers, minus income taxes and social security contributions. This follows the definitions of the Canberra Group.

type IncomesAcrossTheDistributionDatabaseGini2016Dataset 

type IncomesAcrossTheDistributionDatabaseGini2016Dataset struct {
	GiniEquivalisedDisposableHouseholdIncomeEntirePopIncomesAcrossTheDistributionDatabase2016 *float64 `json:"gini_equivalised_disposable_household_income_entire_pop_incomes_across_the_distribution_database_2016"`
	GiniEquivalisedMarketHouseholdIncomeEntirePopIncomesAcrossTheDistributionDatabase2016     *float64 `json:"gini_equivalised_market_household_income_entire_pop_incomes_across_the_distribution_database_2016"`
}

type IncomesAcrossTheDistributionDatabaseNolanThewissenRoserInLevels2016Dataset 

type IncomesAcrossTheDistributionDatabaseNolanThewissenRoserInLevels2016Dataset struct {
	O1stIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o1st_incomes_across_the_distribution_database_2016"`
	O2ndIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o2nd_incomes_across_the_distribution_database_2016"`
	O3rdIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o3rd_incomes_across_the_distribution_database_2016"`
	O4thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o4th_incomes_across_the_distribution_database_2016"`
	O5thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o5th_incomes_across_the_distribution_database_2016"`
	O6thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o6th_incomes_across_the_distribution_database_2016"`
	O7thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o7th_incomes_across_the_distribution_database_2016"`
	O8thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o8th_incomes_across_the_distribution_database_2016"`
	O9thIncomesAcrossTheDistributionDatabase2016 *float64 `json:"o9th_incomes_across_the_distribution_database_2016"`
	MeanIncomesAcrossTheDistributionDatabase2016 *float64 `json:"mean_incomes_across_the_distribution_database_2016"`
}

The decile cut-offs  are calculated on the basis of disposable household income. The measure of disposable household income employed in the Luxembourg Income Study is paid employment and self-employment income, capital income, transfer income, which includes social security transfers (work-related insurance transfers, universal benefits, and assistance benefits) and private transfers, minus income taxes and social security contributions. This follows the definitions of the Canberra Group.

type IndicatorsForWhatIsPppWorldBankDataset 

type IndicatorsForWhatIsPppWorldBankDataset struct {
	PriceLevelRatioOfPppConversionFactorGdpToMarketExchangeRateWorldBank *float64 `json:"price_level_ratio_of_ppp_conversion_factor_gdp_to_market_exchange_rate_world_bank"`
	GdpPerCapitaPppIntMoneyAdjustedWorldBank                             *float64 `json:"gdp_per_capita_ppp_int_money_adjusted_world_bank"`
	GdpPerCapitaUsMoneyMarketExchangeWorldBank                           *float64 `json:"gdp_per_capita_us_money_market_exchange_world_bank"`
}

The following definitions are taken from World Bank's World Development Index description in 'Details'.

Price level ratio of PPP conversion factor (GDP) to market exchange rate:

"Purchasing power parity conversion factor is the number of units of a country's currency required to buy the same amount of goods and services in the domestic market as a U.S. dollar would buy in the United States. The ratio of PPP conversion factor to market exchange rate is the result obtained by dividing the PPP conversion factor by the market exchange rate. The ratio, also referred to as the national price level, makes it possible to compare the cost of the bundle of goods that make up gross domestic product (GDP) across countries. It tells how many dollars are needed to buy a dollar's worth of goods in the country as compared to the United States. PPP conversion factors are based on the 2011 ICP round.

SourceWorld Bank, International Comparison Program database."

GDP per-capita - PPP int-$ adjusted (2015 current prices): "GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars based on the 2011 ICP round.

SourceWorld Bank, International Comparison Program database."

GDP per-capita - US $ market exchange (2015 current prices): "GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.

SourceWorld Bank national accounts data, and OECD National Accounts data files."

Country names were modified according to OWID standardized country names.

type IndustrialMotivePowerInTheUk180070Musson1976Dataset 

type IndustrialMotivePowerInTheUk180070Musson1976Dataset struct {
	NumberOfFactoriesMusson1976        *float64 `json:"number_of_factories_musson_1976"`
	NumberOfSteamEnginesMusson1976     *float64 `json:"number_of_steam_engines_musson_1976"`
	HorsePowerOfSteamEnginesMusson1976 *float64 `json:"horse_power_of_steam_engines_musson_1976"`
	NumberOfWaterWheelsMusson1976      *float64 `json:"number_of_water_wheels_musson_1976"`
	HorsePowerOfWaterWheelsMusson1976  *float64 `json:"horse_power_of_water_wheels_musson_1976"`
}

Data published by: Musson, A. E. “Industrial Motive Power in the United Kingdom, 1800-70.” The Economic History Review, vol. 29, no. 3, 1976, pp. 415–439. JSTOR, JSTOR, www.jstor.org/stable/2595302. Adapted from table on page 424.

type InequalityBeforeAndAfterTaxesOecd2008Dataset 

type InequalityBeforeAndAfterTaxesOecd2008Dataset struct {
	PercentageReductionInGiniOecd2008 *float64 `json:"percentage_reduction_in_gini_oecd_2008"`
}

Inequality before and after taxes measured with the Gini index.

type InequalityInHumanDevelopmentIndicesUndp2019Dataset 

type InequalityInHumanDevelopmentIndicesUndp2019Dataset struct {
	InequalityInLifeExpectancy2015_2020 *float64 `json:"inequality_in_life_expectancy_2015_2020"`
	InequalityInEducation2017           *float64 `json:"inequality_in_education_2017"`
}

type InequalityInLatinAmericaSedlacCedlasAndTheWorldBankDataset 

type InequalityInLatinAmericaSedlacCedlasAndTheWorldBankDataset struct {
	GiniIndexSedlac *float64 `json:"gini_index_sedlac"`
}

type InfantMortalityRateIhme2017Dataset 

type InfantMortalityRateIhme2017Dataset struct {
	InfantMortalityRate *float64 `json:"infant_mortality_rate"`
}

Number of infants (less than one year old) dying per 100,000.

type InheritanceForWomenHowWasLife2014Dataset 

type InheritanceForWomenHowWasLife2014Dataset struct {
	GenderEqualInheritanceForWomenHowWasLife2014 *float64 `json:"gender_equal_inheritance_for_women_how_was_life_2014"`
}

Gendered inheritance practices for immovables in 1920 are obtained from the Murdock data, while 1980 and 2000 are from the World Bank.Information on inheritance practices at the start of the 20th century comes from George Murdock’s Ethnographic Atlas (1969). 

To compare Murdock's figures to contemporary data available from the World Bank’s Fifty Years of Women’s Legal Rights database, a dichotomous scheme had to be used where 0 indicates inequality and 1 equality.For further information of these data, see the link above from page 221, under the heading "Historical sources".

type IntegratedNetworkForSocietalConflictResearchPoliticalInstabilityTaskForcePitfDataset 

type IntegratedNetworkForSocietalConflictResearchPoliticalInstabilityTaskForcePitfDataset struct {
	GenocideIndicatorPitf *float64 `json:"genocide_indicator_pitf"`
}

Death magnitude is a non-linear measure, please see the codebook for further details. For countries that experienced multiple episodes of genocide during the period, the midpoint is taking and summed to calculate the total death magnitude variable. Countries with multiple episodes of genocide include: Angola, Burundi, China, Indonesia, Iraq, Rwanda, Sri Lanka, Sudan, Uganda and the Democratic Republic of Congo.Genocide indicator created for each country as well as a global total.

type IntercontinentalTradeCostaPalmaAndReis2015Dataset 

type IntercontinentalTradeCostaPalmaAndReis2015Dataset struct {
	IntercontinentalTrade1500_1800CostaPalmaAndReis2015 *float64 `json:"intercontinental_trade_1500_1800_costa_palma_and_reis_2015"`
}

Citation: Costa, Palma and Reis (2015). The Great Escape? The Contribution of the Empire to Portugal's Economic Growth, 1500-1800. European Review of Economic History (2015) 19 (1): 1-22

type InternationalHistoricalStatisticsBirthsPer1000BrianMitchell2013Dataset 

type InternationalHistoricalStatisticsBirthsPer1000BrianMitchell2013Dataset struct {
	InternationalHistoricalStatisticsBirthsPer1_000BrianMitchell2013 *float64 `json:"international_historical_statistics_births_per_1_000_brian_mitchell_2013"`
}

Edited by Palgrave Macmillan Ltd (April 2013). International Historical Statistics [Online] Available at: http://www.palgrave.com/us/book/9780230005150 These statistics – originally published under the editorial leadership of Brian Mitchell (since 1983) – are a collection of data sets taken from many primary sources, including both official national and international abstracts dating back to 1750. The books are published in three volumes covering more than 5000 pages.

type InternationalHistoricalStatisticsDeathsPer1000BrianMitchell2013Dataset 

type InternationalHistoricalStatisticsDeathsPer1000BrianMitchell2013Dataset struct {
	InternationalHistoricalStatisticsDeathsPer1_000BrianMitchell2013 *float64 `json:"international_historical_statistics_deaths_per_1_000_brian_mitchell_2013"`
}

Edited by Palgrave Macmillan Ltd . (April 2013). International Historical Statistics . [Online] Available at: http://www.palgrave.com/us/book/9780230005150 These statistics – orignally published under the editorial leadership of Brian Mitchell (since 1983) – are a collection of data sets taken from many primary sources, including both official national and international abstracts dating back to 1750. The books are published in three volumes covering more than 5000 pages.

type InternationalHistoricalStatisticsEuropeanTradeBrianMitchell2015Dataset 

type InternationalHistoricalStatisticsEuropeanTradeBrianMitchell2015Dataset struct {
	HistoricalEuropeanTradeBrianMitchell2015 *float64 `json:"historical_european_trade_brian_mitchell_2015"`
}

National accounts and trade (GDP, imports and exports). The ratio is computed as the sum of imports and exports divided by GDP.

type InterpersonalTrustGeneralSocialSurveyGssDataset 

type InterpersonalTrustGeneralSocialSurveyGssDataset struct {
	InterpersonalTrustPerc *float64 `json:"interpersonal_trust_perc"`
}

The General Social Survey (GSS) is a project of the independent research organization NORC at the University of Chicago, with principal funding from the National Science Foundation.

type InvestmentInRenewablesByRegionIrena2016Dataset 

type InvestmentInRenewablesByRegionIrena2016Dataset struct {
	InvestmentInRenewablesByRegionIrena2016 *float64 `json:"investment_in_renewables_by_region_irena_2016"`
}

Data on investment is included for the following renewable energy projects: all biomass, waste-to-energy, geothermal and wind projects more than 1MW in capacity; all wave and tidal; hydropower projects between 1MW and 50MW; biofuel projects of more than one million litres per year; and all solar projects (with estimates of rooftop installations less than 1MW). Hydropower projects greater than 50MW have not been included in this data, since this technology has been mature for decades and is at a different stage of development versus more recent technologies.

type InvestmentInRenewablesByTechnologyIrena2017Dataset 

type InvestmentInRenewablesByTechnologyIrena2017Dataset struct {
	SolarEnergy             *float64 `json:"solar_energy"`
	WindEnergy              *float64 `json:"wind_energy"`
	BiomassAndWasteToEnergy *float64 `json:"biomass_and_waste_to_energy"`
	LiquidBiofuels          *float64 `json:"liquid_biofuels"`
	SmallHydropower         *float64 `json:"small_hydropower"`
	GeothermalEnergy        *float64 `json:"geothermal_energy"`
	MarineEnergy            *float64 `json:"marine_energy"`
}

Renewable investment figures are measured in billion USD per year. Investment adjusts for re-invested equity, and includes estimates for undisclosed deals.

Figures excludes investments in large hydropower schemes.

type IpccScenariosIiasaDataset 

type IpccScenariosIiasaDataset struct {
	AgriDemandCrops                     *float64 `json:"agri_demand_crops"`
	AgriDemandEnergycrops               *float64 `json:"agri_demand_energycrops"`
	AgriDemandLivestock                 *float64 `json:"agri_demand_livestock"`
	AgriProdCropsEnergy                 *float64 `json:"agri_prod_crops_energy"`
	AgriProdCropsNonenergy              *float64 `json:"agri_prod_crops_nonenergy"`
	AgriProdLivestock                   *float64 `json:"agri_prod_livestock"`
	CarbonPrice                         *float64 `json:"carbon_price"`
	ConcentrationCh4                    *float64 `json:"concentration_ch4"`
	ConcentrationCo2                    *float64 `json:"concentration_co2"`
	ConcentrationN2o                    *float64 `json:"concentration_n2o"`
	EconConsumption                     *float64 `json:"econ_consumption"`
	ElecCapacity                        *float64 `json:"elec_capacity"`
	ElecCapacityBiomass                 *float64 `json:"elec_capacity_biomass"`
	ElecCapacityCoal                    *float64 `json:"elec_capacity_coal"`
	ElecCapacityCsp                     *float64 `json:"elec_capacity_csp"`
	ElecCapacityGas                     *float64 `json:"elec_capacity_gas"`
	ElecCapacityGeothermal              *float64 `json:"elec_capacity_geothermal"`
	ElecCapacityHydro                   *float64 `json:"elec_capacity_hydro"`
	ElecCapacityNuclear                 *float64 `json:"elec_capacity_nuclear"`
	ElecCapacityOffshorewind            *float64 `json:"elec_capacity_offshorewind"`
	ElecCapacityOil                     *float64 `json:"elec_capacity_oil"`
	ElecCapacityOnshorewind             *float64 `json:"elec_capacity_onshorewind"`
	ElecCapacityOther                   *float64 `json:"elec_capacity_other"`
	ElecCapacitySolar                   *float64 `json:"elec_capacity_solar"`
	ElecCapacitySolarpv                 *float64 `json:"elec_capacity_solarpv"`
	ElecCapacityWind                    *float64 `json:"elec_capacity_wind"`
	EmissionsBlackcarbon                *float64 `json:"emissions_blackcarbon"`
	EmissionsCh4                        *float64 `json:"emissions_ch4"`
	EmissionsCh4Fossil                  *float64 `json:"emissions_ch4_fossil"`
	EmissionsCh4Land                    *float64 `json:"emissions_ch4_land"`
	EmissionsCo                         *float64 `json:"emissions_co"`
	EmissionsCo2                        *float64 `json:"emissions_co2"`
	EmissionsCo2Beccs                   *float64 `json:"emissions_co2_beccs"`
	EmissionsCo2Ccs                     *float64 `json:"emissions_co2_ccs"`
	EmissionsCo2Fossil                  *float64 `json:"emissions_co2_fossil"`
	EmissionsCo2Land                    *float64 `json:"emissions_co2_land"`
	EmissionsFgas                       *float64 `json:"emissions_fgas"`
	EmissionsKyotogas                   *float64 `json:"emissions_kyotogas"`
	EmissionsN2o                        *float64 `json:"emissions_n2o"`
	EmissionsN2oLand                    *float64 `json:"emissions_n2o_land"`
	EmissionsNh3                        *float64 `json:"emissions_nh3"`
	EmissionsNox                        *float64 `json:"emissions_nox"`
	EmissionsOc                         *float64 `json:"emissions_oc"`
	EmissionsSulfur                     *float64 `json:"emissions_sulfur"`
	EmissionsVoc                        *float64 `json:"emissions_voc"`
	EnergyTransportFreight              *float64 `json:"energy_transport_freight"`
	EnergyTransportPassenger            *float64 `json:"energy_transport_passenger"`
	FinalEnergy                         *float64 `json:"final_energy"`
	FinalEnergyBiomass                  *float64 `json:"final_energy_biomass"`
	FinalEnergyCoal                     *float64 `json:"final_energy_coal"`
	FinalEnergyElec                     *float64 `json:"final_energy_elec"`
	FinalEnergyGases                    *float64 `json:"final_energy_gases"`
	FinalEnergyHeat                     *float64 `json:"final_energy_heat"`
	FinalEnergyHydrogen                 *float64 `json:"final_energy_hydrogen"`
	FinalEnergyIndustry                 *float64 `json:"final_energy_industry"`
	FinalEnergyLiquids                  *float64 `json:"final_energy_liquids"`
	FinalEnergyResidentialcommercial    *float64 `json:"final_energy_residentialcommercial"`
	FinalEnergySolar                    *float64 `json:"final_energy_solar"`
	FinalEnergySolids                   *float64 `json:"final_energy_solids"`
	FinalEnergyTraditionalbiomass       *float64 `json:"final_energy_traditionalbiomass"`
	FinalEnergyTransport                *float64 `json:"final_energy_transport"`
	Forcing                             *float64 `json:"forcing"`
	ForcingAerosol                      *float64 `json:"forcing_aerosol"`
	ForcingCh4                          *float64 `json:"forcing_ch4"`
	ForcingCo2                          *float64 `json:"forcing_co2"`
	ForcingFgases                       *float64 `json:"forcing_fgases"`
	ForcingKyoto                        *float64 `json:"forcing_kyoto"`
	ForcingN2o                          *float64 `json:"forcing_n2o"`
	Gdp                                 *float64 `json:"gdp"`
	LandCropland                        *float64 `json:"land_cropland"`
	LandForest                          *float64 `json:"land_forest"`
	LandPasture                         *float64 `json:"land_pasture"`
	LandUrban                           *float64 `json:"land_urban"`
	Population                          *float64 `json:"population"`
	PrimaryEnergy                       *float64 `json:"primary_energy"`
	PrimaryEnergyBiomass                *float64 `json:"primary_energy_biomass"`
	PrimaryEnergyBiomassCcs             *float64 `json:"primary_energy_biomass_ccs"`
	PrimaryEnergyBiomassNoCcs           *float64 `json:"primary_energy_biomass_no_ccs"`
	PrimaryEnergyCoal                   *float64 `json:"primary_energy_coal"`
	PrimaryEnergyCoalCcs                *float64 `json:"primary_energy_coal_ccs"`
	PrimaryEnergyCoalNoCcs              *float64 `json:"primary_energy_coal_no_ccs"`
	PrimaryEnergyFossil                 *float64 `json:"primary_energy_fossil"`
	PrimaryEnergyFossilCcs              *float64 `json:"primary_energy_fossil_ccs"`
	PrimaryEnergyFossilNoCcs            *float64 `json:"primary_energy_fossil_no_ccs"`
	PrimaryEnergyGas                    *float64 `json:"primary_energy_gas"`
	PrimaryEnergyGasCcs                 *float64 `json:"primary_energy_gas_ccs"`
	PrimaryEnergyGeothermal             *float64 `json:"primary_energy_geothermal"`
	PrimaryEnergyHydro                  *float64 `json:"primary_energy_hydro"`
	PrimaryEnergyNoCcs                  *float64 `json:"primary_energy_no_ccs"`
	PrimaryEnergyNonbioRenewables       *float64 `json:"primary_energy_nonbio_renewables"`
	PrimaryEnergyNuclear                *float64 `json:"primary_energy_nuclear"`
	PrimaryEnergyOil                    *float64 `json:"primary_energy_oil"`
	PrimaryEnergyOilCcs                 *float64 `json:"primary_energy_oil_ccs"`
	PrimaryEnergyOilNoCcs               *float64 `json:"primary_energy_oil_no_ccs"`
	PrimaryEnergyOther                  *float64 `json:"primary_energy_other"`
	PrimaryEnergySolar                  *float64 `json:"primary_energy_solar"`
	PrimaryEnergyTradbiomass            *float64 `json:"primary_energy_tradbiomass"`
	PrimaryEnergyWind                   *float64 `json:"primary_energy_wind"`
	Temp                                *float64 `json:"temp"`
	Annotation                          *float64 `json:"annotation"`
	SecondaryEnergyElec                 *float64 `json:"secondary_energy_elec"`
	SecondaryEnergyElecBiomass          *float64 `json:"secondary_energy_elec_biomass"`
	SecondaryEnergyElecBiomassCcs       *float64 `json:"secondary_energy_elec_biomass_ccs"`
	SecondaryEnergyElecCoal             *float64 `json:"secondary_energy_elec_coal"`
	SecondaryEnergyElecCoalCcs          *float64 `json:"secondary_energy_elec_coal_ccs"`
	SecondaryEnergyElecGas              *float64 `json:"secondary_energy_elec_gas"`
	SecondaryEnergyElecGasCcs           *float64 `json:"secondary_energy_elec_gas_ccs"`
	SecondaryEnergyElecGeothermal       *float64 `json:"secondary_energy_elec_geothermal"`
	SecondaryEnergyElecHydro            *float64 `json:"secondary_energy_elec_hydro"`
	SecondaryEnergyElecNonbiorenewables *float64 `json:"secondary_energy_elec_nonbiorenewables"`
	SecondaryEnergyElecNuclear          *float64 `json:"secondary_energy_elec_nuclear"`
	SecondaryEnergyElecOil              *float64 `json:"secondary_energy_elec_oil"`
	SecondaryEnergyElecSolar            *float64 `json:"secondary_energy_elec_solar"`
	SecondaryEnergyElecWind             *float64 `json:"secondary_energy_elec_wind"`
	SecondaryEnergyGas                  *float64 `json:"secondary_energy_gas"`
	SecondaryEnergyHeat                 *float64 `json:"secondary_energy_heat"`
	SecondaryEnergyHeatHydro            *float64 `json:"secondary_energy_heat_hydro"`
	SecondaryEnergyHydrogen             *float64 `json:"secondary_energy_hydrogen"`
	SecondaryEnergyLiquidBiofuel        *float64 `json:"secondary_energy_liquid_biofuel"`
	SecondaryEnergyOil                  *float64 `json:"secondary_energy_oil"`
}

type IqDataPietschnigAndVoracek2015Dataset 

type IqDataPietschnigAndVoracek2015Dataset struct {
	AverageIqChangePietschnigAndVoracek2015                    *float64 `json:"average_iq_change_pietschnig_and_voracek_2015"`
	FullscaleIqChangeByRegion1909_2013PietschnigAndVoracek2015 *float64 `json:"fullscale_iq_change_by_region_1909_2013_pietschnig_and_voracek_2015"`
}

Citation: Pietschnig, Jakob, and Martin Voracek. "One Century of Global IQ Gains A Formal Meta-Analysis of the Flynn Effect (1909–2013)." Perspectives on Psychological Science 10, no. 3 (2015): 282-306.

type JobSearchMethodsUsPewResearchCenter2015Dataset 

type JobSearchMethodsUsPewResearchCenter2015Dataset struct {
	PercentageWhoUsedOnlineResourcesAndInformation         *float64 `json:"percentage_who_used_online_resources_and_information"`
	PercentageWhoUsedConnectionsWithCloseFriendsOrFamily   *float64 `json:"percentage_who_used_connections_with_close_friends_or_family"`
	PercentageWhoUsedProfessionalOrWorkConnections         *float64 `json:"percentage_who_used_professional_or_work_connections"`
	PercentageWhoUsedAcquaintancesOrFriendsOfFriends       *float64 `json:"percentage_who_used_acquaintances_or_friends_of_friends"`
	PercentageWhoUsedEmploymentAgenciesGovernmentOrPrivate *float64 `json:"percentage_who_used_employment_agencies_government_or_private"`
	PercentageWhoUsedAdsInPrintPublications                *float64 `json:"percentage_who_used_ads_in_print_publications"`
	PercentageWhoUsedJobFairsConferencesAndOtherEvents     *float64 `json:"percentage_who_used_job_fairs_conferences_and_other_events"`
}

Among Americans who have looked for a new job in the last two years, the percentage who say they used a certain method in their most recent search for a job. Note: based on the 34% of Americans who have looked for a job in the last two years. The survey was conducted June 10 - July 12, 2015. Sample size = 2,001.

type LabGrownMeatPricesNextbigfuture2017AndUnitedStatesBureauOfLaborStatisticsBls2017Dataset 

type LabGrownMeatPricesNextbigfuture2017AndUnitedStatesBureauOfLaborStatisticsBls2017Dataset struct {
	LabGrownBeefNextbigfuture2017 *float64 `json:"lab_grown_beef_nextbigfuture_2017"`
}

Data is based on industry and company reports from lab-scale (not commercial-scale) production of in-vitro beef.Prices have been reported in US$ per pound (lb) of lab-grown beef. We have converted this to US$ per kilogram using a conversion factor of 0.454.

type LaborForceParticipationRatesOfMenAge65AndOverInTheUsOwidBasedOnShort2002AndOecdDataset 

type LaborForceParticipationRatesOfMenAge65AndOverInTheUsOwidBasedOnShort2002AndOecdDataset struct {
	LaborForceParticipationRateInTheUsOfMen65AndOlder *float64 `json:"labor_force_participation_rate_in_the_us_of_men_65_and_older"`
}

The source for the period 1880 to 1990 is Short, Joanna (2002) – “Economic History of Retirement in the United States”. EH.Net Encyclopedia, edited by Robert Whaples. September 30, 2002. URL http://eh.net/encyclopedia/economic-history-of-retirement-in-the-united-states/This data is based on these three sources: – Moen, Jon R. Essays on the Labor Force and Labor Force Participation Rates: The United States from 1860 through 1950. Ph.D. dissertation, University of Chicago, 1987.– Costa, Dora L. The Evolution of Retirement: An American Economic History, 1880-1990. Chicago: University of Chicago Press, 1998.– Bureau of Labor Statistics Data for 2000 to 2015 is from the OECD [OECD.stat web browser – Source: http://stats.oecd.org/viewhtml.aspx?datasetcode=LFS_SEXAGE_I_R&lang=en Labor force participation of men 65 and older from the OECD – http://stats.oecd.org/viewhtml.aspx?datasetcode=LFS_SEXAGE_I_R&lang=en]"Data for 2000 is available from both sources and the discrepancy is minor so that we decided to merge the data as presented in the sources. Short reports 17.5% while the OECD reports 17.73% for 2000.

type LaborProductivityInCottonSpinningAndWeavingEllison1886Dataset 

type LaborProductivityInCottonSpinningAndWeavingEllison1886Dataset struct {
	YarnSpunEllison1886              *float64 `json:"yarn_spun_ellison_1886"`
	HandsEmployedSpinningEllison1886 *float64 `json:"hands_employed_spinning_ellison_1886"`
	OutputPerHandSpunEllison1886     *float64 `json:"output_per_hand_spun_ellison_1886"`
	ClothWovenEllison1886            *float64 `json:"cloth_woven_ellison_1886"`
	HandsEmployedWeavingEllison1886  *float64 `json:"hands_employed_weaving_ellison_1886"`
	OutputPerHandWovenEllison1886    *float64 `json:"output_per_hand_woven_ellison_1886"`
}

Full reference: Ellison, T., 1886. The cotton trade of Great Britain: including a history of the Liverpool cotton market and of the Liverpool cotton brokers' association. E. Wilson. [Please see the table at the bottom of page 68 titled 'YARN' and the table at the top of page 69 titled 'GOODS' for the original data.]

type LaborProductivityInCottonSpinningChapman1972Dataset 

type LaborProductivityInCottonSpinningChapman1972Dataset struct {
	OperativeHoursToProcessOhp100LbsOfCottonChapman1972 *float64 `json:"operative_hours_to_process_ohp_100_lbs_of_cotton_chapman_1972"`
}

The easiest way to illustrate the improvements in labor productivity in cotton spinning over time is to reproduce data on labor productivity from Catling’s study of The Spinning Mule, from which the measure of OHP (operative hours to process 100 lbs of cotton) is applied to other technologies, as shown. [Please see table 2 on page 20 for the original data.]

Catling’s study of The Spinning Mule provides a historical introduction of the invention of the spinning mule in the 18th century. The spinning mule was worked by hand with the spinner manually controlled all its operations. It was around 1825 that Richard Roberts invented the ‘quadrant winding motion’ that enabled the machine to operate automatically.

Full reference: Chapman, S.D., 1987. The cotton industry in the industrial revolution. In The Industrial Revolution A Compendium (pp. 1-64). Palgrave, London.

type LaborProductivityPerHourHillThomasDimsdale2016BankOfEnglandDataset 

type LaborProductivityPerHourHillThomasDimsdale2016BankOfEnglandDataset struct {
	LabourProductivityPerHour2013100 *float64 `json:"labour_productivity_per_hour_2013100"`
}

Uses GDP at factor cost. 

Great Britain growth rates prior to 1855.

type LabourCostRatio4554YearOldPopulation2009Oecd2012Dataset 

type LabourCostRatio4554YearOldPopulation2009Oecd2012Dataset struct {
	LabourCostRatio45_54YearOldPopulation2009Oecd2012 *float64 `json:"labour_cost_ratio_45_54_year_old_population_2009_oecd_2012"`
}

OECD Indicators is the authoritative source for accurate and relevant information on the state of education around the world

type LandUnderCerealProductionIndexWorldBank2017Dataset 

type LandUnderCerealProductionIndexWorldBank2017Dataset struct {
	LandUnderCerealProductionIndexWorldBank2017 *float64 `json:"land_under_cereal_production_index_world_bank_2017"`
}

Land under cereal production index was derived by OurWorldinData based on original data sourced from the World Bank's World Development Indicators (WDI).The land under cereal production index measures annual land use for cereal production as an index to land use in 1961, the first year of the original dataset. This index was calculated by dividing land used for cereals (which is measured in hectares) in any given year by land use in 1961. 1961 = 100. Values >100 indicate an increase in land use vs. 1961, and values <100 indicate a decrease.World/global figures from this World Bank dataset include boundary issues for land under cereal production prior to 1991: this data does not include land within the Former Soviet Union (USSR) and therefore underestimates global land use. To aim to correct for this, we have summed the total land area of production for all countries within the former USSR from 1992, and have added this value to the global total for all years from 1961-1991. Data from 1991 onwards has not been changed.The original dataset from WDI used in this calculation was "Land under cereal production (hectares)", which is defined by the World Bank/FAO as: "Land under cereal production refers to harvested area, although some countries report only sown or cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded."Data available at: http://data.worldbank.org/data-catalog/world-development-indicators [accessed 18th July 2017]

type LandUseDataHyde2017Dataset 

type LandUseDataHyde2017Dataset struct {
	CroplandHyde2017                        *float64 `json:"cropland_hyde_2017"`
	GrazingHyde2017                         *float64 `json:"grazing_hyde_2017"`
	TotalIrrigatedAreaHyde2017              *float64 `json:"total_irrigated_area_hyde_2017"`
	IrrigatedNonRiceCropsHyde2017           *float64 `json:"irrigated_non_rice_crops_hyde_2017"`
	IrrigatedRiceHyde2017                   *float64 `json:"irrigated_rice_hyde_2017"`
	PasturelandHyde2017                     *float64 `json:"pastureland_hyde_2017"`
	RangelandHyde2017                       *float64 `json:"rangeland_hyde_2017"`
	TotalRainfedAreaHyde2017                *float64 `json:"total_rainfed_area_hyde_2017"`
	RainfedNonRiceCropsHyde2017             *float64 `json:"rainfed_non_rice_crops_hyde_2017"`
	TotalRiceAreaHyde2017                   *float64 `json:"total_rice_area_hyde_2017"`
	BuiltUpAreaHyde2017                     *float64 `json:"built_up_area_hyde_2017"`
	TotalPopulationHyde2017                 *float64 `json:"total_population_hyde_2017"`
	UrbanPopulationHyde2017                 *float64 `json:"urban_population_hyde_2017"`
	RuralPopulationHyde2017                 *float64 `json:"rural_population_hyde_2017"`
	AgriculturalAreaCropsAndGrazingHyde2017 *float64 `json:"agricultural_area_crops_and_grazing_hyde_2017"`
	CroplandPerCapitaHyde2017               *float64 `json:"cropland_per_capita_hyde_2017"`
	GrazingLandPerCapitaHyde2017            *float64 `json:"grazing_land_per_capita_hyde_2017"`
	AgriculturalLandPerCapitaHyde2017       *float64 `json:"agricultural_land_per_capita_hyde_2017"`
}

The authors of the database provide the following definition clarifications for land use categories:'Cropland' refers to the same FAO category titled 'arable land and permanent crops'.'Grazing' refers to the FAO category titled 'permanent meadows and pastures'.'Pastureland' refers to "Grazing land with an aridity index > 0.5, assumed to be more intensively managed".'Rangeland' refers to "Grazing land with an aridity index > 0.5, assumed to be less or not managed".Area values have been converted from square kilometres to hectares by multiplying by 100.Full reference: Klein Goldewijk et al., 20xx (in prep). Or in full: Klein Goldewijk, K., A. Beusen, J.Doelman and E. Stehfest, New anthropogenic land use estimates for the Holocene; HYDE 3.2, in prep.

type LandUseMapByAreaOwidBasedOnFaoDataset 

type LandUseMapByAreaOwidBasedOnFaoDataset struct {
	LandUse *float64 `json:"land_use"`
}

Data on global land use breakdown was developed based on land cover figures published by the UN Food and Agricultural Organization (FAO), available at its statistical database: http://www.fao.org/faostat/en/#home [accessed 10th October 2017].Global land use breakdown data has also been visualised and published at: https://ourworldindata.org/yields-and-land-use-in-agriculture/#breakdown-of-global-land-area-todayHere, we have attempted to visualise the extent of global land surface area equivalent to each land cover category using World Bank country area figures.For example, the surface of North America, Latin America and the Caribbean combined is approximately equivalent to the areal extent of global forests.

type LandUseSince10000bcEllisEtAl2020Dataset 

type LandUseSince10000bcEllisEtAl2020Dataset struct {
	AreaDisaggregatedCategories *float64 `json:"area_disaggregated_categories"`
	AreaAggregatedCategories    *float64 `json:"area_aggregated_categories"`
}

The authors produce a global reconstruction and mapping of anthropogenic land use from 10,000 BCE to 2015 CE; the Anthromes 12K dataset. Anthromes were mapped using gridded global estimates of human population density and land use from the History of the Global Environment database (HYDE version 3.2) by a classification procedure similar to that used for prior anthrome maps.Our World in Data has also provided aggregated land use categories based on the Ellis et al. (2020) results, where categories were grouped in the following way:– Urban: "Urban" + "Mixed settlements"– Villages: "Rice villages" + "Irrigated villages" + "Rainfed villages" + "Pastoral villages"– Croplands: "Residential irrigated croplands" + "Residential rainfed croplands" + "Populated rainfed croplands" + "Remote croplands"– Pasture : "Residential rangelands" + "Populated rangelands" + "Remote rangelands"– Semi-natural land: "Residential woodlands" + "Populated woodlands" + "Remote woodlands"

type LargestCitiesByPopulationDensityUnHabitat2014Dataset 

type LargestCitiesByPopulationDensityUnHabitat2014Dataset struct {
	PopulationDensityByCity *float64 `json:"population_density_by_city"`
}

Data on population density (measured as the population per square kilometre) is provided for the world's largest 100 cities based on population.

type LearningAdjustedYearsOfSchoolingWorldBank2018Dataset 

type LearningAdjustedYearsOfSchoolingWorldBank2018Dataset struct {
	LearningAdjustedYearsOfSchool *float64 `json:"learning_adjusted_years_of_school"`
}

The methodology can be read up in Filmer et al. (2018): http://documents.worldbank.org/curated/en/243261538075151093/Learning-Adjusted-Years-of-Schooling-LAYS-Defining-A-New-Macro-Measure-of-Education

type LearningCostsJDoyneFarmerAndFrancoisLafond2016Dataset 

type LearningCostsJDoyneFarmerAndFrancoisLafond2016Dataset struct {
	JDoyneFarmerAndFrancoisLafond2016 *float64 `json:"j_doyne_farmer_and_francois_lafond_2016"`
}

type LengthOfTheWorkDayFrom1890sTo1991Costa2000Dataset 

type LengthOfTheWorkDayFrom1890sTo1991Costa2000Dataset struct {
	PercentageOfMenInEachIndustryGroupCosta2000     *float64 `json:"percentage_of_men_in_each_industry_group_costa_2000"`
	AverageDailyHoursOfWorkByIndustryMenCosta2000   *float64 `json:"average_daily_hours_of_work_by_industry_men_costa_2000"`
	AverageDailyHoursOfWorkByOccupationMenCosta2000 *float64 `json:"average_daily_hours_of_work_by_occupation_men_costa_2000"`
}

See Table 5 and 6 for original data.*The surveys that are used are from California in 1892; Kansas in 1895, 1896, 1897, and 1899; Main in 1890; Michigan stoneworkers in 1888; Michigan railway workers in 1893; Wisconsin in 1895; and women in Indianapolis in 1893

type LengthOfTheWorkdayIn1880AtackAndBateman1992Dataset 

type LengthOfTheWorkdayIn1880AtackAndBateman1992Dataset struct {
	AverageDailyMinutesOfWorkInSummerWeightedByFirmAtackAndBateman1992                                *float64 `json:"average_daily_minutes_of_work_in_summer_weighted_by_firm_atack_and_bateman_1992"`
	AverageDailyMinutesOfWorkInWinterWeightedByFirmAtackAndBateman1992                                *float64 `json:"average_daily_minutes_of_work_in_winter_weighted_by_firm_atack_and_bateman_1992"`
	MeanAbsoluteDifferenceInMinutesOfWorkBetweenSummerAndWinterWeightedByFirmAtackAndBateman1992      *float64 `json:"mean_absolute_difference_in_minutes_of_work_between_summer_and_winter_weighted_by_firm_atack_and_bateman_1992"`
	AverageDailyMinutesOfWorkInSummerWeightedByEmployeesAtackAndBateman1992                           *float64 `json:"average_daily_minutes_of_work_in_summer_weighted_by_employees_atack_and_bateman_1992"`
	AverageDailyMinutesOfWorkInWinterWeightedByEmployeesAtackAndBateman1992                           *float64 `json:"average_daily_minutes_of_work_in_winter_weighted_by_employees_atack_and_bateman_1992"`
	MeanAbsoluteDifferenceInMinutesOfWorkBetweenSummerAndWinterWeightedByEmployeesAtackAndBateman1992 *float64 `json:"mean_absolute_difference_in_minutes_of_work_between_summer_and_winter_weighted_by_employees_atack_and_bateman_1992"`
	AverageScheduledMinutesOfWorkPerDayAtackAndBateman1992                                            *float64 `json:"average_scheduled_minutes_of_work_per_day_atack_and_bateman_1992"`
}

See Table 3 and Table 4 for original data.Estimates of seasonality are derived from the six winter months (November to May) and the six summer months (May to November).

type LevelsOfUrbanizationAndPerCapitaGnpInVariousRegionsBairoch1988Dataset 

type LevelsOfUrbanizationAndPerCapitaGnpInVariousRegionsBairoch1988Dataset struct {
	UrbanPopulationShareBairoch1988          *float64 `json:"urban_population_share_bairoch_1988"`
	PerCapitaGrossNationalProductBairoch1988 *float64 `json:"per_capita_gross_national_product_bairoch_1988"`
	RuralPopulationShareBairoch1988          *float64 `json:"rural_population_share_bairoch_1988"`
}

See Table 29.1 (pp. 459) for original data.

type LgbtMaritalStatusInTheUsGallup2017Dataset 

type LgbtMaritalStatusInTheUsGallup2017Dataset struct {
	PercLgbtMarriedToSameSexSpouse       *float64 `json:"perc_lgbt_married_to_same_sex_spouse"`
	PercLgbtLivingWithSameSexPartner     *float64 `json:"perc_lgbt_living_with_same_sex_partner"`
	PercLgbtSingleneverMarried           *float64 `json:"perc_lgbt_singlenever_married"`
	PercLgbtLivingWithOppositeSexPartner *float64 `json:"perc_lgbt_living_with_opposite_sex_partner"`
	PercLgbtMarriedToOppositeSexSpouse   *float64 `json:"perc_lgbt_married_to_opposite_sex_spouse"`
	PercLgbtDivorced                     *float64 `json:"perc_lgbt_divorced"`
	PercLgbtSeparated                    *float64 `json:"perc_lgbt_separated"`
	PercLgbtWidowed                      *float64 `json:"perc_lgbt_widowed"`
}

Survey method notes from the source:<em> Results for this Gallup poll are based on telephone interviews conducted June 20, 2016-June 19, 2017, on the Gallup U.S. Daily survey, with a random sample of 352,851 adults, aged 18 and older, living in all 50 U.S. states and the District of Columbia. For results based on the total sample of national adults, the margin of sampling error is ±1 percentage point at the 95% confidence level.For results based on the total sample of 12,832 lesbian, gay, bisexual or transgender adults, the margin of sampling error is ±1 percentage point at the 95% confidence level. All reported margins of sampling error include computed design effects for weighting.Each sample of national adults includes a minimum quota of 70% cellphone respondents and 30% landline respondents, with additional minimum quotas by time zone within region. Landline and cellular telephone numbers are selected using random-digit-dial methods. </em>

type LifeCycleImpactsOfEnergySourcesUneceDataset 

type LifeCycleImpactsOfEnergySourcesUneceDataset struct {
	GhgEmissions             *float64 `json:"ghg_emissions"`
	FreshwaterEutrophication *float64 `json:"freshwater_eutrophication"`
	IonisingRadiation        *float64 `json:"ionising_radiation"`
	WaterUse                 *float64 `json:"water_use"`
	MetalMineralUse          *float64 `json:"metal_mineral_use"`
	NoncarcinogenicToxicity  *float64 `json:"noncarcinogenic_toxicity"`
	CarcinogenicToxicity     *float64 `json:"carcinogenic_toxicity"`
	AgricuturalLandUseEnergy *float64 `json:"agricutural_land_use_energy"`
	UrbanLandUseEnergy       *float64 `json:"urban_land_use_energy"`
	LandUseEnergy            *float64 `json:"land_use_energy"`
	AluminiumUse             *float64 `json:"aluminium_use"`
	ChromiumUse              *float64 `json:"chromium_use"`
	CobaltUse                *float64 `json:"cobalt_use"`
	CopperUse                *float64 `json:"copper_use"`
	ManganeseUse             *float64 `json:"manganese_use"`
	MolybdenumUse            *float64 `json:"molybdenum_use"`
	NickelUse                *float64 `json:"nickel_use"`
	SiliconUse               *float64 `json:"silicon_use"`
	ZincUse                  *float64 `json:"zinc_use"`
	UraniumUse               *float64 `json:"uranium_use"`
	DeathsPerTwh             *float64 `json:"deaths_per_twh"`
}

type LifeExpectancy19502015UnPopulationDivision2015Dataset 

type LifeExpectancy19502015UnPopulationDivision2015Dataset struct {
	LifeExpectancy1950_2015UnPopulationDivision2015 *float64 `json:"life_expectancy_1950_2015_un_population_division_2015"`
}

The original data refers to intervals of 5 years. Here the data is assigned the last year of that original interval. For example 1955 refers to 1950-1955.

type LifeExpectancyAtAge1017502100UnitedNationsPopulationDivisionAndHumanMortalityDatabase2015Dataset 

type LifeExpectancyAtAge1017502100UnitedNationsPopulationDivisionAndHumanMortalityDatabase2015Dataset struct {
	E10UnitedNationsPopulationDivisionAndHumanMortalityDatabase2015 *float64 `json:"e10_united_nations_population_division_and_human_mortality_database_2015"`
}

The data file used is called “Life Expectancy at exact age x (ex) - Both Sexes” on the website given under “Mortality indicators”. This original data file has life expectancy at multiple ages for every country and different regions. We use only the countries and “World” data for age 10. Each country has a life table from which we extracted the life expectancy at age 10 (column ex, row where Age is 10 for each country.) We combined the information from the UN Population Division (UNPD) and the Human Mortality Database (HMD) using an R script. In the final data set, UNPD values are used for 1950-2100, and HMD values are used pre-1950, where available. The data set uses the first year of the period to graph the data, i.e. the e10 value for 1950-1954 is graphed at 1950.

We omitted data from the first period for which data is available for each country, because often this first period was not a full 5 years long. For instance, the life table for Australia begins with the 4-year period 1921-1924, and every subsequent period is a full 5 years long. With data manipulation or extrapolation, this first data point can be included in the future.

type LifeExpectancyAtBirthBothGendersClioInfraDataset 

type LifeExpectancyAtBirthBothGendersClioInfraDataset struct {
	LifeExpectancyAtBirthBothGendersClioInfra *float64 `json:"life_expectancy_at_birth_both_genders_clio_infra"`
}

The data are from Clio Infra and the author of the data set is Richard Zijdeman.

type LifeExpectancyAtBirthWorldBank2015Dataset 

type LifeExpectancyAtBirthWorldBank2015Dataset struct {
	LifeExpectancyAtBirthWorldBank2015 *float64 `json:"life_expectancy_at_birth_world_bank_2015"`
}

Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

The source notes that male and female life expectancy at birth is from sources such as: (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

type LifeExpectancyGapminderUnIhmeDataset 

type LifeExpectancyGapminderUnIhmeDataset struct {
	LifeExpectancyInterpolatedGapminderUnIhme    *float64 `json:"life_expectancy_interpolated_gapminder_un_ihme"`
	LifeExpectancyNonInterpolatedGapminderUnIhme *float64 `json:"life_expectancy_non_interpolated_gapminder_un_ihme"`
}

Gapminder data is used up until 1990. Gapminder uses UN data from 1950 onward. We have used IHME life expectancy data from 1990 to today.

type LifeExpectancyJamesRileyForData1990AndEarlierWhoAndWorldBankForLaterDataByMaxRoserDataset 

type LifeExpectancyJamesRileyForData1990AndEarlierWhoAndWorldBankForLaterDataByMaxRoserDataset struct {
	LifeExpectancyJamesRileyForData1990AndEarlierWhoAndWorldBankForLaterDataByMaxRoser *float64 `json:"life_expectancy_james_riley_for_data_1990_and_earlier_who_and_world_bank_for_later_data_by_max_roser"`
}

Changes to Riley's data by Max Roser:I have assigned the 'Life expectancy before health transition' from Riley's data to the year 1770 for all world regions. The reason for doing this is that 1770 is the earliest available data from his paper and I chose that as the start year for the chart.Then I took Riley's estimate for 'Period when earliest health transition in region began' and assigned the 'Life expectancy before health transition' to the mid-point of that period.From then on I show Riley's data until 1990 where his dataset ends.For 2000 and later estimates:Africa: WHO for world region 'Africa'Americas: WHO for world region 'Americas'Asia: World Bank population weighted average of all countries in Riley's definition of Asia http://www.lifetable.de/cgi-bin/countryR.plx?c=AsiaFormer Soviet Union countries: World Bank population weighted average of former Soviet countries ( Russian Federation, Ukraine, Republic of Belarus, Republic of Uzbekistan, Republic of Kazakhstan, Georgia, Republic of Azerbaijan, Republic of Lithuania, Republic of Moldova, Republic of Latvia, Kyrgyz Republic, Republic of Tajikistan, Republic of Armenia, Turkmenistan, and Republic of Estonia.)Europe: Riley's definition of Europe is this: http://www.lifetable.de/cgi-bin/countryR.plx?c=Europe The data here is from the World Bank World Bank: Population weighted average of all countries in Riley's definition of Oceania http://www.lifetable.de/RileyBib.htm)

type LifeExpectancyOecdDataset 

type LifeExpectancyOecdDataset struct {
	LifeExpectancyOecd *float64 `json:"life_expectancy_oecd"`
}

type LifeExpectancyProjectionsUkOnsDataset 

type LifeExpectancyProjectionsUkOnsDataset struct {
	MaleLifeExpectancy2016Projections   *float64 `json:"male_life_expectancy_2016_projections"`
	FemaleLifeExpectancy2016Projections *float64 `json:"female_life_expectancy_2016_projections"`
	MaleLifeExpectancy2014Projections   *float64 `json:"male_life_expectancy_2014_projections"`
	FemaleLifeExpectancy2014Projections *float64 `json:"female_life_expectancy_2014_projections"`
}

Data provides projections from the UK ONS publications in its 2014 and 2016 revisions. 2014 revisions are available at: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/compendium/nationalpopulationprojections/2015-10-29/mortalityassumptions2016 revisions are available at: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/compendium/nationalpopulationprojections/2016basedprojections/mortalityassumptionsHistoric estimates are given on a calendar year basis, whereas future projections are on mid-year basis.

type LifeExpectancyRiley2005AndUnDataset 

type LifeExpectancyRiley2005AndUnDataset struct {
	LifeExpectancy *float64 `json:"life_expectancy"`
}

Data was compiled by Our World in Data based on estimates by James C. Riley and the United Nations Population Division.From 1770-1949, we use data from a study by Riley, which draws from over 700 sources to estimate regional and global life expectancy at birth from 1800 to 2001.Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."For 1950-2015, we use data published by the United Nations Population Division, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database". For the Americas from 1950-2015, we took the population-weighted average of Northern America and Latin America and the Caribbean, using UN Population Division estimates of population size.

type LifeExpectancyRiley2005ClioInfra2015AndUn2019Dataset 

type LifeExpectancyRiley2005ClioInfra2015AndUn2019Dataset struct {
	LifeExpectancy *float64 `json:"life_expectancy"`
}

Data was compiled by Our World in Data based on estimates by James C. Riley, Clio Infra, and the United Nations Population Division.For regional- and global-level data pre-1950, we use data from a study by Riley, which draws from over 700 sources to estimate life expectancy at birth from 1800 to 2001.Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."For country-level data pre-1950, we use Clio Infra's dataset, compiled by Zijdeman and Ribeira da Silva (2015).For country-, regional- and global-level data post-1950, we use data published by the United Nations Population Division, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database". For the Americas from 1950-2015, we took the population-weighted average of Northern America and Latin America and the Caribbean, using UN Population Division estimates of population size.

type LifeExpectationBySexAtAges015And45OwidBasedOnHacker2010AndTheUsSocialSecurityAdministration2017Dataset 

type LifeExpectationBySexAtAges015And45OwidBasedOnHacker2010AndTheUsSocialSecurityAdministration2017Dataset struct {
	LifeExpectationAge0FemalesOwid          *float64 `json:"life_expectation_age_0_females_owid"`
	LifeExpectationAge0MalesOwid            *float64 `json:"life_expectation_age_0_males_owid"`
	LifeExpectationAge15FemalesOwid         *float64 `json:"life_expectation_age_15_females_owid"`
	LifeExpectationAge15MalesOwid           *float64 `json:"life_expectation_age_15_males_owid"`
	LifeExpectationAge45FemalesOwid         *float64 `json:"life_expectation_age_45_females_owid"`
	LifeExpectationAge45MalesOwid           *float64 `json:"life_expectation_age_45_males_owid"`
	MaleMinusFemaleLifeExpectationAge0Owid  *float64 `json:"male_minus_female_life_expectation_age_0_owid"`
	MaleMinusFemaleLifeExpectationAge15Owid *float64 `json:"male_minus_female_life_expectation_age_15_owid"`
	MaleMinusFemaleLifeExpectationAge45Owid *float64 `json:"male_minus_female_life_expectation_age_45_owid"`
}

The Hacker (2010) data is used for the period 1780 to 1890. The year 1790 refers to the period 1790-99; see Table 8: New Life Tables for the White Population of the United States, 1780-1900 for the original data consulted for this period. The US Social Security Administration data is used from 1900 to today.

type LifeSatisfactionEurobarometer2017Dataset 

type LifeSatisfactionEurobarometer2017Dataset struct {
	ShareOfPeopleWhoAreHappyEurobarometer2017 *float64 `json:"share_of_people_who_are_happy_eurobarometer_2017"`
}

The Eurobarometer provides estimates for dated surveys. In some years, there are some countries with multiple surveys. In those cases, we provide yearly averages.

type LifeSatisfactionWorldValueSurvey2014Dataset 

type LifeSatisfactionWorldValueSurvey2014Dataset struct {
	ShareOfPeopleWhoAreHappyWorldValueSurvey2014                  *float64 `json:"share_of_people_who_are_happy_world_value_survey_2014"`
	ShareOfPeopleWhoAreHappyWorldValueSurvey2014FirstAndLastWaves *float64 `json:"share_of_people_who_are_happy_world_value_survey_2014_first_and_last_waves"`
}

The data in the World Value Survey comes from surveys conducted in waves. The years in the dataset correspond to the end year in the corresponding wave. For example, observations from surveys in the wave 1981-1984 are dated 1984.The dataset includes observations for Egypt. However, we have excluded these observations from our analysis. This is because the survey for Egypt in the wave labeled 2014 is from 2012, which was a year characterized by extreme political instability.

type LightingEfficiencyInUkOwidBasedOnFouquetAndPearson2007Dataset 

type LightingEfficiencyInUkOwidBasedOnFouquetAndPearson2007Dataset struct {
	EfficiencyOfLightingOwidBasedOnFouquetAndPearson2007 *float64 `json:"efficiency_of_lighting_owid_based_on_fouquet_and_pearson_2007"`
}

Data was calculated by OWID based on published figures by Fouquet & Pearson (2006). Calculated as the weighted-average efficiency of lighting in the United Kingdom. Calculated based on percentage share and efficiency data of lighting sources from Fouquet & Pearson (2006). Data represents the national average efficiency, calculated as the sum of (% share * efficiency) of each source.For example: (share of lighting from candles * efficiency of candles) + (share of lighting from whale oil * efficiency of whale oil) + (share of lighting from kerosene * efficiency of kerosene).Efficiency is measured in lumen-hours per kilowatt-hour.Reference:Fouquet, R and Pearson, P J G (2006): ‘Seven Centuries of Energy Services: The Price and Use of Light in the United Kingdom (1300-2000)’, The Energy Journal, 27(1). Available at: http://eprints.lse.ac.uk/50460/ [accessed 3rd October 2017].

type LisKeyFiguresLuxumbourgIncomeStudyDataset 

type LisKeyFiguresLuxumbourgIncomeStudyDataset struct {
	GiniDisposableHouseholdIncomeLisKeyFigures *float64 `json:"gini_disposable_household_income_lis_key_figures"`
	PovertyRateLess60percOfMedianLisKeyFigures *float64 `json:"poverty_rate_less60perc_of_median_lis_key_figures"`
}

type LiteracyByYearsOfSchoolingUs1947Oecd2014Dataset 

type LiteracyByYearsOfSchoolingUs1947Oecd2014Dataset struct {
	PercentageLiterate *float64 `json:"percentage_literate"`
}

type LiteracyInEnglandBySexSchofield1973Houston1982Cressy1980Dataset 

type LiteracyInEnglandBySexSchofield1973Houston1982Cressy1980Dataset struct {
	MaleLiteracyRateHouston1982   *float64 `json:"male_literacy_rate_houston_1982"`
	FemaleLiteracyRateHouston1982 *float64 `json:"female_literacy_rate_houston_1982"`
}

See Table 1 (pg 204) for original figures. Table 1 provides the percentage who are illiterate. To arrive at current estimates: % who are literate = 100 - (% who are illiterate)

type LiteracyRatePercOfTotalRespondentsDhsSurveysDataset 

type LiteracyRatePercOfTotalRespondentsDhsSurveysDataset struct {
	LiteracyRateAdultTotalPercOfTotalRespondentsDhsSurveys  *float64 `json:"literacy_rate_adult_total_perc_of_total_respondents_dhs_surveys"`
	LiteracyRateAdultMalePercOfTotalRespondentsDhsSurveys   *float64 `json:"literacy_rate_adult_male_perc_of_total_respondents_dhs_surveys"`
	LiteracyRateAdultFemalePercOfTotalRespondentsDhsSurveys *float64 `json:"literacy_rate_adult_female_perc_of_total_respondents_dhs_surveys"`
}

The adult literacy rate is defined as the percentage of survey respondents, aged 15 and above, who can read. The 155 literacy variable indicates whether a respondent who attended primary schooling can read a whole or part of a sentence showed. A respondent who attended secondary education or higher are coded 2 as well as respondent who could read a whole sentence. Possible categories include: 0 - cannot read at all; 1 - able to read only parts of sentence; 2 - able to read whole sentence; 3 - no card with required language; 4 - blind/visually impaired. Our estimates exclude those in categories 3 and 4. The methodology for constructing this variable is as follows:<ul><li>The DHS surveys for men and women were pooled</li><li>Created a dichotomous literacy variable, combining categories 1 'able to read only parts of sentence' with 2 'able to read whole sentence' to represent literate respondents, and 0 'cannot read at all' for illiterate respondents</li><li>Summed the total number of respondents who were classified as literate and the total number of respondents, for each country in each year</li><li>Calculated the literacy rate (% of total respondents) = (number of people in category 1 and 2 / number of respondents) * 100 </li><li>Calculated the female literacy rate (% of total respondents) = (number of females in category 1 and 2 / number of respondents) * 100 </li><li>Calculated the male literacy rate (% of total respondents) = (number of males in category 1 and 2 / number of respondents) * 100 </li></ul>The number of survey respondents varies from country-to-country and year-to-year. Therefore, comparisons across countries should be made with caution. DHS surveys are conducted approximately every 5 years in phases. The literacy variable (155) is available from phase 4 onward. Hence, Phase 7 corresponds to 2015; phase 6 to 2010; phase 5 to 2005; and phase 4 to 2000. 

As this dataset brings together a number of countries across various phases, the year has been assigned according to the survey phase.

Note the actual year the survey was conducted in a country may differ to the year assigned, but the survey will have been undertaken within the five year interval between each phase. For further information on the DHS surveys visit: https://dhsprogram.com/data/

type LiterateWorldPopulationOurworldindataBasedOnOecdAndUnescoDataset 

type LiterateWorldPopulationOurworldindataBasedOnOecdAndUnescoDataset struct {
	LiteracyRateCiaFactbook2016 *float64 `json:"literacy_rate_cia_factbook_2016"`
}

The year for estimates corresponds to the estimated dates directly reported by the CIA Factbook. For those countries for which the year was reported by the source as NA, we have assigned 2011 (the most common year in the dataset). The countries where this imputation took place are: Andorra, Vatican, Liechtenstein, Norway, Austria, San Marino, Cook Islands, Niue, Solomon Islands, Gibraltar.The source also notes: "This entry includes a definition of literacy and Census Bureau percentages for the total population, males, and females. There are no universal definitions and standards of literacy. Unless otherwise specified, all rates are based on the most common definition - the ability to read and write at a specified age. Detailing the standards that individual countries use to assess the ability to read and write is beyond the scope of the Factbook. Information on literacy, while not a perfect measure of educational results, is probably the most easily available and valid for international comparisons. Low levels of literacy, and education in general, can impede the economic development of a country in the current rapidly changing, technology-driven world."

type LivestockCountsHydeAndFao2017Dataset 

type LivestockCountsHydeAndFao2017Dataset struct {
	AssesHydeAndFao2017    *float64 `json:"asses_hyde_and_fao_2017"`
	BuffaloHydeAndFao2017  *float64 `json:"buffalo_hyde_and_fao_2017"`
	CattleHydeAndFao2017   *float64 `json:"cattle_hyde_and_fao_2017"`
	GoatsHydeAndFao2017    *float64 `json:"goats_hyde_and_fao_2017"`
	HorsesHydeAndFao2017   *float64 `json:"horses_hyde_and_fao_2017"`
	MulesHydeAndFao2017    *float64 `json:"mules_hyde_and_fao_2017"`
	PigsHydeAndFao2017     *float64 `json:"pigs_hyde_and_fao_2017"`
	SheepHydeAndFao2017    *float64 `json:"sheep_hyde_and_fao_2017"`
	ChickensHydeAndFao2017 *float64 `json:"chickens_hyde_and_fao_2017"`
	TurkeysHydeAndFao2017  *float64 `json:"turkeys_hyde_and_fao_2017"`
	PoultryHydeAndFao2017  *float64 `json:"poultry_hyde_and_fao_2017"`
}

This dataset on livestock counts has been compiled by OWID based on the combination of data from the HYDE database and UN Food and Agricultural Organization (FAO) statistics.Data for all livestock from 1890-1950 is sourced from the HYDE Database (History Database of the Global Environment), published by the PBL Netherlands Environmental Assessment Agency. Available at: http://themasites.pbl.nl/tridion/en/themasites/hyde/landusedata/livestock/index-2.html [accessed 12th October 2017].Data from 1961 onwards is sources from the UN Food and Agricultural Organization (FAO) statistics. Available at: http://www.fao.org/faostat/en/#data/QA [accessed 12th October 2017].

type LivingPlanetIndexWwf2020Dataset 

type LivingPlanetIndexWwf2020Dataset struct {
	LivingPlanetIndex *float64 `json:"living_planet_index"`
}

The Living Planet Index (LPI) is a measure of the state of global biological diversity based on population trends of vertebrate species from around the world. The index represents 20,811 populations of 4,392 species. All indices are weighted by species richness, giving species-rich taxonomic groups in terrestrial, marine and freshwater systems more weight than groups with fewer species. Using a method developed by ZSL and WWF, these species population trends are aggregated to produce indices of the state of biodiversity.The index value is measured relative to species' populations in 1970 (i.e. 1970 = 1).To calculate an LPI, a generalised additive modelling framework is used to determine the underlying trend in each population time-series. Average rates of change are then calculated and aggregated to the species level. For the global LPI, the method of aggregation has recently been revised to include a weighting system which gives trends from more species-rich systems, realms and groups more weight in the final index.

type LongRunLifeExpectancyGapminderUnDataset 

type LongRunLifeExpectancyGapminderUnDataset struct {
	LifeExpectancyGapminderUn *float64 `json:"life_expectancy_gapminder_un"`
}

Gapminder definition of life expectancy: The average number of years a newborn child would live if current mortality patterns were to stay the same. Interpolated values of life expectancy have been used.The UN Population Division defines life expectancy at birth as: The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period. Interpolated values have been used.Gapminder data is used up until 1949. UN Population Division (2017 Revision) data is used from 1950 to today.

type LongRunSeriesOfHealthExpenditureWorldBankWdi2017Dataset 

type LongRunSeriesOfHealthExpenditureWorldBankWdi2017Dataset struct {
	PublicExpenditureOnHealthPerCapitaInDevelopingCountriesPppWorldBankWdi2017 *float64 `json:"public_expenditure_on_health_per_capita_in_developing_countries_ppp_world_bank_wdi_2017"`
}

Public health expenditure:According to the definition given by the World Bank "Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds."This scatterplot was inspired by Reeves et al. (2015)Public health expenditure per capita was calculated combining WDI data on Health expenditure, public (%GDP) and GDP per capita, PPP (current international $).See the details in the World Development Indicators for limitations of the original source.Developing countries are defined as all countries in the World excluding high-income countries (as defined by the World Bank):Andorra; Antigua and Barbuda; Aruba; Australia; Austria; Bahamas, The; Bahrain; Barbados; Belgium; Bermuda; British Virgin Islands; Brunei Darussalam; Canada; Cayman Islands; Channel Islands; Chile; Croatia; Curaçao; Cyprus; Czech Republic; Denmark; Estonia; Faroe Islands; Finland; France; French Polynesia; Germany; Gibraltar; Greece; Greenland; Guam; Hong Kong SAR, China; Hungary; Iceland; Ireland; Isle of Man; Israel; Italy; Japan; Korea, Rep.; Kuwait; Latvia; Liechtenstein; Lithuania; Luxembourg; Macao SAR, China; Malta; Monaco; Nauru; Netherlands; New Caledonia; New Zealand; Northern Mariana Islands; Norway; Oman; Poland; Portugal; Puerto Rico; Qatar; San Marino; Saudi Arabia; Seychelles; Singapore; Sint Maarten (Dutch part); Slovak Republic; Slovenia; Spain; St. Kitts and Nevis; St. Martin (French part); Sweden; Switzerland; Taiwan, China; Trinidad and Tobago; Turks and Caicos Islands; United Arab Emirates; United Kingdom; United States; Uruguay; Virgin Islands (U.S.).---Tax revenues per capita:According to the definition given by the World Bank "Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue."This scatterplot was inspired by Reeves et al. (2015).Tax revenue per capita was calculated combining WDI data on Tax revenue (% of GDP) and GDP per capita, PPP (current international $).See the details in the World Development Indicators for limitations of the original source.Developing countries are defined as all countries in the World excluding high-income countries (as defined by the World Bank):Andorra; Antigua and Barbuda; Aruba; Australia; Austria; Bahamas, The; Bahrain; Barbados; Belgium; Bermuda; British Virgin Islands; Brunei Darussalam; Canada; Cayman Islands; Channel Islands; Chile; Croatia; Curaçao; Cyprus; Czech Republic; Denmark; Estonia; Faroe Islands; Finland; France; French Polynesia; Germany; Gibraltar; Greece; Greenland; Guam; Hong Kong SAR, China; Hungary; Iceland; Ireland; Isle of Man; Israel; Italy; Japan; Korea, Rep.; Kuwait; Latvia; Liechtenstein; Lithuania; Luxembourg; Macao SAR, China; Malta; Monaco; Nauru; Netherlands; New Caledonia; New Zealand; Northern Mariana Islands; Norway; Oman; Poland; Portugal; Puerto Rico; Qatar; San Marino; Saudi Arabia; Seychelles; Singapore; Sint Maarten (Dutch part); Slovak Republic; Slovenia; Spain; St. Kitts and Nevis; St. Martin (French part); Sweden; Switzerland; Taiwan, China; Trinidad and Tobago; Turks and Caicos Islands; United Arab Emirates; United Kingdom; United States; Uruguay; Virgin Islands (U.S.).

type LongRunTimeUseInNorwayBySexNorwayStatisticsDataset 

type LongRunTimeUseInNorwayBySexNorwayStatisticsDataset struct {
	TimeSpentOnEducationAllStatisticsNorway              *float64 `json:"time_spent_on_education_all_statistics_norway"`
	TimeSpentOnEducationFemaleStatisticsNorway           *float64 `json:"time_spent_on_education_female_statistics_norway"`
	TimeSpentOnEducationMaleStatisticsNorway             *float64 `json:"time_spent_on_education_male_statistics_norway"`
	TimeSpentOnHouseholdWorkAllStatisticsNorway          *float64 `json:"time_spent_on_household_work_all_statistics_norway"`
	TimeSpentOnHouseholdWorkFemaleStatisticsNorway       *float64 `json:"time_spent_on_household_work_female_statistics_norway"`
	TimeSpentOnHouseholdWorkMaleStatisticsNorway         *float64 `json:"time_spent_on_household_work_male_statistics_norway"`
	TimeSpentOnIncomeProducingWorkAllStatisticsNorway    *float64 `json:"time_spent_on_income_producing_work_all_statistics_norway"`
	TimeSpentOnIncomeProducingWorkFemaleStatisticsNorway *float64 `json:"time_spent_on_income_producing_work_female_statistics_norway"`
	TimeSpentOnIncomeProducingWorkMaleStatisticsNorway   *float64 `json:"time_spent_on_income_producing_work_male_statistics_norway"`
	TimeSpentOnLeisureAllStatisticsNorway                *float64 `json:"time_spent_on_leisure_all_statistics_norway"`
	TimeSpentOnLeisureFemaleStatisticsNorway             *float64 `json:"time_spent_on_leisure_female_statistics_norway"`
	TimeSpentOnLeisureMaleStatisticsNorway               *float64 `json:"time_spent_on_leisure_male_statistics_norway"`
	TimeSpentOnOtherActivitiesAllStatisticsNorway        *float64 `json:"time_spent_on_other_activities_all_statistics_norway"`
	TimeSpentOnOtherActivitiesFemalesStatisticsNorway    *float64 `json:"time_spent_on_other_activities_females_statistics_norway"`
	TimeSpentOnOtherActivitiesMalesStatisticsNorway      *float64 `json:"time_spent_on_other_activities_males_statistics_norway"`
	TimeSpentOnPersonalNeedsAllStatisticsNorway          *float64 `json:"time_spent_on_personal_needs_all_statistics_norway"`
	TimeSpentOnPersonalNeedsFemalesStatisticsNorway      *float64 `json:"time_spent_on_personal_needs_females_statistics_norway"`
	TimeSpentOnPersonalNeedsMalesStatisticsNorway        *float64 `json:"time_spent_on_personal_needs_males_statistics_norway"`
}

Note: Data are normalized to 1440 minutes per day. In other words, for those countries for which the time use does not sum up to 1440 minutes, the missing minutes are equally distributed across all activities.

type LongTermEnergyTransitionInEuropeGalesEtAl2007Dataset 

type LongTermEnergyTransitionInEuropeGalesEtAl2007Dataset struct {
	MuscleGalesEtAl2007             *float64 `json:"muscle_gales_et_al_2007"`
	FirewoodGalesEtAl2007           *float64 `json:"firewood_gales_et_al_2007"`
	WindWaterGalesEtAl2007          *float64 `json:"wind_water_gales_et_al_2007"`
	FossilFuelsGalesEtAl2007        *float64 `json:"fossil_fuels_gales_et_al_2007"`
	PrimaryElectricityGalesEtAl2007 *float64 `json:"primary_electricity_gales_et_al_2007"`
}

The authors note: “Muscle energy corresponds to the input of food by men and working animals; firewood to the actual consumption; wind and water have been estimated from the power of water and wind engines – sailing ships included – and the time, per year, these engines were in use.”

Full citation: Ben Gales, Astrid Kander, Paolo Malanima and Mar Rubio (2007) – North versus South: Energy transition and energy intensity in Europe over 200 years. European Review of Economic History Vol. 11, No. 2 (August 2007), pp. 219-253. Cambridge University Press Available online at: http://dx.doi.org/10.1017/S1361491607001967

type LongTermEnergyTransitionsEnergyHistoryHarvard2016Dataset 

type LongTermEnergyTransitionsEnergyHistoryHarvard2016Dataset struct {
	HumanMuscleEnergyHistoryHarvard2016        *float64 `json:"human_muscle_energy_history_harvard_2016"`
	AnimalMuscleEnergyHistoryHarvard2016       *float64 `json:"animal_muscle_energy_history_harvard_2016"`
	FuelwoodEnergyHistoryHarvard2016           *float64 `json:"fuelwood_energy_history_harvard_2016"`
	WaterAndWindEnergyHistoryHarvard2016       *float64 `json:"water_and_wind_energy_history_harvard_2016"`
	CoalEnergyHistoryHarvard2016               *float64 `json:"coal_energy_history_harvard_2016"`
	OilEnergyHistoryHarvard2016                *float64 `json:"oil_energy_history_harvard_2016"`
	NaturalGasEnergyHistoryHarvard2016         *float64 `json:"natural_gas_energy_history_harvard_2016"`
	PrimaryElectricityEnergyHistoryHarvard2016 *float64 `json:"primary_electricity_energy_history_harvard_2016"`
}

Data published by the Joint Center for History and Economics has been sourced from a range of authors and publications, referenced below.

The authors note:

1. Firewood consumption has been calculated in a variety of ways by the various authors. Sometimes totals are not included for the later part of the series, even if IEA or other organizations report it. The same applies to other forms of energy such as incinerated waste products;

2. this work considers as primary electricity the following sources: geothermal (only for electricity); hydropower, nuclear power, wind, photo­voltaics, tidals, wave and solar thermal (only for electricity). Generally electricity that has been derived from firewood, wastes, or other biomass is not included in ‘primary electricity’ as this entails thermal losses;

3. non-energy uses of oil and natural gas are excluded from the totals.

Original references:

Italy: Malanima, Paolo, Energy consumption in Italy, 1861-2000, CNR (2006) in Kander et al, Power to the People (Princeton, 2013).

Portugal: Henriques, S, Energy Transitions, Economic Growth and Structural Change: Portugal in a Long-run Comparative Perspective, Lund Studies in Economic History (2011).

Spain: Elaborated by Mar Rubio for Kander, Astrid; Gales, Ben; Rubio, Mar; Paolo Malanima, "North versus South: Energy transition and energy intensity in Europe over 200 years", European Review of Economic History 11/2, (2007).

France:Elaborated by Ben Gales, Paolo Malanima and Paul Warde for Kander et al, Power to the People (Princeton, 2013).

Germany: Elaborated by Ben Gales and Paul Warde for Kander et al, Power to the People (Princeton, 2013).

Sweden: Kander, Astrid, Economic growth, energy consumption and CO2 emissions in Sweden, Lund Studies in Economic History, 2002, with further elaborations until 2008 from Swedish National Statistics.

England and Wales:Updated from Warde, Paul, Energy Consumption in England & Wales, 1560-2004 (Naples: CNR, 2007).

Canada: Richard W. Unger, John Thistle, Energy Consumption in Canada in the 19th and 20th Centuries. A Statistical Outline, (Naples: CNR, 2013)

Uruguay: Adapted from: Bertoni, Reto (2011). Energía y desarrollo: la restricción energética en Uruguay como problema, 1882-2000. Montevideo: Universidad de la República.

type LongTermPerCapitaFossilFuelsOwidBasedOnUnGapminderBpEtemadAndLucianaDataset 

type LongTermPerCapitaFossilFuelsOwidBasedOnUnGapminderBpEtemadAndLucianaDataset struct {
	CoalProductionPerCapita *float64 `json:"coal_production_per_capita"`
	OilProductionPerCapita  *float64 `json:"oil_production_per_capita"`
	GasProductionPerCapita  *float64 `json:"gas_production_per_capita"`
}

Average per capita fossil fuel production - for coal, oil and gas - was calculated by OWID based on published population and national fossil fuel production data.

Per capita fossil fuel production was calculated by dividing national production of coal, oil or gas by the total population in any given year.

Population data was sourced from a combination of Gapminder sources (for data pre-1950), and from 1950 onwards was sourced from the UN Population Division's 2017 Revision.

Fossil fuel production data was sourced from a combination of sources.

Global data from 1800-1965 was sourced from Vaclav Smil's Updated and Revised Edition of his book, 'Energy Transitions: Global and National Perspectives' (2017). This book can be found on Smil's website at: http://vaclavsmil.com/2016/12/14/energy-transitions-global-and-national-perspectives-second-expanded-and-updated-edition/.

Global data from 1965 onwards was sourced from BP Statistical Review of Global Energy. Available at: http://www.bp.com/statisticalreview [accessed 18th October 2017].

Data at the national and regional level is sourced from The SHIFT Project Data Portal, who draws upon Etemad & Luciana and US IEA Historical Statistics. Available at: http://www.tsp-data-portal.org/ [accessed 18th October 2017].

Long-run data on UK coal output was sourced from the UK's Department for Energy and Climate Change (DECC). Historical coal data: coal production, availability and consumption 1853 to 2016. Available at: https://www.gov.uk/government/statistical-data-sets/historical-coal-data-coal-production-availability-and-consumption-1853-to-2011 [accessed 18th October 2017].

type LongTermProductivityBergeaudCetteAndLecat2016Dataset 

type LongTermProductivityBergeaudCetteAndLecat2016Dataset struct {
	CapitalIntensityBergeaudCetteAndLecat2016           *float64 `json:"capital_intensity_bergeaud_cette_and_lecat_2016"`
	AgeOfCapitalStockBergeaudCetteAndLecat2016          *float64 `json:"age_of_capital_stock_bergeaud_cette_and_lecat_2016"`
	LaborProductivityBergeaudCetteAndLecat2016          *float64 `json:"labor_productivity_bergeaud_cette_and_lecat_2016"`
	TotalFactorProductivityTfpBergeaudCetteAndLecat2016 *float64 `json:"total_factor_productivity_tfp_bergeaud_cette_and_lecat_2016"`
	GdpPerCapitaBergeaudCetteAndLecat2016               *float64 `json:"gdp_per_capita_bergeaud_cette_and_lecat_2016"`
}

The Long-Term Productivity database was created as a project at the Bank of France in 2013 by Antonin Bergeaud, Gilbert Cette and Remy Lecat. This database covers productivity relevant statistics, at least for the period 1890 to present, and includes 17 countries in the latest version (2016).The starting database used was built by Cette, Kocoglu and Mairesse (2009) on the United States, Japan, France and the United Kingdom over the 1890-2006 period.

type LongTermWheatYieldsFao2017AndBaylissSmith1984Dataset 

type LongTermWheatYieldsFao2017AndBaylissSmith1984Dataset struct {
	WheatFao2017AndBaylissSmith1984 *float64 `json:"wheat_fao_2017_and_bayliss_smith_1984"`
}

This dataset combines data from two key sources.Wheat yields from 1961 onwards are as reported by the UN Food and Agricultural Organization (FAO) from its FAOstat database. Available online: http://www.fao.org/faostat/en/#data/QC [accessed 24th August 2017].Data from prior to 1961 is sourced from Bayliss-Smith & Wanmali (1984). Understanding Green Revolutions: Agrarian Change and Development Planning in South Asia. Available at: https://www-cambridge-org.ezproxy.is.ed.ac.uk/core/books/understanding-green-revolutions/761959C5635C85DB4C36E6B44C19A5EF [accessed 24th August 2017].All values have been converted to a metric of tonnes per hectare.

type LongTermYieldsInTheUnitedKingdom2022Dataset 

type LongTermYieldsInTheUnitedKingdom2022Dataset struct {
	WheatOwid2017     *float64 `json:"wheat_owid_2017"`
	BarleyOwid2017    *float64 `json:"barley_owid_2017"`
	OatsOwid2017      *float64 `json:"oats_owid_2017"`
	PotatoesOwid2017  *float64 `json:"potatoes_owid_2017"`
	SugarBeetOwid2017 *float64 `json:"sugar_beet_owid_2017"`
	RyeOwid2017       *float64 `json:"rye_owid_2017"`
	PulsesOwid2017    *float64 `json:"pulses_owid_2017"`
}

This dataset on agricultural yields in the United Kingdom was constructed from yield data from three key sources.– Data from 1270 - 1870 was taken from Broadberry et al (2015), made available by Bank of England. This comprises crop yield estimates only for England. For this dataset, we have assumed that yields in England are also representative of average UK yields. – Data from 1870 to 1960 is taken from Brassley (2000. This is provided over 5-year periods. We have assumed these figures for the first year in each 5-year set.– Data from 1961 onwards is sourced from the Food and Agriculture Organization of the United Nations.References:Broadberry, S., Campbell, B. M., Klein, A., Overton, M., & Van Leeuwen, B. (2015). British economic growth, 1270–1870. Cambridge University Press.Brassley, P. (2000). Output and technical change in twentieth-century British agriculture. The Agricultural History Review, 60-84.Food and Agriculture Organization of the United Nations. Available at: https://www.fao.org/faostat/en/#data/QCL

type LostSchoolGrantsReinikkaAndSvensson2004Dataset 

type LostSchoolGrantsReinikkaAndSvensson2004Dataset struct {
	ShareOfIntendedCapitationGrantsReceivedBySchoolsInUganda1991_1995 *float64 `json:"share_of_intended_capitation_grants_received_by_schools_in_uganda_1991_1995"`
}

type LowestPayingOccupationsPercentFemaleNwlc2014Dataset 

type LowestPayingOccupationsPercentFemaleNwlc2014Dataset struct {
	PercentWomenNwlc2014 *float64 `json:"percent_women_nwlc_2014"`
}

type LungCancerMortalityRatesPer1000002022Dataset 

type LungCancerMortalityRatesPer1000002022Dataset struct {
	AgeStandardizedDeathRatePer100kBothSexes *float64 `json:"age_standardized_death_rate_per_100k_both_sexes"`
	AgeStandardizedDeathRatePer100kFemale    *float64 `json:"age_standardized_death_rate_per_100k_female"`
	AgeStandardizedDeathRatePer100kMale      *float64 `json:"age_standardized_death_rate_per_100k_male"`
	CrudeDeathRatePer100kBothSexes           *float64 `json:"crude_death_rate_per_100k_both_sexes"`
	CrudeDeathRatePer100kFemale              *float64 `json:"crude_death_rate_per_100k_female"`
	CrudeDeathRatePer100kMale                *float64 `json:"crude_death_rate_per_100k_male"`
}

Age-standardized mortality rates (per 100,000 people) due to lung, bronchus or trachea cancer. The data is from the WHO Mortality database: https://platform.who.int/mortalityThis dataset should be next updated by the source by May 2023. We will update it on Our World in Data soon after the new version is published. At the link above you can directly access the source page and see the latest available data.

type MaddisonProjectDatabase2018BoltEtAl2018Dataset 

type MaddisonProjectDatabase2018BoltEtAl2018Dataset struct {
	RealGdpPerCapitaIn2011usmoneyMultipleBenchmarksMaddisonProjectDatabase2018                 *float64 `json:"real_gdp_per_capita_in_2011usmoney_multiple_benchmarks_maddison_project_database_2018"`
	RealGdpPerCapitaIn2011usmoney2011BenchmarkMaddisonProjectDatabase2018                      *float64 `json:"real_gdp_per_capita_in_2011usmoney_2011_benchmark_maddison_project_database_2018"`
	PopulationMaddisonProjectDatabase2018                                                      *float64 `json:"population_maddison_project_database_2018"`
	TotalRealGdpIn2011usmoneyMultipleBenchmarksMaddisonProjectDatabase2018                     *float64 `json:"total_real_gdp_in_2011usmoney_multiple_benchmarks_maddison_project_database_2018"`
	TotalRealGdpIn2011usmoney2011BenchmarkMaddisonProjectDatabase2018                          *float64 `json:"total_real_gdp_in_2011usmoney_2011_benchmark_maddison_project_database_2018"`
	MaddisonRegionDefinitionMaddison2010                                                       *float64 `json:"maddison_region_definition_maddison_2010"`
	AverageRealGdpPerCapitaAcrossMaddisonsDefinitionsMaddisonProjectDatabase2018               *float64 `json:"average_real_gdp_per_capita_across_maddisons_definitions_maddison_project_database_2018"`
	AverageRealGdpPerCapitaAcrossOwidRegionsOwidCalculationsBasedOnMaddisonProjectDatabase2018 *float64 `json:"average_real_gdp_per_capita_across_owid_regions_owid_calculations_based_on_maddison_project_database_2018"`
	RealGdpPerCapita2011BenchmarksByRegionMaddisonProjectDatabase2018                          *float64 `json:"real_gdp_per_capita_2011_benchmarks_by_region_maddison_project_database_2018"`
	PopulationByRegionMaddisonProjectDatabase2018                                              *float64 `json:"population_by_region_maddison_project_database_2018"`
}

Full citation: Maddison Project Database, version 2018. Bolt, Jutta, Robert Inklaar, Herman de Jong and Jan Luiten van Zanden (2018), “Rebasing ‘Maddison’: new income comparisons and the shape of long-run economic development”, Maddison Project Working paper 10

The original Maddison Project dataset expresses the population "in thousands". In our dataset we have multiplied it by 1000 to avoid expressing it "in thousands".

The Maddison region definitions were adapted from the source from Maddison (2010)'s homepage at: http://www.ggdc.net/maddison/oriindex.htm under the heading "Historical Statistics" and the file titled: Statistics on World Population, GDP and Per Capita GDP, 1-2008 AD (Horizontal file, copyright Angus Maddison, University of Groningen)

To calculate average real GDP per capita across regions, we have calculated a population weight for each country per year by dividing country population in year x by the total regional population. This weight has been used to multiply real GDP per capita (in 2011US$, with multiple benchmarks) to give real regional GDP per capita weighted by population.

type MaddisonProjectDatabase2020BoltAndVanZanden2020Dataset 

type MaddisonProjectDatabase2020BoltAndVanZanden2020Dataset struct {
	GdpPerCapita *float64 `json:"gdp_per_capita"`
	Population   *float64 `json:"population"`
	Gdp          *float64 `json:"gdp"`
}

Full citation: Maddison Project Database, version 2020. Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update”.The database draws on the following work for individual countries:Argentina1800 - 1870 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307 (updated data)1870 - 1900 Bertola, L and Ocampo, J.A. (2012) The Economic Development of Latin America since Independence. Oxford, Oxford U.P Belgium 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911500- 1846 Buyst, E. (2011), “Towards Estimates of Long Term Growth in the Southern Low Countries, ca.1500-1846”, Results presented at the Conference on Quantifying Long Run Economic Development, Venice, 22-24 March, 2011 Bulgaria 1892-1945 Ivanov, M. (2006). “Bulgarian National Income between 1892 and 1924,” Bulgarian National Discussion Papers DP/54/2006 Bosnia and Herzegovina 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project Bolivia (Plurinational State of) 1846-1950 Herranz-Loncán, A. and Peres-Cajías (2016). “Bolivian GDP per capita since the mid-nineteenth century” Cliometrica 10: 99-128 Brazil 1800 - 1870 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307 (updated data)1850–1899 Barro, R.J. and J.F. Ursua, (2008). “Macroeconomic Crises since 1870” Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 39(1 (Spring), pages 255-350 Switzerland 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911850-2011 Stohr, Christian (2016), Trading Gains: new estimates of Swiss GDP, 1851-2008. Economic History Working Papers, 245/2016. London School of Economics and Political Science, Economic History Department, London, UK Chile 1810–2004 Díaz, J.B. Lüders, R. and Wagner, G. (2007) Economia Chilena 1810 - 2000, Producto total y sectorial una nueva mirada, Pontificia universidad Catolica de Chile, Insituto de Economia, Documeno de Trabajo no. 315 China 1000-1661 Broadberry, S.N., Guan, Hanhui and David Daokui Li (2018), “China, Europe and the Great Divergence: a Study in Historical National Accounting, 980-1850”, Journal of Economic History, 78, 4, 955-10001661–1933 Broadberry, S.N., Guan, Hanhui and David Daokui Li (2018), “China, Europe and the Great Divergence: a Study in Historical National Accounting, 980-1850”, Journal of Economic History, 78, 4, 955-10001661–1933 Xu, Y. Z. Shi, B. van Leeuwen, Y Ni, Z Zhang, and Y Ma, (2016) 'Chinese National Income, ca. 1661-1933', Australian Economic History Review 57(3), 368–393 1952–2008 Wu, Harry X. (2014), “China’s growth and productivity performance debate revisited – Accounting for China’s sources of growth with a new data set” The Conference Board Economics Program Working Paper Series EWP#14-01. Colombia 1800 - 1870 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307 (updated data)1870 - 1923 Bertola, L and Ocampo, J.A. (2012) The Economic Development of Latin America since Independence. Oxford, Oxford U.P. Czechoslovakia 1993 - Based on GDP and population data for their successor states Cuba 1690–1895 Santamaria Garcia, A. (2005). Las Cuentas nacionales de Cuba, 1960 - 2005', mimeo1902–1958 Ward, M. and Devereux, J. (2012), “The Road Not Taken: Pre-Revolutionary Cuban Living Standards in Comparative Perspective” Journal of Economic History, 72(1): 104–132 Germany 1500-1850 Pfister, U. (2011). “ Economic growth in Germany, 1500–1850”, Paper presented at the Conference on Quantifying Long Run Economic Development, Venice, 22-24 March, 2011 Ecuador 1833-1938 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307. (updated data). We use the growth rate between 1933 and 1938 from Prados de la Escosura (2009) and link that to the 1939 level of Maddison’s original estimates. 

Egypt	1	Scheidel, W.

and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–91700 – 1500 Pamuk, Ş. and M. Shatzmiller (2011). “Real Wages and GDP per capita in the Medieval Islamic Middle East in Comparative Perspective, 700-1500”, paper presented at the 9th Conference of the European Historical Economics Society, Dublin, September 2-3, 2011.1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Spain 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911270-1850 Álvarez-Nogal, C. and L. Prados de la Escosura (2013). "The Rise and Fall of Spain (1270-1850)," Economic History Review, 66, 1, 1-37, using their annual benchmarks1850–2016 Prados de la Escosura, L. (2017), Spanish Economic Growth, 1850-2015 (London: Palgrave Macmillan) Finland 1600–1860 Eloranta, J., Voutilainen, M. and Nummela, I. (2016). 

“Estimating Finnish Economic Growth Before 1860” mimeo.	France	1	Scheidel, W.

and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911250–1800 Ridolfi, L. (2016) “The French economy in the longue durée. A study on real wages, working days and economic performance from Louis IX to the Revolution (1250-1789)” Dissertation IMT School for Advanced Studies, Lucca, available at http://e-theses.imtlucca.it/211/1/Ridolfi_phdthesis.pdf United Kingdom 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911252–1700 (England) Broadberry, S.N., B. Campbell, A. Klein, M. Overton and B. van Leeuwen (2015), British Economic Growth 1270-1870 Cambridge: Cambridge University Press.1700–1870 Broadberry, S.N., B. Campbell, A. Klein, M. Overton and B. van Leeuwen (2015), British Economic Growth 1270-1870 Cambridge: Cambridge University Press. Greece 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911833-1913 Kostelenos G., S. Petmezas, D. Vasiliou, E. Kounaris and M. Sfakianakis, (2007), "Gross Domestic Product 1830-1939", Sources of Economic History of Modern Greece: Quantitative data and statistical series 1830-1939, Historical Archives of the National Bank of Greece, Athens. 

Croatia	1952-2008	Milanovic (2011).

Estimates provided to the Maddison-Project Indonesia 1815 – 1880 (Java) Van Zanden (2012). “Economic Growth in Java 1815-1939: The Reconstruction of the Historical National Accounts of a Colonial Economy”, Maddison-Project Working Paper WP-3.1880-2008 Van der Eng, P. (2010). The Sources of Long-Term Economic Growth in Indonesia, 1880-2008”, Explorations in Economic History, 47: 294-309 India 1600–1870 Broadberry, S.N., Custodis, J. and Gupta, B. (2015), “India and the great divergence: an Anglo-Indian comparison of GDP per capita, 1600–1871” Explorations in Economic History, 55: 58-75. Iran (Islamic Republic of) 1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Iraq 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–91700 – 1500 Pamuk, Ş. and M. Shatzmiller (2011). “Real Wages and GDP per capita in the Medieval Islamic Middle East in Comparative Perspective, 700-1500”, paper presented at the 9th Conference of the European Historical Economics Society, Dublin, September 2-3, 2011.1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Israel 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–91 Italy 1310-1871 (North Italy) Malanima, P. (2010), “The long decline of a leading economy: GDP in central and northern Italy, 1300–1913” European Review of Economic History 15 (2): 169–219.1871-1990 Baffigi, A. (2011).”Italian National Accounts, 1861-2011”, Banca d’Italia Economic History Working Papers 18. 

Jamaica	1850 - 1938 	Prados de la Escosura, L.

(2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307 (updated data) Jordan 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Japan 724-1874 Bassino, Jean-Pascal & Broadberry, Stephen & Fukao, Kyoji & Gupta, Bishnupriya & Takashima, Masanori, (2018). "Japan and the Great Divergence, 730-1874," CEI Working Paper Series 2018-13, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University1874-1940 Fukao, K., Bassino, J.-P., Makino, T., Paprzycki, R., Settsu, T., Takashima, M., and Tokui, J. (2015) Regional Inequality and Industrial Structure in Japan: 1874-2008, Tokyo: Maruzen Publishing. Republic of Korea 1911-1990 Cha, M.S., Kim, N.N., Park, K.-J., Park, Y. (Eds.) (2020), Historical Statistics of Korea. Studies in Economic History, New York: Springer Publishing Lebanon 1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Mexico 1550–1812 Arroyo Abad, L. and J.L. van Zanden (2016), “Growth under Extractive Institutions? Latin American Per Capita GDP in Colonial Times” Journal of Economic History 76(4): 1182–1215. 1812–1870 Prados de la Escosura, L., (2009), ‘Lost decades? Economic performance in post-independence Latin America’, Journal of Latin America Studies, 41, pp. 279–307. (updated data)1895–2003 Barro, R.J. and J.F. Ursua, (2008). “Macroeconomic Crises since 1870” Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 39(1 (Spring), pages 255-350 TFYR of Macedonia 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project Montenegro 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project Malaysia 1900-1939 Nazrin Shah, S. (2017). Charting the Economy: Early 20th Century Malaya and Contemporary Malaysian Contrasts, Oxford University Press Netherlands 1348–1807 (Holland) Van Zanden, J. L. and van Leeuwen, B. (2012), ‘Persistent but not consistent: the growth of national income in Holland 1347–1807’, Explorations in Economic History, 49 (2012), pp. 119–30.1807-1913 Smits, J.P., E. Horlings and J.L. van Zanden (2000). The Measurement of Gross National Product and its Components 1800-1913 (Groningen Growth and Development Centre Monograph series no 5). 

Norway	1820–1930	Grytten, O.H.

(2015). Norwegian gross domestic product by industry 1830 - 1930, Norges Bank Working paper 19/2015. Population from Maddison (2006) Panama 1906–1945 De Corso, G. (2013). El crecimiento economico de Venuzuela, Desde la Oligarquia Conservadora Hasta La Revolucion Bolivariana: 1830-2012. Uno Vision cuantitativa *: Venezuelan Economic Growth From The Conservative Oligarchy To The Bolivarian Revolution (1830-2012), Revista De Historia Económica / Journal of Iberian and Latin American Economic History, 31(3), 321-357. doi:10.1017/S0212610913000190 Peru 1600–1812 Arroyo Abad, L. and J.L. van Zanden (2016), “Growth under Extractive Institutions? Latin American Per Capita GDP in Colonial Times” Journal of Economic History 76(4): 1182–1215. 1812–1870 Seminario, B. (2015). El Desarrallo de la Economía Peruana en la Era Moderna, Universidad de Pacifico, Lima1870-1901 Bertola, L and Ocampo, J.A. (2012) The Economic Development of Latin America since Independence. Oxford, Oxford U.P Poland 1409–1913 Malinowski, M. and Van Zanden (2017), “National income and its distribution in preindustrial Poland in a global perspective” Cliometrica, Volume 11, Issue 3, pp 375–40 D.P.R. of Korea 1911-1943 Cha, M.S., Kim, N.N., Park, K.-J., Park, Y. (Eds.) (2020), Historical Statistics of Korea. Studies in Economic History, New York: Springer Publishing. 1990-2015 Cha, M.S., Kim, N.N., Park, K.-J., Park, Y. (Eds.) (2020), Historical Statistics of Korea. Studies in Economic History, New York: Springer Publishing. 

Portugal	1	Scheidel, W.

and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–911530–1850 Palma, N., & Reis, J. (2019). From Convergence to Divergence: Portuguese Economic Growth, 1527–1850. The Journal of Economic History, 79(2), 477-506. doi:10.1017/S0022050719000056 Romania 1862–1995 Axenciuc, V. (2012). Produsul intern brut al Romaniei, Vol. 1, Institutl de Economie Nationala, Bucarest Saudi Arabia 1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Singapore 1900–1959 Sugimoto, I. (2011), Economic growth of Singapore in the twentieth century: historical GDP estimates and empirical investigations, Economic Growth Centre Research Monograph ser., 2, http://www.worldscibooks.com/economics/7858.html (accessed on 30 Jan. 2013). Serbia 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project Former USSR 1885-1913 Gregory, P. R. (1982). Russian National Income, 1885–1913, Cambridge: Cambridge University Press1913-1928 Markevich, A. and M. Harrison (2011). “Great War, Civil War, and Recovery: Russia's National Income, 1913 to 1928”, The Journal of Economic History, Volume 71 (3): 672 – 703, table 6.1991 - Based on GDP and population data for their successor states Slovenia 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project Sweden 1300–1560 Krantz, O. (2017) “Swedish GDP 1300-1560 A Tentative Estimate” Lund Papers in Economic History: General Issues; No. 152.1560–1950 Schön, L., and O. Krantz (2015) “New Swedish Historical National Accounts since the 16th Century in Constant and Current Prices” Lund Papers in Economic History no. 140 Syrian Arab Republic 1820, 1870, 1913, 1950 Pamuk, S. (2006), Estimating Economic Growth in the Middle East since 1820, The Journal of Economic History, vol 66, no. 3, pp. 809 - 828 Turkey 1 Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–91700 - 1820 Pamuk, Ş. and M. Shatzmiller (2011). “Real Wages and GDP per capita in the Medieval Islamic Middle East in Comparative Perspective, 700-1500”, paper presented at the 9th Conference of the European Historical Economics Society, Dublin, September 2-3, 2011; Milanovic, B. (2006), “An estimate of average income and inequality in Byzantium around year 1000,” Review of Income and Wealth 52 (3).1500-1820 Pamuk, S. (2009). “Estimating GDP per capita for the Ottoman Empire in a European Comparative Framework, 1500-1820”, paper presented at the XVth World Economic History Congress, August 2009, Utrecht Taiwan, Province of China 1820 - 1940 Fukao, K., D. Ma and T. Yuan (2007). Real GDP in Pre-War East Asia: A 1934-36 Benchmark Purchasing Power Parity Comparison with the U.S. Review of Income and Wealth, 53 (3): 503 - 537. Trend from original Maddison estimates applied to benchmark for 1934/36 Uruguay 1800-1870 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307. (updated data)1870–2014 Bèrtola, L. (2016). El PIB per Capita de Uruguay 1870 - 2016: una reconstruccion. PHES working paper No 48 United States 1650 - 1790 McCusker, John J., ‘Colonial Statistics’, Historical Statistics of the United States: Earliest Time to the Present, in S. B. Carter, S. S. Gartner, M. R. Haineset al. New York, Cambridge University Press. V-671.1790 - 1870 Sutch, R. (2006). National Income and Product. Historical Statistics of the United States: Earliest Time to the Present, in S. B. Carter, S. S. Gartner, M. R. Haineset al. New York, Cambridge University Press III-23-25.1800-1830 Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307. (updated data) Venezuela (Bolivarian Republic of) 1830–2012 De Corso, G. (2013). El crecimiento economico de Venuzuela, Desde la Oligarquia Conservadora Hasta La Revolucion Bolivariana: 1830-2012. Uno Vision cuantitativa *: Venezuelan Economic Growth From The Conservative Oligarchy To The Bolivarian Revolution (1830-2012), Revista De Historia Económica / Journal of Iberian and Latin American Economic History, 31(3), 321-357. doi:10.1017/S0212610913000190 Former Yugoslavia 1952-2008 Milanovic (2011). Estimates provided to the Maddison-Project2008 - Based on GDP and population data for their successor states South Africa 1700–1900 (Cape Colony) Fourie, J. and Van Zanden, J.L. (2013). GDP in the Dutch Cape Colony: the Nationals Accounts of a Slave-Based Society, South African Journal of Economics, vol. 81 (4): 467 - 490

type MalariaDeathsIhme2016Dataset 

type MalariaDeathsIhme2016Dataset struct {
	DeathsFromMalariaIhme2016 *float64 `json:"deaths_from_malaria_ihme_2016"`
}

type MaleAndFemaleLifeExpectancyByAgeInTheLongRunHumanMortalityDatabase2018AndOthersDataset 

type MaleAndFemaleLifeExpectancyByAgeInTheLongRunHumanMortalityDatabase2018AndOthersDataset struct {
	MaleLifeExpectancyAtBirthHmd2018AndOthers              *float64 `json:"male_life_expectancy_at_birth_hmd_2018_and_others"`
	MaleLifeExpectancyAt15Hmd2018AndOthers                 *float64 `json:"male_life_expectancy_at_15_hmd_2018_and_others"`
	MaleLifeExpectancyAt45Hmd2018AndOthers                 *float64 `json:"male_life_expectancy_at_45_hmd_2018_and_others"`
	FemaleLifeExpectancyAtBirthHmd2018AndOthers            *float64 `json:"female_life_expectancy_at_birth_hmd_2018_and_others"`
	FemaleLifeExpectancyAt15Hmd2018AndOthers               *float64 `json:"female_life_expectancy_at_15_hmd_2018_and_others"`
	FemaleLifeExpectancyAt45Hmd2018AndOthers               *float64 `json:"female_life_expectancy_at_45_hmd_2018_and_others"`
	FemaleMinusMaleLifeExpectancyAtBirthHmd2018AndOthers   *float64 `json:"female_minus_male_life_expectancy_at_birth_hmd_2018_and_others"`
	FemaleMinusMaleLifeExpectancyAt15Hmd2018AndOthers      *float64 `json:"female_minus_male_life_expectancy_at_15_hmd_2018_and_others"`
	FemaleMinusMaleLifeExpectancyAt45Hmd2018AndOthers      *float64 `json:"female_minus_male_life_expectancy_at_45_hmd_2018_and_others"`
	FemaleToMaleLifeExpectancyRatioAtBirthHmd2018AndOthers *float64 `json:"female_to_male_life_expectancy_ratio_at_birth_hmd_2018_and_others"`
	FemaleToMaleLifeExpectancyRatioAt15Hmd2018AndOthers    *float64 `json:"female_to_male_life_expectancy_ratio_at_15_hmd_2018_and_others"`
	FemaleToMaleLifeExpectancyRatioAt45Hmd2018AndOthers    *float64 `json:"female_to_male_life_expectancy_ratio_at_45_hmd_2018_and_others"`
}

US estimates are based on Hacker (2010) for the period 1780 to 1890. The year 1790 refers to the period 1790-99; see Table 8: New Life Tables for the White Population of the United States, 1780-1900 for the original data consulted for this period. The US Social Security Administration data is used from 1900 to today.For other countries, estimates are based on the Human Mortality Database period tables. Following <a href="http://www.nber.org/papers/w24716" rel="noopener" target="_blank">Goldin and Lleras-Muney (2018)</a>, our data covers only years in which a census was undertaken. Goldin and Lleras-Muney (2018) explain: "census data are available every ten years starting in 1841 for England, and every ten years starting in 1860 for Sweden. For France they occurred every 5 years between 1836 and 1936 (except for 1871 which was held in 1872, and for 1916 which was cancelled) and then for 1946, 1962, 1968, 1975, 1982, 1990 and 1999."Note: the United Kingdom refers to the total population of England and Wales.

type MaleToFemaleRatioHighSchoolCoursesInUsaGoldinEtAlDataset 

type MaleToFemaleRatioHighSchoolCoursesInUsaGoldinEtAlDataset struct {
	Maths     *float64 `json:"maths"`
	Science   *float64 `json:"science"`
	Chemistry *float64 `json:"chemistry"`
}

Data represents the average male-to-female ratio of students taking maths and science subjects in high school education in the United States.

type MarineEnergyIrenaDataset 

type MarineEnergyIrenaDataset struct {
	InstalledCapacity *float64 `json:"installed_capacity"`
	EnergyProduction  *float64 `json:"energy_production"`
}

Marine energy includes energy generation from both wave and tidal sources.

type MarineStocksByRegionAndTaxaRamlegacyDataset 

type MarineStocksByRegionAndTaxaRamlegacyDataset struct {
	BiomassMeanRegion         *float64 `json:"biomass_mean_region"`
	CatchMeanRegion           *float64 `json:"catch_mean_region"`
	FishingPressureMeanRegion *float64 `json:"fishing_pressure_mean_region"`
	BiomassQ25Region          *float64 `json:"biomass_q25_region"`
	CatchQ25Region            *float64 `json:"catch_q25_region"`
	FishingPressureQ25Region  *float64 `json:"fishing_pressure_q25_region"`
	BiomassQ75Region          *float64 `json:"biomass_q75_region"`
	CatchQ75Region            *float64 `json:"catch_q75_region"`
	FishingPressureQ75Region  *float64 `json:"fishing_pressure_q75_region"`
	BiomassMeanTaxa           *float64 `json:"biomass_mean_taxa"`
	CatchMeanTaxa             *float64 `json:"catch_mean_taxa"`
	FishingPressureMeanTaxa   *float64 `json:"fishing_pressure_mean_taxa"`
	BiomassQ25Taxa            *float64 `json:"biomass_q25_taxa"`
	CatchQ25Taxa              *float64 `json:"catch_q25_taxa"`
	FishingPressureQ25Taxa    *float64 `json:"fishing_pressure_q25_taxa"`
	BiomassQ75Taxa            *float64 `json:"biomass_q75_taxa"`
	CatchQ75Taxa              *float64 `json:"catch_q75_taxa"`
	FishingPressureQ75Taxa    *float64 `json:"fishing_pressure_q75_taxa"`
}

This dataset provides aggregated data on fish biomass, fishing pressure and catch by region and taxa, based on the results of the RAM Legacy Stock Assessment Database: https://www.ramlegacy.org/This aggregated data was provided by Michael Melnychuk, researcher on the RAM Legacy Database.These results were published in PNAS here:Hilborn, R., Amoroso, R. O., Anderson, C. M., Baum, J. K., Branch, T. A., Costello, C., ... & Ye, Y. (2020). Effective fisheries management instrumental in improving fish stock status. Proceedings of the National Academy of Sciences, 117(4), 2218-2224. Available at: https://www.pnas.org/content/117/4/2218Definitions are as follows:– 'Biomass' is a measure of biomass of fish divided by the biomass of fish at the maximum sustainable yield (B / Bmsy)– 'Fishing pressure' is a measure of fishing pressure divided by the pressure at the maximum sustainable yield (U / Umsy)– 'Catch' is a measure of the annual catch divided by the mean catch across the stock's time series

type MarketShareOfIodizedSaltInEuropeanCountriesEuropeanCommission2006Dataset 

type MarketShareOfIodizedSaltInEuropeanCountriesEuropeanCommission2006Dataset struct {
	MarketShareOfIodizedSaltEuropeanCommission2006 *float64 `json:"market_share_of_iodized_salt_european_commission_2006"`
}

Data represents the share of household salt sold on the market which has been fortified with iodine (the most efficient solution to iodine deficiency). This data is available across select European countries.

type MaternalDeathsTo2030BauVsSdgTargetBasedOnWorldBankAndUn2018Dataset 

type MaternalDeathsTo2030BauVsSdgTargetBasedOnWorldBankAndUn2018Dataset struct {
	AnnualMaternalDeathsTo2030BusinessAsUsual     *float64 `json:"annual_maternal_deaths_to_2030_business_as_usual"`
	AnnualMaternalDeathsTo2030RequiredRateForSdgs *float64 `json:"annual_maternal_deaths_to_2030_required_rate_for_sdgs"`
	AnnualMaternalLivesAtStakeTo2030              *float64 `json:"annual_maternal_lives_at_stake_to_2030"`
	CumulativeMaternalDeathsBusinessAsUsual       *float64 `json:"cumulative_maternal_deaths_business_as_usual"`
	CumulativeMaternalDeathsRequiredRate          *float64 `json:"cumulative_maternal_deaths_required_rate"`
	CumulativeMaternalLivesSaved                  *float64 `json:"cumulative_maternal_lives_saved"`
}

Maternal deaths were calculated by Our World in Data under two scenarios:- extrapolation of average annual progress (during the period 2005-2015) in maternal mortality rates through to 2030; this provides a 'business-as-usual' trajectory;- for countries who would not under a business-as-usual trajectory reach the UN SDG Target 3.1 of at least as low as 70 deaths per 100,000 live births, the required trajectory necessary to reach this target.Maternal deaths were derived from these two scenarios by multiplying the maternal mortality ratio (described below from the World Bank) by the annual number of births under the UN Population Division (2018) median fertility scenario from 2015 to 2030.The difference in maternal deaths between these two trajectories is here assumed to be the 'maternal lives at stake' from not reaching SDG Target 3.1. Maternal mortality trajectories were calculated by Our World in Data based on a combination of business-as-usual extrapolations of historical trends published by the World Bank (2018).Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.The World Bank's source data is derived from: WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015.Our future figures, extending from 2015 to 2030 were estimated assuming a 'business-as-usual' continuation of average progress in the latest 10 years of maternal mortality data (2005-2015). Here we calculated the average annual change over this period, and applied it from 2015 onwards to derive estimates by 2030. Note that these figures therefore do not represent a forecast or prediction, but simply represent expected change if progress continues at recent rates.The rates required to reach SDG Target 3.1 (70 per 100,000 live births) were quantified by calculating the average annual rate of decline necessary to reach 70 per 100,000 by 2030 from the 2015 value of a given country. This is shown only for countries who would not reach the SDG Target by 2030 under a business-as-usual trajectory.Sources:The World Bank's World Development Indicators (https://datacatalog.worldbank.org/search/indicators). Data was downloaded on 30th August 2018.United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition (https://esa.un.org/unpd/wpp/Download/Standard/Population/). Data was downloaded on 30th August 2018.

type MaternalMortalityProjectionTo2030BasedOnWorldBank2018Dataset 

type MaternalMortalityProjectionTo2030BasedOnWorldBank2018Dataset struct {
	MaternalMortalityRatioProjectionsTo2030BasedOnWorldBank2018 *float64 `json:"maternal_mortality_ratio_projections_to_2030_based_on_world_bank_2018"`
}

Data was calculated by Our World in Data based on business-as-usual extrapolations of historical trends published by the World Bank.The World Bank's World Development Indicators, available at: https://datacatalog.worldbank.org/search/indicators. Data was downloaded on 30th August 2018.Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.The World Bank's source data is derived from: WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015.Our future figures, extending from 2015 to 2030 were estimated assuming a 'business-as-usual' continuation of average progress in the latest 10 years of maternal mortality data (2005-2015). Here we calculated the average annual change over this period, and applied it from 2015 onwards to derive estimates by 2030. Note that these figures therefore do not represent a forecast or prediction, but simply represent expected change if progress continues at recent rates.

type MaternalMortalityRatioGapminder2010AndWorldBank2015Dataset 

type MaternalMortalityRatioGapminder2010AndWorldBank2015Dataset struct {
	MaternalMortalityRatioGapminder2010AndWorldBank2015 *float64 `json:"maternal_mortality_ratio_gapminder_2010_and_world_bank_2015"`
}

Claudia Hanson has reconstructed the historical estimates for Gapminder in 2010. The work is documented here: https://www.gapminder.org/documentation/documentation/gapdoc010.pdf and can be accessed here: https://docs.google.com/spreadsheets/d/14ZtQy9kd0pMRKWg_zKsTg3qKHoGtflj-Ekal9gIPZ4A/pub# Gapminder has reconstructed the historical data for the following 13 countries: Australia, Belgium, Denmark, Finland, Germany, Ireland, Japan, Malaysia, Netherlands, Sri Lanka, Sweden, the United Kingdom, and the United States.

The original source of the World Bank data is: WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015

The two datasets were combined without any adjustments.

type MdgFinalEvaluationUnMdgReportDataset 

type MdgFinalEvaluationUnMdgReportDataset struct {
	Mdg1aShareInExtremePovertyMoney125PerDay        *float64 `json:"mdg1a_share_in_extreme_poverty_money125_per_day"`
	Mdg1bShareInProductiveEmployment                *float64 `json:"mdg1b_share_in_productive_employment"`
	Mdg1cShareUndernourished                        *float64 `json:"mdg1c_share_undernourished"`
	Mdg2aUniversalPrimaryEducation                  *float64 `json:"mdg2a_universal_primary_education"`
	Mdg3aGenderParityIndex                          *float64 `json:"mdg3a_gender_parity_index"`
	Mdg4aChildMortalityRate                         *float64 `json:"mdg4a_child_mortality_rate"`
	Mdg5aMaternalMortalityRate                      *float64 `json:"mdg5a_maternal_mortality_rate"`
	Mdg5bShareOfPregnantWomenReceivingAntenatalCare *float64 `json:"mdg5b_share_of_pregnant_women_receiving_antenatal_care"`
	Mdg5bShareOfMarriedWomenUsingContraceptives     *float64 `json:"mdg5b_share_of_married_women_using_contraceptives"`
}

Data is based on the UN's final evaluation of the Millennium Development Goals (MDGs) and sourced from its final report (http://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf) and Statistical Database (http://mdgs.un.org/unsd/mdg/Resources/Static/Products/Progress2015/StatAnnex.pdf).The specific Targets with data here are:MDG1.A: halve share of people living in extreme poverty (defined as living on less than $1.25 per day - the historical poverty line).MDG1.B: achieve full and productive employment, as well as decent work for all, including young people and women.MDG1.C: halve the proportion of individuals suffering from hunger.MDG2: ensure that children universally – including both boys and girls – will be able to complete a full course of primary education by 2015.MDG3.A: eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015.MDG4.A: reduce the under-five mortality rate by two-thirds in the period between 1990 and 2015.MDG5.A: reduce the maternal mortality ratio by 75 percent.MDG5.B: achieve universal access to reproductive health.

type MeanBmiNcdRisc2017Dataset 

type MeanBmiNcdRisc2017Dataset struct {
	MeanBmiMale   *float64 `json:"mean_bmi_male"`
	MeanBmiFemale *float64 `json:"mean_bmi_female"`
}

This dataset presents the mean Body Mass Index (BMI) by country, region, and globally for men and women.Body Mass Index (BMI) is a person's weight in kilograms (kg) divided by his or her height in meters squared (m2). The WHO define a BMI <=18.5 as 'underweight'; 18.5 to <25 as 'normal/healthy'; 25.0 to <30 as 'overweight'; and >30.0 as 'obese'.The data was sourced from 2,416 population-based studies with measurements of height and weight on 97.4 million participants aged 20 years and older.NCD Risk Factor Collaboration (NCD-RisC) is a network of health scientists around the world that provides rigorous and timely data on risk factors for non-communicable diseases (NCDs) for 200 countries and territories. The group works closely with the World Health Organisation (WHO), through the WHO Collaborating Centre on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-quality population-based data using advanced statistical methods, designed specifically for analysing NCD risk factors. The Collaboration currently has data from over 2,000 population-based surveys from 189 countries since 1957, with nearly 25 million participants whose risk factor levels have been measured.

type MeanYearsOfSchoolingWomen15To49OurWorldInData2017Dataset 

type MeanYearsOfSchoolingWomen15To49OurWorldInData2017Dataset struct {
	MeanYearsOfSchoolingWomenInReproductiveAge15To49OurWorldInData2017 *float64 `json:"mean_years_of_schooling_women_in_reproductive_age_15_to_49_our_world_in_data_2017"`
}

Shown is the average of mean years of schooling for mean between 15 and 49 years of age. This is calculated as the average of the estimates published by Barro and Lee.

type MeaslesLondonDataset 

type MeaslesLondonDataset struct {
	DeathsFromAllCauses *float64 `json:"deaths_from_all_causes"`
	Measles             *float64 `json:"measles"`
}

To be filled in

type MeasuresAndIndicatorsForPovertyPovcalnetWorldBank2017Dataset 

type MeasuresAndIndicatorsForPovertyPovcalnetWorldBank2017Dataset struct {
	CostOfClosingThePovertyGapInIntMoney2011PovcalnetWorldBank2017    *float64 `json:"cost_of_closing_the_poverty_gap_in_int_money_2011_povcalnet_world_bank_2017"`
	ClosingThePovertyGapIncludingexcludingChinaPovcalnetWorldBank2017 *float64 `json:"closing_the_poverty_gap_includingexcluding_china_povcalnet_world_bank_2017"`
}

Closing the poverty gap:The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers.The cost of closing the poverty gap is calculated as follows: Poverty Gap Index x Poverty Line (1.9 int-$ per day) x 365 x Population.The poverty gap index for each country corresponds to the estimates available via the option 'Replicate the World Bank's regional aggregation' available in the PovcalNet on-line tool.The unit of measure is international-$ (2011 PPP), in order to allow cross-country comparisons. This should not be confused with market dollars. The poverty gap index estimates from the World Bank's PovcalNet data cover low and middle income countries, with observations every three years in the period 1981-2013. To achieve this level of granularity, the World Bank relies on interpolation for countries in which survey data are not available in particular years, but are available either before or after (or both). The process of interpolation requires adjusting the mean income or expenditure observed in the survey year by a growth factor to infer the unobserved level in the missing year. You can read more about this process in http://iresearch.worldbank.org/PovcalNet/methodology.aspx.---Poverty and income groups in non-rich countries:PovcalNet allows to calculate different poverty lines using 2011 PPPs for several reference years (1981, 1984, 1987, 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2010, 2011, 2012 and 2013).OWID team calculated different poverty lines to define the consumption groups. For example, consumption group 1.25 - 1.9 is calculated as follows: 'total population living under the poverty line at 1.9 int-$ per day' minus 'total population living under the poverty line at 1.25 int-$ per day'.Consumption per capita is the preferred welfare indicator for the World Bank’s analysis of global poverty. But not all national statistical agencies report to the World Bank consistent estimates of consumption based on expenditure surveys. For about 25% of the countries, estimates correspond to income, rather than consumption.Warning:'Rich' countries are not included in the PovcalNet tables used for these calculations. Therefore, people belonging to these consumption groups, but living in rich countries (defined by the World Bank) are not represented in these figures.'Non-rich' countries are all countries in the World except: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.---World population in absolute poverty:The share of World population living in Absolute poverty corresponds to the poverty headcount at the World Bank poverty line of 1.9$ per day.Data between 1981 and 2013 are from 'World Total' in PovcalNet's "Regional aggregation using 2011 PPP and $1.9/day poverty line".The 2030 projection of 4.2% is taken from Ferreira at al. (World Bank, 2015), p.37. This projection is "based on the country-specific historic average growth rates for the past 10 years". According to the report, an alternative projection based on the last 20 years would give 5.7%.---Share living in extreme poverty by region:The share of population living in extreme poverty by world region is calculated dividing the absolute number of people living in extreme poverty by the population living in the region.To calculate the share of population living in extreme poverty at world level OWID team uses the total world population, not the sum of the 6 regions. In fact, as explained below, the six world regions do not take into account high-income countries.PovcalNet provides regional aggregation using 2011 PPPs and $1.90/day poverty line for reference years 1987, 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2010, 2011, 2012 and 2013.Unfortunately, for certain regions and years the data survey coverage is too low and the results are suppressed. Therefore, OWID team calculated the number of the missing variable subtracting the sum of the observed regions from the World Total. For example, dis-aggregated data is not available for Middle East and North Africa (MENA) in 2013:MENA 2013 = World Total - (East Asia and Pacific + Europe and Central Asia + Latin America and the Caribbean + South Asia + Sub-Saharan Africa).Warning:The most recent figures from the MENA reason do not necessarily reflect the geopolitical turmoil in the region.High-income countries are not included in PovcalNet tables used for this calculations.---World poverty in absolute number by region:PovcalNet provides regional aggregation using 2011 PPPs and $1.90/day poverty line for reference years 1987, 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2010, 2011, 2012 and 2013.Unfortunately, for certain regions and years the data survey coverage is too low and the results are suppressed. Therefore, OWID team calculated the number of the missing variable subtracting the sum of the observed regions from the World Total. For example, dis-aggregated data is not available for Middle East and North Africa (MENA) in 2013:MENA 2013 = World Total - (East Asia and Pacific + Europe and Central Asia + Latin America and the Caribbean + South Asia + Sub-Saharan Africa).Warning:The most recent figures from the MENA reason do not necessarily reflect the geopolitical turmoil in the region.High-income countries are not included in PovcalNet tables used for this calculations.

type MeatConsumptionInEu28Oecd2018Dataset 

type MeatConsumptionInEu28Oecd2018Dataset struct {
	PerCapitaBeefConsumption    *float64 `json:"per_capita_beef_consumption"`
	PerCapitaPorkConsumption    *float64 `json:"per_capita_pork_consumption"`
	PerCapitaPoultryConsumption *float64 `json:"per_capita_poultry_consumption"`
	PerCapitaSheepConsumption   *float64 `json:"per_capita_sheep_consumption"`
	PerCapitaRedMeatConsumption *float64 `json:"per_capita_red_meat_consumption"`
	PerCapitaMeatConsumption    *float64 `json:"per_capita_meat_consumption"`
}

Per capita meat availability, given as the average of the EU-28 countries. Meat availability is typically taken as an estimate of per capita consumption. Meat availability does not account for consumption (household and retail waste), so actual consumption will be slightly lower than these values.Per capita meat consumption is measured in kilograms per person per year.

type MeatConsumptionInTheUsaUsda2018Dataset 

type MeatConsumptionInTheUsaUsda2018Dataset struct {
	PerCapitaBeefConsumption      *float64 `json:"per_capita_beef_consumption"`
	PerCapitaPorkConsumption      *float64 `json:"per_capita_pork_consumption"`
	PerCapitaRedMeatConsumption   *float64 `json:"per_capita_red_meat_consumption"`
	PerCapitaPoultryConsumption   *float64 `json:"per_capita_poultry_consumption"`
	PerCapitaTotalMeatConsumption *float64 `json:"per_capita_total_meat_consumption"`
}

Per capita meat availability in the United States. Per capita meat availability is typically used as an estimate for meat consumption, although it does not correct for consumption-level waste (households and retail). Actual meat intake will therefore be slightly lower but close to meat availability.Figures are given in lbs and kilograms per person per year. Conversion from lbs to kilograms using a factor of 0.453592 (1 lbs = 0.453592 kilograms).'Red meat' is the sum of beef, pork, sheep and veal, excluding offals.

type MeatConversionEfficienciesAlexanderEtAl2016Dataset 

type MeatConversionEfficienciesAlexanderEtAl2016Dataset struct {
	FeedConversionRatioAlexanderEtAl2016             *float64 `json:"feed_conversion_ratio_alexander_et_al_2016"`
	ProteinFeedConversionEfficiencyAlexanderEtAl2016 *float64 `json:"protein_feed_conversion_efficiency_alexander_et_al_2016"`
	EnergyConversionEfficiencyAlexanderEtAl2016      *float64 `json:"energy_conversion_efficiency_alexander_et_al_2016"`
}

Livestock conversion efficiencies are given as reported in Alexander et al. (2016). Alexander, P., Brown, C., Arneth, A., Finnigan, J., & Rounsevell, M. D. (2016). Human appropriation of land for food: the role of diet. Global Environmental Change, 41, 88-98. Available at: http://www.sciencedirect.com/science/article/pii/S0959378016302370?via%3Dihub#bib0330 [accessed 24th August 2017].'Feed conversion ratio' is defined as the quantity of feed inputs required to produce one kilogram of edible product. This is measured in kilograms of dry-matter feed per kilogram of edible weight product.Protein and energy efficiency are both measured as the percentage of protein or energy converted from feed to animal product.Original data sources as used in this paper are as follows:Smil (2013).Should We Eat Meat? Evolution and Consequences of Modern Carnivory Wiley, New York, USA (2013).Opio et al. (2013). Greenhouse Gas Emissions from Ruminant Supply Chains–A Global Life Cycle Assessment Food and Agriculture Organization of the United Nations (FAO), Rome, Italy (2013).Macleod et al. Greenhouse Gas Emissions from Pig and Chicken Supply Chains – A Global Life Cycle Assessment Food and Agriculture Organization of the United Nations (FAO), Rome, Italy (2013).

type MedianUnPopulationProjectionsGlobalVsAfricaOwidBasedOnUnDataset 

type MedianUnPopulationProjectionsGlobalVsAfricaOwidBasedOnUnDataset struct {
	O65YearsPopulationUnMedianProjections    *float64 `json:"o65_years_population_un_median_projections"`
	Under5YearsPopulationUnMedianProjections *float64 `json:"under_5_years_population_un_median_projections"`
	O25_64YearsPopulationUnMedianProjections *float64 `json:"o25_64_years_population_un_median_projections"`
	O6_11YearsPopulationUnMedianProjections  *float64 `json:"o6_11_years_population_un_median_projections"`
	O12_24YearsPopulationUnMedianProjections *float64 `json:"o12_24_years_population_un_median_projections"`
}

Population data is based on the Medium UN projection (2017 Edition) from 2015-2100. This is presented in thousands.Our World in Data have derived the age category "12-24 years old" based on the sum of the UN age cohorts "12-14" and "15-24" years old. OWID have also derived a value for all non-African countries, using the global projection minus the population projection for Africa.

type MentalAndSubstanceUseDisorderDisaggregatedIhmeDataset 

type MentalAndSubstanceUseDisorderDisaggregatedIhmeDataset struct {
	PrevalenceMentalHealthDisordersBothAgeStandardizedPercent                             *float64 `json:"prevalence_mental_health_disorders_both_age_standardized_percent"`
	PrevalenceMentalHealthDisordersMaleAgeStandardizedPercent                             *float64 `json:"prevalence_mental_health_disorders_male_age_standardized_percent"`
	PrevalenceMentalHealthDisordersFemaleAgeStandardizedPercent                           *float64 `json:"prevalence_mental_health_disorders_female_age_standardized_percent"`
	PrevalenceMentalHealthDisordersBothNumber                                             *float64 `json:"prevalence_mental_health_disorders_both_number"`
	PrevalenceMentalHealthDisordersMaleNumber                                             *float64 `json:"prevalence_mental_health_disorders_male_number"`
	PrevalenceMentalHealthDisordersFemaleNumber                                           *float64 `json:"prevalence_mental_health_disorders_female_number"`
	PrevalenceAlcoholAndSubstanceUseDisordersBothAgeStandardizedPercent                   *float64 `json:"prevalence_alcohol_and_substance_use_disorders_both_age_standardized_percent"`
	PrevalenceAlcoholAndSubstanceUseDisordersMaleAgeStandardizedPercent                   *float64 `json:"prevalence_alcohol_and_substance_use_disorders_male_age_standardized_percent"`
	PrevalenceAlcoholAndSubstanceUseDisordersFemaleAgeStandardizedPercent                 *float64 `json:"prevalence_alcohol_and_substance_use_disorders_female_age_standardized_percent"`
	PrevalenceAlcoholAndSubstanceUseDisordersBothNumber                                   *float64 `json:"prevalence_alcohol_and_substance_use_disorders_both_number"`
	PrevalenceAlcoholAndSubstanceUseDisordersMaleNumber                                   *float64 `json:"prevalence_alcohol_and_substance_use_disorders_male_number"`
	PrevalenceAlcoholAndSubstanceUseDisordersFemaleNumber                                 *float64 `json:"prevalence_alcohol_and_substance_use_disorders_female_number"`
	DeathsAlcoholAndSubstanceUseDisorders                                                 *float64 `json:"deaths_alcohol_and_substance_use_disorders"`
	DeathsAlcoholAndSubstanceUseDisordersAgeStandardizedRate                              *float64 `json:"deaths_alcohol_and_substance_use_disorders_age_standardized_rate"`
	DalysDisabilityAdjustedLifeYearsAlcoholAndSubstanceUseDisordersAgeStandardizedPercent *float64 `json:"dalys_disability_adjusted_life_years_alcohol_and_substance_use_disorders_age_standardized_percent"`
	DalysDisabilityAdjustedLifeYearsMentalHealthDisordersAgeStandardizedPercent           *float64 `json:"dalys_disability_adjusted_life_years_mental_health_disorders_age_standardized_percent"`
	DalysDisabilityAdjustedLifeYearsAlcoholAndSubstanceUseDisordersAgeStandardizedRate    *float64 `json:"dalys_disability_adjusted_life_years_alcohol_and_substance_use_disorders_age_standardized_rate"`
	DalysDisabilityAdjustedLifeYearsMentalHealthDisordersAgeStandardizedRate              *float64 `json:"dalys_disability_adjusted_life_years_mental_health_disorders_age_standardized_rate"`
}

type MentalHealthAsRiskFactorForSubstanceUseSwendsenEtAl2010Dataset 

type MentalHealthAsRiskFactorForSubstanceUseSwendsenEtAl2010Dataset struct {
	IncreasedRiskOfNicotineDependency    *float64 `json:"increased_risk_of_nicotine_dependency"`
	IncreasedRiskOfAlcoholDependency     *float64 `json:"increased_risk_of_alcohol_dependency"`
	IncreasedRiskOfIllicitDrugDependency *float64 `json:"increased_risk_of_illicit_drug_dependency"`
}

Data denotes the increased risk of developing a substance use disorder in individuals with a mental health disorder relative to those without, based on a study on over 5000 individuals over a 10-year period.Full data with confidence intervals can be found at: https://ourworldindata.org/mental-health-disorders-as-risk-for-substance-use

type MentalHealthServicesAcrossIncomesWangEtAl2007Dataset 

type MentalHealthServicesAcrossIncomesWangEtAl2007Dataset struct {
	ShareOfGdpSpentOnHealthcare                            *float64 `json:"share_of_gdp_spent_on_healthcare"`
	ShareReceivingAnyTreatmentForMentalHealth              *float64 `json:"share_receiving_any_treatment_for_mental_health"`
	MentalHealthSpecialtyPerc                              *float64 `json:"mental_health_specialty_perc"`
	GeneralMedicalPerc                                     *float64 `json:"general_medical_perc"`
	HumanServicesPerc                                      *float64 `json:"human_services_perc"`
	ComplementaryAndAlternativeMedicinePerc                *float64 `json:"complementary_and_alternative_medicine_perc"`
	SevereMentalHealthWhoReceivedTreatmentInLastYearPerc   *float64 `json:"severe_mental_health_who_received_treatment_in_last_year_perc"`
	ModerateMentalHealthWhoReceivedTreatmentInLastYearPerc *float64 `json:"moderate_mental_health_who_received_treatment_in_last_year_perc"`
	MildMentalHealthWhoReceivedTreatmentInLastYearPerc     *float64 `json:"mild_mental_health_who_received_treatment_in_last_year_perc"`
}

Data is available only for a small number of countries, and based on household surveys over the period 2001-2004.'Share receiving any treatment for mental health' refers to the percentage of total respondents (with or without mental health burden) who received any form of treatment in the last 12 months.In differentiating the share of people with particular severities of mental illness receiving treatment, the authors define the following categories:- 'Severe' = "bipolar I disorder or substance dependence with a physiological dependence syndrome, making a suicide attempt in conjunction with any other disorder, reporting severe role impairment due to a mental disorder in at least two areas of functioning measured by disorder-specific Sheehan Disability Scales (SDS),12 or having overall functional impairment from any disorder consistent with a Global Assessment of Functioning (GAF)13 score of 50 or less."- 'Moderate' = "substance dependence without a physiological dependence syndrome or at least moderate interference in any SDS domain"- 'Mild' = all other disorders not noted above.Mental health services were classified into the following sectors: - mental health specialty (psychiatrist, psychologist, other mental health professional in any setting, social worker or counsellor in a mental health specialty setting, use of a mental health hotline); - general medical (primary care doctor, other general medical doctor, nurse, any other health professional not previously mentioned); - human services (religious or spiritual advisor, social worker, or counsellor in any setting other than a specialty mental health setting);- complementary and alternative medicine (any other type of healer such as chiropractors, participation in an internet support group, participation in a self-help group).

type MetalProductionClioInfraAndUsgsDataset 

type MetalProductionClioInfraAndUsgsDataset struct {
	NickelProductionClioInfraAndUsgs    *float64 `json:"nickel_production_clio_infra_and_usgs"`
	TungstenProductionClioInfraAndUsgs  *float64 `json:"tungsten_production_clio_infra_and_usgs"`
	ZincProductionClioInfraAndUsgs      *float64 `json:"zinc_production_clio_infra_and_usgs"`
	SilverProductionClioInfraAndUsgs    *float64 `json:"silver_production_clio_infra_and_usgs"`
	BauxiteProductionClioInfraAndUsgs   *float64 `json:"bauxite_production_clio_infra_and_usgs"`
	IronOreProductionClioInfraAndUsgs   *float64 `json:"iron_ore_production_clio_infra_and_usgs"`
	GoldProductionClioInfraAndUsgs      *float64 `json:"gold_production_clio_infra_and_usgs"`
	TinProductionClioInfraAndUsgs       *float64 `json:"tin_production_clio_infra_and_usgs"`
	LeadProductionClioInfraAndUsgs      *float64 `json:"lead_production_clio_infra_and_usgs"`
	CopperProductionClioInfraAndUsgs    *float64 `json:"copper_production_clio_infra_and_usgs"`
	AluminiumProductionClioInfraAndUsgs *float64 `json:"aluminium_production_clio_infra_and_usgs"`
	ManganeseProductionClioInfraAndUsgs *float64 `json:"manganese_production_clio_infra_and_usgs"`
}

Data on metal production is based on the combination of two sources.

Historical data from 1850-2011 is sourced from the ClioInfra database, available at: https://www.clio-infra.eu/ [accessed 9th September 2017].

This has been combined with recent data from the USGS Mineral Statistics from 2012 onwards. Available at: https://minerals.usgs.gov/minerals/pubs/commodity/aluminum/ [accessed 9th September 2017].

Both sources report primary production in thousand metric tonnes. Note: Gold production is measured in metric tons.

type MethaneEmissionsBySectorCait2020Dataset 

type MethaneEmissionsBySectorCait2020Dataset struct {
	AgricultureCh4EmissionsCait                       *float64 `json:"agriculture_ch4_emissions_cait"`
	EnergyCh4EmissionsCait                            *float64 `json:"energy_ch4_emissions_cait"`
	FugitiveEmissionsCh4EmissionsCait                 *float64 `json:"fugitive_emissions_ch4_emissions_cait"`
	IndustryCh4EmissionsCait                          *float64 `json:"industry_ch4_emissions_cait"`
	LandUseChangeAndForestryCh4EmissionsCait          *float64 `json:"land_use_change_and_forestry_ch4_emissions_cait"`
	OtherFuelCombustionCh4EmissionsCait               *float64 `json:"other_fuel_combustion_ch4_emissions_cait"`
	TotalExcludingLucfCh4EmissionsCait                *float64 `json:"total_excluding_lucf_ch4_emissions_cait"`
	TotalIncludingLucfCh4EmissionsCait                *float64 `json:"total_including_lucf_ch4_emissions_cait"`
	WasteCh4EmissionsCait                             *float64 `json:"waste_ch4_emissions_cait"`
	AgriculturePerCapitaCh4EmissionsCait              *float64 `json:"agriculture_per_capita_ch4_emissions_cait"`
	FugitiveEmissionsPerCapitaCh4EmissionsCait        *float64 `json:"fugitive_emissions_per_capita_ch4_emissions_cait"`
	IndustryPerCapitaCh4EmissionsCait                 *float64 `json:"industry_per_capita_ch4_emissions_cait"`
	LandUseChangeAndForestryPerCapitaCh4EmissionsCait *float64 `json:"land_use_change_and_forestry_per_capita_ch4_emissions_cait"`
	TotalExcludingLucfPerCapitaCh4EmissionsCait       *float64 `json:"total_excluding_lucf_per_capita_ch4_emissions_cait"`
	TotalIncludingLucfPerCapitaCh4EmissionsCait       *float64 `json:"total_including_lucf_per_capita_ch4_emissions_cait"`
	WastePerCapitaCh4EmissionsCait                    *float64 `json:"waste_per_capita_ch4_emissions_cait"`
}

Methane (CH4) emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type MethaneEmissionsBySectorCait2021Dataset 

type MethaneEmissionsBySectorCait2021Dataset struct {
	Agriculture                       *float64 `json:"agriculture"`
	AgriculturePerCapita              *float64 `json:"agriculture_per_capita"`
	Energy                            *float64 `json:"energy"`
	EnergyPerCapita                   *float64 `json:"energy_per_capita"`
	FugitiveEmissions                 *float64 `json:"fugitive_emissions"`
	FugitiveEmissionsPerCapita        *float64 `json:"fugitive_emissions_per_capita"`
	Industry                          *float64 `json:"industry"`
	IndustryPerCapita                 *float64 `json:"industry_per_capita"`
	LandUseChangeAndForestry          *float64 `json:"land_use_change_and_forestry"`
	LandUseChangeAndForestryPerCapita *float64 `json:"land_use_change_and_forestry_per_capita"`
	OtherFuelCombustion               *float64 `json:"other_fuel_combustion"`
	OtherFuelCombustionPerCapita      *float64 `json:"other_fuel_combustion_per_capita"`
	TotalExcludingLucf                *float64 `json:"total_excluding_lucf"`
	TotalExcludingLucfPerCapita       *float64 `json:"total_excluding_lucf_per_capita"`
	TotalIncludingLucf                *float64 `json:"total_including_lucf"`
	TotalIncludingLucfPerCapita       *float64 `json:"total_including_lucf_per_capita"`
	Waste                             *float64 `json:"waste"`
	WastePerCapita                    *float64 `json:"waste_per_capita"`
}

Methane (CH4) emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type MilestonesOfWomensPoliticalRepresentationPaxtonEtAl2006Dataset 

type MilestonesOfWomensPoliticalRepresentationPaxtonEtAl2006Dataset struct {
	UniversalSuffrageToWomenOwidBasedOnPaxtonEtAl2006        *float64 `json:"universal_suffrage_to_women_owid_based_on_paxton_et_al_2006"`
	FirstWomenInParliamentOwidBasedOnPaxtonEtAl2006          *float64 `json:"first_women_in_parliament_owid_based_on_paxton_et_al_2006"`
	O10percShareOfWomenInParliamentOwidBasedOnPaxtonEtAl2006 *float64 `json:"o10perc_share_of_women_in_parliament_owid_based_on_paxton_et_al_2006"`
	O20percShareOfWomenInParliamentOwidBasedOnPaxtonEtAl2006 *float64 `json:"o20perc_share_of_women_in_parliament_owid_based_on_paxton_et_al_2006"`
	O30percShareOfWomenInParliamentOwidBasedOnPaxtonEtAl2006 *float64 `json:"o30perc_share_of_women_in_parliament_owid_based_on_paxton_et_al_2006"`
}

Data description:Paxton’s data is downloaded from the Inter-university Consortium for Political and Social Research (ICPSR). It provides country-level data on the start and end year of sovereignty; year in which universal suffrage was granted to all women; the year the first woman was elected to parliament; and percentage share of women in parliament for the years 1893-2003. OWID updates their data through to 2017 using Inter-parliamentary union (IPU) statistical archives and the World Bank’s World Development Indicator (WDI) variable on women’s share in parliament (main sources). Data from 2003 onwards was derived from various other sources. IPU’s statistical archives were used to derive figures on the share of women in parliament (from 2004-2017); the CIA World Factbook provided data on the year of sovereignty and universal suffrage, alongside various additional sources which are listed below. Moreover, data extension to 2017 means that our dataset consists of five additional countries: Libya, Montenegro, Serbia, South Sudan and Timor, which gained independence after 2003.Data construction:Paxton et al (2006) describes women’s political representation as the share of sovereign countries which satisfied the five representation milestones in any given year. Therefore, we have constructed this dataset which indicates the status of sovereign countries using the values "Yes" and "No". More specifically, if the year of universal suffrage is less than 1990 for the United States, we coded ‘Universal suffrage to women’ in the United States in 1990 as Yes. Note that the United States had gained sovereignty by then. In addition, our dataset doesn’t consider these milestones as ordered achievements in women’s political representation. Paxton et al (2006) assumed that a country can only elect a woman MP once the universal suffrage has been granted. However, there are countries whereby a female MP was elected prior to gaining universal suffrage. Therefore, we calculate values for each milestone independently and map countries as per the original source definition of sovereign.Additional data sources: This section provides detailed information on the sources used by variables and countries.Year of sovereignty CIA’s World Factbook Independence field is used to determine the year of sovereignty for Libya, Montenegro, Serbia, South Sudan, Timor, American Samoa, Hungary and Iran. Likewise, we determine end of sovereignty for Libya and Serbia and Montenegro using the same source. Available at: https://www.cia.gov/library/publications/the-world-factbook/fields/2088.html [accessed 16th August 2017].Year of universal suffrageKuwait, Oman and Qatar: UNICEF Gender Equality Profiles. Available at: https://www.unicef.org/gender/gender_62215.html [accessed 16th August 2017].Libya, Serbia (mentioned as Serbia and Montenegro), Montenegro (mentioned as Serbia and Montenegro), South Sudan (mentioned as Sudan), Timor and American Samoa. IPU’s Women in Politics. Available at: http://www.ipu.org/PDF/publications/wmn45-05_en.pdf [accessed 16th August 2017].Saudi Arabia and United Arab Emirates. CIA’s World Factbook Suffrage field. Available at: https://www.cia.gov/library/publications/the-world-factbook/fields/2123.html [accessed 16th August 2017].Brunei: Wikipedia.Year of first woman elected to parliamentTimor, Latvia and American Samoa: IPU’s Women in Politics. Available at: http://www.ipu.org/PDF/publications/wmn45-05_en.pdf [accessed 16th August 2017].Oman, Palau, Saudi Arabia, Qatar, UAE and Tanzania: IPU’s Women in Parliament Annual Reviews.Serbia, Montenegro, Nigeria and South Sudan: IPU’s Parline. Available at: http://www.ipu.org/parline-e/parlinesearch.asp [accessed 16th August 2017].Kuwait: The Guardian. Available at: https://www.theguardian.com/world/2009/may/17/kuwait-women-elected-parliament [accessed 16th August 2017].Libya: Libyan House of Representatives. Available at: https://www.temehu.com/house-of-representatives.htm [accessed 16th August 2017].Sierra Leone: Pathways of Women’s Empowerment, Institute of Development Studies, University of Sussex, UK. Available at: https://assets.publishing.service.gov.uk/media/57a08ac5ed915d3cfd00092c/CS_Women_and_Politics_SL.pdf [accessed 16th August 2017].Share of women in parliament IPU’s Statistical Archive (http://www.ipu.org/wmn-e/classif-arc.htm) for all countries from 2004 to 2017. For each year, we take the share of women in lower house of the parliament. This definition matches both with WDI and IPU’s Women in Parliament 1945-1995. They use the share of women in single house for unicameral parliaments and lower house for bicameral parliaments. Moreover, for consistency, data is taken from December every year. If December data is missing, we take the next latest month of the corresponding year.Handling data issues: For years 1997 - 2003, there is an overlap between Paxton’s dataset and WDI data measuring the share of women in parliament. For most countries, the values match exactly. However, there are 20-40 countries each year where the Paxton and WDI values do not match. As the more complete and comprehensive dataset, we have taken Paxton’s dataset as our primary source in such cases for the period 1997-2003. Nonetheless, we use WDI to fill the remaining gaps. For the same reasoning, we choose manually collected data from IPU’s statistical archive over WDI for the years 2004-2016.

type MilitaryExpenditureAsAShareOfGdpOwidBasedOnCowAndSipri2017Dataset 

type MilitaryExpenditureAsAShareOfGdpOwidBasedOnCowAndSipri2017Dataset struct {
	MilitaryExpenditureAsAShareOfGdpOwidCalculatedBasedOnNmcCowAndSipri2017 *float64 `json:"military_expenditure_as_a_share_of_gdp_owid_calculated_based_on_nmc_cow_and_sipri_2017"`
}

The National Material Capabilities dataset, from the Correlates of War (COW) Project, contains long-run estimates of the military power of countries.The COW data series on military expenditure ends in 2012 and it is measured in current currency units; specifically British Pounds up to the year 1914, and US Dollars thereafter. This reflects the fact that the COW data was originally intended for cross-country comparisons at specific points in time, rather than for tracking changes in military expenditure over the long-run.Considering this, at Our World in Data we have produced a new dataset, based on the COW data, which does allow for comparisons both across countries and time. Here is a description of what we have done. <ol> <li>We first took the COW dataset and used historical exchange rates from the Bank of England to unify the entire series into a single currency (nominal British Pounds).</li> <li>Then we converted this series into shares of GDP. For this we used historical cross-country estimates of nominal GDP, also available in British Pounds from the CEPII research centre.</li> <li>Finally, we extended the series forward up until 2016 by appending data from SIPRI, as published in the World Bank's World Development Indicators <a href="https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?page" rel="noopener" target="_blank">here</a>.</li></ol>Please note that we use exchange rates from the Bank of England because the original exchange rates used to produce the COW estimates are not available. While we are aware that this introduces noise to the series, we believe that this does not affect trends or levels in any systematic way. It simply adds to the margin of error that historical estimates already had.Also note that the entity 'World' shows values from SIPRI for the period 1960-2016.

type MineralProductionBgs2016Dataset 

type MineralProductionBgs2016Dataset struct {
	CadmiumBgs2016  *float64 `json:"cadmium_bgs_2016"`
	CobaltBgs2016   *float64 `json:"cobalt_bgs_2016"`
	DiamondBgs2016  *float64 `json:"diamond_bgs_2016"`
	GraphiteBgs2016 *float64 `json:"graphite_bgs_2016"`
	LithiumBgs2016  *float64 `json:"lithium_bgs_2016"`
	MercuryBgs2016  *float64 `json:"mercury_bgs_2016"`
	PlatinumBgs2016 *float64 `json:"platinum_bgs_2016"`
	UraniumBgs2016  *float64 `json:"uranium_bgs_2016"`
	SiliconBgs2016  *float64 `json:"silicon_bgs_2016"`
}

Where Russia and the Soviet Union both feature in the data, the Soviet Union series has been added to Russia's own figures. The same is true for the Democratic Republic of Congo/Zaire, West Germany/East Germany/Germany, and Serbia and Montenegro/Yugoslavia, Mozambique/Rhodesia.

type MinimumReadingAndMathsProficiencyGemReport20178Dataset 

type MinimumReadingAndMathsProficiencyGemReport20178Dataset struct {
	PercentageOfStudentsAtEndOfLowerSecondaryEducationAchievingAtLeastAMinimumProficiencyLevelInReadingGemReport20178      *float64 `` /* 140-byte string literal not displayed */
	PercentageOfStudentsAtEndOfLowerSecondaryEducationAchievingAtLeastAMinimumProficiencyLevelInMathematicsGemReport20178  *float64 `` /* 144-byte string literal not displayed */
	PercentageOfPupilsInEarlyPrimaryEducationGrades2Or3AchievingAtLeastAMinimumProficiencyLevelInReadingGemReport20178     *float64 `` /* 143-byte string literal not displayed */
	PercentageOfPupilsInEarlyPrimaryEducationGrades2Or3AchievingAtLeastAMinimumProficiencyLevelInMathematicsGemReport20178 *float64 `` /* 147-byte string literal not displayed */
	PercentageOfPupilsAtEndOfPrimaryEducationAchievingAtLeastAMinimumProficiencyLevelInReadingGemReport20178               *float64 `` /* 130-byte string literal not displayed */
	PercentageOfPupilsAtEndOfPrimaryEducationAchievingAtLeastAMinimumProficiencyLevelInMathematicsGemReport20178           *float64 `` /* 134-byte string literal not displayed */
}

type MissingPlasticBudgetLebretonEtAl2019Dataset 

type MissingPlasticBudgetLebretonEtAl2019Dataset struct {
	AccumulatedOceanPlasticMacroplasticsGreater05cm *float64 `json:"accumulated_ocean_plastic_macroplastics_greater05cm"`
	AccumulatedOceanPlasticMicroplasticsLess05cm    *float64 `json:"accumulated_ocean_plastic_microplastics_less05cm"`
}

Data describes modelled and projected ocean plastic under three 'plastic emissions' scenarios.Given is the modelled global accumulation of buoyant macroplastics (>0.5 cm) in the ocean. And the accumulation of microplastics (<0.5 cm), which is degraded plastic material from the ocean surface layer.This is given under three scenarios:(1) Emissions of plastic to the world's oceans stopped by 2020;(2) Emissions stagnated at 2020 levels;(3) Increasing emissions of plastic until 2050 in line with the average growth rate of global plastic production from 2005-2015.

type MissingWomenEstimatesBongaartsAndGuilmoto2015Dataset 

type MissingWomenEstimatesBongaartsAndGuilmoto2015Dataset struct {
	MissingFemalesBongaartsAndGuilmoto2015      *float64 `json:"missing_females_bongaarts_and_guilmoto_2015"`
	ExcessFemaleDeathsBongaartsAndGuilmoto2015  *float64 `json:"excess_female_deaths_bongaarts_and_guilmoto_2015"`
	MissingFemaleBirthsBongaartsAndGuilmoto2015 *float64 `json:"missing_female_births_bongaarts_and_guilmoto_2015"`
}

Missing women are defined as the number of additional women who would be alive in the absence of sex discrimination. Missing women are the sum of women missing at birth (as a result of sex-selective abortion) and excess female mortality through infanticide or neglect.Missing female births and excess female mortality are calculated based on the difference between observed and expected sex ratios.Authors have calculated this historically from 1970 to today in five-year intervals, with projections through to 2100.

type MobileBankAccountsByRegionGsma2019Dataset 

type MobileBankAccountsByRegionGsma2019Dataset struct {
	RegisteredMobileMoneyAccounts *float64 `json:"registered_mobile_money_accounts"`
}

GSMA's definition of mobile money services:- A mobile money service includes transferring money and making payments using the mobile phone.- The service must be available to the unbanked, e.g. people who do not have access to a formal account at a financial institution.- The service must offer a network of physical transactional points which can include agents, outside of bank branches and ATMs, that make the service widely accessible to everyone.- Mobile banking or payment services (such as Apple Pay and Google Wallet) that offer the mobile phone as just another channel to access a traditional banking product are not included.Data refers to the cumulative number of customer accounts at the end the year indicated. Customers who have not been registered but perform transactions over the counter are NOT included.Global data are is available from 2006-2018. Regional data are not available before 2012, due to confidentiality restriction with the survey participants. However, 2006 data for Sub-Saharan Africa are available as published in GSMA, The State of Mobile Money in Sub-Saharan Africa 2016.

type MortalityFromAllFormsOfViolenceIhme2016Dataset 

type MortalityFromAllFormsOfViolenceIhme2016Dataset struct {
	AgeStandardizedDeathRateFromAllFormsOfViolenceIhme2016 *float64 `json:"age_standardized_death_rate_from_all_forms_of_violence_ihme_2016"`
	TotalDeathsFromAllFormsOfViolenceIhme2016              *float64 `json:"total_deaths_from_all_forms_of_violence_ihme_2016"`
	DeathRateFromAllFormsOfViolenceIhme2016                *float64 `json:"death_rate_from_all_forms_of_violence_ihme_2016"`
}

Data was amended by Our World in Data to represent the total number of deaths and age-standardized death rate from all forms of violence. This includes the IHME GBD categories of 'interpersonal violence'; 'conflict and terrorism' and 'executions and police conflict'.

type MotorVehiclesPer1000PeopleNationmaster2014Dataset 

type MotorVehiclesPer1000PeopleNationmaster2014Dataset struct {
	MotorVehiclesPer1000PeopleNationmaster2014 *float64 `json:"motor_vehicles_per_1000_people_nationmaster_2014"`
}

"All countries compared for Transport > Road > Motor vehicles per 1000 people", Wikipedia: List of countries by vehicles per capita. Aggregates compiled by NationMaster. Retrieved from http://www.nationmaster.com/country-info/stats/Transport/Road/Motor-vehicles-per-1000-people

type MultinationalTimeUseStudyMtusGershunyAndFisher2013Dataset 

type MultinationalTimeUseStudyMtusGershunyAndFisher2013Dataset struct {
	MinutesSpentSleepingMtus2013                   *float64 `json:"minutes_spent_sleeping_mtus_2013"`
	MinutesSpentEatingAndDrinkingMtus2013          *float64 `json:"minutes_spent_eating_and_drinking_mtus_2013"`
	MinutesSpentOnSelfCareMtus2013                 *float64 `json:"minutes_spent_on_self_care_mtus_2013"`
	MinutesSpentOnPaidWorkMtus2013                 *float64 `json:"minutes_spent_on_paid_work_mtus_2013"`
	MinutesSpentOnEducationMtus2013                *float64 `json:"minutes_spent_on_education_mtus_2013"`
	MinutesSpentOnFoodPreparationMtus2013          *float64 `json:"minutes_spent_on_food_preparation_mtus_2013"`
	MinutesSpentCleaningMtus2013                   *float64 `json:"minutes_spent_cleaning_mtus_2013"`
	MinutesSpentOnMaintenanceMtus2013              *float64 `json:"minutes_spent_on_maintenance_mtus_2013"`
	MinutesSpentShoppingMtus2013                   *float64 `json:"minutes_spent_shopping_mtus_2013"`
	MinutesSpentGardeningMtus2013                  *float64 `json:"minutes_spent_gardening_mtus_2013"`
	MinutesSpentOnPetCareMtus2013                  *float64 `json:"minutes_spent_on_pet_care_mtus_2013"`
	MinutesSpentOnElderlyCareMtus2013              *float64 `json:"minutes_spent_on_elderly_care_mtus_2013"`
	MinutesSpentOnPhysicalRoutineChildCareMtus2013 *float64 `json:"minutes_spent_on_physical_routine_child_care_mtus_2013"`
	MinutesSpentOnInteractiveChildCareMtus2013     *float64 `json:"minutes_spent_on_interactive_child_care_mtus_2013"`
	MinutesSpentOnReligiousActivitiesMtus2013      *float64 `json:"minutes_spent_on_religious_activities_mtus_2013"`
	MinutesSpentOnVoluntaryWorkMtus2013            *float64 `json:"minutes_spent_on_voluntary_work_mtus_2013"`
	MinutesSpentCommutingMtus2013                  *float64 `json:"minutes_spent_commuting_mtus_2013"`
	MinutesSpentOnTravelMtus2013                   *float64 `json:"minutes_spent_on_travel_mtus_2013"`
	MinutesSpentOnSportsOrExerciseMtus2013         *float64 `json:"minutes_spent_on_sports_or_exercise_mtus_2013"`
	MinutesSpentWatchingTvMtus2013                 *float64 `json:"minutes_spent_watching_tv_mtus_2013"`
	MinutesSpentReadingMtus2013                    *float64 `json:"minutes_spent_reading_mtus_2013"`
	MinutesSpentOnComputerMtus2013                 *float64 `json:"minutes_spent_on_computer_mtus_2013"`
	MinutesSpentOnOutOfHomeLeisureMtus2013         *float64 `json:"minutes_spent_on_out_of_home_leisure_mtus_2013"`
	MinutesSpentOnLeisureMtus2013                  *float64 `json:"minutes_spent_on_leisure_mtus_2013"`
}

The Multinational Time Use Study (MTUS) compiles a cross-nationally harmonised set of time use surveys where recorded variables have been comparably recoded. Activities are sorted into 24 categories for the population aged between 21-65. We restrict our sample to working-age adults to minimise the role of time allocation decisions with a strong inter-temporal component following <a href="https://www.econstor.eu/bitstream/10419/55560/1/508634636.pdf" rel="noopener" target="_blank">Aguiar and Hurst (2006)</a>. The following methodology was used to arrive at the minutes spent across the 24 recorded activities:<ul><li>Using the MTUS simple dataset, the sample was restricted to individuals aged between 21-65.</li><li>The minutes spent on each activity were weighted by the ‘propwt’ variable which brings the sample in line with the population from which it was drawn and ensures only good quality diaries are used. More information on the construction of ‘propwt’ can be found here: https://www.timeuse.org/sites/default/files/9727/chapter-5-weights-in-mtus.pdf </li><li>To calculate the mean number of minutes spent on an activity in a country in a given year, we took an average of the number of minutes spent on each of the 24 activities reported by the diarists. All diary entries where no minutes are recorded for a certain activity are treated as missing and are not included in the calculated average. <a href="https://www.timeuse.org/sites/default/files/9727/mtus-user-guide-r9-february-2016.pdf" rel="noopener" target"_blank">Gershuny and Fisher (2016)</a> mention "it is impossible to say for certain if [entries of zero minutes] is because the diarist did not do the activity or if the diarist actually did undertake the activity but did not report it in the diary". Therefore, estimates should be interpreted as an upper bound for the average number of minutes spent on an activity by those diarists who have reported taking part in an activity.</li><li>The data is normalised to 1440 minutes per day. In other words, countries for which time use does not sum to 1440 minutes after weighting and averaging, the missing minutes are equally distributed across all activities. </li></ul>Please see Table 1.1 for a list of all the surveys and years included and Table 1.2 for technical information on each time use survey: https://www.timeuse.org/sites/default/files/9727/mtus-user-guide-r9-february-2016.pdf

type NationalPovertyLinesJolliffeAndPrydz2016Dataset 

type NationalPovertyLinesJolliffeAndPrydz2016Dataset struct {
	NationalPovertyLinesJolliffeAndPrydz2016 *float64 `json:"national_poverty_lines_jolliffe_and_prydz_2016"`
}

type NaturalDisastersEmdatDataset 

type NaturalDisastersEmdatDataset struct {
	DeathsDrought                        *float64 `json:"deaths_drought"`
	InjuredDrought                       *float64 `json:"injured_drought"`
	AffectedDrought                      *float64 `json:"affected_drought"`
	HomelessDrought                      *float64 `json:"homeless_drought"`
	TotalAffectedDrought                 *float64 `json:"total_affected_drought"`
	ReconstructionCostsDrought           *float64 `json:"reconstruction_costs_drought"`
	InsuredDamagesDrought                *float64 `json:"insured_damages_drought"`
	TotalDamagesDrought                  *float64 `json:"total_damages_drought"`
	AffectedRatePer100kDrought           *float64 `json:"affected_rate_per_100k_drought"`
	HomelessRatePer100kDrought           *float64 `json:"homeless_rate_per_100k_drought"`
	DeathsEarthquake                     *float64 `json:"deaths_earthquake"`
	InjuredEarthquake                    *float64 `json:"injured_earthquake"`
	AffectedEarthquake                   *float64 `json:"affected_earthquake"`
	HomelessEarthquake                   *float64 `json:"homeless_earthquake"`
	TotalAffectedEarthquake              *float64 `json:"total_affected_earthquake"`
	ReconstructionCostsEarthquake        *float64 `json:"reconstruction_costs_earthquake"`
	InsuredDamagesEarthquake             *float64 `json:"insured_damages_earthquake"`
	TotalDamagesEarthquake               *float64 `json:"total_damages_earthquake"`
	AffectedRatePer100kEarthquake        *float64 `json:"affected_rate_per_100k_earthquake"`
	HomelessRatePer100kEarthquake        *float64 `json:"homeless_rate_per_100k_earthquake"`
	DeathsAllDisasters                   *float64 `json:"deaths_all_disasters"`
	InjuredAllDisasters                  *float64 `json:"injured_all_disasters"`
	AffectedAllDisasters                 *float64 `json:"affected_all_disasters"`
	HomelessAllDisasters                 *float64 `json:"homeless_all_disasters"`
	TotalAffectedAllDisasters            *float64 `json:"total_affected_all_disasters"`
	ReconstructionCostsAllDisasters      *float64 `json:"reconstruction_costs_all_disasters"`
	InsuredDamagesAllDisasters           *float64 `json:"insured_damages_all_disasters"`
	TotalDamagesAllDisasters             *float64 `json:"total_damages_all_disasters"`
	AffectedRatePer100kAllDisasters      *float64 `json:"affected_rate_per_100k_all_disasters"`
	HomelessRatePer100kAllDisasters      *float64 `json:"homeless_rate_per_100k_all_disasters"`
	DeathsVolcanic                       *float64 `json:"deaths_volcanic"`
	InjuredVolcanic                      *float64 `json:"injured_volcanic"`
	AffectedVolcanic                     *float64 `json:"affected_volcanic"`
	HomelessVolcanic                     *float64 `json:"homeless_volcanic"`
	TotalAffectedVolcanic                *float64 `json:"total_affected_volcanic"`
	ReconstructionCostsVolcanic          *float64 `json:"reconstruction_costs_volcanic"`
	InsuredDamagesVolcanic               *float64 `json:"insured_damages_volcanic"`
	TotalDamagesVolcanic                 *float64 `json:"total_damages_volcanic"`
	AffectedRatePer100kVolcanic          *float64 `json:"affected_rate_per_100k_volcanic"`
	HomelessRatePer100kVolcanic          *float64 `json:"homeless_rate_per_100k_volcanic"`
	DeathsFlood                          *float64 `json:"deaths_flood"`
	InjuredFlood                         *float64 `json:"injured_flood"`
	AffectedFlood                        *float64 `json:"affected_flood"`
	HomelessFlood                        *float64 `json:"homeless_flood"`
	TotalAffectedFlood                   *float64 `json:"total_affected_flood"`
	ReconstructionCostsFlood             *float64 `json:"reconstruction_costs_flood"`
	InsuredDamagesFlood                  *float64 `json:"insured_damages_flood"`
	TotalDamagesFlood                    *float64 `json:"total_damages_flood"`
	AffectedRatePer100kFlood             *float64 `json:"affected_rate_per_100k_flood"`
	HomelessRatePer100kFlood             *float64 `json:"homeless_rate_per_100k_flood"`
	DeathsMassMovement                   *float64 `json:"deaths_mass_movement"`
	InjuredMassMovement                  *float64 `json:"injured_mass_movement"`
	AffectedMassMovement                 *float64 `json:"affected_mass_movement"`
	HomelessMassMovement                 *float64 `json:"homeless_mass_movement"`
	TotalAffectedMassMovement            *float64 `json:"total_affected_mass_movement"`
	ReconstructionCostsMassMovement      *float64 `json:"reconstruction_costs_mass_movement"`
	InsuredDamagesMassMovement           *float64 `json:"insured_damages_mass_movement"`
	TotalDamagesMassMovement             *float64 `json:"total_damages_mass_movement"`
	AffectedRatePer100kMassMovement      *float64 `json:"affected_rate_per_100k_mass_movement"`
	HomelessRatePer100kMassMovement      *float64 `json:"homeless_rate_per_100k_mass_movement"`
	DeathsStorm                          *float64 `json:"deaths_storm"`
	InjuredStorm                         *float64 `json:"injured_storm"`
	AffectedStorm                        *float64 `json:"affected_storm"`
	HomelessStorm                        *float64 `json:"homeless_storm"`
	TotalAffectedStorm                   *float64 `json:"total_affected_storm"`
	ReconstructionCostsStorm             *float64 `json:"reconstruction_costs_storm"`
	InsuredDamagesStorm                  *float64 `json:"insured_damages_storm"`
	TotalDamagesStorm                    *float64 `json:"total_damages_storm"`
	AffectedRatePer100kStorm             *float64 `json:"affected_rate_per_100k_storm"`
	HomelessRatePer100kStorm             *float64 `json:"homeless_rate_per_100k_storm"`
	DeathsLandslide                      *float64 `json:"deaths_landslide"`
	InjuredLandslide                     *float64 `json:"injured_landslide"`
	AffectedLandslide                    *float64 `json:"affected_landslide"`
	HomelessLandslide                    *float64 `json:"homeless_landslide"`
	TotalAffectedLandslide               *float64 `json:"total_affected_landslide"`
	ReconstructionCostsLandslide         *float64 `json:"reconstruction_costs_landslide"`
	InsuredDamagesLandslide              *float64 `json:"insured_damages_landslide"`
	TotalDamagesLandslide                *float64 `json:"total_damages_landslide"`
	AffectedRatePer100kLandslide         *float64 `json:"affected_rate_per_100k_landslide"`
	HomelessRatePer100kLandslide         *float64 `json:"homeless_rate_per_100k_landslide"`
	DeathsFog                            *float64 `json:"deaths_fog"`
	InjuredFog                           *float64 `json:"injured_fog"`
	AffectedFog                          *float64 `json:"affected_fog"`
	HomelessFog                          *float64 `json:"homeless_fog"`
	TotalAffectedFog                     *float64 `json:"total_affected_fog"`
	ReconstructionCostsFog               *float64 `json:"reconstruction_costs_fog"`
	InsuredDamagesFog                    *float64 `json:"insured_damages_fog"`
	TotalDamagesFog                      *float64 `json:"total_damages_fog"`
	AffectedRatePer100kFog               *float64 `json:"affected_rate_per_100k_fog"`
	HomelessRatePer100kFog               *float64 `json:"homeless_rate_per_100k_fog"`
	DeathsWildfire                       *float64 `json:"deaths_wildfire"`
	InjuredWildfire                      *float64 `json:"injured_wildfire"`
	AffectedWildfire                     *float64 `json:"affected_wildfire"`
	HomelessWildfire                     *float64 `json:"homeless_wildfire"`
	TotalAffectedWildfire                *float64 `json:"total_affected_wildfire"`
	ReconstructionCostsWildfire          *float64 `json:"reconstruction_costs_wildfire"`
	InsuredDamagesWildfire               *float64 `json:"insured_damages_wildfire"`
	TotalDamagesWildfire                 *float64 `json:"total_damages_wildfire"`
	AffectedRatePer100kWildfire          *float64 `json:"affected_rate_per_100k_wildfire"`
	HomelessRatePer100kWildfire          *float64 `json:"homeless_rate_per_100k_wildfire"`
	DeathsTemperature                    *float64 `json:"deaths_temperature"`
	InjuredTemperature                   *float64 `json:"injured_temperature"`
	AffectedTemperature                  *float64 `json:"affected_temperature"`
	HomelessTemperature                  *float64 `json:"homeless_temperature"`
	TotalAffectedTemperature             *float64 `json:"total_affected_temperature"`
	ReconstructionCostsTemperature       *float64 `json:"reconstruction_costs_temperature"`
	InsuredDamagesTemperature            *float64 `json:"insured_damages_temperature"`
	TotalDamagesTemperature              *float64 `json:"total_damages_temperature"`
	AffectedRatePer100kTemperature       *float64 `json:"affected_rate_per_100k_temperature"`
	HomelessRatePer100kTemperature       *float64 `json:"homeless_rate_per_100k_temperature"`
	DeathsGlacialLake                    *float64 `json:"deaths_glacial_lake"`
	InjuredGlacialLake                   *float64 `json:"injured_glacial_lake"`
	AffectedGlacialLake                  *float64 `json:"affected_glacial_lake"`
	HomelessGlacialLake                  *float64 `json:"homeless_glacial_lake"`
	TotalAffectedGlacialLake             *float64 `json:"total_affected_glacial_lake"`
	ReconstructionCostsGlacialLake       *float64 `json:"reconstruction_costs_glacial_lake"`
	InsuredDamagesGlacialLake            *float64 `json:"insured_damages_glacial_lake"`
	TotalDamagesGlacialLake              *float64 `json:"total_damages_glacial_lake"`
	TotalDamagesPctGdpAllDisasters       *float64 `json:"total_damages_pct_gdp_all_disasters"`
	TotalDamagesPctGdpDrought            *float64 `json:"total_damages_pct_gdp_drought"`
	TotalDamagesPctGdpEarthquake         *float64 `json:"total_damages_pct_gdp_earthquake"`
	TotalDamagesPctGdpTemperature        *float64 `json:"total_damages_pct_gdp_temperature"`
	TotalDamagesPctGdpFlood              *float64 `json:"total_damages_pct_gdp_flood"`
	TotalDamagesPctGdpLandslide          *float64 `json:"total_damages_pct_gdp_landslide"`
	TotalDamagesPctGdpMassMovement       *float64 `json:"total_damages_pct_gdp_mass_movement"`
	TotalDamagesPctGdpStorm              *float64 `json:"total_damages_pct_gdp_storm"`
	TotalDamagesPctGdpVolcanic           *float64 `json:"total_damages_pct_gdp_volcanic"`
	TotalDamagesPctGdpWildfire           *float64 `json:"total_damages_pct_gdp_wildfire"`
	DeathsRatePer100kAllDisasters        *float64 `json:"deaths_rate_per_100k_all_disasters"`
	DeathsRatePer100kDrought             *float64 `json:"deaths_rate_per_100k_drought"`
	DeathsRatePer100kEarthquake          *float64 `json:"deaths_rate_per_100k_earthquake"`
	DeathsRatePer100kTemperature         *float64 `json:"deaths_rate_per_100k_temperature"`
	DeathsRatePer100kFlood               *float64 `json:"deaths_rate_per_100k_flood"`
	DeathsRatePer100kFog                 *float64 `json:"deaths_rate_per_100k_fog"`
	DeathsRatePer100kGlacialLake         *float64 `json:"deaths_rate_per_100k_glacial_lake"`
	DeathsRatePer100kLandslide           *float64 `json:"deaths_rate_per_100k_landslide"`
	DeathsRatePer100kMassMovement        *float64 `json:"deaths_rate_per_100k_mass_movement"`
	DeathsRatePer100kStorm               *float64 `json:"deaths_rate_per_100k_storm"`
	DeathsRatePer100kVolcanic            *float64 `json:"deaths_rate_per_100k_volcanic"`
	DeathsRatePer100kWildfire            *float64 `json:"deaths_rate_per_100k_wildfire"`
	InjuredRatePer100kAllDisasters       *float64 `json:"injured_rate_per_100k_all_disasters"`
	InjuredRatePer100kDrought            *float64 `json:"injured_rate_per_100k_drought"`
	InjuredRatePer100kEarthquake         *float64 `json:"injured_rate_per_100k_earthquake"`
	InjuredRatePer100kTemperature        *float64 `json:"injured_rate_per_100k_temperature"`
	InjuredRatePer100kFlood              *float64 `json:"injured_rate_per_100k_flood"`
	InjuredRatePer100kFog                *float64 `json:"injured_rate_per_100k_fog"`
	InjuredRatePer100kGlacialLake        *float64 `json:"injured_rate_per_100k_glacial_lake"`
	InjuredRatePer100kLandslide          *float64 `json:"injured_rate_per_100k_landslide"`
	InjuredRatePer100kMassMovement       *float64 `json:"injured_rate_per_100k_mass_movement"`
	InjuredRatePer100kStorm              *float64 `json:"injured_rate_per_100k_storm"`
	InjuredRatePer100kVolcanic           *float64 `json:"injured_rate_per_100k_volcanic"`
	InjuredRatePer100kWildfire           *float64 `json:"injured_rate_per_100k_wildfire"`
	TotalAffectedRatePer100kAllDisasters *float64 `json:"total_affected_rate_per_100k_all_disasters"`
	TotalAffectedRatePer100kDrought      *float64 `json:"total_affected_rate_per_100k_drought"`
	TotalAffectedRatePer100kEarthquake   *float64 `json:"total_affected_rate_per_100k_earthquake"`
	TotalAffectedRatePer100kTemperature  *float64 `json:"total_affected_rate_per_100k_temperature"`
	TotalAffectedRatePer100kFlood        *float64 `json:"total_affected_rate_per_100k_flood"`
	TotalAffectedRatePer100kFog          *float64 `json:"total_affected_rate_per_100k_fog"`
	TotalAffectedRatePer100kGlacialLake  *float64 `json:"total_affected_rate_per_100k_glacial_lake"`
	TotalAffectedRatePer100kLandslide    *float64 `json:"total_affected_rate_per_100k_landslide"`
	TotalAffectedRatePer100kMassMovement *float64 `json:"total_affected_rate_per_100k_mass_movement"`
	TotalAffectedRatePer100kStorm        *float64 `json:"total_affected_rate_per_100k_storm"`
	TotalAffectedRatePer100kVolcanic     *float64 `json:"total_affected_rate_per_100k_volcanic"`
	TotalAffectedRatePer100kWildfire     *float64 `json:"total_affected_rate_per_100k_wildfire"`
	TotalDamagesPctGdpGlacialLake        *float64 `json:"total_damages_pct_gdp_glacial_lake"`
}

This data has been aggregated by Our World in Data by country and year based on the raw database of disasters published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir).Our World in Data has also calculated each metric, adjusted for population, using a long-run population dataset constructed from Gapminder and UN Population Prospects.https://www.gapminder.org/https://population.un.org/wpp/Our World in Data has also calculated economic damage metrics adjusted for gross domestic product (GDP), using GDP data from the World Bank.http://data.worldbank.org/data-catalog/world-development-indicators

type NaturalDisastersEmdatDecadalDataset 

type NaturalDisastersEmdatDecadalDataset struct {
	AffectedAllDisasters                 *float64 `json:"affected_all_disasters"`
	AffectedDrought                      *float64 `json:"affected_drought"`
	AffectedEarthquake                   *float64 `json:"affected_earthquake"`
	AffectedTemperature                  *float64 `json:"affected_temperature"`
	AffectedFlood                        *float64 `json:"affected_flood"`
	AffectedFog                          *float64 `json:"affected_fog"`
	AffectedGlacialLake                  *float64 `json:"affected_glacial_lake"`
	AffectedLandslide                    *float64 `json:"affected_landslide"`
	AffectedMassMovement                 *float64 `json:"affected_mass_movement"`
	AffectedStorm                        *float64 `json:"affected_storm"`
	AffectedVolcanic                     *float64 `json:"affected_volcanic"`
	AffectedWildfire                     *float64 `json:"affected_wildfire"`
	AffectedRatePer100kAllDisasters      *float64 `json:"affected_rate_per_100k_all_disasters"`
	AffectedRatePer100kDrought           *float64 `json:"affected_rate_per_100k_drought"`
	AffectedRatePer100kEarthquake        *float64 `json:"affected_rate_per_100k_earthquake"`
	AffectedRatePer100kTemperature       *float64 `json:"affected_rate_per_100k_temperature"`
	AffectedRatePer100kFlood             *float64 `json:"affected_rate_per_100k_flood"`
	AffectedRatePer100kFog               *float64 `json:"affected_rate_per_100k_fog"`
	AffectedRatePer100kLandslide         *float64 `json:"affected_rate_per_100k_landslide"`
	AffectedRatePer100kMassMovement      *float64 `json:"affected_rate_per_100k_mass_movement"`
	AffectedRatePer100kStorm             *float64 `json:"affected_rate_per_100k_storm"`
	AffectedRatePer100kVolcanic          *float64 `json:"affected_rate_per_100k_volcanic"`
	AffectedRatePer100kWildfire          *float64 `json:"affected_rate_per_100k_wildfire"`
	DeathsAllDisasters                   *float64 `json:"deaths_all_disasters"`
	DeathsDrought                        *float64 `json:"deaths_drought"`
	DeathsEarthquake                     *float64 `json:"deaths_earthquake"`
	DeathsTemperature                    *float64 `json:"deaths_temperature"`
	DeathsFlood                          *float64 `json:"deaths_flood"`
	DeathsFog                            *float64 `json:"deaths_fog"`
	DeathsGlacialLake                    *float64 `json:"deaths_glacial_lake"`
	DeathsLandslide                      *float64 `json:"deaths_landslide"`
	DeathsMassMovement                   *float64 `json:"deaths_mass_movement"`
	DeathsStorm                          *float64 `json:"deaths_storm"`
	DeathsVolcanic                       *float64 `json:"deaths_volcanic"`
	DeathsWildfire                       *float64 `json:"deaths_wildfire"`
	DeathsRatePer100kAllDisasters        *float64 `json:"deaths_rate_per_100k_all_disasters"`
	DeathsRatePer100kDrought             *float64 `json:"deaths_rate_per_100k_drought"`
	DeathsRatePer100kEarthquake          *float64 `json:"deaths_rate_per_100k_earthquake"`
	DeathsRatePer100kTemperature         *float64 `json:"deaths_rate_per_100k_temperature"`
	DeathsRatePer100kFlood               *float64 `json:"deaths_rate_per_100k_flood"`
	DeathsRatePer100kFog                 *float64 `json:"deaths_rate_per_100k_fog"`
	DeathsRatePer100kLandslide           *float64 `json:"deaths_rate_per_100k_landslide"`
	DeathsRatePer100kMassMovement        *float64 `json:"deaths_rate_per_100k_mass_movement"`
	DeathsRatePer100kStorm               *float64 `json:"deaths_rate_per_100k_storm"`
	DeathsRatePer100kVolcanic            *float64 `json:"deaths_rate_per_100k_volcanic"`
	DeathsRatePer100kWildfire            *float64 `json:"deaths_rate_per_100k_wildfire"`
	HomelessAllDisasters                 *float64 `json:"homeless_all_disasters"`
	HomelessDrought                      *float64 `json:"homeless_drought"`
	HomelessEarthquake                   *float64 `json:"homeless_earthquake"`
	HomelessTemperature                  *float64 `json:"homeless_temperature"`
	HomelessFlood                        *float64 `json:"homeless_flood"`
	HomelessFog                          *float64 `json:"homeless_fog"`
	HomelessGlacialLake                  *float64 `json:"homeless_glacial_lake"`
	HomelessLandslide                    *float64 `json:"homeless_landslide"`
	HomelessMassMovement                 *float64 `json:"homeless_mass_movement"`
	HomelessStorm                        *float64 `json:"homeless_storm"`
	HomelessVolcanic                     *float64 `json:"homeless_volcanic"`
	HomelessWildfire                     *float64 `json:"homeless_wildfire"`
	HomelessRatePer100kAllDisasters      *float64 `json:"homeless_rate_per_100k_all_disasters"`
	HomelessRatePer100kDrought           *float64 `json:"homeless_rate_per_100k_drought"`
	HomelessRatePer100kEarthquake        *float64 `json:"homeless_rate_per_100k_earthquake"`
	HomelessRatePer100kTemperature       *float64 `json:"homeless_rate_per_100k_temperature"`
	HomelessRatePer100kFlood             *float64 `json:"homeless_rate_per_100k_flood"`
	HomelessRatePer100kFog               *float64 `json:"homeless_rate_per_100k_fog"`
	HomelessRatePer100kLandslide         *float64 `json:"homeless_rate_per_100k_landslide"`
	HomelessRatePer100kMassMovement      *float64 `json:"homeless_rate_per_100k_mass_movement"`
	HomelessRatePer100kStorm             *float64 `json:"homeless_rate_per_100k_storm"`
	HomelessRatePer100kVolcanic          *float64 `json:"homeless_rate_per_100k_volcanic"`
	HomelessRatePer100kWildfire          *float64 `json:"homeless_rate_per_100k_wildfire"`
	InjuredAllDisasters                  *float64 `json:"injured_all_disasters"`
	InjuredDrought                       *float64 `json:"injured_drought"`
	InjuredEarthquake                    *float64 `json:"injured_earthquake"`
	InjuredTemperature                   *float64 `json:"injured_temperature"`
	InjuredFlood                         *float64 `json:"injured_flood"`
	InjuredFog                           *float64 `json:"injured_fog"`
	InjuredGlacialLake                   *float64 `json:"injured_glacial_lake"`
	InjuredLandslide                     *float64 `json:"injured_landslide"`
	InjuredMassMovement                  *float64 `json:"injured_mass_movement"`
	InjuredStorm                         *float64 `json:"injured_storm"`
	InjuredVolcanic                      *float64 `json:"injured_volcanic"`
	InjuredWildfire                      *float64 `json:"injured_wildfire"`
	InjuredRatePer100kAllDisasters       *float64 `json:"injured_rate_per_100k_all_disasters"`
	InjuredRatePer100kDrought            *float64 `json:"injured_rate_per_100k_drought"`
	InjuredRatePer100kEarthquake         *float64 `json:"injured_rate_per_100k_earthquake"`
	InjuredRatePer100kTemperature        *float64 `json:"injured_rate_per_100k_temperature"`
	InjuredRatePer100kFlood              *float64 `json:"injured_rate_per_100k_flood"`
	InjuredRatePer100kFog                *float64 `json:"injured_rate_per_100k_fog"`
	InjuredRatePer100kLandslide          *float64 `json:"injured_rate_per_100k_landslide"`
	InjuredRatePer100kMassMovement       *float64 `json:"injured_rate_per_100k_mass_movement"`
	InjuredRatePer100kStorm              *float64 `json:"injured_rate_per_100k_storm"`
	InjuredRatePer100kVolcanic           *float64 `json:"injured_rate_per_100k_volcanic"`
	InjuredRatePer100kWildfire           *float64 `json:"injured_rate_per_100k_wildfire"`
	InsuredDamagesAllDisasters           *float64 `json:"insured_damages_all_disasters"`
	InsuredDamagesDrought                *float64 `json:"insured_damages_drought"`
	InsuredDamagesEarthquake             *float64 `json:"insured_damages_earthquake"`
	InsuredDamagesTemperature            *float64 `json:"insured_damages_temperature"`
	InsuredDamagesFlood                  *float64 `json:"insured_damages_flood"`
	InsuredDamagesFog                    *float64 `json:"insured_damages_fog"`
	InsuredDamagesGlacialLake            *float64 `json:"insured_damages_glacial_lake"`
	InsuredDamagesLandslide              *float64 `json:"insured_damages_landslide"`
	InsuredDamagesMassMovement           *float64 `json:"insured_damages_mass_movement"`
	InsuredDamagesStorm                  *float64 `json:"insured_damages_storm"`
	InsuredDamagesVolcanic               *float64 `json:"insured_damages_volcanic"`
	InsuredDamagesWildfire               *float64 `json:"insured_damages_wildfire"`
	ReconstructionCostsAllDisasters      *float64 `json:"reconstruction_costs_all_disasters"`
	ReconstructionCostsDrought           *float64 `json:"reconstruction_costs_drought"`
	ReconstructionCostsEarthquake        *float64 `json:"reconstruction_costs_earthquake"`
	ReconstructionCostsTemperature       *float64 `json:"reconstruction_costs_temperature"`
	ReconstructionCostsFlood             *float64 `json:"reconstruction_costs_flood"`
	ReconstructionCostsFog               *float64 `json:"reconstruction_costs_fog"`
	ReconstructionCostsGlacialLake       *float64 `json:"reconstruction_costs_glacial_lake"`
	ReconstructionCostsLandslide         *float64 `json:"reconstruction_costs_landslide"`
	ReconstructionCostsMassMovement      *float64 `json:"reconstruction_costs_mass_movement"`
	ReconstructionCostsStorm             *float64 `json:"reconstruction_costs_storm"`
	ReconstructionCostsVolcanic          *float64 `json:"reconstruction_costs_volcanic"`
	ReconstructionCostsWildfire          *float64 `json:"reconstruction_costs_wildfire"`
	TotalAffectedAllDisasters            *float64 `json:"total_affected_all_disasters"`
	TotalAffectedDrought                 *float64 `json:"total_affected_drought"`
	TotalAffectedEarthquake              *float64 `json:"total_affected_earthquake"`
	TotalAffectedTemperature             *float64 `json:"total_affected_temperature"`
	TotalAffectedFlood                   *float64 `json:"total_affected_flood"`
	TotalAffectedFog                     *float64 `json:"total_affected_fog"`
	TotalAffectedGlacialLake             *float64 `json:"total_affected_glacial_lake"`
	TotalAffectedLandslide               *float64 `json:"total_affected_landslide"`
	TotalAffectedMassMovement            *float64 `json:"total_affected_mass_movement"`
	TotalAffectedStorm                   *float64 `json:"total_affected_storm"`
	TotalAffectedVolcanic                *float64 `json:"total_affected_volcanic"`
	TotalAffectedWildfire                *float64 `json:"total_affected_wildfire"`
	TotalAffectedRatePer100kAllDisasters *float64 `json:"total_affected_rate_per_100k_all_disasters"`
	TotalAffectedRatePer100kDrought      *float64 `json:"total_affected_rate_per_100k_drought"`
	TotalAffectedRatePer100kEarthquake   *float64 `json:"total_affected_rate_per_100k_earthquake"`
	TotalAffectedRatePer100kTemperature  *float64 `json:"total_affected_rate_per_100k_temperature"`
	TotalAffectedRatePer100kFlood        *float64 `json:"total_affected_rate_per_100k_flood"`
	TotalAffectedRatePer100kFog          *float64 `json:"total_affected_rate_per_100k_fog"`
	TotalAffectedRatePer100kLandslide    *float64 `json:"total_affected_rate_per_100k_landslide"`
	TotalAffectedRatePer100kMassMovement *float64 `json:"total_affected_rate_per_100k_mass_movement"`
	TotalAffectedRatePer100kStorm        *float64 `json:"total_affected_rate_per_100k_storm"`
	TotalAffectedRatePer100kVolcanic     *float64 `json:"total_affected_rate_per_100k_volcanic"`
	TotalAffectedRatePer100kWildfire     *float64 `json:"total_affected_rate_per_100k_wildfire"`
	TotalDamagesAllDisasters             *float64 `json:"total_damages_all_disasters"`
	TotalDamagesDrought                  *float64 `json:"total_damages_drought"`
	TotalDamagesEarthquake               *float64 `json:"total_damages_earthquake"`
	TotalDamagesTemperature              *float64 `json:"total_damages_temperature"`
	TotalDamagesFlood                    *float64 `json:"total_damages_flood"`
	TotalDamagesFog                      *float64 `json:"total_damages_fog"`
	TotalDamagesGlacialLake              *float64 `json:"total_damages_glacial_lake"`
	TotalDamagesLandslide                *float64 `json:"total_damages_landslide"`
	TotalDamagesMassMovement             *float64 `json:"total_damages_mass_movement"`
	TotalDamagesStorm                    *float64 `json:"total_damages_storm"`
	TotalDamagesVolcanic                 *float64 `json:"total_damages_volcanic"`
	TotalDamagesWildfire                 *float64 `json:"total_damages_wildfire"`
	TotalDamagesPctGdpAllDisasters       *float64 `json:"total_damages_pct_gdp_all_disasters"`
	TotalDamagesPctGdpDrought            *float64 `json:"total_damages_pct_gdp_drought"`
	TotalDamagesPctGdpEarthquake         *float64 `json:"total_damages_pct_gdp_earthquake"`
	TotalDamagesPctGdpTemperature        *float64 `json:"total_damages_pct_gdp_temperature"`
	TotalDamagesPctGdpFlood              *float64 `json:"total_damages_pct_gdp_flood"`
	TotalDamagesPctGdpLandslide          *float64 `json:"total_damages_pct_gdp_landslide"`
	TotalDamagesPctGdpMassMovement       *float64 `json:"total_damages_pct_gdp_mass_movement"`
	TotalDamagesPctGdpStorm              *float64 `json:"total_damages_pct_gdp_storm"`
	TotalDamagesPctGdpVolcanic           *float64 `json:"total_damages_pct_gdp_volcanic"`
	TotalDamagesPctGdpWildfire           *float64 `json:"total_damages_pct_gdp_wildfire"`
}

This data has been aggregated by Our World in Data by country and year based on the raw database of disasters published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir).Our World in Data has also calculated each metric, adjusted for population, using a long-run population dataset constructed from Gapminder and UN Population Prospects.https://www.gapminder.org/https://population.un.org/wpp/Our World in Data has also calculated economic damage metrics adjusted for gross domestic product (GDP), using GDP data from the World Bank.http://data.worldbank.org/data-catalog/world-development-indicators

type NaturalDisastersFrom1900To2019Emdat2020Dataset 

type NaturalDisastersFrom1900To2019Emdat2020Dataset struct {
	NumberOfDisastersEmdat2019   *float64 `json:"number_of_disasters_emdat_2019"`
	TotalDeathsEmdat2019         *float64 `json:"total_deaths_emdat_2019"`
	InjuredEmdat2019             *float64 `json:"injured_emdat_2019"`
	AffectedEmdat2019            *float64 `json:"affected_emdat_2019"`
	HomelessEmdat2019            *float64 `json:"homeless_emdat_2019"`
	TotalAffectedEmdat2019       *float64 `json:"total_affected_emdat_2019"`
	TotalEconomicDamageEmdat2019 *float64 `json:"total_economic_damage_emdat_2019"`
}

The data presented here includes all categories classified as "natural disasters" (distinguished from technological disasters, such as oil spills and industrial accidents). This includes those from drought, floods, extreme weather, extreme temperature, landslides, dry mass movements, wildfires, volcanic activity and earthquakes.emdat defines the following variables as:Affected: "People requiring immediate assistance during a period of emergency, i.e. requiring basic survival needs such as food, water, shelter, sanitation and immediate medical assistance."Injured: "People suffering from physical injuries, trauma or an illness requiring immediate medical assistance as a direct result of a disaster."Homeless: "Number of people whose house is destroyed or heavily damaged and therefore need shelter after an event."Total affected: "In EM-DAT, it is the sum of the injured, affected and left homeless after a disaster."Estimated economic damage: "The amount of damage to property, crops, and livestock. In EM-DAT estimated damage are given in US$ (‘000). For each disaster, the registered figure corresponds to the damage value at the moment of the event, i.e. the figures are shown true to the year of the event."Total deaths: "In EM-DAT, it is the sum of deaths and missing."

type NeglectedTropicalDiseasesLymphaticFilariasisPopulationRequiringPcNotTreatedAndTreatedEnricJane2016Dataset 

type NeglectedTropicalDiseasesLymphaticFilariasisPopulationRequiringPcNotTreatedAndTreatedEnricJane2016Dataset struct {
	ReportedNumberOfPeopleTreatedEnricJane2016           *float64 `json:"reported_number_of_people_treated_enric_jane_2016"`
	PopulationRequiringPcForLfButNotTreatedEnricJane2016 *float64 `json:"population_requiring_pc_for_lf_but_not_treated_enric_jane_2016"`
}

type NeonatalMortalityRateViaChildmortalityorg2015Dataset 

type NeonatalMortalityRateViaChildmortalityorg2015Dataset struct {
	NeonatalMortalityRateViaChildmortalityorg2015 *float64 `json:"neonatal_mortality_rate_via_childmortalityorg_2015"`
}

Estimates generated by the UN Inter-agency Group for Child Mortality Estimation (IGME) in 2015.

type NeonatalTetanusIncidenceDataset 

type NeonatalTetanusIncidenceDataset struct {
	YearOfMaternalNeonatalTetanusMntElimination *float64 `json:"year_of_maternal_neonatal_tetanus_mnt_elimination"`
}

type NewEstimatesOfHoursOfWorkPerWeek19001957Jones1963Dataset 

type NewEstimatesOfHoursOfWorkPerWeek19001957Jones1963Dataset struct {
	AverageHoursPerWeekWorkedInManufacturingBls    *float64 `json:"average_hours_per_week_worked_in_manufacturing_bls"`
	AverageHoursPerWeekWorkedInManufacturingJones  *float64 `json:"average_hours_per_week_worked_in_manufacturing_jones"`
	AverageHoursOfWorkPerWeekOnRailroadsBls        *float64 `json:"average_hours_of_work_per_week_on_railroads_bls"`
	AverageHoursOfWorkPerWeekOnRailroadsJones      *float64 `json:"average_hours_of_work_per_week_on_railroads_jones"`
	AverageHoursOfWorkPerWeekInBituminousCoalBls   *float64 `json:"average_hours_of_work_per_week_in_bituminous_coal_bls"`
	AverageHoursOfWorkPerWeekInBituminousCoalJones *float64 `json:"average_hours_of_work_per_week_in_bituminous_coal_jones"`
	AverageHoursOfWorkPerWeekInAnthraciteCoalBls   *float64 `json:"average_hours_of_work_per_week_in_anthracite_coal_bls"`
	AverageHoursOfWorkPerWeekInAnthraciteCoalJones *float64 `json:"average_hours_of_work_per_week_in_anthracite_coal_jones"`
}

type NewsworthinessOfDisastersByDisasterTypeAndRegionEisenseeAndStromberg2007Dataset 

type NewsworthinessOfDisastersByDisasterTypeAndRegionEisenseeAndStromberg2007Dataset struct {
	ShareInNewsEisenseeAndStromberg2007                  *float64 `json:"share_in_news_eisensee_and_stromberg_2007"`
	FixedEffectsRegressionEisenseeAndStromberg2007       *float64 `json:"fixed_effects_regression_eisensee_and_stromberg_2007"`
	EqualCoverageCasualtiesRatioEisenseeAndStromberg2007 *float64 `json:"equal_coverage_casualties_ratio_eisensee_and_stromberg_2007"`
}

type NihDnaSequencingCostsDataset 

type NihDnaSequencingCostsDataset struct {
	CostPerMbDnaSequenceGsp                 *float64 `json:"cost_per_mb_dna_sequence_gsp"`
	CostPerGenomeGsp                        *float64 `json:"cost_per_genome_gsp"`
	NumberOfBasePairsSequencedPerUsmoneyGsp *float64 `json:"number_of_base_pairs_sequenced_per_usmoney_gsp"`
}

Full reference: Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: www.genome.gov/sequencingcostsdata [accessed 2017-07-11].The NHGRI program describe genome sequencing costs relative to two metrics, quoted below:"(1) "Cost per Megabase of DNA Sequence" - the cost of determining one megabase (Mb; a million bases) of DNA sequence of a specified quality;(2) "Cost per Genome" - the cost of sequencing a human-sized genome."Cost data from the NHGRI is presented either biannually or seasonally for each year. Here, we have summarized this data on an annual basis by calculating the yearly average cost.The number of DNA base pairs sequenced per US$ was calculated by dividing 1 by the metric, "Cost per Megabase of DNA Sequence", and multiplying by 1 million.

type NitrogenFertilizerConsumptionFao2017Dataset 

type NitrogenFertilizerConsumptionFao2017Dataset struct {
	NitrogenFertilizerConsumptionFao2017 *float64 `json:"nitrogen_fertilizer_consumption_fao_2017"`
}

Data combines two UN FAO datasets related to nitrogen fertilizer consumption: "Nitrogen Fertilizers - Consumption" which extends from 2003-2014 and "Fertilizers Archive - Nitrogenous Fertilizers - Consumption", which extends from 1961-2002.Data is measured in tonnes of total nutrient consumption.

type NitrogenFertilizerProductionFao2017Dataset 

type NitrogenFertilizerProductionFao2017Dataset struct {
	NitrogenFertilizerProductionFao2017 *float64 `json:"nitrogen_fertilizer_production_fao_2017"`
}

Data combines two UN FAO datasets related to nitrogen fertilizer production: "Nitrogen Fertilizers - Production Quantity" which extends from 2003-2014 and "Fertilizers Archive - Nitrogenous Fertilizers - Production Quantity", which extends from 1961-2002.Data is measured in tonnes of nitrogen production.

type NitrousOxideEmissionsBySectorCait2020Dataset 

type NitrousOxideEmissionsBySectorCait2020Dataset struct {
	AgricultureN2oEmissionsCait              *float64 `json:"agriculture_n2o_emissions_cait"`
	EnergyN2oEmissionsCait                   *float64 `json:"energy_n2o_emissions_cait"`
	FugitiveEmissionsN2oEmissionsCait        *float64 `json:"fugitive_emissions_n2o_emissions_cait"`
	IndustryN2oEmissionsCait                 *float64 `json:"industry_n2o_emissions_cait"`
	LandUseChangeAndForestryN2oEmissionsCait *float64 `json:"land_use_change_and_forestry_n2o_emissions_cait"`
	OtherFuelCombustionN2oEmissionsCait      *float64 `json:"other_fuel_combustion_n2o_emissions_cait"`
	TotalExcludingLucfN2oEmissionsCait       *float64 `json:"total_excluding_lucf_n2o_emissions_cait"`
	TotalIncludingLucfN2oEmissionsCait       *float64 `json:"total_including_lucf_n2o_emissions_cait"`
	WasteN2oEmissionsCait                    *float64 `json:"waste_n2o_emissions_cait"`
	AgriculturePerCapitaCait                 *float64 `json:"agriculture_per_capita_cait"`
	FugitiveEmissionsPerCapitaCait           *float64 `json:"fugitive_emissions_per_capita_cait"`
	IndustryPerCapitaCait                    *float64 `json:"industry_per_capita_cait"`
	LandUseChangeAndForestryPerCapitaCait    *float64 `json:"land_use_change_and_forestry_per_capita_cait"`
	TotalExcludingLucfPerCapitaCait          *float64 `json:"total_excluding_lucf_per_capita_cait"`
	TotalIncludingLucfPerCapita              *float64 `json:"total_including_lucf_per_capita"`
	WastePerCapitaCait                       *float64 `json:"waste_per_capita_cait"`
}

Nitrous oxide (N2O) emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type NitrousOxideEmissionsBySectorCait2021Dataset 

type NitrousOxideEmissionsBySectorCait2021Dataset struct {
	Agriculture                       *float64 `json:"agriculture"`
	AgriculturePerCapita              *float64 `json:"agriculture_per_capita"`
	Energy                            *float64 `json:"energy"`
	EnergyPerCapita                   *float64 `json:"energy_per_capita"`
	FugitiveEmissions                 *float64 `json:"fugitive_emissions"`
	FugitiveEmissionsPerCapita        *float64 `json:"fugitive_emissions_per_capita"`
	Industry                          *float64 `json:"industry"`
	IndustryPerCapita                 *float64 `json:"industry_per_capita"`
	LandUseChangeAndForestry          *float64 `json:"land_use_change_and_forestry"`
	LandUseChangeAndForestryPerCapita *float64 `json:"land_use_change_and_forestry_per_capita"`
	OtherFuelCombustion               *float64 `json:"other_fuel_combustion"`
	OtherFuelCombustionPerCapita      *float64 `json:"other_fuel_combustion_per_capita"`
	TotalExcludingLucf                *float64 `json:"total_excluding_lucf"`
	TotalExcludingLucfPerCapita       *float64 `json:"total_excluding_lucf_per_capita"`
	TotalIncludingLucf                *float64 `json:"total_including_lucf"`
	TotalIncludingLucfPerCapita       *float64 `json:"total_including_lucf_per_capita"`
	Waste                             *float64 `json:"waste"`
	WastePerCapita                    *float64 `json:"waste_per_capita"`
}

Nitrous oxide (N2O) emissions are measured in tonnes of carbon dioxide equivalents (CO₂e), based on 100-year global warming potential factors for non-CO₂ gases.This data is published by country and sector from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal. Available here: https://www.climatewatchdata.org/data-explorer/historical-emissions

type NonCommercialFlightDistanceRecordsWikipediaDataset 

type NonCommercialFlightDistanceRecordsWikipediaDataset struct {
	NonCommercialFlightDistanceRecordKmWikipedia *float64 `json:"non_commercial_flight_distance_record_km_wikipedia"`
}

This data has been sourced from the compilation of flight distance records Wiki available at: https://en.wikipedia.org/wiki/Flight_distance_record [accessed 2017-07-11]. References to the individual sources for each record can be found at this link.

Only non-commercial powered aircraft are included in this dataset, which contains only those flights taken without mid-flight refueling.

Where more than one record was set in any given year, only the highest of the given year has been included here.

type NorthAtlanticHurricanesHudratNoaaDataset 

type NorthAtlanticHurricanesHudratNoaaDataset struct {
	NumberOfNorthAtlanticHurricanesHudratNoaa      *float64 `json:"number_of_north_atlantic_hurricanes_hudrat_noaa"`
	NumberOfMajorNorthAtlanticHurricanesHudratNoaa *float64 `json:"number_of_major_north_atlantic_hurricanes_hudrat_noaa"`
	NumberOfUsHurricanesHudratNoaa                 *float64 `json:"number_of_us_hurricanes_hudrat_noaa"`
	NumberOfMajorUsHurricanesHudratNoaa            *float64 `json:"number_of_major_us_hurricanes_hudrat_noaa"`
	AccumulatedCycloneEnergyAceHudratNoaa          *float64 `json:"accumulated_cyclone_energy_ace_hudrat_noaa"`
	CyclonePowerDissipationIndexPdiHudratNoaa      *float64 `json:"cyclone_power_dissipation_index_pdi_hudrat_noaa"`
}

Data on the number of hurricanes in the Atlantic Ocean, Caribbean, and Gulf of Mexico, in addition to the number that made landfall in the United States, as published in the HURDAT (Hurricane Database) of the National Oceanic & Atmospheric Administration (NOAA).This data runs from 1851 through to the latest annual data.Hurricanes are categorised by the Saffir–Simpson hurricane wind scale (SSHWS) which classifies by five categories (1 being the lowest; 5 the highest) based on the intensity of sustained winds. This scale estimates potential property damage. Major hurricanes are defined as those with a 3, 4 or 5 rating on the SSHWS; these are considered major hurricanes because of their potential for significant loss of life and damage.The NOAA notes that because of the sparseness of towns and cities before 1900 in some coastal locations along the United States, the data prior to 1900 may not be complete for all states."ACE" is an abbreviation for "Accumulated Cyclone Energy". ACE is an index that combines the numbers of systems, how long they existed and how intense they became. It is calculated by squaring the maximum sustained surface wind in the system every six hours that the cyclone is a Named Storm and summing it up for the season.The Power Dissipation Index (PDI) accounts for cyclone strength, duration, and frequency. he lines have been smoothed using a five-year weighted average, plotted at the middle year. The most recent average (2011–2015) is plotted at 2013.

type NuclearWarheadStockpilesFederationOfAmericanScientistsDataset 

type NuclearWarheadStockpilesFederationOfAmericanScientistsDataset struct {
	NuclearWeaponsStockpile *float64 `json:"nuclear_weapons_stockpile"`
}

This dataset provides information on the number of stockpiled nuclear warheads by the nuclear powers, using data from the Federation of American Scientists, prepared by Hans M. Kristensen, Matt Korda, and Robert Norris.

type NuclearWeaponsProliferationOwidBasedOnBleek2017NuclearThreatInitiative2022Dataset 

type NuclearWeaponsProliferationOwidBasedOnBleek2017NuclearThreatInitiative2022Dataset struct {
	NuclearWeaponsStatus        *float64 `json:"nuclear_weapons_status"`
	NuclearWeaponsConsideration *float64 `json:"nuclear_weapons_consideration"`
	NuclearWeaponsPursuit       *float64 `json:"nuclear_weapons_pursuit"`
	NuclearWeaponsPossession    *float64 `json:"nuclear_weapons_possession"`
}

This dataset provides information on nuclear weapons states, using data from Bleek (2017), which we double-checked with information from the Nuclear Threat Initiative (2022) for recent years.The dataset distinguishes between countries not considering (score 0), considering (score of 1), pursuing (score 2), possessing nuclear weapons (score 3). A country is coded as not considering nuclear weapons if it neither considers, pursues, or possesses nuclear weapons.A country is coded as considering nuclear weapons if its leaders explore whether it is possible and desirable for them to attempt to acquire nuclear weapons, or they work to increase their nuclear weapons capabilities, but without launching a dedicated program.A country is coded as pursuing nuclear weapons if it has an active program to acquire nuclear weapons or to obtain the ability to construct them on short notice.A country is coded as possessing nuclear weapons if it has a nuclear-explosive device that it can deliver. Conducting an explosive nuclear test is therefore neither sufficient nor nor necessary.Belarus, Kazakhstan, and Ukraine are not coded as possessing nuclear weapons because they never had operational control of the nuclear weapons left over from the Soviet Union.You can read more about specific countries in the profiles by Bleek (2017) and the Nuclear Threat Initiative (2022) by following the links.

type NuclearWeaponsProliferationTotalOwidBasedOnBleek2017NuclearThreatInitiative2022Dataset 

type NuclearWeaponsProliferationTotalOwidBasedOnBleek2017NuclearThreatInitiative2022Dataset struct {
	NumberNuclweapConsideration *float64 `json:"number_nuclweap_consideration"`
	NumberNuclweapPursuit       *float64 `json:"number_nuclweap_pursuit"`
	NumberNuclweapPossession    *float64 `json:"number_nuclweap_possession"`
}

This dataset provides information on the number of nuclear weapons states, using data from Bleek (2017), which we double-checked with information from the Nuclear Threat Initiative (2022) for recent years.A country is coded as considering nuclear weapons if its leaders explore whether it is possible and desirable for them to attempt to acquire nuclear weapons, or they work to increase their nuclear weapons capabilities, but without launching a dedicated program.A country is coded as pursuing nuclear weapons if it has an active program to acquire nuclear weapons or to obtain the ability to construct them on short notice.A country is coded as possessing nuclear weapons if it has a nuclear-explosive device that it can deliver. Conducting an explosive nuclear test is therefore neither sufficient nor nor necessary.You can read more about specific countries in the profiles by Bleek (2017) and the Nuclear Threat Initiative (2022) by following the links.

type NuclearWeaponsTestsArmsControlAssociation2020Dataset 

type NuclearWeaponsTestsArmsControlAssociation2020Dataset struct {
	NuclearWeaponsTests *float64 `json:"nuclear_weapons_tests"`
}

This datasets provides the number of nuclear weapons tests by country using data from the Arms Control Association (2020).

type NumberAndPercentageOfCurrentSmokersBySexAmericanLungAssociation2011Dataset 

type NumberAndPercentageOfCurrentSmokersBySexAmericanLungAssociation2011Dataset struct {
	NumberOfAdultsWhoWereCurrentSmokersMaleAmericanLungAssociation2011    *float64 `json:"number_of_adults_who_were_current_smokers_male_american_lung_association_2011"`
	NumberOfAdultsWhoWereCurrentSmokersFemaleAmericanLungAssociation2011  *float64 `json:"number_of_adults_who_were_current_smokers_female_american_lung_association_2011"`
	PercentOfAdultsWhoWereCurrentSmokersMaleAmericanLungAssociation2011   *float64 `json:"percent_of_adults_who_were_current_smokers_male_american_lung_association_2011"`
	PercentOfAdultsWhoWereCurrentSmokersFemaleAmericanLungAssociation2011 *float64 `json:"percent_of_adults_who_were_current_smokers_female_american_lung_association_2011"`
}

For the original data, please see Table 3: Number of Adults Who Were Current Smokers By Sex, Race and Age, Selected Years, 1965-2009. Due to the redesign of the NHIS survey in 1997, comparisons with data from prior years must be conducted with caution.

type NumberOfChildDeaths19502017Ihme2017Dataset 

type NumberOfChildDeaths19502017Ihme2017Dataset struct {
	NumberOfChildDeaths1950_2017Ihme2017 *float64 `json:"number_of_child_deaths_1950_2017_ihme_2017"`
}

The number of deaths of children under five years old.This data is available from 1950 to 2017 in 5-year intervals from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease study, available at: http://ghdx.healthdata.org/record/ihme-data/gbd-2017-all-cause-mortality-and-life-expectancy-1950-2017

type NumberOfChildrenWhoAreStuntedOwidBasedOnUnicefwhoDataset 

type NumberOfChildrenWhoAreStuntedOwidBasedOnUnicefwhoDataset struct {
	NumberOfChildrenWhoAreStunted *float64 `json:"number_of_children_who_are_stunted"`
}

This dataset multiplies the stunting prevalence figures from UNICEF/WHO by the under 5 population (UN Population Division 2017)

type NumberOfCountriesWithMinimumUrbanPopulationThresholdUn2018Dataset 

type NumberOfCountriesWithMinimumUrbanPopulationThresholdUn2018Dataset struct {
	NumberOfCountriesWithMinimumUrbanPopulationThresholdUn2018 *float64 `json:"number_of_countries_with_minimum_urban_population_threshold_un2018"`
}

Data was assessed by Our World in Data based on UN documentation of its Urbanization Prospects (2018). The Urbanization Prospects presents data on the number and share of the population residing in urban areas for each country from 1950 with projections to 2050.There is no consistent definition of what constitutes an 'urban area'. This is highly variable across countries. This data presents the number of countries with a given minimum threshold of inhabitants needed for it to be defined an 'urban area'. For many countries, there is no defined threshold based on inhabitants; other metrics such as population density, infrastructure, or even pre-defined cities may be used.Note that some countries with minimum inhabitant thresholds noted here also include additional qualities (such as population density) to be met.

type NumberOfDeathsDueToTetanusDataset 

type NumberOfDeathsDueToTetanusDataset struct {
	NumberOfDeathsFromTetanusGbd2016 *float64 `json:"number_of_deaths_from_tetanus_gbd_2016"`
}

type NumberOfDeathsInEnglandAndWalesByAgeOnsDataset 

type NumberOfDeathsInEnglandAndWalesByAgeOnsDataset struct {
	MaleNumberOfDeaths      *float64 `json:"male_number_of_deaths"`
	FemaleNumberOfDeaths    *float64 `json:"female_number_of_deaths"`
	BothSexesNumberOfDeaths *float64 `json:"both_sexes_number_of_deaths"`
}

Total annual number of deaths by age group in England and Wales across all causes. Data for infants <1 years old are not included.

type NumberOfDirectNationalElectionsNelda2015Dataset 

type NumberOfDirectNationalElectionsNelda2015Dataset struct {
	NumberOfDirectNationalElections *float64 `json:"number_of_direct_national_elections"`
}

This dataset gives the number of direct national elections held in each country in each year 1945–2012. These data are taken entirely from Susan Hyde and Nikolay Marinov's NELDA dataset (Version 4, 2015).For a summary of the most important information about the NELDA dataset (e.g. which elections and countries are included), see their overview: https://nelda.co/#about. See also their 2012 paper, “Which Elections can be Lost?” Political Analysis, 20(2), 191–210, available at: https://www.cambridge.org/core/services/aop-cambridge-core/content/view/0474B124646DF486D1FD9A8E26D31DEC/S1047198700013097a.pdf/div-class-title-which-elections-can-be-lost-div.pdfNotes: “Serbia” refers to Yugoslavia for 1945–91 and to “Serbia and Montenegro” for 1992–2006. “Germany” refers to West Germany for 1945–89.How the dataset "Number of direct national elections — NELDA (2015)" was produced: https://drive.google.com/open?id=1bfmN0bd00-7PVFf9W4eRPRHdOTTHm6cB

type NumberOfInfantDeathsIhme2017Dataset 

type NumberOfInfantDeathsIhme2017Dataset struct {
	NumberOfInfantDeathsIhme2017 *float64 `json:"number_of_infant_deaths_ihme_2017"`
}

Number of infants dying before reaching one year of age.

type NumberOfInternetUsersOwidBasedOnWbAndUnwppDataset 

type NumberOfInternetUsersOwidBasedOnWbAndUnwppDataset struct {
	NumberOfInternetUsersOwidBasedOnWbAndUn *float64 `json:"number_of_internet_users_owid_based_on_wb_and_un"`
}

The number of internet users is calculated by Our World in Data based on internet access figures as a share of the total population, published in the World Bank, World Development Indicators (http://data.worldbank.org/data-catalog/world-development-indicators) and total population figures from the UN World Population Prospects (2017) (https://esa.un.org/unpd/wpp/Download/Standard/Population/).The World Bank defines internet users as: "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc."

type NumberOfNeonatalDeathsIhme2017Dataset 

type NumberOfNeonatalDeathsIhme2017Dataset struct {
	NumberOfNeonatalDeathsIhme2017 *float64 `json:"number_of_neonatal_deaths_ihme_2017"`
}

Number of newborns dying before reaching 28 days of age.

type NumberOfObservationsInPovcalPerDecadeOwid2017Dataset 

type NumberOfObservationsInPovcalPerDecadeOwid2017Dataset struct {
	ObservationsInPovcalnetPerDecadeOwid2017 *float64 `json:"observations_in_povcalnet_per_decade_owid_2017"`
}

This is the number of available surveys with data on poverty measures in Povcal by decade. 2014 refers to the available surveys in the period 2005-2014; 2004 refers to 1995-2004; 1994 refers to 1985-1994.

The idea for this measure is inspired by Serajuddin, Uematsu, Wieser, Yoshida, and Dabalen (2015) – ‘Data deprivation: another deprivation to end’, World Bank Policy Research Working Paper no. WPS 7252

type NumberOfPartiesToMultilateralEnvironmentalAgreementsUnctadDataset 

type NumberOfPartiesToMultilateralEnvironmentalAgreementsUnctadDataset struct {
	CartagenaProtocolOnBiosafety                                              *float64 `json:"cartagena_protocol_on_biosafety"`
	ConventionOnBiologicalDiversityCbd                                        *float64 `json:"convention_on_biological_diversity_cbd"`
	CitesConventionOnInternationalTradeInEndangeredSpeciesOfWildFaunaNadFlora *float64 `json:"cites_convention_on_international_trade_in_endangered_species_of_wild_fauna_nad_flora"`
	ConventionOnTheConservationOfMigratorySpeciesOfWildAnimals                *float64 `json:"convention_on_the_conservation_of_migratory_species_of_wild_animals"`
	WorldHeritageConvention                                                   *float64 `json:"world_heritage_convention"`
	KyotoProtocol                                                             *float64 `json:"kyoto_protocol"`
	ViennaConventionOzone                                                     *float64 `json:"vienna_convention_ozone"`
	RamsarConvention                                                          *float64 `json:"ramsar_convention"`
	RotterdamConvention                                                       *float64 `json:"rotterdam_convention"`
	StockholmConventionOnPersistentOrganicPollutants                          *float64 `json:"stockholm_convention_on_persistent_organic_pollutants"`
	UnConventionToCombatDesertificationUnccd                                  *float64 `json:"un_convention_to_combat_desertification_unccd"`
	UnConventionOnTheLawOfTheSeaUnclos                                        *float64 `json:"un_convention_on_the_law_of_the_sea_unclos"`
	UnFrameworkConventionOnClimateChangeUnfccc                                *float64 `json:"un_framework_convention_on_climate_change_unfccc"`
	CitesConventionOnInternationalTradeInEndangeredSpeciesOfWildFaunaAndFlora *float64 `json:"cites_convention_on_international_trade_in_endangered_species_of_wild_fauna_and_flora"`
}

Data represents the number of parties signed into international, multilateral environmental agreements.Acronyms:CBD: Convention on Biological Diversity. CMS: Convention on the Conservation of Migratory Species of Wild Animals UNCCD: United Nations Convention to Combat Desertification UNCLOS: United Nations Convention on the Law of the Sea UNFCCC: United Nations Framework Convention on Climate Change Stockholm: Stockholm Convention on Persistent Organic Pollutants Cartagena: Cartagena Protocol on Biosafety CITES: Convention on International Trade in Endangered Species of Wild Fauna and Flora Heritage: World Heritage Convention. Kyoto: Kyoto Protocol Ozone: Ozone Vienna Convention Ramsar: Ramsar Convention Rotterdam: Rotterdam Convention.

type NumberOfPeopleWhoAreUndernourishedFaoSofi2018AndWorldBank2017Dataset 

type NumberOfPeopleWhoAreUndernourishedFaoSofi2018AndWorldBank2017Dataset struct {
	NumberOfPeopleUndernourishedFao2017AndWorldBank2017              *float64 `json:"number_of_people_undernourished_fao_2017_and_world_bank_2017"`
	NumberOfPeopleWithSufficientCaloricIntakeFao2017AndWorldBank2017 *float64 `json:"number_of_people_with_sufficient_caloric_intake_fao_2017_and_world_bank_2017"`
	NumberOfPeopleUndernourishedFaoSofi2018AndWorldBank2017          *float64 `json:"number_of_people_undernourished_fao_sofi_2018_and_world_bank_2017"`
}

The number of people who are undernourished was derived by combining several datasets: data prior to 2005 was derived using the prevalence of undernourishment (%) from World Bank, World Development Indicators and the UN FAO State of Food Insecurity 2017; and global population figures utilised by the UN FAO database. Multiplying these figures allow for the calculation of the absolute number of people undernourished.Data from 2005 onwards is taken from the UN SOFI (2018) report.The number of people with sufficient caloric intake (i.e. not undernourished) was calculated as the difference between total population and number of people undernourished.References:World Bank, World Development Indicators. https://data.worldbank.org/indicator [accessed 25th September 2017].FAO, IFAD, UNICEF, WFP and WHO. 2017. The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome, FAO.FAO, IFAD, UNICEF, WFP and WHO. 2018. The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Rome, FAO.

type NumberOfPeopleWithAndWithoutAccessToImprovedSanitationOwidBasedOnWdiDataset 

type NumberOfPeopleWithAndWithoutAccessToImprovedSanitationOwidBasedOnWdiDataset struct {
	PopulationWithAccessToImprovedSanitation    *float64 `json:"population_with_access_to_improved_sanitation"`
	PopulationWithoutAccessToImprovedSanitation *float64 `json:"population_without_access_to_improved_sanitation"`
}

The absolute number of people with and without access to improved sanitation facilities has been calculated by OurWorldinData based on data of the share of the population with access, and the total population: both of these datasets sourced from the World Bank, World Development Indicators.The World Bank note: "Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet."

type NumberOfPeopleWithAndWithoutAccessToImprovedWaterOwidBasedOnWdiDataset 

type NumberOfPeopleWithAndWithoutAccessToImprovedWaterOwidBasedOnWdiDataset struct {
	PopulationWithAccessToImprovedWaterSource    *float64 `json:"population_with_access_to_improved_water_source"`
	PopulationWithoutAccessToImprovedWaterSource *float64 `json:"population_without_access_to_improved_water_source"`
}

The absolute number of people with and without access to an improved water source has been calculated by OurWorldinData based on data of the share of the population with access, and the total population: both of these datasets sourced from the World Bank, World Development Indicators.The World Bank note that an improved water drinking source: "includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)."

type NumberOfPeopleWithAndWithoutAccessToImprovedWaterSourcesWorldBankAndUnDataset 

type NumberOfPeopleWithAndWithoutAccessToImprovedWaterSourcesWorldBankAndUnDataset struct {
	NumberOfPeopleWithImprovedWaterSourcesWorldBankAndUn    *float64 `json:"number_of_people_with_improved_water_sources_world_bank_and_un"`
	NumberOfPeopleWithoutImprovedWaterSourcesWorldBankAndUn *float64 `json:"number_of_people_without_improved_water_sources_world_bank_and_un"`
}

Dataset derived through the combination of percentage electricity access statistics and UN population figures.

The absolute number of people with and without access to improved water sources was derived by multiplying the percentage of the global population with access to improved water sources (from World Bank, World Development Indicators) by United Nations figures on global population in any given year.

As defined by the World Bank: "improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)."

Access to improved water sources (%) data was derived from World Bank World Development Indicators (WDI). Available at: http://data.worldbank.org/ [accessed 2017-06-12].

Global population figures were sourced from the United Nations Population Prospects Division. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/ [accessed 2017-06-12]

type NumberOfPeopleWithAndWithoutEnergyAccessOwidBasedOnWorldBank2021Dataset 

type NumberOfPeopleWithAndWithoutEnergyAccessOwidBasedOnWorldBank2021Dataset struct {
	NumberOfPeopleWithAccessToElectricity    *float64 `json:"number_of_people_with_access_to_electricity"`
	NumberOfPeopleWithoutAccessToElectricity *float64 `json:"number_of_people_without_access_to_electricity"`
	NumberWithCleanFuelsCooking              *float64 `json:"number_with_clean_fuels_cooking"`
	NumberWithoutCleanFuelsCooking           *float64 `json:"number_without_clean_fuels_cooking"`
}

This was calculated by Our World in Data based on estimates the share of the population with access to electricity and clean fuels for cooking – published by the World Bank – and total population estimates from the UN World Population Prospects (UNWPP). Original metrics of energy access shares and total population can be accessed from the World Bank World Development Indicators (available at: http://data.worldbank.org/data-catalog/world-development-indicators).

type NumberOfPeopleWithoutAccessToSafeWaterAndSanitationWhoWash2019Dataset 

type NumberOfPeopleWithoutAccessToSafeWaterAndSanitationWhoWash2019Dataset struct {
	NumberOfPeopleWithoutAccessToSafeDrinkingWaterWhoWash2019 *float64 `json:"number_of_people_without_access_to_safe_drinking_water_who_wash_2019"`
	NumberOfPeopleWithoutAccessToSafeSanitationWhoWash2019    *float64 `json:"number_of_people_without_access_to_safe_sanitation_who_wash_2019"`
}

Absolute numbers of people without access to safely managed drinking water and sanitation was calculated by Our World in Data based on original data sourced from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) and World Bank's World Development Indicators.Our World in Data calculated absolute numbers based on original data on the number with access to safely managed drinking water and sanitation, and the total population numbers.– Number of people without access to safely managed drinking water = [population – number of people with access to safe drinking water]– Number of people without access to sanitation = [population – number of people with access to sanitation]Safely managed drinking water: “Safely managed drinking water” is defined as an “Improved source located on premises, available when needed, and free from microbiological and priority chemical contamination.”Safely managed sanitation facilities: “Safely managed sanitation” is defined as the use of an improved sanitation facility which is not shared with other households and where:• excreta is safely disposed in situ or• excreta is transported and treated off-site.

type NumberOfPolioCasesPerOneMillionPopulationWho2017Dataset 

type NumberOfPolioCasesPerOneMillionPopulationWho2017Dataset struct {
	PolioCasesPer1MillionPopulationWho2017 *float64 `json:"polio_cases_per_1_million_population_who_2017"`
}

The Polio case data from the WHO (2017) were divided by the UN Population estimates (which were reported in thousands). The result was then divided by one thousand to obtain the number of polio cases per one million population.

type NumberOfPublishedTitlesSimons2001Dataset 

type NumberOfPublishedTitlesSimons2001Dataset struct {
	NumberOfPublishedTitlesSimons2001 *float64 `json:"number_of_published_titles_simons_2001"`
}

A “title” is for the editors of the ESTC an edition. The eight editions of Robinson Crusoe published in 1719 create eight ESTC-entries

type NumberOfStateBasedConflictsByConflictTypeAndRegionUcdpPrioDataset 

type NumberOfStateBasedConflictsByConflictTypeAndRegionUcdpPrioDataset struct {
	NumberOfColonialOrImperialConflicts                *float64 `json:"number_of_colonial_or_imperial_conflicts"`
	NumberOfConflictsBetweenStates                     *float64 `json:"number_of_conflicts_between_states"`
	NumberOfCivilConflicts                             *float64 `json:"number_of_civil_conflicts"`
	NumberOfCivilConflictsWithForeignStateIntervention *float64 `json:"number_of_civil_conflicts_with_foreign_state_intervention"`
	NumberOfConflictsAllTypes                          *float64 `json:"number_of_conflicts_all_types"`
}

This dataset aggregates the number of conflicts per year (by conflict type and region) listed in UCDP/PRIO Armed Conflict Dataset version 21.1.Conflicts within this dataset refer only to 'State-based' conflicts. UCDP defines state-based armed conflict as: “a contested incompatibility that concernsgovernment and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in a calendar year.”The conflict is listed in all years that the 25 deaths threshold is met (but not otherwise).The data is organised by year and conflict. We aggregate this to provide the number of conflicts in each year, broken down by the conflict type and region. This dataset runs from 1946 and aims to have global coverage over this period. Accordingly, we add a zero deaths observation for year-region-conflict type combinations that do appear in the dataset.The labels for the conflict types we have used paraphrase UCDP/PRIO's technical definitions of 'Extrasystemic', 'Internal', 'Internationalised internal' and 'Interstate'.Note that in this dataset the location refers not (necessarily) to where fighting and deaths took place, but rather to the location of the 'incompatibility' between the participants that defines the conflict: usually the country or territory whose possession or governance is in dispute.

type NumberOfTouristDeparturesPer1000WorldBankAndUn2019Dataset 

type NumberOfTouristDeparturesPer1000WorldBankAndUn2019Dataset struct {
	NumberOfDeparturesPer1000People *float64 `json:"number_of_departures_per_1000_people"`
}

Number of tourist departures per 1000 was derived based on the number of departures per year, divided by population figures from the World Bank's World Development Indicators (WDI).Number of tourist departures sourced from the World Bank's World Development Indicators (WDI). Available at: https://data.worldbank.org/indicator/ST.INT.DPRT.Population data sourced from the World Bank's World Development Indicators (WDI); figures were divided by 1000. Available at: https://databank.worldbank.org/indicator/SP.POP.TOTL/1ff4a498/Popular-Indicators.

type O20thCenturyDeathsInUsCdcDataset 

type O20thCenturyDeathsInUsCdcDataset struct {
	AccidentsExclRoadDeaths                                  *float64 `json:"accidents_excl_road_deaths"`
	AccidentsTotalDeaths                                     *float64 `json:"accidents_total_deaths"`
	ArteriosclerosisDeaths                                   *float64 `json:"arteriosclerosis_deaths"`
	BronchitisDeaths                                         *float64 `json:"bronchitis_deaths"`
	CancersDeaths                                            *float64 `json:"cancers_deaths"`
	CopdDeaths                                               *float64 `json:"copd_deaths"`
	DementiaDeaths                                           *float64 `json:"dementia_deaths"`
	DiabetesDeaths                                           *float64 `json:"diabetes_deaths"`
	DiarrhealDiseaseDeaths                                   *float64 `json:"diarrheal_disease_deaths"`
	HeartDiseaseDeaths                                       *float64 `json:"heart_disease_deaths"`
	HomicideDeaths                                           *float64 `json:"homicide_deaths"`
	KidneyInfectionDeaths                                    *float64 `json:"kidney_infection_deaths"`
	LiverDiseaseDeaths                                       *float64 `json:"liver_disease_deaths"`
	NeonatalDisordersDeaths                                  *float64 `json:"neonatal_disorders_deaths"`
	NephritisDeaths                                          *float64 `json:"nephritis_deaths"`
	OtherCausesDeaths                                        *float64 `json:"other_causes_deaths"`
	PneumoniaAndInfluenzaDeaths                              *float64 `json:"pneumonia_and_influenza_deaths"`
	RespiratoryDiseaseDeaths                                 *float64 `json:"respiratory_disease_deaths"`
	RoadAccidentsDeaths                                      *float64 `json:"road_accidents_deaths"`
	SepticemiaDeaths                                         *float64 `json:"septicemia_deaths"`
	StrokeDeaths                                             *float64 `json:"stroke_deaths"`
	SuicideDeaths                                            *float64 `json:"suicide_deaths"`
	SyphilisDeaths                                           *float64 `json:"syphilis_deaths"`
	TotalDeathsDeaths                                        *float64 `json:"total_deaths_deaths"`
	TuberculosisDeaths                                       *float64 `json:"tuberculosis_deaths"`
	NonCommunicableDiseasesNcdsDeaths                        *float64 `json:"non_communicable_diseases_ncds_deaths"`
	CommunicableInfectiousNeonatalAndOtherDeathsDeaths       *float64 `json:"communicable_infectious_neonatal_and_other_deaths_deaths"`
	AccidentsExclRoadDeathRates                              *float64 `json:"accidents_excl_road_death_rates"`
	AccidentsTotalDeathRates                                 *float64 `json:"accidents_total_death_rates"`
	ArteriosclerosisDeathRates                               *float64 `json:"arteriosclerosis_death_rates"`
	BronchitisDeathRates                                     *float64 `json:"bronchitis_death_rates"`
	CancersDeathRates                                        *float64 `json:"cancers_death_rates"`
	CopdDeathRates                                           *float64 `json:"copd_death_rates"`
	DementiaDeathRates                                       *float64 `json:"dementia_death_rates"`
	DiabetesDeathRates                                       *float64 `json:"diabetes_death_rates"`
	DiarrhealDiseaseDeathRates                               *float64 `json:"diarrheal_disease_death_rates"`
	HeartDiseaseDeathRates                                   *float64 `json:"heart_disease_death_rates"`
	HomicideDeathRates                                       *float64 `json:"homicide_death_rates"`
	KidneyInfectionDeathRates                                *float64 `json:"kidney_infection_death_rates"`
	LiverDiseaseDeathRates                                   *float64 `json:"liver_disease_death_rates"`
	NeonatalDisordersDeathRates                              *float64 `json:"neonatal_disorders_death_rates"`
	NephritisDeathRates                                      *float64 `json:"nephritis_death_rates"`
	OtherCausesDeathRates                                    *float64 `json:"other_causes_death_rates"`
	PneumoniaAndInfluenzaDeathRates                          *float64 `json:"pneumonia_and_influenza_death_rates"`
	RespiratoryDiseaseDeathRates                             *float64 `json:"respiratory_disease_death_rates"`
	RoadAccidentsDeathRates                                  *float64 `json:"road_accidents_death_rates"`
	SepticemiaDeathRates                                     *float64 `json:"septicemia_death_rates"`
	StrokeDeathRates                                         *float64 `json:"stroke_death_rates"`
	SuicideDeathRates                                        *float64 `json:"suicide_death_rates"`
	SyphilisDeathRates                                       *float64 `json:"syphilis_death_rates"`
	TotalDeathsDeathRates                                    *float64 `json:"total_deaths_death_rates"`
	TuberculosisDeathRates                                   *float64 `json:"tuberculosis_death_rates"`
	NonCommunicableDiseasesNcdsDeathRates                    *float64 `json:"non_communicable_diseases_ncds_death_rates"`
	CommunicableInfectiousNeonatalAndOtherDeathsDeathRates   *float64 `json:"communicable_infectious_neonatal_and_other_deaths_death_rates"`
	AccidentsExclRoadPercOfDeaths                            *float64 `json:"accidents_excl_road_perc_of_deaths"`
	AccidentsTotalPercOfDeaths                               *float64 `json:"accidents_total_perc_of_deaths"`
	ArteriosclerosisPercOfDeaths                             *float64 `json:"arteriosclerosis_perc_of_deaths"`
	BronchitisPercOfDeaths                                   *float64 `json:"bronchitis_perc_of_deaths"`
	CancersPercOfDeaths                                      *float64 `json:"cancers_perc_of_deaths"`
	CopdPercOfDeaths                                         *float64 `json:"copd_perc_of_deaths"`
	DementiaPercOfDeaths                                     *float64 `json:"dementia_perc_of_deaths"`
	DiabetesPercOfDeaths                                     *float64 `json:"diabetes_perc_of_deaths"`
	DiarrhealDiseasePercOfDeaths                             *float64 `json:"diarrheal_disease_perc_of_deaths"`
	HeartDiseasePercOfDeaths                                 *float64 `json:"heart_disease_perc_of_deaths"`
	HomicidePercOfDeaths                                     *float64 `json:"homicide_perc_of_deaths"`
	KidneyInfectionPercOfDeaths                              *float64 `json:"kidney_infection_perc_of_deaths"`
	LiverDiseasePercOfDeaths                                 *float64 `json:"liver_disease_perc_of_deaths"`
	NeonatalDisordersPercOfDeaths                            *float64 `json:"neonatal_disorders_perc_of_deaths"`
	NephritisPercOfDeaths                                    *float64 `json:"nephritis_perc_of_deaths"`
	OtherBronchopulmonicDiseasesPercOfDeaths                 *float64 `json:"other_bronchopulmonic_diseases_perc_of_deaths"`
	OtherCausesPercOfDeaths                                  *float64 `json:"other_causes_perc_of_deaths"`
	PneumoniaAndInfluenzaPercOfDeaths                        *float64 `json:"pneumonia_and_influenza_perc_of_deaths"`
	RespiratoryDiseasePercOfDeaths                           *float64 `json:"respiratory_disease_perc_of_deaths"`
	RoadAccidentsPercOfDeaths                                *float64 `json:"road_accidents_perc_of_deaths"`
	SepticemiaPercOfDeaths                                   *float64 `json:"septicemia_perc_of_deaths"`
	StrokePercOfDeaths                                       *float64 `json:"stroke_perc_of_deaths"`
	SuicidePercOfDeaths                                      *float64 `json:"suicide_perc_of_deaths"`
	SyphilisPercOfDeaths                                     *float64 `json:"syphilis_perc_of_deaths"`
	TotalDeathsPercOfDeaths                                  *float64 `json:"total_deaths_perc_of_deaths"`
	TuberculosisPercOfDeaths                                 *float64 `json:"tuberculosis_perc_of_deaths"`
	NonCommunicableDiseasesNcdsPercOfDeaths                  *float64 `json:"non_communicable_diseases_ncds_perc_of_deaths"`
	CommunicableInfectiousNeonatalAndOtherDeathsPercOfDeaths *float64 `json:"communicable_infectious_neonatal_and_other_deaths_perc_of_deaths"`
}

Data is compiled based on the list of top 10 causes of death published by the Centre for Diseases Control (CDC). This is measured across both sexes and all ages. Death rates are reported per 100,000 and are not age-standardized.Data for specific causes of death may be missing or intermittent where it enters or falls out of the top 10 reported causes of deaths in any year.

type OecdConsumptionTaxTrends2016Dataset 

type OecdConsumptionTaxTrends2016Dataset struct {
	CountriesWithVatOecd2016                *float64 `json:"countries_with_vat_oecd_2016"`
	ValueAddedTaxesOecd2016                 *float64 `json:"value_added_taxes_oecd_2016"`
	TaxesOnSpecificGoodsAndServicesOecd2016 *float64 `json:"taxes_on_specific_goods_and_services_oecd_2016"`
}

type OecdEducationPisaTestScoresPisa2015Dataset 

type OecdEducationPisaTestScoresPisa2015Dataset struct {
	OecdPisaEducationScorePisa2015        *float64 `json:"oecd_pisa_education_score_pisa_2015"`
	OecdPisaReadingScore2000_2012Pisa2015 *float64 `json:"oecd_pisa_reading_score_2000_2012_pisa_2015"`
}

PISA education test scores (reading, mathematics, and science)OECD PISA average variable is the mean average taken over the mathematics, reading and sciences scores to create an overall ranking. Note also that a problem solving test was included in 2003 and also used to calculate the average. Data is missing for US Reading in 2006 as it was disqualified. Method of averaging over different tests is not endorsed by OECD

type OecdEducationStatistics2017Dataset 

type OecdEducationStatistics2017Dataset struct {
	SharePublicEducationExpenditureOecdEducationStatistics2017 *float64 `json:"share_public_education_expenditure_oecd_education_statistics_2017"`
	PrivateExpenditureOnEducationOecdEducationStatistics2017   *float64 `json:"private_expenditure_on_education_oecd_education_statistics_2017"`
	PublicExpenditureOnEducationOecdEducationStatistics2017    *float64 `json:"public_expenditure_on_education_oecd_education_statistics_2017"`
	CapitalExpenditureOecdEducationStatistics2017              *float64 `json:"capital_expenditure_oecd_education_statistics_2017"`
	CurrentExpenditureStaffOecdEducationStatistics2017         *float64 `json:"current_expenditure_staff_oecd_education_statistics_2017"`
	CurrentExpenditureNonStaffOecdEducationStatistics2017      *float64 `json:"current_expenditure_non_staff_oecd_education_statistics_2017"`
}

type OecdSocialSpendingFamilyDataset 

type OecdSocialSpendingFamilyDataset struct {
	Family *float64 `json:"family"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingHealthDataset 

type OecdSocialSpendingHealthDataset struct {
	Health *float64 `json:"health"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingHousingDataset 

type OecdSocialSpendingHousingDataset struct {
	Housing *float64 `json:"housing"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingIncapacityRelatedDataset 

type OecdSocialSpendingIncapacityRelatedDataset struct {
	IncapacityRelated *float64 `json:"incapacity_related"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingOldAgeDataset 

type OecdSocialSpendingOldAgeDataset struct {
	OldAge *float64 `json:"old_age"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingOtherSocialPolicyAreasDataset 

type OecdSocialSpendingOtherSocialPolicyAreasDataset struct {
	OtherSocialPolicyAreas *float64 `json:"other_social_policy_areas"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingSurvivorsDataset 

type OecdSocialSpendingSurvivorsDataset struct {
	Survivors *float64 `json:"survivors"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdSocialSpendingUnemploymentDataset 

type OecdSocialSpendingUnemploymentDataset struct {
	Unemployment *float64 `json:"unemployment"`
}

Dimension of social spending given as the percentage of a country's gross domestic product (%GDP)

type OecdTrustInGovernmentDataset 

type OecdTrustInGovernmentDataset struct {
	TrustGovernment *float64 `json:"trust_government"`
}

Trust in government refers to the share of people who report having confidence in the national government. The data shown reflect the share of respondents answering “yes” (the other response categories being “no”, and “don’t know”) to the survey question: “In this country, do you have confidence in… national government? Due to small sample sizes, country averages for horizontal inequalities (by age, gender, and education) are pooled between 2010-18 to improve the accuracy of the estimates. The sample is ex-ante designed to be nationally representative of the population aged 15 and over. This indicator is measured as a percentage of all survey respondents.We show this metric as the population-weighted average of all OECD member states.

type OilProductionEtemadAndLucianaDataset 

type OilProductionEtemadAndLucianaDataset struct {
	OilProductionEtemadAndLuciana *float64 `json:"oil_production_etemad_and_luciana"`
}

Data from 1900-1980 is sourced from Bouda Etemad and Jean Luciani, World Energy Production 1800 – 1985, ISBN 2-600-56007-6.Data from 1980 onwards is source from U.S. Energy Information Administration, International Energy Statistics.

type OilSpillsItopf2021Dataset 

type OilSpillsItopf2021Dataset struct {
	NumberOilSpills                *float64 `json:"number_oil_spills"`
	QuantityOilSpilt               *float64 `json:"quantity_oil_spilt"`
	NumberOilSpillsDecadalAverage  *float64 `json:"number_oil_spills_decadal_average"`
	QuantityOilSpiltDecadal        *float64 `json:"quantity_oil_spilt_decadal"`
	QuantityOilSpiltDecadalAverage *float64 `json:"quantity_oil_spilt_decadal_average"`
}

The International Tanker Owners Pollution Federation (ITOPF) publishes annual data on the number and quantities of oil spilt from tankers globally.The latest report from the ITOPF is published here: https://www.itopf.org/fileadmin/uploads/itopf/data/Documents/Company_Lit/Oil_Spill_Stats_2021.pdfWe have extracted the data from the published tables of annual statistics.Decadal figures are given as the total, or annual average over the ten years starting at the decadal base year: for example, the figures for the 1990s is the 10 years from 1990 (inclusive) to 1999.

type OilandgasEmploymentAndRigCountUsBureauOfLaborStatistics2017Dataset 

type OilandgasEmploymentAndRigCountUsBureauOfLaborStatistics2017Dataset struct {
	OilAndGasEmployeesUsBureauOfLaborStatistics2017 *float64 `json:"oil_and_gas_employees_us_bureau_of_labor_statistics_2017"`
}

Dataset from BLS includes all employees working in oil & gas extraction in the United States.

type OilcropYieldProductionAndLandUseFao2021Dataset 

type OilcropYieldProductionAndLandUseFao2021Dataset struct {
	VegetableOilProductionTonnes               *float64 `json:"vegetable_oil_production_tonnes"`
	ShareOfTotalVegetableOilProduction         *float64 `json:"share_of_total_vegetable_oil_production"`
	AreaHarvestedHa                            *float64 `json:"area_harvested_ha"`
	ShareOfTotalOilcropArea                    *float64 `json:"share_of_total_oilcrop_area"`
	OilYieldTha                                *float64 `json:"oil_yield_tha"`
	AreaNeededToProduce1TonneOfOilHa           *float64 `json:"area_needed_to_produce_1_tonne_of_oil_ha"`
	AreaNeededToMeetGlobalVegetableOilDemandHa *float64 `json:"area_needed_to_meet_global_vegetable_oil_demand_ha"`
	ShareOfTotalOilcropAreaNegative            *float64 `json:"share_of_total_oilcrop_area_negative"`
}

Data on vegetable oil production and land use by crop type is sourced directly from the UN Food and Agriculture Organization (FAO) database.Our World in Data have calculated each crop's share of total vegetable oil production and land use based on this data.Land use data is sourced as the 'Area harvested' variable from its crops database: http://www.fao.org/faostat/en/#data/QCOil production data is sourced from its Food Balances Sheets as the variable 'Production Quantity':http://www.fao.org/faostat/en/#data/FBSOur World in Data has also calculated oil yields per crop by dividing oil production by land area used to grow the crop. This is measured in tonnes per hectare. Note that this calculates the oil yield per hectare, which is different from the yield of the total crop: this is because not all of the crop can be used for oil.

type OlympicCompetingNationsAndAthletesOlympicDatabaseDataset 

type OlympicCompetingNationsAndAthletesOlympicDatabaseDataset struct {
	NationsParticipatingInSummerOlympics     *float64 `json:"nations_participating_in_summer_olympics"`
	AthletesParticipatingInSummerOlympics    *float64 `json:"athletes_participating_in_summer_olympics"`
	NationsParticipatingInWinterOlympics     *float64 `json:"nations_participating_in_winter_olympics"`
	AthletesParticipatingInWinterOlympics    *float64 `json:"athletes_participating_in_winter_olympics"`
	NationsParticipatingInSummerParalympics  *float64 `json:"nations_participating_in_summer_paralympics"`
	AthletesParticipatingInSummerParalympics *float64 `json:"athletes_participating_in_summer_paralympics"`
	NationsParticipatingInWinterParalympics  *float64 `json:"nations_participating_in_winter_paralympics"`
	AthletesParticipatingInWinterParalympics *float64 `json:"athletes_participating_in_winter_paralympics"`
}

Number of nations and athletes competing in each Olympic and Paralympic games. This is noted separately for the Summer and Winter games.

type OnshoreWindCostBreakdownIrena2018Dataset 

type OnshoreWindCostBreakdownIrena2018Dataset struct {
	CivilWorks     *float64 `json:"civil_works"`
	Development    *float64 `json:"development"`
	Foundation     *float64 `json:"foundation"`
	GridConnection *float64 `json:"grid_connection"`
	Land           *float64 `json:"land"`
	Other          *float64 `json:"other"`
	Planning       *float64 `json:"planning"`
	WindTurbine    *float64 `json:"wind_turbine"`
}

Cost breakdown of onshore wind energy projects in select countries, given as each component's share of total installed project costs.This data is published in IRENA's report 'Renewable Power Generation Costs in 2017'. Available at: http://www.irena.org/publications/2018/Jan/Renewable-power-generation-costs-in-2017. IRENA retains copyright of the underlying data.

type OnshoreWindInstalledProjectCostIrena2018Dataset 

type OnshoreWindInstalledProjectCostIrena2018Dataset struct {
	AverageOnshoreWindInstalledCost2016Moneykw *float64 `json:"average_onshore_wind_installed_cost_2016_moneykw"`
	MaxOnshoreWindInstalledCost2016Moneykw     *float64 `json:"max_onshore_wind_installed_cost_2016_moneykw"`
	MinOnshoreWindInstalledCost2016Moneykw     *float64 `json:"min_onshore_wind_installed_cost_2016_moneykw"`
}

The total installed costs of onshore wind projects given as the global weighted average, with maximum and minimum costs of projects also available.The LCOE is given in 2016 USD per kW.This data is published in IRENA's report 'Renewable Power Generation Costs in 2017'. Available at: http://www.irena.org/publications/2018/Jan/Renewable-power-generation-costs-in-2017. IRENA retains copyright of the underlying data.

type OnshoreWindLcoeIrenaCostDatabase2018Dataset 

type OnshoreWindLcoeIrenaCostDatabase2018Dataset struct {
	OnshoreWindLcoe2016moneykwh *float64 `json:"onshore_wind_lcoe_2016moneykwh"`
}

The weighted average levelised cost of electricity (LCOE) of commissioned onshore wind projects; this is given as the global average and for select countries where data is available.The LCOE is given in 2016 USD per kWh.This data is published in IRENA's report 'Renewable Power Generation Costs in 2017'. Available at: http://www.irena.org/publications/2018/Jan/Renewable-power-generation-costs-in-2017. IRENA retains copyright of the underlying data.

type OphiMultidimensionalPovertyIndexAlkireAndRobles2016Dataset 

type OphiMultidimensionalPovertyIndexAlkireAndRobles2016Dataset struct {
	MultidimensionalPovertyHeadcountRatioAlkireAndRobles2016                                 *float64 `json:"multidimensional_poverty_headcount_ratio_alkire_and_robles_2016"`
	PercentageContributionOfDeprivationsInEducationToOverallPovertyAlkireAndRobles2016       *float64 `json:"percentage_contribution_of_deprivations_in_education_to_overall_poverty_alkire_and_robles_2016"`
	PercentageContributionOfDeprivationsInHealthToOverallPovertyAlkireAndRobles2016          *float64 `json:"percentage_contribution_of_deprivations_in_health_to_overall_poverty_alkire_and_robles_2016"`
	PercentageContributionOfDeprivationsInLivingStandardsToOverallPovertyAlkireAndRobles2016 *float64 `json:"percentage_contribution_of_deprivations_in_living_standards_to_overall_poverty_alkire_and_robles_2016"`
}

Definitions

People are considered 'multidimensional poor' if they are deprived in at least one third of the weighted indicators.

The ten indicators relate to: Years of Schooling, Child School Attendance, Child Mortality, Nutrition, Electricity, Sanitation, Drinking Water, Floor, Cooking Fuel, Asset Ownership. The first four of these indicators carry a weight of one sixth each (i.e. 0.166). The other six have a weight of one eighteenth each (i.e. 0.055)

Survey data

Years correspond to the year reported for the underlying survey used as source in each country. Whenever surveys correspond to year intervals (e.g. 2011-2012), we display the end year for that interval.

The source notes that "The global MPI was first released in 2010, for 104 developing countries, and has been updated yearly with the latest data available. Starting from winter 2014/2015, updates are now made twice a year, in summer and winter. In June 2015, the Global MPI was updated for 32 countries and 6 new countries have been added to the list of those last reported in 2014. In total it now covers 101 developing countries, using data ranging from 2005 to 2014."

type OpioidDeathsDueToOveruseInTheUsCdcWonder2017Dataset 

type OpioidDeathsDueToOveruseInTheUsCdcWonder2017Dataset struct {
	TotalOverdoseDeathsCdcWonder2017                                       *float64 `json:"total_overdose_deaths_cdc_wonder_2017"`
	PrescriptionDrugsCdcWonder2017                                         *float64 `json:"prescription_drugs_cdc_wonder_2017"`
	OpioidPainRelieversCdcWonder2017                                       *float64 `json:"opioid_pain_relievers_cdc_wonder_2017"`
	BenzodiazepinesCdcWonder2017                                           *float64 `json:"benzodiazepines_cdc_wonder_2017"`
	IllicitDrugsCdcWonder2017                                              *float64 `json:"illicit_drugs_cdc_wonder_2017"`
	CocaineCdcWonder2017                                                   *float64 `json:"cocaine_cdc_wonder_2017"`
	HeroinCdcWonder2017                                                    *float64 `json:"heroin_cdc_wonder_2017"`
	AnyOpioidsCdcWonder2017                                                *float64 `json:"any_opioids_cdc_wonder_2017"`
	OpioidPainRelieversOtherThanSyntheticOpioidsCdcWonder2017              *float64 `json:"opioid_pain_relievers_other_than_synthetic_opioids_cdc_wonder_2017"`
	SyntheticOpioidsOtherThanMethadoneCdcWonder2017                        *float64 `json:"synthetic_opioids_other_than_methadone_cdc_wonder_2017"`
	IllicitOpioidsHeroinAndSyntheticOpioidsOtherThanMethadoneCdcWonder2017 *float64 `json:"illicit_opioids_heroin_and_synthetic_opioids_other_than_methadone_cdc_wonder_2017"`
}

The total number of deaths as cited by the National Institute of Drug Abuse (2017) were divided by the US national population figures as cited on the US Census website and subsequently multiplied by 100,000.

type OutputOfKeyIndustrialSectorsInEnglandBankOfEngland2017Dataset 

type OutputOfKeyIndustrialSectorsInEnglandBankOfEngland2017Dataset struct {
	MetalsAndMiningBankOfEngland2017    *float64 `json:"metals_and_mining_bank_of_england_2017"`
	TextilesAndLeatherBankOfEngland2017 *float64 `json:"textiles_and_leather_bank_of_england_2017"`
	OtherIndustriesBankOfEngland2017    *float64 `json:"other_industries_bank_of_england_2017"`
	TotalIndustryBankOfEngland2017      *float64 `json:"total_industry_bank_of_england_2017"`
}

This dataset is produced using 'A4. Ind Production 1270-1870' sheet in BoE dataset. For each variables, output figures are indexed those in year 1700 (i.e. 1700 = 100), and they are measured on a log scale.

type OutputOfKeyIndustriesInEnglandUsingBankOfEngland2017Dataset 

type OutputOfKeyIndustriesInEnglandUsingBankOfEngland2017Dataset struct {
	Tin          *float64 `json:"tin"`
	Iron         *float64 `json:"iron"`
	Coal         *float64 `json:"coal"`
	Wooltextiles *float64 `json:"wooltextiles"`
	Leather      *float64 `json:"leather"`
	Foodstuffs   *float64 `json:"foodstuffs"`
	Construction *float64 `json:"construction"`
	PrintedBooks *float64 `json:"printed_books"`
}

This dataset is produced using 'A4. Ind Production 1270-1870' sheet in BoE dataset. For each variables, output figures are indexed those in year 1700 (i.e. 1700 = 100), and they are measured on a log scale.

type OutputOfKeyServicesSectorsInEnglandUsingBankOfEngland2017Dataset 

type OutputOfKeyServicesSectorsInEnglandUsingBankOfEngland2017Dataset struct {
	GovernmentServices         *float64 `json:"government_services"`
	TradeAndTransport          *float64 `json:"trade_and_transport"`
	FinancialServices          *float64 `json:"financial_services"`
	HousingAndDomesticServices *float64 `json:"housing_and_domestic_services"`
	TotalServices              *float64 `json:"total_services"`
}

This dataset is produced using 'A5. Service Sector 1270-1870' sheet in BoE dataset. For each variables, output figures are indexed those in year 1700 (i.e. 1700 = 100), and they are measured on a log scale.

type OwidCountryToWhoRegionsDataset 

type OwidCountryToWhoRegionsDataset struct {
	WhoRegion *float64 `json:"who_region"`
}

type OzoneAndChlorineProjectionsTo2100ScientificAssessment2014Dataset 

type OzoneAndChlorineProjectionsTo2100ScientificAssessment2014Dataset struct {
	OzoneConcentration1960_0                *float64 `json:"ozone_concentration_1960_0"`
	EquivalentStratosphericChorineEsc1960_0 *float64 `json:"equivalent_stratospheric_chorine_esc_1960_0"`
}

Figures represent stratospheric ozone, and effective stratospheric chlorine (ESC) based on historical measurement and future projections from chemistry-climate models.Chemistry-climate models are used to make projections of total ozone amounts that account for the effects of ozone-depleting substances (ODSs) and climate change. Regional and global projections are shown for total ozone and ESC for the period1960–2100, referenced to 1960 values (i.e. 1960 = 0).Data is based on those in Q20-2 in 'Twenty questions and answers about the ozone layer: 2014 update', published as the 2014 edition of the Scientific Assessment Panel of the Montreal Protocol.Data was extracted from the static figure, Q0-1, using the extraction tool WebPlotDigitizer (https://apps.automeris.io/wpd/).

type OzoneConcentrationStateofglobalairDataset 

type OzoneConcentrationStateofglobalairDataset struct {
	OzoneConcentrationStateofglobalair *float64 `json:"ozone_concentration_stateofglobalair"`
}

Data is gathered based on a combination of air quality observations from satellites combined with information from global chemical transport models, and available ground measurements. A global chemical transport model was used to calculate a seasonal (summer, when temperatures are highest) average concentration. Variation in the timing of the ozone (summer) season in different parts of the world is accounted for in these datasets. Global exposure to PM2.5 is then systematically estimated based on blocks or grid cells covering 0.1° x 0.1° of longitude and latitude (approximately 11 km x 11 km at the equator).Taking into account the population in each block within a country, this data is then aggregated as estimated exposure concentrations to national-level population-weighted averages for a given year.

type OzoneDepletingEmissionsIndexEeaDataset 

type OzoneDepletingEmissionsIndexEeaDataset struct {
	OzoneDepletingEmissionsIndex1986_100 *float64 `json:"ozone_depleting_emissions_index_1986_100"`
}

Data represents the consumption of ozone-depleting substances (in ozone-depleting potential tonnes), measured as an index relative to emissions in the year 1986 (1986 = 100).

type OzoneDepletingEmissionsSince1960ScientificAssessment2014Dataset 

type OzoneDepletingEmissionsSince1960ScientificAssessment2014Dataset struct {
	OzoneDepletingSubstanceEmissionsScientificAssessment2014 *float64 `json:"ozone_depleting_substance_emissions_scientific_assessment_2014"`
}

Figures represent emissions of ozone-depleting substances, with substances weighted by their potential to destroy ozone (their ozone-depleting potential). This gives a total value of emissions normalised to their CFC11-equivalents. Total emissions is inclusive of naturally-occurring and man-made emissions.Data is based on those in Q0-1 in 'Twenty questions and answers about the ozone layer: 2014 update', published as the 2014 edition of the Scientific Assessment Panel of the Montreal Protocol.Data was extracted from the static figure, Q0-1, using the extraction tool WebPlotDigitizer (https://apps.automeris.io/wpd/).

type OzoneDepletionImpactsOnSkinCancerIncidenceSlaperEtAlDataset 

type OzoneDepletionImpactsOnSkinCancerIncidenceSlaperEtAlDataset struct {
	ExcessSkinCancerCasesPerMillionNoRestrictions       *float64 `json:"excess_skin_cancer_cases_per_million_no_restrictions"`
	ExcessSkinCancerCasesPerMillionMontrealProtocol     *float64 `json:"excess_skin_cancer_cases_per_million_montreal_protocol"`
	ExcessSkinCancerCasesPerMillionCopenhagenAmendments *float64 `json:"excess_skin_cancer_cases_per_million_copenhagen_amendments"`
}

The authors modelled the number of excess skin cancer cases they would expect among fair-skinned populations in the United States and Northwest Europe as a result of stratospheric ozone depletion.This was modelled for a scenario of no restrictions on ozone-depleting substances, where the authors assumed a 3% annual increase in emissions of chloroflourocarbons (CFCs), halons and methyl chloroforms. The Montreal Protocol scenario assumed a decline of five important ozone-depleting substances by 50% by the end of 1999 as agreed in the protocol in 1987.The Copenhagen Amendment assumed the production of 21 ozone-depleting substances reduced to zero by the end of 1995.

type OzoneHoleAreaAndConcentrationNasaDataset 

type OzoneHoleAreaAndConcentrationNasaDataset struct {
	MaximumOzoneHoleAreaNasa      *float64 `json:"maximum_ozone_hole_area_nasa"`
	MeanOzoneHoleArea             *float64 `json:"mean_ozone_hole_area"`
	MinimumDailyConcentrationNasa *float64 `json:"minimum_daily_concentration_nasa"`
	MeanDailyConcentrationNasa    *float64 `json:"mean_daily_concentration_nasa"`
}

Annual maximum and Antarctic stratospheric ozone hole area, resultant from the emission of ozone-depleting substances.Minimum and mean Southern Hemisphere daily ozone concentrations, measured in Dobson Units (DU).

type PartiesToMontrealProtocolUnepDataset 

type PartiesToMontrealProtocolUnepDataset struct {
	ViennaConventionAndMontrealProtocolPartiesSubscribingEachYear *float64 `json:"vienna_convention_and_montreal_protocol_parties_subscribing_each_year"`
	ViennaConventionAndMontrealProtocolCumulativeParties          *float64 `json:"vienna_convention_and_montreal_protocol_cumulative_parties"`
}

Through The Vienna Convention on the Protection of the Ozone Layer governments committed themselves to protect the ozone layer and to co-operate with each other in scientific research to improve understanding of the atmospheric processes.The Montreal Protocol on Substances that Deplete the Ozone Layer was adopted by Governments in 1987 and has been modified five times so far. Its control provisions were strengthened through four adjustments to the Protocol adopted in London (1990), Copenhagen (1992), Vienna (1995), Montreal (1997) and Beijing (1999). The Protocol aims to reduce and eventually eliminate the emissions of man-made ozone depleting substances

type PatentAndPublicationRatesOwidBasedOnWorldBankAndUnDataset 

type PatentAndPublicationRatesOwidBasedOnWorldBankAndUnDataset struct {
	PatentApplicationRatesOwidBasedOnWorldBankAndUn                              *float64 `json:"patent_application_rates_owid_based_on_world_bank_and_un"`
	PatentApplicationRatesNonresidentsOwidBasedOnWorldBankAndUn                  *float64 `json:"patent_application_rates_nonresidents_owid_based_on_world_bank_and_un"`
	ScientificAndTechnicalJournalPublicationRatesOwidBasedOnWorldBankAndUn       *float64 `json:"scientific_and_technical_journal_publication_rates_owid_based_on_world_bank_and_un"`
	PatentApplicationRatesAged15OwidBasedOnWorldBankAndUn                        *float64 `json:"patent_application_rates_aged_15_owid_based_on_world_bank_and_un"`
	PatentApplicationRatesAged15NonresidentsOwidBasedOnWorldBankAndUn            *float64 `json:"patent_application_rates_aged_15_nonresidents_owid_based_on_world_bank_and_un"`
	ScientificAndTechnicalJournalPublicationRatesAged15OwidBasedOnWorldBankAndUn *float64 `json:"scientific_and_technical_journal_publication_rates_aged_15_owid_based_on_world_bank_and_un"`
}

Rates of patent application and scientific/technical publication were calculated by OurWorldinData based on a combination of World Bank, World Development Indicators (WDI) and United Nations Population Prospects estimates.

Rates were calculated per million people by dividing the absolute number of patent applications and scientific & technical journal articles (as published by the World Bank) by UN population estimates for any given year.

The World Bank indicators were based on "patent applications, residents" which measures the number of patent applications from residents of a given country; "patent applications, nonresidents" which measures the number of patent applications for any given country from individuals outside of that country. Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.

"Scientific and technical journal articles" which measures the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.

World Bank, World Development Indicators available at: https://data.worldbank.org/data-catalog/world-development-indicators

UN World Population Prospects data available at: http://www.un.org/en/development/desa/population/theme/trends/index.shtml

type PatentsAwardedInEnglandScotlandAndWalesBottomleyDataset 

type PatentsAwardedInEnglandScotlandAndWalesBottomleyDataset struct {
	NumberOfPatentsAwarded *float64 `json:"number_of_patents_awarded"`
}

The annual number of patents awarded across all industries and sectors in England, Scotland and Ireland across the period of the Industrial Revolution (1700-1852).

type PeakFarmlandProjectionAusbuelEtAl2013Dataset 

type PeakFarmlandProjectionAusbuelEtAl2013Dataset struct {
	ArableLandAndPermanentCropsAusbuelEtAl2013 *float64 `json:"arable_land_and_permanent_crops_ausbuel_et_al_2013"`
	AusubelEtAl2013ProjectionAusbuelEtAl2013   *float64 `json:"ausubel_et_al_2013_projection_ausbuel_et_al_2013"`
}

This dataset is based on global arable land use projections from Ausubel et al. (2013), and combines two sources.Data from 1961-2014 is based on estimated global arable land use from the UN Food and Agricultural Organization (FAO) database, available at: http://www.fao.org/faostat/en/#home [accessed 12th August 2017].Data projections for 2015-2060 is based on model predications from the paper by Ausubel et al. (2013). This work projects that over the period 2010-2060, global arable land will decrease by 0.2 percent per year as a result of slowing population growth and agricultural productivity factors. We have taken this estimation from 2015 onwards.References:Ausubel, J. H., Wernick, I. K. and Waggoner, P. E. (2013), Peak Farmland and the Prospect for Land Sparing. Population and Development Review, 38: 221–242. doi:10.1111/j.1728-4457.2013.00561.x

type PeopleExperiencingHomelessnessInTheUsaPitByShelteringStatusHud2016Dataset 

type PeopleExperiencingHomelessnessInTheUsaPitByShelteringStatusHud2016Dataset struct {
	ShelteredHomelessHud2016   *float64 `json:"sheltered_homeless_hud_2016"`
	UnshelteredHomelessHud2016 *float64 `json:"unsheltered_homeless_hud_2016"`
}

HUD's description of the report:

"This report outlines the key findings of the 2016 Point-In-Time (PIT) count and Housing Inventory Count (HIC) conducted in January 2016. Specifically, this report provides 2016 national, state, and CoC-level PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth".

All HUD reports are available at https://www.hudexchange.info/programs/hdx/guides/ahar/#reports

type PercentageDeathsAttributableToRiskFactorsIhmeDataset 

type PercentageDeathsAttributableToRiskFactorsIhmeDataset struct {
	ShareOfDeathsAttributableToRiskFactors *float64 `json:"share_of_deaths_attributable_to_risk_factors"`
}

Data represents the share of deaths which can be attributed to the prevalence of risk factors, including smoking, diet & nutrition, obesity, alcohol and drug consumption, unsafe sex, poor sanitation, water access, air pollution and environmental exposures. The remaining share is therefore the share of deaths which would occur in the absence of any linked risk factors.

type PercentageGainedAccessToImprovedWaterAndSanitation19902015WhoDataset 

type PercentageGainedAccessToImprovedWaterAndSanitation19902015WhoDataset struct {
	PopulationWhoGainedAccessToWaterFrom1990_2015      *float64 `json:"population_who_gained_access_to_water_from_1990_2015"`
	PopulationWhoGainedAccessToSanitationFrom1990_2015 *float64 `json:"population_who_gained_access_to_sanitation_from_1990_2015"`
}

This metric measures the proportion of the 2015 population who gained access to improved water sources or sanitation since 1990. This marks the start and end dates of the Millennium Development Goals (MDGs).

type PercentageOfAdultsLivingAloneInTheUsAndCanadaUsCensusBureauAndStatisticsCanadaDataset 

type PercentageOfAdultsLivingAloneInTheUsAndCanadaUsCensusBureauAndStatisticsCanadaDataset struct {
	PercentageOfAdultsLivingAlone *float64 `json:"percentage_of_adults_living_alone"`
}

Data for the United States corresponds to population aged 18 and over. Data for Canada corresponds to population aged 15 and over.

type PercentageOfAmericansLivingAloneByAgeIpumsDataset 

type PercentageOfAmericansLivingAloneByAgeIpumsDataset struct {
	PercentageOfAmericansLivingAloneByYearTotalIpums  *float64 `json:"percentage_of_americans_living_alone_by_year_total_ipums"`
	PercentageOfAmericansLivingAloneByYearMaleIpums   *float64 `json:"percentage_of_americans_living_alone_by_year_male_ipums"`
	PercentageOfAmericansLivingAloneByYearFemaleIpums *float64 `json:"percentage_of_americans_living_alone_by_year_female_ipums"`
	PercentageOfAmericansLivingAloneByAgeTotalIpums   *float64 `json:"percentage_of_americans_living_alone_by_age_total_ipums"`
	PercentageOfAmericansLivingAloneByAgeMaleIpums    *float64 `json:"percentage_of_americans_living_alone_by_age_male_ipums"`
	PercentageOfAmericansLivingAloneByAgeFemaleIpums  *float64 `json:"percentage_of_americans_living_alone_by_age_female_ipums"`
}

Samples of the decennial Census (1900-2000) and the 2018 ACS from IPUMS-USA has been used to construct variables on the percentage of Americans living alone, by age and sex. We include only those people who are reported to be living in a one-person household and who do not live in group quarters. The IPUMS defines group quarters as "largely institutions and other group living arrangements, such as rooming houses and military barracks. The definitions vary from year to year, but the pre-1940 samples have generally used a definition of group quarters that includes units with 10 or more individuals unrelated to the householder."We have excluded those recorded to be living in institutions, other group quarters, and additional households under the 2000 definition.

type PercentageOfIndividualsUsingTheInternetIctItu2015Dataset 

type PercentageOfIndividualsUsingTheInternetIctItu2015Dataset struct {
	PercentageOfIndividualsUsingTheInternetIct2015 *float64 `json:"percentage_of_individuals_using_the_internet_ict_2015"`
}

These are data on the 'Percentage of Individuals using the Internet (excel)' from 2000 to 2014. Downloaded on 14th July 2015.

type PercentageOfPersonsWithoutHealthInsuranceCouncilOfEconomicAdvisersAndNationalCenterForHealthStatisticsDataset 

type PercentageOfPersonsWithoutHealthInsuranceCouncilOfEconomicAdvisersAndNationalCenterForHealthStatisticsDataset struct {
	PercentageOfPersonsWithoutHealthInsurancePerc *float64 `json:"percentage_of_persons_without_health_insurance_perc"`
}

For 1978-2015, both CEA and NCHS relied on historical data from the National Health Interview Survey (NHIS).  For years prior to 1978 CEA estimated the overall uninsured rate by combining information from the NHIS on trends in private coverage, with administrative data on Medicare and Medicaid enrollment during those years. See the links above for further details.

type PerceptionsOfSpendingOnHealthExpenditureIpsos2016Dataset 

type PerceptionsOfSpendingOnHealthExpenditureIpsos2016Dataset struct {
	HowMuchWeThinkWeSpendOnHealthExpenditureIpsos2016  *float64 `json:"how_much_we_think_we_spend_on_health_expenditure_ipsos_2016"`
	HowMuchWeActuallySpendOnHealthExpenditureIpsos2016 *float64 `json:"how_much_we_actually_spend_on_health_expenditure_ipsos_2016"`
}

Ipsos MORI conducted 27,250 interviews across 50 countries between September-November 2016 via the Ipsos MORI Online Panel system. In the Czech Republic, Montenegro, Netherlands, Norway and Serbia, interviews were carried out through a combination of the online system and face-to-face methodologies.

Data are weighted to match the profile of the population.

type PhosphateFertilizersFao2017Dataset 

type PhosphateFertilizersFao2017Dataset struct {
	PhosphateProductionFao2017  *float64 `json:"phosphate_production_fao_2017"`
	PhosphateConsumptionFao2017 *float64 `json:"phosphate_consumption_fao_2017"`
}

Data combines UN FAO datasets related to phosphate fertilizer production and consumption:For production figures: "Phosphate Fertilizers - Production Quantity" which extends from 2003-2014 and "Fertilizers Archive - Phosphate Fertilizers - Production Quantity", which extends from 1961-2002.For consumption figures: "Phosphate Fertilizers - Consumption" which extends from 2003-2014 and "Fertilizers Archive - Phosphate Fertilizers - Consumption", which extends from 1961-2002.Data is measured in tonnes of total nutrient production or consumption.

type PiecesOfMailAndNumberOfPostOfficesUnitedStatesPostalService2018Dataset 

type PiecesOfMailAndNumberOfPostOfficesUnitedStatesPostalService2018Dataset struct {
	PiecesOfMailHandledUnPostalService2018      *float64 `json:"pieces_of_mail_handled_un_postal_service_2018"`
	NumberOfPostOfficesUnPostalService2018      *float64 `json:"number_of_post_offices_un_postal_service_2018"`
	MailHandledPerPostOfficeUnPostalService2018 *float64 `json:"mail_handled_per_post_office_un_postal_service_2018"`
}

type PlasticBagSubstituteComparisonsDanishEpa2018Dataset 

type PlasticBagSubstituteComparisonsDanishEpa2018Dataset struct {
	GreenhouseGasEmissions     *float64 `json:"greenhouse_gas_emissions"`
	AllEnvironmentalIndicators *float64 `json:"all_environmental_indicators"`
}

The Danish Environmental Protection Agency conducted full life-cycle analysis (LCA) of environmental impacts of a range of grocery bag types. LCAs measure the total environmental impacts (such as greenhouse gas emissions) of a product across their full value chain (including inputs needed for their production).This was quantified for greenhouse gas emissions, as well as a comparison of 'all environmental indicators' which was a combined value for greenhouse gas emissions, ozone depletion, human toxicity (cancer effects), human toxicity (non-cancer effects), photochemical ozone formation, ionizing radiation, particulate matter, terrestrial acidification, terrestrial eutrophication, marine eutrophication, ecosystem toxicity, resource depletion (fossil), resource depletion (abiotic), and water resource depletion.Values are given relative to a standard LDPE (Low-density polyethylene) single-use plastic bag, with values indicating the number of reuses a given bag would need to result in an equal environment impact. For example, a value of 5 would indicate a bag would have to be reused 5 times in order to have as low an environmental impact as the LDPE bag.

type PlasticDiscardedRecycledIncineratedGeyerEtAl2017Dataset 

type PlasticDiscardedRecycledIncineratedGeyerEtAl2017Dataset struct {
	EstimatedHistoricPlasticFate *float64 `json:"estimated_historic_plastic_fate"`
	ExtrapolationOfPlasticFate   *float64 `json:"extrapolation_of_plastic_fate"`
}

Estimates of the proportion of plastic waste discarded, recycled and incinerated based on data in Geyer et al. (2017).Geyer et al. (2017) note an average linear rate of increase in recycling of 0.7% per year from 1990 onwards, and 0.7% per year for incineration from 1980 onwards. Data for 1980-2015 is based on historical estimates. Data from 2016-2050 is based solely on extrapolation of consistent growth trends of 0.7% for recycling and incineration; it therefore represents business-as-usual growth but should not be interpreted as a direct projection.

type PlasticImportersToChinaBrooksEtAl2018Dataset 

type PlasticImportersToChinaBrooksEtAl2018Dataset struct {
	PlasticExportsToChinaTonnes      *float64 `json:"plastic_exports_to_china_tonnes"`
	ShareOfChinesePlasticImportsPerc *float64 `json:"share_of_chinese_plastic_imports_perc"`
}

Quantity of plastic exports to China by the top 10 exporting countries in 2016. This is given in tonnes of plastic waste imported by China.Data is based on the UN Comtrade Database.

type PlasticImportsByChinaAndImpactOfBanBrooksEtAl2018Dataset 

type PlasticImportsByChinaAndImpactOfBanBrooksEtAl2018Dataset struct {
	DomesticPlasticWasteGeneratedTonnes   *float64 `json:"domestic_plastic_waste_generated_tonnes"`
	ImportedRecycledWasteTonnes           *float64 `json:"imported_recycled_waste_tonnes"`
	TotalWasteToManageTonnes              *float64 `json:"total_waste_to_manage_tonnes"`
	ShareOfPlasticWasteImportedPerc       *float64 `json:"share_of_plastic_waste_imported_perc"`
	DisplacedPlasticWaste100percBanTonnes *float64 `json:"displaced_plastic_waste_100perc_ban_tonnes"`
	DisplacedPlasticWaste75percBanTonnes  *float64 `json:"displaced_plastic_waste_75perc_ban_tonnes"`
	DisplacedPlasticWaste50percBanTonnes  *float64 `json:"displaced_plastic_waste_50perc_ban_tonnes"`
}

Data represents two series related to Chinese plastic waste:- the estimated quantity of domestic waste generation, imported recycled waste, and total plastic waste to manage, measured in tonnes per year (from 2010 to 2016).- the projected cumulative quantity of plastic waste displaced as a result of the Chinese plastic imported ban (over the period 2018-2030). This is shown under scenarios of a 100%, 75% or 50% Chinese ban on plastic waste imports.

type PlasticOceanPollutionMeijerEtAl2021Dataset 

type PlasticOceanPollutionMeijerEtAl2021Dataset struct {
	ProbabilityOfPlasticBeingEmittedToOcean         *float64 `json:"probability_of_plastic_being_emitted_to_ocean"`
	MismanagedPlasticWasteMetricTonsYear1           *float64 `json:"mismanaged_plastic_waste_metric_tons_year_1"`
	MismanagedWasteEmittedToTheOceanMetricTonsYear1 *float64 `json:"mismanaged_waste_emitted_to_the_ocean_metric_tons_year_1"`
	ShareOfMismanagedPlasticWasteEmittedToOcean     *float64 `json:"share_of_mismanaged_plastic_waste_emitted_to_ocean"`
	ShareOfGlobalMismanagedPlasticWaste             *float64 `json:"share_of_global_mismanaged_plastic_waste"`
	ShareOfGlobalPlasticsEmittedToOcean             *float64 `json:"share_of_global_plastics_emitted_to_ocean"`
	MismanagedPlasticWastePerCapitaKgPerYear        *float64 `json:"mismanaged_plastic_waste_per_capita_kg_per_year"`
	MismanagedPlasticWasteToOceanPerCapitaKgPerYear *float64 `json:"mismanaged_plastic_waste_to_ocean_per_capita_kg_per_year"`
}

type PlasticPollutionByTop50RiversMeijerEtAl2021Dataset 

type PlasticPollutionByTop50RiversMeijerEtAl2021Dataset struct {
	MismanagedWasteEmittedToTheOceanMetricTonsYear1 *float64 `json:"mismanaged_waste_emitted_to_the_ocean_metric_tons_year_1"`
	MismanagedWasteEmittedToTheOceanGramsPerSecond  *float64 `json:"mismanaged_waste_emitted_to_the_ocean_grams_per_second"`
	ShareOfGlobalPlasticsEmittedToOcean             *float64 `json:"share_of_global_plastics_emitted_to_ocean"`
}

type PlasticProductLifetimeProductionWasteBySourceGeyerEtAl2017Dataset 

type PlasticProductLifetimeProductionWasteBySourceGeyerEtAl2017Dataset struct {
	MeanProductLifetimeYears                   *float64 `json:"mean_product_lifetime_years"`
	PrimaryPlasticProductionMillionTonnes      *float64 `json:"primary_plastic_production_million_tonnes"`
	PrimaryPlasticWasteGenerationMillionTonnes *float64 `json:"primary_plastic_waste_generation_million_tonnes"`
}

Data is given as mean product lifetimes of plastic products (measured in years), plastic production, and plastic waste by industrial sector/use (measured in tonnes per year). Data is also given for primary production and plastic waste by polymer type. Polymer types are as follows:- LDPE: Low-density polyethylene- HDPE: High-density polyethylene- PP: Polypropylene- PS: Polystyrene- PVC: Polyvinyl chloride- PET: Polyethylene terephthalate- PUT: Polyurethanes- PP&A fibres: polyester, polyamide, and acrylic fibres- Other polymers- Additives

type PlasticWasteGenerationByCountryOwidBasedOnJambeckEtAlAndWorldBankDataset 

type PlasticWasteGenerationByCountryOwidBasedOnJambeckEtAlAndWorldBankDataset struct {
	PlasticWasteGenerationTonnesTotal *float64 `json:"plastic_waste_generation_tonnes_total"`
}

Data on total national plastic waste generation was calculated by Our World in Data based on per capita plastic waste generation data published in Jambeck et al. (2015), and total population data published in the World Bank, World Development Indicators (available at: https://datacatalog.worldbank.org/dataset/world-development-indicators).Jambeck et al. quantified municipal and plastic waste streams from coastal populations in 2010. Their estimates were therefore multiplied by coastal population numbers to derive national plastic waste at risk of entering oceans and waterways. Here we calculate the total plastic waste generation by instead multiplying by total population figures.

type PlasticWasteJambeckEtAl2015Dataset 

type PlasticWasteJambeckEtAl2015Dataset struct {
	CoastalPopulation                 *float64 `json:"coastal_population"`
	PerCapitaWasteGenerationRate      *float64 `json:"per_capita_waste_generation_rate"`
	ShareOfPlasticsInWasteStream      *float64 `json:"share_of_plastics_in_waste_stream"`
	ShareOfPlasticInadequatelyManaged *float64 `json:"share_of_plastic_inadequately_managed"`
	MunicipalWasteGenerated           *float64 `json:"municipal_waste_generated"`
	PlasticWasteGenerated             *float64 `json:"plastic_waste_generated"`
	InadequatelyManagedPlasticWaste   *float64 `json:"inadequately_managed_plastic_waste"`
	PlasticWasteLittered              *float64 `json:"plastic_waste_littered"`
	PerCapitaMismanagedPlasticWaste   *float64 `json:"per_capita_mismanaged_plastic_waste"`
	TotalMismanagedPlasticWasteIn2010 *float64 `json:"total_mismanaged_plastic_waste_in_2010"`
	TotalMismanagedPlasticWasteIn2025 *float64 `json:"total_mismanaged_plastic_waste_in_2025"`
	PerCapitaPlasticWasteKgpersonday  *float64 `json:"per_capita_plastic_waste_kgpersonday"`
}

Jambeck et al. quantified municipal and plastic waste streams from coastal populations in 2010 with projections to the year 2025.The authors' definition of a coastal population is based on those who live within 50km of a coastal water. Such populations are those for which plastic waste is at risk of leading to ocean debris. Sources further inland are significantly less likely to end up as ocean debris.The authors define mismanaged and inadequately managed waste as follows: "mismanaged waste is material that is either littered or inadequately disposed. Inadequately disposed waste is not formally managed and includes disposal in dumps or open, uncontrolled landfills, where it is not fully contained. Mismanaged waste could eventually enter the ocean via inland waterways,wastewater outflows, and transport by wind or tides."In October 2019, per capita plastic waste figures for Trinidad and Tobago were updated from 3.6kg to 0.29kg per person per day. This change was the result of error in the original waste figures published by the World Bank, which have since been revised and amended.

type PolcalnetGlobalPoverty2017Dataset 

type PolcalnetGlobalPoverty2017Dataset struct {
	MeanMonthlyPerCapitaExpenditureIn2011IntMoneyPovcalnet2017   *float64 `json:"mean_monthly_per_capita_expenditure_in_2011_int_money_povcalnet_2017"`
	MedianMonthlyPerCapitaExpenditureIn2011IntMoneyPovcalnet2017 *float64 `json:"median_monthly_per_capita_expenditure_in_2011_int_money_povcalnet_2017"`
}

Countries for which both consumption and income expenditure data was available, we present mean and median per capita consumption expenditure only.

type PoliticalCompetitionAndParticipationHowWasLifeOecd2014Dataset 

type PoliticalCompetitionAndParticipationHowWasLifeOecd2014Dataset struct {
	PoliticalParticipationHowWasLifeOecd2014 *float64 `json:"political_participation_how_was_life_oecd_2014"`
	PoliticalCompetitionHowWasLifeOecd2014   *float64 `json:"political_competition_how_was_life_oecd_2014"`
	IndexOfDemocracyHowWasLifeOecd2014       *float64 `json:"index_of_democracy_how_was_life_oecd_2014"`
	Polity2HowWasLifeOecd2014                *float64 `json:"polity2_how_was_life_oecd_2014"`
}

type PoliticalRegimesBertelsmannTransformationIndex2022Dataset 

type PoliticalRegimesBertelsmannTransformationIndex2022Dataset struct {
	RegimeBti                 *float64 `json:"regime_bti"`
	StateBasicBti             *float64 `json:"state_basic_bti"`
	ElectfreefairBti          *float64 `json:"electfreefair_bti"`
	EffectivePowerBti         *float64 `json:"effective_power_bti"`
	FreeassocBti              *float64 `json:"freeassoc_bti"`
	FreeexprBti               *float64 `json:"freeexpr_bti"`
	SepPowerBti               *float64 `json:"sep_power_bti"`
	CivRightsBti              *float64 `json:"civ_rights_bti"`
	DemocracyBti              *float64 `json:"democracy_bti"`
	StateBti                  *float64 `json:"state_bti"`
	PoliticalParticipationBti *float64 `json:"political_participation_bti"`
	RuleOfLawBti              *float64 `json:"rule_of_law_bti"`
	StabilityDemInstBti       *float64 `json:"stability_dem_inst_bti"`
	PolSocIntegrBti           *float64 `json:"pol_soc_integr_bti"`
	NumberHardautBti          *float64 `json:"number_hardaut_bti"`
	NumberModautBti           *float64 `json:"number_modaut_bti"`
	NumberHdefdemBti          *float64 `json:"number_hdefdem_bti"`
	NumberDefdemBti           *float64 `json:"number_defdem_bti"`
	NumberConsdemBti          *float64 `json:"number_consdem_bti"`
	PopHardautBti             *float64 `json:"pop_hardaut_bti"`
	PopModautBti              *float64 `json:"pop_modaut_bti"`
	PopHdefdemBti             *float64 `json:"pop_hdefdem_bti"`
	PopDefdemBti              *float64 `json:"pop_defdem_bti"`
	PopConsdemBti             *float64 `json:"pop_consdem_bti"`
	PopMissregBti             *float64 `json:"pop_missreg_bti"`
	PopwDemocracyBti          *float64 `json:"popw_democracy_bti"`
	Region                    *float64 `json:"region"`
}

This dataset provides information on political regimes, using data from Bertelsmann Foundation's Bertelsmann Transformation Index (2022).You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/democracy

type PoliticalRegimesEconomistIntelligenceUnit2022Dataset 

type PoliticalRegimesEconomistIntelligenceUnit2022Dataset struct {
	RegimeEiu        *float64 `json:"regime_eiu"`
	DemocracyEiu     *float64 `json:"democracy_eiu"`
	ElectFreefairEiu *float64 `json:"elect_freefair_eiu"`
	FunctGovEiu      *float64 `json:"funct_gov_eiu"`
	PolPartEiu       *float64 `json:"pol_part_eiu"`
	DemCultureEiu    *float64 `json:"dem_culture_eiu"`
	CivlibEiu        *float64 `json:"civlib_eiu"`
	NumberAutregEiu  *float64 `json:"number_autreg_eiu"`
	NumberHybregEiu  *float64 `json:"number_hybreg_eiu"`
	NumberFlawdemEiu *float64 `json:"number_flawdem_eiu"`
	NumberFulldemEiu *float64 `json:"number_fulldem_eiu"`
	PopAutregEiu     *float64 `json:"pop_autreg_eiu"`
	PopHybregEiu     *float64 `json:"pop_hybreg_eiu"`
	PopFlawdemEiu    *float64 `json:"pop_flawdem_eiu"`
	PopFulldemEiu    *float64 `json:"pop_fulldem_eiu"`
	PopMissregEiu    *float64 `json:"pop_missreg_eiu"`
	PopwDemocracyEiu *float64 `json:"popw_democracy_eiu"`
	Region           *float64 `json:"region"`
}

This dataset provides information on political regimes, using data from the Economist Intelligence Unit's Democracy Index (2022).You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/democracy

type PoliticalRegimesFreedomHouse2022Dataset 

type PoliticalRegimesFreedomHouse2022Dataset struct {
	RegimeFh            *float64 `json:"regime_fh"`
	PolrightsFh         *float64 `json:"polrights_fh"`
	CivlibsFh           *float64 `json:"civlibs_fh"`
	ElectdemFh          *float64 `json:"electdem_fh"`
	PolrightsScoreFh    *float64 `json:"polrights_score_fh"`
	CivlibsScoreFh      *float64 `json:"civlibs_score_fh"`
	CountryFh           *float64 `json:"country_fh"`
	NumberNotfreeFh     *float64 `json:"number_notfree_fh"`
	NumberPartlyfreeFh  *float64 `json:"number_partlyfree_fh"`
	NumberFreeFh        *float64 `json:"number_free_fh"`
	NumberNonelectdemFh *float64 `json:"number_nonelectdem_fh"`
	NumberElectdemFh    *float64 `json:"number_electdem_fh"`
	PopNotfreeFh        *float64 `json:"pop_notfree_fh"`
	PopPartlyfreeFh     *float64 `json:"pop_partlyfree_fh"`
	PopFreeFh           *float64 `json:"pop_free_fh"`
	PopMissregFh        *float64 `json:"pop_missreg_fh"`
	PopNonelectdemFh    *float64 `json:"pop_nonelectdem_fh"`
	PopElectdemFh       *float64 `json:"pop_electdem_fh"`
	PopMissdemFh        *float64 `json:"pop_missdem_fh"`
	Region              *float64 `json:"region"`
	ElectprocessFh      *float64 `json:"electprocess_fh"`
}

This dataset provides information on political regimes, using data from Freedom House's Freedom in the World (2022).You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/democracy

type PoliticalRegimesOwidBasedOnBoixEtAl2013Dataset 

type PoliticalRegimesOwidBasedOnBoixEtAl2013Dataset struct {
	RegimeBmrOwid               *float64 `json:"regime_bmr_owid"`
	DemAgeBmrOwid               *float64 `json:"dem_age_bmr_owid"`
	DemAgeGroupBmrOwid          *float64 `json:"dem_age_group_bmr_owid"`
	RegimeWomsuffrBmrOwid       *float64 `json:"regime_womsuffr_bmr_owid"`
	DemWsAgeBmrOwid             *float64 `json:"dem_ws_age_bmr_owid"`
	DemWsAgeGroupBmrOwid        *float64 `json:"dem_ws_age_group_bmr_owid"`
	RegimeImputedBmrOwid        *float64 `json:"regime_imputed_bmr_owid"`
	RegimeImputedCountryBmrOwid *float64 `json:"regime_imputed_country_bmr_owid"`
	DemExpBmrOwid               *float64 `json:"dem_exp_bmr_owid"`
	DemWsExpBmrOwid             *float64 `json:"dem_ws_exp_bmr_owid"`
	NumberNondemBmrOwid         *float64 `json:"number_nondem_bmr_owid"`
	NumberDemBmrOwid            *float64 `json:"number_dem_bmr_owid"`
	NumberNondemWomsuffrBmrOwid *float64 `json:"number_nondem_womsuffr_bmr_owid"`
	NumberDemWomsuffrBmrOwid    *float64 `json:"number_dem_womsuffr_bmr_owid"`
	NumberDem18BmrOwid          *float64 `json:"number_dem_18_bmr_owid"`
	NumberDem30BmrOwid          *float64 `json:"number_dem_30_bmr_owid"`
	NumberDem60BmrOwid          *float64 `json:"number_dem_60_bmr_owid"`
	NumberDem90BmrOwid          *float64 `json:"number_dem_90_bmr_owid"`
	NumberDem91plusBmrOwid      *float64 `json:"number_dem_91plus_bmr_owid"`
	NumberDemWs18BmrOwid        *float64 `json:"number_dem_ws_18_bmr_owid"`
	NumberDemWs30BmrOwid        *float64 `json:"number_dem_ws_30_bmr_owid"`
	NumberDemWs60BmrOwid        *float64 `json:"number_dem_ws_60_bmr_owid"`
	NumberDemWs90BmrOwid        *float64 `json:"number_dem_ws_90_bmr_owid"`
	NumberDemWs91plusBmrOwid    *float64 `json:"number_dem_ws_91plus_bmr_owid"`
	PopNondemBmrOwid            *float64 `json:"pop_nondem_bmr_owid"`
	PopDemBmrOwid               *float64 `json:"pop_dem_bmr_owid"`
	PopMissregBmrOwid           *float64 `json:"pop_missreg_bmr_owid"`
	PopNondemWomsuffrBmrOwid    *float64 `json:"pop_nondem_womsuffr_bmr_owid"`
	PopDemWomsuffrBmrOwid       *float64 `json:"pop_dem_womsuffr_bmr_owid"`
	PopDem18BmrOwid             *float64 `json:"pop_dem_18_bmr_owid"`
	PopDem30BmrOwid             *float64 `json:"pop_dem_30_bmr_owid"`
	PopDem60BmrOwid             *float64 `json:"pop_dem_60_bmr_owid"`
	PopDem90BmrOwid             *float64 `json:"pop_dem_90_bmr_owid"`
	PopDem91plusBmrOwid         *float64 `json:"pop_dem_91plus_bmr_owid"`
	PopDemWs18BmrOwid           *float64 `json:"pop_dem_ws_18_bmr_owid"`
	PopDemWs30BmrOwid           *float64 `json:"pop_dem_ws_30_bmr_owid"`
	PopDemWs60BmrOwid           *float64 `json:"pop_dem_ws_60_bmr_owid"`
	PopDemWs90BmrOwid           *float64 `json:"pop_dem_ws_90_bmr_owid"`
	PopDemWs91plusBmrOwid       *float64 `json:"pop_dem_ws_91plus_bmr_owid"`
	Region                      *float64 `json:"region"`
}

This dataset provides information on political regimes, using data from Boix et al. (2013).We slightly expand the countries and years covered.You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/political_regimes

type PoliticalRegimesOwidBasedOnVDemV12AndLuhrmannEtAl2018Dataset 

type PoliticalRegimesOwidBasedOnVDemV12AndLuhrmannEtAl2018Dataset struct {
	RegimeRowOwid                *float64 `json:"regime_row_owid"`
	ElectdemAgeRowOwid           *float64 `json:"electdem_age_row_owid"`
	ElectdemAgeGroupRowOwid      *float64 `json:"electdem_age_group_row_owid"`
	LibdemAgeRowOwid             *float64 `json:"libdem_age_row_owid"`
	LibdemAgeGroupRowOwid        *float64 `json:"libdem_age_group_row_owid"`
	ElectmulparRowOwid           *float64 `json:"electmulpar_row_owid"`
	ElectmulparHoeRowOwid        *float64 `json:"electmulpar_hoe_row_owid"`
	ElectmulparLegRowOwid        *float64 `json:"electmulpar_leg_row_owid"`
	ElectfreefairRowOwid         *float64 `json:"electfreefair_row_owid"`
	ElectdemDichRowOwid          *float64 `json:"electdem_dich_row_owid"`
	AccessjustMRowOwid           *float64 `json:"accessjust_m_row_owid"`
	AccessjustWRowOwid           *float64 `json:"accessjust_w_row_owid"`
	TransplawsRowOwid            *float64 `json:"transplaws_row_owid"`
	ElectdemVdemOwid             *float64 `json:"electdem_vdem_owid"`
	ElectdemVdemLowOwid          *float64 `json:"electdem_vdem_low_owid"`
	ElectdemVdemHighOwid         *float64 `json:"electdem_vdem_high_owid"`
	LibdemVdemOwid               *float64 `json:"libdem_vdem_owid"`
	LibdemVdemLowOwid            *float64 `json:"libdem_vdem_low_owid"`
	LibdemVdemHighOwid           *float64 `json:"libdem_vdem_high_owid"`
	ParticipdemVdemOwid          *float64 `json:"participdem_vdem_owid"`
	ParticipdemVdemLowOwid       *float64 `json:"participdem_vdem_low_owid"`
	ParticipdemVdemHighOwid      *float64 `json:"participdem_vdem_high_owid"`
	DelibdemVdemOwid             *float64 `json:"delibdem_vdem_owid"`
	DelibdemVdemLowOwid          *float64 `json:"delibdem_vdem_low_owid"`
	DelibdemVdemHighOwid         *float64 `json:"delibdem_vdem_high_owid"`
	EgaldemVdemOwid              *float64 `json:"egaldem_vdem_owid"`
	EgaldemVdemLowOwid           *float64 `json:"egaldem_vdem_low_owid"`
	EgaldemVdemHighOwid          *float64 `json:"egaldem_vdem_high_owid"`
	FreeexprVdemOwid             *float64 `json:"freeexpr_vdem_owid"`
	FreeexprVdemLowOwid          *float64 `json:"freeexpr_vdem_low_owid"`
	FreeexprVdemHighOwid         *float64 `json:"freeexpr_vdem_high_owid"`
	FreeassocVdemOwid            *float64 `json:"freeassoc_vdem_owid"`
	FreeassocVdemLowOwid         *float64 `json:"freeassoc_vdem_low_owid"`
	FreeassocVdemHighOwid        *float64 `json:"freeassoc_vdem_high_owid"`
	SuffrVdemOwid                *float64 `json:"suffr_vdem_owid"`
	ElectfreefairVdemOwid        *float64 `json:"electfreefair_vdem_owid"`
	ElectfreefairVdemLowOwid     *float64 `json:"electfreefair_vdem_low_owid"`
	ElectfreefairVdemHighOwid    *float64 `json:"electfreefair_vdem_high_owid"`
	ElectoffVdemOwid             *float64 `json:"electoff_vdem_owid"`
	LibVdemOwid                  *float64 `json:"lib_vdem_owid"`
	LibVdemLowOwid               *float64 `json:"lib_vdem_low_owid"`
	LibVdemHighOwid              *float64 `json:"lib_vdem_high_owid"`
	CivlibVdemOwid               *float64 `json:"civlib_vdem_owid"`
	CivlibVdemLowOwid            *float64 `json:"civlib_vdem_low_owid"`
	CivlibVdemHighOwid           *float64 `json:"civlib_vdem_high_owid"`
	JudicialConstrVdemOwid       *float64 `json:"judicial_constr_vdem_owid"`
	JudicialConstrVdemLowOwid    *float64 `json:"judicial_constr_vdem_low_owid"`
	JudicialConstrVdemHighOwid   *float64 `json:"judicial_constr_vdem_high_owid"`
	LegisConstrVdemOwid          *float64 `json:"legis_constr_vdem_owid"`
	LegisConstrVdemLowOwid       *float64 `json:"legis_constr_vdem_low_owid"`
	LegisConstrVdemHighOwid      *float64 `json:"legis_constr_vdem_high_owid"`
	ParticipVdemOwid             *float64 `json:"particip_vdem_owid"`
	ParticipVdemLowOwid          *float64 `json:"particip_vdem_low_owid"`
	ParticipVdemHighOwid         *float64 `json:"particip_vdem_high_owid"`
	CivsocParticipVdemOwid       *float64 `json:"civsoc_particip_vdem_owid"`
	CivsocParticipVdemLowOwid    *float64 `json:"civsoc_particip_vdem_low_owid"`
	CivsocParticipVdemHighOwid   *float64 `json:"civsoc_particip_vdem_high_owid"`
	DirpopVoteVdemOwid           *float64 `json:"dirpop_vote_vdem_owid"`
	LocelectVdemOwid             *float64 `json:"locelect_vdem_owid"`
	LocelectVdemLowOwid          *float64 `json:"locelect_vdem_low_owid"`
	LocelectVdemHighOwid         *float64 `json:"locelect_vdem_high_owid"`
	RegelectVdemOwid             *float64 `json:"regelect_vdem_owid"`
	RegelectVdemLowOwid          *float64 `json:"regelect_vdem_low_owid"`
	RegelectVdemHighOwid         *float64 `json:"regelect_vdem_high_owid"`
	DelibVdemOwid                *float64 `json:"delib_vdem_owid"`
	DelibVdemLowOwid             *float64 `json:"delib_vdem_low_owid"`
	DelibVdemHighOwid            *float64 `json:"delib_vdem_high_owid"`
	JustifiedPolchVdemOwid       *float64 `json:"justified_polch_vdem_owid"`
	JustifiedPolchVdemLowOwid    *float64 `json:"justified_polch_vdem_low_owid"`
	JustifiedPolchVdemHighOwid   *float64 `json:"justified_polch_vdem_high_owid"`
	JustcomgdPolchVdemOwid       *float64 `json:"justcomgd_polch_vdem_owid"`
	JustcomgdPolchVdemLowOwid    *float64 `json:"justcomgd_polch_vdem_low_owid"`
	JustcomgdPolchVdemHighOwid   *float64 `json:"justcomgd_polch_vdem_high_owid"`
	CounterargPolchVdemOwid      *float64 `json:"counterarg_polch_vdem_owid"`
	CounterargPolchVdemLowOwid   *float64 `json:"counterarg_polch_vdem_low_owid"`
	CounterargPolchVdemHighOwid  *float64 `json:"counterarg_polch_vdem_high_owid"`
	EliteconsPolchVdemOwid       *float64 `json:"elitecons_polch_vdem_owid"`
	EliteconsPolchVdemLowOwid    *float64 `json:"elitecons_polch_vdem_low_owid"`
	EliteconsPolchVdemHighOwid   *float64 `json:"elitecons_polch_vdem_high_owid"`
	SocconsPolchVdemOwid         *float64 `json:"soccons_polch_vdem_owid"`
	SocconsPolchVdemLowOwid      *float64 `json:"soccons_polch_vdem_low_owid"`
	SocconsPolchVdemHighOwid     *float64 `json:"soccons_polch_vdem_high_owid"`
	TurnoutVdemOwid              *float64 `json:"turnout_vdem_owid"`
	EgalVdemOwid                 *float64 `json:"egal_vdem_owid"`
	EgalVdemLowOwid              *float64 `json:"egal_vdem_low_owid"`
	EgalVdemHighOwid             *float64 `json:"egal_vdem_high_owid"`
	EqualRightsVdemOwid          *float64 `json:"equal_rights_vdem_owid"`
	EqualRightsVdemLowOwid       *float64 `json:"equal_rights_vdem_low_owid"`
	EqualRightsVdemHighOwid      *float64 `json:"equal_rights_vdem_high_owid"`
	EqualAccessVdemOwid          *float64 `json:"equal_access_vdem_owid"`
	EqualAccessVdemLowOwid       *float64 `json:"equal_access_vdem_low_owid"`
	EqualAccessVdemHighOwid      *float64 `json:"equal_access_vdem_high_owid"`
	EqualRessVdemOwid            *float64 `json:"equal_ress_vdem_owid"`
	EqualRessVdemLowOwid         *float64 `json:"equal_ress_vdem_low_owid"`
	EqualRessVdemHighOwid        *float64 `json:"equal_ress_vdem_high_owid"`
	GoveffectiveVdemWbgiOwid     *float64 `json:"goveffective_vdem_wbgi_owid"`
	RegimeImputedVdemOwid        *float64 `json:"regime_imputed_vdem_owid"`
	RegimeImputedCountryVdemOwid *float64 `json:"regime_imputed_country_vdem_owid"`
	ElectdemExpRowOwid           *float64 `json:"electdem_exp_row_owid"`
	LibdemExpRowOwid             *float64 `json:"libdem_exp_row_owid"`
	NumberClosedautRowOwid       *float64 `json:"number_closedaut_row_owid"`
	NumberElectautRowOwid        *float64 `json:"number_electaut_row_owid"`
	NumberElectdemRowOwid        *float64 `json:"number_electdem_row_owid"`
	NumberLibdemRowOwid          *float64 `json:"number_libdem_row_owid"`
	NumberElectdem18RowOwid      *float64 `json:"number_electdem_18_row_owid"`
	NumberElectdem30RowOwid      *float64 `json:"number_electdem_30_row_owid"`
	NumberElectdem60RowOwid      *float64 `json:"number_electdem_60_row_owid"`
	NumberElectdem90RowOwid      *float64 `json:"number_electdem_90_row_owid"`
	NumberElectdem91plusRowOwid  *float64 `json:"number_electdem_91plus_row_owid"`
	NumberLibdem18RowOwid        *float64 `json:"number_libdem_18_row_owid"`
	NumberLibdem30RowOwid        *float64 `json:"number_libdem_30_row_owid"`
	NumberLibdem60RowOwid        *float64 `json:"number_libdem_60_row_owid"`
	NumberLibdem90RowOwid        *float64 `json:"number_libdem_90_row_owid"`
	NumberLibdem91plusRowOwid    *float64 `json:"number_libdem_91plus_row_owid"`
	PopClosedautRowOwid          *float64 `json:"pop_closedaut_row_owid"`
	PopElectautRowOwid           *float64 `json:"pop_electaut_row_owid"`
	PopElectdemRowOwid           *float64 `json:"pop_electdem_row_owid"`
	PopLibdemRowOwid             *float64 `json:"pop_libdem_row_owid"`
	PopMissregRowOwid            *float64 `json:"pop_missreg_row_owid"`
	PopwElectdemVdemOwid         *float64 `json:"popw_electdem_vdem_owid"`
	PopwElectdemLVdemOwid        *float64 `json:"popw_electdem_l_vdem_owid"`
	PopwElectdemHVdemOwid        *float64 `json:"popw_electdem_h_vdem_owid"`
	PopwLibdemVdemOwid           *float64 `json:"popw_libdem_vdem_owid"`
	PopwLibdemLVdemOwid          *float64 `json:"popw_libdem_l_vdem_owid"`
	PopwLibdemHVdemOwid          *float64 `json:"popw_libdem_h_vdem_owid"`
	PopwParticipdemVdemOwid      *float64 `json:"popw_participdem_vdem_owid"`
	PopwParticipdemLVdemOwid     *float64 `json:"popw_participdem_l_vdem_owid"`
	PopwParticipdemHVdemOwid     *float64 `json:"popw_participdem_h_vdem_owid"`
	PopwDelibdemVdemOwid         *float64 `json:"popw_delibdem_vdem_owid"`
	PopwDelibdemLVdemOwid        *float64 `json:"popw_delibdem_l_vdem_owid"`
	PopwDelibdemHVdemOwid        *float64 `json:"popw_delibdem_h_vdem_owid"`
	PopwEgaldemVdemOwid          *float64 `json:"popw_egaldem_vdem_owid"`
	PopwEgaldemLVdemOwid         *float64 `json:"popw_egaldem_l_vdem_owid"`
	PopwEgaldemHVdemOwid         *float64 `json:"popw_egaldem_h_vdem_owid"`
	Region                       *float64 `json:"region"`
	PopElectdem18RowOwid         *float64 `json:"pop_electdem_18_row_owid"`
	PopElectdem30RowOwid         *float64 `json:"pop_electdem_30_row_owid"`
	PopElectdem60RowOwid         *float64 `json:"pop_electdem_60_row_owid"`
	PopElectdem90RowOwid         *float64 `json:"pop_electdem_90_row_owid"`
	PopElectdem91plusRowOwid     *float64 `json:"pop_electdem_91plus_row_owid"`
	PopLibdem18RowOwid           *float64 `json:"pop_libdem_18_row_owid"`
	PopLibdem30RowOwid           *float64 `json:"pop_libdem_30_row_owid"`
	PopLibdem60RowOwid           *float64 `json:"pop_libdem_60_row_owid"`
	PopLibdem90RowOwid           *float64 `json:"pop_libdem_90_row_owid"`
	PopLibdem91plusRowOwid       *float64 `json:"pop_libdem_91plus_row_owid"`
	RegimeAmbRowOwid             *float64 `json:"regime_amb_row_owid"`
	ElectmulparHighRowOwid       *float64 `json:"electmulpar_high_row_owid"`
	ElectmulparLowRowOwid        *float64 `json:"electmulpar_low_row_owid"`
	ElectmulparLegHighRowOwid    *float64 `json:"electmulpar_leg_high_row_owid"`
	ElectmulparLegLowRowOwid     *float64 `json:"electmulpar_leg_low_row_owid"`
	ElectmulparHoeHighRowOwid    *float64 `json:"electmulpar_hoe_high_row_owid"`
	ElectmulparHoeLowRowOwid     *float64 `json:"electmulpar_hoe_low_row_owid"`
	ElectfreefairHighRowOwid     *float64 `json:"electfreefair_high_row_owid"`
	ElectfreefairLowRowOwid      *float64 `json:"electfreefair_low_row_owid"`
	ElectdemDichHighRowOwidOwid  *float64 `json:"electdem_dich_high_row_owid_owid"`
	ElectdemDichLowRowOwidOwid   *float64 `json:"electdem_dich_low_row_owid_owid"`
	AccessjustMHighRowOwid       *float64 `json:"accessjust_m_high_row_owid"`
	AccessjustMLowRowOwid        *float64 `json:"accessjust_m_low_row_owid"`
	AccessjustWHighRowOwid       *float64 `json:"accessjust_w_high_row_owid"`
	AccessjustWLowRowOwid        *float64 `json:"accessjust_w_low_row_owid"`
	TransplawsHighRowOwid        *float64 `json:"transplaws_high_row_owid"`
	TransplawsLowRowOwid         *float64 `json:"transplaws_low_row_owid"`
	LibDichRowOwid               *float64 `json:"lib_dich_row_owid"`
	LibDichHighRowOwid           *float64 `json:"lib_dich_high_row_owid"`
	LibDichLowRowOwid            *float64 `json:"lib_dich_low_row_owid"`
	NumberClosedautAmbRowOwid    *float64 `json:"number_closedaut_amb_row_owid"`
	NumberClosedautHAmbRowOwid   *float64 `json:"number_closedaut_h_amb_row_owid"`
	NumberElectautLAmbRowOwid    *float64 `json:"number_electaut_l_amb_row_owid"`
	NumberElectautAmbRowOwid     *float64 `json:"number_electaut_amb_row_owid"`
	NumberElectautHAmbRowOwid    *float64 `json:"number_electaut_h_amb_row_owid"`
	NumberElectdemLAmbRowOwid    *float64 `json:"number_electdem_l_amb_row_owid"`
	NumberElectdemAmbRowOwid     *float64 `json:"number_electdem_amb_row_owid"`
	NumberElectdemHAmbRowOwid    *float64 `json:"number_electdem_h_amb_row_owid"`
	NumberLibdemLAmbRowOwid      *float64 `json:"number_libdem_l_amb_row_owid"`
	NumberLibdemAmbRowOwid       *float64 `json:"number_libdem_amb_row_owid"`
	PopClosedautAmbRowOwid       *float64 `json:"pop_closedaut_amb_row_owid"`
	PopClosedautHAmbRowOwid      *float64 `json:"pop_closedaut_h_amb_row_owid"`
	PopElectautLAmbRowOwid       *float64 `json:"pop_electaut_l_amb_row_owid"`
	PopElectautAmbRowOwid        *float64 `json:"pop_electaut_amb_row_owid"`
	PopElectautHAmbRowOwid       *float64 `json:"pop_electaut_h_amb_row_owid"`
	PopElectdemLAmbRowOwid       *float64 `json:"pop_electdem_l_amb_row_owid"`
	PopElectdemAmbRowOwid        *float64 `json:"pop_electdem_amb_row_owid"`
	PopElectdemHAmbRowOwid       *float64 `json:"pop_electdem_h_amb_row_owid"`
	PopLibdemLAmbRowOwid         *float64 `json:"pop_libdem_l_amb_row_owid"`
	PopLibdemAmbRowOwid          *float64 `json:"pop_libdem_amb_row_owid"`
}

This dataset provides information on political regimes, using data from the Varieties of Democracy project (v11.1), and the Regimes of the World classification by Lührmann et al. (2018).We expand the countries and years covered, and refine the coding of the Regimes of the World classification. You can read a detailed description of the data in this post: https://ourworldindata.org/regimes-of-the-world-dataYou can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/political_regimes

type PoliticalRegimesPolity5Dataset 

type PoliticalRegimesPolity5Dataset struct {
	DemocracyPolity       *float64 `json:"democracy_polity"`
	RegimePolity          *float64 `json:"regime_polity"`
	DemAgePolity          *float64 `json:"dem_age_polity"`
	DemAgeGroupPolity     *float64 `json:"dem_age_group_polity"`
	ExecReccompPolity     *float64 `json:"exec_reccomp_polity"`
	ExecRecopenPolity     *float64 `json:"exec_recopen_polity"`
	ExecConstrPolity      *float64 `json:"exec_constr_polity"`
	PolpartRegPolity      *float64 `json:"polpart_reg_polity"`
	PolpartCompPolity     *float64 `json:"polpart_comp_polity"`
	DemExpPolity          *float64 `json:"dem_exp_polity"`
	NumberAutPolity       *float64 `json:"number_aut_polity"`
	NumberAnoPolity       *float64 `json:"number_ano_polity"`
	NumberDemPolity       *float64 `json:"number_dem_polity"`
	NumberDem18Polity     *float64 `json:"number_dem_18_polity"`
	NumberDem30Polity     *float64 `json:"number_dem_30_polity"`
	NumberDem60Polity     *float64 `json:"number_dem_60_polity"`
	NumberDem90Polity     *float64 `json:"number_dem_90_polity"`
	NumberDem91plusPolity *float64 `json:"number_dem_91plus_polity"`
	PopAutPolity          *float64 `json:"pop_aut_polity"`
	PopAnoPolity          *float64 `json:"pop_ano_polity"`
	PopDemPolity          *float64 `json:"pop_dem_polity"`
	PopMissregPolity      *float64 `json:"pop_missreg_polity"`
	PopDem18Polity        *float64 `json:"pop_dem_18_polity"`
	PopDem30Polity        *float64 `json:"pop_dem_30_polity"`
	PopDem60Polity        *float64 `json:"pop_dem_60_polity"`
	PopDem90Polity        *float64 `json:"pop_dem_90_polity"`
	PopDem91plusPolity    *float64 `json:"pop_dem_91plus_polity"`
	PopwDemocracyPolity   *float64 `json:"popw_democracy_polity"`
	Region                *float64 `json:"region"`
}

This dataset provides information on political regimes, using data from Polity (2021).You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/democracy

type PoliticalRegimesSkaaningEtAl2015Dataset 

type PoliticalRegimesSkaaningEtAl2015Dataset struct {
	RegimeLied               *float64 `json:"regime_lied"`
	ExelecLied               *float64 `json:"exelec_lied"`
	LegelecLied              *float64 `json:"legelec_lied"`
	OppositionLied           *float64 `json:"opposition_lied"`
	CompetitionLied          *float64 `json:"competition_lied"`
	MaleSuffrageLied         *float64 `json:"male_suffrage_lied"`
	FemaleSuffrageLied       *float64 `json:"female_suffrage_lied"`
	ElectdemAgeLied          *float64 `json:"electdem_age_lied"`
	ElectdemAgeGroupLied     *float64 `json:"electdem_age_group_lied"`
	PolyarchyAgeLied         *float64 `json:"polyarchy_age_lied"`
	PolyarchyAgeGroupLied    *float64 `json:"polyarchy_age_group_lied"`
	PolibertiesLied          *float64 `json:"poliberties_lied"`
	ElectdemExpLied          *float64 `json:"electdem_exp_lied"`
	PolyarchyExpLied         *float64 `json:"polyarchy_exp_lied"`
	NumberNonelectautLied    *float64 `json:"number_nonelectaut_lied"`
	NumberOnepautLied        *float64 `json:"number_onepaut_lied"`
	NumberMultipautneLied    *float64 `json:"number_multipautne_lied"`
	NumberMultipautLied      *float64 `json:"number_multipaut_lied"`
	NumberExcldemLied        *float64 `json:"number_excldem_lied"`
	NumberMaledemLied        *float64 `json:"number_maledem_lied"`
	NumberElectdemLied       *float64 `json:"number_electdem_lied"`
	NumberPolyLied           *float64 `json:"number_poly_lied"`
	NumberElectdem18Lied     *float64 `json:"number_electdem_18_lied"`
	NumberElectdem30Lied     *float64 `json:"number_electdem_30_lied"`
	NumberElectdem60Lied     *float64 `json:"number_electdem_60_lied"`
	NumberElectdem90Lied     *float64 `json:"number_electdem_90_lied"`
	NumberElectdem91plusLied *float64 `json:"number_electdem_91plus_lied"`
	NumberPoly18Lied         *float64 `json:"number_poly_18_lied"`
	NumberPoly30Lied         *float64 `json:"number_poly_30_lied"`
	NumberPoly60Lied         *float64 `json:"number_poly_60_lied"`
	NumberPoly90Lied         *float64 `json:"number_poly_90_lied"`
	NumberPoly91plusLied     *float64 `json:"number_poly_91plus_lied"`
	PopNonelectautLied       *float64 `json:"pop_nonelectaut_lied"`
	PopOnepautLied           *float64 `json:"pop_onepaut_lied"`
	PopMultipautneLied       *float64 `json:"pop_multipautne_lied"`
	PopMultipautLied         *float64 `json:"pop_multipaut_lied"`
	PopExcldemLied           *float64 `json:"pop_excldem_lied"`
	PopMaledemLied           *float64 `json:"pop_maledem_lied"`
	PopElectdemLied          *float64 `json:"pop_electdem_lied"`
	PopPolyLied              *float64 `json:"pop_poly_lied"`
	PopMissregLied           *float64 `json:"pop_missreg_lied"`
	PopElectdem18Lied        *float64 `json:"pop_electdem_18_lied"`
	PopElectdem30Lied        *float64 `json:"pop_electdem_30_lied"`
	PopElectdem60Lied        *float64 `json:"pop_electdem_60_lied"`
	PopElectdem90Lied        *float64 `json:"pop_electdem_90_lied"`
	PopElectdem91plusLied    *float64 `json:"pop_electdem_91plus_lied"`
	PopPoly18Lied            *float64 `json:"pop_poly_18_lied"`
	PopPoly30Lied            *float64 `json:"pop_poly_30_lied"`
	PopPoly60Lied            *float64 `json:"pop_poly_60_lied"`
	PopPoly90Lied            *float64 `json:"pop_poly_90_lied"`
	PopPoly91plusLied        *float64 `json:"pop_poly_91plus_lied"`
	Region                   *float64 `json:"region"`
}

This dataset provides information on political regimes, using data from Skaaning et al. (2015).You can download the code and complete dataset, including supplementary variables, from GitHub: https://github.com/owid/notebooks/tree/main/BastianHerre/democracy

type PopulationByAgeGroupTo2100BasedOnUnwpp2017MediumScenarioDataset 

type PopulationByAgeGroupTo2100BasedOnUnwpp2017MediumScenarioDataset struct {
	Under15YearsOldUnwpp2017         *float64 `json:"under_15_years_old_unwpp_2017"`
	WorkingAge15_64YearsOldUnwpp2017 *float64 `json:"working_age_15_64_years_old_unwpp_2017"`
	O65YearsOldUnwpp2017             *float64 `json:"o65_years_old_unwpp_2017"`
	Under5YearsOldUnwpp2017          *float64 `json:"under_5_years_old_unwpp_2017"`
	O5_14YearsOldUnwpp2017           *float64 `json:"o5_14_years_old_unwpp_2017"`
	O15_24YearsOldUnwpp2017          *float64 `json:"o15_24_years_old_unwpp_2017"`
	O25_64YearsOldUnwpp2017          *float64 `json:"o25_64_years_old_unwpp_2017"`
}

Dataset was compiled by Our World in Data by combining historical estimates (1950-2015) published by the UN World Population Prospects (2017) Revision, and its medium scenario projected to 2100.

type PopulationByCountry1800To2100GapminderAndUnDataset 

type PopulationByCountry1800To2100GapminderAndUnDataset struct {
	PopulationByCountryHistoricAndProjectionsGapminderAndUn *float64 `json:"population_by_country_historic_and_projections_gapminder_and_un"`
	PopulationByCountry1800To2015GapminderAndUn             *float64 `json:"population_by_country_1800_to_2015_gapminder_and_un"`
}

Population data by country is taken from Version 5 of Gapminder. This provides data by country from 1800 through to 2100.From 1950 to 2015, estimates come from the UN Population Revision (2017), and projections to 2100 from the UN Population Revision (2017) medium scenario.Full documentation of Gapminder's sources and process are available here: https://www.gapminder.org/data/documentation/gd003/.UN population revision citation:United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/.

type PopulationClioInfra2016WithIslandOfIrelandRepNorthernDataset 

type PopulationClioInfra2016WithIslandOfIrelandRepNorthernDataset struct {
	PopulationClioInfra2016WithIslandOfIrelandFigures *float64 `json:"population_clio_infra_2016_with_island_of_ireland_figures"`
}

Prior to 1921 (partition of Ireland) the Clio-infra series is used. After 1921 this series presents the Clio-infra data (which represents only the Republic of Ireland) added to the population of Northern Ireland as given in the closest census year to the reference year. For 1930, the population figure for Northern Ireland used is that of the 1926 census. That of 1940, the 1937 census. From 1950 onwards, decadal census figures from 1951, 1961, etc are used.

type PopulationCoveredByTheInternetInternetWorldStats2019Dataset 

type PopulationCoveredByTheInternetInternetWorldStats2019Dataset struct {
	ShareOfPopulationCoveredByTheInternetInPerc *float64 `json:"share_of_population_covered_by_the_internet_in_perc"`
}

The numbers were obtained by dividing each country's number of internet users by its population (both data points provided by Internet World Stats).

type PopulationDataGapminderUpTo1949UnPopulationDivision1950To2015Dataset 

type PopulationDataGapminderUpTo1949UnPopulationDivision1950To2015Dataset struct {
	TotalPopulationGapminderUpTo1949UnPopulationDivision1950To2015 *float64 `json:"total_population_gapminder_up_to_1949_un_population_division_1950_to_2015"`
}

type PopulationDensityWorldBankGapminderHydeAndUnDataset 

type PopulationDensityWorldBankGapminderHydeAndUnDataset struct {
	PopulationDensity *float64 `json:"population_density"`
}

Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on three key sources: HYDE, Gapminder, and the UN World Population Prospects. This combines historical population estimates with median scenario projections to 2100. You can find more information on these sources and how our time series is constructed on this page: <a href="https://ourworldindata.org/population-sources">What sources do we rely on for population estimates?</a>We combine this population dataset with the <a href="https://ourworldindata.org/grapher/land-area-km">land area estimates published by the World Bank</a>, to produce a long-run dataset of population density.In all sources that we rely on, population estimates and land area estimates are based on today’s geographical borders.

type PopulationDynamicsAndGlobalHumanCapitalIiasa2015Dataset 

type PopulationDynamicsAndGlobalHumanCapitalIiasa2015Dataset struct {
	IiasaRatesOfNoEducationProjectionsIiasa2015                 *float64 `json:"iiasa_rates_of_no_education_projections_iiasa_2015"`
	TotalNumberOfPeopleAged15WithNoEducationMillionsIiasa2015   *float64 `json:"total_number_of_people_aged_15_with_no_education_millions_iiasa_2015"`
	NoEducation1970_2050Iiasa2015                               *float64 `json:"no_education_1970_2050_iiasa_2015"`
	PrimaryEducation1970_2050Iiasa2015                          *float64 `json:"primary_education_1970_2050_iiasa_2015"`
	SecondaryEducation1970_2050Iiasa2015                        *float64 `json:"secondary_education_1970_2050_iiasa_2015"`
	TertiaryEducation1970_2050Iiasa2015                         *float64 `json:"tertiary_education_1970_2050_iiasa_2015"`
	Aged0_14_1970_2050Iiasa2015                                 *float64 `json:"aged_0_14_1970_2050_iiasa_2015"`
	TertiaryEducationForThoseAged15Proportion1970_2050Iiasa2015 *float64 `json:"tertiary_education_for_those_aged_15_proportion_1970_2050_iiasa_2015"`
}

The data on past and projected rates of educational attainment comes from the International Institute for Applied Systems Analysis (IIASA) and can be found here. These projections are constructed using current Global Economic Trends (GET). There are other scenarios available, including a best and worst case.

type PopulationEstimatesAndProjectionWittgensteinCentreForDemographyAndGlobalHumanCapitalDataset 

type PopulationEstimatesAndProjectionWittgensteinCentreForDemographyAndGlobalHumanCapitalDataset struct {
	PopulationTotal          *float64 `json:"population_total"`
	PopulationYoungerThan5   *float64 `json:"population_younger_than_5"`
	PopulationYoungerThan15  *float64 `json:"population_younger_than_15"`
	PopulationtotalSsp2      *float64 `json:"populationtotal_ssp2"`
	Populationunder5Ssp2     *float64 `json:"populationunder5_ssp2"`
	Populationunder15Ssp2    *float64 `json:"populationunder15_ssp2"`
	PopulationtotalSsp2Ft    *float64 `json:"populationtotal_ssp2_ft"`
	Populationunder5Ssp2Ft   *float64 `json:"populationunder5_ssp2_ft"`
	Populationunder15Ssp2Ft  *float64 `json:"populationunder15_ssp2_ft"`
	PopulationtotalSsp2Cer   *float64 `json:"populationtotal_ssp2_cer"`
	Populationunder5Ssp2Cer  *float64 `json:"populationunder5_ssp2_cer"`
	Populationunder15Ssp2Cer *float64 `json:"populationunder15_ssp2_cer"`
}

The new set of projections by levels of educational attainment was produced by a large team of researchers at the Wittgenstein Centre for Demography and Global Human Capital and at other institutions. It also includes population projections developed for the 5th assessment report of the Intergovernmental Panel on Climate Change (IPCC) according to a set of Shared Socioeconomic Pathways (SSP) scenarios.

type PopulationFedByHaberBoschFertilizersFao2017Dataset 

type PopulationFedByHaberBoschFertilizersFao2017Dataset struct {
	WorldPopulation                          *float64 `json:"world_population"`
	WorldPopulationFedByHaberBoschNitrogen   *float64 `json:"world_population_fed_by_haber_bosch_nitrogen"`
	WorldPopulationWithoutHaberBoschNitrogen *float64 `json:"world_population_without_haber_bosch_nitrogen"`
}
Additional information:

Estimates of population sustained with and without the production of Haber-Bosch nitrogen are derived based on Figure 1 in Erisman et al. (2008) and its sources. World population figures were sourced from Gapminder for the years 1900-1949, and from the UN Population Division from 1950 onwards. The share of the world's population reliant on Haber-Bosch fertilizer production for food production was estimated based on Figure 1 in Erisman et al. (2008). These figures are based on approximation, although coincide with several published projections of these estimates. Erisman et al. estimate that by 2000, 44% of the global population were sustained by Haber-Bosch nitrogen, rising to 48% by 2008. This approximately coincides with Smil (2002) estimates of 40% by 2000. OWID have extrapolated these estimates to 2015, assuming that approximately 50% (±2%) rely on Haber-Bosch nitrogen. Erisman, J. W., Sutton, M. A., Galloway, J., Klimont, Z., & Winiwarter, W. (2008). How a century of ammonia synthesis changed the world. Nature Geoscience, 1(10), 636-639. Available at: https://www.nature.com/articles/ngeo325 [accessed 14th October 2017]. Smil, V. (2002). Nitrogen and food production: proteins for human diets. AMBIO: A Journal of the Human Environment, 31(2), 126-131. Available at: http://www.bioone.org/doi/abs/10.1579/0044-7447-31.2.126 [accessed 24th October 2017]. Stewart, W. M., Dibb, D. W., Johnston, A. E., & Smyth, T. J. (2005). The contribution of commercial fertilizer nutrients to food production. Agronomy Journal, 97(1), 1-6. Available at: https://dl.sciencesocieties.org/publications/aj/abstracts/97/1/0001 [accessed 24th October 2017].

type PopulationGapminderHydeAndUnDataset 

type PopulationGapminderHydeAndUnDataset struct {
	PopulationHistoricalEstimates                     *float64 `json:"population_historical_estimates"`
	PopulationHistoricalEstimatesAndFutureProjections *float64 `json:"population_historical_estimates_and_future_projections"`
	ShareOfWorldPopulation                            *float64 `json:"share_of_world_population"`
	PopulationFutureProjections                       *float64 `json:"population_future_projections"`
}

Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on three key sources: HYDE, Gapminder, and the UN World Population Prospects. You can find more information on these sources and how our time series is constructed on this page: <a href="https://ourworldindata.org/population-sources">What sources do we rely on for population estimates?</a>

type PopulationGrowth19922015Listed2017UnPopulationDivision2015Dataset 

type PopulationGrowth19922015Listed2017UnPopulationDivision2015Dataset struct {
	PopulationGrowth1992_2015UnPopulationDivision *float64 `json:"population_growth_1992_2015_un_population_division"`
}

type PopulationGrowthRateByRegion20202100MediumVariantProjectionUnPopulationDivision2015RevisionDataset 

type PopulationGrowthRateByRegion20202100MediumVariantProjectionUnPopulationDivision2015RevisionDataset struct {
	ProjectedGrowthRateUnPopulationDivision2015Revision *float64 `json:"projected_growth_rate_un_population_division_2015_revision"`
}

type PopulationGrowthUnPopulationDivision2015RevisionDataset 

type PopulationGrowthUnPopulationDivision2015RevisionDataset struct {
	PopulationGrowthUnPopulationDivision2015Revision *float64 `json:"population_growth_un_population_division_2015_revision"`
}

Years refer to the average over the preceding 5 year interval (1955 refers to the period 1950 to 1955 etc.)

type PopulationUsingInformalSavingPercWorldBankWorldDevelopmentReport2013Dataset 

type PopulationUsingInformalSavingPercWorldBankWorldDevelopmentReport2013Dataset struct {
	PopulationUsingInformalSavingPercWorldBankWorldDevelopmentReport2013 *float64 `json:"population_using_informal_saving_perc_world_bank_world_development_report_2013"`
}

Underlying source: World Bank, Global Financial Inclusion Database, at http://data.worldbank.org/data-catalog/financial_inclusion

type PostageRatesUnitedStatesPostalService2018Dataset 

type PostageRatesUnitedStatesPostalService2018Dataset struct {
	NominalRatesForDomesticLettersUnitedStatesPostalService2018          *float64 `json:"nominal_rates_for_domestic_letters_united_states_postal_service_2018"`
	NominalRatesForStampedCardsAndPostcardsUnitedStatesPostalService2018 *float64 `json:"nominal_rates_for_stamped_cards_and_postcards_united_states_postal_service_2018"`
	RealRatesForStampedCardsAndPostcardsUnitedStatesPostalService2018    *float64 `json:"real_rates_for_stamped_cards_and_postcards_united_states_postal_service_2018"`
	RealRatesForDomesticLettersUnitedStatesPostalService2018             *float64 `json:"real_rates_for_domestic_letters_united_states_postal_service_2018"`
}

Postage rate refers to 1/2 ounces for 1863 and 1883. 1885 onward postage rates refer to 1 ounce letters. Two observations were reported for 1981, the average of the two observations is reported.The real postage rates were calculated: Real postage rate = Nominal postage rate / ( Price Index / 100 )Real rates for stamped cards and postcards, and domestic letters were calculated using the annual consumer price index for the United States, available at: https://www.measuringworth.com/datasets/uscpi/Full reference: Samuel H. Williamson, "The Annual Consumer Price Index for the United States, 1774-Present," MeasuringWorth, 2018.

type PotashFertilizersFao2017Dataset 

type PotashFertilizersFao2017Dataset struct {
	PotashProductionFao2017  *float64 `json:"potash_production_fao_2017"`
	PotashConsumptionFao2017 *float64 `json:"potash_consumption_fao_2017"`
}

Data combines UN FAO datasets related to potash fertilizer production and consumption:For production figures: "Potash Fertilizers - Production Quantity" which extends from 2003-2014 and "Fertilizers Archive - Potash Fertilizers - Production Quantity", which extends from 1961-2002.For consumption figures: "Potash Fertilizers - Consumption" which extends from 2003-2014 and "Fertilizers Archive - Potash Fertilizers - Consumption", which extends from 1961-2002.Data is measured in tonnes of total nutrient production or consumption.

type PovertyHeadcountAtMoney190ADay2011PppHighIncomeWorldBankPovcal2017Dataset 

type PovertyHeadcountAtMoney190ADay2011PppHighIncomeWorldBankPovcal2017Dataset struct {
	ExtremePovertyHeadcountHighIncomeWorldBankPovcal2017 *float64 `json:"extreme_poverty_headcount_high_income_world_bank_povcal_2017"`
}

According to the World Bank's definition: "PovcalNet is an interactive computational tool that allows you to replicate the calculations made by the World Bank's researchers in estimating the extent of absolute poverty in the world".

Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet. (http://iresearch.worldbank.org/PovcalNet/methodology.aspx).

type PovertyRateLess50percOfMedianLisKeyFigures2018Dataset 

type PovertyRateLess50percOfMedianLisKeyFigures2018Dataset struct {
	PovertyRateLess50percOfMedianLisKeyFigures2018 *float64 `json:"poverty_rate_less50perc_of_median_lis_key_figures_2018"`
}

type PrecipitationAnomalyInUsNoaaDataset 

type PrecipitationAnomalyInUsNoaaDataset struct {
	AnnualPrecipitationAnomalyInches *float64 `json:"annual_precipitation_anomaly_inches"`
}

Data shows US precipitation patterns, based on rainfall and snowfall measurements from land-based weather stations worldwide. This indicator shows annual anomalies, or differences, compared with the average precipitation from 1901 to 2000. At each weather station, annual precipitation anomalies were calculated from total annual precipitation in inches. Anomalies for the contiguous 48 states and Alaska have been determined by calculating average precipitation anomalies for areas within each state based on station density and topography. These regional anomalies are then averaged together in proportion to their area to develop national results.

type PressFreedomFreedomHouse2017Dataset 

type PressFreedomFreedomHouse2017Dataset struct {
	FreedomOfThePress *float64 `json:"freedom_of_the_press"`
}

This dataset provides information on press freedoms, using data from Freedom House's (2017) Freedom of the Press.For the years 1979-1987, we use the worse classification across the two dimensions of print and broadcast media. For the data covering January 1981 to August 1982 we code the year 1982, and for the data covering August 1982 to November 1983 we code the year 1983.We show the data for Czechoslovakia as Czechia, for the USSR as Russia, and for Yugoslavia as Serbia.

type PrevalenceOfAlcoholDrinkingInTheUsaCdcDataset 

type PrevalenceOfAlcoholDrinkingInTheUsaCdcDataset struct {
	BingeDrinking              *float64 `json:"binge_drinking"`
	HeavyDrinking              *float64 `json:"heavy_drinking"`
	AdultsWhoDrankInLast30Days *float64 `json:"adults_who_drank_in_last_30_days"`
}

"Binge drinking is defined as a pattern of alcohol consumption that brings the blood alcohol concentration (BAC) level to 0.08% or more. This pattern of drinking usually corresponds to 5 or more drinks on a single occasion for men or 4 or more drinks on a single occasion for women, generally within about 2 hours.""For men, heavy drinking is typically defined as consuming 15 drinks or more per week. For women, heavy drinking is typically defined as consuming 8 drinks or more per week."

type PrevalenceOfUndernourishmentByRegionUnFaoSofi2017And2018Dataset 

type PrevalenceOfUndernourishmentByRegionUnFaoSofi2017And2018Dataset struct {
	PrevalenceOfUndernourishmentUnFaoSofi2018And2017 *float64 `json:"prevalence_of_undernourishment_un_fao_sofi_2018_and_2017"`
}

Regional figures on the prevalence of undernourishment from 2005-2017 are based on latest estimates from the UN FAO SOFI (2018) report. Figures for the year 2000 are from the UN FAO SOFI (2017) report.References:FAO, IFAD, UNICEF, WFP and WHO. 2018. The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Rome, FAO.FAO, IFAD, UNICEF, WFP and WHO. 2017. The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome, FAO.

type PrevalenceOfUndernourishmentInDevelopingCountriesFaoFoodSecurityIndicators2017Dataset 

type PrevalenceOfUndernourishmentInDevelopingCountriesFaoFoodSecurityIndicators2017Dataset struct {
	PrevalenceOfUndernourishmentInDevelopingCountriesFaoFoodSecurityIndicators2017 *float64 `json:"prevalence_of_undernourishment_in_developing_countries_fao_food_security_indicators_2017"`
}

The prevalence of undernourishment expresses the probability that a randomly selected individual from the population consumes an amount of calories that is insufficient to cover her/his energy requirement for an active and healthy life. The indicator is computed by comparing a probability distribution of habitual daily dietary energy consumption with a threshold level called the minimum dietary energy requirement. Both are based on the notion of an average individual in the reference population.This is the traditional FAO hunger indicator, adopted as official Millennium Development Goal indicator for Goal 1, Target 1.9.The indicator is calculated in three year averages, from 1969-71 to 2014-16 to reduce the impact of possible errors in estimated DES, due to the difficulties in properly accounting of stock variations in major food.The FAO has maintained a constant definition of "developing countries" throughout the data series, with the following definition: "Includes all countries other than developed countries, namely: all countries in Africa except South Africa, all countries in Asia except Israel and Japan, all countries in Oceania except Australia and New Zealand, and all countries in North and Central America except Canada and USA, and all countries in South America."The aggregates are computed using a weighted population average.Earlier estimates for the prevalence of undernourishment in developing countries for 1970 and 1980. Over this period, methodological methods for the estimation of undernourishment have been refined for use in tracking progress on MDGs from 1990 onwards- this means earlier estimates have a higher level of uncertainty. Figures given here for 1970 and 1980 have been taken as the average between two FAO estimates from annual "State of Food Insecurity in the World" reports in 2006 and 2010 (referenced below). These estimates should therefore be utilised with caution, but have been included for longer-term perspective on reduction trends.References:FAO. 2006. The State of Food Insecurity in the World 2006. Rome. Available at: http://www.fao.org/docrep/009/a0750e/a0750e00.htmFAO, 2010. The State of Food Insecurity in the World: Addressing food insecurity in protracted crises. Rome. Available at: http://www.fao.org/docrep/016/i3027e/i3027e.pdf

type PrevalenceOfUndernourishmentSince2000Faostats2018Dataset 

type PrevalenceOfUndernourishmentSince2000Faostats2018Dataset struct {
	PrevalenceOfUndernourishmentFaostat2018       *float64 `json:"prevalence_of_undernourishment_faostat_2018"`
	NumberOfPeopleWhoAreUndernourishedFaostat2018 *float64 `json:"number_of_people_who_are_undernourished_faostat_2018"`
}

Estimated prevalence and total number of undernourished individuals as reported by the UN Food and Agriculture Organization (FAO).Data is based on its latest statistics on FAOstats (http://www.fao.org/faostat/en/#data/FS). This data extends from the year 2000 through to 2016 at national levels, and 2017 estimates by region and at the global level.Figures at the national level are reported as 3-year averages; here we have allocated these figures to the mid-year. For example, the UN FAO reports national data for "2014-2016" which we allocate to the year 2015.Some data, particularly for high-income countries, is not reported by the UN FAO. This is often the case for countries where the prevalence of undernourishment is less than 2.5% of the total population.The categories of Extreme Fragility and Fragile have been added as a variable based on the country groupings defined in the OECD's States of Fragility (2018) Report.

type PrevalenceOfUndernourishmentWorldBank2017AndUnSofi2018Dataset 

type PrevalenceOfUndernourishmentWorldBank2017AndUnSofi2018Dataset struct {
	PrevalenceOfUndernourishmentWorldBank2017AndUnFaoSofi2018 *float64 `json:"prevalence_of_undernourishment_world_bank_2017_and_un_fao_sofi_2018"`
}

The share of people who are undernourished was derived from the World Bank, World Development Indicators and the UN FAO State of Food Insecurity 2017. Global figures from 2005 onwards are from the UN SOFI (2018) report.References:World Bank, World Development Indicators. https://data.worldbank.org/indicator [accessed 25th September 2017].FAO, IFAD, UNICEF, WFP and WHO. 2017. The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome, FAO.FAO, IFAD, UNICEF, WFP and WHO. 2018. The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Rome, FAO.

type PrevalenceOfVitaminADeficiencyInChildrenWho2017Dataset 

type PrevalenceOfVitaminADeficiencyInChildrenWho2017Dataset struct {
	PrevalenceOfVitaminADeficiencyWho2017 *float64 `json:"prevalence_of_vitamin_a_deficiency_who_2017"`
	PrevalenceOfNightBlindnessWho2017     *float64 `json:"prevalence_of_night_blindness_who_2017"`
}

Data on vitamin-A deficiency is based on the reported prevalence of risk by the World Health Organization, which has collated national and household level survey data on indicators extending the period 1995-2005. Note that data on vitamin-A deficiency is typically not measured on an annual basis, therefore the year of measurement will vary by country, but lie within the period 1995-2005.All countries with a GDP per capita ≥US$15,000 were assumed by the WHO to be free from vitamin-A deficiency of a public health significance and were therefore excluded. None of these 37 countries had retinol or night blindness data reported for either preschool-age children or pregnant women.Two sets of indicators of VAD are commonly used for population surveys: clinically assessed eye signs and biochemically determined concentrations of retinol in plasma or serum. The incidence of night blindness in individuals indicates moderate-to-severe systemic VAD. VAD can also be identified when serum retinol concentrations fall below below a cut-off value of 0.70 µmol/l.Vitamin A deficiency (VAD) is a major nutritional concern in poor societies, especially in lower income countries. Its presence as a public health problem is assessed by measuring the prevalence of deficiency in a population, represented by specific biochemical and clinical indicators of status. The main underlying cause of VAD as a public health problem is a diet that is chronically insufficient in vitamin A that can lead to lower body stores and fail to meet physiologic needs (e.g. support tissue growth, normal metabolism, resistance to infection). Deficiency of sufficient duration or severity can lead to disorders that are common in vitamin A deficient populations such as xerophthalmia (xeros = dryness; -ophthalmia = pertaining to the eye), the leading cause of preventable childhood blindness, anaemia, and weakened host resistance to infection, which can increase the severity of infectious diseases and risk of death.

type PrevalenceOfVitaminADeficiencyInPregnantWomenWho2009Dataset 

type PrevalenceOfVitaminADeficiencyInPregnantWomenWho2009Dataset struct {
	PrevalenceOfVitaminADeficiencyWho2009 *float64 `json:"prevalence_of_vitamin_a_deficiency_who_2009"`
	PrevalenceOfNightBlindnessWho2009     *float64 `json:"prevalence_of_night_blindness_who_2009"`
}

Data on vitamin-A deficiency is based on the reported prevalence of risk by the World Health Organization, which has collated national and household level survey data on indicators extending the period 1995-2005. Note that data on vitamin-A deficiency is typically not measured on an annual basis, therefore the year of measurement will vary by country, but lie within the period 1995-2005.All countries with a GDP per capita ≥US$15,000 were assumed by the WHO to be free from vitamin-A deficiency of a public health significance and were therefore excluded.. None of these 37 countries had retinol or night blindness data reported for either preschool-age children or pregnant women.Two sets of indicators of VAD are commonly used for population surveys: clinically assessed eye signs and biochemically determined concentrations of retinol in plasma or serum. The incidence of night blindness in individuals indicates moderate-to-severe systemic VAD. VAD can also be identified when serum retinol concentrations fall below below a cut-off value of 0.70 µmol/l.Vitamin A deficiency (VAD) is a major nutritional concern in poor societies, especially in lower income countries. Its presence as a public health problem is assessed by measuring the prevalence of deficiency in a population, represented by specific biochemical and clinical indicators of status. The main underlying cause of VAD as a public health problem is a diet that is chronically insufficient in vitamin A that can lead to lower body stores and fail to meet physiologic needs (e.g. support tissue growth, normal metabolism, resistance to infection). Deficiency of sufficient duration or severity can lead to disorders that are common in vitamin A deficient populations such as xerophthalmia (xeros = dryness; -ophthalmia = pertaining to the eye), the leading cause of preventable childhood blindness, anaemia, and weakened host resistance to infection, which can increase the severity of infectious diseases and risk of death.

type PrevalenceOfWeightCategoriesInFemalesNcdrisc2017Dataset 

type PrevalenceOfWeightCategoriesInFemalesNcdrisc2017Dataset struct {
	UnderweightNcdrisc2017       *float64 `json:"underweight_ncdrisc_2017"`
	HealthyNcdrisc2017           *float64 `json:"healthy_ncdrisc_2017"`
	OverweightNcdrisc2017        *float64 `json:"overweight_ncdrisc_2017"`
	ObeseNcdrisc2017             *float64 `json:"obese_ncdrisc_2017"`
	OverweightOrObeseNcdrisc2017 *float64 `json:"overweight_or_obese_ncdrisc_2017"`
}

Body Mass Index (BMI) is a person's weight in kilograms (kg) divided by his or her height in meters squared (m2). The WHO define a BMI <=18.5 as 'underweight'; 18.5 to <25 as 'normal/healthy'; 25.0 to <30 as 'overweight'; and >30.0 as 'obese'.NCD Risk Factor Collaboration (NCD-RisC) is a network of health scientists around the world that provides rigorous and timely data on risk factors for non-communicable diseases (NCDs) for 200 countries and territories. The group works closely with the World Health Organisation (WHO), through the WHO Collaborating Centre on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-quality population-based data using advanced statistical methods, designed specifically for analysing NCD risk factors. The Collaboration currently has data from over 2,000 population-based surveys from 189 countries since 1957, with nearly 25 million participants whose risk factor levels have been measured.

type PrevalenceOfWeightCategoriesInMalesNcdrisc2017Dataset 

type PrevalenceOfWeightCategoriesInMalesNcdrisc2017Dataset struct {
	UnderweightNcdrisc2017       *float64 `json:"underweight_ncdrisc_2017"`
	HealthyNcdrisc2017           *float64 `json:"healthy_ncdrisc_2017"`
	OverweightNcdrisc2017        *float64 `json:"overweight_ncdrisc_2017"`
	ObeseNcdrisc2017             *float64 `json:"obese_ncdrisc_2017"`
	OverweightOrObeseNcdrisc2017 *float64 `json:"overweight_or_obese_ncdrisc_2017"`
}

Body Mass Index (BMI) is a person's weight in kilograms (kg) divided by his or her height in meters squared (m2). The WHO define a BMI <=18.5 as 'underweight'; 18.5 to <25 as 'normal/healthy'; 25.0 to <30 as 'overweight'; and >30.0 as 'obese'.NCD Risk Factor Collaboration (NCD-RisC) is a network of health scientists around the world that provides rigorous and timely data on risk factors for non-communicable diseases (NCDs) for 200 countries and territories. The group works closely with the World Health Organisation (WHO), through the WHO Collaborating Centre on NCD Surveillance and Epidemiology at Imperial College London. NCD-RisC pools high-quality population-based data using advanced statistical methods, designed specifically for analysing NCD risk factors. The Collaboration currently has data from over 2,000 population-based surveys from 189 countries since 1957, with nearly 25 million participants whose risk factor levels have been measured.

type PrevalenceOfZincDeficiencyWessellsEtAl2012Dataset 

type PrevalenceOfZincDeficiencyWessellsEtAl2012Dataset struct {
	PrevalenceOfZincDeficiencyWessellsEtAl2012 *float64 `json:"prevalence_of_zinc_deficiency_wessells_et_al_2012"`
}

Estimates of zinc inadequacy were measured on the basis of national dietary intakes and esimates physiological requirements.The authors note: "National food balance sheet data were obtained from the Food and Agriculture Organization of the United Nations. Country-specific estimated prevalence of inadequate zinc intake were calculated based on the estimated absorbable zinc content of the national food supply, International Zinc Nutrition Consultative Group estimated physiological requirements for absorbed zinc, and demographic data obtained from United Nations estimates. Stunting data were obtained from a recent systematic analysis based on World Health Organization growth standards."

type PriceForLightFouquetDataset 

type PriceForLightFouquetDataset struct {
	PriceForLightFouquetAndPearson2012 *float64 `json:"price_for_light_fouquet_and_pearson_2012"`
}

type PriceOfMobileDataAllianceForAffordableInternet2019Dataset 

type PriceOfMobileDataAllianceForAffordableInternet2019Dataset struct {
	CostOf1gbOfMobileDataPercOfMeanGniPerCapita *float64 `json:"cost_of_1gb_of_mobile_data_perc_of_mean_gni_per_capita"`
}

type PriceOfNailsSince1695DanielSichels2017Dataset 

type PriceOfNailsSince1695DanielSichels2017Dataset struct {
	UkForgedCentslbDanielSichel2017                         *float64 `json:"uk_forged_centslb_daniel_sichel_2017"`
	MixedCentslbDanielSichel2017                            *float64 `json:"mixed_centslb_daniel_sichel_2017"`
	CutCentslbDanielSichel2017                              *float64 `json:"cut_centslb_daniel_sichel_2017"`
	PpiWireCentslbDanielSichel2017                          *float64 `json:"ppi_wire_centslb_daniel_sichel_2017"`
	PpiWire1974100DanielSichel2017                          *float64 `json:"ppi_wire_1974100_daniel_sichel_2017"`
	SearsCentslb6d2DanielSichel2017                         *float64 `json:"sears_centslb_6d_2_daniel_sichel_2017"`
	CpiU2010100WrpiSpliceDanielSichel2017                   *float64 `json:"cpi_u_2010100_wrpi_splice_daniel_sichel_2017"`
	MatchedModelRealPriceCentsnail2010moneyDanielSichel2017 *float64 `json:"matched_model_real_price_centsnail_2010money_daniel_sichel_2017"`
	UkForgedRealDanielSichel2017                            *float64 `json:"uk_forged_real_daniel_sichel_2017"`
	MixedCentslbRealDanielSichel2017                        *float64 `json:"mixed_centslb_real_daniel_sichel_2017"`
	CutCentslbRealDanielSichel2017                          *float64 `json:"cut_centslb_real_daniel_sichel_2017"`
	PpiWireCentslbRealDanielSichel2017                      *float64 `json:"ppi_wire_centslb_real_daniel_sichel_2017"`
	CountPerPoundFor2NailsDanielSichel2017                  *float64 `json:"count_per_pound_for_2_nails_daniel_sichel_2017"`
	UkForgedCentsnailRealDanielSichel2017                   *float64 `json:"uk_forged_centsnail_real_daniel_sichel_2017"`
	MixedCentsnailRealDanielSichel2017                      *float64 `json:"mixed_centsnail_real_daniel_sichel_2017"`
	CutCentsnailRealDanielSichel2017                        *float64 `json:"cut_centsnail_real_daniel_sichel_2017"`
	PpiWireCentsnailRealDanielSichel2017                    *float64 `json:"ppi_wire_centsnail_real_daniel_sichel_2017"`
}

There are three main types of nails: hand forged, machine cut, and wire. Blacksmiths produced hand forged nails until about 1820. Cut nails have been produced using a machine which cuts nails from sheets of iron or steel - technology patents for this machinery emerging in the late 18th century. Wire nails became more widely produced in the late 19th century with individual nails cut from a coil of drawn wire, one end sharpened at the tip, and the head of the nail added to the other (Sichel (2017)).

Real prices are calculated using Sichel’s (2017) consumer price index (CPI) dating back to 1695 constructed from the UK retail price index (RPI) from 1695-1784, and US CPI from 1784-2011. 2010 is used as the base year, so all prices adjusted for inflation are in terms of 2010 dollars. To calculate real price of nails in cents/lb, we use the following procedure:

Real price of nails (cents/lb) = Nominal price of nails (cents/lb) divided by (CPI /100)

However, the real price of nails measured in units of cents per pound can obscure quality differences. To adjust for differences in quality, Sichel (2017) standardizes nail sizes, converting all nails to be as close as possible to a 2”, size 6d nail, to construct real prices in cents per nail. The counts of nails per pound used for each year can be found in Table 3 of Sichel’s (2017) working paper.

In calculating the real price of nails (cents/nail), we first convert units from cents per pound to cents per nail, and then convert from nominal to real prices.

Nominal price of nails (cents/nail) = Nominal price of nail (cents/lb) x 1/[count of nails per pound]

Real price of nails (cents/nail) = Nominal price of nails (cents/nail) divided by (CPI/100)

The nominal price series for nails, from which the real series have been constructed, are comprised of multiple data sources.

From 1695 to 1792, prices are from Beveridge (1939) in the United Kingdom. Where data is not reported for selected years, the values for these years are interpolated. Prices have been converted from shillings to cents.

For the period 1784 to 1813 prices are sourced from Cole (1938) although the type of nails have not been specified and are thus assumed to be mixed (forged and cut) nails.

From 1814 to 1890 data covers machine-cut nail prices. Between 1814 and 1828, prices are taken from Cole (1938). Quotes for later years are from various sources with the data reproduced in ‘Historical Statistics of the United States’.

The period covering 1890 up until 2011 refers to wire nails. This section of the data series includes data from the Bureau of Labor Statistics.

For greater detail in the construction of this dataset, please see Sichel’s (2017)* working paper referenced below.

References:

Beveridge, William (1939), Prices and Wages in England from the Twelfth to the Nineteenth Century, Vol. I, Price Tables: Mercantile Era, Longmans, Green, and Co., London.

Cole, Arthur Harrison (1938), Wholesale Commodity Prices in the United States 1700-1861, Harvard University Press, Cambridge, MA.

*Sichel, Daniel E. (2017), The Price of Nails since 1695: Even Simple Products Experienced Large Price Declines Over the Centuries.

United States Bureau of the Census (1975), Historical Statistics of the United States: Colonial Times to the Present, Washington, D.C.

type PrimaryEnergyConsumptionBpAndEia2022Dataset 

type PrimaryEnergyConsumptionBpAndEia2022Dataset struct {
	PrimaryEnergyConsumptionTwh              *float64 `json:"primary_energy_consumption_twh"`
	AnnualChangePrimaryEnergyConsumptionPerc *float64 `json:"annual_change_primary_energy_consumption_perc"`
	AnnualChangePrimaryEnergyConsumptionTwh  *float64 `json:"annual_change_primary_energy_consumption_twh"`
	EnergyPerCapitaKwh                       *float64 `json:"energy_per_capita_kwh"`
	EnergyPerGdpKwhPerMoney                  *float64 `json:"energy_per_gdp_kwh_per_money"`
}

Primary energy consumption data was compiled by Our World in Data based on two key data sources: 1. BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html2. International data from the U.S. Energy Information Administration (EIA): https://www.eia.gov/international/data/world/total-energy/more-total-energy-dataBP provides the longest and most up-to-date time-series of primary energy. However, it does not provide data for all countries. We have therefore supplemented this dataset with energy data from the EIA. Where BP provides data for a given country, this data is adopted; for countries where this data is missing, we rely on EIA Energy figures.Per capita figures have been calculated using a population dataset that is built and maintained by Our World in Data, based on different sources:https://ourworldindata.org/population-sourcesTo calculate energy per unit of GDP, we use total real GDP figures from the Maddison Project Database, version 2020: https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020This dataset is based on Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update ”. GDP is measured in 2011$ which are PPP-adjusted.

type PrimaryEnergyConsumptionBpAndShift2020Dataset 

type PrimaryEnergyConsumptionBpAndShift2020Dataset struct {
	PrimaryEnergyConsumptionTwh              *float64 `json:"primary_energy_consumption_twh"`
	AnnualChangePrimaryEnergyConsumptionPerc *float64 `json:"annual_change_primary_energy_consumption_perc"`
	AnnualChangePrimaryEnergyConsumptionTwh  *float64 `json:"annual_change_primary_energy_consumption_twh"`
	EnergyConsumptionPerCapitaKwh            *float64 `json:"energy_consumption_per_capita_kwh"`
	EnergyConsumptionPerGdpKwhPerMoney       *float64 `json:"energy_consumption_per_gdp_kwh_per_money"`
}

Primary energy consumption data was compiled by Our World in Data based on two key data sources: 1. BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html2. Shift Energy Data Portal: https://www.theshiftdataportal.org/energyBP provides the longest and most up-to-date time-series of primary energy. However, it does provide data for all countries. We have therefore supplemented this dataset with energy data from the Shift Energy Data Portal. Where BP provides data for a given country, this data is adopted; for countries where this data is missing, we rely on Shift Energy figures.The Shift Project draws on two sources: – Etemad & Luciani for the period 1900-1980 Bouda Etemad and Jean Luciani, World Energy Production 1900 – 1985, ISBN 2-600-56007-6, Data digitalized and published with agreement of B. Etemad– US EIA Historical Statistics for 1980-2016. U.S. Energy Information AdministrationTo convert from total primary energy to per capita figures we use population figures from the UN World Population Prospects: https://population.un.org/wpp/.To calculate energy per unit of GDP, we use total real GDP figures from the Maddison Project Database (2018): http://www.ggdc.net/maddison/oriindex.htm. This is measured in 2011$ which are PPP-adjusted.

type PrisonersPer100000FromWorldPrisonBriefDownloadedSeptember2018CountryStandardizedDataset 

type PrisonersPer100000FromWorldPrisonBriefDownloadedSeptember2018CountryStandardizedDataset struct {
	PrisonPopulationRateWorldPrisonBrief2018 *float64 `json:"prison_population_rate_world_prison_brief_2018"`
}

type ProjectedChangeInUnder5PopulationByCountry201520502100OwidBasedOnUnPopulation2017Dataset 

type ProjectedChangeInUnder5PopulationByCountry201520502100OwidBasedOnUnPopulation2017Dataset struct {
	ChangeInUnder5Population2015To2050_2050To2100 *float64 `json:"change_in_under_5_population_2015_to_2050_2050_to_2100"`
}

Population data is based on the Medium UN projection (2017 Edition) from 2015-2100. Our World in Data have derived the change in the projected under-5 population between the year 2015 to 2050 (here, denoted as '2050') and the change between 2050 to 2100 (here, denoted as '2100'). This was calculated simply through the subtraction of 2015 under-5 numbers from projected 2050 figures; and 2050 figures subtracted from 2100 projections.

type ProjectedExtremePovertyAmongDifferentGroupingsOfFragileStatesCrespoCuaresmaEtAl2018OecdWorldBankDataset 

type ProjectedExtremePovertyAmongDifferentGroupingsOfFragileStatesCrespoCuaresmaEtAl2018OecdWorldBankDataset struct {
	ProjectedGlobalExtremePovertyRate                      *float64 `json:"projected_global_extreme_poverty_rate"`
	ProjectedExtremePoorInFragileStates                    *float64 `json:"projected_extreme_poor_in_fragile_states"`
	ProjectedShareOfGlobalExtremePoorLivingInFragileStates *float64 `json:"projected_share_of_global_extreme_poor_living_in_fragile_states"`
	ProjectedShareOfWorldPopulationLivingInFragileStates   *float64 `json:"projected_share_of_world_population_living_in_fragile_states"`
	ProjectedExtremePovertyRateAmongstFragileStates        *float64 `json:"projected_extreme_poverty_rate_amongst_fragile_states"`
	ProjectedExtremePoorNotInFragileStates                 *float64 `json:"projected_extreme_poor_not_in_fragile_states"`
}

The poverty projections are based on the Shared Socioeconomic Pathways framework developed at IIASA. In particular these figures relate to SSP2, the baseline scenario employed by Crespo Cuaresma et al. (2018). Population projections from UN World Population Prospects: The 2017 Revision.The sources of the fragile states lists are as follows:OECD States of Fragility, 2018OECD Resource Flows to Fragile and Conflict-Affected States: Annual Report 2008World Bank 2019 – FY19 List of fragile situationsWorld Bank 2009 – FY09 Fragile States list

type ProjectionsOfPeakAgriculturalLandFao2006Oecd2012Mea2005Dataset 

type ProjectionsOfPeakAgriculturalLandFao2006Oecd2012Mea2005Dataset struct {
	AgriculturalAreaFao2006Oecd2012Mea2005    *float64 `json:"agricultural_area_fao_2006_oecd_2012_mea_2005"`
	FaoimageFao2006Oecd2012Mea2005            *float64 `json:"faoimage_fao_2006_oecd_2012_mea_2005"`
	IaastdFao2006Oecd2012Mea2005              *float64 `json:"iaastd_fao_2006_oecd_2012_mea_2005"`
	OecdOutlookBaselineFao2006Oecd2012Mea2005 *float64 `json:"oecd_outlook_baseline_fao_2006_oecd_2012_mea_2005"`
	MeaScenario1Fao2006Oecd2012Mea2005        *float64 `json:"mea_scenario_1_fao_2006_oecd_2012_mea_2005"`
	MeaScenario2Fao2006Oecd2012Mea2005        *float64 `json:"mea_scenario_2_fao_2006_oecd_2012_mea_2005"`
	MeaScenario3Fao2006Oecd2012Mea2005        *float64 `json:"mea_scenario_3_fao_2006_oecd_2012_mea_2005"`
	MeaScenario4Fao2006Oecd2012Mea2005        *float64 `json:"mea_scenario_4_fao_2006_oecd_2012_mea_2005"`
}

This dataset combines actual reported trends in global agricultural land area from the UN Food and Agricultural Organization from 1961-2014, with future projections from various sources.Data for the period 1961-2014 is based on the metric "Agricultural land" from the FAO Database. Available at: http://www.fao.org/faostat/en/#data/RL [accessed 22nd August 2017].Agricultural land includes arable land, permanent crops and meadows and pasture land.Projections to 2050 are based on those reported in OECD (2012) – OECD Environmental Outlook to 2050: The Consequences of Inaction. Available at: http://www.oecd.org/environment/indicators-modelling-outlooks/oecdenvironmentaloutlookto2050theconsequencesofinaction.htm [accessed 22nd August 2017].The original sources for these projections are as follows:FAO/IMAGE = FAO (UN Food and Agriculture Organisation) (2006), World Agriculture Towards 2030/2050, FAO, Rome.IAASTD = International Assessment of Agricultural Knowledge, Science and Technology for Development (2009). Global report, Island Press, Washington, DC.MEA = Millennium Ecosystem Assessment (2005), Synthesis report, Island Press, Washington, DC.

type ProportionOf4554YearOldsWithTertiaryEducation2009Oecd2012Dataset 

type ProportionOf4554YearOldsWithTertiaryEducation2009Oecd2012Dataset struct {
	ProportionOf45_54YearOldsWithTertiaryEducation2009Oecd2012 *float64 `json:"proportion_of_45_54_year_olds_with_tertiary_education_2009_oecd_2012"`
}

Education at a Glance: OECD Indicators is the authoritative source for accurate and relevant information on the state of education around the world.

type PublicExpenditureOnEducationOecdTanziAndSchuknecht2000Dataset 

type PublicExpenditureOnEducationOecdTanziAndSchuknecht2000Dataset struct {
	PublicExpenditureOnEducationTanziAndSchuktnecht2000 *float64 `json:"public_expenditure_on_education_tanzi_and_schuktnecht_2000"`
}

The underlying sources for Tanzi & Schuknecht (2000) include: League of Nations Statistical Yearbook (various years), Mitchell (1962), OECD Education at a Glance (1996), UNESCO World Education Report (1993), UNDP Human Development Report (1996), UN World Economics Survey (various years). To the extent that the authors do not specify which sources were prioritised for each year/country, it is not possible for us to reliably extend the time series with newer data. For instance, the OECD Education at a Glance report (1998), which presents estimates for the years 1990 and 1995, suggests discrepancies with the values reported by Tanzi & Schuknecht (2000) for 1993.

type PublicSupportAndOppositionToNuclearEnergyIpsosMori2011Dataset 

type PublicSupportAndOppositionToNuclearEnergyIpsosMori2011Dataset struct {
	SupportForNuclearEnergyIpsosMori2011 *float64 `json:"support_for_nuclear_energy_ipsos_mori_2011"`
	OpposedToNuclearEnergyIpsosMori2011  *float64 `json:"opposed_to_nuclear_energy_ipsos_mori_2011"`
}

The Ipsos MORI survey was conducted in 24 countries. An international sample of 18,787 adults aged 18-64 in the US and Canada, and age 16-64 in all other countries, were interviewed between May 6 and May 21, 2011 via the Ipsos Online Panel system.

Results presented here are in response to the question: "Please indicate whether you strongly support, somewhat support, somewhat oppose, or strongly oppose each way of producing electricity" [results for nuclear electricity production].

Four responses were accepted for this question: "strongly support"; "somewhat support"; "somewhat oppose"; and "strongly oppose". We have simplified these results to "support" and "oppose" by summing the former and latter two responses, respectively.

type RaisedBloodPressurePrevalenceNcdRisc2017Dataset 

type RaisedBloodPressurePrevalenceNcdRisc2017Dataset struct {
	AgeStandardisedRaisedBloodPressurePrevalenceMale   *float64 `json:"age_standardised_raised_blood_pressure_prevalence_male"`
	AgeStandardisedRaisedBloodPressurePrevalenceFemale *float64 `json:"age_standardised_raised_blood_pressure_prevalence_female"`
	CrudeRaisedBloodPressurePrevalenceMale             *float64 `json:"crude_raised_blood_pressure_prevalence_male"`
	CrudeRaisedBloodPressurePrevalenceFemale           *float64 `json:"crude_raised_blood_pressure_prevalence_female"`
}

This dataset presents crude and age-standardised estimates of the prevalence of raised blood pressure by country, region, and globally for men and women.Raised blood pressure (BP) is defined as systolic BP ≥140 mmHg, or diastolic BP ≥90 mmHg.The data was pooled from 1,479 studies that had measured the blood pressures of 19.1 million adults.

type RandDatabaseOfWorldwideTerrorismIncidentsDataset 

type RandDatabaseOfWorldwideTerrorismIncidentsDataset struct {
	CountOfTerroristIncidentsRand *float64 `json:"count_of_terrorist_incidents_rand"`
	InjuriesDueToTerrorismRand    *float64 `json:"injuries_due_to_terrorism_rand"`
	FatalitiesDueToTerrorismRand  *float64 `json:"fatalities_due_to_terrorism_rand"`
}

Several changes were made to the coding of terrorist incidents:Azores added to PortugalCanary Islands added to SpainChechnya added to RussiaCroatians added to CroatiaNorthern Ireland added to United KingdomSri Lanka (Ceylon) added to Sri LankaSouth Vietnam added to VietnamTranskei added to South AfricaTrucial Oman States added to United Arab EmiratesZimbabwe (Rhodesia) added to ZimbabweWest Bank/Gaza coded to Palestine

type RateOfInternationallyObservedElectionsHydeAndMarinov2012Dataset 

type RateOfInternationallyObservedElectionsHydeAndMarinov2012Dataset struct {
	PercentOfElectionsObservedAndCriticizedHydeAndMarinov2012               *float64 `json:"percent_of_elections_observed_and_criticized_hyde_and_marinov_2012"`
	PercentOfElectionsObservedAndNotCriticizedHydeAndMarinov2012            *float64 `json:"percent_of_elections_observed_and_not_criticized_hyde_and_marinov_2012"`
	NumberOfInternationalElectionsNotObservedHydeAndMarinov2012             *float64 `json:"number_of_international_elections_not_observed_hyde_and_marinov_2012"`
	NumberOfInternationalElectionsObservedAndCriticizedHydeAndMarinov2012   *float64 `json:"number_of_international_elections_observed_and_criticized_hyde_and_marinov_2012"`
	NumberOfInternationalElectionsObservedAndNotCritcizedHydeAndMarinov2012 *float64 `json:"number_of_international_elections_observed_and_not_critcized_hyde_and_marinov_2012"`
}

Codebook notes:" The National Elections across Democracy and Autocracy (NELDA) dataset provides detailed information on all election events from 1945-2012. To be included, elections must be for a national executive figure, such as a president, or for a national legislative body, such as a parliament, legislature, constituent assembly, or other directly elected representative bodies. In order for an election to be included, voters must directly elect the person or persons appearing on the ballot to the national post in question.Elections may or may not be competitive, and may have any number of other ostensible flaws. In fact, the inclusion of flawed elections is a feature of the NELDA dataset. "The unit of observation is the NELDA dataset is the election round. The ElectionID variable has been used to collapse multi-round elections into one observation for OWID’s purposes. In three election events, Equatorial Guinea's 1993 legislative/parliamentary election, Equatorial Guinea's 1996 presidential/other direct executive election, and Zimbabwe's 2005 legislative/parliamentary election, these election events were coded as unobserved by Western monitors, yet criticized with allegations of significant vote-fraud. For our purposes, these three election events have been re-coded as unobserved and not criticized as no Western monitors were in the country at the time to monitor the election.The percent of observed and criticized elections was calculated as:% observed and criticized elections = (no.of criticized elections/ total no. of elections) * 100The percent of observed and not criticized elections was calculated as:% observed and not criticized elections = [(no. of observed elections - no. of criticized elections)/total number of elections] * 100Microstates, defined as those countries with less than 500,000 citizens at the time of election, have been excluded. Five independent states that did not hold any direct elections between 1945-2012 - China, Eritrea, Qatar, Saudi Arabia, and the United Arab Emirates - have also been excluded.

type RateOfInternationallyObservedElectionsSusanHyde2011Dataset 

type RateOfInternationallyObservedElectionsSusanHyde2011Dataset struct {
	PercentOfElectionsObservedAndCriticizedHyde2011    *float64 `json:"percent_of_elections_observed_and_criticized_hyde_2011"`
	PercentOfElectionsObservedAndNotCriticizedHyde2011 *float64 `json:"percent_of_elections_observed_and_not_criticized_hyde_2011"`
}

International election monitoring can be defined as the observation of an election by one or more independent parties, typically from another country or non-governmental organization (NGO). Election observers primarily assess the conduct of elections and factors influencing the prevailing electoral environment. As Hyde (2011) notes "nearly 80 percent of all national elections are now monitored". For further background information see Hyde's 2011 paper referenced above.The percent of observed and criticized elections was calculated as:% observed and criticized elections = (no.of criticized elections/ total no. of elections) * 100The percent of observed and not criticized elections was calculated as:% observed and not criticized elections = [(no. of observed elections - no. of criticized elections)/total number of elections] * 100 Countries excluded from the analysis are microstates and the five independent states that did not hold any direct national elections between 1960 and 2006: China, Eritrea, Qatar, Saudi Arabia, and the United Arab Emirates.

type RateOfNaturalPopulationIncreaseUnPopulationDivision2015Dataset 

type RateOfNaturalPopulationIncreaseUnPopulationDivision2015Dataset struct {
	RateOfNaturalPopulationIncreaseUnPopulationDivision2015 *float64 `json:"rate_of_natural_population_increase_un_population_division_2015"`
}

The Rate of Natural Population Increase is the crude birth rate minus the crude death rate. Represents the portion of population growth (or decline) determined exclusively by births and deaths. 

The data refers to the 5 year interval preceding the shown year (e.g.

1955 is '1950 to 1955')

In the original dataset it is expressed per 1,000 population annually. Here it is expressed in percentage terms (original value divided by 10).

type RealCommodityPriceIndexSince1850Jacks2016Dataset 

type RealCommodityPriceIndexSince1850Jacks2016Dataset struct {
	Beef       *float64 `json:"beef"`
	Hides      *float64 `json:"hides"`
	Lamb       *float64 `json:"lamb"`
	Pork       *float64 `json:"pork"`
	Coal       *float64 `json:"coal"`
	NaturalGas *float64 `json:"natural_gas"`
	Petroleum  *float64 `json:"petroleum"`
	Barley     *float64 `json:"barley"`
	Corn       *float64 `json:"corn"`
	Rice       *float64 `json:"rice"`
	Rye        *float64 `json:"rye"`
	Wheat      *float64 `json:"wheat"`
	Aluminum   *float64 `json:"aluminum"`
	Chromium   *float64 `json:"chromium"`
	Copper     *float64 `json:"copper"`
	Lead       *float64 `json:"lead"`
	Manganese  *float64 `json:"manganese"`
	Nickel     *float64 `json:"nickel"`
	Steel      *float64 `json:"steel"`
	Tin        *float64 `json:"tin"`
	Zinc       *float64 `json:"zinc"`
	Bauxite    *float64 `json:"bauxite"`
	IronOre    *float64 `json:"iron_ore"`
	Phosphate  *float64 `json:"phosphate"`
	Potash     *float64 `json:"potash"`
	Sulfur     *float64 `json:"sulfur"`
	Gold       *float64 `json:"gold"`
	Platinum   *float64 `json:"platinum"`
	Silver     *float64 `json:"silver"`
	Cocoa      *float64 `json:"cocoa"`
	Coffee     *float64 `json:"coffee"`
	Cotton     *float64 `json:"cotton"`
	Cottonseed *float64 `json:"cottonseed"`
	PalmOil    *float64 `json:"palm_oil"`
	Peanuts    *float64 `json:"peanuts"`
	Rubber     *float64 `json:"rubber"`
	Sugar      *float64 `json:"sugar"`
	Tea        *float64 `json:"tea"`
	Tobacco    *float64 `json:"tobacco"`
	Wool       *float64 `json:"wool"`
}

Commodity prices are given as a price index relative to real prices in 1900, where 1900 = 100.

type RealGdpPerCapitaLondonAndDelhiOwidDataset 

type RealGdpPerCapitaLondonAndDelhiOwidDataset struct {
	RealGdpPerCapitaOwid *float64 `json:"real_gdp_per_capita_owid"`
}

The data presented here from 1990 onwards is from the World Bank. It is GDP per capita in 2011 international-$ as published here: http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD (accessed on April 16, 2017).

Data earlier than 1990 is backwards extended from the World Bank observation for 1990 based on the growth rates implied by Maddison data. The Maddison data is published here: http://www.ggdc.net/maddison/maddison-project/data.htm

Here we have assumed GDP per capita for the UK and India is a proxy for London and Delhi, respectively.

type RecycledPlasticExportsBrooksEtAl2018Dataset 

type RecycledPlasticExportsBrooksEtAl2018Dataset struct {
	CumulativeRecycledPlasticTradeValueBillionUsd *float64 `json:"cumulative_recycled_plastic_trade_value_billion_usd"`
	CumulativeNetExportedPlasticMillionTonnes     *float64 `json:"cumulative_net_exported_plastic_million_tonnes"`
	ShareOfGlobalExportsPerc                      *float64 `json:"share_of_global_exports_perc"`
}

Cumulative export of plastics over the period 1988-2016 by the top 10 exporting countries. This is measured across several variables: economic value, tonnage, and share of global exports of plastics.

type RegimePopulationsOwidBasedOnLuhrmannEtAl2018VDemV12Owid2021GapminderV6HydeV32AndUn2019Dataset 

type RegimePopulationsOwidBasedOnLuhrmannEtAl2018VDemV12Owid2021GapminderV6HydeV32AndUn2019Dataset struct {
	PopulationClosedAut    *float64 `json:"population_closed_aut"`
	PopulationElectoralAut *float64 `json:"population_electoral_aut"`
	PopulationElectoralDem *float64 `json:"population_electoral_dem"`
	PopulationLiberalDem   *float64 `json:"population_liberal_dem"`
	PopulationMissingData  *float64 `json:"population_missing_data"`
}

This dataset provides information on the size of populations living under different political systems.Population data comes from our own calculations based on Gapminder (v6), HYDE (v3.2), and UN (2019).Political regimes are identified using the Regimes of the World classification from Lührmann et al. (2018), data from the Varieties of Democracy Project (v12), and expanding the data and refining the classification ourselves.The classification distinguishes between closed autocracies, electoral autocracies, electoral democracies, and liberal democracies.

type RelativeEarningsOfAdultsByEducationalAttainmentEducationAtAGlance2017OecdIndicators2017Dataset 

type RelativeEarningsOfAdultsByEducationalAttainmentEducationAtAGlance2017OecdIndicators2017Dataset struct {
	EarningsOfTertiaryEducatedWorkersRelativeToTheEarningsOfWorkersWithUpperSecondaryEducationOecd2017 *float64 `json:"earnings_of_tertiary_educated_workers_relative_to_the_earnings_of_workers_with_upper_secondary_education_oecd_2017"`
}

The year of reference is 2015 for all countries except: Denmark (2014), Finland (2014), Belgium (2014), Italy (2013), Canada (2014), Netherlands (2014), Japan (2012), Spain (2014), France (2013), Luxembourg (2014), Poland (2014), and Lithuania (2014). Earnings are net of income tax only for: Latvia, Ireland, and Mexico.For further details, see Table A6.1 in the <a href="https://www.oecd-ilibrary.org/docserver/eag-2017-en.pdf?expires=1533729082&id=id&accname=guest&checksum=78D200C9AD96EA2A893AEC34BFB793F8" rel="noopener" target="_blank">Education at a Glance 2017</a> report.

type RelativeWagesOfCraftsmenToLabourers12002000Clark2005Dataset 

type RelativeWagesOfCraftsmenToLabourers12002000Clark2005Dataset struct {
	RelativeWagesOfCraftsmenToLabourers1200_2000Clark2005 *float64 `json:"relative_wages_of_craftsmen_to_labourers_1200_2000_clark_2005"`
}

Data taken from Table A2.

type RenewableEnergyCapacityByRegionIrena2017Dataset 

type RenewableEnergyCapacityByRegionIrena2017Dataset struct {
	RenewableEnergyCapacityByRegionIrena2017 *float64 `json:"renewable_energy_capacity_by_region_irena_2017"`
}

The data presented in the IRENA REsource database comes from a variety of sources. Most of the data are official statistics submitted by countries to IRENA using the IRENA renewable energy statistics questionnaire during its annual data collection cycle or taken from official publications. Where official statistics are unavailable, the statistics are supplemented with IRENA estimates or third party data such as that from industry associations.

type RenewableEnergyCapacityByTechnologyIrena2017Dataset 

type RenewableEnergyCapacityByTechnologyIrena2017Dataset struct {
	RenewableEnergyCapacityByTechnologyIrena2017 *float64 `json:"renewable_energy_capacity_by_technology_irena_2017"`
}

The data presented in the IRENA REsource database comes from a variety of sources. Most of the data are official statistics submitted by countries to IRENA using the IRENA renewable energy statistics questionnaire during its annual data collection cycle or taken from official publications. Where official statistics are unavailable, the statistics are supplemented with IRENA estimates or third party data such as that from industry associations.

type RenewableEnergyCostsIrena2020Dataset 

type RenewableEnergyCostsIrena2020Dataset struct {
	OnshoreWindTurbineNameplateCapacityMw    *float64 `json:"onshore_wind_turbine_nameplate_capacity_mw"`
	OnshoreTurbineRotorDiameterM             *float64 `json:"onshore_turbine_rotor_diameter_m"`
	TotalInstalledCostOnshoreWind2019Usdkw   *float64 `json:"total_installed_cost_onshore_wind_2019_usdkw"`
	OnshoreWindCapacityFactor                *float64 `json:"onshore_wind_capacity_factor"`
	OnshoreWindLcoe2019Usdkwh                *float64 `json:"onshore_wind_lcoe_2019_usdkwh"`
	TotalInstalledSolarCost2019Usdkw         *float64 `json:"total_installed_solar_cost_2019_usdkw"`
	SolarLcoe2019Usdkwh                      *float64 `json:"solar_lcoe_2019_usdkwh"`
	TotalInstalledSolarCostResidential       *float64 `json:"total_installed_solar_cost_residential"`
	TotalInstalledSolarCostCommercial        *float64 `json:"total_installed_solar_cost_commercial"`
	SolarCapacityFactor                      *float64 `json:"solar_capacity_factor"`
	SolarLcoeResidentialUsdkw                *float64 `json:"solar_lcoe_residential_usdkw"`
	SolarLcoeCommercialUsdkw                 *float64 `json:"solar_lcoe_commercial_usdkw"`
	SolarPvModulePrice2019Usdw               *float64 `json:"solar_pv_module_price_2019_usdw"`
	OffshoreWindAverageTurbineRatingMw       *float64 `json:"offshore_wind_average_turbine_rating_mw"`
	OffshoreWindAverageProjectCapacityMw     *float64 `json:"offshore_wind_average_project_capacity_mw"`
	OffshoreWindTotalInstalledCosts2019Usdkw *float64 `json:"offshore_wind_total_installed_costs_2019_usdkw"`
	OffshoreWindCapacityFactor               *float64 `json:"offshore_wind_capacity_factor"`
	OffshoreWindLcoe2019Usdkwh               *float64 `json:"offshore_wind_lcoe_2019_usdkwh"`
	HydroTotalInstalledCosts2018Usdkw        *float64 `json:"hydro_total_installed_costs_2018_usdkw"`
	HydroTotalInstalledCostsLarge            *float64 `json:"hydro_total_installed_costs_large"`
	HydroTotalInstalledCostsSmall            *float64 `json:"hydro_total_installed_costs_small"`
	HydroCapacityFactor                      *float64 `json:"hydro_capacity_factor"`
	HydroLcoe219Usdkwh                       *float64 `json:"hydro_lcoe_219_usdkwh"`
	GeothermalTotalInstalledCosts2018Usdkw   *float64 `json:"geothermal_total_installed_costs_2018_usdkw"`
	GeothermalCapacityFactor                 *float64 `json:"geothermal_capacity_factor"`
	GeothermalLcoe219Usdkwh                  *float64 `json:"geothermal_lcoe_219_usdkwh"`
	BioenergyTotalInstalledCosts2018Usdkw    *float64 `json:"bioenergy_total_installed_costs_2018_usdkw"`
	BioenergyCapacityFactor                  *float64 `json:"bioenergy_capacity_factor"`
	BioenergyLcoe2019Usdkwh                  *float64 `json:"bioenergy_lcoe_2019_usdkwh"`
	CspTotalInstalledCosts2019Usdkw          *float64 `json:"csp_total_installed_costs_2019_usdkw"`
	CspCapacityFactor                        *float64 `json:"csp_capacity_factor"`
	CspLcoe2019Usdkwh                        *float64 `json:"csp_lcoe_2019_usdkwh"`
}

type RenewableEnergyPercElectricityProductionWorldBank2015Dataset 

type RenewableEnergyPercElectricityProductionWorldBank2015Dataset struct {
	RenewableElectricityPercElectricityProductionWorldBank2015 *float64 `json:"renewable_electricity_perc_electricity_production_world_bank_2015"`
}

Countries aggregated by income group have been classified based on the World Bank's own definition of income level (low, lower middle, middle, middle upper, and high)

type RenewableInvestmentAsPercOfGdpBnepAndWorldBankDataset 

type RenewableInvestmentAsPercOfGdpBnepAndWorldBankDataset struct {
	RenewableInvestmentPercOfGdpBnepAndWorldBank *float64 `json:"renewable_investment_perc_of_gdp_bnep_and_world_bank"`
}

Renewable energy investment as a percentage of GDP was calculated based on renewable energy investment figures (measured in US$, from Bloomberg New Energy Finance) and national GDP figures (measured in current US$ from the World Bank) in 2015 for the world's largest ten investors.This data includes both asset finance and small-scale renewable investments.References:Bloomberg New Energy Finance. Global trends in renewable energy finance 2016. Available at: https://www.actu-environnement.com/media/pdf/news-26477-rapport-pnue-enr.pdf (accessed 2017-05-10)The World Bank. World Development Indicators. Available at: http://databank.worldbank.org/data/home.aspx (accessed 2017-05-10)

type RenewablesPatentsIrena2016Dataset 

type RenewablesPatentsIrena2016Dataset struct {
	Wind                   *float64 `json:"wind"`
	SolarPv                *float64 `json:"solar_pv"`
	SolarThermal           *float64 `json:"solar_thermal"`
	SolarPvThermalHybrid   *float64 `json:"solar_pv_thermal_hybrid"`
	TotalSolarTechnologies *float64 `json:"total_solar_technologies"`
	Bioenergy              *float64 `json:"bioenergy"`
	Geothermal             *float64 `json:"geothermal"`
	Hydropower             *float64 `json:"hydropower"`
	Marine                 *float64 `json:"marine"`
	Other                  *float64 `json:"other"`
	TotalPatents           *float64 `json:"total_patents"`
	CumulativePatents      *float64 `json:"cumulative_patents"`
}

Data reports the annual number of new patents filed under the category of each renewable technology. Annual number of patents by technology for the years 2014-2016 was calculated by OWID based on IRENA's cumulative number of patents for each technology in these years (i.e. annual number in 2014 = cumulative total in 2015 minus cumulative total in 2014).

Note that figures for 2014-16 may be subject to a time lag; processing times of patent applications vary and some patents submitted over this period may not yet be recorded in statistics. These figures will be updated with time if additional patent applications are recorded.

type ReportedGuineaWormCasesWho2021Dataset 

type ReportedGuineaWormCasesWho2021Dataset struct {
	GuineaWormReportedCases     *float64 `json:"guinea_worm_reported_cases"`
	YearCertifiedGuineaWormFree *float64 `json:"year_certified_guinea_worm_free"`
	Certifcation                *float64 `json:"certifcation"`
}

Data sources:1986-2017: https://apps.who.int/dracunculiasis/dradata/html/report_Countries_i2.html2018: Table 1a https://apps.who.int/iris/rest/bitstreams/1230660/retrieve2019: Table 1a https://apps.who.int/iris/rest/bitstreams/1277901/retrieve2020: Table 1a https://apps.who.int/iris/rest/bitstreams/1349256/retrieve2021*: https://www.cartercenter.org/resources/gallery/images/highres/guinea-worm-current-case-count-by-country-chart.pdf*Figures are considered provisional until officially confirmed.

type ReportedNumberAndDifferentEstimationsOfPolioCasesWho2018Dataset 

type ReportedNumberAndDifferentEstimationsOfPolioCasesWho2018Dataset struct {
	EstimatedNumberOfParalyticPolioCasesDisregardingCasesAfterPolioCeasedToBeEndemicWho2018AndTebbensEtAl2011          *float64 `` /* 132-byte string literal not displayed */
	ReportedNumberOfParalyticPolioCasesWho2018                                                                         *float64 `json:"reported_number_of_paralytic_polio_cases_who_2018"`
	EstimatedNumberOfParalyticPolioCasesWho2018AndTebbensEtAl2011                                                      *float64 `json:"estimated_number_of_paralytic_polio_cases_who_2018_and_tebbens_et_al_2011"`
	EstimatedNumberOfParalyticPolioCasesUsingReportedNumberOfCasesAfterPolioFreeCertificationWho2018AndTebbensEtAl2011 *float64 `` /* 142-byte string literal not displayed */
}

For the estimated number of polio cases using Tebbens et al.’s 2010 model: The WHO’s dataset of reported polio cases from 1980 onwards (the first link above) is used to produce an estimate of the actual number of polio cases. An explanation of the correction factor algorithm can be found here: https://ourworldindata.org/tebbens-et-al-2011-estimation-of-the-number-of-paralytic-polio-cases/ Part of the model input consists of the WHO’s surveillance system that can be found under the second link cited above.

For the estimated number of polio cases that disregard any reported cases after a country was certified polio-free and use Tebbens et al.’s (2010) model: The WHO’s dataset from 1980 onwards (the first link above) was modified by deleting all documented polio cases after the year in which polio ceased to be endemic in each country (see map and data here: https://ourworldindata.org/grapher/the-decade-of-the-last-recorded-case-of-paralytic-polio-by-country). Then, Tebbens et al.’s (2010) model is applied in the same way as for the “Estimated number of polio cases”, for which we provide an explanation here: https://ourworldindata.org/tebbens-et-al-2011-estimation-of-the-number-of-paralytic-polio-cases/.

type RequiredRateOfMaternalMortalityDeclineForSdgBasedOnWorldBank2018Dataset 

type RequiredRateOfMaternalMortalityDeclineForSdgBasedOnWorldBank2018Dataset struct {
	RequiredRateOfMaternalMortalityDeclineForSdgs *float64 `json:"required_rate_of_maternal_mortality_decline_for_sdgs"`
}

Data was calculated by Our World in Data based on 2015 estimates of maternal mortality published by the World Bank, and UN Sustainable Development Goal (SDG) Target 3.1.The World Bank's World Development Indicators, available at: https://datacatalog.worldbank.org/search/indicators. Data was downloaded on 30th August 2018.Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.The World Bank's source data is derived from: WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015.Here we calculate the average annual rate of decline required for each country to attain a maternal mortality ratio as low as 70 per 100,000 live births over the period 2015 to 2030. Data for countries with maternal mortality rates already below 70 per 100,000 (in 2015) are not included.Note that these figures therefore do not represent a forecast or prediction, but simply represent required rates of progress to attain a maternal mortality ratio as low as 70 per 100,000 live births by 2030.

type ReservesProductionRatioBpStatistics2016Dataset 

type ReservesProductionRatioBpStatistics2016Dataset struct {
	CoalBpStatistics2016                    *float64 `json:"coal_bp_statistics_2016"`
	OilBpStatistics2016                     *float64 `json:"oil_bp_statistics_2016"`
	NaturalGasBpStatistics2016              *float64 `json:"natural_gas_bp_statistics_2016"`
	ReservesProductionRatioBpStatistics2016 *float64 `json:"reserves_production_ratio_bp_statistics_2016"`
}

The Reserves-to-Production (R/P) Ratio measures the number of years of fuel supplies left based on current annual consumption rates. Note that this can change through time through the discovery of new fuel reserves, and increases in annual consumption.

type RevenueSharesFromTaxFlora1983AndIctd2016Dataset 

type RevenueSharesFromTaxFlora1983AndIctd2016Dataset struct {
	ShareOfRevenueFromIncomeTaxFlora1983AndIctd2016 *float64 `json:"share_of_revenue_from_income_tax_flora_1983_and_ictd_2016"`
}

Data from the ICTD corresponds to estimates from OECD. More details in Prichard, W. (2016). Reassessing Tax and Development Research: A New Dataset, New Findings, and Lessons for Research. World Development, 80, 48-60.

type RhinoPoachingRatesAfrsg2019Dataset 

type RhinoPoachingRatesAfrsg2019Dataset struct {
	RhinosPoachedAfrsg2019                     *float64 `json:"rhinos_poached_afrsg_2019"`
	RhinosPoacheddayAfrsg2019                  *float64 `json:"rhinos_poachedday_afrsg_2019"`
	WeightOfSeizedRhinoHornsAfrsg2019          *float64 `json:"weight_of_seized_rhino_horns_afrsg_2019"`
	NumberOfSeizedRhinoHornsAndPiecesAfrsg2019 *float64 `json:"number_of_seized_rhino_horns_and_pieces_afrsg_2019"`
}

Estimates of rhino poaching numbers and rates are collected and published periodically by the African and Asian Rhino Specialist Groups and TRAFFIC groups.Data on the number of rhinos poached is annual, where available. Rhino horn seizures data is the total over the period from 2009 to September 2018.The majority of figures on number of rhinos poached, and seizures are sourced from:Emslie, R.H. et al., 2019. African and Asian rhinoceroses - status, conservation and trade. A report from the IUCN Species Survival Commission (IUCN/SSC) African and Asian Rhino Specialist Groups and TRAFFIC to the CITES Secretariat pursuant to Resolution Conf. 9.14 (Rev. CoP17). Report to CITES 17th meeting (Colombo, June 2019), CoP 18 Doc.83.1 annex 3: 1-38. Available at: http://www.rhinoresourcecenter.com/pdf_files/156/1560170144.pdf.This was supplemented, where suitable, by statistics from: http://www.poachingfacts.com/poaching-statistics/rhino-poaching-statistics/.

type RhinoPopulationsAfrsgAndOtherSources2022Dataset 

type RhinoPopulationsAfrsgAndOtherSources2022Dataset struct {
	SumatranRhinoPopulationAfrsgAndOtherSources2019      *float64 `json:"sumatran_rhino_population_afrsg_and_other_sources_2019"`
	IndianRhinoPopulationAfrsgAndOtherSources2019        *float64 `json:"indian_rhino_population_afrsg_and_other_sources_2019"`
	JavanRhinoPopulationAfrsgAndOtherSources2019         *float64 `json:"javan_rhino_population_afrsg_and_other_sources_2019"`
	SouthernWhiteRhinoPopulationAfrsgAndOtherSources2019 *float64 `json:"southern_white_rhino_population_afrsg_and_other_sources_2019"`
	NorthernWhiteRhinoPopulationAfrsgAndOtherSources2019 *float64 `json:"northern_white_rhino_population_afrsg_and_other_sources_2019"`
	WhiteRhinoPopulationAfrsgAndOtherSources2019         *float64 `json:"white_rhino_population_afrsg_and_other_sources_2019"`
	BlackRhinoPopulationAfrsgAndOtherSources2019         *float64 `json:"black_rhino_population_afrsg_and_other_sources_2019"`
}

Data was compiled by Our World in Data based on a number of historical sources. Data is based on best estimates: there is greater uncertainty in earlier historical data but is provided as the best estimate of the time. The main data source for rhino populations is the African and Asian Rhino Specialist Groups (AfRSG). AfRSG periodically produce the latest estimates of rhino populations in Africa and Asia, in update reports of their status. The available literature from AfRSG can be accessed at: http://www.rhinoresourcecenter.com/index.php?s=1&act=refs.Citation of the latest report is as follows:Emslie, R.H. et al., 2019. African and Asian rhinoceroses - status, conservation and trade. A report from the IUCN Species Survival Commission (IUCN/SSC) African and Asian Rhino Specialist Groups and TRAFFIC to the CITES Secretariat pursuant to Resolution Conf. 9.14 (Rev. CoP17). Report to CITES 17th meeting (Colombo, June 2019), CoP 18 Doc.83.1 annex 3: 1-38We here estimates from AfRSG is available, we have used its data. However, we have also combined these statistics with earlier estimates from multiple academic sources. These include:Pusparini, W., Sievert, P. R., Fuller, T. K., Randhir, T. O., & Andayani, N. (2015). Rhinos in the parks: an island-wide survey of the last wild population of the Sumatran rhinoceros. PloS one, 10(9), e0136643.Emslie, R. and Brooks, M. (1999) African Rhino. Status Survey and Conservation Action Plan. IUCN/SSC African RhinoSpecialist Group. IUCN, Gland, Switzerland and Cambridge, UK. ix + 92 pp.Thapa, K., Nepal, S., Thapa, G., Bhatta, S. R., & Wikramanayake, E. (2013). Past, present and future conservation of the greater one-horned rhinoceros Rhinoceros unicornis in Nepal. Oryx, 47(3), 345-351.2020 estimated from the State of the Rhino report (from the International Rhino Foundation): https://rhinos.org/about-rhinos/state-of-the-rhino/

type RiskAttributionOfCancerDeathsToTobaccoSmokingIhmeDataset 

type RiskAttributionOfCancerDeathsToTobaccoSmokingIhmeDataset struct {
	AgeStandardizedCancerDeathRateAttributedToTobaccoPer100_000 *float64 `json:"age_standardized_cancer_death_rate_attributed_to_tobacco_per_100_000"`
	AgeStandardizedShareOfCancerDeathsAttributedToTobacco       *float64 `json:"age_standardized_share_of_cancer_deaths_attributed_to_tobacco"`
}

IHME, Global Burden of Disease provide figures on disease burden and deaths attributed to a range of environmental and lifestyle risk factors. Here data from IHME represents the age-standardized cancer death rate (per 100,000 individuals) and share of total cancer deaths attributed to tobacco smoking (which is inclusive of smoking and secondhand smoke). This impact is measured across total cancer types.

type RoadDeathsAndInjuriesOecdDataset 

type RoadDeathsAndInjuriesOecdDataset struct {
	AccidentsInvolvingCasualties             *float64 `json:"accidents_involving_casualties"`
	Deaths                                   *float64 `json:"deaths"`
	DeathsPerMillionVehicles                 *float64 `json:"deaths_per_million_vehicles"`
	Injuries                                 *float64 `json:"injuries"`
	RoadDeathsPer100MillionVehicleKilometres *float64 `json:"road_deaths_per_100_million_vehicle_kilometres"`
}

The majority of series data is sourced from the OECD Statistics.For particular countries, longer series have been sourced from specific national records as referenced below.Germany: Statistisches Bundesamt (https://www.destatis.de/EN/FactsFigures/EconomicSectors/TransportTraffic/TrafficAccidents/Tables_/RoadTrafficAccidents.html)New Zealand: http://www.transport.govt.nz/research/roadtoll/annualroadtollhistoricalinformation/Australia: https://bitre.gov.au/publications/ongoing/road_deaths_australia_annual_summaries.aspx and https://infrastructure.gov.au/roads/safety/publications/2008/pdf/Ann_Stats_2007.pdfScotland: https://www.transport.gov.scot/media/39307/sct05174402361.pdfNorthern Ireland: https://www.psni.police.uk/inside-psni/Statistics/road-traffic-collision-statistics/United States: https://www.bts.gov/content/transportation-fatalities-mode

type RotavirusDeathsAndCasesInUnder5sIhme2018Dataset 

type RotavirusDeathsAndCasesInUnder5sIhme2018Dataset struct {
	ChildDeathsFromRotavirus                            *float64 `json:"child_deaths_from_rotavirus"`
	ChildMortalityFromRotavirusPer100_000               *float64 `json:"child_mortality_from_rotavirus_per_100_000"`
	RotavirusIncidencePer1_000Children                  *float64 `json:"rotavirus_incidence_per_1_000_children"`
	RotavirusCasesInChildren                            *float64 `json:"rotavirus_cases_in_children"`
	DeathsAvertedFromRotavirusVaccine                   *float64 `json:"deaths_averted_from_rotavirus_vaccine"`
	AvertableDeathsIfRotavirusVaccineCoverageWas100perc *float64 `json:"avertable_deaths_if_rotavirus_vaccine_coverage_was_100perc"`
}

Data is the summary of research published by Troeger et al. (2018) based on statistics from the IHME, Global Burden of Disease study.Data presents the estimated number of child (under five years old) deaths; child mortality rate; and number of cases from rotavirus. Also given is the estimated number of deaths averted from the rotavirus vaccine, and potential avertable deaths if full rotavirus vaccine coverage was achieved.Rotavirus is a diarrheal disease responsible for an estimated 29% of diarrheal disease deaths in children under five years old.Estimates of potentially avertable deaths from the rotavirus vaccine take into consideration its efficacy in different regions: whilst efficacy in children in high-income countries is typically greater than 90%, in lower-income countries, particularly in Sub-Saharan Africa, this efficacy rate is closer to 50%.

type RoughSleepingInEnglandInThe2010sOwidBasedOnUkNationalStatistics2018Dataset 

type RoughSleepingInEnglandInThe2010sOwidBasedOnUkNationalStatistics2018Dataset struct {
	ShareOfPeopleExperiencingRoughSleepingPer100_000InEngland2010_2017OwidBasedOnUkNationalStatistics2018 *float64 `json:"share_of_people_experiencing_rough_sleeping_per_100_000_in_england_2010_2017_owid_based_on_uk_national_statistics_2018"`
	TotalNumberOfPeopleExperiencingRoughSleepingInEnglandInThe2010sOwidBasedOnUkNationalStatistics2018    *float64 `json:"total_number_of_people_experiencing_rough_sleeping_in_england_in_the_2010s_owid_based_on_uk_national_statistics_2018"`
}

The UK Government defines Rough Sleepers as: are defined for the purposes of rough sleeping counts and estimates as: "people sleeping, about to bed down (sitting on/in or standing next to their bedding) or actually bedded down in the open air (such as on the streets, in tents, doorways, parks, bus shelters or encampments); people in buildings or other places not designed for habitation (such as stairwells, barns, sheds, car parks, cars, derelict boats, stations, or ‘bashes’); The definition does not include people in hostels or shelters, people in campsites or other sites used for recreational purposes or organised protest, squatters or travellers."

type SameSexMarriageLawPewResearchCenterCfrDataset 

type SameSexMarriageLawPewResearchCenterCfrDataset struct {
	SameSexMarriageAndCivilUnionsLegal                *float64 `json:"same_sex_marriage_and_civil_unions_legal"`
	NumberOfCountriesThatHaveLegalisedSameSexMarriage *float64 `json:"number_of_countries_that_have_legalised_same_sex_marriage"`
}

The United Kingdom in the dataset refers to England/Wales. In the United Kingdom, England and Wales were first to legalise same-sex marriage in 2013, Scotland followed in 2014, and Northern Ireland introduced its legalisation in 2019.

type SameSexMarriagesBySexInTheNetherlandsCbs2016Dataset 

type SameSexMarriagesBySexInTheNetherlandsCbs2016Dataset struct {
	AnnualNumberOfTwoMenMarriages   *float64 `json:"annual_number_of_two_men_marriages"`
	AnnualNumberOfTwoWomenMarriages *float64 `json:"annual_number_of_two_women_marriages"`
}

Annual number of same-sex marriages

type SameSexMarriedHouseholdsInTheUsDataset 

type SameSexMarriedHouseholdsInTheUsDataset struct {
	MaleMaleMarriedCouples     *float64 `json:"male_male_married_couples"`
	FemaleFemaleMarriedCouples *float64 `json:"female_female_married_couples"`
}

The series includes data for the period 2005-2008, but these estimates are not comparable to those for the period 2008-2018. This is because of changes in the survey questionnaires. The source notes: "Changes in the questionnaire format and data capture procedures between 2007 and 2008 resulted in significant declines in the estimated number of same-sex partner households in 2008. For more explanation, see the following working paper: https://www.census.gov/library/working-papers/2010/demo/oconnell-01.html"

type SelfReportedLonelinessInOlderAdultsOwid2018Dataset 

type SelfReportedLonelinessInOlderAdultsOwid2018Dataset struct {
	SelfReportedFeelingsOfLonelinessAmongOlderAdults *float64 `json:"self_reported_feelings_of_loneliness_among_older_adults"`
}

Share of survey respondents who report feeling lonely at least some of the time. For all countries except US, England and Finland, the estimates correspond to the year 2005, and include population 65+. For the US and UK estimates correspond to the year 2018, and populations 72+ and 65-74 respectively. For Finland estimates correspond to 2002 and ages 75+. Estimates aggregate people who report feeling lonely some of the time, most of the time, or almost all the time. You find the exact wording of the questions for each survey in our supporting documentation <a href="https://owid.cloud/wp-content/uploads/2019/06/Our-World-in-Data-based-on-Sundström-et-al.-2009-Savikko-et-al-2005-ONS-2019-and-CIGNA-2018.xlsx" rel="noopener" target="_blank">here</a>.

type SexRatioAtBirthByBirthOrderInSkoreaAndChinaJiangEtAl2017AndNsoKoreaDataset 

type SexRatioAtBirthByBirthOrderInSkoreaAndChinaJiangEtAl2017AndNsoKoreaDataset struct {
	TotalSexRatioAtBirthSrbJiangEtAl2017            *float64 `json:"total_sex_ratio_at_birth_srb_jiang_et_al_2017"`
	FirstChildSrbJiangEtAl2017                      *float64 `json:"first_child_srb_jiang_et_al_2017"`
	SecondChildSrbJiangEtAl2017                     *float64 `json:"second_child_srb_jiang_et_al_2017"`
	ThirdChildSrbJiangEtAl2017                      *float64 `json:"third_child_srb_jiang_et_al_2017"`
	FourthChildAndHigherSrbJiangEtAl2017AndNsoKorea *float64 `json:"fourth_child_and_higher_srb_jiang_et_al_2017_and_nso_korea"`
}

Data measures the sex ratio at birth (SRB) depending on the birth order of children (first, second, third-born or fourth-born and higher children). SRB is measured as the number of male births per 100 female births. The natural 'sex ratio at birth' is noted to be 105 male births per 100 female births.Data for China is sourced from Jiang, Q., Yu, Q., Yang, S., & Sánchez-Barricarte, J. J. (2017). Changes in sex ratio at birth in china: a decomposition by birth order. Journal of Biosocial Science, 49(6), 826-841. Available at: https://www.cambridge.org/core/services/aop-cambridge-core/content/view/51D48C611E5176A042FF28684041D997/S0021932016000547a.pdf/changes_in_sex_ratio_at_birth_in_china_a_decomposition_by_birth_order.pdfData for South Korea is sourced from the National Statistical Office of Korea (NSO Korea). Available at: http://kosis.kr/eng/statisticsList/statisticsListIndex.do?menuId=M_01_01&vwcd=MT_ETITLE&parmTabId=M_01_01&parentId=A.1;A2.2;A21.3;#A21.3

type SexRatioAtBirthChaoEtAl2019Dataset 

type SexRatioAtBirthChaoEtAl2019Dataset struct {
	SexRatioAtBirthChaoEtAl2019 *float64 `json:"sex_ratio_at_birth_chao_et_al_2019"`
}

Sex ratio at birth, measured as the number of male births per 100 female births. Birth ratios are slightly male-biased, with an expected biological ratio of 105 male per 100 female births.Data is available for every country from 1950, with modelled estimates based on a range of sources (census, household surveys and other population information sources) by the study authors.

type SexRatioByAgeOwidBasedOnUnwpp2017Dataset 

type SexRatioByAgeOwidBasedOnUnwpp2017Dataset struct {
	SexRatioByAgeOneYearOlds  *float64 `json:"sex_ratio_by_age_one_year_olds"`
	SexRatioByAgeFiveYearOlds *float64 `json:"sex_ratio_by_age_five_year_olds"`
	SexRatioByAge10YearOlds   *float64 `json:"sex_ratio_by_age_10_year_olds"`
	SexRatioByAge15YearOlds   *float64 `json:"sex_ratio_by_age_15_year_olds"`
	SexRatioByAge20YearOlds   *float64 `json:"sex_ratio_by_age_20_year_olds"`
	SexRatioByAge30YearOlds   *float64 `json:"sex_ratio_by_age_30_year_olds"`
	SexRatioByAge40YearOlds   *float64 `json:"sex_ratio_by_age_40_year_olds"`
	SexRatioByAge50YearOlds   *float64 `json:"sex_ratio_by_age_50_year_olds"`
	SexRatioByAge60YearOlds   *float64 `json:"sex_ratio_by_age_60_year_olds"`
	SexRatioByAge70YearOlds   *float64 `json:"sex_ratio_by_age_70_year_olds"`
	SexRatioByAge80YearOlds   *float64 `json:"sex_ratio_by_age_80_year_olds"`
	SexRatioByAge90YearOlds   *float64 `json:"sex_ratio_by_age_90_year_olds"`
	SexRatioByAge100YearOlds  *float64 `json:"sex_ratio_by_age_100_year_olds"`
}

Sex ratio is here defined as the number of males per 100 females. Data is given at different life stages: one-year olds, 5-year olds, 10-year olds, 15-year olds, 20-year olds and each decade thereafter (to 100 year olds).This was calculated by Our World in Data based on male and female population estimates published in the United Nations World Population Prospects (2017 Revision).

type SexualViolenceUnicef2017Dataset 

type SexualViolenceUnicef2017Dataset struct {
	SexualViolencePrevalenceAmongGirls15To19UnicefGlobalDatabases2014 *float64 `json:"sexual_violence_prevalence_among_girls_15_to_19_unicef_global_databases_2014"`
	PrevalenceOfForcedSexAmongBoys                                    *float64 `json:"prevalence_of_forced_sex_among_boys"`
	PrevalenceOfForcedSexAmongGirls                                   *float64 `json:"prevalence_of_forced_sex_among_girls"`
}

The source notes that in some instances observations differ from the standard definition or refer to only part of a country.

type ShareOfArableLandWhichIsOrganicOwidBasedOnFaoDataset 

type ShareOfArableLandWhichIsOrganicOwidBasedOnFaoDataset struct {
	ShareOfArableLandWhichIsOrganic *float64 `json:"share_of_arable_land_which_is_organic"`
}

Share of arable land which is organic is derived by Our World in Data based on UN Food and Agriculture Organization (FAO) Statistics on total arable land area, and organic arable land area. The share is calculated as: total arable area (ha) / organic arable land area (ha).Organic arable land area is the sum of the area certified as organic by official standards, and land area in the conversion process to organic (which is assumed by the FAO as a two-year period prior to certification).

type ShareOfCountriesWhereHomosexualityIsLegalOwidBasedOnKennyAndPatel2017Dataset 

type ShareOfCountriesWhereHomosexualityIsLegalOwidBasedOnKennyAndPatel2017Dataset struct {
	NumberOfCountriesWhereHomosexualityIsLegal    *float64 `json:"number_of_countries_where_homosexuality_is_legal"`
	NumberOfCountriesWhereHomosexualityIsIlllegal *float64 `json:"number_of_countries_where_homosexuality_is_illlegal"`
}

Estimates are based on Figure 2 in <em>Kenny, C., & Patel, D. (2017). Norms and Reform: Legalizing Homosexuality Improves Attitudes. Center for Global Development Working Paper, (465).</em>We updated the classification for those countries where same-sex sexual activity was decriminalized after 2017, namely: Angola (2019), Botswana (2019), Micronesia (2018), India (2018) and Trinidad & Tobago (2018).

type ShareOfDeathsAttributedToAirPollutionIhme2019Dataset 

type ShareOfDeathsAttributedToAirPollutionIhme2019Dataset struct {
	ShareOfDeathsFromAirPollutionOutdoorAndIndoorIhme2019 *float64 `json:"share_of_deaths_from_air_pollution_outdoor_and_indoor_ihme_2019"`
	ShareOfDeathsFromIndoorAirPollutionIhme2019           *float64 `json:"share_of_deaths_from_indoor_air_pollution_ihme_2019"`
	ShareOfDeathsFromOutdoorAirPollutionIhme2019          *float64 `json:"share_of_deaths_from_outdoor_air_pollution_ihme_2019"`
}

Share of total deaths which are attributed to to air pollution as a risk factor.Figures for 'Outdoor air pollution' have been calculated by Our World in Data as the sum of figures from the IHME for 'Ambient ozone pollution' and 'Ambient particulate matter pollution'.

type ShareOfEmploymentInTheFinancialSectorGgdc2017Dataset 

type ShareOfEmploymentInTheFinancialSectorGgdc2017Dataset struct {
	ShareOfWorkersInFinancialSectorGgdc2017    *float64 `json:"share_of_workers_in_financial_sector_ggdc_2017"`
	ShareOfPopulationInFinancialSectorGgdc2017 *float64 `json:"share_of_population_in_financial_sector_ggdc_2017"`
	ShareOfWorkersInFinancialSector            *float64 `json:"share_of_workers_in_financial_sector"`
}

Population data taken from World Bank Health Nutrition and Population Statistics

type ShareOfEnergyFromCerealsRootsAndTubersFao2017Dataset 

type ShareOfEnergyFromCerealsRootsAndTubersFao2017Dataset struct {
	ShareOfEnergyFromCerealsRootsAndTubersFao2017 *float64 `json:"share_of_energy_from_cereals_roots_and_tubers_fao_2017"`
}

This data details the average share of dietary energy supplied by cereals, roots or tuber food commodities. It is often used as a key indicator of dietary diversity, with individuals at lower-income levels relying on cereals, roots and tubers for a high percentage of total energy intake (indicating poor dietary diversity).

type ShareOfFoodLostByFoodTypeAndRegionFao2019Dataset 

type ShareOfFoodLostByFoodTypeAndRegionFao2019Dataset struct {
	PercentageOfFoodLossByFoodGroupFao2019 *float64 `json:"percentage_of_food_loss_by_food_group_fao_2019"`
	PercentageOfFoodLossByRegionFao2019    *float64 `json:"percentage_of_food_loss_by_region_fao_2019"`
}

Data represents the share of food lost (from post-harvest through to, but not including, retail level) by food group and by region. From the source: “Percentage of food loss refers to the physical quantity lost for different commodities divided by the amount produced. An economic weight is used to aggregate percentages at regional or commodity group levels, so that higher-value commodities carry more weight in loss estimation than lower-value ones”.This data is sourced from the UN FAO’s 2019 State of Food and Agriculture report.FAO. 2019. The State of Food and Agriculture 2019. Moving forward on food loss and waste reduction. Rome. Available at: http://www.fao.org/3/ca6030en/ca6030en.pdfThe data was made available in the UN FAO’s interactive report accompanying the chart: http://www.fao.org/state-of-food-agriculture/en/.

type ShareOfLandownersWhoAreFemaleFao2017Dataset 

type ShareOfLandownersWhoAreFemaleFao2017Dataset struct {
	ShareOfLandownersWhichAreFemalePercFao2017 *float64 `json:"share_of_landowners_which_are_female_perc_fao_2017"`
}

Data on agricultural land ownership by gender is available and reported by the FAO across 102 countries. Currently this data is not available as a time-series, and is limited to measurement in a single census year. Note that this census year is not consistent across all countries. Data for most European and North American countries is based on 2010-11 datasets, whereas other countries can extend from 1993-2011. For graphing consistency, this has been shown as a single year (e.g. assuming these figures are representative of 2011). The actual years of measurement for each country can be found in the referenced FAO report and database.The FAO's publication on its Gender and Land Rights Database notes the following definition:"The agricultural landowner is defined as the legal owner of the agricultural land; however, definitions of ownership may vary across countries and surveys. The indicator may not necessarily reflect documented ownership certified by a legal document. Especially in places where much of the land is not formally titled or documented, surveys often simply ask whether someone in the household owns the land, and if so, who owns it. In addition to officially titled ownership, it may also include proxies, such as the right to use, sell or bequeath the land, or the right to use it as collateral. This enables the indicator to capture different aspects of the “bundle of rights” related to land, rather than land ownership in the strictest sense of the term. The current indicator in the GLRD uses different definitions of ownership; they are specified for each country in the data notes. As data for more countries become available, it will be useful to calculate these measures using more than one definition of ownership.An individual is defined as a landowner whether they own land solely (they are the only owner of a plot of land) or jointly with someone inside or outside the household. Thus, households may have multiple landowners. In addition, households may own multiple plots of land with different owners identified for each plot."References:FAO (2015). Gender and Land Statistics Recent developments in FAO’s Gender and Land Rights Database. Rome. Available at: http://www.fao.org/3/a-i4862e.pdf [accessed 29/05/2017]

type ShareOfMarriagesInEnglandAndWalesThatEndedInDivorceUkOns2020Dataset 

type ShareOfMarriagesInEnglandAndWalesThatEndedInDivorceUkOns2020Dataset struct {
	ShareOfMarriedCouplesThatHadDivorcedInEnglandAndWalesUkOns *float64 `json:"share_of_married_couples_that_had_divorced_in_england_and_wales_uk_ons"`
}

Data measures the percentage of marriages in England and Wales ending in divorce by anniversary and year of marriage.The UK Office for National Statistics notes the following points:– When calculating these percentages, it has been assumed that the couples who married each year have not moved out of England and Wales, couples who divorced each year have not moved into England and Wales since getting married, and couples marry in the country where they usually live.– The Divorce Reform Act 1969, which came into effect on 1 January 1971, made it easier for couples to divorce upon separation.

type ShareOfPeopleExperiencingHomelessnessInTheUsa20072016Per100000Hud2016AndUsCensusBureau2010Dataset 

type ShareOfPeopleExperiencingHomelessnessInTheUsa20072016Per100000Hud2016AndUsCensusBureau2010Dataset struct {
	ShelteredHomelessHud2016AndUsCensusBureau2010   *float64 `json:"sheltered_homeless_hud_2016_and_us_census_bureau_2010"`
	UnshelteredHomelessHud2016AndUsCensusBureau2010 *float64 `json:"unsheltered_homeless_hud_2016_and_us_census_bureau_2010"`
}

Additional info on Homelessness data:

HUD's description of the report:

"The Annual Homeless Assessment Report (AHAR) is a HUD report to the U.S. Congress that provides nationwide estimates of homelessness, including information about the demographic characteristics of homeless persons, service use patterns, and the capacity to house homeless persons. The report is based primarily on Homeless Management Information Systems (HMIS) data about persons who experience homelessness during a 12-month period".

All HUD reports are available at https://www.hudexchange.info/programs/hdx/guides/ahar/#reports.

Additional info on Population data:

As explained by their Guidance for Data: "the Census Bureau’s Population Estimates Program (PEP) releases several different data series over the course of each decade".

Total U.S. population figures for the period 2007-2010 are taken from the 'Intercensal Estimates 2000-2010'. Each decade, PEP releases these 'Intercensal Estimates' which constitutes the preferred data series for the decade.

Total U.S. population figures for the period 2011-2016 are taken from the 'National Population Totals Tables: 2010-2016'. These estimates "are produced using a similar procedure to that used for our current postcensal estimates series, and with no knowledge of the Census count at the end of the decade."

type ShareOfPeopleWhoReportHavingIntentionsToStartBusinessGlobalEntrepreneurshipMonitorDataset 

type ShareOfPeopleWhoReportHavingIntentionsToStartBusinessGlobalEntrepreneurshipMonitorDataset struct {
	ShareOfPeopleWhoReportHavingIntentionsToStartBusinessGlobalEntrepreneurshipMonitor *float64 `json:"share_of_people_who_report_having_intentions_to_start_business_global_entrepreneurship_monitor"`
}

type ShareOfPopulationCoveredBySocialProtectionAspireWorldBank2019Dataset 

type ShareOfPopulationCoveredBySocialProtectionAspireWorldBank2019Dataset struct {
	ShareOfPopulationCoveredBySocialProtectionInPerc *float64 `json:"share_of_population_covered_by_social_protection_in_perc"`
}

type ShareOfPrimarySchoolChildrenAchievingMinimumReadingProficiencyRichVsPoorUnescoDataset 

type ShareOfPrimarySchoolChildrenAchievingMinimumReadingProficiencyRichVsPoorUnescoDataset struct {
	ShareOfChildrenWhoPassedTheMinimumPasecProficiencyLevelPoorUnesco *float64 `json:"share_of_children_who_passed_the_minimum_pasec_proficiency_level_poor_unesco"`
	ShareOfChildrenWhoPassedTheMinimumPasecProficiencyLevelRichUnesco *float64 `json:"share_of_children_who_passed_the_minimum_pasec_proficiency_level_rich_unesco"`
}

type ShareOfServicesInTotalExportsWdi2017Dataset 

type ShareOfServicesInTotalExportsWdi2017Dataset struct {
	ShareOfServicesInTotalExportsWdi *float64 `json:"share_of_services_in_total_exports_wdi"`
}

In the series for China, the original data shows a large temporary shift over the period 1997-2002. We have contacted the source for clarification, but in the meantime we have suppressed these observations from our data, since we have reasons to believe the shift is a mistake in the source. The deleted observations are: (1997, 40.95%; 1998, 44.13%; 1999, 44.09%; 2000, 41.42%; 2001, 43.20%; 2002, 41.26%).Goods exports (BoP, current US$): https://data.worldbank.org/indicator/BX.GSR.MRCH.CD?view=chartServices exports (BoP, current US$): https://data.worldbank.org/indicator/BX.GSR.NFSV.CD?view=chart

type ShareOfSingleParentFamiliesUnPopulationDivision2018Dataset 

type ShareOfSingleParentFamiliesUnPopulationDivision2018Dataset struct {
	ShareOfSingleParentFamilies *float64 `json:"share_of_single_parent_families"`
}

The variable for the share of single parent families is taken from the <a href="https://population.un.org/Household/index.html#/countries/840">United Nations Database on Household Size and Composition (2018)</a>. The UN database pulls from 4 different sources: 1) Demographic and Health Surveys (DHS); 2) the Demographic Yearbook (DYB) of the United Nations; 3) IPUMS-International;4) Labor Force Surveys (LFS) of the European Union, Eurostat. Where a country time series was composed of multiple sources, we favoured the source covering the most years. In cases where there was a tie between sources, we favoured the DYB, then IPUMS, DHS, and lastly LFS estimates. The DYB covered the largest number of countries and the LFS the least. 

Where multiple country-year observations came from the same source, we favoured the observation with the reference date occurring later in the year.

This is true for Senegal in 2013 and Tanzania in 2004.The source for each observation can be found in the metadata spreadsheet <a href="https://owid.cloud/app/uploads/2020/01/un-single-parent-final-metadata-stan.csv">here</a>.

type ShareOfTop1percInNetPersonalWealthWorldWealthAndIncomeDatabase2018Dataset 

type ShareOfTop1percInNetPersonalWealthWorldWealthAndIncomeDatabase2018Dataset struct {
	ShareOfTop1percInNetPersonalWealth *float64 `json:"share_of_top_1perc_in_net_personal_wealth"`
}

type ShareOfWomenInTopIncomeGroupsAtkinsonCasaricoAndVoitchovsky2018OldDataset 

type ShareOfWomenInTopIncomeGroupsAtkinsonCasaricoAndVoitchovsky2018OldDataset struct {
	ShareOfWomenInTop10perc  *float64 `json:"share_of_women_in_top_10perc"`
	ShareOfWomenInTop5perc   *float64 `json:"share_of_women_in_top_5perc"`
	ShareOfWomenInTop1perc   *float64 `json:"share_of_women_in_top_1perc"`
	ShareOfWomenInTop01perc  *float64 `json:"share_of_women_in_top_01perc"`
	ShareOfWomenInTop05perc  *float64 `json:"share_of_women_in_top_05perc"`
	ShareOfWomenInTop025perc *float64 `json:"share_of_women_in_top_025perc"`
	Top10percSeries2         *float64 `json:"top_10perc_series_2"`
	Top5percSeries2          *float64 `json:"top_5perc_series_2"`
	Top1percSeries2          *float64 `json:"top_1perc_series_2"`
	Top01percSeries2         *float64 `json:"top_01perc_series_2"`
	Top05percSeries2         *float64 `json:"top_05perc_series_2"`
	Top025percSeries2        *float64 `json:"top_025perc_series_2"`
	Top05percSeries3         *float64 `json:"top_05perc_series_3"`
}

Share of women in pre-tax top income groups. Based on tax data. Income includes earned, self-employment and investment income. Capital gains is in general excluded, but to varying degrees between countries.

type ShareOfWorldMerchandiseTradeByTypeOfTradeFouquinAndHugotCepii2016DyadicDataDataset 

type ShareOfWorldMerchandiseTradeByTypeOfTradeFouquinAndHugotCepii2016DyadicDataDataset struct {
	ValueOfWorldMerchandiseTradeRichToRichFouquinAndHugotCepii2016DyadicData       *float64 `json:"value_of_world_merchandise_trade_rich_to_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ValueOfWorldMerchandiseTradeRichToNonRichFouquinAndHugotCepii2016DyadicData    *float64 `json:"value_of_world_merchandise_trade_rich_to_non_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ValueOfWorldMerchandiseTradeNonRichToRichFouquinAndHugotCepii2016DyadicData    *float64 `json:"value_of_world_merchandise_trade_non_rich_to_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ValueOfWorldMerchandiseTradeNonRichToNonRichFouquinAndHugotCepii2016DyadicData *float64 `json:"value_of_world_merchandise_trade_non_rich_to_non_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ShareOfWorldMerchandiseTradeRichToRichFouquinAndHugotCepii2016DyadicData       *float64 `json:"share_of_world_merchandise_trade_rich_to_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ShareOfWorldMerchandiseTradeRichToNonRichFouquinAndHugotCepii2016DyadicData    *float64 `json:"share_of_world_merchandise_trade_rich_to_non_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ShareOfWorldMerchandiseTradeNonRichToRichFouquinAndHugotCepii2016DyadicData    *float64 `json:"share_of_world_merchandise_trade_non_rich_to_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
	ShareOfWorldMerchandiseTradeNonRichToNonRichFouquinAndHugotCepii2016DyadicData *float64 `json:"share_of_world_merchandise_trade_non_rich_to_non_rich_fouquin_and_hugot_cepii_2016_dyadic_data"`
}

'Non-rich' countries are all countries in the world except: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland,Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.Calculations use Fouquin and Hugot (CEPII 2016) dyadic trade data.

type SharkAttacksAndFatalitiesGlobalSharkAttackFileGsaf2018Dataset 

type SharkAttacksAndFatalitiesGlobalSharkAttackFileGsaf2018Dataset struct {
	TotalNumberOfSharkAttacksGsaf2018          *float64 `json:"total_number_of_shark_attacks_gsaf_2018"`
	TotalNumberOfSharkAttackFatalitiesGsaf2018 *float64 `json:"total_number_of_shark_attack_fatalities_gsaf_2018"`
}

The Global Shark Attack File (GSAF) keeps an incident log that is updated with each new shark attack. Four types of shark attack categories are included: provoked, unprovoked, boating incidents, and sea disasters. The GSAF defines provoked incidents as incidents in which "the shark was speared, hooked, captured or in which a human drew 'first blood'" versus unprovoked incidents where this behaviour did not occur. Boating incidents are defined as cases where "a boat was bitten or rammed by a shark." Sea disasters are "accidents that place people in the day-to-day business of sharks." Shark attacks classified as "Invalid" or "Questionable" have been excluded from estimates.The link above provides more details about the type, area, location, activity, whether the victim was a male or female, their age, description of the injury, time of the incident, shark species, and investigator/source for each observation. Where a range of dates is provided for a particular incident, we have used the earliest year when plotting the data. For example, where the date is provided as "Between 1918 and 1939" we plot this as 1918. For years entered as "Before xxxx", we have simply input the previous year. The observations 'Portugal / India' for 1580, and 'Africa' for 1846 have been dropped due to their geographical inaccuracy.The 'Global Shark Attack File' figures reflect reported/recorded attacks or deaths only. For less recent years, these records could be incomplete.

type SignificantEarthquakeEventsNgdcNasaDataset 

type SignificantEarthquakeEventsNgdcNasaDataset struct {
	SignificantEarthquakeEventsNgdcNasa *float64 `json:"significant_earthquake_events_ngdc_nasa"`
}

The Significant Earthquake Database is a global listing of over 5,700 earthquakes from 2150 BC to the present. A significant earthquake is classified as one that meets at least one of the following criteria: caused deaths, caused moderate damage (approximately $1 million or more), magnitude 7.5 or greater, Modified Mercalli Intensity (MMI) X or greater, or the earthquake generated a tsunami.Our World in Data have aggregated significant earthquake numbers by country/location per year. Due to data availability, reporting and evidence, it's expected that more recent data will be more complete than the long historical record.

type SignificantVolcanicEruptionsNgdcWdsDataset 

type SignificantVolcanicEruptionsNgdcWdsDataset struct {
	NumberOfSignificantVolcanicEruptionsNgdcWds *float64 `json:"number_of_significant_volcanic_eruptions_ngdc_wds"`
}

The Significant Volcanic Eruption Database is a global listing of over 500 significant eruptions which includes information on the latitude, longitude, elevation, type of volcano, and last known eruption. A significant eruption is classified as one that meets at least one of the following criteria: caused fatalities, caused moderate damage (approximately $1 million or more), with a Volcanic Explosivity Index (VEI) of 6 or larger, caused a tsunami, or was associated with a major earthquake.Note that since this data is very long-term it's expected that most recent data on eruptions will be the most complete than long historic events.

type SipriMilitaryExpenditureDatabaseDataset 

type SipriMilitaryExpenditureDatabaseDataset struct {
	MilitaryExpenditure          *float64 `json:"military_expenditure"`
	MilitaryExpenditureShareGdp  *float64 `json:"military_expenditure_share_gdp"`
	MilitaryExpenditurePerCapita *float64 `json:"military_expenditure_per_capita"`
}

The SIPRI Military Expenditure Database contains consistent time series on the military spending of countries since 1949. The database is updated annually.Military expenditure figures are adjusted for inflation and expressed in constant (2019) US Dollars.The availability of data varies considerably by country, but for a majority of countries that were independent at the time, data is available from at least the late 1950s. Estimates for regional military expenditure have been extended backward depending on the availability of data for countries in the region, but no estimates for total world military expenditure are available before 1988 due to the lack of data for the Soviet Union.SIPRI military expenditure data is based on open sources only.

type SmallpoxCasesByCountry19201977Dataset 

type SmallpoxCasesByCountry19201977Dataset struct {
	ReportedSmallpoxCasesWho *float64 `json:"reported_smallpox_cases_who"`
	WorldRegionWorldBank2017 *float64 `json:"world_region_world_bank_2017"`
}

Data until 1966 was compiled from relevant tables in Fenner et al. (1988)'s Chapter 8 (link above): Fenner, F., Henderson, D. A., Arita, I., Jezek, Z., Ladnyi, I. D., & World Health Organization. (1988). Smallpox and its eradication.1967 data retrieved from World Health Organization. (‎1969)‎. Weekly Epidemiological Record, 1969, vol. 44, 4 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 44 (‎4)‎, 69-88. http://apps.who.int/iris/bitstream/handle/10665/217012/WER4404.PDF?sequence=1&isAllowed=y1968 and 1969 data retrieved from World Health Organization. (‎1970)‎. Weekly Epidemiological Record, 1970, vol. 45, 3 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 45 (‎3)‎, 17-36. http://apps.who.int/iris/bitstream/handle/10665/217559/WER4503.PDF?sequence=1&isAllowed=y1970 data retrieved from World Health Organization. (‎1971)‎. Weekly Epidemiological Record, 1971, vol. 46, 51 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 46 (‎51)‎, 521-532. http://apps.who.int/iris/bitstream/handle/10665/218642/WER4651.PDF?sequence=1&isAllowed=y1971 data retrieved from World Health Organization. (‎1972)‎. Weekly Epidemiological Record, 1972, vol. 47, 26 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 47 (‎26)‎, 249-256. http://apps.who.int/iris/bitstream/handle/10665/218953/WER4726.PDF?sequence=1&isAllowed=y1972 data retrieved from World Health Organization. (‎1974)‎. Weekly Epidemiological Record, 1974, vol. 49, 2 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 49 (‎2)‎, 9-24. http://apps.who.int/iris/bitstream/handle/10665/219695/WER4902.PDF?sequence=1&isAllowed=y1973 and 1974 data retrieved from World Health Organization. (‎1975)‎. Table 5 Smallpox Surveillance. http://apps.who.int/iris/bitstream/handle/10665/220183/WER5003_21-22.PDF?sequence=1&isAllowed=y1975 data retrieved from World Health Organization. (‎1976)‎. Smallpox surveillance End of Year 1975 summary. http://apps.who.int/iris/bitstream/handle/10665/220670/WER5103_9-18.PDF?sequence=1&isAllowed=y1976 data retrieved from World Health Organization. (‎1977)‎. Weekly Epidemiological Record, 1977, vol. 52, 2 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 52 (‎2)‎, 9-20. http://apps.who.int/iris/bitstream/handle/10665/221189/WER5202.PDF?sequence=1&isAllowed=y1977 data retrieved from World Health Organization. (‎1978)‎. Weekly Epidemiological Record, 1978, vol. 53, 2 [‎full issue]‎. Weekly Epidemiological Record = Relevé épidémiologique hebdomadaire, 52 (‎2)‎, 9-20. http://apps.who.int/iris/bitstream/handle/10665/221687/WER5302.PDF?sequence=1&isAllowed=ySome data cleaning was performed: East Pakistan was changed into Bangladesh and West Pakistan was changed into Pakistan. Upper Volta was changed into Burkina Faso, Southern Rhodesia to Zimbabwe, Ceylon to Sri Lanka but Dahomey (a region in today's Nigeria) was left as it is. For all countries that approached zero cases and then were not included in the Weekly Epidemiological Record (WER) anymore subsequently were assumed to have zero cases. This was done for 1967-1977.

type SmallpoxCasesReportedAndRevisedFennerEtAl1988Dataset 

type SmallpoxCasesReportedAndRevisedFennerEtAl1988Dataset struct {
	SmallpoxCasesAsRecordedInApril1967 *float64 `json:"smallpox_cases_as_recorded_in_april_1967"`
	SmallpoxCasesAsRevisedIn1987       *float64 `json:"smallpox_cases_as_revised_in_1987"`
}

type SmokingCigaretteSalesInternationalSmokingStatistics2017Dataset 

type SmokingCigaretteSalesInternationalSmokingStatistics2017Dataset struct {
	SalesOfCigarettesPerAdultPerDayInternationalSmokingStatistics2017 *float64 `json:"sales_of_cigarettes_per_adult_per_day_international_smoking_statistics_2017"`
	NumberOfCigarettesSmokedPerSmokerPerDayFemalesIss2017             *float64 `json:"number_of_cigarettes_smoked_per_smoker_per_day_females_iss_2017"`
	NumberOfCigarettesSmokedPerSmokerPerDayMalesIss2017               *float64 `json:"number_of_cigarettes_smoked_per_smoker_per_day_males_iss_2017"`
	PrevalenceOfSmokingFemalesIss2017                                 *float64 `json:"prevalence_of_smoking_females_iss_2017"`
	PrevalenceOfSmokingMalesIss2017                                   *float64 `json:"prevalence_of_smoking_males_iss_2017"`
}

Total cigarettes include both manufactured and hand-rolled cigarettes. For Latvia, total cigarettes comprise of manufactured cigarettes and Papyrosi cigarettes. In some countries the original data reports 0 for a specific year, with otherwise large reported values for the years before and after. In these cases we treat 0 as a mistake and report missing data for that year.The 'number of cigarettes smoked per person per day' for both males and females has been averaged across all years in which multiple estimates were provided in the ISS dataset for the United States, to arrive at one estimate for each year.The time series for Germany includes West Germany cigarette sales for the 1948-1989 period .

type SmokingPrevalenceAndCigaretteConsumptionIhmeGhdx2012Dataset 

type SmokingPrevalenceAndCigaretteConsumptionIhmeGhdx2012Dataset struct {
	CigaretteConsumptionPerSmokerPerDayIhmeGhdx2012       *float64 `json:"cigarette_consumption_per_smoker_per_day_ihme_ghdx_2012"`
	ConsumptionPerSmokerPerDayLowerBoundIhmeGhdx2012      *float64 `json:"consumption_per_smoker_per_day_lower_bound_ihme_ghdx_2012"`
	ConsumptionPerSmokerPerDayUpperBoundIhmeGhdx2012      *float64 `json:"consumption_per_smoker_per_day_upper_bound_ihme_ghdx_2012"`
	LowerBoundForDailySmokingPrevalenceBothIhmeGhdx2012   *float64 `json:"lower_bound_for_daily_smoking_prevalence_both_ihme_ghdx_2012"`
	UpperBoundForDailySmokingPrevalenceBothIhmeGhdx2012   *float64 `json:"upper_bound_for_daily_smoking_prevalence_both_ihme_ghdx_2012"`
	DailySmokingPrevalenceBothIhmeGhdx2012                *float64 `json:"daily_smoking_prevalence_both_ihme_ghdx_2012"`
	LowerBoundForDailySmokingPrevalenceMaleIhmeGhdx2012   *float64 `json:"lower_bound_for_daily_smoking_prevalence_male_ihme_ghdx_2012"`
	UpperBoundForDailySmokingPrevalenceMaleIhmeGhdx2012   *float64 `json:"upper_bound_for_daily_smoking_prevalence_male_ihme_ghdx_2012"`
	DailySmokingPrevalenceMaleIhmeGhdx2012                *float64 `json:"daily_smoking_prevalence_male_ihme_ghdx_2012"`
	LowerBoundForDailySmokingPrevalenceFemaleIhmeGhdx2012 *float64 `json:"lower_bound_for_daily_smoking_prevalence_female_ihme_ghdx_2012"`
	UpperBoundForDailySmokingPrevalenceFemaleIhmeGhdx2012 *float64 `json:"upper_bound_for_daily_smoking_prevalence_female_ihme_ghdx_2012"`
	DailySmokingPrevalenceFemaleIhmeGhdx2012              *float64 `json:"daily_smoking_prevalence_female_ihme_ghdx_2012"`
	NumberOfDailySmokersBothIhmeGhdx2012                  *float64 `json:"number_of_daily_smokers_both_ihme_ghdx_2012"`
	LowerBoundForNumberOfDailySmokersBothIhmeGhdx2012     *float64 `json:"lower_bound_for_number_of_daily_smokers_both_ihme_ghdx_2012"`
	UpperBoundForNumberOfDailySmokersBothIhmeGhdx2012     *float64 `json:"upper_bound_for_number_of_daily_smokers_both_ihme_ghdx_2012"`
	NumberOfDailyFemaleSmokersIhmeGhdx2012                *float64 `json:"number_of_daily_female_smokers_ihme_ghdx_2012"`
	LowerBoundForNumberOfFemaleDailySmokersIhmeGhdx2012   *float64 `json:"lower_bound_for_number_of_female_daily_smokers_ihme_ghdx_2012"`
	UpperBoundForNumberOfFemaleDailySmokersIhmeGhdx2012   *float64 `json:"upper_bound_for_number_of_female_daily_smokers_ihme_ghdx_2012"`
	NumberOfDailyMaleSmokersIhmeGhdx2012                  *float64 `json:"number_of_daily_male_smokers_ihme_ghdx_2012"`
	LowerBoundForNumberOfDailyMaleSmokersIhmeGhdx2012     *float64 `json:"lower_bound_for_number_of_daily_male_smokers_ihme_ghdx_2012"`
	UpperBoundForNumberOfDailyMaleSmokersIhmeGhdx2012     *float64 `json:"upper_bound_for_number_of_daily_male_smokers_ihme_ghdx_2012"`
}

Consumption per smoker per day refers to all ages and both sexes. The unit of measurement is in cigarettes.

Daily smoking prevalence for male, female, and both (male and female) also refers to all ages of each particular category. Daily male smoking prevalence is interpreted as the percentage of men, across all ages, who smoke daily.

The number of daily male smokers refers to the number of men, by country, who smoke cigarettes at least daily.

type So2EmissionsByCountry18502000ClioInfraDataset 

type So2EmissionsByCountry18502000ClioInfraDataset struct {
	So2EmissionsByCountry1850_2000ClioInfra *float64 `json:"so2_emissions_by_country_1850_2000_clio_infra"`
}

type So2EmissionsByRegionOecd2014AndKlimontEtAl2013Dataset 

type So2EmissionsByRegionOecd2014AndKlimontEtAl2013Dataset struct {
	So2EmissionsOecd2014AndKlimontEtAl2013 *float64 `json:"so2_emissions_oecd_2014_and_klimont_et_al_2013"`
}

For the period 1850-2000 we have used regional data aggregated in OECD (2014) report based on Clio Infra national emissions data. Clio Infra data is available online at: https://www.clio-infra.eu/Data for 2010 has been derived using figures from Klimont et al. (2013).Metrics of emissions are the same in both sources and based on methodology developed in Smith et al. (2011). This methodology estimates emissions based on energy and industrial production and mass-balance statistics.

type So2EmissionsChinaAndIndiaKlimontEtAl2013Dataset 

type So2EmissionsChinaAndIndiaKlimontEtAl2013Dataset struct {
	So2EmissionsInChinaAndIndiaKlimontEtAl2013 *float64 `json:"so2_emissions_in_china_and_india_klimont_et_al_2013"`
}

Metrics of emissions are based on methodology developed in Smith et al. (2011). This methodology estimates emissions based on energy and industrial production and mass-balance statistics.

References:

Klimont, Z; S J Smith and J Cofala (2013) – The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions. Environmental Research Letters, Volume 8, Number 1. Published 9 January 2013. Online at: http://iopscience.iop.org/article/10.1088/1748-9326/8/1/014003

S. J. Smith, J. van Aardenne, Z. Klimont, R. J. Andres, A. Volke, and S. Delgado Arias (2011) – Anthropogenic sulfur dioxide emissions: 1850–2005. Atmospheric Chemistry and Physics, 11, 1101-1116, doi:10.5194/acp-11-1101-2011, 2011. Online at: http://www.atmos-chem-phys.net/11/1101/2011/

type So2PerCapitaClioInfraDataset 

type So2PerCapitaClioInfraDataset struct {
	So2EmissionsPerCapitaClioInfra *float64 `json:"so2_emissions_per_capita_clio_infra"`
}

type SocialExpenditureInTheLongRunLindert2004Oecd1985OecdSocxDataset 

type SocialExpenditureInTheLongRunLindert2004Oecd1985OecdSocxDataset struct {
	SocialExpenditurePercgdpOwidExtrapolatedSeries *float64 `json:"social_expenditure_percgdp_owid_extrapolated_series"`
}

DefinitionsSocial spending includes, among others, the following areas: health, old age, incapacity-related benefits, family, active labor market programmes, unemployment, and housing. In some studies and reports, education is included in social spending. This is not the case in this dataset.Handling of underlying sourcesThis dataset was constructed by combining three sources. These are the OECD Social Expenditure Database (OECD SOCX), OECD (1985) and Lindert (2004).These three sources were combined as follows. For the period 1980-2016 we report the figures as published in OECD SOCX. For the period 1960-1979, we extended the recent figures (from OECD SOCX) backwards by using the rates of change as reported in OECD (1985). And for the period 1880-1930, we took observations from Lindert (2004).To be precise, the data for the period 1960-1979 was obtained by relying on the earliest available observation from OECD SOCX, and then successively extending the series backwards by using the year-by-year rate of change implied by the estimates in OECD (1985). Here is an example:OECD_SOCX_(Year-1) = [OECD_SOCX_(Year)] x [OECD_1985_(year-1)]/[ OECD_1985_(year)]Backward extrapolation for the period 1960-1979 was necessary because the levels in the estimates from OECD SOCX and OECD (1985) do not exactly coincide for the overlapping years. The implicit assumption, then, is that the estimates from OECD SOCX and OECD (1985) have different levels, but common trends.In this visualization (https://ourworldindata.org/grapher/various-measures-of-social-expenditure-as-share-of-gdp?country=AUS) you can see how the various underlying sources compare to our constructed series.Further remarksAccording to the Manual to the OECD Social Expenditure Database (SOCX), the OECD defines social expenditure as “The provision by public and private institutions of benefits to, and financial contributions targeted at, households and individuals in order to provide support during circumstances which adversely affect their welfare, provided that the provision of the benefits and financial contributions constitutes neither a direct payment for a particular good or service nor an individual contract or transfer”The series on Social expenditure for France and Denmark in OECD(1985) are not complete. However, Lindert (2004) uses the data available to estimate social expenditure in 1960 and 1970. Therefore, we use his estimations to extrapolate backwards for those two points in time.In OECD (1985) social expenditure figures include expenditure on education. To ensure consistency with Lindert (2004) and OECD (SOCX), we subtracted education.

type SolarPvModuleCostsAndCapacityLafondEtAl2017AndIrenaDataset 

type SolarPvModuleCostsAndCapacityLafondEtAl2017AndIrenaDataset struct {
	CumulativeCapacity *float64 `json:"cumulative_capacity"`
	UnitCost           *float64 `json:"unit_cost"`
}

Solar photovoltaic (PV) data for the years 1976-2009 are sourced from Lafond et al. (2017). The authors sourced this data from the Navigant Research series (https://www.navigantresearch.com/).Our World in Data have extended this series up to 2019 based on the latest data from the International Renewable Energy Agency (IRENA) Resource, which reports global solar PV installed capacity and solar PV model prices. Available at: http://resourceirena.irena.org/gateway/dashboard/https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019IRENA presents solar PV module price series for a number of different module technologies. Here we have adopted the series for Thin film a-Si/u-Si, which it adopts as the Global Index.Prices from LaFond et al. (2017) have been converted to 2019 US$ using the US GDP deflator: https://www.multpl.com/gdp-deflator/table/by-year

type SolarPvSystemsCostsBarboseAndDarghouth2016Dataset 

type SolarPvSystemsCostsBarboseAndDarghouth2016Dataset struct {
	TotalInstalledPriceBarboseAndDarghouth2016 *float64 `json:"total_installed_price_barbose_and_darghouth_2016"`
	ModulePriceBarboseAndDarghouth2016         *float64 `json:"module_price_barbose_and_darghouth_2016"`
	NonModuleCostsBarboseAndDarghouth2016      *float64 `json:"non_module_costs_barbose_and_darghouth_2016"`
}

Data is based on median residential solar photovolatic (PV) prices in the United States only.

Installed non-module prices include hardware costs, such as inverters and racking equipment; and the wide assortment of soft costs, including such things as marketing and customer acquisition, system design, installation labor, permitting and inspection costs, and installer margins.

Non-module costs have been assumed to be the total solar PV system price minus the solar module price.

type SolidFuelUseForCookingByRegionBonjourEtAl2013Dataset 

type SolidFuelUseForCookingByRegionBonjourEtAl2013Dataset struct {
	HouseholdsUsingSolidFuelsAsTheMainCookingFuel *float64 `json:"households_using_solid_fuels_as_the_main_cooking_fuel"`
}
95% Confidence Intervals for the years 1990, 2000, and 2010 are given in the Supplemental Material, Table S3.

To give one example: The world’s households primarily relying on solid fuels for cooking declined from 62% to 41% between 1980 and 2010. The 95% confidence interval for 1980 is from 58% to 66% and for 2010 from 37% to 44%.Countries are grouped by WHO region and income category (WHO 2012e; see Supplemental Material, Table S2).

type SplitOfExportsToDifferentCountryGroupsOwidCalculationsBasedOnFouquinAndHugotCepii2016DyadicDataDataset 

type SplitOfExportsToDifferentCountryGroupsOwidCalculationsBasedOnFouquinAndHugotCepii2016DyadicDataDataset struct {
	ExportsToAfricaFouquinAndHugotCepii2016Dyadic                    *float64 `json:"exports_to_africa_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToAntarcticaFouquinAndHugotCepii2016Dyadic                *float64 `json:"exports_to_antarctica_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToAsiaFouquinAndHugotCepii2016Dyadic                      *float64 `json:"exports_to_asia_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToEasternEuropeFouquinAndHugotCepii2016Dyadic             *float64 `json:"exports_to_eastern_europe_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToNorthAmericaFouquinAndHugotCepii2016Dyadic              *float64 `json:"exports_to_north_america_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToOceaniaFouquinAndHugotCepii2016Dyadic                   *float64 `json:"exports_to_oceania_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToSouthAmericaFouquinAndHugotCepii2016Dyadic              *float64 `json:"exports_to_south_america_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToWesternEuropeFouquinAndHugotCepii2016Dyadic             *float64 `json:"exports_to_western_europe_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToLowerMiddleIncomeGroupingFouquinAndHugotCepii2016Dyadic *float64 `json:"exports_to_lower_middle_income_grouping_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToHighIncomeGroupingFouquinAndHugotCepii2016Dyadic        *float64 `json:"exports_to_high_income_grouping_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToLowIncomeGroupingFouquinAndHugotCepii2016Dyadic         *float64 `json:"exports_to_low_income_grouping_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToUpperMiddleIncomeGroupingFouquinAndHugotCepii2016Dyadic *float64 `json:"exports_to_upper_middle_income_grouping_fouquin_and_hugot_cepii_2016_dyadic"`
	ExportsToTheWorldFouquinAndHugotCepii2016Dyadic                  *float64 `json:"exports_to_the_world_fouquin_and_hugot_cepii_2016_dyadic"`
}

To calculate country exports to the rest of the world, the total value of exports by country, per year, is divided by the country's GDP. Calculations use Fouquin and Hugot (CEPII 2016) dyadic trade data.The time series 'World' corresponds to the World's total exports (i.e. the sum of exports reported by all countries in the dataset).The total export values of regional income aggregates have been calculated using the World Bank's income groupings.Germany's time series is comprised of West Germany, and Germany. East Germany has been excluded for the purposes of Germany's calculations but is included in the World series.Russia's time series comprises Russia and the USSR. Continental groupings are defined according to OWID's classification. Europe has been split into Western Europe including: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Italy, the Netherlands, Norway, Portugal, Russia, Spain, Sweden, Switzerland, and the United Kingdom. Remaining European countries have been classified as Eastern Europe.

type StateBasedConflictDeathsSince1946ByRegionAndConflictTypePrioUcdp2022Dataset 

type StateBasedConflictDeathsSince1946ByRegionAndConflictTypePrioUcdp2022Dataset struct {
	DeathsInCivilConflicts                                       *float64 `json:"deaths_in_civil_conflicts"`
	DeathsInColonialOrImperialConflicts                          *float64 `json:"deaths_in_colonial_or_imperial_conflicts"`
	DeathsInCivilConflictsWithForeignStateIntervention           *float64 `json:"deaths_in_civil_conflicts_with_foreign_state_intervention"`
	DeathsInConflictsBetweenStates                               *float64 `json:"deaths_in_conflicts_between_states"`
	DeathsInAllStateBasedConflictTypes                           *float64 `json:"deaths_in_all_state_based_conflict_types"`
	DeathsInColonialOrImperialConflictsPer100_000                *float64 `json:"deaths_in_colonial_or_imperial_conflicts_per_100_000"`
	DeathsInConflictsBetweenStatesPer100_000                     *float64 `json:"deaths_in_conflicts_between_states_per_100_000"`
	DeathsInCivilConflictsPer100_000                             *float64 `json:"deaths_in_civil_conflicts_per_100_000"`
	DeathsInCivilConflictsWithForeignStateInterventionPer100_000 *float64 `json:"deaths_in_civil_conflicts_with_foreign_state_intervention_per_100_000"`
	DeathsInAllStateBasedConflictTypesPer100_000                 *float64 `json:"deaths_in_all_state_based_conflict_types_per_100_000"`
}

The UCDP Battle-related Deaths Dataset provides data on direct deaths arising from 'state-based' conflicts. 

UCDP defines state-based armed conflict as: “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in a calendar year.”Both civilian and military deaths are included.

Deaths due to disease or famine caused by conflict, as well as extra-judicial killings in custody, are excluded.The data is organised by year and conflict. We aggregate this to provide total numbers of deaths each year, broken down by the conflict type and region. This dataset runs from 1989 and aims to have global coverage over this period. Accordingly, we add a zero deaths observation for year-region-conflict type combinations that do appear in the dataset.The labels for the conflict types we have used paraphrase UCDP/PRIO's technical definitions of 'Extrasystemic', 'Internal', 'Internationalised internal' and 'Interstate'.Note that in this dataset the location refers not (necessarily) to where fighting and deaths took place, but rather to the location of the 'incompatibility' between the participants that defines the conflict: usually the country or territory whose possession or governance is in dispute.To extend the data back further in time we rely on the PRIO Battledeaths Datset, produced according to the same definitions and structure as the UCDP Battle-related Deaths Dataset. The PRIO data runs from 1946 to 2008 and is no longer maintained. We aggregated the PRIO data in the same way as the UCDP data, and then constructed a final joint series which consists of the PRIO data from 1946 to 1988 and the UCDP data from 1989 onwards.

type StateOfVaccineConfidenceLarsonEtAl2016Dataset 

type StateOfVaccineConfidenceLarsonEtAl2016Dataset struct {
	ImportanceLarsonEtAl2016             *float64 `json:"importance_larson_et_al_2016"`
	SafetyLarsonEtAl2016                 *float64 `json:"safety_larson_et_al_2016"`
	EffectivenessLarsonEtAl2016          *float64 `json:"effectiveness_larson_et_al_2016"`
	ReligiousCompatibilityLarsonEtAl2016 *float64 `json:"religious_compatibility_larson_et_al_2016"`
}

Possible answers for all questions were those on the five-point Likert scale (strongly agree, tend to agree, do not know, tend to disagree, strongly disagree).

type SubnationalInequalityOecdBasedOnRoyuelaEtAl2014Dataset 

type SubnationalInequalityOecdBasedOnRoyuelaEtAl2014Dataset struct {
	OecdSubnationalGiniCoefficientsOecdBasedOnRoyuelaEtAl2014       *float64 `json:"oecd_subnational_gini_coefficients_oecd_based_on_royuela_et_al_2014"`
	AverageAnnualGrowthRateOfGdpPerCapitaOecdBasedOnRoyuelaEtAl2014 *float64 `json:"average_annual_growth_rate_of_gdp_per_capita_oecd_based_on_royuela_et_al_2014"`
}

Each observation corresponds to a different sub-national region. France, for example, is divided in 22 different regions. The source notes that these regions correspond in most cases to the principal sub-national unit of government (states or provinces)

type SuicideRatesBySexAndAgeIhme2019Dataset 

type SuicideRatesBySexAndAgeIhme2019Dataset struct {
	MaleSuicideRateAgeStandardized   *float64 `json:"male_suicide_rate_age_standardized"`
	FemaleSuicideRateAgeStandardized *float64 `json:"female_suicide_rate_age_standardized"`
	MalefemaleSuicideRatio           *float64 `json:"malefemale_suicide_ratio"`
	SuicideRate15_19YearsMale        *float64 `json:"suicide_rate_15_19_years_male"`
	SuicideRate15_19YearsFemale      *float64 `json:"suicide_rate_15_19_years_female"`
	SuicideRate15_19YearsBothSexes   *float64 `json:"suicide_rate_15_19_years_both_sexes"`
	SuicideRate20_24YearsMale        *float64 `json:"suicide_rate_20_24_years_male"`
	SuicideRate20_24YearsFemale      *float64 `json:"suicide_rate_20_24_years_female"`
	SuicideRate20_24YearsBothSexes   *float64 `json:"suicide_rate_20_24_years_both_sexes"`
	SuicideRate25_29YearsMale        *float64 `json:"suicide_rate_25_29_years_male"`
	SuicideRate25_29YearsFemale      *float64 `json:"suicide_rate_25_29_years_female"`
	SuicideRate25_29YearsBothSexes   *float64 `json:"suicide_rate_25_29_years_both_sexes"`
	SuicideRate30_34YearsMale        *float64 `json:"suicide_rate_30_34_years_male"`
	SuicideRate30_34YearsFemale      *float64 `json:"suicide_rate_30_34_years_female"`
	SuicideRate30_34YearsBothSexes   *float64 `json:"suicide_rate_30_34_years_both_sexes"`
	SuicideRate35_39YearsMale        *float64 `json:"suicide_rate_35_39_years_male"`
	SuicideRate35_39YearsFemale      *float64 `json:"suicide_rate_35_39_years_female"`
	SuicideRate35_39YearsBothSexes   *float64 `json:"suicide_rate_35_39_years_both_sexes"`
	SuicideRate40_44YearsMale        *float64 `json:"suicide_rate_40_44_years_male"`
	SuicideRate40_44YearsFemale      *float64 `json:"suicide_rate_40_44_years_female"`
	SuicideRate40_44YearsBothSexes   *float64 `json:"suicide_rate_40_44_years_both_sexes"`
	SuicideRate45_49YearsMale        *float64 `json:"suicide_rate_45_49_years_male"`
	SuicideRate45_49YearsFemale      *float64 `json:"suicide_rate_45_49_years_female"`
	SuicideRate45_49YearsBothSexes   *float64 `json:"suicide_rate_45_49_years_both_sexes"`
	SuicideRate50_69YearsMale        *float64 `json:"suicide_rate_50_69_years_male"`
	SuicideRate50_69YearsFemale      *float64 `json:"suicide_rate_50_69_years_female"`
	SuicideRate50_69YearsBothSexes   *float64 `json:"suicide_rate_50_69_years_both_sexes"`
	SuicideRate70YearsMale           *float64 `json:"suicide_rate_70_years_male"`
	SuicideRate70YearsFemale         *float64 `json:"suicide_rate_70_years_female"`
	SuicideRate70YearsBothSexes      *float64 `json:"suicide_rate_70_years_both_sexes"`
}

Death rates from suicide are measured as the number of suicide deaths measured per 100,000 individuals in a given demographic group.The male-female suicide ratio is calculated by Our World in Data by dividing the male age-standardized suicide rate by the female age-standardized rate.

type SuicidesFromPesticidesMewEtAl2017Dataset 

type SuicidesFromPesticidesMewEtAl2017Dataset struct {
	ShareOfSuicideDeathsFromPesticidesMewEtAl2017 *float64 `json:"share_of_suicide_deaths_from_pesticides_mew_et_al_2017"`
	AnnualSuicideDeathsFromPesticidesMewEtAl2017  *float64 `json:"annual_suicide_deaths_from_pesticides_mew_et_al_2017"`
}

Mew et al. (2017) estimated the share and number of suicide deaths globally and regionally which resulted from pesticide poisoning.The authors did so using:"WHO method-specific suicide data were supplemented by a systematic review of the literature between 2006 and 2015, including searches of thirteen electronic databases and Google, citation searching and a review of reference lists and personal collections. Our primary outcome was the proportion of total suicides due to pesticide poisoning. Weighted estimates were calculated for seven WHO regional and income strata."

type SupercomputerPowerFlopsTop500DatabaseDataset 

type SupercomputerPowerFlopsTop500DatabaseDataset struct {
	FloatingPointOperationsPerSecond *float64 `json:"floating_point_operations_per_second"`
}

Data on supercomputer power is sourced from the TOP500 database, which tracks and reports the 500 largest supercomputers on a bi-annual basis. Supercomputer power is measured in Floating-Point Operations per Second (FLOPS), a measure of calculations per second for floating-point operations. Floating-point operations are needed for very large or very small real numbers, or computations that require a large dynamic range. It is therefore a more accurate measured than simply instructions per second.

type SurfaceOceanPlasticByMassEriksenEtAl2014Dataset 

type SurfaceOceanPlasticByMassEriksenEtAl2014Dataset struct {
	SmallMicroplasticMass033_1mm   *float64 `json:"small_microplastic_mass_033_1mm"`
	LargeMicroplasticMass101_475mm *float64 `json:"large_microplastic_mass_101_475mm"`
	MesoplasticMass476_200mm       *float64 `json:"mesoplastic_mass_476_200mm"`
	MacroplasticMassGreater200mm   *float64 `json:"macroplastic_mass_greater200mm"`
	AllSizesTotalMass              *float64 `json:"all_sizes_total_mass"`
}

Estimates by Eriksen et al. (2014) on the mass of surface ocean debris floating at sea, differentiated by ocean basin and particle size.Particle size categories range from small microplastics to macroplastics. Estimates are based on results from 24 expeditions (over the period 2007–2013) across all five sub-tropical gyres, costal Australia, Bay of Bengal and the Mediterranean Sea conducting surface net tows (N = 680) and visual survey transects of large plastic debris (N = 891). These field-based results were combined with oceanographic modelling of floating debris dispersal and wind-driven vertical mixing to derive total oceanic figures.

type SurfaceOceanPlasticByParticleCountEriksenEtAl2014Dataset 

type SurfaceOceanPlasticByParticleCountEriksenEtAl2014Dataset struct {
	SmallMicroplasticCount033_1mm   *float64 `json:"small_microplastic_count_033_1mm"`
	LargeMicroplasticCount101_475mm *float64 `json:"large_microplastic_count_101_475mm"`
	MesoplasticCount476_200mm       *float64 `json:"mesoplastic_count_476_200mm"`
	MacroplasticCountGreater200mm   *float64 `json:"macroplastic_count_greater200mm"`
	AllSizesTotalCount              *float64 `json:"all_sizes_total_count"`
}

Estimates by Eriksen et al. (2014) on the number of plastic particles of surface ocean debris floating at sea, differentiated by ocean basin and particle size.Particle size categories range from small microplastics to macroplastics. Estimates are based on results from 24 expeditions (over the period 2007–2013) across all five sub-tropical gyres, costal Australia, Bay of Bengal and the Mediterranean Sea conducting surface net tows (N = 680) and visual survey transects of large plastic debris (N = 891). These field-based results were combined with oceanographic modelling of floating debris dispersal and wind-driven vertical mixing to derive total oceanic figures.

type SwedishHistoricalNationalAccountsSchonAndKrantz200720122015Dataset 

type SwedishHistoricalNationalAccountsSchonAndKrantz200720122015Dataset struct {
	AgricultureGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015               *float64 `json:"agriculture_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	ManufacturingAndIndustryGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015  *float64 `json:"manufacturing_and_industry_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	BuildingAndConstructionGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015   *float64 `json:"building_and_construction_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	TransportAndComunicationsGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015 *float64 `json:"transport_and_comunications_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	PrivateServicesGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015           *float64 `json:"private_services_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	PublicServicesGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015            *float64 `json:"public_services_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	ServicesOfDwellingsGdpSekConstantPrices191012PriceLevelSchonAndKrantz2007_2012_2015       *float64 `json:"services_of_dwellings_gdp_sek_constant_prices_191012_price_level_schon_and_krantz_2007_2012_2015"`
	LaborProductivityGdpworkerSchonAndKrantz2007_2012_2015                                    *float64 `json:"labor_productivity_gdpworker_schon_and_krantz_2007_2012_2015"`
}

The source notes: "One should note that present time levels of GDP and sector value added in these series differ from data in contemporary official national accounts. It is natural that a number of shifts and redefinitions are performed in contemporary statistics in relation to structural and technological changes, but it is also reasonable that levels in the short contemporary series are adjusted to the long historical series rather than the other way around. "

type TaxCompositionArroyoAbadAndPLindert2016Dataset 

type TaxCompositionArroyoAbadAndPLindert2016Dataset struct {
	IncomeAndWealthArroyoAbadAndLindert2016 *float64 `json:"income_and_wealth_arroyo_abad_and_lindert_2016"`
	ConsumptionArroyoAbadAndLindert2016     *float64 `json:"consumption_arroyo_abad_and_lindert_2016"`
	TradeArroyoAbadAndLindert2016           *float64 `json:"trade_arroyo_abad_and_lindert_2016"`
	ResourcesArroyoAbadAndLindert2016       *float64 `json:"resources_arroyo_abad_and_lindert_2016"`
}

type TaxCompositionTodaroAndSmith2014Dataset 

type TaxCompositionTodaroAndSmith2014Dataset struct {
	CorporateIncomeTaxesTodaroAndSmith2014    *float64 `json:"corporate_income_taxes_todaro_and_smith_2014"`
	PersonalIncomeTaxesTodaroAndSmith2014     *float64 `json:"personal_income_taxes_todaro_and_smith_2014"`
	GeneralConsumptionTaxesTodaroAndSmith2014 *float64 `json:"general_consumption_taxes_todaro_and_smith_2014"`
	ExciseTaxesTodaroAndSmith2014             *float64 `json:"excise_taxes_todaro_and_smith_2014"`
	TradeTaxesTodaroAndSmith2014              *float64 `json:"trade_taxes_todaro_and_smith_2014"`
	SocialSecurityTodaroAndSmith2014          *float64 `json:"social_security_todaro_and_smith_2014"`
}

Estimates are provided by the source in two windows of time: 1985-1987 and 1995-1997. We report the estimates for the year in the middle of the corresponding estimates (i.e. 1986 and 1996)

type TaxRevenuePiketty2014Dataset 

type TaxRevenuePiketty2014Dataset struct {
	TaxRevenuePiketty2014               *float64 `json:"tax_revenue_piketty_2014"`
	TopMarignalIncomeTaxRatePiketty2014 *float64 `json:"top_marignal_income_tax_rate_piketty_2014"`
}

Data on tax revenues include all taxes, fees, social contributions, and other payments that citizens mush pay under penalty of law.Data on top marginal tax rates includes general income tax supplements, but excludes all other taxes and social contributions (e.g. NHS in the UK)

type TaxesIctdGrd2021Dataset 

type TaxesIctdGrd2021Dataset struct {
	TotalTaxRevenuePercOfGdpIctd2019                          *float64 `json:"total_tax_revenue_perc_of_gdp_ictd_2019"`
	TotalTaxesOnGoodsAndServicesPercOfGdpIctd2019             *float64 `json:"total_taxes_on_goods_and_services_perc_of_gdp_ictd_2019"`
	TotalTaxesOnIncomeProfitsAndCapitalGainsPercOfGdpIctd2019 *float64 `json:"total_taxes_on_income_profits_and_capital_gains_perc_of_gdp_ictd_2019"`
	TotalTaxesOnPayrollAndWorkforcePercOfGdpIctd2019          *float64 `json:"total_taxes_on_payroll_and_workforce_perc_of_gdp_ictd_2019"`
	OtherTaxesPercOfGdpIctd2019                               *float64 `json:"other_taxes_perc_of_gdp_ictd_2019"`
	SocialContributionsPercOfGdpIctd2019                      *float64 `json:"social_contributions_perc_of_gdp_ictd_2019"`
}

This data comes from the 'merged' dataset provided by ICTD, after excluding observations flagged as unreliable. Specifically, we excluded the observations where the ICTD indicated concerns about their quality, accuracy or comparability. Details regarding these flags, as well as the dataset more generally, are available Prichard, W. (2016). Reassessing Tax and Development Research: A New Dataset, New Findings, and Lessons for Research. World Development, 80, 48-60.The August 2021 release differs from the previous release with the inclusion of new data up until 2019/2020, where data is available. For more details on the Government Revenue Dataset see its <a href="https://www.wider.unu.edu/about-grd">update history</a>. UNU-WIDER Government Revenue Dataset. Version 2021. https://doi.org/10.35188/UNU-WIDER/GRD-2021

type TeacherAbsenteeismBoldEtAl2017Dataset 

type TeacherAbsenteeismBoldEtAl2017Dataset struct {
	PercentageOfTeachersAbsentFromClass  *float64 `json:"percentage_of_teachers_absent_from_class"`
	PercentageOfTeachersAbsentFromSchool *float64 `json:"percentage_of_teachers_absent_from_school"`
}

The data comes from Table 2 in the source paper with the following note: " The table reports the absence rate for all teachers, the scheduled teaching time, actual teaching time and number of orphaned classrooms for all governmentschools. Teachers are marked as absent from school if during the second unannounced visit, they are not found anywhere on the school premises. Otherwise, they are marked as present. Teachers are marked as absent from class if during the second unannounced visit, they are absent from school or present at school but absent from the classroom. Otherwise, they are marked as present. The scheduled teaching time is the length of the school day minus break time. Time spent teaching adjusts the length of the school day by the share of teachers who are present in the classroom, on average, and the time the teacher spends teaching while in the classroom. The orphaned classrooms measure is the ratio of the classrooms with students but no teacher to the number of classrooms with students with or without a teacher. All individual country statistics are calculated using country-specific sampling weights. The average for all countries is taken by averaging over the country columns. Hence, each country is given equal weight. Further details on the construction of the variables and sampling weights are available in an Appendix available from the authors upon request.

type TeachingTimeLostWorldDevelopmentReport2018Dataset 

type TeachingTimeLostWorldDevelopmentReport2018Dataset struct {
	PercentageOfScheduledTimeTeacherIsPresentInClassroom *float64 `json:"percentage_of_scheduled_time_teacher_is_present_in_classroom"`
	PercentageOfScheduledTimeTeacherIsTeaching           *float64 `json:"percentage_of_scheduled_time_teacher_is_teaching"`
}

The years for each observation correspond to the year of publication of the underlying source.

The full list of underlying sources is as follows: Abadzi (2009): Brazil (Pernambuco state), Ghana, Morocco, and Tunisia; Benveniste, Marshall, and Araujo (2008): Cambodia; Benveniste, Marshall, and Santibañez (2007): Lao People’s Democratic Republic; Millot and Lane (2002): Arab Republic of Egypt, Lebanon, and Republic of Yemen; World Bank (2016a): Madagascar; World Bank (2016b): Zambia; World Bank’s Service Delivery Indicators, 2012–13 (http://www.worldbank .org/sdi): Kenya, Mozambique, Nigeria, Senegal, Tanzania, Togo, and Uganda.

For Brazil, Cambodia, Ghana, Lao PDR, Senegal, Tanzania, and Tunisia, data include public schools. For all other countries, data include both public and private schools.

Also, for Brazil, the data corresponds only to Pernambuco state.

type TechnologyAdoptionIsard1942AndOthersDataset 

type TechnologyAdoptionIsard1942AndOthersDataset struct {
	CanalsIsard1942                                           *float64 `json:"canals_isard_1942"`
	RoadsUsCensusBureau2017                                   *float64 `json:"roads_us_census_bureau_2017"`
	DieselLocomotivesInServiceUsCensusBureau2017              *float64 `json:"diesel_locomotives_in_service_us_census_bureau_2017"`
	AgriculturalTractorCominAndHobijn2004                     *float64 `json:"agricultural_tractor_comin_and_hobijn_2004"`
	AtmCominAndHobijn2004                                     *float64 `json:"atm_comin_and_hobijn_2004"`
	AviationPassengerKmCominAndHobijn2004                     *float64 `json:"aviation_passenger_km_comin_and_hobijn_2004"`
	CreditAndDebitPaymentsCominAndHobijn2004                  *float64 `json:"credit_and_debit_payments_comin_and_hobijn_2004"`
	CardPaymentsCominAndHobijn2004                            *float64 `json:"card_payments_comin_and_hobijn_2004"`
	MailMitchell1998                                          *float64 `json:"mail_mitchell_1998"`
	MriUnitsCominAndHobijn2004                                *float64 `json:"mri_units_comin_and_hobijn_2004"`
	NewspapersCominAndHobijn2004                              *float64 `json:"newspapers_comin_and_hobijn_2004"`
	RetailLocationsAcceptingCardCominAndHobijn2004            *float64 `json:"retail_locations_accepting_card_comin_and_hobijn_2004"`
	RailPassengerKmCominAndHobijn2004                         *float64 `json:"rail_passenger_km_comin_and_hobijn_2004"`
	SteamshipsTonsCominAndHobijn2004                          *float64 `json:"steamships_tons_comin_and_hobijn_2004"`
	CrudeSteelProductionBlastOxygenFurnacesCominAndHobijn2004 *float64 `json:"crude_steel_production_blast_oxygen_furnaces_comin_and_hobijn_2004"`
	CrudeSteelProductionElectricFurnacesCominAndHobijn2004    *float64 `json:"crude_steel_production_electric_furnaces_comin_and_hobijn_2004"`
	TelegramsMitchell1998                                     *float64 `json:"telegrams_mitchell_1998"`
	SyntheticNonCellulosicFibresCominAndHobijn2004            *float64 `json:"synthetic_non_cellulosic_fibres_comin_and_hobijn_2004"`
	CommercialVehiclesCominAndHobijn2004                      *float64 `json:"commercial_vehicles_comin_and_hobijn_2004"`
}

Roads - Historical Statistics of the United States, Colonial Times to 1970, Volume 1 and 2. Bureau of the Census, Washington D.C. see Chapter Q - Transportation, Q50-63. Link: https://www2.census.gov/library/publications/1975/compendia/hist_stats_colonial-1970/hist_stats_colonial-1970p2-chQ.pdf;Diesel locomotives - Historical Statistics of the United States, Colonial Times to 1970, Volume 1 and 2. Bureau of the Census, Washington D.C. see Chapter Q - Transportation, Series Q284-312: Railroad mileage, equipment, and passenger traffic and revenue: 1890 to 1970. Link: https://www2.census.gov/library/publications/1975/compendia/hist_stats_colonial-1970/hist_stats_colonial-1970p2-chQ.pdf;Agricultural tractor, ATM, Aviation passenger-km, Credit and debit payments, Card payments, MRI units, Newspapers, Retail locations accepting card, Rail passenger-km, Steamships (tons), Crude steel production (blast oxygen furnaces)/(electric furnaces), Synthetic (non-cellulosic) fibres, Commercial vehicles - Comin and Hobijn (2004). Link: http://www.nber.org/data/chat/;Mail and telegrams - Mitchell (1998) International Historical Statistics: the Americas, 1970-2000, 5th Ed

type TechnologyDiffusionCominAndHobijn2004AndOthersDataset 

type TechnologyDiffusionCominAndHobijn2004AndOthersDataset struct {
	TechnologyDiffusionCominAndHobijn2004AndOthers *float64 `json:"technology_diffusion_comin_and_hobijn_2004_and_others"`
}

A similar dataset was previously assembled by Horace Dediu who writes at: http://www.asymco.com/author/asymco/. We are thankful for Horace Dediu's generosity in making his dataset available to us. We have tracked down all of his original sources and assembled our dataset based on these original sources. The latest update to the dataset are detailed <a href="https://owid.cloud/wp-content/uploads/2019/07/Tech_Adoption_Update_documentation.xlsx">here (ver 27.07.19)</a>. We thank Adam Ferris for his help in collating this data.This dataset is a compilation of multiple sources to construct a broad overview of the adoption of technology in the United States. The dataset is comprised of the following sources:Isard (1942) A Neglected Cycle: The Transport-Building Cycle; Arnulf Grubler (1990), The rise and fall of infrastructures: dynamics of evolution and technological change; Lebergott (1993) Pursuring Happiness: American Consumers in the Twentieth Century - for 1989; Nicholas Felton NTY (2014), http://www.nytimes.com/imagepages/2008/02/10/opinion/10op.graphic.ready.html, from 2006 to 2011, data sourced from the US Census Bureau's Extended well-being; Bowden and Offer (1994), Household appliances and the use of time; Lebergott (1976), The American Economy: Income, Wealth and Want; Gisela Rua (2013) Federal Reserve Board, Fixed costs, network effects, and the diffusion of container shipping; Nielsen Television Audience (2008); David Popp (2006), Exploring links between innovation and diffusion; Popp, Hafner, Johnstone (2007), Policy vs. consumer pressure; Bech and Hobijn (2006), Technology diffusion within central banking; Pew Research Centre; Statista;World Bank;Consumer Intelligence Research Partners (CIRP);Greenwood, Seshadri & Yorikoglu (2005), Engines of Liberation. Data on dishwashers, dryers, freezers, microwaves, refrigerators, and washers uses the US Census Bureau's data from 1992 to 2011, using Greenwood et al. (2005) data for previous years;

type TemporaryAccommodationInEnglandUkGovernment2018Dataset 

type TemporaryAccommodationInEnglandUkGovernment2018Dataset struct {
	TotalNumberOfHouseholdsInTaTemporaryAccomodation              *float64 `json:"total_number_of_households_in_ta_temporary_accomodation"`
	TotalNumberOfHouseholdsInTaTemporaryAccommodationWithChildren *float64 `json:"total_number_of_households_in_ta_temporary_accommodation_with_children"`
	TotalNumberOfChildrenInTaTemporaryAccommodation               *float64 `json:"total_number_of_children_in_ta_temporary_accommodation"`
}

Figures report the number of households, households with children and number of children in 'temporary accommodation' (TA).Temporary accommodation (typically given for a period of 28 days or less) is allocated to households without permanent housing, and are therefore included in annual homelessness accounts. Temporary accommodation includes relocation to bed and breakfasts, hostels, shelters, local authority or privately-owned temporary accommodation.The UK National Statistics note: "Complete temporary accommodation data was provided by 299 (92%) local authorities. A further 23 local authorities did provide a return but their totals have been omitted from the release due to quality concerns that placements may have been underreported or double reported."Temporary accommodation data is reported quarterly: to maintain consistency with rough sleeper statistics (recorded in Autumn) we have assumed Q3 results for each year.

type TerrainRuggednessIndexNunnAndPuga2012Dataset 

type TerrainRuggednessIndexNunnAndPuga2012Dataset struct {
	TerrainRuggednessIndex100mNunnAndPuga2012 *float64 `json:"terrain_ruggedness_index_100m_nunn_and_puga_2012"`
}

type TerrorismIncidentsFatalitiesAndInjuriesGlobalTerrorismDatabase2018Dataset 

type TerrorismIncidentsFatalitiesAndInjuriesGlobalTerrorismDatabase2018Dataset struct {
	TerrorismFatalitiesGtd                                 *float64 `json:"terrorism_fatalities_gtd"`
	NumberOfTerroristIncidentsGdt2018                      *float64 `json:"number_of_terrorist_incidents_gdt_2018"`
	NumberOfSuccessfulTerroristIncidentsGdt2018            *float64 `json:"number_of_successful_terrorist_incidents_gdt_2018"`
	NumberOfUnsuccessfulTerroristIncidentsGdt2018          *float64 `json:"number_of_unsuccessful_terrorist_incidents_gdt_2018"`
	ShareOfTerroristIncidentsSuccessfulGdt2018             *float64 `json:"share_of_terrorist_incidents_successful_gdt_2018"`
	NumberOfSuicideTerroristIncidentsGdt2018               *float64 `json:"number_of_suicide_terrorist_incidents_gdt_2018"`
	NumberOfNonSuicideTerroristIncidentsGdt2018            *float64 `json:"number_of_non_suicide_terrorist_incidents_gdt_2018"`
	ShareOfTerroristIncidentsWithSuicideAttemptGdt2018     *float64 `json:"share_of_terrorist_incidents_with_suicide_attempt_gdt_2018"`
	NumberWoundedFromTerroristAttacksGdt2018               *float64 `json:"number_wounded_from_terrorist_attacks_gdt_2018"`
	NumberOfIncidentsWhichResultedInFatalityGdt2018        *float64 `json:"number_of_incidents_which_resulted_in_fatality_gdt_2018"`
	ShareOfTerroristIncidentsWithAtLeastOneFatalityGdt2018 *float64 `json:"share_of_terrorist_incidents_with_at_least_one_fatality_gdt_2018"`
	AverageFatalitiesPerIncidentGdt2018                    *float64 `json:"average_fatalities_per_incident_gdt_2018"`
}

The Global Terrorism Database definitions can be found at (https://www.start.umd.edu/gtd/downloads/Codebook.pdf) and are as follows:A terrorist incident is included in GTD if it is the following:- The incident must be intentional – the result of a conscious calculation on the part of a perpetrator.- The incident must entail some level of violence or immediate threat of violence -including property violence, as well as violence against people.- The perpetrators of the incidents must be sub-national actors. The database does not include acts of state terrorism.In addition, at least two of the following three criteria must be present for an incident to be included in the GTD:- Criterion 1: The act must be aimed at attaining a political, economic, religious, or social goal. In terms of economic goals, the exclusive pursuit of profit does not satisfy this criterion. It must involve the pursuit of more profound, systemic economic change.- Criterion 2: There must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience (or audiences) than the immediate victims. It is the act taken as a totality that is considered, irrespective if every individual involved in carrying out the act was aware of this intention. As long as any of the planners or decision-makers behind the attack intended to coerce, intimidate or publicize, the intentionality criterion is met.- Criterion 3: The action must be outside the context of legitimate warfare activities. That is, the act must be outside the parameters permitted by international humanitarian law (particularly the prohibition against deliberately targeting civilians or non-combatants).Is it defined as a suicide attack?This variable is coded “Yes” in those cases where there is evidence that the perpetrator did not intend to escape from the attack alive. Successful vs. unsuccessful attacks:"The GTD does include attacks that were attempted but ultimately unsuccessful. The circumstances vary depending on tactics (for details see the success variable, below). However, in general if a bomb is planted but fails to detonate; if an arsonist is intercepted by authorities before igniting a fire; or, if an assassin attempts and fails to kill his or her intended target, the attack is considered for inclusion in the GTD, but denoted as unsuccessful.Success of a terrorist strike is defined according to the tangible effects of the attack. Success is not judged in terms of the larger goals of the perpetrators. For example, a bomb that exploded in a building would be counted as a success even if it did not succeed in bringing the building down or inducing government repression. The definition of a successful attack depends on the type of attack. Essentially, the key question is whether or not the attack type took place. If a case has multiple attack types, it is successful if any of the attack types are successful, with the exception of assassinations, which are only successful if the intended target is killed."Fatalities are defined as:"The number of total confirmed fatalities for the incident. The number includes all victims and attackers who died as a direct result of the incident. Where there is evidence of fatalities, but a figure is not reported or it is too vague to be of use, this field remains blank. If information is missing regarding the number of victims killed in an attack, but perpetrator fatalities are known, this value will reflect only the number of perpetrators who died as a result of the incident. Likewise, if information on the number of perpetrators killed in an attack is missing, but victim fatalities are known, this field will only report the number of victims killed in the incident."Injuries are:"the number of confirmed non-fatal injuries to both perpetrators and victims."

type TerroristAttackByTargetTypeGlobalTerrorismDatabase2018Dataset 

type TerroristAttackByTargetTypeGlobalTerrorismDatabase2018Dataset struct {
	AbortionRelated              *float64 `json:"abortion_related"`
	AirportsAndAircraft          *float64 `json:"airports_and_aircraft"`
	Business                     *float64 `json:"business"`
	EducationalInstitution       *float64 `json:"educational_institution"`
	FoodOrWaterSupply            *float64 `json:"food_or_water_supply"`
	GovernmentDiplomatic         *float64 `json:"government_diplomatic"`
	GovernmentGeneral            *float64 `json:"government_general"`
	JournalistsAndMedia          *float64 `json:"journalists_and_media"`
	Maritime                     *float64 `json:"maritime"`
	Military                     *float64 `json:"military"`
	Ngo                          *float64 `json:"ngo"`
	Other                        *float64 `json:"other"`
	Police                       *float64 `json:"police"`
	PrivateCitizensAndProperty   *float64 `json:"private_citizens_and_property"`
	ReligiousFiguresinstitutions *float64 `json:"religious_figuresinstitutions"`
	Telecommunications           *float64 `json:"telecommunications"`
	TerroristsnonStateMilitia    *float64 `json:"terroristsnon_state_militia"`
	Tourists                     *float64 `json:"tourists"`
	Transportation               *float64 `json:"transportation"`
	Unknown                      *float64 `json:"unknown"`
	Utilities                    *float64 `json:"utilities"`
	ViolentPoliticalParty        *float64 `json:"violent_political_party"`
}

As explained by the Global Terrorism Database (https://www.start.umd.edu/gtd/downloads/Codebook.pdf):Data denotes the general type of target/victim. When a victim is attacked specifically because of his or her relationship to a particular person, such as a prominent figure, the target type reflects that motive. Forexample, if a family member of a government official is attacked because of his or her relationship to that individual, the type of target is “government.” This variable consists of the following 22 categories:1 = BUSINESSBusinesses are defined as individuals or organizations engaged in commercial or mercantile activity as a means of livelihood. Any attack on a business or private citizens patronizing a business such as a restaurant, gas station, music store, bar, café, etc. This includes attacks carried out against corporate offices or employees of firms like mining companies, or oil corporations. Furthermore, includes attacks conducted on business people or corporate officers. Included in this value as well are hospitals and chambers of commerce and cooperatives. Does not include attacks carried out in public or quasi-public areas such as “business district or commercial area”, or generic business-related individuals such as“businessmen” (these attacks are captured under “Private Citizens and Property”, see below.) Also does not include attacks against generic business-related individuals such as “businessmen.” Unless the victims were targeted because of their specific business affiliation, these attacks belong in “Private Citizens and Property.”2 = GOVERNMENT (GENERAL)Any attack on a government building; government member, former members, including members of political parties in official capacities, their convoys, or events sponsored by political parties; political movements; or a government sponsored institution where the attack is expressly carried out to harm the government. This value includes attacks on judges, public attorneys (e.g., prosecutors), courts and court systems, politicians, royalty, head of state, government employees (unless police or military), election-related attacks, or intelligence agencies and spies. This value does not include attacks on political candidates for office or members of political parties that do not hold an elected office (these attacks are captured in “Private Citizens and Property”).3 = POLICEThis value includes attacks on members of the police force or police installations; this includes police boxes, patrols headquarters, academies, cars, checkpoints, etc. Includes attacks against jails or prison facilities, or jail or prison staff or guards.4 = MILITARYIncludes attacks against military units, patrols, barracks, convoys, jeeps, and aircraft. Also includes attacks on recruiting sites, and soldiers engaged in internal policing functions such as at checkpoints and in anti narcotics activities. This category also includes peacekeeping units that conduct military operations (e.g., AMISOM). Excludes attacks against non-state militias and guerrillas, these types of attacks are coded as “Terrorist/Non-state Militias” see below.5 = ABORTION RELATEDAttacks on abortion clinics, employees, patrons, or security personnel stationed at clinics.6 = AIRPORTS & AIRCRAFTAn attack that was carried out either against an aircraft or against an airport. Attacks against airline employees while on board are also included in this value. Includes attacks conducted against airport business offices and executives. Military aircraft are not included.7 = GOVERNMENT (DIPLOMATIC)Attacks carried out against foreign missions, including embassies, consulates, etc. This value includes cultural centers that have diplomatic functions, and attacks against diplomatic staff and their families (when the relationship is relevant to the motive of the attack) and property. The United Nations is a diplomatic target.8 = EDUCATIONAL INSTITUTIONAttacks against schools, teachers, or guards protecting school sites. Includes attacks against university professors, teaching staff and school buses. Moreover, includes attacks against religious schools in this value. As noted below in the “Private Citizens and Property” value, the GTD has several attacks against students. If attacks involving students are not expressly against a school, university or other educational institution or are carried out in an educational setting, they are coded as private citizens and property. Excludes attacks against military schools (attacks on military schools are coded as “Military,” see below).9 = FOOD OR WATER SUPPLYAttacks on food or water supplies or reserves are included in this value. This generally includes attacks aimed at the infrastructure related to food and water for human consumption.10 = JOURNALISTS & MEDIAIncludes, attacks on reporters, news assistants, photographers, publishers, as well as attacks on media headquarters and offices. Attacks on transmission facilities such as antennae or transmission towers, or broadcast infrastructure are coded as “Telecommunications,” see below.11 = MARITIME (INCLUDES PORTS AND MARITIME FACILITIES)Includes civilian maritime: attacks against fishing ships, oil tankers, ferries, yachts, etc. (Attacks on fishermen are coded as “Private Citizens and Property,” see below).12 = NGOIncludes attacks on offices and employees of non-governmental organizations (NGOs). NGOs here include large multinational non-governmental organizations such as the Red Cross and Doctors without Borders, as well as domestic organizations.13= OTHERThis value includes acts of terrorism committed against targets which do not fit into other categories. Some examples include ambulances, firefighters, and international demilitarized zones.14= PRIVATE CITIZENS & PROPERTYThis value includes attacks on individuals, the public in general or attacks in public areas including markets, commercial streets, busy intersections and pedestrian malls. Also includes ambiguous cases where the target/victim was a named individual, or where the target/victim of an attack could be identified by name, age, occupation, gender or nationality. This value also includes ceremonial events, such as weddings and funerals. The GTD contains a number of attacks against students. If these attacks are not expressly against a school, university or other educational institution or are not carried out in an educational setting, these attacks are coded using this value. Also, includes incidents involving political supporters as private citizens and property, provided that these supporters are not part of a government-sponsored event. Finally, this value includes police informers. Does not include attacks causing civilian casualties in businesses such as restaurants, cafes or movie theaters (these categories are coded as “Business” see above).15 = RELIGIOUS FIGURES/INSTITUTIONSThis value includes attacks on religious leaders, (Imams, priests, bishops, etc.), religious institutions (mosques, churches), religious places or objects (shrines, relics, etc.). This value also includes attacks on organizations that are affiliated with religious entities that are not NGOs, businesses or schools. Attacks on religious pilgrims are considered “Private Citizens and Property;” attacks on missionaries are considered religious figures.16 = TELECOMMUNICATIONThis includes attacks on facilities and infrastructure for the transmission of information. More specifically this value includes things like cell phone towers, telephone booths, television transmitters, radio, and microwave towers.17 = TERRORISTS/NON-STATE MILITIASTerrorists or members of identified terrorist groups within the GTD are included in this value. Membership is broadly defined and includes informants for terrorist groups, but excludes former or surrendered terrorists.This value also includes cases involving the targeting of militias and guerillas.18 = TOURISTSThis value includes the targeting of tour buses, tourists, or “tours.” Tourists are persons who travel primarily for the purposes of leisure or amusement. Government tourist offices are included in this value. The attack must clearly target tourists, not just an assault on a business or transportation system used by tourists. Travel agencies are coded as business targets.19 = TRANSPORTATION (OTHER THAN AVIATION)Attacks on public transportation systems are included in this value. This can include efforts to assault public buses, minibuses, trains, metro/subways, highways (if the highway itself is the target of the attack), bridges, roads, etc. The GTD contains a number of attacks on generic terms such as “cars” or “vehicles.” These attacks are assumed to be against “Private Citizens and Property” unless shown to be against public transportation systems. In this regard, buses are assumed to be public transportation unless otherwise noted.20 = UNKNOWNThe target type cannot be determined from the available information.21 = UTILITIESThis value pertains to facilities for the transmission or generation of energy. For example, power lines, oil pipelines, electrical transformers, high tension lines, gas and electric substations, are all included in this value. This value also includes lampposts or street lights. Attacks on officers, employees or facilities of utility companies excluding the type of facilities above are coded as business.22 = VIOLENT POLITICAL PARTIESThis value pertains to entities that are both political parties (and thus, coded as “government” in this coding scheme) and terrorists. It is operationally defined as groups that engage in electoral politics and appear as “Perpetrators” in the GTD.

type TerroristAttacksByTypeGlobalTerrorismDatabase2018Dataset 

type TerroristAttacksByTypeGlobalTerrorismDatabase2018Dataset struct {
	Assassination                  *float64 `json:"assassination"`
	HostageTakingKidnapping        *float64 `json:"hostage_taking_kidnapping"`
	Bombingexplosion               *float64 `json:"bombingexplosion"`
	FacilityinfrastructureAttack   *float64 `json:"facilityinfrastructure_attack"`
	ArmedAssault                   *float64 `json:"armed_assault"`
	Hijacking                      *float64 `json:"hijacking"`
	Unknown                        *float64 `json:"unknown"`
	HostageTakingBarricadeIncident *float64 `json:"hostage_taking_barricade_incident"`
	UnarmedAssault                 *float64 `json:"unarmed_assault"`
}

This metric captures the general method of a terrorist attack and often reflects the broad class of tactics used. It consists of nine categories, which are defined below.1 = ASSASSINATIONAn act whose primary objective is to kill one or more specific, prominent individuals. Usually carried out on persons of some note, such as highranking military officers, government officials, celebrities, etc. Not to include attacks on non-specific members of a targeted group. The killing of a police officer would be an armed assault unless there is reason to believe the attackers singled out a particularly prominent officer for assassination.2 = ARMED ASSAULTAn attack whose primary objective is to cause physical harm or death directly to human beings by use of a firearm, incendiary, or sharp instrument (knife, etc.). Not to include attacks involving the use of fists, rocks, sticks, or other handheld (less-than-lethal) weapons. Also includes attacks involving certain classes of explosive devices in addition to firearms, incendiaries, or sharp instruments. The explosive device subcategories that are included in this classification are grenades, projectiles, and unknown or other explosive devices that are thrown.3 = BOMBING/EXPLOSIONAn attack where the primary effects are caused by an energetically unstable material undergoing rapid decomposition and releasing a pressure wave that causes physical damage to the surrounding environment. Can include either high or low explosives (including a dirty bomb) but does not include a nuclear explosive device that releases energy from fission and/or fusion, or an incendiary device where decomposition takes place at a much slower rate. If an attack involves certain classes of explosive devices along with firearms, incendiaries, or sharp objects, then the attack is coded as an armed assault only. The explosive device subcategories that are included in this classification are grenades, projectiles, and unknown or other explosive devices that are thrown in which the bombers are also using firearms or incendiary devices.4 = HIJACKINGAn act whose primary objective is to take control of a vehicle such as an aircraft, boat, bus, etc. for the purpose of diverting it to an unprogrammed destination, force the release of prisoners, or some other political objective. Obtaining payment of a ransom should not the sole purpose of a Hijacking, but can be one element of the incident so long as additional objectives have also been stated. Hijackings are distinct from Hostage Taking because the target is a vehicle, regardless of whether there are people/passengers in the vehicle.5 = HOSTAGE TAKING (BARRICADE INCIDENT)An act whose primary objective is to take control of hostages for the purpose of achieving a political objective through concessions or through disruption of normal operations. Such attacks are distinguished from kidnapping since the incident occurs and usually plays out at the target location with little or no intention to hold the hostages for an extended period in a separate clandestine location.6 = HOSTAGE TAKING (KIDNAPPING)An act whose primary objective is to take control of hostages for the purpose of achieving a political objective through concessions or through disruption of normal operations. Kidnappings are distinguished from Barricade Incidents (above) in that they involve moving and holding the hostages in another location.7 = FACILITY / INFRASTRUCTURE ATTACKAn act, excluding the use of an explosive, whose primary objective is to cause damage to a non-human target, such as a building, monument, train, pipeline, etc. Such attacks include arson and various forms of sabotage (e.g., sabotaging a train track is a facility/infrastructure attack, even if passengers are killed). Facility/infrastructure attacks can include acts which aim to harm an installation, yet also cause harm to people incidentally (e.g. an arson attack primarily aimed at damaging a building, but causes injuries or fatalities).8 = UNARMED ASSAULTAn attack whose primary objective is to cause physical harm or death directly to human beings by any means other than explosive, firearm, incendiary, or sharp instrument (knife, etc.). Attacks involving chemical, biological or radiological weapons are considered unarmed assaults.9 = UNKNOWNThe attack type cannot be determined from the available information.

type TerroristAttacksByWeaponTypeGlobalTerrorismDatabase2018Dataset 

type TerroristAttacksByWeaponTypeGlobalTerrorismDatabase2018Dataset struct {
	Biological        *float64 `json:"biological"`
	Chemical          *float64 `json:"chemical"`
	Explosives        *float64 `json:"explosives"`
	FakeWeapons       *float64 `json:"fake_weapons"`
	Firearms          *float64 `json:"firearms"`
	Incendiary        *float64 `json:"incendiary"`
	Melee             *float64 `json:"melee"`
	Other             *float64 `json:"other"`
	Radiological      *float64 `json:"radiological"`
	SabotageEquipment *float64 `json:"sabotage_equipment"`
	Unknown           *float64 `json:"unknown"`
	Vehicle           *float64 `json:"vehicle"`
}

The general weapon used in a given terrorist attack is recorded in the following categories:1 = BiologicalA weapon whose components are produced from pathogenic microorganisms or toxic substances of biological origins.2 = ChemicalA weapon produced from toxic chemicals that is contained in a delivery system and dispersed as a liquid, vapor, or aerosol. This category includes chemical weapons delivered via explosive device.3 = RadiologicalA weapon whose components are produced from radioactive materials that emit ionizing radiation and can take many forms.4 = NuclearA weapon which draws its explosive force from fission, fusion, or a combination of these methods.5 = FirearmsA weapon which is capable of firing a projectile using an explosive charge as a propellant.6 = ExplosivesA weapon composed of energetically unstable material undergoing rapid decomposition and releasing a pressure wave that causes physical damage to the surrounding environment. Note that chemical weapons delivered via explosive are classified as “Chemical” with weapon subtype “Explosives.”7 = Fake WeaponsA weapon that was claimed by the perpetrator at the time of the incident to be real but was discovered after-the-fact to be non-existent or incapable of producing the desired effects.8 = IncendiaryA weapon that is capable of catching fire, causing fire, or burning readily and produces intensely hot fire when exploded.9 = MeleeA weapon—targeting people rather than property—that does not involve a projectile in which the user and target are in contact with it simultaneously.10 = VehicleAn automobile that is used in an incident that does not incorporate the use of explosives such as a car bomb or truck bomb.11 = Sabotage EquipmentA weapon that is used in the demolition or destruction of property (e.g., removing bolts from a train tracks).12 = OtherA weapon that has been identified but does not fit into one of the above categories.13 = UnknownThe weapon type cannot be determined from the available information.

type TetanusNeonatalRateCalculatedFromWhoIncidence2017AndWdiPopulationDataHannahBehrensDataset 

type TetanusNeonatalRateCalculatedFromWhoIncidence2017AndWdiPopulationDataHannahBehrensDataset struct {
	NumberOfNeonatalTetanusCasesWho2017Per100_000PeoplePopulationFromWdi *float64 `json:"number_of_neonatal_tetanus_cases_who_2017_per_100_000_people_population_from_wdi"`
}

Neonatal tetanus incidence per 1,000,000 people based on WHO neonatal tetanus incidence dataset 2017 (already on grapher) and WDI total population dataset (already on grapher). No population data was available for Niue (all years), Kuwait (1992-1994), Eritrea (2013-2016). The neonatal tetanus incidence was zero. Population size does therefore not matter to calculate the rate, as it will be zero regardless of the population size.No population data was available for Swaziland (all years) and (Serbia 1980-1989). They were therefore excluded.

type TheAllocationOfTimeOverFiveDecadesAguiarAndHurst2006Dataset 

type TheAllocationOfTimeOverFiveDecadesAguiarAndHurst2006Dataset struct {
	HoursPerWeekSpentInMarketAndNonMarketWorkAguiarAndHurst2006 *float64 `json:"hours_per_week_spent_in_market_and_non_market_work_aguiar_and_hurst_2006"`
	HoursPerWeekSpentOnChildCareAguiarAndHurst2006              *float64 `json:"hours_per_week_spent_on_child_care_aguiar_and_hurst_2006"`
	HoursPerWeekSpentInLeisureAguiarAndHurst2006                *float64 `json:"hours_per_week_spent_in_leisure_aguiar_and_hurst_2006"`
}

type TheWorldsNumberAndShareOfVaccinatedOneYearOldsDataset 

type TheWorldsNumberAndShareOfVaccinatedOneYearOldsDataset struct {
	NumberOfOneYearOldsUnPopulationDivision2017                            *float64 `json:"number_of_one_year_olds_un_population_division_2017"`
	ShareOfOneYearOldsWithBcgTuberculosisImmunizationWho2017               *float64 `json:"share_of_one_year_olds_with_bcg_tuberculosis_immunization_who_2017"`
	ShareOfOneYearOldsWithHepatitisB3ImmunizationWho2017                   *float64 `json:"share_of_one_year_olds_with_hepatitis_b3_immunization_who_2017"`
	ShareOfOneYearOldsWithDiphtheriaPertussisAndTetanusImmunizationWho2017 *float64 `json:"share_of_one_year_olds_with_diphtheria_pertussis_and_tetanus_immunization_who_2017"`
	ShareOfOneYearOldsWithPolioImmunizationWho2017                         *float64 `json:"share_of_one_year_olds_with_polio_immunization_who_2017"`
	ShareOfOneYearOldsWithMeaslesImmunizationWho2017                       *float64 `json:"share_of_one_year_olds_with_measles_immunization_who_2017"`
	NumberOfOneYearOldsWithBcgTuberculosisImmunizationWho2017              *float64 `json:"number_of_one_year_olds_with_bcg_tuberculosis_immunization_who_2017"`
	NumberOfOneYearOldsWithHepatitisB3ImmunizationWho2017                  *float64 `json:"number_of_one_year_olds_with_hepatitis_b3_immunization_who_2017"`
	NumberOfOneYearOldsWithDiphteriaPertussisAndTetanusImmunizationWho2017 *float64 `json:"number_of_one_year_olds_with_diphteria_pertussis_and_tetanus_immunization_who_2017"`
	NumberOfOneYearOldsWithPolioImmunizationWho2017                        *float64 `json:"number_of_one_year_olds_with_polio_immunization_who_2017"`
	NumberOfOneYearOldsWithMeaslesImmunizationWho2017                      *float64 `json:"number_of_one_year_olds_with_measles_immunization_who_2017"`
}

type TimeSpentOnDomesticWorkUn2017AndOecd2014Dataset 

type TimeSpentOnDomesticWorkUn2017AndOecd2014Dataset struct {
	DailyTimeSpentOnDomesticWorkWomenUn2017 *float64 `json:"daily_time_spent_on_domestic_work_women_un_2017"`
}

This dataset is an excerpt from the data provided by the United Nations Statistics Division under the Minimum Set of Gended Indicators. Cross-country comparability is limited due to lack of uniform definitions. Importantly, the ages of the relevant population differ from country to country (and in a few instances, they also differ from year to year for the same country). For most low-income countries, the figures include children among the relevant population. For most high-income countries, children below 15 are excluded. Additionally, in some countries the estimates correspond only to specific regions (e.g. urban). This file provides a column with notes explaining the particular reference population in each case.Regarding the definitions, the original source notes: "Average number of hours spent on unpaid domestic work derives from time use statistics that is collected through stand-alone time-use surveys or a time-use module in multi-purpose household surveys. Data on time-use may be summarized and presented as either (1) average time spent for participants only or (2) average time spent for all population of certain age. In the former type of averages the total time spent by the individuals who performed an activity is divided by the number of persons who performed it (participants). In the latter type of averages the total time is divided by the total relevant population (or a sub-group thereof) regardless of whether people performed the activity or not. All statistics presented in the Minimum Set on Gender Indicators on time spent in various activities are averages based on all total relevant population. This type of averages can be used to compare groups and assess changes over time. Differences among groups or over time may be due to a difference (or change) in the proportion of those participating in the specific activity or a difference (or change) in the amount of time spent by participants or both. Data presented for this indicator are expressed as an average per day. It is averaged over seven days of the week (weekdays and weekends are not differentiated)."

type TimeSpentParticipationTimeAndParticipationRatesEurostatDataset 

type TimeSpentParticipationTimeAndParticipationRatesEurostatDataset struct {
	TimeSpentOnPersonalCareEurostat                                      *float64 `json:"time_spent_on_personal_care_eurostat"`
	TimeSpentInEmploymentEurostat                                        *float64 `json:"time_spent_in_employment_eurostat"`
	TimeSpentInStudyEurostat                                             *float64 `json:"time_spent_in_study_eurostat"`
	TimeSpentOnHouseholdAndFamilyCareEurostat                            *float64 `json:"time_spent_on_household_and_family_care_eurostat"`
	TimeSpentOnLeisureSocialAndAssociativeLifeEurostat                   *float64 `json:"time_spent_on_leisure_social_and_associative_life_eurostat"`
	TimeSpentOnTravelEurostat                                            *float64 `json:"time_spent_on_travel_eurostat"`
	ParticipationTimeInPersonalCareEurostat                              *float64 `json:"participation_time_in_personal_care_eurostat"`
	ParticipationTimeInEmploymentEurostat                                *float64 `json:"participation_time_in_employment_eurostat"`
	ParticipationTimeInStudyEurostat                                     *float64 `json:"participation_time_in_study_eurostat"`
	ParticipationTimeInHouseholdAndFamilyCareEurostat                    *float64 `json:"participation_time_in_household_and_family_care_eurostat"`
	ParticipationTimeInLeisureSocialAndAssociativeLifeEurostat           *float64 `json:"participation_time_in_leisure_social_and_associative_life_eurostat"`
	ParticipationTimeInTravelEurostat                                    *float64 `json:"participation_time_in_travel_eurostat"`
	ParticipationRateInPersonalCareEurostat                              *float64 `json:"participation_rate_in_personal_care_eurostat"`
	ParticipationRateInEmploymentEurostat                                *float64 `json:"participation_rate_in_employment_eurostat"`
	ParticipationRateInStudyEurostat                                     *float64 `json:"participation_rate_in_study_eurostat"`
	ParticipationRateInHouseholdAndFamilyCareEurostat                    *float64 `json:"participation_rate_in_household_and_family_care_eurostat"`
	ParticipationRateInLeisureSocialAndAssociativeLifeActivitiesEurostat *float64 `json:"participation_rate_in_leisure_social_and_associative_life_activities_eurostat"`
	ParticipationRateInTravelEurostat                                    *float64 `json:"participation_rate_in_travel_eurostat"`
}

This Eurostat data set measures the time spent, participation time, and participation rate in 55 activities. For the purposes of illustrating how time use has changed over time, we have aggregated the 55 activities under six main headings provided in the original Eurostat source. The six headings are (1) Personal care; (2) Employment, related activities, and travel; (3) Study; (4) Household and family care; (5) Leisure, social, and associative life; and (6) Travel and unspecified time use. The data has been normalised to 1440 minutes per day. The survey population includes private participants only - individuals living in institutions (nursing homes, homes for the elderly, children’s homes, rehabilitation centres and penitentiary) are excluded. For further information on the time use surveys see: http://ec.europa.eu/eurostat/cache/metadata/en/tus_esms.htm

type TimeThatDoctorsSpendWithAPatientDasHammerAndLeonard2008Dataset 

type TimeThatDoctorsSpendWithAPatientDasHammerAndLeonard2008Dataset struct {
	TimeThatDoctorsSpendWithAPatientDasHammerAndLeonard2008 *float64 `json:"time_that_doctors_spend_with_a_patient_das_hammer_and_leonard_2008"`
}

India figures refer to Delhi and cannot be applied nationwide. Values for the United Kingdom and Spain represent the time spent with the doctor after a nurse or assistant has taken basic health measures from the patient. Data taken from Table 2 of the paper linked above.Underlying sources: : India—Das and Hammer (2007); Paraguay—Das and Sohnesen (2007); Tanzania—based on calculations by Kenneth Leonard; International Comparisons—Hogelzeir et al. (1993) and Deveugele, Derese, Brink-Muinen, Bensing, and De Maeseneer (2003).The timing of recorded values differ country to country. Hence, cross country comparisons should be made with caution. See the underlying sources for further information on the countries included and dates surveys were conducted.

type TimeUseInFinlandStatisticsFinlandDataset 

type TimeUseInFinlandStatisticsFinlandDataset struct {
	TimeAllocationAllStatisticsFinland   *float64 `json:"time_allocation_all_statistics_finland"`
	TimeAllocationMenStatisticsFinland   *float64 `json:"time_allocation_men_statistics_finland"`
	TimeAllocationWomenStatisticsFinland *float64 `json:"time_allocation_women_statistics_finland"`
}

Statistics Finland has 26 time use categories aggregated under 6 main headings: gainful employment, domestic work, personal care, study, free time, and time left unspecified. Activity definitions are outlined below:<ul><li>Gainful employment includes time spent at work as well as travel to and from work. </li><li>Domestic work includes housekeeping, maintenance work, other domestic work, childcare, shopping and services, and travel related to domestic work.</li><li>Personal care includes sleep, meals, washing and dressing. </li><li>Study includes time spent in school or university, travel related to study, and free time study (incl. related travel). </li><li>Free time includes participatory activity, sports and outdoor activities, entertainment and culture, reading, listening to radio, TV, socialising with friends and family, hobbies, other free time, and travel related to free time.</li></ul>

type TimeUseInSwedenStatisticsSwedenDataset 

type TimeUseInSwedenStatisticsSwedenDataset struct {
	TimeAllocationWeekdayWomen    *float64 `json:"time_allocation_weekday_women"`
	TimeAllocationWeekendWomen    *float64 `json:"time_allocation_weekend_women"`
	TimeAllocationWeekdayMen      *float64 `json:"time_allocation_weekday_men"`
	TimeAllocationWeekendMen      *float64 `json:"time_allocation_weekend_men"`
	TimeAllocationAverageDayWomen *float64 `json:"time_allocation_average_day_women"`
	TimeAllocationAverageDayMen   *float64 `json:"time_allocation_average_day_men"`
}

Time spent on activities has been categorised under six main headings: gainful employment, personal needs, housework, leisure, studies, and other activities. The definition for each activity is outlined below:<ul><li>Gainful employment includes time spent at work and time spent commuting to and from work. </li><li>Personal needs includes meals, personal care, and travel for personal needs. </li><li>Housework includes cooking, washing, ironing, cleaning, caring for own children, caring for others, maintenance work, purchase of goods and services, other housework, and related travel.</li><li>Leisure time includes sports and outdoor activities, TV and radio, hobbies, entertainment, cultural, and social activities, other free time, and related travel. </li><li>Study time includes time spent studying and travel for studies. </li></ul>For the original data, see Table 1 in the <a href="http://www.scb.se/statistik/_publikationer/LE0103_1990I91_BR_LE80SA9201.pdf" rel="noopener" target="_blank">1990 Sweden Statistics time use report</a>; Table B:4 in the <a href="http://www.scb.se/statistik/LE/LE0103/2003M00/LE99SA0301.pdf" rel="noopener" target="_blank">2000 report</a>; and Table B:1a in the <a href="http://www.scb.se/statistik/_publikationer/LE0103_2010A01_BR_LE123BR1201.pdf" rel="noopener" target="_blank">2010 report</a>.

type Top1percWealthSharesChartbookOfEconomicInequality2017Dataset 

type Top1percWealthSharesChartbookOfEconomicInequality2017Dataset struct {
	ShareOfWealthHeldByTop1percChartbookOfEconomicInequality2017 *float64 `json:"share_of_wealth_held_by_top_1perc_chartbook_of_economic_inequality_2017"`
}

type TopIncomeSharesWorldWealthAndIncomeDatabase2018Dataset 

type TopIncomeSharesWorldWealthAndIncomeDatabase2018Dataset struct {
	ShareOfTop1percInPreTaxNationalIncome *float64 `json:"share_of_top_1perc_in_pre_tax_national_income"`
}

type TopMarginalIncomeTaxRateReynolds2008Dataset 

type TopMarginalIncomeTaxRateReynolds2008Dataset struct {
	TopMarginalIncomeTaxRateReynolds2008 *float64 `json:"top_marginal_income_tax_rate_reynolds_2008"`
}

Hong Kong’s maximum tax (the “standard rate”) has normally been 15 percent, effectively capping the marginal rate at high income levels (in exchange for no personal exemptions). The highest U.S. tax rate of 39.6 percent after 1993 was reduced to 38.6 percent in 2002 and to 35 percent in 2003.

type TopNetPersonalWealthSharesWid2018Dataset 

type TopNetPersonalWealthSharesWid2018Dataset struct {
	Top10percShareOfNetPersonalWealthWid2018 *float64 `json:"top_10perc_share_of_net_personal_wealth_wid_2018"`
	Top1percShareOfNetPersonalWealthWid2018  *float64 `json:"top_1perc_share_of_net_personal_wealth_wid_2018"`
}

type TotalCasesOfPoliomyelitisVirusByCountryAndYearFrom1980OnwardsWho2020Dataset 

type TotalCasesOfPoliomyelitisVirusByCountryAndYearFrom1980OnwardsWho2020Dataset struct {
	NumberOfPolioCasesWho2017 *float64 `json:"number_of_polio_cases_who_2017"`
}

The WHO link above has the dataset saved under point "3.1 Reported incidence time series". The WHO last updated this dataset on 15 July 2020 which comprises the data shown here.

type TotalEconomyProductivityGrowthOecdDataset 

type TotalEconomyProductivityGrowthOecdDataset struct {
	AnnualProductivityGrowthRate *float64 `json:"annual_productivity_growth_rate"`
}

Data reports the annual growth rate (%) in productivity, measured as gross domestic product (GDP) per hour worked across all sectors of the economy. Percentage growth data is derived from underlying productivity data measured in 2010 USD PPP. The OECD note: "Productivity is a key driver of economic growth and changes in living standards. Labour productivity growth implies a higher level of output for unit of labour input (hours worked or persons employed). This can be achieved if more capital is used in production or through improved overall efficiency with which labour and capital are used together, i.e., higher multifactor productivity growth (MFP). Productivity is also a key driver of international competitiveness, e.g. as measured by Unit Labour Costs (ULC)."

type TotalFertilityByRegion20202100MediumVariantProjectionUnPopulationDivision2015RevisionDataset 

type TotalFertilityByRegion20202100MediumVariantProjectionUnPopulationDivision2015RevisionDataset struct {
	ProjectedFertilityRateUnPopulationDivision2015Revision *float64 `json:"projected_fertility_rate_un_population_division_2015_revision"`
}

type TotalGrossOfficialDisbursementsForMedicalResearchAndBasicHeathSectorsOecdDataset 

type TotalGrossOfficialDisbursementsForMedicalResearchAndBasicHeathSectorsOecdDataset struct {
	TotalGrossOfficialDisbursementsForMedicalResearchAndBasicHeathSectors *float64 `json:"total_gross_official_disbursements_for_medical_research_and_basic_heath_sectors"`
}

The sum of Official Development Assistance (ODA) flows from all donors to developing countries for medical research and basic health.ODA: The DAC defines ODA as “those flows to countries and territories on the DAC List of ODA Recipientsand to multilateral institutions which are:i) provided by official agencies, including state and local governments, or by their executiveagencies; andii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent).Medical research and basic health sectors are as defined by the DAC. Medical research refers to CRSsector code 12182 and basic health covers all codes in the 122 series (see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm).

type TotalPopulationByBroadAgeGroupBothSexes19502100UnPopulationDivision2015Dataset 

type TotalPopulationByBroadAgeGroupBothSexes19502100UnPopulationDivision2015Dataset struct {
	O0To4UnPopulationDivision2015   *float64 `json:"o0_to_4_un_population_division_2015"`
	O5To14UnPopulationDivision2015  *float64 `json:"o5_to_14_un_population_division_2015"`
	O15To24UnPopulationDivision2015 *float64 `json:"o15_to_24_un_population_division_2015"`
	O25To60UnPopulationDivision2015 *float64 `json:"o25_to_60_un_population_division_2015"`
	O70UnPopulationDivision2015     *float64 `json:"o70_un_population_division_2015"`
}

The data file used is called “Annual Population by Age Groups - Both Sexes” on the website given. This original data file divides the population into 5-year age groups. These columns were added together to create the broad age groups in this file.

type TotalPopulationGapminderUnPopulationDivisionDataset 

type TotalPopulationGapminderUnPopulationDivisionDataset struct {
	TotalPopulationGapminderUnPopulationDivision *float64 `json:"total_population_gapminder_un_population_division"`
}

Gapminder's total population estimates are used until 1949; the UN Population Division's estimates are used from 1950-2015; and the UN's population projections are used to extend the series to 2016.

type TotalValueOfExportsByCountryToWorldPercgdpOwidCalculationsBasedOnFouquinAndHugotCepii2016AndOtherSourcesDataset 

type TotalValueOfExportsByCountryToWorldPercgdpOwidCalculationsBasedOnFouquinAndHugotCepii2016AndOtherSourcesDataset struct {
	TotalMerchandiseTradeAsShareOfGdpFouquinAndHugotCepii2016DyadicData *float64 `json:"total_merchandise_trade_as_share_of_gdp_fouquin_and_hugot_cepii_2016_dyadic_data"`
}

Whenever possible, the source reports data on merchandise trade, excluding trade in services and re-exports. Some specific goods are also excluded (the authors highlight the omission of bullion and species in historical data).Since re-exports are excluded, special import data is favored over general import data (special imports have the importing country as their final destination, whereas general trade is composed of special trade, together with transit trade).To calculate total exports to the rest of the world as a share of GDP, using the UK as an example, figures have been calculated as follows:UK-exports-to-the-world-as-share-of-GDP = [exports-to-A + exports-to-B + exports-to-C] / GDP-of-UK (this calculation would assume there are a total of three countries in the entire sample constituting the rest of the world).Export values used rely on the estimate given by the importer as the authors note: "importers have a greater incentive to properly assess trade flows as they are commonly subject to duties". Bilateral trade flows are recorded in current British pounds. Fouquin and Hugot (CEPII 2016) data uses the IMF's Direction of Trade Statistics (DOTS) dataset extensively from 1948 until the present day.West Germany estimates have been used between 1950 - 1990, and Germany's trade estimates used before 1950 and after 1990. Estimates for East Germany have been excluded. Please see the original document providing a detailed description of the data set used, including country and territory definitions, available at: http://www.cepii.fr/CEPII/fr/publications/wp/abstract.asp?NoDoc=9134Micronesia, Marshall Islands, Monaco, Montenegro, San Marino, and the Bahamas have also been excluded from the sample due to insufficient data availability.

type TourismDataByWorldRegionUnwto2019Dataset 

type TourismDataByWorldRegionUnwto2019Dataset struct {
	InternationalTouristArrivals *float64 `json:"international_tourist_arrivals"`
}

The UNWTO World Tourism Barometer monitors short-term tourism trends on a regular basis to provide global tourism stakeholders with up-to-date analysis on international tourism.

type TradeGiovanniAndTenaJunguito2016Dataset 

type TradeGiovanniAndTenaJunguito2016Dataset struct {
	WorldTradeRelativeTo1913FedericoAndTenaJunguito2016 *float64 `json:"world_trade_relative_to_1913_federico_and_tena_junguito_2016"`
}

type TradeShareByTypeOfTradeOwidCalculationsBasedOnNberUnitedNationsTradeData19622000Dataset 

type TradeShareByTypeOfTradeOwidCalculationsBasedOnNberUnitedNationsTradeData19622000Dataset struct {
	ImportTradeShareFromLaborIntensiveCountriesOwidCalculationsBasedOnNberUnitedNationsTradeData1962_2000   *float64 `json:"import_trade_share_from_labor_intensive_countries_owid_calculations_based_on_nber_united_nations_trade_data_1962_2000"`
	ImportTradeShareFromCapitalIntensiveCountriesOwidCalculationsBasedOnNberUnitedNationsTradeData1962_2000 *float64 `` /* 126-byte string literal not displayed */
	ExportTradeShareByLaborIntensiveCountriesOwidCalculationsBasedOnNberUnitedNationsTradeData1962_2000     *float64 `json:"export_trade_share_by_labor_intensive_countries_owid_calculations_based_on_nber_united_nations_trade_data_1962_2000"`
	ExportTradeShareByCapitalIntensiveCountriesOwidCalculationsBasedOnNberUnitedNationsTradeData1962_2000   *float64 `json:"export_trade_share_by_capital_intensive_countries_owid_calculations_based_on_nber_united_nations_trade_data_1962_2000"`
}

The total value of exports calculated for the NBER-United Nations Datasets includes only trade in goods, classified according to the 4 digit Standard International Trade Classification, Revision 2. Authors give primacy to importers' reports whenever they are available, assumed to be more accurate.

OWID calculations have excluded all observations for the 'World' with the focus on bilateral country trade flows. Countries Comoros, Saint Kitts and Nevis, and the Netherlands Antilles have also been excluded from the sample as, in the case of the former two, multiple countries are included in these labels in the NBER-UN data, and hence would be inaccurate to calculate the trade share by national GDP. For the Netherlands Antilles there is insufficient alternative trade data by the World Bank and other sources to check whether the figures are correctly estimated.

The United Kingdom consists of: the UK, British Antarctic Territories, the Falkland Islands, and Saint Helena.

Germany consists of: East Germany, West Germany, and Germany.

Russia: the former USSR and Russia.

France: French Guinea, Guadeloupe, Saint Pierre and Miquelon, and France.

Yemen: the Yemen Arab Republic, the Yemen People's Republic, and Yemen.

All countries defined to be resource rich have been excluded from the sample. The list of these countries include: the Democratic Republic of Congo, Liberia, Niger, Guinea, Mali, Chad, Mauritania, Laos, Zambia, Vietnam, Yemen, Nigeria, Cameroon, Papua New Guinea, Sudan, Uzbekistan, Cote d'Ivoire, Bolivia, Mongolia, Congo, Iraq, Indonesia, Timor, Syria, Guyana, Turkmenistan, Angola, Gabon, Equatorial Guinea. The list is taken from the IMF resource: https://www.imf.org/external/np/pp/eng/2012/082412.pdf on page 48, Appendix 1, Table 1.

Capital vs labor intensive countries have been categorised using the World Bank's 2016 income classification. Low and lower-middle income countries have been classified as labor intensive while upper-middle and high-income countries as capital intensive. The 2016 classification has been applied to all previous years for which there is data.

To calculate export trade share to labor-intensive countries, using the UK as an example, figures have been calculated as follows: UK-exports-to-labor-intensive-countries-as-share-of-GDP = [exports-to-A + exports-to-B + exports-to-C] / GDP-of-UK (this calculation would assume there are a total of three developing countries in the entire sample).

type TradeShareWithCapitalAndLaborIntensiveCountriesOwidCalculationsBasedOnFouquinAndHugotCepii2016Dataset 

type TradeShareWithCapitalAndLaborIntensiveCountriesOwidCalculationsBasedOnFouquinAndHugotCepii2016Dataset struct {
	ValueOfExportsToLaborIntensiveCountriesPercOfGdpOwidCalculationsBasedOnCepii2016   *float64 `json:"value_of_exports_to_labor_intensive_countries_perc_of_gdp_owid_calculations_based_on_cepii_2016"`
	ValueOfExportsToCapitalIntensiveCountriesPercOfGdpOwidCalculationsBasedOnCepii2016 *float64 `json:"value_of_exports_to_capital_intensive_countries_perc_of_gdp_owid_calculations_based_on_cepii_2016"`
	ValueOfImportsToLaborIntensiveCountriesPercOfGdpOwidCalculationsBasedOnCepii2016   *float64 `json:"value_of_imports_to_labor_intensive_countries_perc_of_gdp_owid_calculations_based_on_cepii_2016"`
	ValueOfImportsToCapitalIntensiveCountriesPercOfGdpOwidCalculationsBasedOnCepii2016 *float64 `json:"value_of_imports_to_capital_intensive_countries_perc_of_gdp_owid_calculations_based_on_cepii_2016"`
}

Capital and labor-intensive countries have been defined according to the World Bank’s 2016 income classifications as our starting point. Low and lower-middle income countries have been classified as labor-intensive while upper-middle income and high income countries as capital-intensive. The 2016 classification has been applied to all previous years for which there is data available. Where no World Bank classification is available for self-governing territories, the observation is assigned the country classification to which it is related. For example, Saint Pierre and Miquelon would be assigned France's income classification. All countries defined to be resource rich have been excluded from the sample to focus on the trade between rich countries and countries that are labor-abundant but capital and resource poor. The list of resource rich countries excluded are: the Democratic Republic of Congo, Liberia, Niger, Guinea, Mali, Chad, Mauritania, Laos, Zambia, Vietnam, Yemen, Nigeria, Cameroon, Papua New Guinea, Sudan, Uzbekistan, Cote d'Ivoire, Bolivia, Mongolia, Congo, Iraq, Indonesia, Timor, Syria, Guyana, Turkmenistan, Angola, Gabon, Equatorial Guinea. The list is taken from the IMF resource: https://www.imf.org/external/np/pp/eng/2012/082412.pdf on page 48, Appendix 1, Table 1. To calculate export trade share to labor-intensive countries, using the UK as an example, figures have been calculated as follows:UK-exports-to-labor-intensive-countries-as-share-of-GDP = [exports-to-A + exports-to-B + exports-to-C] / GDP-of-UK (this calculation would assume there are a total of three labor-intensive countries in the entire sample). Export values used rely on the estimate given by the importer as the authors note: " importers have a greater incentive to properly assess trade flows as they are commonly subject to duties". Bilateral trade flows are recorded in current British pounds.West Germany estimates have been used between 1950 - 1990, and Germany's trade estimates used before 1950 and after 1990. 

Estimates for East Germany have been excluded.

Please see the original document providing a detailed description of the data set used, including country and territory definitions, available at: http://www.cepii.fr/CEPII/fr/publications/wp/abstract.asp?NoDoc=9134Micronesia, Marshall Islands, Monaco, Montenegro, San Marino, and the Bahamas have also been excluded from the sample due to insufficient data availability.

type TransistorsPerMicroprocessorRuppAndHorowitzDataset 

type TransistorsPerMicroprocessorRuppAndHorowitzDataset struct {
	TransistorsPerMicroprocessor *float64 `json:"transistors_per_microprocessor"`
}

Sourced from Karl Rupp series on microprocessor trend data. Rupp's data for the years prior to 2000 from M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten. '35 Years of Microprocessor Trend Data'.This data also correlates to that published in 'The Singularity Is Near: When Humans Transcend Biology' by Ray Kurzweil up to 2003. Available at: http://www.singularity.com/index.htmlWhere data of several microprocessors is given for a single year, we have shown the highest transistor count per chip of that year.Data for 2015 and 2017 have been updated by OurWorldinData based on highest transistor count noted at: https://en.wikipedia.org/wiki/Transistor_count. The 2015 record was set by Oracle's 32-core SPARC M7 (https://www.enterprisetech.com/2014/08/13/oracle-cranks-cores-32-sparc-m7-chip/) and in 2017 by the 32-core AMD Epyc microprocessor.

type TreeDensityCrowtherEtAl2015Dataset 

type TreeDensityCrowtherEtAl2015Dataset struct {
	NumberOfTrees             *float64 `json:"number_of_trees"`
	TreeDensityTreesPerKm2    *float64 `json:"tree_density_trees_per_km2"`
	TreeDensityTreesPerCapita *float64 `json:"tree_density_trees_per_capita"`
}

The authors used 429,775 ground-sourced measurements of tree density from every continent on Earth except Antarctica to generate a global map of forest trees.They define a tree as a plant with woody stems larger than 10 cm diameter at breast height (DBH).Tree density (per square kilometer and per capita) were calculated by the authors based on population and land area data sourced from the World Bank for 2014.

type TropicalDeforestationByCountryOrRegionPendrillEtAl2019Dataset 

type TropicalDeforestationByCountryOrRegionPendrillEtAl2019Dataset struct {
	ForestLossHa                         *float64 `json:"forest_loss_ha"`
	ShareOfCommodityDrivenDeforestation  *float64 `json:"share_of_commodity_driven_deforestation"`
	DeforestationEmissionsMtco2          *float64 `json:"deforestation_emissions_mtco2"`
	ShareOfDeforestationEmissions        *float64 `json:"share_of_deforestation_emissions"`
	PeatEmissions                        *float64 `json:"peat_emissions"`
	ShareOfPeatEmissions                 *float64 `json:"share_of_peat_emissions"`
	TotalDeforestationAndPeatEmissions   *float64 `json:"total_deforestation_and_peat_emissions"`
	ShareOfDeforestationAndPeatEmissions *float64 `json:"share_of_deforestation_and_peat_emissions"`
}

Pendrill et al. (2019) developed a land-balance model which attributed detected forest loss across the world to the expansion of croplands, pasture and tree plantations. This is then linked to particular agricultural commodities based on national land use, crop and forest product statistics published in the UN Food and Agricultural Organization balance sheets.This study also maps deforestation and related CO2 emissions embedded in the international trade of these products using both a physical trade model, and a MRIO (multi-regional input-output) model. This allows for the quantification of deforestation and related emissions embedded in imported food and forestry products.

type TrustEurostatDataset 

type TrustEurostatDataset struct {
	TrustInOthersEurostat2015 *float64 `json:"trust_in_others_eurostat_2015"`
	TrustInPoliceEurostat2015 *float64 `json:"trust_in_police_eurostat_2015"`
}

type TrustWorldValueSurveyDataset 

type TrustWorldValueSurveyDataset struct {
	TrustInOthersWorldValuesSurvey2014 *float64 `json:"trust_in_others_world_values_survey_2014"`
}

type UcdpprioArmedConflictDatasetVersion172DirectFormUcdpDataset 

type UcdpprioArmedConflictDatasetVersion172DirectFormUcdpDataset struct {
	NumberOfConflictsAndIncidencesOfOneSidedViolence *float64 `json:"number_of_conflicts_and_incidences_of_one_sided_violence"`
}

type UkButterflyPopulationsUkOnsDataset 

type UkButterflyPopulationsUkOnsDataset struct {
	ButterflyPopulationUnsmoothedIndex *float64 `json:"butterfly_population_unsmoothed_index"`
	ButterflyPopulationSmoothedIndex   *float64 `json:"butterfly_population_smoothed_index"`
	NoSpeciesLongtermIncrease          *float64 `json:"no_species_longterm_increase"`
	NoSpeciesLongtermDecrease          *float64 `json:"no_species_longterm_decrease"`
	NoSpeciesLongtermNochange          *float64 `json:"no_species_longterm_nochange"`
	NoSpeciesShorttermIncrease         *float64 `json:"no_species_shortterm_increase"`
	NoSpeciesShorttermDecrease         *float64 `json:"no_species_shortterm_decrease"`
	NoSpeciesShorttermNochange         *float64 `json:"no_species_shortterm_nochange"`
}

Butterfly population indices are compiled by Butterfly Conservation (BC) and the UK Centre for Ecology & Hydrology (UKCEH) from data collated through the UK Butterfly Monitoring Scheme (UKBMS) including from the Wider Countryside Butterfly Survey (WCBS).Data is collected for 26 habitat specialist butterflies (low mobility species restricted to semi-natural habitats) and 25 more widespread butterflies (which use both semi-natural and general countryside habitats) using data collected at 5,737 sample locations.The year-to-year fluctuations in butterfly numbers are often linked to natural environmental variation, especially weather conditions. Therefore, in order to identify underlying patterns in population trends, the assessment of change is based on smoothed indices.

type UkCholeraDeathOverTheLongTermOnsDataset 

type UkCholeraDeathOverTheLongTermOnsDataset struct {
	CholeraDeaths *float64 `json:"cholera_deaths"`
}

Data has been compiled by Our World in Data across various historical sources.

Data on 19th century outbreaks of cholera were sourced from Thomas, A. J. (2015) and Pearson Schools and Colleges, Poverty, public health and the state in Britain, c1780–1939. Note that this data represents total death toll for major pandemic years (and is given as the total death toll for the outbreak period e.g. in data for 1832 represents the total deaths across the 1831-32 outbreak). The death toll from cholera between these outbreaks was much lower than such peaks, although exact figures are not known. These are given as the total death toll in Great Britain (representing England, Wales and Scotland).

Data from 1910 onward is for England and Wales only. This data is derived from the Office of National Statistics (ONS).

'The 20th century mortality files' available at: http://webarchive.nationalarchives.gov.uk/20160108034247tf_/http://www.ons.gov.uk/ons/rel/subnational-health1/the-20th-century-mortality-files/20th-century-deaths/index.html 'The 21st century mortality files' available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/the21stcenturymortalityfilesdeathsdataset

References: Thomas, A. J. (2015). Cholera: The Victorian Plague. Pen and Sword.

Pearson Schools and Colleges, 'Poverty, public health and the state in Britain, c1780–1939'. Available at: https://www.pearsonschoolsandfecolleges.co.uk/AssetsLibrary/SECTORS/Secondary/SUBJECT/HistoryandSocialScience/PDFs/A%20Level%202015/Paper-3-Sample-Chapters/GCE%20A%20Level%20History_Poverty%20watermarked%20pdf%2021-07-2016_Ch1.pdf

type UkDefenceSpendingUkpublicspendingcomDataset 

type UkDefenceSpendingUkpublicspendingcomDataset struct {
	DefenceSpendingAsAPercentOfGdpUkpublicspendingcom *float64 `json:"defence_spending_as_a_percent_of_gdp_ukpublicspendingcom"`
}

Historical data provided by International Historical Statistics (Brian Mitchell) and the Measuring Worth project (measuringworth.com). Recent data comes from official sources (Office for Budget Responsibility and HM Treasury).

type UkNominalWageDataPriceDataAndRealWageBankOfEnglandThreeCenturiesOfMacroeconomicData2017Dataset 

type UkNominalWageDataPriceDataAndRealWageBankOfEnglandThreeCenturiesOfMacroeconomicData2017Dataset struct {
	NominalAverageWeeklyWagesBankOfEngland2017        *float64 `json:"nominal_average_weekly_wages_bank_of_england_2017"`
	SplicedConsumerPriceIndex2015100BankOfEngland2017 *float64 `json:"spliced_consumer_price_index_2015100_bank_of_england_2017"`
	RealAverageWeeklyWagesBankOfEngland2017           *float64 `json:"real_average_weekly_wages_bank_of_england_2017"`
}

Data on nominal weekly wages was spliced by the Bank of England and is based on the following sources: N. Crafts and T. Mills Trends in Real Wages in Britain, 1750-1913, Explorations in Economic History (1994) Feinstein (1998) C. H. Feinstein New Estimates of Average Earnings in the United Kingdom, Economic History Review (1990) C.H. Feinstein, National Income Expenditure and Output of the United Kingdom 1855-1965 ONS: Series code LNMQ ONS: Series code KAB9

The cost of living index is referred to by the Bank of England as "Headline Consumer Price Index (CPI) - original method in version 1.0" and is indexed to 2015. It is spliced by the BoE based on the following sources: Feinstein (1998), Allen (2007), Feinstein (1991), Capie and Collins (1983), Ministry of Labour, ONS retail prices 1919-1990, O'Donoghue et al (2004), and the ONS website.

The real weekly wage was calculated based on these two series.

For all details see the original source.

type UkraineRussiaContributionToGlobalFoodDataset 

type UkraineRussiaContributionToGlobalFoodDataset struct {
	RussiaProduction             *float64 `json:"russia_production"`
	UkraineProduction            *float64 `json:"ukraine_production"`
	RussiaExports                *float64 `json:"russia_exports"`
	UkraineExports               *float64 `json:"ukraine_exports"`
	RussiaShareGlobalProduction  *float64 `json:"russia_share_global_production"`
	UkraineShareGlobalProduction *float64 `json:"ukraine_share_global_production"`
	RussiaShareGlobalExports     *float64 `json:"russia_share_global_exports"`
	UkraineShareGlobalExports    *float64 `json:"ukraine_share_global_exports"`
}

type UkraineRussiaGlobalFoodBasedOnUnFaoDataset 

type UkraineRussiaGlobalFoodBasedOnUnFaoDataset struct {
	WheatDomesticSupply                          *float64 `json:"wheat_domestic_supply"`
	WheatExports                                 *float64 `json:"wheat_exports"`
	WheatImports                                 *float64 `json:"wheat_imports"`
	WheatStocks                                  *float64 `json:"wheat_stocks"`
	WheatImportsShareDomesticSupply              *float64 `json:"wheat_imports_share_domestic_supply"`
	WheatStocksShareDomesticSupply               *float64 `json:"wheat_stocks_share_domestic_supply"`
	WheatExportsCapita                           *float64 `json:"wheat_exports_capita"`
	WheatImportsCapita                           *float64 `json:"wheat_imports_capita"`
	WheatStocksCapita                            *float64 `json:"wheat_stocks_capita"`
	WheatSupplyCapita                            *float64 `json:"wheat_supply_capita"`
	UkraineWheatImports                          *float64 `json:"ukraine_wheat_imports"`
	RussiaWheatImports                           *float64 `json:"russia_wheat_imports"`
	UkraineRussiaWheatImports                    *float64 `json:"ukraine_russia_wheat_imports"`
	UkraineWheatImportsPerCapita                 *float64 `json:"ukraine_wheat_imports_per_capita"`
	RussiaWheatImportsPerCapita                  *float64 `json:"russia_wheat_imports_per_capita"`
	UkraineRussiaWheatImportsPerCapita           *float64 `json:"ukraine_russia_wheat_imports_per_capita"`
	UkraineWheatShareAllImports                  *float64 `json:"ukraine_wheat_share_all_imports"`
	RussiaWheatShareAllImports                   *float64 `json:"russia_wheat_share_all_imports"`
	UkraineRussiaWheatShareAllImports            *float64 `json:"ukraine_russia_wheat_share_all_imports"`
	UkraineWheatShareDomesticSupply              *float64 `json:"ukraine_wheat_share_domestic_supply"`
	RussiaWheatShareDomesticSupply               *float64 `json:"russia_wheat_share_domestic_supply"`
	UkraineRussiaWheatShareDomesticSupply        *float64 `json:"ukraine_russia_wheat_share_domestic_supply"`
	MaizeDomesticSupply                          *float64 `json:"maize_domestic_supply"`
	MaizeExports                                 *float64 `json:"maize_exports"`
	MaizeImports                                 *float64 `json:"maize_imports"`
	MaizeStocks                                  *float64 `json:"maize_stocks"`
	MaizeImportsShareDomesticSupply              *float64 `json:"maize_imports_share_domestic_supply"`
	MaizeStocksShareDomesticSupply               *float64 `json:"maize_stocks_share_domestic_supply"`
	MaizeExportsCapita                           *float64 `json:"maize_exports_capita"`
	MaizeImportsCapita                           *float64 `json:"maize_imports_capita"`
	MaizeStocksCapita                            *float64 `json:"maize_stocks_capita"`
	MaizeSupplyCapita                            *float64 `json:"maize_supply_capita"`
	UkraineMaizeImports                          *float64 `json:"ukraine_maize_imports"`
	RussiaMaizeImports                           *float64 `json:"russia_maize_imports"`
	UkraineRussiaMaizeImports                    *float64 `json:"ukraine_russia_maize_imports"`
	UkraineMaizeImportsPerCapita                 *float64 `json:"ukraine_maize_imports_per_capita"`
	RussiaMaizeImportsPerCapita                  *float64 `json:"russia_maize_imports_per_capita"`
	UkraineRussiaMaizeImportsPerCapita           *float64 `json:"ukraine_russia_maize_imports_per_capita"`
	UkraineMaizeShareAllImports                  *float64 `json:"ukraine_maize_share_all_imports"`
	RussiaMaizeShareAllImports                   *float64 `json:"russia_maize_share_all_imports"`
	UkraineRussiaMaizeShareAllImports            *float64 `json:"ukraine_russia_maize_share_all_imports"`
	UkraineMaizeShareDomesticSupply              *float64 `json:"ukraine_maize_share_domestic_supply"`
	RussiaMaizeShareDomesticSupply               *float64 `json:"russia_maize_share_domestic_supply"`
	UkraineRussiaMaizeShareDomesticSupply        *float64 `json:"ukraine_russia_maize_share_domestic_supply"`
	BarleyDomesticSupply                         *float64 `json:"barley_domestic_supply"`
	BarleyExports                                *float64 `json:"barley_exports"`
	BarleyImports                                *float64 `json:"barley_imports"`
	BarleyStocks                                 *float64 `json:"barley_stocks"`
	BarleyImportsShareDomesticSupply             *float64 `json:"barley_imports_share_domestic_supply"`
	BarleyStocksShareDomesticSupply              *float64 `json:"barley_stocks_share_domestic_supply"`
	BarleyExportsCapita                          *float64 `json:"barley_exports_capita"`
	BarleyImportsCapita                          *float64 `json:"barley_imports_capita"`
	BarleyStocksCapita                           *float64 `json:"barley_stocks_capita"`
	BarleySupplyCapita                           *float64 `json:"barley_supply_capita"`
	SunfloweroilDomesticSupply                   *float64 `json:"sunfloweroil_domestic_supply"`
	SunfloweroilExports                          *float64 `json:"sunfloweroil_exports"`
	SunfloweroilImports                          *float64 `json:"sunfloweroil_imports"`
	SunfloweroilStocks                           *float64 `json:"sunfloweroil_stocks"`
	SunfloweroilImportsShareDomesticSupply       *float64 `json:"sunfloweroil_imports_share_domestic_supply"`
	SunfloweroilStocksShareDomesticSupply        *float64 `json:"sunfloweroil_stocks_share_domestic_supply"`
	SunfloweroilExportsCapita                    *float64 `json:"sunfloweroil_exports_capita"`
	SunfloweroilImportsCapita                    *float64 `json:"sunfloweroil_imports_capita"`
	SunfloweroilStocksCapita                     *float64 `json:"sunfloweroil_stocks_capita"`
	SunfloweroilSupplyCapita                     *float64 `json:"sunfloweroil_supply_capita"`
	WheatNetImports                              *float64 `json:"wheat_net_imports"`
	WheatNetImportsCapita                        *float64 `json:"wheat_net_imports_capita"`
	MaizeNetImports                              *float64 `json:"maize_net_imports"`
	MaizeNetImportsCapita                        *float64 `json:"maize_net_imports_capita"`
	BarleyNetImports                             *float64 `json:"barley_net_imports"`
	BarleyNetImportsCapita                       *float64 `json:"barley_net_imports_capita"`
	UkraineBarleyImports                         *float64 `json:"ukraine_barley_imports"`
	RussiaBarleyImports                          *float64 `json:"russia_barley_imports"`
	UkraineRussiaBarleyImports                   *float64 `json:"ukraine_russia_barley_imports"`
	UkraineBarleyImportsPerCapita                *float64 `json:"ukraine_barley_imports_per_capita"`
	RussiaBarleyImportsPerCapita                 *float64 `json:"russia_barley_imports_per_capita"`
	UkraineRussiaBarleyImportsPerCapita          *float64 `json:"ukraine_russia_barley_imports_per_capita"`
	UkraineBarleyShareAllImports                 *float64 `json:"ukraine_barley_share_all_imports"`
	RussiaBarleyShareAllImports                  *float64 `json:"russia_barley_share_all_imports"`
	UkraineRussiaBarleyShareAllImports           *float64 `json:"ukraine_russia_barley_share_all_imports"`
	UkraineBarleyShareDomesticSupply             *float64 `json:"ukraine_barley_share_domestic_supply"`
	RussiaBarleyShareDomesticSupply              *float64 `json:"russia_barley_share_domestic_supply"`
	UkraineRussiaBarleyShareDomesticSupply       *float64 `json:"ukraine_russia_barley_share_domestic_supply"`
	SunfloweroilNetImports                       *float64 `json:"sunfloweroil_net_imports"`
	SunfloweroilNetImportsCapita                 *float64 `json:"sunfloweroil_net_imports_capita"`
	UkraineSunfloweroilImports                   *float64 `json:"ukraine_sunfloweroil_imports"`
	RussiaSunfloweroilImports                    *float64 `json:"russia_sunfloweroil_imports"`
	UkraineRussiaSunfloweroilImports             *float64 `json:"ukraine_russia_sunfloweroil_imports"`
	UkraineSunfloweroilImportsPerCapita          *float64 `json:"ukraine_sunfloweroil_imports_per_capita"`
	RussiaSunfloweroilImportsPerCapita           *float64 `json:"russia_sunfloweroil_imports_per_capita"`
	UkraineRussiaSunfloweroilImportsPerCapita    *float64 `json:"ukraine_russia_sunfloweroil_imports_per_capita"`
	UkraineSunfloweroilShareAllImports           *float64 `json:"ukraine_sunfloweroil_share_all_imports"`
	RussiaSunfloweroilShareAllImports            *float64 `json:"russia_sunfloweroil_share_all_imports"`
	UkraineRussiaSunfloweroilShareAllImports     *float64 `json:"ukraine_russia_sunfloweroil_share_all_imports"`
	UkraineSunfloweroilShareDomesticSupply       *float64 `json:"ukraine_sunfloweroil_share_domestic_supply"`
	RussiaSunfloweroilShareDomesticSupply        *float64 `json:"russia_sunfloweroil_share_domestic_supply"`
	UkraineRussiaSunfloweroilShareDomesticSupply *float64 `json:"ukraine_russia_sunfloweroil_share_domestic_supply"`
}

type UnPopulationDivision2015Dataset 

type UnPopulationDivision2015Dataset struct {
	PopulationGrowthRateUnPopulationDivision2015 *float64 `json:"population_growth_rate_un_population_division_2015"`
}

type UnPopulationDivisionMedianAge2015Dataset 

type UnPopulationDivisionMedianAge2015Dataset struct {
	UnPopulationDivisionMedianAge2015 *float64 `json:"un_population_division_median_age_2015"`
}

Median age is the age that divides the population in two parts of equal size, that is, there are as many persons with ages above the median as there are with ages below the median. It is expressed as years.

type UnPopulationDivisionMedianAge2017Dataset 

type UnPopulationDivisionMedianAge2017Dataset struct {
	UnPopulationDivisionMedianAge2017 *float64 `json:"un_population_division_median_age_2017"`
}

Median age is the age that divides the population in two parts of equal size, that is, there are as many persons with ages above the median as there are with ages below the median. It is expressed as years.

type UnadjustedFemaleMaleHourlyWageRatiosByPercentileBlauKahn2017Dataset 

type UnadjustedFemaleMaleHourlyWageRatiosByPercentileBlauKahn2017Dataset struct {
	TenthPercentileBlauKahn2017     *float64 `json:"tenth_percentile_blau_kahn_2017"`
	FiftiethPercentileBlauKahn2017  *float64 `json:"fiftieth_percentile_blau_kahn_2017"`
	NinetiethPercentileBlauKahn2017 *float64 `json:"ninetieth_percentile_blau_kahn_2017"`
}

type Under5MortalityRateOurWorldInDataDataset 

type Under5MortalityRateOurWorldInDataDataset struct {
	Under5MortalityRateOurWorldInData *float64 `json:"under_5_mortality_rate_our_world_in_data"`
}

We have combined two sources: The UN Child Mortality Estimates (CME) data and the Human Mortality Database (HMD).

All data points from the CME used. HMD has been used as a supplement when CME data is not available for a given year. HMD values are multiplied by factor of 1.1 for Belarus, Hungary and Lithuania (to match CME data as described in Hill et al. 2012). HMD values are multiplied by a factor of 1.2 for Bulgaria, Czech Republic, Latvia, Russia, Slovakia, Spain and Estonia (before 1992) (to match CME as described in Hill et al. 2012).

Hill et al. 2012 adjust the CME data by these same factors because of concerns about the low levels of early neonatal death recorded in the civil registration systems of these countries compared to western European countries as a result of differences in the definition of live births.

More information can be found in their paper: Hill K, You D, Inoue M, Oestergaard MZ, Technical Advisory Group of the United Nations Inter-agency Group for Child Mortality Estimation (2012) Child Mortality Estimation: Accelerated Progress in Reducing Global Child Mortality, 1990-2010. PLoS Med 9(8): e1001303. doi:10.1371/journal.pmed.1001303

type UnderFiveMortalityRateUnWorldPopulationProspects2015Dataset 

type UnderFiveMortalityRateUnWorldPopulationProspects2015Dataset struct {
	UnderFiveMortalityRateUnWorldPopulationProspects2015 *float64 `json:"under_five_mortality_rate_un_world_population_prospects_2015"`
}

– The original data is presented in deaths per 1,000 live births. Here it is changed to percentages by dividing the original estimates by 100.– Importantly data refer to 5 year intervals preceding the indicated year. E.g. 1955 refers to 1950-1955 in the original dataset.

type UnemploymentRateAges2554ByEducationIlostat2017Dataset 

type UnemploymentRateAges2554ByEducationIlostat2017Dataset struct {
	UnemploymentRateAges25_54AdvancedEducation       *float64 `json:"unemployment_rate_ages_25_54_advanced_education"`
	UnemploymentRateAges25_54BasicEducation          *float64 `json:"unemployment_rate_ages_25_54_basic_education"`
	UnemploymentRateAges25_54LessThanBasicEducation  *float64 `json:"unemployment_rate_ages_25_54_less_than_basic_education"`
	UnemploymentRateAges25_54AllLevelsOfEducation    *float64 `json:"unemployment_rate_ages_25_54_all_levels_of_education"`
	UnemploymentRateAges25_54IntermediateEducation   *float64 `json:"unemployment_rate_ages_25_54_intermediate_education"`
	UnemploymentRateAges25_54EducationLevelNotStated *float64 `json:"unemployment_rate_ages_25_54_education_level_not_stated"`
}

The unemployment rate is the number of persons who are unemployed as a percent of the total number of employed and unemployed persons (i.e., the labour force). Data by level of education are provided on the highest level of education completed.According to the ISCED-11 classification, a 'less than basic education' may refer to no schooling, or early childhood education. 

'Basic education' includes primary or lower secondary education; 'Intermediate education' to upper secondary or post-secondary non-tertiary education; 'Advanced education' can refer to short-cycle tertiary education, Bachelor's or equivalent level, Master's or equivalent level, or Doctoral or equivalent level, while 'level not stated' is an education level not elsewhere classified.

For more information see the ILOSTAT's <a href="https://www.ilo.org/ilostat-files/Documents/description_EDU_EN.pdf" rel="noopener" target=_blank">'Employment by education' document</a>.

type UnescoMetadataOnLiteracyUis2017Dataset 

type UnescoMetadataOnLiteracyUis2017Dataset struct {
	DataInstrumentsUsedToEstimateLiteracyUis2017 *float64 `json:"data_instruments_used_to_estimate_literacy_uis_2017"`
	MethodologiesUsedForMeasuringLiteracyUis2017 *float64 `json:"methodologies_used_for_measuring_literacy_uis_2017"`
}

The UNESCO Institute for Statistics (UIS) collects cross-national education statistics. Data on literacy rates come from a variety of sources (such as national censuses and surveys) and reporting modes (self-reporting vs household declarations).

Data instruments used to estimate literacy are grouped under three main types: i) a census; ii) a survey; or iii) indirect estimates. Indirect estimates are extrapolated - based on educational attainment, or estimated based on the country's census.

Methodologies used for measuring literacy have been grouped into four categories: household declarations, self declarations, literacy tests, and national estimates. Both household and self-declared literacy are self-reports; in the former, it is the head of the household responding to the survey, and in the latter it is directly reported by the individuals themselves.

UNESCO categorised Chile's (2009) and the Philippines' (2003) mode of literacy reporting as 'Household/Self declaration'. We have categorised the two countries under the household declaration category.

type UnionDensityQualityOfGovernmentQog2017Dataset 

type UnionDensityQualityOfGovernmentQog2017Dataset struct {
	UnionDensityRateQualityOfGovernmentQog2017 *float64 `json:"union_density_rate_quality_of_government_qog_2017"`
}

type UnitedNationsHumanDevelopmentIndexHdiDataset 

type UnitedNationsHumanDevelopmentIndexHdiDataset struct {
	HumanDevelopmentIndexHdi1980_2014Un *float64 `json:"human_development_index_hdi_1980_2014_un"`
}

type UnitedNationsPeacekeepingDataset 

type UnitedNationsPeacekeepingDataset struct {
	NumberOfPeacekeepingMissionsUnitedNationsPeacekeeping  *float64 `json:"number_of_peacekeeping_missions_united_nations_peacekeeping"`
	SizeOfTotalPeacekeepingForceUnitedNationsPeacekeeping  *float64 `json:"size_of_total_peacekeeping_force_united_nations_peacekeeping"`
	CivilianPolicePeacekeepersUnitedNationsPeacekeeping    *float64 `json:"civilian_police_peacekeepers_united_nations_peacekeeping"`
	MilitaryObserversPeacekeepersUnitedNationsPeacekeeping *float64 `json:"military_observers_peacekeepers_united_nations_peacekeeping"`
	TroopsPeacekeepersUnitedNationsPeacekeeping            *float64 `json:"troops_peacekeepers_united_nations_peacekeeping"`
}

All data is taken directly from the United Nations, with the exception for the historical size of the peacekeeping force (1947-1991), which is instead taken from the Global Policy Forum. Available at: https://www.globalpolicy.org/security-council/peacekeeping/peacekeeping-data.htmlThe data for the size and breakdown of the peacekeeping force from the United Nations is taken as the value reported in the December report at the end of the year (with the exception for 1998, which is taken in November).For example, summaries from 2010 onwards are given here: https://peacekeeping.un.org/sites/default/files/00-front_page_msr_december_2021.pdf. We have taken figures from December in that report as the figures for a given year.

type UrbanAndRuralPopulation19502050UnWorldUrbanizationProspects2018Dataset 

type UrbanAndRuralPopulation19502050UnWorldUrbanizationProspects2018Dataset struct {
	UrbanPopulation1950_2050UnWorldUrbanizationProspects2018 *float64 `json:"urban_population_1950_2050_un_world_urbanization_prospects_2018"`
	RuralPopulation1950_2050UnWorldUrbanizationProspects2018 *float64 `json:"rural_population_1950_2050_un_world_urbanization_prospects_2018"`
}

type UrbanAndRuralPopulationsInTheUnitedStatesUsCensusBureau2010Dataset 

type UrbanAndRuralPopulationsInTheUnitedStatesUsCensusBureau2010Dataset struct {
	UrbanPopulationUsCensusBureau2010                       *float64 `json:"urban_population_us_census_bureau_2010"`
	RuralPopulationUsCensusBureau2010                       *float64 `json:"rural_population_us_census_bureau_2010"`
	TotalPopulationUsCensusBureau2010                       *float64 `json:"total_population_us_census_bureau_2010"`
	PercentageOfThePopulationInUrbanAreasUsCensusBureau2010 *float64 `json:"percentage_of_the_population_in_urban_areas_us_census_bureau_2010"`
	PercentageOfThePopulationInRuralAreasUsCensusBureau2010 *float64 `json:"percentage_of_the_population_in_rural_areas_us_census_bureau_2010"`
}

For further information on the US Census Bureau's urban and rural classifications see: https://www.census.gov/geo/reference/ua/uafaq.html or https://www.census.gov/geo/reference/ua/urban-rural-2010.html

type UrbanDefinitionPopulationThresholdUn2018Dataset 

type UrbanDefinitionPopulationThresholdUn2018Dataset struct {
	MinimumPopulationThresholdForUrbanAreaUn2018 *float64 `json:"minimum_population_threshold_for_urban_area_un_2018"`
}

Data was assessed by Our World in Data based on UN documentation of its Urbanization Prospects (2018). The Urbanization Prospects presents data on the number and share of the population residing in urban areas for each country from 1950 with projections to 2050.There is no consistent definition of what constitutes an 'urban area'. This is highly variable across countries. This data presents a given country's minimum threshold of inhabitants needed for it to be defined an 'urban area'. For many countries, there is no defined threshold based on inhabitants; other metrics such as population density, infrastructure, or even pre-defined cities may be used.Note that some countries with minimum inhabitant thresholds noted here also include additional qualities (such as population density) to be met.

type UrbanPopulationLivingInSlumsWbWdiDataset 

type UrbanPopulationLivingInSlumsWbWdiDataset struct {
	UrbanPopulationLivingInSlums    *float64 `json:"urban_population_living_in_slums"`
	UrbanPopulationNotLivingInSlums *float64 `json:"urban_population_not_living_in_slums"`
}

Data on the total size of urban populations living in slums was calculated by Our World in Data based on metrics published within the World Bank, World Development Indicators: 'Urban population (total)' and '% urban population living in slums'.Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.

type UrbanizationInTheLongRunOwidBasedOnTheUnWorldUrbanizationProspects2018AndOthersDataset 

type UrbanizationInTheLongRunOwidBasedOnTheUnWorldUrbanizationProspects2018AndOthersDataset struct {
	UrbanPopulationPercLongRunWith2050ProjectionsOwid *float64 `json:"urban_population_perc_long_run_with_2050_projections_owid"`
	RuralPopulationPercLongRunWith2050ProjectionsOwid *float64 `json:"rural_population_perc_long_run_with_2050_projections_owid"`
	UrbanPopulationLongRunWith2050ProjectionsOwid     *float64 `json:"urban_population_long_run_with_2050_projections_owid"`
	RuralPopulationLongRunWith2050ProjectionsOwid     *float64 `json:"rural_population_long_run_with_2050_projections_owid"`
	UrbanPopulationPercLongRunTo2016Owid              *float64 `json:"urban_population_perc_long_run_to_2016_owid"`
	RuralPopulationPercLongRunTo2016Owid              *float64 `json:"rural_population_perc_long_run_to_2016_owid"`
	UrbanPopulationLongRunTo2016Owid                  *float64 `json:"urban_population_long_run_to_2016_owid"`
	RuralPopulationLongRunTo2016Owid                  *float64 `json:"rural_population_long_run_to_2016_owid"`
}

To construct the long run urbanization series we began with the UN World Urbanization Prospects database covering the period from 1950 to 2050. Then we extended the series backwards using additional data sources including the US Census Bureau, Bairoch (1988), Kuroda (1984), HYDE 3.1 (2010), and De Vries (1984) to arrive at the longest single series possible for each country and region included. For further information on the source used for particular observations by country-year, see the following <a href="https://docs.google.com/spreadsheets/d/1tl3FmKTD_FaQ-i4VUlusS-OcZHOl30zkK7oIsuQPtJ4/edit?usp=sharing" rel="noopener" target="_blank">documentation</a>. To calculate the absolute rural and urban populations, we have multiplied the rural and urban percentages by the historic population series published by Gapminder until 1949 and extended using the UN Population Division data from 1950 to 2016. The long run 'Gapminder + UN Population' population series is available <a href="https://ourworldindata.org/grapher/population-by-country-gapminder+un" rel="noopener" target="_blank">here</a>. 

For absolute population estimates before 1950 for the 'World', we used the <a href="https://ourworldindata.org/grapher/world-population-1750-2015-and-un-projection-until-2100" rel="noopener" target="_blank">'World Population over 12,000 years' series</a>.Note: Urbanization data for Japan in 1900 has been excluded as the estimate (HYDE 3.1) was inconsistent with figures for neighbouring years (Kuroda (1986)).

type UrbanizationShareEuropeanCommissionAtlasOfTheHumanPlanetDataset 

type UrbanizationShareEuropeanCommissionAtlasOfTheHumanPlanetDataset struct {
	DegreeOfUrbanizationPercOfPopulation *float64 `json:"degree_of_urbanization_perc_of_population"`
}

Data reports the share of people living in urban areas, as reported by the European Commission project. These results differ significantly from UN and nationally-defined figures based on differences in methodology.The European Commission combines high-resolution satellite imagery with national census data to derive its estimates of urban and rural settlements.The European Commission applied a universal definition of settlements across all countries:- Urban centre: must have a minimum of 50,000 inhabitants plus a population density of at least 1500 people per square kilometre (km2) or density of build-up area greater than 50 percent.- Urban cluster: must have a minimum of 5,000 inhabitants plus a population density of at least 300 people per square kilometre (km2).- Rural: fewer than 5,000 inhabitants.The urban share is thereafter defined as the sum of urban centres and urban clusters.

type UsCornYieldsUsda2017AndFao2017Dataset 

type UsCornYieldsUsda2017AndFao2017Dataset struct {
	CornYieldUsda2017AndFao2017 *float64 `json:"corn_yield_usda_2017_and_fao_2017"`
}

This dataset comprises of a combination of two datasets: one from the United States Department of Agriculture (USDA) and the UN Food and Agricultural Organization (FAO).Data for the years 1866-1960 has been taken from the USDA-NASS database. This reports survey data on corn yields in bushels per acre. To convert these values to tonnes per hectare, we have used a conversion factor of 0.06725.Data from 1961 onwards has been sourced from the UN FAOstats database. Here, corn yields are termed 'maize'. The FAO yields are reported in 100g per hectare. To convert to tonnes per hectare, we applied a conversion factor of 0.0001.Full USDA reference:USDA-NASS (2017). Quick Stats. United States Dept. of Agr - Nat'l Ag. Statistics Service, Washington, D.C. Available at: https://quickstats.nass.usda.gov [accessed 3rd August 2017].

type UsFemaleLaborForceParticipation18902005Olivetti2013Dataset 

type UsFemaleLaborForceParticipation18902005Olivetti2013Dataset struct {
	FemaleLaborForceParticipationOlivetti2013 *float64 `json:"female_labor_force_participation_olivetti_2013"`
}

type UsMaternalMortalityAndFlfpIndexOwid2017Dataset 

type UsMaternalMortalityAndFlfpIndexOwid2017Dataset struct {
	MaternalMortalityIndexOwid2017 *float64 `json:"maternal_mortality_index_owid_2017"`
}

The maternal mortality index was calculated by OWID based on maternal mortality data compiled from Gapminder (historical data) and the World Bank (recent data). The original data shows maternal death per 100,000 live births.For more detail see: https://ourworldindata.org/grapher/maternal-mortality.The index measures maternal mortality as an index to 1900. This was calculated by dividing maternal mortality in each year by maternal mortality in 1900 (therefore 1900 = 100). Values less than 100 indicate a decrease in maternal mortality relative to 1900; values greater than 100 indicate an increase in maternal mortality relative to 1900.

type UsMeaslesCasesAndDeathsOwid2017Dataset 

type UsMeaslesCasesAndDeathsOwid2017Dataset struct {
	ReportedMeaslesCasesOwid2017     *float64 `json:"reported_measles_cases_owid_2017"`
	ReportedMeaslesCaseRateOwid2017  *float64 `json:"reported_measles_case_rate_owid_2017"`
	ReportedMeaslesDeathsOwid2017    *float64 `json:"reported_measles_deaths_owid_2017"`
	ReportedMeaslesDeathRateOwid2017 *float64 `json:"reported_measles_death_rate_owid_2017"`
	UsPopulation                     *float64 `json:"us_population"`
}

Cases: 1921: Public Health Reports Vol. 37, No. 41 "The Notifiable Diseases: Prevalence during 1921 in States. Anthrax, Cerebrospinal Meningitis, Dengue, Diphtheria, Gonorrhea, Influenza, Malaria, Measles, Pneumonia, Poliomyelitis, Rabies, Rocky Mountain Spotted Fever, Scarlet Fever, Septic Sore Throat, Smallpox, Syphilis, Tuberculosis (All Forms and Pulmonary), Typhoid Fever, and Typhus Fever” 1924: Public Health Reports Vol. 40, No. 51 “The notifiable diseases: Prevalence during 1924 in States”1925: Public Health Reports Vol. 42, No. 1 “The notifiable diseases: Prevalence during 1924 in States”1938-1943: US Census Bureau “Statistical Abstract of the United States 1944-45” available here: https://www.census.gov/library/publications/1945/compendia/statab/66ed.html1944-1993: Morbidity and Mortality Weekly Report Annual Supplement Summary 1993 Vol. 42, No. 53 by the US Public Health Service 1994-1999: Morbidity and Mortality Weekly Report 2001 Vol. 48 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm4853a1.htm2000-2005: Morbidity and Mortality Weekly Report 2005 Vol. 54 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5453a1.htm2006-2010: Morbidity and Mortality Weekly Report 2010 Vol. 59 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5953a1.htm2011-2012: Morbidity and Mortality Weekly Report 2012 Vol. 61 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6153a1.htm2013: Morbidity and Mortality Weekly Report 2015 Vol. 62 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6253a1.htm2014: Morbidity and Mortality Weekly Report 2016 Vol. 63 Nr. 54 DOI: http://dx.doi.org/10.15585/mmwr.mm6354a12015: Morbidity and Mortality Weekly Report 2017 Vol. 64 Nr. 53 DOI: http://dx.doi.org/10.15585/mmwr.mm6453a1Deaths: 1921: Public Health Reports Vol. 37, No. 41 "The Notifiable Diseases: Prevalence during 1921 in States. Anthrax, Cerebrospinal Meningitis, Dengue, Diphtheria, Gonorrhea, Influenza, Malaria, Measles, Pneumonia, Poliomyelitis, Rabies, Rocky Mountain Spotted Fever, Scarlet Fever, Septic Sore Throat, Smallpox, Syphilis, Tuberculosis (All Forms and Pulmonary), Typhoid Fever, and Typhus Fever” 1922-23: US Census Bureau “Statistical Abstract of the United States: 1924” available here: https://www.census.gov/library/publications/1925/compendia/statab/47ed.html1924: Public Health Reports Vol. 40, No. 51 “The notifiable diseases: Prevalence during 1924 in States”1925: Public Health Reports Vol. 42, No. 1 “The notifiable diseases: Prevalence during 1924 in States”1926: US Census Bureau “Statistical Abstract of the United States: 1928” available here: https://www.census.gov/library/publications/1928/compendia/statab/50ed.html1927: US Census Bureau “Statistical Abstract of the United States: 1929” available here: https://www.census.gov/library/publications/1929/compendia/statab/50ed.html1928: US Census Bureau “Statistical Abstract of the United States: 1930” available here: https://www.census.gov/library/publications/1930/compendia/statab/52ed.html1929: US Census Bureau “Statistical Abstract of the United States: 1931” available here: https://www.census.gov/library/publications/1931/compendia/statab/53ed.html1931: US Census Bureau “Statistical Abstract of the United States: 1933” available here: https://www.census.gov/library/publications/1933/compendia/statab/55ed.html1932: US Census Bureau “Statistical Abstract of the United States: 1934” available here:https://www.census.gov/library/publications/1934/compendia/statab/56ed.html1933: US Census Bureau “Statistical Abstract of the United States: 1935” available here:https://www.census.gov/library/publications/1935/compendia/statab/57ed.html1934: US Census Bureau “Statistical Abstract of the United States: 1936” available here:https://www.census.gov/library/publications/1936/compendia/statab/58ed.html1935: US Census Bureau “Statistical Abstract of the United States: 1937” available here:https://www.census.gov/library/publications/1938/compendia/statab/59ed.html1936: US Census Bureau “Statistical Abstract of the United States: 1938” available here:https://www.census.gov/library/publications/1939/compendia/statab/60ed.html1937: US Census Bureau “Statistical Abstract of the United States: 1938” available here:https://www.census.gov/library/publications/1940/compendia/statab/61ed.html1938: Public Health Reports Vol. 55 No. 7 Notifiable Diseases in the United States, 1938: Morbidity and Mortality Summaries for Certain Important Communicable Diseases1939: Public Health Reports Vol. 56 No. 7 Notifiable Diseases in the United States, 1939: Morbidity and Mortality Summaries for Certain Important Communicable Diseases1940: Public Health Reports Vol. 57 No. 7 Notifiable Diseases in the United States, 1940: Morbidity and Mortality Summaries for Certain Important Communicable Diseases1949: US Census Bureau “Statistical Abstract of the United States: 1952” available here:https://www.census.gov/library/publications/1952/compendia/statab/73ed.html1950-1959: Morbidity and Mortality Weekly Report Annual Supplement Summary Vol. 9, No. 53 by the US Public Health Service1960-1968: Morbidity and Mortality Weekly Report Annual Supplement Summary 1970 Vol. 19, No. 53 by the US Public Health Service 1969: Morbidity and Mortality Weekly Report Annual Supplement Summary 1972 Vol. 21, No. 53 by the US Public Health Service 1970-1978: Morbidity and Mortality Weekly Report Annual Supplement Summary 1980 Vol. 29, No. 54 by the US Public Health Service 1982-1991: Morbidity and Mortality Weekly Report Annual Supplement Summary 1993 Vol. 42, No. 53 by the US Public Health Service 1992-1998: Morbidity and Mortality Weekly Report 2001 Vol. 48 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm4853a1.htm1999-2001: Morbidity and Mortality Weekly Report 2003 Vol. 52 Nr. 54 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5254a1.htm2002-2008: Morbidity and Mortality Weekly Report 2010 Vol. 59 Nr. 53 https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5953a1.htm2009-2014: Morbidity and Mortality Weekly Report 2016 Vol. 63 Nr. 54 DOI: http://dx.doi.org/10.15585/mmwr.mm6354a1US Population estimates: 1921-1949: US Census Bureau “Statistical Abstract of the United States: 1950” available here:https://www.census.gov/library/publications/1950/compendia/statab/71ed.html1950-2015: United Nations DESA Population Division (2017) “World Population Prospects Report” available here: https://esa.un.org/unpd/wpp/DVD/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2017_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx

type UsOpinionOnWivesWorking19361998OwidCompilationDataset 

type UsOpinionOnWivesWorking19361998OwidCompilationDataset struct {
	PercApproveOfWifeWorkingIfHusbandCanSupportOwid2017 *float64 `json:"perc_approve_of_wife_working_if_husband_can_support_owid_2017"`
}

The question asked in the Fogli and Veldkamp data varies slightly, see their appendix (B.2 Survey data) for more detail. GSS survey respondents answered the question "Do you approve or disapprove of a married woman earning money in business or industry if she has a husband capable of supporting her?"

type UsPublicTrustInGovernmentPewResearchCenterDataset 

type UsPublicTrustInGovernmentPewResearchCenterDataset struct {
	PublicTrustGovernment *float64 `json:"public_trust_government"`
}

For those years with estimates from more than one poll, the reported figure corresponds to the average for that year.

type UsRevenuePublicSchoolsUsBureauOfTheCensusAndNces2017Dataset 

type UsRevenuePublicSchoolsUsBureauOfTheCensusAndNces2017Dataset struct {
	TotalUsBureauOfTheCensusAndNces2017             *float64 `json:"total_us_bureau_of_the_census_and_nces_2017"`
	FederalunassignedUsBureauOfTheCensusAndNces2017 *float64 `json:"federalunassigned_us_bureau_of_the_census_and_nces_2017"`
	StateUsBureauOfTheCensusAndNces2017             *float64 `json:"state_us_bureau_of_the_census_and_nces_2017"`
	LocalUsBureauOfTheCensusAndNces2017             *float64 `json:"local_us_bureau_of_the_census_and_nces_2017"`
}

type UsaConsumerPriceIndexGoodsAndServices19972017UsBureauOfLaborStatistics2017Dataset 

type UsaConsumerPriceIndexGoodsAndServices19972017UsBureauOfLaborStatistics2017Dataset struct {
	HouseholdFurnishingsUsBureauOfLaborStatistics2017 *float64 `json:"household_furnishings_us_bureau_of_labor_statistics_2017"`
	HouseholdEnergyUsBureauOfLaborStatistics2017      *float64 `json:"household_energy_us_bureau_of_labor_statistics_2017"`
	ChildcareUsBureauOfLaborStatistics2017            *float64 `json:"childcare_us_bureau_of_labor_statistics_2017"`
	CollegeTuitionFeesUsBureauOfLaborStatistics2017   *float64 `json:"college_tuition_fees_us_bureau_of_labor_statistics_2017"`
	EducationUsBureauOfLaborStatistics2017            *float64 `json:"education_us_bureau_of_labor_statistics_2017"`
	ToysUsBureauOfLaborStatistics2017                 *float64 `json:"toys_us_bureau_of_labor_statistics_2017"`
	FoodAndBeveragesUsBureauOfLaborStatistics2017     *float64 `json:"food_and_beverages_us_bureau_of_labor_statistics_2017"`
	HousingUsBureauOfLaborStatistics2017              *float64 `json:"housing_us_bureau_of_labor_statistics_2017"`
	MedicalCareUsBureauOfLaborStatistics2017          *float64 `json:"medical_care_us_bureau_of_labor_statistics_2017"`
	NewCarsUsBureauOfLaborStatistics2017              *float64 `json:"new_cars_us_bureau_of_labor_statistics_2017"`
	ClothingUsBureauOfLaborStatistics2017             *float64 `json:"clothing_us_bureau_of_labor_statistics_2017"`
	TvsUsBureauOfLaborStatistics2017                  *float64 `json:"tvs_us_bureau_of_labor_statistics_2017"`
	SoftwareUsBureauOfLaborStatistics2017             *float64 `json:"software_us_bureau_of_labor_statistics_2017"`
	AllItemsUsBureauOfLaborStatistics2017             *float64 `json:"all_items_us_bureau_of_labor_statistics_2017"`
	PublicTransportationUsBureauOfLaborStatistics2017 *float64 `json:"public_transportation_us_bureau_of_labor_statistics_2017"`
}

The Bureau of Labor Statistics report on the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at a national, city and state level.

Data used in this sequence are based on the USA national average of urban consumers, relative to December 1997 (which has been given the value of zero). CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.

type UsaPatentsGrantedUsPatentAndTrademarkOfficeDataset 

type UsaPatentsGrantedUsPatentAndTrademarkOfficeDataset struct {
	TotalPatentsGranted        *float64 `json:"total_patents_granted"`
	Chemical                   *float64 `json:"chemical"`
	ComputersAndCommunications *float64 `json:"computers_and_communications"`
	Medical                    *float64 `json:"medical"`
	Electronics                *float64 `json:"electronics"`
	Mechanical                 *float64 `json:"mechanical"`
	Other                      *float64 `json:"other"`
}

Total patents granted from 1790-1839, as well as 2015 is based on the US Patent and Trademark Office's reported "Utility Patents" which refers to "patents for inventions". Available at: https://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm'Chemical' patents includes those from agriculture, food, textiles, coatings, gas, organic compounds, and resins.'Computers & communications' includes those from communications, computer hardware and software, peripherals, information storage, electronic business methods and software.'Medical' includes drugs, surgery, medical instruments and biotechnology. 'Electrical & electronics' includes electrical devices, electrical lighting, measuring, testing, nuclear, x-rays, power systems, and semiconductor devices.'Mechanical' includes materials processing & handling, metal working, motors, engines, parts, optics and transportation.'Others' includes agriculture, husbandry, food, amusement devices, apparel & textile, earth working & wells, furniture, house fixtures, heating, pipes & joints, and receptacles.

type UsaPolioCasesAndDeaths19102010OwidBasedOnUsPublicHealthService19101951UsCenterForDiseaseControl19602010AndWho2011Dataset 

type UsaPolioCasesAndDeaths19102010OwidBasedOnUsPublicHealthService19101951UsCenterForDiseaseControl19602010AndWho2011Dataset struct {
	PolioCasesOwidBasedOnUsPublicHealthService1910_1951AndUsCenterForDiseaseControl1960_2010     *float64 `json:"polio_cases_owid_based_on_us_public_health_service_1910_1951_and_us_center_for_disease_control_1960_2010"`
	PolioDeathsOwidBasedOnUsPublicHealthService1910_1951AndUsCenterForDiseaseControl1960_2010    *float64 `json:"polio_deaths_owid_based_on_us_public_health_service_1910_1951_and_us_center_for_disease_control_1960_2010"`
	PolioCaseRateOwidBasedOnUsPublicHealthService1910_1951AndUsCenterForDiseaseControl1960_2010  *float64 `json:"polio_case_rate_owid_based_on_us_public_health_service_1910_1951_and_us_center_for_disease_control_1960_2010"`
	PolioDeathRateOwidBasedOnUsPublicHealthService1910_1951AndUsCenterForDiseaseControl1960_2010 *float64 `json:"polio_death_rate_owid_based_on_us_public_health_service_1910_1951_and_us_center_for_disease_control_1960_2010"`
}

Polio case counts: 1910-1911: Public Health Reports Vol. 27, No. 16 “Poliomyelitis in the United States, 1910 and 1911” 1913: Public Health Reports Vol. 29, No. 28 “The Notifiable Diseases: Their Prevalence by States during 1913” 1914: Public Health Reports Vol. 39, No. 36 “The Notifiable Diseases: Reported Prevalence during 1914 by States” 1915-1937: Public Health Reports Vol. 54, No. 21 “Prevalence of Poliomyelitis in the United States in 1938” 1938: Public Health Reports Vol. 55, No. 10 “Notifiable Diseases in the United States, 1938: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1939: Public Health Reports Vol. 56, No. 7 “Notifiable Diseases in the United States, 1939: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1940: Public Health Reports Vol. 57, No. 7 “Notifiable Diseases in the United States, 1940: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1941: US Census Bureau “67th Statistical Abstract of the United States, 1946” https://www.census.gov/library/publications/1946/compendia/statab/67ed.html 1942-1947: Public Health Reports Vol. 63, No. 13 “Incidence of Poliomyelitis in 1947” 1948: Public Health Reports Vol. 64, No. 23 “Prevalence of Poliomyelitis in 1948” 1949 - 1950: Public Health Reports Vol. 67, No. 6 “Poliomyelitis in the United States, 1951” 1951-1960: Morbidity and Mortality Weekly Report Annual Supplement Vol. 9, No. 53 by the US Public Health Service 1961-1970: Morbidity and Mortality Weekly Report Annual Supplement Summary 1970 Vol. 19, No. 53 by the US Public Health Service 1971-1980: Morbidity and Mortality Weekly Report Annual Supplement Summary 1980 Vol. 29, No. 54 by the US Public Health Service 1981-2000: Morbidity and Mortality Weekly Report Annual Supplement Summary 2000 Vol. 49, No. 53 by the US Public Health Service 2001-2002: Morbidity and Mortality Weekly Report Annual Supplement Summary 2002 Vol. 51, No. 53 by the US Public Health Service 2003-2010: Morbidity and Mortality Weekly Report Annual Supplement Summary 2002 Vol. 59, No. 53 by the US Public Health Service Polio death counts: 1910-1911: Public Health Reports Vol. 27, No. 16 “Poliomyelitis in the United States, 1910 and 1911” 1913: Public Health Reports Vol. 29, No. 28 “The Notifiable Diseases: Their Prevalence by States during 1913” 1914: Public Health Reports Vol. 39, No. 36 “The Notifiable Diseases: Reported Prevalence during 1914 by States” 1915-1937: Public Health Reports Vol. 54, No. 21 “Prevalence of Poliomyelitis in the United States in 1938” 1938: Public Health Reports Vol. 55, No. 10 “Notifiable Diseases in the United States, 1938: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1939: Public Health Reports Vol. 56, No. 7 “Notifiable Diseases in the United States, 1939: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1940: Public Health Reports Vol. 57, No. 7 “Notifiable Diseases in the United States, 1940: Morbidity and Mortality Summaries for Certain Important Communicable Diseases” 1943-1944: Public Health Reports Vol. 61, No. 25 “Incidence of Poliomyelitis in the United States in 1945” 1945: Public Health Reports Vol. 62, No. 25 “Incidence of Poliomyelitis in 1946” 1946: Public Health Reports Vol. 63, No. 13 “Incidence of Poliomyelitis in 1947” 1947-1948: Public Health Reports Vol. 64, No. 23 “Prevalence of Poliomyelitis in 1948” 1949: Public Health Reports Vol. 67, No. 6 “Poliomyelitis in the United States, 1951” 1950-1959: Morbidity and Mortality Weekly Report Annual Supplement Vol. 9, No. 53 by the US Public Health Service 1960-1968: Morbidity and Mortality Weekly Report Annual Supplement Summary 1970 Vol. 19, No. 53 by the US Public Health Service 1969: Morbidity and Mortality Weekly Report Annual Supplement Summary 1972 Vol. 21, No. 53 by the US Public Health Service 1970-1979: Morbidity and Mortality Weekly Report Annual Supplement Summary 1980 Vol. 29, No. 54 by the US Public Health Service 1980-1982: Morbidity and Mortality Weekly Report Annual Supplement Summary 1988 Vol. 37, No. 54 by the US Public Health Service 1983-1991: Morbidity and Mortality Weekly Report Annual Supplement Summary 1993 Vol. 42, No. 53 by the US Public Health Service 1992-1998: Morbidity and Mortality Weekly Report Annual Supplement Summary 2000 Vol. 49, No. 53 by the US Public Health Service 1992-1998: Morbidity and Mortality Weekly Report Annual Supplement Summary 2000 Vol. 49, No. 53 by the US Public Health Service 1999-2000: Morbidity and Mortality Weekly Report Annual Supplement Summary 2002 Vol. 51, No. 53 by the US Public Health Service 2002-2008: Morbidity and Mortality Weekly Report Annual Supplement Summary 2002 Vol. 59, No. 53 by the US Public Health Service US Population estimates: 1910-1919: US Census Bureau “43rd Statistical Abstract of the United States 1920” https://www.census.gov/library/publications/1921/compendia/statab/43ed.html US Census Bureau “71st Statistical Abstract of the United States 1950” https://www.census.gov/library/publications/1950/compendia/statab/71ed.html The United Nations DESA Population Division latest "World Population Prospects" Report from 2017 (Link above) The number of cases of polio and the deaths due to polio include any type of poliomyelitis, i.e. both the wild and vaccine-derived type as well as both indigenous and imported cases. This explains why, even though the US were declared polio free in 1979, there are still cases reported.

type UseOfDifferentSocialMediaSitesByDemographicGroupsDataset 

type UseOfDifferentSocialMediaSitesByDemographicGroupsDataset struct {
	SocialMediaUseAmongMenInTheUsPerc   *float64 `json:"social_media_use_among_men_in_the_us_perc"`
	SocialMediaUseAmongWomenInTheUsPerc *float64 `json:"social_media_use_among_women_in_the_us_perc"`
}

Respondents who did not give an answer are excluded from the estimates.

type VaccineCoverageAndDiseaseBurdenWho2017Dataset 

type VaccineCoverageAndDiseaseBurdenWho2017Dataset struct {
	BcgImmunizationCoverageAmong1YearOldsWho2017                            *float64 `json:"bcg_immunization_coverage_among_1_year_olds_who_2017"`
	HepatitisBHepb3ImmunizationCoverageAmong1YearOldsWho2017                *float64 `json:"hepatitis_b_hepb3_immunization_coverage_among_1_year_olds_who_2017"`
	Dtp3ImmunizationCoverageAmong1YearOldsWho2017                           *float64 `json:"dtp3_immunization_coverage_among_1_year_olds_who_2017"`
	PolioPol3ImmunizationCoverageAmong1YearOldsWho2017                      *float64 `json:"polio_pol3_immunization_coverage_among_1_year_olds_who_2017"`
	MeaslesMcvImmunizationCoverageAmong1YearOldsWho2017                     *float64 `json:"measles_mcv_immunization_coverage_among_1_year_olds_who_2017"`
	NumberOfConfirmedTetanusCasesWho2017                                    *float64 `json:"number_of_confirmed_tetanus_cases_who_2017"`
	NumberConfirmedPolioCasesWho2017                                        *float64 `json:"number_confirmed_polio_cases_who_2017"`
	NumberOfConfirmedPertussisCasesWho2017                                  *float64 `json:"number_of_confirmed_pertussis_cases_who_2017"`
	NumberOfConfirmedMeaslesCasesWho2017                                    *float64 `json:"number_of_confirmed_measles_cases_who_2017"`
	NumberOfConfirmedDiphtheriaCasesWho2017                                 *float64 `json:"number_of_confirmed_diphtheria_cases_who_2017"`
	EstimatedDeathsDueToTuberculosisPer100_000PopulationExcludingHivWho2017 *float64 `json:"estimated_deaths_due_to_tuberculosis_per_100_000_population_excluding_hiv_who_2017"`
	EstimatedNumberOfDeathsDueToTuberculosisExcludingHivWho2017             *float64 `json:"estimated_number_of_deaths_due_to_tuberculosis_excluding_hiv_who_2017"`
}

type ValueOfGlobalMerchandiseImportsAndExportsFouquinAndHugotCepii2016NationalDataDataset 

type ValueOfGlobalMerchandiseImportsAndExportsFouquinAndHugotCepii2016NationalDataDataset struct {
	ValueOfGlobalMerchandiseImportsAsAShareOfGdpFouquinAndHugotCepii2016NationalData *float64 `json:"value_of_global_merchandise_imports_as_a_share_of_gdp_fouquin_and_hugot_cepii_2016_national_data"`
	ValueOfGlobalMerchandiseExportsAsAShareOfGdpFouquinAndHugotCepii2016NationalData *float64 `json:"value_of_global_merchandise_exports_as_a_share_of_gdp_fouquin_and_hugot_cepii_2016_national_data"`
}

To calculate country exports (imports) to (from) the rest of the world, the total value of exports (imports) by country, per year, is divided by the country's GDP. Calculations use Fouquin and Hugot (CEPII 2016) national trade data.The time series 'World' corresponds to the World's total exports (imports) (i.e. the sum of exports (imports) reported by all countries in the dataset).The total export (import) values of regional income aggregates have been calculated using the World Bank's income groupings. These time series begin in 1970, where the number of countries are more representative. Similarly, total export (import) values by continental grouping begin in 1960. Germany's time series is comprised of West Germany, and Germany. East Germany has been excluded for the purposes of Germany's calculations.Russia's time series comprises Russia and the USSR.

type ViolentDeathsInConflictsAndOneSidedViolenceSince1989ByRegionAndTypeOfViolenceUcdp2022Dataset 

type ViolentDeathsInConflictsAndOneSidedViolenceSince1989ByRegionAndTypeOfViolenceUcdp2022Dataset struct {
	DeathsInStateBasedConflict           *float64 `json:"deaths_in_state_based_conflict"`
	DeathsInNonStateConflict             *float64 `json:"deaths_in_non_state_conflict"`
	DeathsInOneSidedViolence             *float64 `json:"deaths_in_one_sided_violence"`
	DeathsInAllTypesOfConflict           *float64 `json:"deaths_in_all_types_of_conflict"`
	DeathsInStateBasedConflictPer100_000 *float64 `json:"deaths_in_state_based_conflict_per_100_000"`
	DeathsInNonStateConflictPer100_000   *float64 `json:"deaths_in_non_state_conflict_per_100_000"`
	DeathsInOneSidedViolencePer100_000   *float64 `json:"deaths_in_one_sided_violence_per_100_000"`
	DeathsInAllTypesOfConflictPer100_000 *float64 `json:"deaths_in_all_types_of_conflict_per_100_000"`
}

This data counts civilian and military deaths in conflicts and one-sided violence. The data counts only direct violent deaths (i.e. excluding deaths from disease or famine).It is based on the UCDP Georeferenced Event Dataset (GED) Global version 21.1. This presents deaths estimates for individual events. We aggregate deaths across years, broken down by conflict/violence type and by region.

type ViolentDisciplineInTheUsUsGeneralSocialSurvey2017Dataset 

type ViolentDisciplineInTheUsUsGeneralSocialSurvey2017Dataset struct {
	FavorSpankingToDisciplineChildStronglyAgreeUsGeneralSocialSurvey2017    *float64 `json:"favor_spanking_to_discipline_child_strongly_agree_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildAgreeUsGeneralSocialSurvey2017            *float64 `json:"favor_spanking_to_discipline_child_agree_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildDisagreeUsGeneralSocialSurvey2017         *float64 `json:"favor_spanking_to_discipline_child_disagree_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildStronglyDisagreeUsGeneralSocialSurvey2017 *float64 `json:"favor_spanking_to_discipline_child_strongly_disagree_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildDontKnowUsGeneralSocialSurvey2017         *float64 `json:"favor_spanking_to_discipline_child_dont_know_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildNoAnswerUsGeneralSocialSurvey2017         *float64 `json:"favor_spanking_to_discipline_child_no_answer_us_general_social_survey_2017"`
	FavorSpankingToDisciplineChildNotApplicableUsGeneralSocialSurvey2017    *float64 `json:"favor_spanking_to_discipline_child_not_applicable_us_general_social_survey_2017"`
}

The General Social Survey in the US asks: Do you strongly agree, agree, disagree, or strongly disagree that it is sometimes necessary to discipline a child with a good, hard spanking?

type ViolentDisciplineUnicef2017Dataset 

type ViolentDisciplineUnicef2017Dataset struct {
	PercentageOfChildren2_14WhoExperienceAnyViolentDisciplineUnicefGlobalDatabases2016             *float64 `json:"percentage_of_children_2_14_who_experience_any_violent_discipline_unicef_global_databases_2016"`
	PercentageOfBoys2_14WhoExperienceAnyViolentDisciplineUnicefGlobalDatabases2016                 *float64 `json:"percentage_of_boys_2_14_who_experience_any_violent_discipline_unicef_global_databases_2016"`
	PercentageOfGirls2_14WhoExperienceAnyViolentDisciplineUnicefGlobalDatabases2016                *float64 `json:"percentage_of_girls_2_14_who_experience_any_violent_discipline_unicef_global_databases_2016"`
	PercentageOfChildren2_14InUrbanAreasWhoExperienceAnyViolentDisciplineUnicefGlobalDatabases2016 *float64 `json:"percentage_of_children_2_14_in_urban_areas_who_experience_any_violent_discipline_unicef_global_databases_2016"`
	PercentageOfChildren2_14InRuralAreasWhoExperienceAnyViolentDisciplineUnicefGlobalDatabases2016 *float64 `json:"percentage_of_children_2_14_in_rural_areas_who_experience_any_violent_discipline_unicef_global_databases_2016"`
}

– The source notes that in some instances observations differ from the standard definition or refer to only part of a country.

– The dates associated to each observation correspond to the end of the survey used as underlying source

– Most estimates come from the Multiple Indicator Cluster Surveys (MICS). The MICS include a standard set of questions covering different disciplinary methods. It does allow survey respondents to report both violent and nonviolent forms of discipline.

–The source notes that estimates reported in UNICEF publications and in MICS country reports prior to 2010 were calculated using household weights that did not take into account the last-stage selection of children for the administration of the child discipline module in MICS surveys. (A random selection of one child aged 2–14 is undertaken for the administration of the child discipline module.) In January 2010, it was decided that more accurate estimates are produced by using a household weight that takes the last-stage selection into account. MICS3 data were recalculated using this approach. All estimates produced after 2010 use the revised estimates.

– When it was first implemented in MICS3, the child discipline module was administered only to mothers/primary caregivers, who were asked whether any of the disciplinary methods covered in the module had been used by any member of the household during the month preceding the interview. Beginning with MICS4, the methodology was changed: Any adult household member, not just the mother or primary caregiver, can now respond to the questions on child discipline.

type ViolentVictimizationUsBureauOfJusticeStatistics2017Dataset 

type ViolentVictimizationUsBureauOfJusticeStatistics2017Dataset struct {
	UsViolentVictimizationAtSchoolRatesPer1000People12UsBureauOfJusticeStatistics2017 *float64 `json:"us_violent_victimization_at_school_rates_per_1000_people_12_us_bureau_of_justice_statistics_2017"`
}

type VolcanicEruptionDeathsNgdcnoaaDataset 

type VolcanicEruptionDeathsNgdcnoaaDataset struct {
	VolcanicEruptionDeathsNgdcnoaa *float64 `json:"volcanic_eruption_deaths_ngdcnoaa"`
}

Data represents the estimated number of deaths from volcanic eruption events. This includes direct deaths from volcanic eruptions, in addition to secondary impacts (such as a tsunami or earthquake triggered by an eruption).Our World in Data have aggregated significant earthquake numbers by country/location per year. Due to data availability, reporting and evidence, it's expected that more recent data will be more complete than the long historical record.

type WagesInTheManufacturingSectorVsSeveralFoodPricesInTheUsUsBureauOfLaborStatistics2013Dataset 

type WagesInTheManufacturingSectorVsSeveralFoodPricesInTheUsUsBureauOfLaborStatistics2013Dataset struct {
	WageAndFoodPricesUsBureauOfLaborStatistics2013 *float64 `json:"wage_and_food_prices_us_bureau_of_labor_statistics_2013"`
}

Wages in the Manufacturing Sector vs. Several Food Prices USA  – BLS Data

type WaterAndSanitationWhoWash2021Dataset 

type WaterAndSanitationWhoWash2021Dataset struct {
	WatImp                   *float64 `json:"wat_imp"`
	WatBas                   *float64 `json:"wat_bas"`
	WatLim                   *float64 `json:"wat_lim"`
	WatUnimp                 *float64 `json:"wat_unimp"`
	WatSur                   *float64 `json:"wat_sur"`
	WatSm                    *float64 `json:"wat_sm"`
	SanImp                   *float64 `json:"san_imp"`
	SanBas                   *float64 `json:"san_bas"`
	SanLim                   *float64 `json:"san_lim"`
	SanUnimp                 *float64 `json:"san_unimp"`
	SanOd                    *float64 `json:"san_od"`
	SanSm                    *float64 `json:"san_sm"`
	HygBas                   *float64 `json:"hyg_bas"`
	HygLim                   *float64 `json:"hyg_lim"`
	HygNfac                  *float64 `json:"hyg_nfac"`
	WatImpNumber             *float64 `json:"wat_imp_number"`
	WatBasNumber             *float64 `json:"wat_bas_number"`
	WatLimNumber             *float64 `json:"wat_lim_number"`
	WatUnimpNumber           *float64 `json:"wat_unimp_number"`
	WatSurNumber             *float64 `json:"wat_sur_number"`
	WatSmNumber              *float64 `json:"wat_sm_number"`
	SanImpNumber             *float64 `json:"san_imp_number"`
	SanBasNumber             *float64 `json:"san_bas_number"`
	SanLimNumber             *float64 `json:"san_lim_number"`
	SanUnimpNumber           *float64 `json:"san_unimp_number"`
	SanOdNumber              *float64 `json:"san_od_number"`
	SanSmNumber              *float64 `json:"san_sm_number"`
	HygBasNumber             *float64 `json:"hyg_bas_number"`
	HygLimNumber             *float64 `json:"hyg_lim_number"`
	HygNfacNumber            *float64 `json:"hyg_nfac_number"`
	WatImpRural              *float64 `json:"wat_imp_rural"`
	WatBasRural              *float64 `json:"wat_bas_rural"`
	WatLimRural              *float64 `json:"wat_lim_rural"`
	WatUnimpRural            *float64 `json:"wat_unimp_rural"`
	WatSurRural              *float64 `json:"wat_sur_rural"`
	WatSmRural               *float64 `json:"wat_sm_rural"`
	SanImpRural              *float64 `json:"san_imp_rural"`
	SanBasRural              *float64 `json:"san_bas_rural"`
	SanLimRural              *float64 `json:"san_lim_rural"`
	SanUnimpRural            *float64 `json:"san_unimp_rural"`
	SanOdRural               *float64 `json:"san_od_rural"`
	SanSmRural               *float64 `json:"san_sm_rural"`
	HygBasRural              *float64 `json:"hyg_bas_rural"`
	HygLimRural              *float64 `json:"hyg_lim_rural"`
	HygNfacRural             *float64 `json:"hyg_nfac_rural"`
	WatImpNumberRural        *float64 `json:"wat_imp_number_rural"`
	WatBasNumberRural        *float64 `json:"wat_bas_number_rural"`
	WatLimNumberRural        *float64 `json:"wat_lim_number_rural"`
	WatUnimpNumberRural      *float64 `json:"wat_unimp_number_rural"`
	WatSurNumberRural        *float64 `json:"wat_sur_number_rural"`
	WatSmNumberRural         *float64 `json:"wat_sm_number_rural"`
	SanImpNumberRural        *float64 `json:"san_imp_number_rural"`
	SanBasNumberRural        *float64 `json:"san_bas_number_rural"`
	SanLimNumberRural        *float64 `json:"san_lim_number_rural"`
	SanUnimpNumberRural      *float64 `json:"san_unimp_number_rural"`
	SanOdNumberRural         *float64 `json:"san_od_number_rural"`
	SanSmNumberRural         *float64 `json:"san_sm_number_rural"`
	HygBasNumberRural        *float64 `json:"hyg_bas_number_rural"`
	HygLimNumberRural        *float64 `json:"hyg_lim_number_rural"`
	HygNfacNumberRural       *float64 `json:"hyg_nfac_number_rural"`
	WatLimUrban              *float64 `json:"wat_lim_urban"`
	WatImpUrban              *float64 `json:"wat_imp_urban"`
	WatBasUrban              *float64 `json:"wat_bas_urban"`
	WatUnimpUrban            *float64 `json:"wat_unimp_urban"`
	WatSurUrban              *float64 `json:"wat_sur_urban"`
	WatSmUrban               *float64 `json:"wat_sm_urban"`
	SanImpUrban              *float64 `json:"san_imp_urban"`
	SanBasUrban              *float64 `json:"san_bas_urban"`
	SanLimUrban              *float64 `json:"san_lim_urban"`
	SanUnimpUrban            *float64 `json:"san_unimp_urban"`
	SanOdUrban               *float64 `json:"san_od_urban"`
	SanSmUrban               *float64 `json:"san_sm_urban"`
	HygBasUrban              *float64 `json:"hyg_bas_urban"`
	HygLimUrban              *float64 `json:"hyg_lim_urban"`
	HygNfacUrban             *float64 `json:"hyg_nfac_urban"`
	WatImpNumberUrban        *float64 `json:"wat_imp_number_urban"`
	WatBasNumberUrban        *float64 `json:"wat_bas_number_urban"`
	WatLimNumberUrban        *float64 `json:"wat_lim_number_urban"`
	WatUnimpNumberUrban      *float64 `json:"wat_unimp_number_urban"`
	WatSurNumberUrban        *float64 `json:"wat_sur_number_urban"`
	WatSmNumberUrban         *float64 `json:"wat_sm_number_urban"`
	SanImpNumberUrban        *float64 `json:"san_imp_number_urban"`
	SanBasNumberUrban        *float64 `json:"san_bas_number_urban"`
	SanLimNumberUrban        *float64 `json:"san_lim_number_urban"`
	SanUnimpNumberUrban      *float64 `json:"san_unimp_number_urban"`
	SanOdNumberUrban         *float64 `json:"san_od_number_urban"`
	SanSmNumberUrban         *float64 `json:"san_sm_number_urban"`
	HygBasNumberUrban        *float64 `json:"hyg_bas_number_urban"`
	HygLimNumberUrban        *float64 `json:"hyg_lim_number_urban"`
	HygNfacNumberUrban       *float64 `json:"hyg_nfac_number_urban"`
	WatBasMinusSm            *float64 `json:"wat_bas_minus_sm"`
	WatBasMinusSmNumber      *float64 `json:"wat_bas_minus_sm_number"`
	SanBasMinusSm            *float64 `json:"san_bas_minus_sm"`
	SanBasMinusSmNumber      *float64 `json:"san_bas_minus_sm_number"`
	WatBasMinusSmRural       *float64 `json:"wat_bas_minus_sm_rural"`
	WatBasMinusSmNumberRural *float64 `json:"wat_bas_minus_sm_number_rural"`
	SanBasMinusSmRural       *float64 `json:"san_bas_minus_sm_rural"`
	SanBasMinusSmNumberRural *float64 `json:"san_bas_minus_sm_number_rural"`
	WatBasMinusSmUrban       *float64 `json:"wat_bas_minus_sm_urban"`
	WatBasMinusSmNumberUrban *float64 `json:"wat_bas_minus_sm_number_urban"`
	SanBasMinusSmUrban       *float64 `json:"san_bas_minus_sm_urban"`
	SanBasMinusSmNumberUrban *float64 `json:"san_bas_minus_sm_number_urban"`
	WatImpNumberWithout      *float64 `json:"wat_imp_number_without"`
	WatBasNumberWithout      *float64 `json:"wat_bas_number_without"`
	WatSmNumberWithout       *float64 `json:"wat_sm_number_without"`
	WatImpWithout            *float64 `json:"wat_imp_without"`
	WatBasWithout            *float64 `json:"wat_bas_without"`
	WatSmWithout             *float64 `json:"wat_sm_without"`
	SanImpNumberWithout      *float64 `json:"san_imp_number_without"`
	SanBasNumberWithout      *float64 `json:"san_bas_number_without"`
	SanSmNumberWithout       *float64 `json:"san_sm_number_without"`
	SanImpWithout            *float64 `json:"san_imp_without"`
	SanBasWithout            *float64 `json:"san_bas_without"`
	SanSmWithout             *float64 `json:"san_sm_without"`
	HygBasNumberWithout      *float64 `json:"hyg_bas_number_without"`
	HygBasWithout            *float64 `json:"hyg_bas_without"`
}

type WaterResourcesByContinentFaoAquastatDataset 

type WaterResourcesByContinentFaoAquastatDataset struct {
	Precipitation                        *float64 `json:"precipitation"`
	InternalRenewableFreshwaterResources *float64 `json:"internal_renewable_freshwater_resources"`
	PerCapitaRenewableResources          *float64 `json:"per_capita_renewable_resources"`
}

Precipitation is measured in terms of average volumetric precipitation rates per year (based on trends to the year 2015). Internal renewable freshwater resources measures the total volume of resources which could be sustainably used per year, without depleting internal reserves. Per capita renewable resources are based on the average volume per person which could be used in 2015 without depleting reserves.

type WaterWithdrawalsAndConsumptionAquastatDataset 

type WaterWithdrawalsAndConsumptionAquastatDataset struct {
	TotalWaterWithdrawalPerCapita             *float64 `json:"total_water_withdrawal_per_capita"`
	TotalWaterWithdrawal                      *float64 `json:"total_water_withdrawal"`
	MunicipalWaterAsShareOfTotalWithdrawal    *float64 `json:"municipal_water_as_share_of_total_withdrawal"`
	MunicipalWaterWithdrawal                  *float64 `json:"municipal_water_withdrawal"`
	IrrigationWaterWithdrawal                 *float64 `json:"irrigation_water_withdrawal"`
	IrrigationWaterRequirement                *float64 `json:"irrigation_water_requirement"`
	IndustrialWaterAsShareOfTotalWithdrawal   *float64 `json:"industrial_water_as_share_of_total_withdrawal"`
	IndustrialWaterWithdrawal                 *float64 `json:"industrial_water_withdrawal"`
	AgriculturalWaterWithdrawal               *float64 `json:"agricultural_water_withdrawal"`
	AgriculturalWaterAsShareOfTotalWithdrawal *float64 `json:"agricultural_water_as_share_of_total_withdrawal"`
}

Data definitions for each variable included in the AQUASTAT Database is as follows:Agricultural water withdrawal: "Annual quantity of self-supplied water withdrawn for irrigation, livestock and aquaculture purposes. It can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. Water for the dairy and meat industries and industrial processing of harvested agricultural products is included under industrial water withdrawal."Industrial water withdrawal: "Annual quantity of self-supplied water withdrawn for industrial uses. It can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. This sector refers to self-supplied industries not connected to the public distribution network. The ratio between net consumption and withdrawal is estimated at less than 5%. It includes water for the cooling of thermoelectric and nuclear power plants, but it does not include hydropower. Water withdrawn by industries that are connected to the public supply network is generally included in municipal water withdrawal."Municipal water withdrawal: "Annual quantity of water withdrawn primarily for the direct use by the population. It can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. It is usually computed as the total water withdrawn by the public distribution network. It can include that part of the industries and urban agriculture, which is connected to the municipal network. The ratio between the net consumption and the water withdrawn can vary from 5 to 15% in urban areas and from 10 to 50% in rural areas."Irrigation water requirement: "The quantity of water exclusive of precipitation and soil moisture (i.e. quantity of irrigation water) required for normal crop production. It consists of water to ensure that the crop receives its full crop water requirement (i.e. irrigation consumptive water use, as well as extra water for flooding of paddy fields to facilitate land preparation and protect the plant and for leaching salt when necessary to allow for plant growth). It is usually expressed in water depth (millimetres) or water volume (m3) and may be stated in monthly, seasonal or annual terms, or for a crop period. It corresponds to net irrigation water requirement."Irrigation water withdrawal: "Annual quantity of water withdrawn for irrigation purposes. In the AQUASTAT database water withdrawal for irrigation is part of agricultural water withdrawal, together with water withdrawal for livestock (watering and cleaning) and water withdrawal for aquaculture. It can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. The amount of water withdrawn for irrigation by far exceeds the consumptive use of irrigation because of water lost in its distribution from its source to the crops. The term "water requirement ratio" (sometimes also called "irrigation efficiency") is used to indicate the ratio between the net irrigation water requirements or crop water requirements, which is the volume of water needed to compensate for the deficit between potential evapotranspiration and effective precipitation over the growing period of the crop, and the amount of water withdrawn for irrigation including the losses. In the specific case of paddy rice irrigation, additional water is needed for flooding to facilitate land preparation and for plant protection. In that case, irrigation water requirements are the sum of rainfall deficit and the water needed to flood paddy fields. At scheme level, water requirement ratio values can vary from less than 20 percent to more than 80 percent."Total water withdrawal: "Annual quantity of water withdrawn for agricultural, industrial and municipal purposes. It can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. It does not include in-stream uses, which are characterized by a very low net consumption rate, such as recreation, navigation, hydropower, inland capture fisheries, etc."

type WattsPerMipsKurzweilDataset 

type WattsPerMipsKurzweilDataset struct {
	ComputingEfficiency *float64 `json:"computing_efficiency"`
}

Computing efficiency is measured as the number of watts used per computation/operation per second. Here it is measured in Watts per MIPS (Millions of instructions per second).

type WealthAsPercentNationalIncomeByWealthTypePiketty2014Dataset 

type WealthAsPercentNationalIncomeByWealthTypePiketty2014Dataset struct {
	LandWealthPercOfNationalIncome                                *float64 `json:"land_wealth_perc_of_national_income"`
	HousingWealthPercOfNationalIncome                             *float64 `json:"housing_wealth_perc_of_national_income"`
	OtherDomesticWealthPercOfNationalIncome                       *float64 `json:"other_domestic_wealth_perc_of_national_income"`
	NetForeignWealthPercOfNationalIncome                          *float64 `json:"net_foreign_wealth_perc_of_national_income"`
	TotalWealthAsPercOfNationalIncome                             *float64 `json:"total_wealth_as_perc_of_national_income"`
	OtherDomesticWealthPlusNetForeignWealthAsPercOfNationalIncome *float64 `json:"other_domestic_wealth_plus_net_foreign_wealth_as_perc_of_national_income"`
}

National wealth held in different forms, expressed as a percentage of national income at the time.Taken from Thomas Piketty's book, 'Capital in the 21st Century'. All the data used in the book is made available by Piketty on his website here: http://piketty.pse.ens.fr/files/capital21c/en/

type WealthPerCapitaByComponentByCountryWorldBank2017Dataset 

type WealthPerCapitaByComponentByCountryWorldBank2017Dataset struct {
	TotalWealthPerCapita                                         *float64 `json:"total_wealth_per_capita"`
	ProducedCapital                                              *float64 `json:"produced_capital"`
	NaturalCapitalRenewableExcludingSubSoilAssets                *float64 `json:"natural_capital_renewable_excluding_sub_soil_assets"`
	ForestTimber                                                 *float64 `json:"forest_timber"`
	ForestNonTimber                                              *float64 `json:"forest_non_timber"`
	ProtectedAreas                                               *float64 `json:"protected_areas"`
	Cropland                                                     *float64 `json:"cropland"`
	Pastureland                                                  *float64 `json:"pastureland"`
	SubSoilAssetsFossilEnergyResourcesMetalsAndMinerals          *float64 `json:"sub_soil_assets_fossil_energy_resources_metals_and_minerals"`
	FossilEnergyResourcesOilNaturalGasCoal                       *float64 `json:"fossil_energy_resources_oil_natural_gas_coal"`
	Oil                                                          *float64 `json:"oil"`
	NaturalGas                                                   *float64 `json:"natural_gas"`
	Coal                                                         *float64 `json:"coal"`
	MetalsAndMinerals                                            *float64 `json:"metals_and_minerals"`
	HumanCapital                                                 *float64 `json:"human_capital"`
	HumanCapitalMale                                             *float64 `json:"human_capital_male"`
	HumanCapitalFemale                                           *float64 `json:"human_capital_female"`
	NetForeignAssets                                             *float64 `json:"net_foreign_assets"`
	AdjustedNetNationalIncome                                    *float64 `json:"adjusted_net_national_income"`
	AdjustedNetSaving                                            *float64 `json:"adjusted_net_saving"`
	Gdp                                                          *float64 `json:"gdp"`
	GrossNationalIncome                                          *float64 `json:"gross_national_income"`
	NaturalCapitalTimberNonTimberProtectedAreasLandSubSoilAssets *float64 `json:"natural_capital_timber_non_timber_protected_areas_land_sub_soil_assets"`
	LandCropPasture                                              *float64 `json:"land_crop_pasture"`
}

type WealthPerCapitaByComponentForVariousCountryGroupingsWorldBank2017Dataset 

type WealthPerCapitaByComponentForVariousCountryGroupingsWorldBank2017Dataset struct {
	TotalWealth                                                  *float64 `json:"total_wealth"`
	ProducedCapitalIncludingUrbanLand                            *float64 `json:"produced_capital_including_urban_land"`
	NaturalCapitalTimberNonTimberProtectedAreasLandSubSoilAssets *float64 `json:"natural_capital_timber_non_timber_protected_areas_land_sub_soil_assets"`
	NaturalCapitalRenewableExcludingSubSoilAssets                *float64 `json:"natural_capital_renewable_excluding_sub_soil_assets"`
	ForestTimber                                                 *float64 `json:"forest_timber"`
	ForestNonTimber                                              *float64 `json:"forest_non_timber"`
	ProtectedAreas                                               *float64 `json:"protected_areas"`
	LandCropPasture                                              *float64 `json:"land_crop_pasture"`
	Cropland                                                     *float64 `json:"cropland"`
	Pastureland                                                  *float64 `json:"pastureland"`
	SubSoilAssetsFossilEnergyResourcesMetalsAndMinerals          *float64 `json:"sub_soil_assets_fossil_energy_resources_metals_and_minerals"`
	FossilEnergyResourcesOilNaturalGasCoal                       *float64 `json:"fossil_energy_resources_oil_natural_gas_coal"`
	Oil                                                          *float64 `json:"oil"`
	NaturalGas                                                   *float64 `json:"natural_gas"`
	Coal                                                         *float64 `json:"coal"`
	MetalsAndMinerals                                            *float64 `json:"metals_and_minerals"`
	HumanCapital                                                 *float64 `json:"human_capital"`
	HumanCapitalTotalMale                                        *float64 `json:"human_capital_total_male"`
	HumanCapitalTotalFemale                                      *float64 `json:"human_capital_total_female"`
	NetForeignAssets                                             *float64 `json:"net_foreign_assets"`
	AdjustedNetNationalIncome                                    *float64 `json:"adjusted_net_national_income"`
	AdjustedNetSaving                                            *float64 `json:"adjusted_net_saving"`
	GrossDomesticProduct                                         *float64 `json:"gross_domestic_product"`
	GrossNationalIncome                                          *float64 `json:"gross_national_income"`
}

type WealthTotalByComponentForVariousCountryGroupingsWorldBank2017Dataset 

type WealthTotalByComponentForVariousCountryGroupingsWorldBank2017Dataset struct {
	TotalWealth                                                  *float64 `json:"total_wealth"`
	ProducedCapitalIncludingUrbanLand                            *float64 `json:"produced_capital_including_urban_land"`
	NaturalCapitalTimberNonTimberProtectedAreasLandSubSoilAssets *float64 `json:"natural_capital_timber_non_timber_protected_areas_land_sub_soil_assets"`
	ForestTimber                                                 *float64 `json:"forest_timber"`
	ForestNonTimber                                              *float64 `json:"forest_non_timber"`
	ProtectedAreas                                               *float64 `json:"protected_areas"`
	LandCropPasture                                              *float64 `json:"land_crop_pasture"`
	Cropland                                                     *float64 `json:"cropland"`
	Pastureland                                                  *float64 `json:"pastureland"`
	SubSoilAssetsFossilEnergyResourcesMetalsAndMinerals          *float64 `json:"sub_soil_assets_fossil_energy_resources_metals_and_minerals"`
	FossilEnergyResourcesOilNaturalGasCoal                       *float64 `json:"fossil_energy_resources_oil_natural_gas_coal"`
	Oil                                                          *float64 `json:"oil"`
	NaturalGas                                                   *float64 `json:"natural_gas"`
	Coal                                                         *float64 `json:"coal"`
	MetalsAndMinerals                                            *float64 `json:"metals_and_minerals"`
	HumanCapital                                                 *float64 `json:"human_capital"`
	HumanCapitalMale                                             *float64 `json:"human_capital_male"`
	HumanCapitalFemale                                           *float64 `json:"human_capital_female"`
	NetForeignAssets                                             *float64 `json:"net_foreign_assets"`
	AdjustedNetNationalIncome                                    *float64 `json:"adjusted_net_national_income"`
	AdjustedNetSaving                                            *float64 `json:"adjusted_net_saving"`
	GrossDomesticProduct                                         *float64 `json:"gross_domestic_product"`
	GrossNationalIncome                                          *float64 `json:"gross_national_income"`
	NaturalCapitalRenewableExcludingSubSoilAssets                *float64 `json:"natural_capital_renewable_excluding_sub_soil_assets"`
}

type WeatherFatalityRatesInTheUsOwidBasedOnNoaaAndLopezHolleAndPopulationDataDataset 

type WeatherFatalityRatesInTheUsOwidBasedOnNoaaAndLopezHolleAndPopulationDataDataset struct {
	Lightning  *float64 `json:"lightning"`
	Tornado    *float64 `json:"tornado"`
	Flood      *float64 `json:"flood"`
	Hurricane  *float64 `json:"hurricane"`
	Heat       *float64 `json:"heat"`
	Cold       *float64 `json:"cold"`
	Winter     *float64 `json:"winter"`
	RipCurrent *float64 `json:"rip_current"`
	Wind       *float64 `json:"wind"`
}

Data on the annual fatalities (since 1940) to various weather events in the US is from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service. This dataset is online here: http://www.lightningsafety.noaa.gov/victims.shtmlData on lightning from lightning earlier than 1991 is not from this dataset from NOAA, but from the Lopez and Holle paper cited below. I have calculated the fatality rate based on the NOAA fatality counts and estimates of the US population. These estimates are based on US census data for the period before 1960 and on World Bank data for the period since then.The US census data is online here: https://www2.census.gov/programs-surveys/popest/tables/1900-1980/national/totals/popclockest.txtThe World Bank estimates are online here: http://data.worldbank.org/indicator/SP.POP.TOTLData on the lightning fatality rate until 1990 is from Raúl E. Lopez and Ronald L. Holle (1997) – Changes in the Number of Lightning Deaths in the United States during the Twentieth Century. Journal of Climate; Volume 11.This fatality rate is not calculated for the entire US since in earlier periods not all US states reported the number of annual fatalities. (In the calculation of the fatality rate Lopez and Holle have taken only the population of the reporting states into account so that the population in the numerator and the denominator refer to the same group of people.)

type WeeklyHomeProductionHoursInTheUsaRamey2009AndRameyFrancis2009Dataset 

type WeeklyHomeProductionHoursInTheUsaRamey2009AndRameyFrancis2009Dataset struct {
	HomeProductionWorkingHoursPerWeekInTheUsByGenderAndDemographicGroupRameyAndFrancis2009 *float64 `json:"home_production_working_hours_per_week_in_the_us_by_gender_and_demographic_group_ramey_and_francis_2009"`
}

Following Ramey and Francis (2009), "the activities included in home production are: planning, purchasing goods and services (except medical and personal care services), care of children and adults (both in the household and outside the household), general cleaning, care and repair of the house and grounds (including yard work, but excluding gardening), preparing and clearing food, making, mending, and laundering of clothing and other household textiles.".Ramey and Francis (2009) estimations are based on data assembled by Ramey (2009).Ramey (2009)'s data are assembled and estimated from samples based on a multitude of different sources, including diaries, U.S. census, historical statistics, and surveys from the Bureau of Labour Statistics and the American Heritage Time Use Survey. For more details on how these data are constructed see Ramey (2009), pp.3-4, and Appendix A.

type WellcomeGlobalMonitorTrustDataset 

type WellcomeGlobalMonitorTrustDataset struct {
	TrustPeopleInNeighborhood                                      *float64 `json:"trust_people_in_neighborhood"`
	TrustTheNationalGovernmentInThisCountry                        *float64 `json:"trust_the_national_government_in_this_country"`
	TrustScientistsInThisCountry                                   *float64 `json:"trust_scientists_in_this_country"`
	TrustJournalistsInThisCountry                                  *float64 `json:"trust_journalists_in_this_country"`
	TrustDoctorsAndNursesInThisCountry                             *float64 `json:"trust_doctors_and_nurses_in_this_country"`
	TrustPeopleWhoWorkAtCharitableOrganizationsOrNgosInThisCountry *float64 `json:"trust_people_who_work_at_charitable_organizations_or_ngos_in_this_country"`
	TrustTraditionalHealersInThisCountry                           *float64 `json:"trust_traditional_healers_in_this_country"`
	TrustScience                                                   *float64 `json:"trust_science"`
	TrustScientistsToFindAccurateInformationAboutTheWorld          *float64 `json:"trust_scientists_to_find_accurate_information_about_the_world"`
	TrustScientistsToDoWorkWithIntentionOfBenefitingPublic         *float64 `json:"trust_scientists_to_do_work_with_intention_of_benefiting_public"`
}

The Wellcome Global Monitor is the world’s largest study into how people around the world think and feel about science and major health challenges. It surveys over 140,000 people from more than 140 countries.Survey respondents were asked, "How much do you trust each of the following: other people in your neighborhood; your national government; scientists; journalists; doctors and nurses; people who work at non-governmental or non-profit organizations; healers? Do you trust them a lot, some, not much, or not at all?"The "share of people who trust" is the sum of those who responded "a lot", and "some".

type WhaleCatchByDecadeRochaEtAlAndIwcDataset 

type WhaleCatchByDecadeRochaEtAlAndIwcDataset struct {
	BlueWhaleRochaEtAlAndIwc        *float64 `json:"blue_whale_rocha_et_al_and_iwc"`
	FinWhaleRochaEtAlAndIwc         *float64 `json:"fin_whale_rocha_et_al_and_iwc"`
	SpermWhaleRochaEtAlAndIwc       *float64 `json:"sperm_whale_rocha_et_al_and_iwc"`
	HumpbackWhaleRochaEtAlAndIwc    *float64 `json:"humpback_whale_rocha_et_al_and_iwc"`
	SeiWhaleRochaEtAlAndIwc         *float64 `json:"sei_whale_rocha_et_al_and_iwc"`
	BrydesWhaleRochaEtAlAndIwc      *float64 `json:"brydes_whale_rocha_et_al_and_iwc"`
	MinkeWhaleRochaEtAlAndIwc       *float64 `json:"minke_whale_rocha_et_al_and_iwc"`
	GrayWhaleRochaEtAlAndIwc        *float64 `json:"gray_whale_rocha_et_al_and_iwc"`
	RightWhaleRochaEtAlAndIwc       *float64 `json:"right_whale_rocha_et_al_and_iwc"`
	UnspecifiedotherRochaEtAlAndIwc *float64 `json:"unspecifiedother_rocha_et_al_and_iwc"`
	AllSpeciesRochaEtAlAndIwc       *float64 `json:"all_species_rocha_et_al_and_iwc"`
}

Data is presented as decadal totals where '1900' is equal to the total catch from 1900 to 1909; '1910' is from 1910 to 1919 etc. For the decade '2010', only the years 2010 to 2015 are currently included.Data on whale catch is sourced from two sources:Figures over the 20th century (1900-1999) is from: Rocha, R. C., Clapham, P. J., & Ivashchenko, Y. V. (2014). Emptying the oceans: a summary of industrial whaling catches in the 20th century. Marine Fisheries Review, 76(4), 37-48.The above paper draws on data originally published by the International Whaling Commission (IWC).Data from 2000 onwards is sourced directly from the International Whaling Commissions (IWC) which recorded the number of catches at: https://iwc.int/catches.

type WhaleCatchRochaEtAlIwcDataset 

type WhaleCatchRochaEtAlIwcDataset struct {
	BlueWhaleRochaEtAlIwc        *float64 `json:"blue_whale_rocha_et_al_iwc"`
	FinWhaleRochaEtAlIwc         *float64 `json:"fin_whale_rocha_et_al_iwc"`
	SpermWhaleRochaEtAlIwc       *float64 `json:"sperm_whale_rocha_et_al_iwc"`
	HumpbackWhaleRochaEtAlIwc    *float64 `json:"humpback_whale_rocha_et_al_iwc"`
	SeiWhaleRochaEtAlIwc         *float64 `json:"sei_whale_rocha_et_al_iwc"`
	BrydesWhaleRochaEtAlIwc      *float64 `json:"brydes_whale_rocha_et_al_iwc"`
	MinkeWhaleRochaEtAlIwc       *float64 `json:"minke_whale_rocha_et_al_iwc"`
	GrayWhaleRochaEtAlIwc        *float64 `json:"gray_whale_rocha_et_al_iwc"`
	RightWhaleRochaEtAlIwc       *float64 `json:"right_whale_rocha_et_al_iwc"`
	UnspecifiedotherRochaEtAlIwc *float64 `json:"unspecifiedother_rocha_et_al_iwc"`
	AllWhaleSpeciesRochaEtAlIwc  *float64 `json:"all_whale_species_rocha_et_al_iwc"`
}

Data on whale catch is sourced from two sources:Figures over the 20th century (1900-1999) is from: Rocha, R. C., Clapham, P. J., & Ivashchenko, Y. V. (2014). Emptying the oceans: a summary of industrial whaling catches in the 20th century. Marine Fisheries Review, 76(4), 37-48.The above paper draws on data originally published by the International Whaling Commission (IWC).Data from 2000 onwards is sourced directly from the International Whaling Commissions (IWC) which recorded the number of catches at: https://iwc.int/catches

type WhalePopulationsPershingEtAl2010Dataset 

type WhalePopulationsPershingEtAl2010Dataset struct {
	WhalePopulationsPershingEtAl2010 *float64 `json:"whale_populations_pershing_et_al_2010"`
}

Estimates of whale populations pre-whaling and in 2001 were published by Pershing et al. (2010), and originally sourced from Christensen (2006). See full references below.The dates of pre-whaling differs depending on the whale species and oceanic region. This ranges from as far back as 1530 for Right whales. The most common 'pre-whaling' date in the original source was in the late 19th and early 20th century. Here we have coded all pre-whaling dates as 1890 for consistency, but it should be noted that this date varies by species. The latest date – 2001 – is consistent across species.All estimates are of the global total.

type WheatPricesLongRunInEnglandMakridakisEtAl1997Dataset 

type WheatPricesLongRunInEnglandMakridakisEtAl1997Dataset struct {
	WheatPriceIn1996PoundsMakridakisEtAl1997 *float64 `json:"wheat_price_in_1996_pounds_makridakis_et_al_1997"`
}

Long-run series on wheat prices in England, measured in constant 1996 pounds per tonne.Data is sourced from Makridakis, Wheelwright, and Hyndman (1997) - Forecasting: Methods and Applications. Wiley.The data is available at the book's accompanying website here: https://robjhyndman.com/forecasting/This book is online here: http://otexts.com/fpp/

type WhoAmericansSpendTimeWithAmericanTimeUseSurvey20092019Dataset 

type WhoAmericansSpendTimeWithAmericanTimeUseSurvey20092019Dataset struct {
	TimeSpentAloneByAgeOfRespondentUnitedStates                       *float64 `json:"time_spent_alone_by_age_of_respondent_united_states"`
	TimeSpentWithChildrenByAgeOfRespondentUnitedStates                *float64 `json:"time_spent_with_children_by_age_of_respondent_united_states"`
	TimeSpentWithCoworkersByAgeOfRespondentUnitedStates               *float64 `json:"time_spent_with_coworkers_by_age_of_respondent_united_states"`
	TimeSpentWithFamilyByAgeOfRespondentUnitedStates                  *float64 `json:"time_spent_with_family_by_age_of_respondent_united_states"`
	TimeSpentWithFriendsByAgeOfRespondentUnitedStates                 *float64 `json:"time_spent_with_friends_by_age_of_respondent_united_states"`
	TimeSpentWithOtherPeopleByAgeOfRespondentUnitedStates             *float64 `json:"time_spent_with_other_people_by_age_of_respondent_united_states"`
	TimeSpentWithPartnerByAgeOfRespondentUnitedStates                 *float64 `json:"time_spent_with_partner_by_age_of_respondent_united_states"`
	TimeSpentWithOtherUnclassifiedPeopleByAgeOfRespondentUnitedStates *float64 `json:"time_spent_with_other_unclassified_people_by_age_of_respondent_united_states"`
}

These estimates are based on a classification of time spent on various activities while being in company of others, and the presentation of the data was inspired by earlier work from Henrik Lindberg.The time-use data comes from the American Time Use Survey (ATUS), and the classification of activities and relationships relies on the code from Henrik Lindberg, available from <a href="https://gist.github.com/halhen/d969234077c9b70df4c4b8dd902bea38">Lindberg's GitHub Repo</a>. All estimates correspond to pooled data for the period 2009-2019, using population weights.Estimates rely on the ATUS "Who File". This is a file that indicates who was present during each activity recorded. People can be counted twice, which means that attending a party with friends and your spouse, for example, would count for both categories. Sleeping is excluded.<b> Further notes </b>- Some of the original "Who Codes" have be re-coded and grouped (e.g. 'roommate' has been coded as 'friend')- In the ATUS "Who File" there is one record for each person reported present. Therefore, there will be one record for activities done alone and multiple records for activities with multiple people present. For some activities, no “who” codes are collected (such as sleeping and grooming).

type WildfireDataInTheUsNifcDataset 

type WildfireDataInTheUsNifcDataset struct {
	NumberOfWildfiresComparableDataNifc  *float64 `json:"number_of_wildfires_comparable_data_nifc"`
	AcresBurnedComparableDataNifc        *float64 `json:"acres_burned_comparable_data_nifc"`
	NumberOfWildfiresFullSeriesNifc      *float64 `json:"number_of_wildfires_full_series_nifc"`
	AcresBurnedFullSeriesNifc            *float64 `json:"acres_burned_full_series_nifc"`
	AcresBurnedPerFireComparableDataNifc *float64 `json:"acres_burned_per_fire_comparable_data_nifc"`
	AcresBurnedPerFireFullSeriesNifc     *float64 `json:"acres_burned_per_fire_full_series_nifc"`
}

The National Interagency Coordination Center at NIFC compiles annual wildland fire statistics for federal and state agencies. This information is provided through Situation Reports, which have been in use for several decades. **Prior to 1983, sources of these figures are not known, or cannot be confirmed, and were not derived from the current situation reporting process. As a result the figures prior to 1983 should not be compared to later data.The average acres burned per wildfire has been calculated by Our World in Data by dividing the total number of acres burned by the number of wildfires in a given year. Note this simplifies and does not account for the distribution of wildfire extents.

type WomensEconomicOpportunity2012EconomistIntelligenceUnit2012Dataset 

type WomensEconomicOpportunity2012EconomistIntelligenceUnit2012Dataset struct {
	WeoIndexOverallScoreEconomistIntelligenceUnit2012 *float64 `json:"weo_index_overall_score_economist_intelligence_unit_2012"`
}

The Women's Economic Opportunity (WEO) Index measures five categories to determine whether the environment for both women employees and women entrepreneurs is favourable. Five category scores are calculated from the unweighted mean of underlying indicators and scaled 0-100, where 100=most favourable. These categories are: Labor policy and practice (comprising two sub-categories: Labor policy and labor practice); Access to Finance; Education and training; Women's legal and social status; and the General business environment. Each category or sub-category features either four or five underlying indicators.The overall score (from 0-100) is calculated from a simple average of the unweighted category and indicator variables. That is, every indicator contributes equally to their parent category and every category contributes equally to the overall score.EIU note: The criteria used in this study were chosen in close consultation between the Economist Intelligence Unit and panels of experts, mostly in 2009 and 2010. The indicator list was reviewed and revised at an experts meeting held at the offices of UN Women in July 2011.World Bank classifications include entities: High income (OECD), High income, High income (non-OECD), Upper-middle income, Lower-middle income, Low income, Europe & Central Asia, Latin America & the Caribbean, East Asia & Pacific, Middle East & North Africa, South Asia, and Sub-Saharan Africa. UN classifications include entities: Europe, Americas, Oceania, Asia, and Africa.

type WomensPoliticalRepresentationUsingPaxtonEtAl2006Ipu2017AndWdi2017Dataset 

type WomensPoliticalRepresentationUsingPaxtonEtAl2006Ipu2017AndWdi2017Dataset struct {
	NumberOfSovereignCountriesHavingGrantedUniversalSuffrageToWomenRelativeToOrderedMilestones   *float64 `json:"number_of_sovereign_countries_having_granted_universal_suffrage_to_women_relative_to_ordered_milestones"`
	NumberOfSovereignCountriesHavingElectedFirstFemaleMpRelativeToOrderedMilestones              *float64 `json:"number_of_sovereign_countries_having_elected_first_female_mp_relative_to_ordered_milestones"`
	NumberOfSovereignCountriesHavingHadAtLeast10percWomenInParliamentRelativeToOrderedMilestones *float64 `json:"number_of_sovereign_countries_having_had_at_least_10perc_women_in_parliament_relative_to_ordered_milestones"`
	NumberOfSovereignCountriesHavingHadAtLeast20percWomenInParliamentRelativeToOrderedMilestones *float64 `json:"number_of_sovereign_countries_having_had_at_least_20perc_women_in_parliament_relative_to_ordered_milestones"`
	NumberOfSovereignCountriesHavingHadAtLeast30percWomenInParliamentRelativeToOrderedMilestones *float64 `json:"number_of_sovereign_countries_having_had_at_least_30perc_women_in_parliament_relative_to_ordered_milestones"`
	ShareOfSovereignCountriesHavingGrantedUniversalSuffrageToWomenRelativeToOrderedMilestones    *float64 `json:"share_of_sovereign_countries_having_granted_universal_suffrage_to_women_relative_to_ordered_milestones"`
	ShareOfSovereignCountriesHavingElectedFirstFemaleMpRelativeToOrderedMilestones               *float64 `json:"share_of_sovereign_countries_having_elected_first_female_mp_relative_to_ordered_milestones"`
	ShareOfSovereignCountriesHavingHadAtLeast10percWomenInParliamentRelativeToOrderedMilestones  *float64 `json:"share_of_sovereign_countries_having_had_at_least_10perc_women_in_parliament_relative_to_ordered_milestones"`
	ShareOfSovereignCountriesHavingHadAtLeast20percWomenInParliamentRelativeToOrderedMilestones  *float64 `json:"share_of_sovereign_countries_having_had_at_least_20perc_women_in_parliament_relative_to_ordered_milestones"`
	ShareOfSovereignCountriesHavingHadAtLeast30percWomenInParliamentRelativeToOrderedMilestones  *float64 `json:"share_of_sovereign_countries_having_had_at_least_30perc_women_in_parliament_relative_to_ordered_milestones"`
	NumberOfSovereignCountries                                                                   *float64 `json:"number_of_sovereign_countries"`
	NumberOfSovereignCountriesHavingGrantedUniversalSuffrageToWomen                              *float64 `json:"number_of_sovereign_countries_having_granted_universal_suffrage_to_women"`
	NumberOfSovereignCountriesHavingElectedFirstFemaleMp                                         *float64 `json:"number_of_sovereign_countries_having_elected_first_female_mp"`
	NumberOfSovereignCountriesHavingHadAtLeast10percWomenInParliament                            *float64 `json:"number_of_sovereign_countries_having_had_at_least_10perc_women_in_parliament"`
	NumberOfSovereignCountriesHavingHadAtLeast20percWomenInParliament                            *float64 `json:"number_of_sovereign_countries_having_had_at_least_20perc_women_in_parliament"`
	NumberOfSovereignCountriesHavingHadAtLeast30percWomenInParliament                            *float64 `json:"number_of_sovereign_countries_having_had_at_least_30perc_women_in_parliament"`
	ShareOfSovereignCountriesHavingGrantedUniversalSuffrageToWomen                               *float64 `json:"share_of_sovereign_countries_having_granted_universal_suffrage_to_women"`
	ShareOfSovereignCountriesHavingElectedFirstFemaleMp                                          *float64 `json:"share_of_sovereign_countries_having_elected_first_female_mp"`
	ShareOfSovereignCountriesHavingHadAtLeast10percWomenInParliament                             *float64 `json:"share_of_sovereign_countries_having_had_at_least_10perc_women_in_parliament"`
	ShareOfSovereignCountriesHavingHadAtLeast20percWomenInParliament                             *float64 `json:"share_of_sovereign_countries_having_had_at_least_20perc_women_in_parliament"`
	ShareOfSovereignCountriesHavingHadAtLeast30percWomenInParliament                             *float64 `json:"share_of_sovereign_countries_having_had_at_least_30perc_women_in_parliament"`
}

Data construction:Paxton et al (2006) provides data on five milestones of women’s political representation from 1893 to 2003. Our dataset updates their data through to 2017 using Inter-parliamentary union (IPU) statistical archives and the World Bank’s World Development Indicator (WDI) variable on women’s share in parliament (main sources).Paxton’s data is downloaded from the Inter-university Consortium for Political and Social Research (ICPSR). It provides country-level data on the start and end year of sovereignty; year in which universal suffrage was granted to all women; the year the first woman was elected to parliament; and percentage share of women in parliament for the years 1945-2003. Data from 2003 onwards was derived from various sources. IPU’s statistical archives were used to derive figures on the share of women in parliament (from 2004-2017); the CIA World Factbook provided data on the year of sovereignty and universal suffrage, alongside various additional sources which are listed below. Moreover, this data extension to 2017 means our dataset consists of five additional countries: Libya, Montenegro, Serbia, South Sudan and Timor, which gained independence after 2003. Paxton et al (2006) describes women’s political representation as the share of sovereign countries which satisfied the five representation milestones in any given year. We have therefore derived the number of sovereign countries generated in any given year, as well as the number which have attained each of the five milestones.To overcome methodological differences, the attainment of milestones was assessed in two ways. Firstly, the number and share of sovereign countries were assessed relative to ‘ordered’ milestones. This results from Paxton et al’s (2006) assumption that a country only has an elected woman MP after universal suffrage is granted. However, there were 17 countries - Australia, Belgium, Brazil, Canada, Guinea-Bissau, Hungary, Ireland, Netherlands, Norway, Portugal, Samoa, Saudi Arabia, Socialist Federal Republic of Yugoslavia, South Africa, USSR, United Kingdom, and the United States – whereby a female MP was elected prior to gaining universal suffrage. Secondly, we assessed the number and share of sovereign countries which independently satisfied five milestones.Additional information on data sources: This section provides detailed information on the sources used by variables and countries.Sovereignty CIA’s World Factbook Independence field is used to determine the year of sovereignty for Libya, Montenegro, Serbia, South Sudan, Timor, American Samoa, Hungary and Iran. Likewise, we determine end of sovereignty for Libya and Serbia and Montenegro using the same source. Available at: https://www.cia.gov/library/publications/the-world-factbook/fields/2088.html [accessed 16th August 2017].Universal SuffrageKuwait, Oman and Qatar: UNICEF Gender Equality Profiles. Available at: https://www.unicef.org/gender/gender_62215.html [accessed 16th August 2017].Libya, Serbia (mentioned as Serbia and Montenegro), Montenegro (mentioned as Serbia and Montenegro), South Sudan (mentioned as Sudan), Timor and American Samoa. IPU’s Women in Politics. Available at: http://www.ipu.org/PDF/publications/wmn45-05_en.pdf [accessed 16th August 2017].Saudi Arabia and United Arab Emirates. CIA’s World Factbook Suffrage field. Available at: https://www.cia.gov/library/publications/the-world-factbook/fields/2123.html [accessed 16th August 2017].Brunei: Wikipedia.First woman elected to parliamentTimor, Latvia and American Samoa: IPU’s Women in Politics. Available at: http://www.ipu.org/PDF/publications/wmn45-05_en.pdf [accessed 16th August 2017].Oman, Palau, Saudi Arabia, Qatar, UAE and Tanzania: IPU’s Women in Parliament Annual Reviews.Serbia, Montenegro, Nigeria and South Sudan: IPU’s Parline. Available at: http://www.ipu.org/parline-e/parlinesearch.asp [accessed 16th August 2017].Kuwait: The Guardian. Available at: https://www.theguardian.com/world/2009/may/17/kuwait-women-elected-parliament [accessed 16th August 2017].Libya: Libyan House of Representatives. Available at: https://www.temehu.com/house-of-representatives.htm [accessed 16th August 2017].Sierra Leone: Pathways of Women’s Empowerment, Institute of Development Studies, University of Sussex, UK. Available at: https://assets.publishing.service.gov.uk/media/57a08ac5ed915d3cfd00092c/CS_Women_and_Politics_SL.pdf [accessed 16th August 2017].Share of women in parliament IPU’s Statistical Archive (http://www.ipu.org/wmn-e/classif-arc.htm) for all countries from 2004 to 2017. For each year, we take the share of women in lower house of the parliament. This definition matches both with WDI and IPU’s Women in Parliament 1945-1995. They use the share of women in single house for unicameral parliaments and lower house for bicameral parliaments. Moreover, for consistency, data is taken from December every year. If December data is missing, we take the next latest month of the corresponding year.Handling data issues: For years 1997 - 2003, there is an overlap between Paxton’s dataset and WDI data measuring the share of women in parliament. For most countries, the values match exactly. However, there are 20-40 countries each year where the Paxton and WDI values do not match. As the more complete and comprehensive dataset, we have taken Paxton’s dataset as our primary source in such cases for the period 1997-2003. Nonetheless, we use WDI to fill the remaining gaps. For the same reasoning, we choose manually collected data from IPU’s statistical archive over WDI for the years 2004-2016.

type WomensWeeklyEarningsAsAPercentageOfMensBureauOfLaborStatistics2017Dataset 

type WomensWeeklyEarningsAsAPercentageOfMensBureauOfLaborStatistics2017Dataset struct {
	WomensWeeklyEarningsAsAPercentageOfMensByAgeBureauOfLaborStatistics2017 *float64 `json:"womens_weekly_earnings_as_a_percentage_of_mens_by_age_bureau_of_labor_statistics_2017"`
}

Full citation: Bureau of Labor Statistics, U.S. Department of Labor, The Economics Daily, Women’s earnings as a percentage of men’s, 1979-2005 on the Internet at https://www.bls.gov/opub/ted/2006/oct/wk1/art02.htm (visited December 05, 2017).

type WorkingHoursDataHubermanAndMinns2007Dataset 

type WorkingHoursDataHubermanAndMinns2007Dataset struct {
	FullTimeProductionWorkersMaleAndFemaleInNonAgriculturalActivitiesWeeklyWorkHoursHubermanAndMinns2007 *float64 `json:"full_time_production_workers_male_and_female_in_non_agricultural_activities_weekly_work_hours_huberman_and_minns_2007"`
	FullTimeProductionWorkersMaleAndFemaleInNonAgriculturalActivitiesAnnualWorkHoursHubermanAndMinns2007 *float64 `json:"full_time_production_workers_male_and_female_in_non_agricultural_activities_annual_work_hours_huberman_and_minns_2007"`
	WeeksWorked                                                                                          *float64 `json:"weeks_worked"`
	DaysOfVacationAndHolidays                                                                            *float64 `json:"days_of_vacation_and_holidays"`
}

type WorldBankEducationDatasetWorldBank2015Dataset 

type WorldBankEducationDatasetWorldBank2015Dataset struct {
	TotalGovernmentExpenditureOnEducationWorldBank2015                        *float64 `json:"total_government_expenditure_on_education_world_bank_2015"`
	ShareOfEducationInGovernmentExpenditureWorldBank2015                      *float64 `json:"share_of_education_in_government_expenditure_world_bank_2015"`
	ExpenditureOnPrimaryAsPercOfGovernmentExpenditureOnEducationWorldBank2015 *float64 `json:"expenditure_on_primary_as_perc_of_government_expenditure_on_education_world_bank_2015"`
}

Educational attainment, enrolment and expenditure data

type WorldBankIncomeThresholdsWorldBank2017Dataset 

type WorldBankIncomeThresholdsWorldBank2017Dataset struct {
	LowIncomeWorldBank2017         *float64 `json:"low_income_world_bank_2017"`
	LowerMiddleIncomeWorldBank2017 *float64 `json:"lower_middle_income_world_bank_2017"`
	UpperMiddleIncomeWorldBank2017 *float64 `json:"upper_middle_income_world_bank_2017"`
	HighIncomeWorldBank2017        *float64 `json:"high_income_world_bank_2017"`
}

The Atlas methodology is used to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes.

type WorldConflictDeathRateSince1989VariousSourcesDataset 

type WorldConflictDeathRateSince1989VariousSourcesDataset struct {
	ConflictDeathsPer100_000WorldVariousSources *float64 `json:"conflict_deaths_per_100_000_world_various_sources"`
}

Some sources release data only at the conflict level. In these cases we have assumed an even death toll across the duration of the conflict and aggregated by year across all conflicts.

type WorldConflictDeathsVariousSourcesDataset 

type WorldConflictDeathsVariousSourcesDataset struct {
	CorrelatesOfWar               *float64 `json:"correlates_of_war"`
	ConflictCatalogueTotal        *float64 `json:"conflict_catalogue_total"`
	ConflictCatalogueMilitaryOnly *float64 `json:"conflict_catalogue_military_only"`
	PrioUcdp                      *float64 `json:"prio_ucdp"`
}

For more details on the construction and sources of these series, see our data appendix here: https://ourworldindata.org/uploads/2018/09/Notes-on-five-sources-of-the-world-conflict-death-rate-since-1989.pdf

type WorldGdpIn2011IntMoneyOwidBasedOnWorldBankMaddison2017Dataset 

type WorldGdpIn2011IntMoneyOwidBasedOnWorldBankMaddison2017Dataset struct {
	WorldGdpIn2011IntmoneyOwidBasedOnWorldBankAndMaddison2017 *float64 `json:"world_gdp_in_2011_intmoney_owid_based_on_world_bank_and_maddison_2017"`
}

The data presented here from 1990 onwards is from the World Bank. It is total global GDP in 2011 international-$ as published here: http://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD (accessed on April 16, 2017). Data earlier than 1990 is backwards extended from the World Bank observation for 1990 based on the growth rates implied by Maddison data. The Maddison data is published here: http://www.ggdc.net/maddison/oriindex.htm

type WorldHappinessReport2022Dataset 

type WorldHappinessReport2022Dataset struct {
	LifeSatisfactionInCantrilLadderWorldHappinessReport2022 *float64 `json:"life_satisfaction_in_cantril_ladder_world_happiness_report_2022"`
}

Life evaluation was measured by the individual answers to the Cantril ladder question: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”The value shown in a given year is the average of that year and the previous and following year. For example, the value given for 2020 is an average of the values for 2019-2021.This dataset should be next updated by the source in April 2023. We will update it on Our World in Data soon after the new version is published. At the link above you can directly access the source page and see the latest available data.

type WorldPopulationByPoliticalRegimeTheyLiveInOwid2016Dataset 

type WorldPopulationByPoliticalRegimeTheyLiveInOwid2016Dataset struct {
	CountryInTransitionOrNoDataOwid2016 *float64 `json:"country_in_transition_or_no_data_owid_2016"`
	PopulationInColonyOwid2016          *float64 `json:"population_in_colony_owid_2016"`
	PopulationInAutocracyOwid2016       *float64 `json:"population_in_autocracy_owid_2016"`
	PopulationInClosedAnocracyOwid2016  *float64 `json:"population_in_closed_anocracy_owid_2016"`
	PopulationInOpenAnocracyOwid2016    *float64 `json:"population_in_open_anocracy_owid_2016"`
	PopulationInDemocracyOwid2016       *float64 `json:"population_in_democracy_owid_2016"`
}

The data on political regimes is taken from the Polity IV dataset and I have added the information on colonial regimes based on Wimmer and Min.Wimmer and Min (2006) – “From empire to nation-state: Explaining war in the modern world, 1816-2001”, American Sociological Review 71(6):867-897, 2006.Countries receiving Polity scores of -10 to 5 are classified as autocracies, scores of -5 to 0 as closed anocracies, scores of 1-5 as open anocracies, 6 to 10 are classified as democracies.Three changes have been made to the Polity IV measures:*For the period 1937 to 1945 I interpolated for China with -5 (the score in the year before and after). This is the time of the Second Sino-Japanese War (1937–45). * For the years 1861 and 1860 China has no data in Polity IV. Here the score -6 which is the score in the period before and after is assigned. 1861 is the year of the Battle of Shanghai during the Taiping Rebellion. * For India the Polity IV has no data for 1947, 1948, and 1949 – the time of the "Partition of India". For the period after the Polity IV score is 9; here this score is used from 1947 (the year of independence from Britain).The data on the countries’ population is taken from Gapminder and for the total world population from Our World In Data. The difference between the total of number of people for which information on the country regime was available and the total world population is reported as "Country in tranistion or no data". Population by country data comes from the following sources:* Before 1950: From Gapminder.org* 1950-2015: UN Population Division (2015 Revision)* 2016 onwards: Medium Variant – UN Population Division (2015 Revision)

type WorldPovertyClockDataset 

type WorldPovertyClockDataset struct {
	ShareOfPopulationInExtremePovertyWorldDataLab2018 *float64 `json:"share_of_population_in_extreme_poverty_world_data_lab_2018"`
	NumberOfPeopleInExtremePovertyWorldDataLab2018    *float64 `json:"number_of_people_in_extreme_poverty_world_data_lab_2018"`
	PopulationWorldDataLab2018                        *float64 `json:"population_world_data_lab_2018"`
}

For the years prior to 2020, the values for January were taken.

type WorldPressFreedomIndexReportersSansFrontieres2022Dataset 

type WorldPressFreedomIndexReportersSansFrontieres2022Dataset struct {
	PressFreedomScore  *float64 `json:"press_freedom_score"`
	PressFreedomStatus *float64 `json:"press_freedom_status"`
}

This dataset provides information on press freedom, using data from the Reporters sans Frontieres' World Press Freedom Index.

type WorldRegionsAccordingToTheWorldBankDataset 

type WorldRegionsAccordingToTheWorldBankDataset struct {
	WorldRegionAccordingToTheWorldBank *float64 `json:"world_region_according_to_the_world_bank"`
}

This dataset simply lists how the World Bank defines world regions.

type YearOfLastRecordedWildPoliomyelitisVirusWhoGpei2017Dataset 

type YearOfLastRecordedWildPoliomyelitisVirusWhoGpei2017Dataset struct {
	YearOfLastPolioCaseWhoGpei2017 *float64 `json:"year_of_last_polio_case_who_gpei_2017"`
}

In three countries polio remains endemic today: Afghanistan, Nigeria and Pakistan. Switzerland's data point was missing in the original dataset and was obtained from the WHO (2017) time series dataset "Reported Cases of Selected Vaccine Preventable Diseases (VPDs)" which can be found under point "3.1 Reported incidence time series" here: http://www.who.int/immunization/monitoring_surveillance/data/en/ Somalia was listed twice in the GPEI table, so again the WHO (2017) dataset cited above was consulted to make out which year was in fact the correct information. Timor's datapoint originally read "pre 1985" and was replaced with 1985 for this visualization.

type YearOfLastRinderpestCaseOie2018Dataset 

type YearOfLastRinderpestCaseOie2018Dataset struct {
	YearOfTheLastReportedRinderpestCase *float64 `json:"year_of_the_last_reported_rinderpest_case"`
}

Mauritania and Pakistan's values were obtained from the 2003 Archives of the OIE that can be accessed here: http://web.oie.int/hs2/sit_mald_cont.asp?c_mald=5&c_cont=6&annee=2003China's value was obtained from page 109 in Roeder, P. L., Taylor, W. P. & Rweyemamu, M. M. (2006) Rinderpest in the twentieth and twenty-first centuries. Academic Press. Abstract available online here: https://www.sciencedirect.com/science/article/pii/B9780120883851500368

type YearOfMaternalAndNeonatalTetanusMntEliminationWhoAndKiwanis2018Dataset 

type YearOfMaternalAndNeonatalTetanusMntEliminationWhoAndKiwanis2018Dataset struct {
	YearOfMaternalNeonatalTetanusMntElimination *float64 `json:"year_of_maternal_neonatal_tetanus_mnt_elimination"`
}

The WHO described 57 countries as at risk of maternal and neonatal tetanus in 1999 and 2020 here: http://web.archive.org/web/20220409111206/https://www.who.int/initiatives/maternal-and-neonatal-tetanus-elimination-(mnte)/progress-towards-global-mnt-eliminationFor the countries that eliminated MNT before 2011, the list of references was found here (http://www.who.int/immunization/diseases/MNTE_resources/en/) but the individual references are linked below as well:Bangladesh: http://www.who.int/wer/2008/wer8334.pdf?ua=1Benin: http://www.who.int/immunization/diseases/MNTEStrategicPlan_E.pdfBurundi: http://www.who.int/wer/2011/wer8628.pdf?ua=1Comoros: http://www.who.int/wer/2011/wer8628.pdf?ua=1Congo: http://www.who.int/wer/2009/wer8445.pdf?ua=1Egypt: http://www.who.int/wer/2007/wer8226_27.pdf?ua=1Eritrea: http://www.who.int/wer/2004/en/wer7924.pdf?ua=1Malawi: http://www.who.int/wer/2004/en/wer7901.pdf?ua=1Morocco: http://www.who.int/immunization/diseases/WER_2002_Morocco.pdf?ua=1Mozambique: http://www.who.int/wer/2011/wer8644.pdf?ua=1Myanmar: http://www.who.int/wer/2010/wer8543.pdf?ua=1Namibia: http://www.who.int/immunization/diseases/MNTEStrategicPlan_E.pdfNepal: http://www.who.int/wer/2006/wer8113.pdf?ua=1Rwanda: http://www.who.int/wer/2004/en/wer7946.pdf?ua=1South Africa: https://www.sajei.co.za/index.php/SAJEI/article/viewFile/74/68Togo: http://www.who.int/wer/2006/wer8104.pdf?ua=1Turkey: http://www.who.int/wer/2009/wer8417.pdf?ua=1Uganda: http://www.who.int/pmnch/media/news/2011/20110726_uganda_mnctetanus/en/Vietnam: http://www.who.int/wer/2006/wer8127.pdf?ua=1Zambia: http://www.who.int/immunization/diseases/WER_2008_Zambia.pdf?ua=1Zimbabwe: http://www.who.int/docstore/wer/pdf/2001/wer7614.pdf?ua=1

type YearOfSmallpoxEradicationByCountryWho1988Dataset 

type YearOfSmallpoxEradicationByCountryWho1988Dataset struct {
	YearOfSmallpoxEradicationWho1988 *float64 `json:"year_of_smallpox_eradication_who_1988"`
}

The only data not taken from the maps but from the text are the data for Canada, USA, Australia and New Zealand: For Canada the authors reported that “endemic smallpox was eliminated by 1944”, for the USA only that it was eliminated “the latter half of the 1940s” (I chose 1948 for the visualization). Dates for the last outbreaks of smallpox in Australia (1917) and New Zealand (1914) are reported in the text – in both countries smallpox has always been rare.

The dates for Madagascar and Namibia are not exactly known – the authors only report that smallpox was eradicated in Madagascar before 1918 and for Namibia before 1955 (so I chose these years for eradication).

Countries are shown in their current borders – successor countries of Yugoslavia, the USSR and the Sudan are assigned the eradication date of these states: 1925 for Yugoslavia, 1936 for the USSR, and 1972 for the Sudan.

type YearsOfSchoolingBasedOnLeeLee2016BarroLee2018AndUndp2018Dataset 

type YearsOfSchoolingBasedOnLeeLee2016BarroLee2018AndUndp2018Dataset struct {
	AverageTotalYearsOfSchoolingForAdultPopulationLeeLee2016BarroLee2018AndUndp2018 *float64 `json:"average_total_years_of_schooling_for_adult_population_lee_lee_2016_barro_lee_2018_and_undp_2018"`
}

This series combines figures from three published datasets.For the period 1870-1949 inclusive, the estimates correspond to population aged 25-64, and are taken from Lee-Lee (2016). For the period 1950-1990 inclusive, the estimates correspond to population aged 25+, and are taken from Barro-Lee (2018). For the period 1991-2017 inclusive, the estimated correspond to population 25+, and are taken from the UNDP, HDR (2018).

type YougovImperialCovid19BehaviorTrackerDataset 

type YougovImperialCovid19BehaviorTrackerDataset struct {
	HouseholdMembersContact                          *float64 `json:"household_members_contact"`
	PeopleContactOutsideHousehold                    *float64 `json:"people_contact_outside_household"`
	TimesLeftHomeYesterday                           *float64 `json:"times_left_home_yesterday"`
	HandwashingYesterday                             *float64 `json:"handwashing_yesterday"`
	WillingnessIsolateIfSymptoms                     *float64 `json:"willingness_isolate_if_symptoms"`
	DifficultToIsolate                               *float64 `json:"difficult_to_isolate"`
	WillingnessIsolateIfAdvised                      *float64 `json:"willingness_isolate_if_advised"`
	MaskOutsideHome                                  *float64 `json:"mask_outside_home"`
	WashedHands                                      *float64 `json:"washed_hands"`
	HandSanitiser                                    *float64 `json:"hand_sanitiser"`
	CoveredMouthSneeze                               *float64 `json:"covered_mouth_sneeze"`
	AvoidedPeopleWithSymptoms                        *float64 `json:"avoided_people_with_symptoms"`
	AvoidedGoingOut                                  *float64 `json:"avoided_going_out"`
	AvoidedHealthcareSettings                        *float64 `json:"avoided_healthcare_settings"`
	AvoidedPublicTransport                           *float64 `json:"avoided_public_transport"`
	AvoidedWorkingOutsideHome                        *float64 `json:"avoided_working_outside_home"`
	ChildrenAvoidedSchool                            *float64 `json:"children_avoided_school"`
	AvoidedGuestsAtHome                              *float64 `json:"avoided_guests_at_home"`
	AvoidedSmallGatherings                           *float64 `json:"avoided_small_gatherings"`
	AvoidedMediumGatherings                          *float64 `json:"avoided_medium_gatherings"`
	AvoidedLargeGatherings                           *float64 `json:"avoided_large_gatherings"`
	AvoidedCrowdedAreas                              *float64 `json:"avoided_crowded_areas"`
	AvoidedShops                                     *float64 `json:"avoided_shops"`
	SleptSeparateBedrooms                            *float64 `json:"slept_separate_bedrooms"`
	EatenSeparately                                  *float64 `json:"eaten_separately"`
	CleanedSurfacesHome                              *float64 `json:"cleaned_surfaces_home"`
	AvoidedObjectsPublic                             *float64 `json:"avoided_objects_public"`
	ScaredContractingCovid                           *float64 `json:"scared_contracting_covid"`
	HappierTwoWeeksAgo                               *float64 `json:"happier_two_weeks_ago"`
	GovernmentRespondedWell                          *float64 `json:"government_responded_well"`
	MaskAtHome                                       *float64 `json:"mask_at_home"`
	MaskGroceryStore                                 *float64 `json:"mask_grocery_store"`
	MaskClothingStore                                *float64 `json:"mask_clothing_store"`
	MaskAtWork                                       *float64 `json:"mask_at_work"`
	MaskPublicTransport                              *float64 `json:"mask_public_transport"`
	CovidDangerousToMe                               *float64 `json:"covid_dangerous_to_me"`
	LikelyGetCovidFuture                             *float64 `json:"likely_get_covid_future"`
	MaskProtectMe                                    *float64 `json:"mask_protect_me"`
	MaskProtectOthers                                *float64 `json:"mask_protect_others"`
	MaskNotPossible                                  *float64 `json:"mask_not_possible"`
	ActivitiesImproveHealth                          *float64 `json:"activities_improve_health"`
	LifeGreatlyAffected                              *float64 `json:"life_greatly_affected"`
	AvoidedPublicEvents                              *float64 `json:"avoided_public_events"`
	ActivitiesImproveHealthNumResponses              *float64 `json:"activities_improve_health_num_responses"`
	AvoidedCrowdedAreasNumResponses                  *float64 `json:"avoided_crowded_areas_num_responses"`
	AvoidedGoingOutNumResponses                      *float64 `json:"avoided_going_out_num_responses"`
	AvoidedGuestsAtHomeNumResponses                  *float64 `json:"avoided_guests_at_home_num_responses"`
	AvoidedHealthcareSettingsNumResponses            *float64 `json:"avoided_healthcare_settings_num_responses"`
	AvoidedLargeGatheringsNumResponses               *float64 `json:"avoided_large_gatherings_num_responses"`
	AvoidedMediumGatheringsNumResponses              *float64 `json:"avoided_medium_gatherings_num_responses"`
	AvoidedObjectsPublicNumResponses                 *float64 `json:"avoided_objects_public_num_responses"`
	AvoidedPeopleWithSymptomsNumResponses            *float64 `json:"avoided_people_with_symptoms_num_responses"`
	AvoidedPublicEventsNumResponses                  *float64 `json:"avoided_public_events_num_responses"`
	AvoidedPublicTransportNumResponses               *float64 `json:"avoided_public_transport_num_responses"`
	AvoidedShopsNumResponses                         *float64 `json:"avoided_shops_num_responses"`
	AvoidedSmallGatheringsNumResponses               *float64 `json:"avoided_small_gatherings_num_responses"`
	AvoidedWorkingOutsideHomeNumResponses            *float64 `json:"avoided_working_outside_home_num_responses"`
	ChildrenAvoidedSchoolNumResponses                *float64 `json:"children_avoided_school_num_responses"`
	CleanedSurfacesHomeNumResponses                  *float64 `json:"cleaned_surfaces_home_num_responses"`
	CoveredMouthSneezeNumResponses                   *float64 `json:"covered_mouth_sneeze_num_responses"`
	CovidDangerousToMeNumResponses                   *float64 `json:"covid_dangerous_to_me_num_responses"`
	CovidVaccineImportantHealth                      *float64 `json:"covid_vaccine_important_health"`
	CovidVaccineImportantHealthNumResponses          *float64 `json:"covid_vaccine_important_health_num_responses"`
	CovidVaccineWillPreventTransmission              *float64 `json:"covid_vaccine_will_prevent_transmission"`
	CovidVaccineWillPreventTransmissionNumResponses  *float64 `json:"covid_vaccine_will_prevent_transmission_num_responses"`
	CovidVaccineWillProtectHealth                    *float64 `json:"covid_vaccine_will_protect_health"`
	CovidVaccineWillProtectHealthNumResponses        *float64 `json:"covid_vaccine_will_protect_health_num_responses"`
	DifficultToIsolateNumResponses                   *float64 `json:"difficult_to_isolate_num_responses"`
	EatenSeparatelyNumResponses                      *float64 `json:"eaten_separately_num_responses"`
	GovernmentRespondedWellNumResponses              *float64 `json:"government_responded_well_num_responses"`
	GovtWillProvideEffectiveCovidVaccine             *float64 `json:"govt_will_provide_effective_covid_vaccine"`
	GovtWillProvideEffectiveCovidVaccineNumResponses *float64 `json:"govt_will_provide_effective_covid_vaccine_num_responses"`
	HandSanitiserNumResponses                        *float64 `json:"hand_sanitiser_num_responses"`
	HandwashingYesterdayNumResponses                 *float64 `json:"handwashing_yesterday_num_responses"`
	HappierTwoWeeksAgoNumResponses                   *float64 `json:"happier_two_weeks_ago_num_responses"`
	HouseholdMembersContactNumResponses              *float64 `json:"household_members_contact_num_responses"`
	LifeGreatlyAffectedNumResponses                  *float64 `json:"life_greatly_affected_num_responses"`
	LikelyGetCovidFutureNumResponses                 *float64 `json:"likely_get_covid_future_num_responses"`
	MaskAtHomeNumResponses                           *float64 `json:"mask_at_home_num_responses"`
	MaskAtWorkNumResponses                           *float64 `json:"mask_at_work_num_responses"`
	MaskClothingStoreNumResponses                    *float64 `json:"mask_clothing_store_num_responses"`
	MaskGroceryStoreNumResponses                     *float64 `json:"mask_grocery_store_num_responses"`
	MaskNotPossibleNumResponses                      *float64 `json:"mask_not_possible_num_responses"`
	MaskOutsideHomeNumResponses                      *float64 `json:"mask_outside_home_num_responses"`
	MaskProtectMeNumResponses                        *float64 `json:"mask_protect_me_num_responses"`
	MaskProtectOthersNumResponses                    *float64 `json:"mask_protect_others_num_responses"`
	MaskPublicTransportNumResponses                  *float64 `json:"mask_public_transport_num_responses"`
	PeopleContactOutsideHouseholdNumResponses        *float64 `json:"people_contact_outside_household_num_responses"`
	ScaredContractingCovidNumResponses               *float64 `json:"scared_contracting_covid_num_responses"`
	SleptSeparateBedroomsNumResponses                *float64 `json:"slept_separate_bedrooms_num_responses"`
	TimesLeftHomeYesterdayNumResponses               *float64 `json:"times_left_home_yesterday_num_responses"`
	TrustCovidVaccines                               *float64 `json:"trust_covid_vaccines"`
	TrustCovidVaccinesNumResponses                   *float64 `json:"trust_covid_vaccines_num_responses"`
	WashedHandsNumResponses                          *float64 `json:"washed_hands_num_responses"`
	WillingnessCovidVaccinateThisWeek                *float64 `json:"willingness_covid_vaccinate_this_week"`
	WillingnessCovidVaccinateThisWeekNumResponses    *float64 `json:"willingness_covid_vaccinate_this_week_num_responses"`
	WillingnessIsolateIfAdvisedNumResponses          *float64 `json:"willingness_isolate_if_advised_num_responses"`
	WillingnessIsolateIfSymptomsNumResponses         *float64 `json:"willingness_isolate_if_symptoms_num_responses"`
	WorriedCovidVaccineSideEffects                   *float64 `json:"worried_covid_vaccine_side_effects"`
	WorriedCovidVaccineSideEffectsNumResponses       *float64 `json:"worried_covid_vaccine_side_effects_num_responses"`
	CovidVaccinatedOrWilling                         *float64 `json:"covid_vaccinated_or_willing"`
	CovidVaccinatedOrWillingNumResponses             *float64 `json:"covid_vaccinated_or_willing_num_responses"`
	CovidVaccineReceivedOneOrTwoDoses                *float64 `json:"covid_vaccine_received_one_or_two_doses"`
	CovidVaccineReceivedOneOrTwoDosesNumResponses    *float64 `json:"covid_vaccine_received_one_or_two_doses_num_responses"`
	UnwillingnessCovidVaccinateThisWeek              *float64 `json:"unwillingness_covid_vaccinate_this_week"`
	UncertainCovidVaccinateThisWeek                  *float64 `json:"uncertain_covid_vaccinate_this_week"`
	UnwillingnessCovidVaccinateThisWeekNumResponses  *float64 `json:"unwillingness_covid_vaccinate_this_week_num_responses"`
	UncertainCovidVaccinateThisWeekNumResponses      *float64 `json:"uncertain_covid_vaccinate_this_week_num_responses"`
}

YouGov has partnered with the Institute of Global Health Innovation (IGHI) at Imperial College London to gather global insights on people’s behaviors in response to COVID-19. The research will cover 29 countries, interviewing around 21,000 people each week.It is designed to provide behavioral analysis on how different populations are responding to the pandemic, helping public health bodies in their efforts to limit the impact of the disease. Anonymized respondent level data will be available for all public health and academic institutions globally.The questions in the survey, led by IGHI, cover data on testing, symptoms, self-isolating in response to symptoms and the ability and willingness to self-isolate if needed. It also looks at behaviors, including going outdoors, working outside the home, contact with others, handwashing and the extent of compliance with 20 common preventative measures.

type YouthMortalityRateUnIgme2018Dataset 

type YouthMortalityRateUnIgme2018Dataset struct {
	YouthMortalityRates0_14 *float64 `json:"youth_mortality_rates_0_14"`
}

Youth Mortality Rate measures the share of newborns who die before reaching the age of 15. This dataset was constructed by Our World in Data based on data from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME).We define and calculate 'youth mortality' based on the combination of:- under-5 mortality rates (the share of newborns who die before reaching the age of five)- 5-14 mortality rates (the share of children aged 5 who die before reaching the age of 15).Both of these metrics are available from the UN IGME. Based on these estimates we calculate 'youth mortality': the share of newborns who die before reaching the age of 15.

Source Files

View all Source files

Jump to

Keyboard shortcuts

? : This menu
/ : Search site
f or F : Jump to
y or Y : Canonical URL
go.dev uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more.