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.

The Gross Domestic Product (GDP) In the Euro Area was worth 16406.13 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Euro Area represents 14.74 percent of the world economy. This dataset provides the latest reported value for - Euro Area GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The dataset used in this paper is a combination of monthly macroeconomic and weekly financial data.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

Context

This dataset provides country-level GDP (Gross Domestic Product) in current US dollars from 2000 to 2025, mapped to the seven classic continents (Asia, Africa, Europe, North America, South America, Australia, and Antarctica). It is designed to make global economic data easier to explore, compare, and visualize by combining both geographic and temporal dimensions.

GDP is one of the most widely used indicators to measure the size of an economy, its growth trends, and relative economic performance across regions.

Source

Data Provider: World Bank Open Data

Indicator Used: NY.GDP.MKTP.CD → GDP (current US$)

License: World Bank Dataset Terms of Use (aligned with CC BY 4.0)

Note: 2024–2025 values may be incomplete or missing for some countries, depending on World Bank publication updates.

Dataset Structure

Name of country → Country name

Continent → One of the 7 continents

2000–2025 → GDP values in current US$ (float, may contain missing values NaN)

Format: wide panel data (one row per country, one column per year).

Inspiration & Use Cases

This dataset was prepared to make economic analysis, visualization, and forecasting more accessible. It can be used for:

• Time-series forecasting (predicting GDP growth into the future)
• Cross-country comparisons (e.g., comparing GDP trends of India vs. USA vs. Brazil)
• Continent-level aggregation (summing GDP by continent per year)
• Data visualization (heatmaps, line charts, world choropleths)
• Machine Learning applications (e.g., clustering countries by GDP trajectory)

Citation

If you use this dataset, please cite:

Source: World Bank, World Development Indicators (NY.GDP.MKTP.CD). Licensed under the World Bank Terms of Use.

The dataset contains GDP growth data for 15+ European Countries, spanning from 1960 to 2023. The indicator name is GDP (current US$), and the corresponding indicator code is NY.GDP.MKTP.CD.

This dataset is made possible with Collaboration of @Batros Jamali

Laos LA: Net Bilateral Aid Flows from Development Assistance Committee Donors: European Commission data was reported at 27.330 USD mn in 2016. This records an increase from the previous number of 10.780 USD mn for 2015. Laos LA: Net Bilateral Aid Flows from Development Assistance Committee Donors: European Commission data is updated yearly, averaging 8.600 USD mn from Dec 1978 (Median) to 2016, with 37 observations. The data reached an all-time high of 27.330 USD mn in 2016 and a record low of 0.200 USD mn in 1982. Laos LA: Net Bilateral Aid Flows from Development Assistance Committee Donors: European Commission data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Defense and Official Development Assistance. Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.; ; Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline.; Sum;

Imports: swda: EU 27E: United States: Food, Drink and Tobacco data was reported at 0.902 EUR bn in Feb 2025. This records an increase from the previous number of 0.820 EUR bn for Jan 2025. Imports: swda: EU 27E: United States: Food, Drink and Tobacco data is updated monthly, averaging 0.499 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 0.902 EUR bn in Feb 2025 and a record low of 0.296 EUR bn in Mar 2007. Imports: swda: EU 27E: United States: Food, Drink and Tobacco data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA050: Eurostat: Trade Statistics: By SITC: European Union: United States.

U.S. Public Debt vs. GDP

Trends and Comparisons

By Charlie Hutcheson [source]

About this dataset

This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades

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How to use the dataset

This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.

The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.

By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time

Research Ideas

• Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
• Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
• Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Gains from Harmonizing US and EU Auto Regulations under the Transatlantic Trade and Investment Partnership, PIIE Policy Brief 15-10. If you use the data, please cite as: Freund, Caroline, and Sarah Oliver. (2015). Gains from Harmonizing US and EU Auto Regulations under the Transatlantic Trade and Investment Partnership. PIIE Policy Brief 15-10. Peterson Institute for International Economics.

Exports to European Union in the United States increased to 30423.97 USD Million in February from 29808.03 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports to European Union.

Water and a Green Economy in Latin America and the Caribbean (LAC)

This dataset contains data about two ISEWs for the EU27, its individual Member States (MS), the UK and the US. Following Van der Slycken and Bleys (2023) (1), two variants of the ISEW are presented in this dataset: the ISEW_BCE accounts for the benefits and costs of the present and pasts activities experienced in the present and within a specific country (Benefits and Costs Experienced); the ISEW_BCPA accounts for the benefits and costs of present activities experienced in the present and in the future, both domestically and internationally (Benefits and Costs of Present economic Activities).

This document contains different datasets. Two datasets contain a summary of the values of the ISEWs and their components in ‘per capita’ terms. One summary presents the results for the EU27 (and MS) and the other one presents the results for the UK and the US (Non-EU countries). Additionally, each component is presented in some details in different pages, allowing to see the value of the different subcomponents included in each component (and even the value of some items with subcomponents for some components).

The period covered by this dataset is 1995-2020.

All the components are described in the accompanying table and in the report.

(1) Van der Slycken, J. and Bleys, B. (2023). Towards ISEW and GPI 2.0: Dealing with Cross-Time and Cross-Boundary Issues in a Case Study for Belgium. Social Indicators Research, 168(1):557-583.

This opinion survey was conducted on June 1-July 6, 2002 via telephone (except Poland, which held face-to-face interviews) with a sample size of 6,001. The survey was conducted in six different nations: Great Britain, Netherlands, Italy, Poland, France, and Germany. The survey was designed by an independent team of experts on public opinion and foreign policy from the United States and Europe. The topics covered include: what issues the respondent thinks are particularly important, including foreign policy, the economy, social issues, health issues, war/security issues, and the environment; the status of government programs; their thoughts on US government programs; their rating of countries' influence in the world; respondents' political leanings; their thoughts on the strength of the European Union, economic versus military strength; George W. Bush's handling of problems; threats to Europe; feelings toward other countries; thoughts on NATO; thoughts on the US potentially invading Iraq; and thoughts about events in the Middle East.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31085381. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

This collection provides users with data about R&D expenditure and R&D personnel broken down by the following institutional sectors: business enterprise (BES); government (GOV); higher education (HES); private non-profit (PNP), total of all sectors.

The R&D expenditure is broken down by source of funds; sector of performance; type of costs; type of R&D; fields of research and development (FORD); https://circabc.europa.eu/ui/group/c1b49c83-24a7-4ff2-951c-621ac0a89fd8/library/b4b841e5-d200-41bc-8f23-d0b1e034f689?p=1&n=10&sort=modified_DESC">socio-economic objectives (NABS 2007) and by regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level). The business enterprise sector is further broken down by economic activity (https://showvoc.op.europa.eu/#/datasets/ESTAT_Statistical_Classification_of_Economic_Activities_in_the_European_Community_Rev._2/data">NACE Rev.2); size class; industry orientation.

R&D personnel data are broken down by professional position; sector of performance; educational attainment level; sex; field of research and development (https://www.oecd.org/innovation/frascati-manual-2015-9789264239012-en.htm">FORD); regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level); for the business enterprise sector is further broken down in size class and economic activity (NACE Rev.2). Researchers are further broken down by age class and citizenship.

The periodicity of R&D data are every two years, except for the key R&D indicators (R&D expenditure, R&D personnel (in Full Time Equivalent - FTE) and Researchers (in FTE) by sectors of performance) which are transmitted annually by the EU Member States (from 2003 onwards based on a legal obligation). Some other breakdowns of the data may appear on an annual basis based on voluntary data provisions.

The data are collected through sample or census surveys, from administrative registers or through a combination of sources.

R&D data are available for following countries and country groups:

• All EU Member States; Candidate Countries; EFTA Countries; The Organisation for Economic Cooperation and Development (OECD) is data provider for the United States of America, Japan, South Korea and China.
• Country groups: EU Member States, Euro Area States.

R&D data are compiled in accordance to the guidelines laid down in OECD (2015), https://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm">Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities and the European business statistics methodological manual for R&D statistics – 2023 edition - Manuals and guidelines - Eurostat

This data package includes the underlying data files to replicate the data and charts presented in How trade cooperation by the United States, the European Union, and China can fight climate change, PIIE Working Paper 23-8.

If you use the data, please cite as: Bown, Chad P., and Kimberly A. Clausing. 2023. How trade cooperation by the United States, the European Union, and China can fight climate change. PIIE Working Paper 23-8. Washington, DC: Peterson Institute for International Economics.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Earmarked Revenues: How the European Union Can Learn from US Budgeting Experience, PIIE Policy Brief 18-2. If you use the data, please cite as: Kirkegaard, Jacob Funk. (2018). Earmarked Revenues: How the European Union Can Learn from US Budgeting Experience. PIIE Policy Brief 18-2. Peterson Institute for International Economics.

This data set contains annual and monthly data for exchange rates important to U.S. agriculture. It includes both nominal and real exchange rates for 79 countries, plus the European Union (EU), as well as real trade-weighted exchange rate indexes for many commodities and aggregations.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

Comprehensive Trade Agreements (CTAs) constitute a new generation of free trade agreements, which challenge traditional models of trade preferences. To understand preferences toward CTAs I present a new predictor, trust in government, that explains support for CTAs in the European Parliament. I develop a unified framework that includes economic and noneconomic factors to explain trade preferences, and analyze support for three recent CTAs: the Transatlantic Trade and Investment Partnership (TTIP), the Comprehensive Economic Trade Agreement with Canada (CETA), and the EU-Korea Free Trade Agreement. Using an original dataset on trade voting and a multilevel model, I show that higher levels of citizens’ trust in government make Members of the European Parliament more likely to vote in favor of CTAs. My research offers a novel theoretical argument and insights on the connection between public trust and elite position-taking.