The Gross Domestic Product per capita in European Union was last recorded at 34859.60 US dollars in 2024. The GDP per Capita in European Union is equivalent to 276 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product per capita in European Union was last recorded at 54290.99 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in European Union, when adjusted by Purchasing Power Parity is equivalent to 306 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita Ppp - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data from 1st of June 2022. For most recent GDP data, consult dataset nama_10_gdp. Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that the index, calculated from PPS figures and expressed with respect to EU27_2020 = 100, is intended for cross-country comparisons rather than for temporal comparisons."

This dataset provides values for GDP PER CAPITA PPP 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 the United States was last recorded at 66682.61 US dollars in 2024. The GDP per Capita in the United States is equivalent to 528 percent of the world's average. This dataset provides - United States GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gross domestic product (GDP) is a measure for the economic activity. It refers to the value of the total output of goods and services produced by an economy, less intermediate consumption, plus net taxes on products and imports. GDP per capita is calculated as the ratio of GDP to the average population in a specific year. Basic figures are expressed in purchasing power standards (PPS), which represents a common currency that eliminates the differences in price levels between countries to allow meaningful volume comparisons of GDP. The values are also offered as an index calculated in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Please note that this index is intended for cross-country comparisons rather than for temporal comparisons. Finally, the disparities indicator offered for EU aggregates is calculated as the coefficient of variation of the national figures. This time series offers a measure of the convergence of economic activity between the EU Member States. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

Administrative unitsRepresents the administrative units used for GDP per capita (PPP) and HDI data products. National administrative units have id 1-999, sub-national ones 1001-admin_areas_GDP_HDI.ncGDP_per_capita_PPP_1990_2015The GDP per capita (PPP) dataset represents average gross domestic production per capita in a given administrative area unit. GDP is given in 2011 international US dollars. Gap-filled sub-national data were used, supplemented by national data where necessary. Datagaps were filled by using national temporal pattern. Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_1990_2015_5arcminThis global dataset represents the gross domestic production (GDP) of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE 3.2 (the years of population data not available (1991-1999) were linearly interpolated at grid scale based on data from years 1990 and 2000). Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.HDI_1990_2015HDI is a composite index of average achievement in key dimensions of human development (dimensionless indicator between 0 and 1). This index is based on method introduced 2010 and updated 2011. The subnational data for HDI were collected from multiple national-level datasets, and national-level HDI was collected from UNDP. Years with missing data were interpolated over time thin plate spines, assuming smooth trend over time. The dataset has a global extent at 5 arc-min resolution, and the annual data is available for each year over 1990-2015. HDI sub-national data covers 39 countries and 66% of global population in 2015.pedigree_GDP_per_capita_PPP_1990_2015This is the source data for GDP per capita (PPP), published as an indication of accuracy and precision. Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.pedigree_HDI_1990_2015This is the source data for Human Development Index (HDI), published as an indication of accuracy and precision. Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_30arcsecThe GDP (PPP) data represents average gross domestic production of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP), which is multiplied by gridded population data from Global Human Settlement (GHS). Dataset has a global extent at 30 arc-second resolution for three time steps: 1990, 2000, and 2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.kummu_etal_scidata_codeThis file contains the scripts for data handling and production An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990–2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015).

We publish our out-of-sample estimates on historical GDP per capita levels between the years 1300 and 2000 together with the collected source data on countries and regions in a comprehensive dataset comprising 5,690 observations (1,313 source data observations, and 4,377 out-of-sample estimates). All references to the source data are provided in the manuscript.Locations refer to NUTS-2 regions in Europe (2021 edition), metro- and micropolitan statistical areas for the United States, metropolitan areas for Canada, and regions of similar size for other countries, e.g. oblasts in Russia. For countries, we use ISO 3166-1 alpha-3 country codes.The column GDPpc is denoted in 2011 USD PPP, matching the unit provided in the Maddison project. The column flag describes whether the value in column GDPpc is taken from source data (see manuscript for references) or an out-of-sample estimate. If it is an out-of-sample estimate, the columns GDPpc_lower and GDPpc_upper provide 90 percent confidence intervals obtained by bootstrapping.The code to generate all estimates will be published soon to ensure reproducibility of results.

This table shows Gross Domestic Product (GDP) per capita (or per person), household final consumption expenditure per capita and actual individual consumption per capita. Final consumption expenditure is the expenditure of resident households on consumption goods or services, while individual consumption is the sum of household consumption plus the individual (not collective) consumption of the non-profit institutions serving households (NPISH) and General Government sectors. The indicators are in volume terms and are converted to US dollars using constant Purchasing Power Parities (PPPs).

When using the filters, please note that GDP is selected by default in the ‘Transaction’ filter but you can select the consumption measures using the ‘Transaction’ filter. The ‘Institutional sector’ filter shows that GDP and actual individual consumption relate to the total economy, while household final consumption expenditure relates to households.

The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area . These can be selected using the ‘Reference area’ filter.

These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset.
See ANA Changes for information on changes in methodology: ANA Changes
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. GDP per person employed is intended to give an overall impression of the productivity of national economies expressed in relation to the European Union average. If the index of a country is higher than 100, this country's level of GDP per person employed is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that 'persons employed' does not distinguish between full-time and part-time employment. Labour productivity per hour worked is calculated as real output per unit of labour input (measured by the total number of hours worked). Measuring labour productivity per hour worked provides a better picture of productivity developments in the economy than labour productivity per person employed, as it eliminates differences in the full time/part time composition of the workforce across countries and years. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

These are the results obtained by conducting the experiment "Average Height of 19-year-old Males and Females and GDP per Capita in 2019 for 164 Countries". The CSV file contains the raw data produced by processing, filtering and merging the input datasets. There are two rows for each of the 164 countries. In both rows, the country name, country code and GDP per capita are given. However, one row contains the average height of 19-year-old males (indicated by the value 'Boys' in the 'Sex' column) whereas the other displays the average height of 19-year-old females (indicated by the value 'Girls'). Furthermore, there are two PNG files which display the regression plots for the average height of 19-year-old males and females, respectively. Note that the x-scale (for the GDP per capita) is logarithmic. {"references": ["The World Bank, GDP per capita (current US$), Washington, DC: The World Bank, 2021. Accessed on: Apr. 13, 2021. [Online] Available: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD.", "NCD Risk Factor Collaboration, Height - Evolution of adult height over time, NCD Risk Factor Collaboration, 2021. Accessed on: Apr. 18, 2021. [Online] Available: https://ncdrisc.org/data-downloads-height.html under "Country-specific data for all countries"."]}

This dataset was created in order to document self-reported life evaluations among small-scale societies that exist on the fringes of mainstream industrialized socieities. The data were produced as part of the LICCI project, through fieldwork carried out by LICCI partners. The data include individual responses to a life satisfaction question, and household asset values. Data from Gallup World Poll and the World Values Survey are also included, as used for comparison. TABULAR DATA-SPECIFIC INFORMATION --------------------------------- 1. File name: LICCI_individual.csv Number of rows and columns: 2814,7 Variable list: Variable names: User, Site, village Description: identification of investigator and location Variable name: Well.being.general Description: numerical score for life satisfaction question Variable names: HH_Assets_US, HH_Assets_USD_capita Description: estimated value of representative assets in the household of respondent, total and per capita (accounting for number of household inhabitants) 2. File name: LICCI_bySite.csv Number of rows and columns: 19,8 Variable list: Variable names: Site, N Description: site name and number of respondents at the site Variable names: SWB_mean, SWB_SD Description: mean and standard deviation of life satisfaction score Variable names: HHAssets_USD_mean, HHAssets_USD_sd Description: Site mean and standard deviation of household asset value Variable names: PerCapAssets_USD_mean, PerCapAssets_USD_sd Description: Site mean and standard deviation of per capita asset value 3. File name: gallup_WVS_GDP_pk.csv Number of rows and columns: 146,8 Variable list: Variable name: Happiness Score, Whisker-high, Whisker-low Description: from Gallup World Poll as documented in World Happiness Report 2022. Variable name: GDP-PPP2017 Description: Gross Domestic Product per capita for year 2020 at PPP (constant 2017 international $). Accessed May 2022. Variable name: pk Description: Produced capital per capita for year 2018 (in 2018 US$) for available countries, as estimated by the World Bank (accessed February 2022). Variable names: WVS7_mean, WVS7_std Description: Results of Question 49 in the World Values Survey, Wave 7.

The Gross Domestic Product (GDP) in European Union was worth 19423.32 billion US dollars in 2024, according to official data from the World Bank. The GDP value of European Union represents 18.29 percent of the world economy. This dataset provides the latest reported value for - European Union GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These are research indicators of comparative empirical investigation of Central and Eastern European Countries (CEECs) and the BRICS that were compiled from the criteria and factors of the World Bank. This dataset consists of data for CEECs and the BRICS for the period of 2000 to 2016. The World Bank Research Indicators consist of (1) GNI, Atlas Method (Current US$); (2) GNI per capita, Atlas; (3) GNI PPP (Current International $); (4) GNI per capita, PPP (Current International $); (5) Energy Use (kg of Oil Equivalent per capita); (6) Electric Power Consumption (kWh per capita); (7) GDP (Current US$); (8) GDP Growth (Annual %); (9) Inflation, GDP Deflator (Annual %); (10) Agriculture, Value Added (% of GDP); (11) Industry, Value Added (% of GDP); (12) Service, etc., Value Added (% of GDP); (13) Exports of Goods and Services (% of GDP); (14) Imports of Goods and Services (% of GDP); (15) Gross Capital Formation (% of GDP); (16) Revenue, excluding Grants (% of GDP); (17) Time Required to Start a Business (Days); (18) Domestic Credit Provided by Financial Sector (% of GDP); (19) Tax Revenue (% of GDP); (20) High-Technology Exports (% of Manufactured Exports); (21) Merchandise Trade (% of GDP); (22) Net Barter Terms of Trade Index (2000 = 100); (23) External Debt Stock, Total (DOD, Current US$); (24) Total Debt Service (% of Exports of Goods, Services and Primary Income); (25) Personal Remittances, Received (Current US$); (26) Foreign Direct Investment, Net Flows (BoP, Current US$); and (27) Net Official Development Assistance and Official Aid Received (Current US$). Furthermore, statistical data of CEECs and the BRICS were retrieved from Atlas 2.1 – Growth Lab at the Center for International Development at Harvard University; UN Comtrade Maps; WITS – UNSD Comtrade and ITC.

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>

These are research indicators of comparative empirical investigation of South Eastern European Countries (SEECs) and People’s Republic of China (PRC) that were compiled from the criteria and factors of the World Bank. This dataset consists of data for SEECs and PRC for the period of 2000 to 2016. The World Bank Research Indicators consist of (1) GNI, Atlas Method (Current US$); (2) GNI per capita, Atlas; (3) GNI PPP (Current International $); (4) GNI per capita, PPP (Current International $); (5) Energy Use (kg of Oil Equivalent per capita); (6) Electric Power Consumption (kWh per capita); (7) GDP (Current US$); (8) GDP Growth (Annual %); (9) Inflation, GDP Deflator (Annual %); (10) Agriculture, Value Added (% of GDP); (11) Industry, Value Added (% of GDP); (12) Service, etc., Value Added (% of GDP); (13) Exports of Goods and Services (% of GDP); (14) Imports of Goods and Services (% of GDP); (15) Gross Capital Formation (% of GDP); (16) Revenue, excluding Grants (% of GDP); (17) Time Required to Start a Business (Days); (18) Domestic Credit Provided by Financial Sector (% of GDP); (19) Tax Revenue (% of GDP); (20) High-Technology Exports (% of Manufactured Exports); (21) Merchandise Trade (% of GDP); (22) Net Barter Terms of Trade Index (2000 = 100); (23) External Debt Stock, Total (DOD, Current US$); (24) Total Debt Service (% of Exports of Goods, Services and Primary Income); (25) Personal Remittances, Received (Current US$); (26) Foreign Direct Investment, Net Flows (BoP, Current US$); and (27) Net Official Development Assistance and Official Aid Received (Current US$). Furthermore, statistical data of SEECs and PRC were retrieved from Atlas 2.1 – Growth Lab at the Center for International Development at Harvard University and WITS – UNSD COMPTRADE.

Net lending/borrowing of a country corresponds to the sum of total current and capital accounts’ balances in the Balance of Payments. It represents the net resources that the total economy makes available to the rest of the world (if it is positive) or receives from the rest of the world (if it is negative). In another words when the variable is positive (meaning that it shows a financing capacity), it should be called net lending (+); when it is negative (meaning that it shows a borrowing need), it should be called net borrowing (–). The net lending (+) or borrowing (–) of the total economy is equal but of opposite sign to the net borrowing (–) or lending (+) of the rest of the world. The MIP scoreboard indicator is expressed in percentage of GDP and in million of national currency, and calculated as: (CAB+CAK)%GDP=(CAB+CAK)*100/GDP . The indicator is based on the Balance of Payments data reported to Eurostat by the EU Member States. Starting from October 2014 definitions are based on the IMF's Sixth Balance of Payments Manual (BPM6). Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

The data in this collection consists of historical data relating to trade patterns and development indicators which enabled the testing of, firstly, the role of a reduction in shipping times (brought about through steam technology) in the expansion of world trade in the 19th Century and, secondly, the impact of these changing trade patterns on economic development. Five datasets are included: 1) information on shipping times for different sailing technologies (sail/steam) across roughly 16,000 country pairs; 2) 23,000 bilateral trade observations for nearly 1,000 distinct country pairs (1850-1900); 3) data on the duration of voyages of sailing ships from 1750-1854; 4) country-level data on per-capita GDP, population, exports, urban population; 5) data on freight rates for shipping materials and coal from the ports of Cardiff and Newcastle (1855-1900). The first dataset, consisting of information on shipping times for different sailing technologies (sail/steam) across roughly 16,000 country pairs, was calculated by the author using geographical information from the Centre for International Earth Science Information Network and the US National Oceanic and Atmospheric Administration. The second dataset, consisting of 23,000 bilateral trade observations for nearly 1,000 distinct country pairs (1850-1900), was constructed by the author from several primary data sources (given in the paper). The third dataset, consisting of the duration of voyages of sailing ships from 1750-1854, was obtained from the Royal Netherlands Metereological Institute. The fourth dataset consists of country-level data on per-capita GDP, population, exports, urban population: data on per-capita GDP was obtained from the Maddison Project Database (Bolt and van Zanden, 2014); population data were obtained from many different sources listed in the online appendix (link given below in related resources); urban population was obtained for the majority of countries form the Cross-National Time-Series Data Archive (Banks and Wilson, 2013), and for the remaining countries from a large number of sources listed in the appendix. The fifth dataset, consisting of freight rates for shipping materials and coal from the ports of Cardiff and Newcastle (1855-1900), was constructed by the author using three different primary sources: the Newcastle Courant (newspaper); the Mitchell’s Maritime Register (weekly journal of shipping and commerce); a publication of freight rates between 1869-1919 (Angrier, 1920). Please see the paper (provided with the collection) for further details, including the references mentioned above.

The municipal GDP aims to provide as complete a view as possible of the economic reality of the Basque Country at municipal level in terms of GDP per capita, GDP per job, distribution of Gross Added Value and Jobs, fully consistent with the macromagnitudes obtained in the Annual Economic Accounts of the Basque Country prepared by EUSTAT.The objective of this statistical operation is to provide information at municipal level that allows us to know closely the economic structure of our municipalities, which are the economic sectors in which it is based, as well as the effects of economic cycles on their level of activity and employment.