Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

The Gross Domestic Product per capita in the United States was last recorded at 75491.61 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in the United States, when adjusted by Purchasing Power Parity is equivalent to 425 percent of the world's average. This dataset provides - United States GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product per capita in Philippines was last recorded at 3925.30 US dollars in 2024. The GDP per Capita in Philippines is equivalent to 31 percent of the world's average. This dataset provides - Philippines GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

The indicator is calculated as the ratio of real GDP to the average population of a specific year. GDP measures the value of total final output of goods and services produced by an economy within a certain period of time. It includes goods and services that have markets (or which could have markets) and products which are produced by general government and non-profit institutions. It is a measure of economic activity and is also used as a proxy for the development in a country’s material living standards. However, it is a limited measure of economic welfare. For example, neither does GDP include most unpaid household work nor does GDP take account of negative effects of economic activity, like environmental degradation.

The Gross Domestic Product per capita in Canada was last recorded at 44401.72 US dollars in 2024. The GDP per Capita in Canada is equivalent to 352 percent of the world's average. This dataset provides - Canada GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The indicator is calculated as the ratio of real GDP to the average population of a specific year. GDP measures the value of total final output of goods and services produced by an economy within a certain period of time. It includes goods and services that have markets (or which could have markets) and products which are produced by general government and non-profit institutions. It is a measure of economic activity and is also used as a proxy for the development in a country’s material living standards. However, it is a limited measure of economic welfare. For example, neither does GDP include most unpaid household work nor does GDP take account of negative effects of economic activity, like environmental degradation.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

Analysis of ‘Country Socioeconomic Status Scores: 1880-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdorius/globses on 14 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

--- Original source retains full ownership of the source dataset ---

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

The Gross Domestic Product per capita in Jamaica was last recorded at 5312.40 US dollars in 2024. The GDP per Capita in Jamaica is equivalent to 42 percent of the world's average. This dataset provides - Jamaica GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

MIP23 - European Gross Domestic Product (GDP) per capita in Purchasing Power Standards (PPS). Published by Eurostat. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).European Gross Domestic Product (GDP) per capita in Purchasing Power Standards (PPS)...

This dataset provides economic indicators for FUAs of more than 250 000 inhabitants, including GDP, GDP per capita, jobs and labour productivity.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   When economic statistics are unavailable at a more granular level than the FUA (e.g. municipal level), indicators are estimated by adjusting regional (OECD TL2 and TL3 regions) values to FUA boundaries, based on the population distribution in each region. Regional values (GDP and jobs) in TL3 regions are used as data inputs and combined with gridded population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA boundaries are intersected with TL3 borders to compute the share of the regional population that lives within FUAs in each region. This share is then applied to the variable of interest (e.g. GDP) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   When a more granular level is available, data is aggregated for each FUA. For example in the United States, GDP estimates are available at the county-level (<a href=https://www.bea.gov/data/employment/employment-county-metro-and-other-areas>US Bureau of Economic Analysis</a>), and then aggregated by FUA.
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href="http://data-explorer.oecd.org/s/1e5">Economy - FUAs</a>), <a href=http://oe.cd/geostats>http://oe.cd/geostats</a></p>

   <h3>Further information</h3>
   <ul> 
   <li> <a href=https://localdataportal.oecd.org/>OECD Local Data Portal </a> </li>
   <li> <a href=https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2024_f42db3bf-en.html/>OECD Regions and Cities at a Glance </a> </li>
   </ul>
   <p align="justify">For questions and/or comments, please email <a href="mailto:CitiesStat@oecd.org">CitiesStat@oecd.org</a>

This dataset contains the annual historical series of CO2 Emissions and Drivers ( Kaya Decomposition) from 1971-2020Note: Identifying drivers of CO2 emissions trends This table presents the decomposition of CO2 emissions into four driving factors following the Kaya identity1, which is generally presented in the form: Kaya identity C = P (G/P) (E/G) (C/E) where: "C = CO2 emissions; P = populationG = GDPE = primary energy consumption" "The identity expresses, for a given time, CO2 emissions as the product of population, per capita economic output (G/P), energy intensity of the economy (E/G) and carbon intensity of the energy mix (C/E).Because of possible non-linear interactions between terms, the sum of the percentage changes of the four factors, e.g. (Py-Px)/Px, will not generally add up to the percentage change of CO2 emissions (Cy-Cx)/Cx. However, relative changes of CO2 emissions in time can be obtained from relative changes of the four factors as follows:" Kaya identity: relative changes in time Cy/Cx = Py/Px (G/P)y/(G/P)x (C/E)y/(C/E)x where x and y represent for example two different years. In this table, the Kaya decomposition is presented as: "CO2 emissions and driversCO2 = P (GDP/P) (TES/GDP) (CO2/TES) " where: "C = CO2 emissions; P = populationGDP/P = GDP/population *TES/GDP = Total primary energy consumption per GDP *CO2/TES = CO2 emissions per unit TES" * GDP in 2015 USD, based on purchasing power parities. "The Kaya identity can be used to discuss the primary driving forces of CO2 emissions. For example, it shows that, globally, increases in population and GDP per capita have been driving upwards trends in CO2 emissions, more than offsetting the reduction in energy intensity. In fact, the carbon intensity of the energy mix is almost unchanged, due to the continued dominance of fossil fuels - particularly coal - in the energy mix, and to the slow uptake of low-carbon technologies.However, it should be noted that there are important caveats in the use of the Kaya identity. Most important, the four terms on the right-hand side of equation should be considered neither as fundamental driving forces in themselves, nor as generally independent from each other."

The Gross Domestic Product per capita in Tanzania was last recorded at 1120.77 US dollars in 2024. The GDP per Capita in Tanzania is equivalent to 9 percent of the world's average. This dataset provides the latest reported value for - Tanzania GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides values for GDP PER CAPITA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Gross Domestic Product per capita in Philippines was last recorded at 10375.94 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in Philippines, when adjusted by Purchasing Power Parity is equivalent to 58 percent of the world's average. This dataset provides - Philippines GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains the variables used in this power sector policy paper, expect CO2 emission intensity (IEA database) data due to IEA licensing agreement as the product was purchased from them. The dataset contains data on 34 OECD countries (while the analysis only included 34) and 5 BRICS countries, from 2000 - 2018.

The variables used in the analysis are: - CO2 emission intensity per KWh (Data not included due to copy right from IEA, 3 example data points included for refererence). - Emission trading system price, both national and with adjusted price by relative size for sub-national systems (Cross-referenced from several sources). - Feed-in tariffs for solar PV and wind (OECD.Stat database). - Public environmental R&D and patent data on climate change mitigation technologies related to (1) energy generation, transmission or distribution (OECD.Stat green growth database). - Industrial energy consumption (OECD.Stat database). - GDP per capita, Industry share of GDP, Residental electricity consumption (World bank indicators). - Installed renewable energy capacity (IRENA database).

The data set records the GDP consumption and composition of Qinghai Province from 1990 to 2019. The data are divided by year. The data is compiled from the statistical yearbook of Qinghai Province issued by Qinghai Provincial Bureau of statistics. The data set contains 17 data tables with the same structure. For example, the data table from 1990 to 2003 has 8 fields: Field 1: year Field 2: total consumption Field 3: Agricultural residents Field 4: non-agricultural residents Field 5: government consumption Field 6: per capita of all residents Field 7: per capita of agricultural residents Field 8: per capita of non-agricultural residents

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..