Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q2 2025 about per capita, real, GDP, and USA.

Context

GDP or Gross Domestic Product, as defined by the International Monetary Fund, "measures the monetary value of final goods and services—that is, those that are bought by the final user—produced in a country in a given period of time". GDP per capita shows a country's GDP divided by its total population. It is theoretically the amount of money that each individual earns in that particular country.

GDP per capita is a very handy data to have access to in complement to other datasets, especially when working with prices, salaries, etc. As there was no such updated dataset on kaggle, I uploaded this dataset which is updated up until 2021, with predictions going up to 2026, so that other kagglers can have easy access to it when needed. This data was extracted from the International Monetary Fund official website.

Content

This dataset contains the GDP per capita of 229 countries, from 1980 to 2021, with predictions up until 2026. It is organized as follows: each year is represented by a column, and each country by a row.

Acknowledgements

This data set was extracted from the following website: https://www.imf.org/external/datamapper/NGDPDPC@WEO/OEMDC/ADVEC/WEOWORLD .

I created this dataset for beginners such as myself to get GDP per capita into playground competition more easily.

Here is a code on how to implement it.

dataset = pd.read_csv('../train.csv')
gdp = pd.read_csv('../GDP_Playground.csv')

def get_gdp(dataset):
    # Rename the columns in GDP df 
    gdp.columns = ['Argentina', 'Canada', 'Estonia', 'Spain', 'Japan']

    # Create a dictionary
    GDP_dictionary = gdp.unstack().to_dict()
    
    # Create GDP column
    dataset['GDP']=dataset.set_index(['country', 'year']).index.map(GDP_dictionary.get)
    
    # Split GDP by country (for linear model)
    dataset['GDP_Argentina']=dataset['GDP'] * (dataset['country']=='Argentina')
    dataset['GDP_Canada']=dataset['GDP'] * (dataset['country']=='Canada')
    dataset['GDP_Estonia']=dataset['GDP'] * (dataset['country']=='Estonia')
    dataset['GDP_Spain']=dataset['GDP'] * (dataset['country']=='Spain')
    dataset['GDP_Japan']=dataset['GDP'] * (dataset['country']=='Japan')
    
    # Drop column
    dataset=dataset.drop(['GDP','year'],axis=1)
    
    return dataset

Source of this function can be found in the older playground series from January 2022 Link is here: Click here

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."

Context

This data set contains global economic income indicators per country. The data has been prepared for ease of use.

The data is divided into: Male, female, dimestic credit, gross domestic product, gross national income, fixed capital formation, labour share. The individual files are briefly described below:

Income index:

Dimension: Income/composition of resources Definition: GNI per capita (2011 PPP International $, using natural logarithm) expressed as an index using a minimum value of $100 and a maximum value $75,000.

Domestic credit provided by financial sector (% of GDP)

Dimension: Income/composition of resources Definition: Credit to various sectors on a gross basis (except credit to the central government, which is net), expressed as a percentage of GDP.

Estimated gross national income per capita, female (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

Estimated gross national income per capita, male (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

GDP per capita (2011 PPP $)

Dimension: Income/composition of resources Definition: GDP in a particular period divided by the total population in the same period.

Gross domestic product (GDP), total (2011 PPP $ billions)

Dimension: Income/composition of resources Definition: 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, expressed in 2011 international dollars using purchasing power parity (PPP) rates.

Gross fixed capital formation (% of GDP)

Dimension: Income/composition of resources Definition: Value of acquisitions of new or existing fixed assets by the business sector, governments and households (excluding their unincorporated enterprises) less disposals of fixed assets, expressed as a percentage of GDP. No adjustment is made for depreciation of fixed assets.

Gross national income (GNI) per capita (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Aggregate income of an economy generated by its production and its ownership of factors of production, less the incomes paid for the use of factors of production owned by the rest of the world, converted to international dollars using PPP rates, divided by midyear population.

Labour share of GDP, comprising wages and social protection transfers (%)

Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality Dimension: Income/composition of resources Definition: Total compensation of employees given as a percent of GDP, which is a measure of total output. Total compensation refers to the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period.

Additional Information

For more information see : http://hdr.undp.org/sites/default/files/hdr2019_technical_notes.pdf

The title picture is from https://searchengineland.com/international-ppc-deal-currency-fluctuations-245601

Economic growth is easy to understand: it means that people have access to goods and services of increasing quantity and quality. What is hard, however, is to measure economic growth. This chart shows two ways of doing this for US growth over the past 160 years. The purple lines represent a straightforward approach: each line tracks the share of households with access to one specific good or service. Starting from the top, you see the rising provision of basic infrastructure like running water, flush toilets, and electric power. You can also see the increasing availability of communication technology: radios, TVs, the Internet, and mobile phones. And further down, you see the rise of technologies that reduced work at home: vacuum cleaners, washing machines, dryers, and dishwashers. This approach is very concrete; it shows practical ways in which the production and consumption of specific goods increased over time. The downside is that it only captures a limited number of particular goods. Millions of goods and services are produced and consumed, and most are not recorded with such precision. A way to measure how people’s access to the full range of goods and services changes is to measure people’s incomes. This way of measuring growth is shown in the top left panel. The data on average income, here measured by GDP per capita, tells us that the average American was 13 times poorer in 1860 than in 2022 (adjusted for inflation). These two ways of measuring economic growth have pros and cons: one is concrete but not comprehensive, the other is comprehensive but quite abstract. If we want to understand what growth means for our societies, I find it helpful to combine them both.

Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

Faroe Islands GDP per Capita: PPP data was reported at 78,103.483 Intl $ in 2023. This records an increase from the previous number of 74,282.307 Intl $ for 2022. Faroe Islands GDP per Capita: PPP data is updated yearly, averaging 54,000.238 Intl $ from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 78,103.483 Intl $ in 2023 and a record low of 39,330.596 Intl $ in 2009. Faroe Islands GDP per Capita: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Faroe Islands – Table FO.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

Greenland GDP per Capita: PPP data was reported at 68,086.460 Intl $ in 2021. This records an increase from the previous number of 64,857.629 Intl $ for 2020. Greenland GDP per Capita: PPP data is updated yearly, averaging 40,283.810 Intl $ from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 68,086.460 Intl $ in 2021 and a record low of 20,092.096 Intl $ in 1993. Greenland GDP per Capita: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.

Statistical concept and methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.

Variable time span 2000 – 2018

Link: https://ourworldindata.org/alcohol-consumption

This Alberta Official Statistic presents annual per cent change for Alberta and Canada real Gross Domestic Product (GDP) for Primary Agriculture Industries, 2008-2014. Gross Domestic Product (GDP) is a measure of the economic production which takes place within a geographical area. The term "gross" in GDP means that capital consumption costs, that is the costs associated with the depreciation of capital assets (buildings, machinery and equipment), are included. The production estimates are prepared for 215 separate industries using the North American Industrial Classification System (NAICS). Real GDP is gross domestic product adjusted for price changes. By taking out the impact of fluctuation in prices, real GDP allows people to more accurately measure the changes in total output and service for a jurisdiction. GDP measures are part of the Canadian System of National Accounts (SNA). The SNA provides a conceptually integrated framework of statistics for studying the state and behavior of the Canadian economy. The accounts are centered on the measurement of activities associated with the production of goods and services, the sales of goods and services in final markets, the supporting financial transactions, and the resulting wealth positions.

Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

Demographic characteristics of selected participants.

United States US: Military Expenditure: % of GDP data was reported at 3.149 % in 2017. This records a decrease from the previous number of 3.222 % for 2016. United States US: Military Expenditure: % of GDP data is updated yearly, averaging 4.864 % from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.063 % in 1967 and a record low of 2.908 % in 1999. United States US: Military Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

The Gross Domestic Product per capita in Pakistan was last recorded at 1643.68 US dollars in 2024. The GDP per Capita in Pakistan is equivalent to 13 percent of the world's average. This dataset provides - Pakistan GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides statistics on gross domestic product and gross domestic product per capita, for large regions (TL2) and small regions (TL3).

Regions and territorial levels

Regions are subnational units below national boundaries and correspond to administrative divisions defined autonomously by countries according to different criteria. The OECD classifies regions into two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). This classification facilitates greater comparability of geographic units at the same territorial level.

The list and maps of OECD regions are presented in the OECD Territorial grid (pdf).

Regional typologies

Regional typologies helps to assess differences in socio-economic trends in regions, both within and across countries. Small (TL3) regions are categorized based on shared characteristics into two types of typology:

• Typology based on the proximity of regions to functional urban areas of different sizes (FUAs encompass the economic and functional extent of cities based on daily people’s movements). See the methodology (pdf).
• Typology based on urban-rural population: traditionally the OECD has classified small regions as predominantly urban, intermediate, or predominantly rural regions. See the methodology (pdf).

The list of OECD regions and typologies are presented in the OECD Territorial correspondence table (xlsx).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

Subnational statistics can be further explored with:

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

For any question or comment, please write to RegionStat@oecd.org