This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GDP GROWTH RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The indicator is part of the EU Sustainable Development Goals (SDG) indicator set. It is used to monitor progress towards SDG 8 on decent work and economic growth; which is embedded in the European Commission’s Priorities under the European Green Deal, Economy that works for people. SDG 8 recognises the importance of sustained economic growth and high levels of economic productivity for the creation of well-paid quality jobs and the achievement of global prosperity. That said, it envisions inclusive and sustainable economic growth, which leaves no one behind and does not harm the environment.

Indicator can be considered as identical to global SDG indicator 8.1.1 "Annual growth rate of real GDP per capita". Furthermore, it is part of the impact indicators for Strategic plan 2020-2024 referring to the 6 Commission priorities.

The EU supports growth, job creation and competitiveness through funding instrumentssuch as the European Fund for Strategic Investments, the European Social Fund and its successor, the European Social Fund Plus, the European Structural and Investment Funds, Horizon 2020, the Programme for Employment and Social Innovation (EaSI), the Programme for the Competitiveness of Enterprises and Small and Medium-sized Enterprises (COSME), the Emergency Support Instrument, the Connecting Europe Facility and the Creative Europe Programme (CAP).

The indicator is calculated as the ratio of real GDP to the average population of a specific year. GDP measures the value of total final output of goods and services produced by an economy within a certain period of time. It includes goods and services that have markets (or which could have markets) and products which are produced by general government and non-profit institutions.

It is a measure of economic activity and is commonly used as a proxy for the development in a country’s material living standards. However, it is not a complete measure of economic welfare. For example, GDP does not include most unpaid household work. Neither does GDP take account of negative effects of economic activity, like environmental degradation.

Real GDP per capita is calculated as the ratio of real GDP to the average population of a specific year and is based on rounded figures.

All EU MS

Comparability across all EU Member States respectively other presented countries is ensured by the application of the legal framework represented by ESA 2010 and SNA 2008. Comparability of GDP for EU countries is regularly monitored in the context of the work of the Gross National Income (GNI) Committee. In addition, international harmonisation of techniques and, to some extent, compilation tools is ensured by the work of the national accounts working groups (Eurostat, OECD, UN).

Comparability across countries of the population figures is ensured by application of the same concept (ESA 2010) across countries.

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.

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."

Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia. Data is sourced from the World Bank and turned into a standard normalized CSV.

This dataset provides values for GDP PER CAPITA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_European_countries_by_GDP_growth. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

this project was realized in tableu :

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Governments around the world rely on tax revenues as a primary means to sustainably finance their operations, including providing infrastructure, public services, and paying for their employees. However, the extent to which countries collect taxes varies significantly, as illustrated by data from the United Nations showing government tax revenues as a share of gross domestic product (GDP).

In many European nations, tax revenues represent over a third of GDP, with countries like France and Denmark reaching levels as high as about half. These figures underscore the significant role of taxation in funding public expenditures in these countries.

Conversely, in most other parts of the world, tax revenues constitute a smaller portion of GDP. In some countries, taxes make up only a few percent of GDP, reflecting lower levels of government intervention in the economy or differing tax structures.

It's essential to recognize that variations in tax revenues are not solely attributable to differences in the capacity to collect taxes. While some variations may indeed reflect disparities in administrative capabilities or enforcement mechanisms, others stem from deliberate policy choices and political preferences regarding the level of taxation.

Moreover, reliance on alternative revenue sources, such as revenues from natural resources or foreign aid, can introduce volatility and uncertainty into a government's fiscal position. Therefore, the ability to effectively collect taxes remains crucial for ensuring stability and predictability in financing government activities.

Taxation also serves broader economic and social objectives beyond revenue generation. For instance, progressive taxation can contribute to reducing income inequality by redistributing wealth and funding social welfare programs. Conversely, lower tax rates may stimulate economic growth by incentivizing investment and consumption.

However, the optimal level and structure of taxation are subjects of ongoing debate and vary depending on economic conditions, societal preferences, and political ideologies. Governments must strike a balance between raising sufficient revenue to finance public expenditures and minimizing distortions and inefficiencies caused by taxation.

Furthermore, tax policies should be designed with consideration for their potential impact on economic behavior, investment decisions, and international competitiveness. International cooperation and coordination are also essential, particularly in addressing issues such as tax evasion, avoidance, and base erosion in an increasingly interconnected global economy.

In conclusion, while countries differ significantly in the extent to which they collect taxes, taxation remains a fundamental tool for financing government activities and achieving broader economic and social objectives. Effective tax policies must strike a balance between revenue generation, economic efficiency, equity, and international competitiveness to ensure sustainable fiscal outcomes and support inclusive growth and development.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/e7XSsvplRDKyJkFqPz9B2w Please be aware that annual and quarterly national accounts' volume figures are expressed in chain-linked volumes with a reference year 2005. Thus, the figures for this indicator are not comparable with previous releases. For more information, click here. GDP includes goods and services that have markets (or which could have markets) and products which are produced by general government and non-profit institutions. For measuring the growth rate of real GDP, the GDP at current prices are valued in prices of the previous year and the thus computed volume changes are imposed on the level of a reference year; this is called a chain-linked series. Accordingly, price movements will not inflate the growth rate. Real GDP per capita is calculated as the ratio of real GDP to the average population of a specific year. It is often used as an indicator of how well off a country is, since it is a measure of average real income in that country. However, it is not a complete measure of economic welfare. For example, GDP does not include most unpaid household work. Neither does GDP take account of negative effects of economic activity, like environmental degradation. Real GDP per capita is based on rounded figures. Discrepancies in tables between totals and percentages are due to rounding.

European Union EU27: DG ECFIN Forecast: Gross Domestic Product (GDP) per Capita data was reported at 42.598 EUR th in 2026. This records an increase from the previous number of 41.048 EUR th for 2025. European Union EU27: DG ECFIN Forecast: Gross Domestic Product (GDP) per Capita data is updated yearly, averaging 25.636 EUR th from Dec 1995 (Median) to 2026, with 32 observations. The data reached an all-time high of 42.598 EUR th in 2026 and a record low of 14.914 EUR th in 1995. European Union EU27: DG ECFIN Forecast: Gross Domestic Product (GDP) per Capita data remains active status in CEIC and is reported by European Commission's Directorate-General for Economic and Financial Affairs. The data is categorized under Global Database’s European Union – Table EU.DG ECFIN.AMECO: GDP per Capita: Forecast.

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.

The presented dataset shows that most countries increased their R&D spending - 16 of those 26 members of the European Union for which Eurostat has complete data did so. This also translated into the average ratio for the entire Community, in 2012 it amounted to 2.06% of GDP compared to 2.01% in 2009. The most severely limited their expenses were the leaders of the 2009 ranking, including Finland, Sweden and Denmark. Despite this, they kept their places and, more importantly, maintained the ideal, according to the "Strategy 2020" ratio of R&D expenditure to GDP (it is 3% and more). How important an element of innovation policy is the R&D sector can be seen by comparing the leaders of the above list with those of the "Summary Innovation Index" presented in the previous chapter. In two rankings, the same countries are in the top four. As for Poland, it increased R&D spending from 0.67% of GDP to 0.90% of GDP. This puts seven countries ahead of them.

The value of goods and services purchased or produced by general government and directly supplied to private households for consumption purposes. Expressed in million euro (current prices) and as a percentage of the gross domestic product (GDP).

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: - Frequent: Data is provided every month from January 2015. This is more frequent than quarterly official datasets. - Timely: Data is provided with a three week lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 3-6 month lag of official datasets. - Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

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

We have created this dataset for the UK, Switzerland and 28 EU Countries.

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: - Frequent: Data is provided every month from January 2015. This is more frequent than quarterly official datasets. - Timely: Data is provided with a three week lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 3-6 month lag of official datasets. - Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

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

We have created this dataset for the UK, Switzerland and 28 EU Countries.

The dataset provides information on exposure to natural hazards for 37 European countries and territories from 1870 to 2020, regridded to EURO-CORDEX 0.11 degree rotated-pole grid and geographical 5 arc minute grid (WGS84). The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively simple and explicit models and disaggregation techniques. It can be utilized to study changes in exposure, vulnerability and risk to various natural hazards. This page provides a revised version of the netCDF files previously published with the remainder of the HANZE database.

This dataset provides values for GDP FROM MANUFACTURING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Energy intensity is calculated as units of energy per unit of GDP. Indicator expressed in chain linked volumes is more suitable to be used to compare different time periods in one country, while PPS values are more suitable to be used to compare across countries in one specific year.

Energy data are from Eurostat's dataset nrg_bal_s and GDP data are from Eurostat's dataset nama_10_gdp.

nrg_bal_s:

• SIEC: TOTAL
• NRG_BAL: GAE
• UNIT: KTOE

nama_10_gdp:

• NA_ITEM: B1GQ
• UNIT: CLV05_MEUR, CLV10_MEUR, CLV15_MEUR, CP_MPPS_EU27_2020

Calculation examples:

Energy per GDP = 1000 * KTOE / CLV10_MEUR (Chain linked volumes, index 2010=100)
Energy per GDPPPS = 1000* KTOE / CP_MPPS (Current prices, million purchasing power standards)

Resulting calculations are rounded to 2 decimal places.