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.

The Gross Domestic Product (GDP) in France expanded 0.50 percent in the third quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - France GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The following dataset provides the results of the TdF social and economic impact survey, conducted during the Tour de France 2014. Please note We are aware of data quality errors in this dataset. This is currently under review. Any personal data collected has been removed and postcode restricted to district area. This is a one off publication and will only be updated to improve the quality of data.

The dataset contains data for 8 countries and one special administrative region (China, France, Germany, Hong Kong, India, Japan, Spain, United Kingdom and United States of America) from 1980 through 2020. It include major macroeconomic factors like inflation, unemployment, GDP, exchange rate (base USD) and per capita income. Apart from that it has the stock prices of the respective country's major stock index which can help in analysing the data set to identify the impact of major macroeconomic variables on the movement of stock index prices.

This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR07644.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

Title: Monthly War Data on WWI and WWII (Synthetic)

Description: This dataset provides a synthetic month-by-month, country-level representation of key metrics during World War I and World War II. It encompasses key nations involved in the conflicts and aims to showcase patterns and events influenced by historical timelines.

Key Features:

Year: The year of the data entry. Month: The month of the data entry. Country: The nation in focus. The dataset includes the USA, UK, Germany, USSR/Russia, and France. War Status: A binary variable indicating if the war was ongoing for that nation during that month (1 for Yes, 0 for No). Civilian Deaths: An estimated count of civilian deaths during that month. Military Deaths: An estimated count of military deaths during that month. Economic Impact Factor: A fictional index from 0 to 100 indicating the economic strain on the nation (a higher score indicates more strain). Population: Estimated population of the nation during that month. Note:

The data provided in this dataset is synthetically generated and is influenced by historical events and timelines. However, it is not an accurate representation of actual events and should be used with caution for analytical purposes. It is primarily designed for educational and illustrative tasks, allowing users to practice data analysis techniques in a historically-inspired context.

Imports from United Kingdom in France increased to 2310 EUR Million in January from 2218 EUR Million in December of 2023. This dataset includes a chart with historical data for France Imports from Uk.

hello, this is France, Germany and London stock index daily historical daily data. who can use various historical analysis for investment or show direction of economy.

The information relates to the economic boost from the Tour de France 2014, for the City of Bradford Metropolitan District and wider. How various teams worked together seamlessly to provide a service, and the overall impact on tourism, business, cycling and the region.

185 views (3 recent) Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. 2. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. 3. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

Techsalerator’s News Event Data in Europe is a comprehensive and meticulously curated dataset designed to provide businesses, analysts, journalists, and researchers with an extensive view of significant news events across Europe. This dataset captures and categorizes key events reported from a variety of news sources, offering valuable insights into industry developments, economic changes, political shifts, and other noteworthy occurrences throughout the continent.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide range of sources including press releases, industry news sites, blogs, PR platforms, and traditional news outlets. This broad coverage ensures that users receive a diverse array of information from multiple reporting channels. Categorization of Events:

News events are meticulously categorized into various types such as business and financial updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to specific interests or sectors. Real-Time Updates:

Data is updated regularly to include the most current events. This ensures that users have access to the latest information and can stay informed about recent developments as they unfold. Geographic Segmentation:

Events are tagged with their respective countries and regions within Europe. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes detailed information such as the date of occurrence, source of the news, event description, and relevant keywords. This comprehensive detail aids in understanding the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and analyze changes over time. This feature supports longitudinal studies and comparative analysis of historical and recent events. Advanced Search and Filter Options:

Users can search and filter news events based on various criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. European Countries Covered: Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Benefits of the Dataset: Informed Decision-Making: Businesses and analysts can leverage the dataset to stay updated on key developments that may impact their operations, market conditions, or strategic decisions. Market and Industry Analysis: The dataset provides valuable insights into industry trends, economic changes, and political events, helping users analyze market dynamics and make informed decisions. Media and PR Monitoring: Journalists and PR professionals can track relevant news and events across Europe, allowing them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Purposes: Researchers can use the dataset for longitudinal studies, trend analysis, and academic research on various topics related to European news and events. Techsalerator’s News Event Data in Europe is a vital resource for accessing and analyzing significant news events across the continent. By offering detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

This study explores the causal relationship between the economy and the elderly population in 15 European countries. The economy was measured by the Per Capita Gross Domestic Product growth rate, while the population aged above 65 as a percentage of the total was considered the elderly population. The data were obtained from a time series dataset published by the World Bank for six decades from 1961 to 2021. The Granger causality test was employed in the study to analyse the impact between the economy and the elderly population. An alternate approach, wavelet coherence, was used to demonstrate the changes to the relationship between the two variables in Europe over the 60 years. The findings from the Granger causality test indicate a unidirectional Granger causality from the economy to the elderly population for Luxembourg, Austria, Denmark, Spain, and Sweden, while vice versa for Greece and the United Kingdom. Furthermore, for Belgium, Finland, France, Italy, Netherlands, Norway, Portugal, and Turkey, Granger causality does not exist between the said variables. Moreover, wavelet coherence analysis depicts that for Europe, the elderly population negatively affected the economic growth in the 1960s, and vice versa in the 1980s.

150 views (3 recent) Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. Data collection is carried out by NSIs and research institutes working on their behalf. See a 2018 presentation for a full list of sources. https://circabc.europa.eu/faces/jsp/extension/wai/navigation/container.jsp

Context

This data set covers all aspects of the pre-WWI and interwar economies, including production, construction, employment, money, prices, asset market transactions, foreign trade, and government activity. Many series are highly disaggregated, and many exist at the monthly or quarterly frequency. The data set has some coverage of the United Kingdom, France and Germany, although it predominantly covers the United States. For information see:

• Improving the Accessibility of the NBER's Historical Data , by Daniel Feenberg and Jeff Miron. (NBER Working Paper #5186). Published in the Journal of Business and Economic Statistics, Volume 15 Number 3 (July 1997) pages 293-299.

Information about seasonal adjustments is available, but in most cases only unadjusted series have been made available here.

Content

The data.csv is organized in a long format with columns for the date, variable, and value. The dates are always the beginning of period date for whatever period existed in the original data. This means that '1920' was converted to January 1st, 1920 while Q2 1920 was converted to April 1, 1920. This is intended as a convenience to make it easier to work with multiple time series from the original mixed frequency data.

The data is currently organized into 16 chapters:

• Chapter1: Production of Commodities
• Chapter2: Construction
• Chapter3: Transportation and Public Utilities
• Chapter4: Prices
• Chapter5: Stocks of Commodities
• Chapter6: Distribution of Commodities
• Chapter7: Foreign Trade
• Chapter8: Income and Employment
• Chapter9: Financial Status of Business
• Chapter10: Savings and Investment
• Chapter11: Security Markets
• Chapter12: Volume of Transactions
• Chapter13: Interest Rates
• Chapter14: Money and Banking
• Chapter15: Government and Finance
• Chapter16: Leading Indicators

The dataset has been transformed from its original format. You can find the data preparation code here.

Acknowledgements

This dataset was kindly made available by the National Bureau of Economic Research (NBER). You can find the original dataset here.

Inspiration

• Which major historical events can you detect from the data?
• With roughly 3,500 time series in the dataset, finding relevant information can be challenging. Can you find a better way of organizing or indexing the data?

Gross Domestic Product (GDP) is a measure of the total economic output of a country. It is the sum of all the goods and services produced within a country over a given period. The GDP of a country is an important indicator of its economic health and can be used to compare the economic performance of different countries.

According to the World Bank, the United States has the highest GDP of any country in the world, with a value of $23.3 trillion. The American economy is one of the most diversified and technologically advanced in the world which contributes to the US’s large GDP. China is the second-largest economy in the world, with a GDP of $17.7 trillion. Japan, Germany, India, the United Kingdom, and France round out the top seven, all with GDPs over $3 trillion.

On the other hand, there are countries with low GDPs. The country with the lowest GDP in the world is Nauru, with a value of $133.2 million. Palau, Marshall Islands, Federated States of Micronesia, and São Tomé and Príncipe are some other countries with low GDPs. These countries are typically characterized by limited natural resources, small populations, geographic isolation, and a heavy reliance on tourism or foreign aid.

It is important to note that GDP is not necessarily an accurate reflection of the economic well-being of a country’s citizens. While a high GDP indicates a large and productive economy, it does not necessarily mean that all citizens are equally prosperous. Countries with lower GDPs may also have a higher standard of living if income is distributed more equally among the population.

The Life in Transition Survey, after the crisis (LiTS II), is the second round of LiTS surveys, previously conducted in 2006 (LiTS I). In late 2006, the EBRD and World Bank carried out the first comprehensive survey of individuals and households across virtually the whole transition region. The purpose was to gain a better understanding of how people's lives had been shaped and affected by the upheavals of the previous 15 years.Four years later, the EBRD and World Bank commissioned a second round of the survey. The circumstances facing most people were significantly different between the first and second rounds. The Life in Transition Survey I (LiTS I) was carried out at a time when the region's economies were, with few exceptions, growing strongly. In contrast, LiTS II took place in late 2010, at a time when most countries were still facing the aftershocks of a severe global economic crisis.LiTS II advances and improves on LiTS I in two important ways. First, the questionnaire was substantially revised. The new questionnaire includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes. Second, the coverage has been expanded to include five western European "comparator" countries - France, Germany, Italy, Sweden and the UK. This allows us to benchmark the transition region against some advanced market economies, thereby giving a clearer perspective on the remaining challenges facing transition countries.

The OECD Banking statistics database includes data from 1979 to 2009 on classification of bank assets and liabilities, income statement and balance sheet and structure of the financial system for OECD countries. The OECD have discontinued this dataset, so no further updates will be made.

The OECD Banking Statistics are presented in the following tables (some tables will include missing data):

Classification of bank assets and liabilities

This dataset provides the composition of bank assets and liabilities of residents and non-residents denominated in domestic and foreign currencies based on financial statements of banks in each OECD member country and Russia. Data are reported at current prices in millions of national currency and in millions of Euros for OECD countries. The data covers the years starting from 2005 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and Russian Federation.

Income statement and balance sheet

This comparative tables comprises statistics on country’s financial profiles by presenting their respective extensive income statements, balance sheets and capital adequacy by banking group that can be further analyzed by type of financial institution such as commercial banks, savings banks co-operative banks and other monetary institutions. This dataset provides information on income statements, balance sheets and capital adequacy by banking group. Data are reported at current prices in millions of national currency. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation.

Structure of the financial system

This dataset provides information on the overall structure of the financial system per country by type of institution and their components: Central banks, other monetary institutions, other financial institutions and insurance institutions. Data relate to number of institutions, number of branches, number of employees, total assets and liabilities and total financial assets. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation.

These data were first provided by the UK Data Service in December 2014.

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.