The statistic shows global gross domestic product (GDP) from 1985 to 2024, with projections up until 2030. In 2020, global GDP amounted to about 85.76 trillion U.S. dollars, two and a half trillion lower than in 2019. Gross domestic product Gross domestic product, also known as GDP, is the accumulated value of all finished goods and services produced in a country, often measured annually. GDP is significant in determining the economic health, growth and productivity in the country, and is a stat often used when comparing several countries at a time, most likely in order to determine which country has seen the most progress. Until 2020, Global GDP had experienced a growth every year since 2010. However, a strong growth rate does not necessarily lead to all positive outcomes and often has a negative effect on inflation rates. A severe growth in GDP leads to lower unemployment, however lower unemployment often leads to higher inflation rates due to demand increasing at a much higher rate than supply and as a result prices rise accordingly. In terms of unemployment, growth had been fairly stagnant since the economic downturn of 2007-2009, but it remains to be seen what the total impact of the coronavirus pandemic will be on total employment.

This table presents annual data on the output components, the final expenditure categories and the income components of gross domestic product of the Netherlands. In the national accounts gross domestic product is approached from three points of view: from the output, from the generation of income and from the final expenditure. Gross domestic product is a main macroeconomic indicator. The volume change of gross domestic product is a measure for the economic growth of a country.

Data available from: 1995.

Status of the figures: Data from 1995 up to and including 2023 are final. Data of 2024 are provisional.

Changes as of June 24th 2025: Data of 2024 have been added to this table.

When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year.

The statistic shows the gross domestic product (GDP) per capita in the United States from 1987 to 2024, with projections up until 2030. In 2024, the gross domestic product per capita in the United States amounted to around 85,812.18 U.S. dollars. Thus, the United States is one of the countries with the largest GDP per capita worldwide. See the U.S. GDP growth rate here and the US GDP for further information. For comparison, per capita GDP in China had reached about 5,553 U.S. dollars in 2011. Gross domestic product of the United States The gross domestic product (GDP) of a country is an economic key figure, as it represents the market value of goods and services produced in a country within one year. The United States’ GDP) is increasing consistently, and it is expected to continue growing. On a global scale, the U.S. share of GDP adjusted for Purchasing Power Parity has been in the range of 20 percent over the last few years, give or take a few percentage points. The United States has the largest GDP worldwide, with a significant lead over China, Japan and Germany. Gross domestic product per capita is annual GDP divided by the average population from the same year, which allows for a GDP calculation per inhabitant of a country. Thus, a country with a high GDP, like the United States, can still have a low GDP per capita. Consequently, if compared to other countries, the United States does not rank among the top ten on this list .

As of the first quarter of 2025, the GDP of the U.S. fell by 0.5 percent from the fourth quarter of 2024. GDP, or gross domestic product, is effectively a count of the total goods and services produced in a country over a certain period of time. It is calculated by first adding together a country’s total consumer spending, government spending, investments and exports; and then deducting the country’s imports. The values in this statistic are the change in ‘constant price’ or ‘real’ GDP, which means this basic calculation is also adjusted to factor in the regular price changes measured by the U.S. inflation rate. Because of this adjustment, U.S. real annual GDP will differ from the U.S. 'nominal' annual GDP for all years except the baseline from which inflation is calculated. What is annualized GDP? The important thing to note about the growth rates in this statistic is that the values are annualized, meaning the U.S. economy has not actually contracted or grown by the percentage shown. For example, the fall of 29.9 percent in the second quarter of 2020 did not mean GDP is suddenly one third less than a year before. In fact, it means that if the decline seen during that quarter continued at the same rate for a full year, then GDP would decline by this amount. Annualized values can therefore exaggerate the effect of short-term economic shocks, as they only look at economic output during a limited period. This effect can be seen by comparing annualized quarterly growth rates with the annual GDP growth rates for each calendar year.

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.

The World Bank is a treasure trove of information. :- https://data.worldbank.org/

Generally the Gross Domestic Product of a country = the total output of the country = measure of development/total affluence of the country is measured by indicators such as household spending, government spending, level of investments etc.

Please see Bank of England explanation of GDP here :- http://edu.bankofengland.co.uk/knowledgebank/what-is-gdp/

I have argued that GDP could instead be measured better by primary indicators that lead to these what I call "secondary indicators".

Primary indicators are such as :- level of education. I hypothesize that a higher level of education leads to higher household income and hence higher household spending. So does knowing education levels of a country allow us to predict the GDP of the country?

I have used the list of primary indicators below to do a regression of the GDP per person :- (1) Women making informed choices regarding healthcare - The null hypotheses (H0)----> is the higher the level of women's education - the higher the level of national education and lesser infant mortality rates(which might be a stretch) and hence higher household income --> higher household spending ---> higher GDP. (2) Rural Population % - The null hypotheses (H0) is -----> higher rural population ----> lower per capita household income----> lower level of household spending----> lower GDP. (3) Ratio of Population having education ----> similar to above. You get the point hopefully by now... if not read a introductory macroeconomics textbook or course like this :- https://www.edx.org/course/introduction-economics-macroeconomics-snux-snu044-088-2x-0 (4) Legal Rights Strength Index-----> This actually comes from Islam. In Islam - the affluence of a country is related to truthfulness, rule of law being abided in the country etc.. For those who can understand Urdu/Hindi - please watch this video :- https://www.youtube.com/watch?v=XLjicUv0KYs (5) Credit to Private Sector -----> easier it is to open a business, work on ideas-----> higher should be the output of the country (6) Births attended by Skilled Staff ------> less infant mortality ----> indicates higher level of education and health care in the country ------> can indicate higher government spending among other factors ------>and should translate to higher level of GDP. (6) ATMMachines Ratio per 1000 people ---------> Higher level -----> shows finance is easily available -----> institutions are developed -----> maybe even indicates better public infrastructure-----> should indicate higher personal and government funding. (7) Agricultural Machines per hectare of land ------> higher automation -----> better access to finance for rural areas ------> should lead to higher GDP. (8) Literacy Rate Adults -----> the higher level of education in adults ----> higher private spending -----> should lead to higher GDP. (9) Accounts Ratio Financial Institutions -----> how many people have bank accounts who are male and over 15 ------> shows level of private spending-----> level of finance and infrastructure and hence government funding maybe -----> higher GDP.

Gross domestic product today is only modestly bigger than it was 100 years ago, at least if it’s measured in tons! While this may seem an absurd way to measure GDP, the point is that how economic variables are measured is important.

Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP++, undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia.

Nominal GDP is an assessment of economic production in an economy but includes the current prices of goods and services in its calculation. GDP is typically measured as the monetary value of goods and services produced.

**Real gross domestic product **(real GDP for short) is a macroeconomic measure of the value of economic output adjusted for price changes (i.e. inflation or deflation). This adjustment transforms the money-value measure, nominal GDP, into an index for quantity of total output.estions do you want to see answered?

In 2024, the U.S. GDP increased from the previous year to about 29.18 trillion U.S. dollars. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2024, the United States has the largest economy in the world. What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs). GDP and national debt Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living. While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.

This dataset provides both quarterly and annual estimates of the value of the goods and services produced in Iowa as provided by the U.S. Department of Commerce, Bureau of Economic Analysis in tables SAGDP2N, SAGDP9N, SAGDP10N, SQGDP2, and SQGDP9. Annual data is available beginning in 1997, and quarterly beginning 2005. The data include breakdowns of industries' contributions. Quarterly estimates are presented as an annual rate. Gross domestic product (GDP) is the measure of the market value of all final goods and services produced within Iowa in a particular period of time. In concept, an industry's GDP by state, referred to as its "value added", is equivalent to its gross output (sales or receipts and other operating income, commodity taxes, and inventory change) minus its intermediate inputs (consumption of goods and services purchased from other U.S. industries or imported). The Iowa GDP a state counterpart to the Nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Iowa GDP differs from national GDP for the following reasons: Iowa GDP excludes and national GDP includes the compensation of federal civilian and military personnel stationed abroad and government consumption of fixed capital for military structures located abroad and for military equipment, except office equipment; and Iowa GDP and national GDP have different revision schedules. GDP is reported in millions of current dollars. Real GDP is an inflation-adjusted measure of Iowa's gross product that is based on national prices for the goods and services produced within Iowa. The real estimates of gross domestic product (GDP) are measured in millions of chained dollars. The annual per capita real GDP is also provided and is measured in chained dollars. In calculating the per capita real GDP, the real GDP is divided by the Census Bureau’s annual midyear (July 1) population estimates for the year.

Quarterly national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B (transmission programme of data) of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013, amended by Regulation (EU) 2023/734 of 15 March 2023. ESA 2010 is aligned with the national accounts framework at the international level, the System of National Accounts (SNA 2008).

GDP is one of the key aggregates in national accounts. GDP is a measure of the total economic activity taking place on an economic territory.

There are three approaches to measuring GDP:

1. the production approach, as the sum of the value added by all activities which produce goods and services, plus taxes less subsidies on products;
2. the expenditure approach, as the sum of all final expenditures made in either consuming the final output of the economy, or in adding to wealth, plus exports less imports of goods and services;
3. the income approach, as the sum of all incomes earned in the process of producing goods and services plus taxes on production and imports less subsidies

Data published in Eurobase reflect these 3 approaches. The current metadata is associated with the following data collections:

Geographical entities covered are the European Union, the euro area, EU Member States, EFTA countries and Candidate Countries. Data from other countries (e.g. US, Japan and other countries) are received via the OECD and IMF and published in Eurobase in the naid_10 collection.

Data sources: National Statistical Institutes.

National accounts are a coherent and consistent set of macroeconomic indicators, which provide an overall picture of the economic situation and are widely used for economic analysis and forecasting, policy design and policy making.

Annual national accounts are compiled in accordance with the European System of Accounts - ESA 2010 as defined in Annex B of the Council Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013, amended by Council Regulation (EU) 2023/734 of 15 March 2023.

Gross domestic product (GDP) is one of the key aggregates in the European system of accounts (ESA). GDP is a measure of the total economic activity taking place on an economic territory which leads to output meeting the final demands of the economy.

There are three ways of measuring GDP at market prices:

1. the production approach, as the sum of the values added by all activities which produce goods and services, plus taxes less subsidies on products;
2. the expenditure approach, as the total of all final expenditures made in either consuming the final output of the economy, or in adding to wealth, plus exports less imports of goods and services;
3. the income approach, as the total of all incomes earned in the process of producing goods and services plus taxes on production and imports less subsidies.

Data published in the following tables reflect these 3 approaches.

Breakdowns provided are based on the ESA Transmission Programme, which list all tables requested from the countries.

The annual tables under this collection are the following:

• nama_10_gdp GDP and main components (output, expenditure and income)
• nama_10_pc Main GDP aggregates per capita
• nama_10_a10 Gross value added and income components by A*10 industry
• nama_10_a64 Gross value added and income components by A*64 industry

Geographical entities covered are the European Union, the euro area, EU Member States, EFTA countries and Candidate Countries. Data from other countries (e.g. US, Japan and other countries) are received via the OECD and IMF and published in Eurobase in the naid_10 collection.

Data sources: National Statistical Institutes, Eurostat (for European aggregates).

Netherlands NL: Gross Domestic Product (GDP): Current PPP data was reported at 1,398.445 USD bn in 2023. This records an increase from the previous number of 1,319.462 USD bn for 2022. Netherlands NL: Gross Domestic Product (GDP): Current PPP data is updated yearly, averaging 554.119 USD bn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 1,398.445 USD bn in 2023 and a record low of 165.710 USD bn in 1981. Netherlands NL: Gross Domestic Product (GDP): Current PPP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Netherlands – Table NL.OECD.MSTI: Gross Domestic Product, GDP PPP and GDP Deflator: OECD Member: Annual.

In the Netherlands, beginning with the 2013 data, the following methodological improvements led to breaks in series in the business sector (increase), the government sector (decrease), and at the total economy level (increase): better collection and treatment methods for measuring and reporting R&D expenditures related to external R&D personnel (alignment with the 2015 Frascati Manual); reclassification from the government to the business sector of public corporations engaged in market production; and a better follow-up of non-respondents. In 2012, the method for sampling enterprises included in ISIC industries 84 to 99 (community, social, and personal services) as well as the breakdown of personnel data by occupation were modified leading to breaks in series in the business and government sectors. In 2011, the method for producing business enterprise data changed: all observed enterprises are included whereas before 2011, only enterprises with substantial R&D activities (i.e. with a minimum number of R&D personnel) were incorporated. Subsequent changes affected the higher education sector: before 1999, a large number of PhD candidates were formally employed by research institutes (in the government sector) financing their research. From 1999, universities became the formal employer of PhD candidates and their research activities moved from the Government sector to the Higher Education sector. Besides this, the R&D activities of the Universities of Applied Sciences (HBO) were taken into account for the first time. Finally the R&D activities of the Academic hospitals were increasingly underestimated due to the merging of the Academic hospitals and (parts) of the Faculties of Medicine of the universities into so-called University Medical Centers (UMC's). This started in 1998 and meant for instance that staff of the Faculty of Medicine of the university became employees of the UMC. As a result, data on R&D in the field of medical sciences were also revised. As of 2000, newly-recruited researchers on the payroll of the Netherlands Organisation for Scientific Research (NOW), previously included in the Government sector, were included with personnel in the higher education sector. In 1982 and 1990, the methodology of the survey on R&D expenditure changed.

In 2003, Statistics Netherlands revised the panel of the R&D survey for the Government and PNP sectors, resulting in breaks in series for both. Also beginning in 2003, R&D personnel in the PNP sector are grouped with Government sector R&D personnel.

In 1994 and 1996 there were major expansions of the scope of the Business Enterprise sector survey; R&D expenditure and personnel data in the latter sector and in the whole economy are thus not comparable with those for the previous years.

In 1990 and 1999, new methods for calculating GUF are introduced for GBARD series.

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

In 2024, the United States accounted for 14.88 percent of global gross domestic product (GDP) after adjusting for purchasing power parity (PPP). This share was expected to decrease to 13.98 percent by 2030, which is roughly a seventh of the global total. What is PPP? The easiest way to understand purchasing power parity is the Big Mac Index, a measure developed by The Economist. The index tracks the price of the McDonald’s Big Mac burger, sold at each of its thousands of restaurants worldwide. Countries where the Big Mac is most expensive have higher purchasing power, meaning one can buy more for each unit of that currency. To calculate PPP, economists use a group of goods to calculate the ratio of the price of this group in each country. This ratio is then used to convert all countries into a standardized price level, on parity with each other. Why use PPP? A U.S. dollar in the United States does not have the same purchasing power as a dollar in China, even after considering the exchange rate. For this reason, adjusting for PPP gives an idea of what the rest of the world could buy in the United States, if prices were the same as in their home country. However, some economists argue that using PPP for comparisons between countries is inaccurate because it changes the price level differently for each country. Still, because it accounts not only for country-specific effects but also inflation and exchange rate fluctuations, PPP is a very popular metric.

In the US, income and expenditure-side estimates of GDP (GDPI and GDPE) measure true GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (that is, the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

Netherlands NL: GDP: Value Added: Current PPP data was reported at 799.221 USD bn in 2022. This records an increase from the previous number of 708.528 USD bn for 2021. Netherlands NL: GDP: Value Added: Current PPP data is updated yearly, averaging 361.117 USD bn from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 799.221 USD bn in 2022 and a record low of 111.316 USD bn in 1981. Netherlands NL: GDP: Value Added: Current PPP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Netherlands – Table NL.OECD.MSTI: Gross Domestic Product, GDP PPP and GDP Deflator: OECD Member: Annual. In the Netherlands, beginning with the 2013 data, the following methodological improvements led to breaks in series in the business sector (increase), the government sector (decrease), and at the total economy level (increase): better collection and treatment methods for measuring and reporting R&D expenditures related to external R&D personnel (alignment with the 2015 Frascati Manual); reclassification from the government to the business sector of public corporations engaged in market production; and a better follow-up of non-respondents. In 2012, the method for sampling enterprises included in ISIC industries 84 to 99 (community, social, and personal services) as well as the breakdown of personnel data by occupation were modified leading to breaks in series in the business and government sectors. In 2011, the method for producing business enterprise data changed: all observed enterprises are included whereas before 2011, only enterprises with substantial R&D activities (i.e. with a minimum number of R&D personnel) were incorporated. Subsequent changes affected the higher education sector: before 1999, a large number of PhD candidates were formally employed by research institutes (in the government sector) financing their research. From 1999, universities became the formal employer of PhD candidates and their research activities moved from the Government sector to the Higher Education sector. Besides this, the R&D activities of the Universities of Applied Sciences (HBO) were taken into account for the first time. Finally the R&D activities of the Academic hospitals were increasingly underestimated due to the merging of the Academic hospitals and (parts) of the Faculties of Medicine of the universities into so-called University Medical Centers (UMC's). This started in 1998 and meant for instance that staff of the Faculty of Medicine of the university became employees of the UMC. As a result, data on R&D in the field of medical sciences were also revised. As of 2000, newly-recruited researchers on the payroll of the Netherlands Organisation for Scientific Research (NOW), previously included in the Government sector, were included with personnel in the higher education sector. In 1982 and 1990, the methodology of the survey on R&D expenditure changed.In 2003, Statistics Netherlands revised the panel of the R&D survey for the Government and PNP sectors, resulting in breaks in series for both. Also beginning in 2003, R&D personnel in the PNP sector are grouped with Government sector R&D personnel.In 1994 and 1996 there were major expansions of the scope of the Business Enterprise sector survey; R&D expenditure and personnel data in the latter sector and in the whole economy are thus not comparable with those for the previous years.In 1990 and 1999, new methods for calculating GUF are introduced for GBARD series.

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.