The OECD Family database is an on-line database on family outcomes and family policies with indicators for all OECD countries. Coverage also includes EU Member States that are not OECD members. To date the database brings together 58 indicators on family structure, labor market participation, public policies and child outcomes. When possible, indicators are updated on a regular basis.

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OECD Revenue Statistics: Comparative Tables Introduction

The OECD Revenue Statistics database provides detailed and internationally comparable data on the taxes and social contributions paid by businesses and individuals in OECD countries. The data is collected annually from national governments and covers a wide range of taxes, including personal income tax, corporate income tax, social security contributions, and value-added tax.

Data

The database is divided into two main parts:

Part 1: Revenue by Level of Government This part of the database provides data on the total revenue collected by each level of government (central, state, and local) in each OECD country. The data is broken down by type of tax and by source of revenue (e.g., taxes on income, profits, and capital gains; taxes on goods and services; social security contributions).

Part 2: Revenue by Tax Type This part of the database provides data on the revenue collected from each type of tax in each OECD country. The data is broken down by level of government and by source of revenue.

Uses

The OECD Revenue Statistics database can be used for a variety of purposes, including:

Cross-country comparisons of tax levels and structures The database can be used to compare the tax levels and structures of different OECD countries. This information can be used by policymakers to assess the effectiveness of their tax systems and to identify potential areas for reform.

Analysis of the impact of tax policies The database can be used to analyze the impact of tax policies on economic growth, income distribution, and other outcomes. This information can be used by policymakers to design tax policies that are more effective and efficient.

Research on tax policy The database can be used by researchers to study the effects of tax policy on a variety of economic outcomes. This research can help to inform the design of tax policy and to improve our understanding of the economic effects of taxation.

Conclusion

The OECD Revenue Statistics database is a valuable resource for policymakers, researchers, and anyone interested in the taxation of businesses and individuals in OECD countries. The database provides detailed and internationally comparable data on a wide range of taxes, making it an essential tool for understanding the tax systems of OECD countries.

Data Access

The OECD Revenue Statistics database is available online to subscribers. Subscribers can access the data through the OECD's website.

Access OECD countries and selected non-member economies data through the OECD API.

Graph and download economic data for Gross National Income for OECD Members (NYGNPMKTPCDOED) from 1960 to 2024 about OECD Economies, GNI, and income.

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis: each variable in the dataset is made publicly available as soon as it is updated in the sources databases. The productivity database contains data on labour productivity both measured using employment or hours worked and the compenents of capital and labour inputs. The productivity database in levels, in growth rates and by industry contains annual data, while the database on productivity and unit labour costs are quarterly estimates. Further information for all datasets and the methodology may be found in the attached file OECD-Productivity-Statistics-Database-metadata

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented as growth rates. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns.

The data is presented for OECD countries individually, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

United States Exports: FAS: OECD data was reported at 96.769 USD bn in May 2018. This records an increase from the previous number of 92.128 USD bn for Apr 2018. United States Exports: FAS: OECD data is updated monthly, averaging 15.201 USD bn from Jan 1988 (Median) to May 2018, with 365 observations. The data reached an all-time high of 98.504 USD bn in Mar 2018 and a record low of 6.490 USD bn in Jan 1988. United States Exports: FAS: OECD data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA009: Trade Statistics: Census Basis: By Region. Significant change between Dec-2003 and Jan-2004 was due to the increased number of OECD countries. OECD includes 24 countries until Dec-2003 and increased to 29 countries beginning Jan-2004 to present.

United States Imports: Customs: OECD data was reported at 116.823 USD bn in Sep 2018. This records a decrease from the previous number of 127.074 USD bn for Aug 2018. United States Imports: Customs: OECD data is updated monthly, averaging 22.344 USD bn from Jan 1988 (Median) to Sep 2018, with 369 observations. The data reached an all-time high of 127.074 USD bn in Aug 2018 and a record low of 6.651 USD bn in Jan 1990. United States Imports: Customs: OECD data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA009: Trade Statistics: Census Basis: By Region. Significant change between Dec-2003 and Jan-2004 was due to the increased number of OECD countries. OECD includes 24 countries until Dec-2003 and increased to 29 countries beginning Jan-2004 to present.

Germany DE: Total Business Enterprise R&D Personnel: % of National Total data was reported at 63.417 % in 2021. This records a decrease from the previous number of 63.699 % for 2020. Germany DE: Total Business Enterprise R&D Personnel: % of National Total data is updated yearly, averaging 63.417 % from Dec 1981 (Median) to 2021, with 35 observations. The data reached an all-time high of 70.450 % in 1987 and a record low of 61.011 % in 1996. Germany DE: Total Business Enterprise R&D Personnel: % of National Total 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 Germany – Table DE.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual. The data in this publication for Germany cover unified Germany from 1991 and western Germany only until 1990.In 2016, the method for calculating R&D coefficients was revised, introducing a break in series in the Higher Education sector. In particular, coefficients are thereafter based on time-use surveys.From reference year 2014, the distribution of R&D personnel by occupation is requested in the government survey whereas it was previously estimated from data by qualification.The method for calculating public-financed R&D in the business enterprise sector was reviewed, resulting in the revision of business enterprise R&D and the national total back to 1991.In 1992 the methodology of the survey on resources devoted to R&D in the Government sector was changed. From 1991, the data for the Private Non-Profit sector have been included in the Government sector.For 1997, the methodology for allocating GBARD by socio-economic objective changed. For 1997 and from 2001 to 2015, the global budget reduction was not distributed proportionally across SEO by the Federal Ministry of Education and Research. Therefore, the sum of the breakdown for those years does not add to the total. From 2016 onwards the global reduction is distributed across SEO proportionally.

This dataset provides the indicators to measure OECD countries' efforts for Sustainable Development Goal 17 - Strengthen the means of implementation and revitalize the global partnership for sustainable development.

The OECD dataset on the SDGs has been built using global UNSDG data and complementary OECD data, which allows the analysis to be tailored to the policy challenges faced by OECD countries. The methodology builds on data from the UN Global SDG Database and relevant OECD Databases such as Green Growth Indicators, OECD Environment Statistics, OECD Main Science and Technology Indicators, OECD Science, Technology and R&D Statistics, OECD Compendium of Productivity Indicators, amongst others. The OECD acts as the (co-)custodian of a number of indicators and directly supplies data to the UN Global Database in areas including ODA and other international flows, gender-based legal discrimination, access to civil justice and others.

Details on the construction of the database can be found in the methodology note.

Graph and download economic data for Population Growth for OECD Members (SPPOPGROWOED) from 1961 to 2024 about OECD Economies, population, and rate.

This dataset provides a country–year panel for OECD countries covering the period 2010–2024. It combines annual data on public, private and total social expenditure as a share of GDP with the World Happiness Index (WHI) and the Human Development Index (HDI).The data are constructed to analyze the relationships between social spending, subjective well-being and human development in OECD countries. The panel structure (one observation per country per year) makes the dataset suitable for descriptive analysis as well as regression-based empirical research.ContentsThe main Excel file contains a single data sheet:Sheet: data_setEach row corresponds to a specific country–year observation for an OECD member state.Variables:Country: Country name (OECD member; e.g., “Australia”, “Türkiye”, “United States”).iso3: ISO 3166-1 alpha-3 country code (e.g., “AUS”, “TUR”, “USA”).year: Calendar year (2010–2024).pub_socexp_gdp: Public social expenditure as a percentage of GDP (%).priv_socexp_gdp: Private (mandatory and voluntary) social expenditure as a percentage of GDP (%).tot_socexp_gdp: Total social expenditure (public + private) as a percentage of GDP (%).WHI: World Happiness Index; average national happiness score on a 0–10 scale based on the Cantril ladder question.HDI: Human Development Index; composite index of three basic dimensions of human development (health, education, and standard of living).income_group: Binary country income group indicator used in the analysis. High‑income OECD countries are coded as 1 (“High”), and all other OECD members (upper‑middle, lower‑middle and low income) are coded as 0 (“NonHigh”). Income groups were constructed using data from the OECD Data Explorer (2024) and the World Bank country income classification for 2024, based on PPP (purchasing power parity) income thresholds.Empty cells indicate that data for the corresponding country–year observation are not available in the original sources or were not included in the analytical sample due to missingness.Data sourcesSocial expenditure (pub_socexp_gdp, priv_socexp_gdp, tot_socexp_gdp)Data are taken from the OECD Social Expenditure Database (SOCX). SOCX provides reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at the program level for 38 OECD countries (and some accession countries), with coverage from 1980 and estimates for more recent years.Reference: OECD Social Expenditure Database (SOCX), https://www.oecd.org/en/data/datasets/social-expenditure-database-socx.html.World Happiness Index (WHI)Happiness data are drawn from the World Happiness Report, accessed via HumanProgress.org (World Happiness Report section). The index is based on average national values for answers to the Cantril ladder question, which asks respondents to evaluate their current life on a 0–10 scale, with the worst possible life as 0 and the best possible life as 10.Reference: World Happiness Report; HumanProgress.org, https://humanprogress.org.Human Development Index (HDI)HDI data are drawn from the Human Development Index series compiled by the United Nations Development Programme (UNDP), accessed via HumanProgress.org (Human Development Index section). The HDI measures three basic dimensions of human development: life expectancy at birth; an education component (adult literacy rate and school enrollment); and GDP per capita (purchasing power parity, PPP, in U.S. dollars), combined into a composite index.Reference: United Nations Development Programme (UNDP), Human Development Reports; HumanProgress.org, https://humanprogress.org.Data construction and coverageThe dataset is restricted to OECD member countries and the years 2010–2024.WHI and HDI series are matched to OECD social expenditure data using ISO3 country codes and calendar years.In addition, a binary income group variable (income_group) was created to distinguish high‑income OECD countries from other OECD members, using the World Bank’s 2024 income thresholds (PPP‑based) and country information from the OECD Data Explorer (2024).Some country–year combinations, particularly in later years (e.g., 2022–2024), contain missing values where the original sources do not provide data or only provide partial estimates. These are retained as empty cells.The empirical analyses in the associated study are conducted on subsets of the data restricted to complete cases for the relevant variables.Researchers can use this dataset to replicate the results of the associated study or to conduct additional analyses on the links between social expenditure, happiness and human development within the OECD context.If you use this dataset, please cite both this data file and the original data providers (OECD, World Happiness Report, UNDP, and HumanProgress.org).

OECD statistics contact: stat.contact@oecd.org

The OECD Secretariat collects a wide range of statistics on businesses and business activity. The Structural Business Statistics by size class dataset is part of the Structural and Demographic Business Statistics (SDBS) database featuring the harmonised data collection of the OECD Statistics and Data Directorate relating to a number of key variables, such as value added, operating surplus, employment, and the number of business units.

Data are broken down to class (4-digit) level of International Standard of Industrial Classification (ISIC Revision 4), and by enterprise size class based on the number of persons employed.

Data cover OECD member and partner countries, non-OECD countries that are members of the European Statistical System who provide data to Eurostat, as well as countries participating in OECD Regional initiatives.

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa).

It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level).

FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset supports FDI aggregates indicators available from the FDI in Figures.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates

The average for 2023 based on 38 countries was 108.84 percent. The highest value was in Luxembourg: 404.46 percent and the lowest value was in the USA: 24.9 percent. The indicator is available from 1960 to 2024. Below is a chart for all countries where data are available.

The data have been collected via the official OECD Application Programming Interface (API) and includes the following indicators:

• EmpPlaRes - Employment at place of residence
• LfPartRa - Labour Force and Participation rate
• UnemReg - Unemployment in regions
• RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2)
• GDPLT3 - Gross Domestic Product (Small regions TL3)
• RegEmIndu - Regional Employment by industry (ISIC rev 4)
• RegGVAWorker - Regional GVA per worker
• RegIncPC - Regional income per capita

Source: https://data.oecd.org/api/

This dataset FDI positions by counterpart area, BMD4 includes inward and outward Foreign Direct Investment (FDI) positions by partner country for OECD reporting economies:

• Inward FDI positions by partner country measure the total level of direct investment in the reporting economy at the end of the year, by source countries, enabling, for example, the identification of the major sources of FDI for a specific OECD economy. Inward FDI positions are allocated to the immediate investing country but inward FDI positions allocated to the ultimate investing country are also available for some OECD economies.
• Outward FDI positions by partner country measure the total level of direct investment from the reporting economy at the end of the year, by destination countries, enabling, for example, the identification of the major destinations of FDI for a specific OECD economy. Outward FDI positions are allocated to the immediate counterpart country for all OECD countries.

Inward and outward FDI positions by partner country are presented according to the directional principle (unless otherwise specified in the country level metadata); they are measured in USD millions, in millions of national currency and as a share of total FDI positions.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI positions by counterpart area under the previous BMD3 methodology can be accessed in the archived dataset FDI positions by partner country.

Historical dataset from OECD on R&D and Innovation indicators.Source : Data extracted from OECD Database.Use : Was used to study, and analyse innovation management indicators for industries across Countries with such available data. These set of data of R&D indicators or measures(expenditure, personnel) was to investigate innovation management using Data Analysis and applying Machine Learning Algorithm for data prediction.To Use : For each Excel file, see description and meanings of parameters in "Notes" tab.Furthermore, for the specific R&D Expenditures and Personnel links :R&D Expenditure: Business enterprise R&D expenditure by industryR&D Personnel: R&D personnel by sector of employment and function

The Better Life Index is an initiative created by the OECD to compare the well-being priorities of people around the world. It consists of 11 social indicators: “housing, income, jobs, community, education, environment, governance, health, life satisfaction, safety, work-life balance” that contribute to well-being in OECD countries. This initiative aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives.

The 11 indicators in turn are composed of 20 sub-indicators through averaging and normalization. The visualization tool is available here. By selecting a set of weights to the sub-indicators, a user can rank countries according to their weighted sum.