The Gross Domestic Product (GDP) in European Union was worth 19423.32 billion US dollars in 2024, according to official data from the World Bank. The GDP value of European Union represents 18.29 percent of the world economy. This dataset provides the latest reported value for - European Union GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product per capita in European Union was last recorded at 34859.60 US dollars in 2024. The GDP per Capita in European Union is equivalent to 276 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented in US dollars. 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 table shows OECD countries and some other economies, 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

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.

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

The Gross Domestic Product per capita in European Union was last recorded at 54290.99 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in European Union, when adjusted by Purchasing Power Parity is equivalent to 306 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita Ppp - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

EDA has compared the 2005 to 2011 defence expenditure of its participating Member States with that of the United States for the same year.

The EU data represent spending of 26 EDA participating Member States*. The US data are based on public sources, mainly US Government publications. Data on GDP, General Government Expenditure, Population and Euro/US Dollar exchange rates were collected from Eurostat.

This table shows Gross Domestic Product (GDP) per capita (or per person), household final consumption expenditure per capita and actual individual consumption per capita. Final consumption expenditure is the expenditure of resident households on consumption goods or services, while individual consumption is the sum of household consumption plus the individual (not collective) consumption of the non-profit institutions serving households (NPISH) and General Government sectors. The indicators are in volume terms and are converted to US dollars using constant Purchasing Power Parities (PPPs).

When using the filters, please note that GDP is selected by default in the ‘Transaction’ filter but you can select the consumption measures using the ‘Transaction’ filter. The ‘Institutional sector’ filter shows that GDP and actual individual consumption relate to the total economy, while household final consumption expenditure relates to households.

The table shows OECD countries and selected economies, as well as the OECD total, OECD Europe, European Union and euro area . These can be selected using the ‘Reference area’ filter.

These indicators were presented in the previous dissemination system in the SNA_TABLE1 dataset.
See ANA Changes for information on changes in methodology: ANA Changes
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

Abstract: This empirical study analyses the potential determinants of GDP growth in selected European countries. The study is conducted on the data for 19 countries from Central, Eastern and South-Eastern Europe within 2014 to 2020 time - framework. The influence of possible drivers of economic growth are investigated by employing dynamic panel data modeling, specifically System GMM method. The insights made by the study reveal that fiscal responsibility, initial development, inflation rate, EU membership are the main GDP growth drivers. In addition, we control for the institutional determinants of economic growth, as well as the role of R&D. These results provide further support for the hypothesis that macroeconomic policies conducted in a responsible and sustainable way can significantly improve countries growth perspectives. These findings may help us to understand that trinity between policies, institutions and technology is conditio sine qua non of economic growth.

We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.

Diverse News Segments for In-Depth Analysis

This extensive extraction spans multiple segments, such as:

• Business and Market Analysis: Stay updated on major companies, mergers, and acquisitions.
• Technology and Innovation: Explore developments in AI, cybersecurity, and digital transformation.
• Economic Forecasts: Access insights into GDP, employment rates, inflation, and other economic indicators.
• Geopolitical Developments: Understand the impact of political events and global trade dynamics on markets.
• Personal Finance: Learn about saving strategies, investment tips, and real estate trends.

Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.

Why Choose This Dataset?

The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:

• Trend Analysis
• Sentiment Analysis
• Predictive Modeling

Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.

Access More News Datasets

Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.

Sample Dataset Available

To get a preview, check out the CSV sample of the CNBC economy articles dataset.

The Gross Domestic Product (GDP) In the Euro Area expanded 0.10 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Euro Area GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Balance of Payments is the statistical statement that systematically summarises transactions between residents and non-residents. It consists of the goods and services account, the primary income account, the secondary income account, the capital account and the financial account (BPM6 – 2.12) The current account is an important grouping of accounts within the Balance of Payments. The current account balance shows the difference between the sum of exports and income receivable and the sum of imports and income payable (exports and imports refer to both goods and services, while income refers to both primary and secondary income). The value of current account balance equals the saving-investment gap for the economy. The balance of current account is thus related to understanding domestic transactions (BPM6 – 2.15). Data are expressed in million euros. Data are presented in raw form. Source of euro area data: European Central Bank (ECB). Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

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

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

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

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

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

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

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

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

These are research indicators of comparative empirical investigation of South Eastern European Countries (SEECs) and People’s Republic of China (PRC) that were compiled from the criteria and factors of the World Bank. This dataset consists of data for SEECs and PRC for the period of 2000 to 2016. The World Bank Research Indicators consist of (1) GNI, Atlas Method (Current US$); (2) GNI per capita, Atlas; (3) GNI PPP (Current International $); (4) GNI per capita, PPP (Current International $); (5) Energy Use (kg of Oil Equivalent per capita); (6) Electric Power Consumption (kWh per capita); (7) GDP (Current US$); (8) GDP Growth (Annual %); (9) Inflation, GDP Deflator (Annual %); (10) Agriculture, Value Added (% of GDP); (11) Industry, Value Added (% of GDP); (12) Service, etc., Value Added (% of GDP); (13) Exports of Goods and Services (% of GDP); (14) Imports of Goods and Services (% of GDP); (15) Gross Capital Formation (% of GDP); (16) Revenue, excluding Grants (% of GDP); (17) Time Required to Start a Business (Days); (18) Domestic Credit Provided by Financial Sector (% of GDP); (19) Tax Revenue (% of GDP); (20) High-Technology Exports (% of Manufactured Exports); (21) Merchandise Trade (% of GDP); (22) Net Barter Terms of Trade Index (2000 = 100); (23) External Debt Stock, Total (DOD, Current US$); (24) Total Debt Service (% of Exports of Goods, Services and Primary Income); (25) Personal Remittances, Received (Current US$); (26) Foreign Direct Investment, Net Flows (BoP, Current US$); and (27) Net Official Development Assistance and Official Aid Received (Current US$). Furthermore, statistical data of SEECs and PRC were retrieved from Atlas 2.1 – Growth Lab at the Center for International Development at Harvard University and WITS – UNSD COMPTRADE.

An international comparison of productivity across the G7 nations, in terms of the level of and growth in GDP per hour and GDP per worker. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: ICP

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.

Since the oil price shock in 1974 unemployment increased significantly and also did not really decline in periods of economic upswings in Europe. This is especially the case for the countries of the European Union; therefore we face a special need for explanation. Looking at the member states on finds considerable differences. Since 1977 the unemployment rate within the EU is higher than the average unemployment rate of all OECD countries. The economic upswing in the second half of the 80s relaxed the labor market but nevertheless the unemployment rate remained on a high level. This study deals with the development of unemployment between 1974 and 1993 in four different G7 countries: Germany, France, Great Britain and Italy. Besides the common trend of an increasing unemployment rate, there are significantly different developments within the four countries. The analysis is divided in two parts: the first part looks at the reasons for the increase in unemployment in the considered countries; the second part aims to explain the difference between the developments of unemployment during economic cycles in the different countries. After the description of similarities and differences of labor markets in the four countries it follows a long term analysis based on annual data as well as a short and medium term analysis on quarterly data. This is due to the fact that short and medium term developments are mainly influenced by cyclical economic developments but long term developments are mainly influenced by other factors like demographical and structural changes. A concrete question within this framework is if an increase in production potential can contribute to a decrease in unemployment. For the long term analysis among others the Hysteresis-hypothesis (Hysteresis = Greek: to remain; denotes the remaining effect; in this context: remaining of unemployment) used for the explanation of the persistence of a high unemployment rate. According to this approach consisting unemployment is barely decreased after economic recovery despite full utilization of capacity. According to the Hysteresis-hypothesis there are two reasons for this. The first reason is that for long term unemployed the abilities to work and the qualification level decreased, their human capital is partly devalued. The second reason is that employees give up wage restraint, because they do not fear unemployment anymore and therefore enforce higher real wages. Besides economic recovery companies are not willing to hire long term unemployed with a lower expected productivity for the higher established tariff wages. In the context of the empirical investigation a multiple explanatory approach is chosen which takes supply side and demand side factors into consideration. The short and medium term analysis refers to Okun´s law (=an increase in the unemployment rate is connected with a decrease of the GDP; if the unemployment rate stays unchanged, the GDP grows with 3% p.a.) and aims to analyze more detailed the reactions of unemployment to economic cycles. A geometrical lag-model is compared with a lag-model ager Almon. This should ensure a precise as possible analysis of the Okun´s relations and coefficients. Register of tables in HISTAT: A.: Unemployment in the European G7 countries B.: Analysis of unemployment in the Federal Republic of Germany C.: Basic numbers: International comparison A.: Unemployment in the European G7 countries A.1. Determinates of unemployment in the EU, Germany (1974-1993) A.2. Determinates of unemployment in the EU, France (1974-1993) A.3. Determinates of unemployment in the EU, Great Britain (1974-1993) A.4. Determinates of unemployment in the EU, Italy (1974-1993) B: Analysis of unemployment in the Federal Republic of Germany B.1. Growth of unemployment in the Federal Republic of Germany (1984-1991) B.2. Output and unemployment in the Federal Republic of Germany (1961-1990) C: Basic numbers: International comparison C.1. Unemployment in EU countries, the USA, Japan and Switzerland (1960-1996) C.2. Gainful employments in EU countries, the USA, Japan and Switzerland (after inland and residency concept) (1960-1996) C.3. Employees in EU countries, the USA and Japan (1960-1996) C.4. Population in EU countries, the USA and Japan (1960-1996)

• Attention and interest on international issues related to different countries.
• Most important objectives for Spain in international politics.
• Image of the Ibero-American countries in Spain and for the interviewee.
• Spain's relations with Latin America in economic, political, cultural, scientific/technical and sporting aspects.
• Most important objectives for Spain in international policy with Latin America.
• Similarities, common interests and union in the future between Spain and Latin American or European countries.
• Knowledge of the annual celebration of the Ibero-American Summit.
• Importance of the Ibero-American Summit for the countries of Latin America and for Spain.
• Agreement with various statements on the Ibero-American Summits: They strengthen political and economic ties, poor practical results, boost cooperation, and recent summits have lost relevance.
• Main problems that currently exist in the world. Opinion on the cooperation of Spain to the development of other peoples and the role of the State in international aid and cooperation.
• Countries to which Spanish development cooperation is directed and to which it should be directed.
• Evaluation of the resources that Spain dedicates to international cooperation for development and knowledge of the reduction of funds in recent years due to the crisis. Knowledge of the objective of industrialized countries to devote 0.7% of their GDP to help less developed countries. Opinion on whether Spain should dedicate 0.7% of its GDP. Knowledge of the percentage of Spanish GDP that is dedicated to helping less developed countries.
• Important aspects in development cooperation: human rights, health, indigenous peoples, poverty,... Knowledge of the United Nations Millennium Goals. Likelihood of achieving the various Goals. Knowledge of the resources that your Autonomous Community or City Council dedicates to cooperation with developing countries. Opinion on whether they should devote part of their resources.
• Agreement on the form of financing of NGOs.
• Most important task to be performed by NGDOs. Composition of NGOs to make their work effective.
• Participation in the interviewee's development cooperation.
• Box marked for allocation in the Income Statement.
• Influence on relations between Spain and Latin America of Latin American immigrants.
• Main cause of the immigration that Spain receives. Changes in the image of Latin America due to the presence of Latin American immigrants and the degree of agreement with a series of opinions about them.
• Beneficiary of the work done by immigrants from less developed countries.
• Agreement that there would be fewer immigrants if cooperation between countries were greater.
• Effect of the economic situation on immigrant and Spanish workers.
• Opinions on Spanish immigration policy and changes that should be introduced.
• Knowledge of Spanish companies with interests in Latin America. Investment.
• Effect of the performance of Spanish companies investing in Latin America has on the image of Spain.
• Equal performance of Spanish companies in Spain and Latin America.
• The Spanish Government should encourage greater involvement of Spanish companies abroad.
• Opinion on the action of Spanish companies in Latin America Larina in environmental and social matters
• Effect of the performance of Spanish companies investing in Latin America has on relations between Spain and these countries.
• Opinion on who benefits from the presence of Spanish companies in Latin America.
• Ideological self-location scale (1-10).
• Electoral participation in the 2008 general elections.
• Religious practice.

PhD project about the empirical effects of government spending. Thesis defended on June 22, 2021 by Thomas van Gemert, containing the following main chapters: Chapter 2: The role of the anticipation horizon in government spending news; Chapter 3: Local fiscal multipliers of different government spending categories; Chapter 4: Sectoral and regional spillover effects of government spending. Data on government spending and macroeconomic variables (e.g. GDP, private consumption, investment, state-level income, sectoral production). National data US, quarterly, 1948 - 2014; State-level data US, yearly, 1963 - 2014; NUTS2-regions level data, yearly, 1989 - 2015.