Graph and download economic data for Gross Domestic Product: All Industries in Oxford County, ME (GDPALL23017) from 2001 to 2023 about Oxford County, ME; ME; industry; GDP; and USA.

Graph and download economic data for Total Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) (RGMP11500) from 2001 to 2023 about Anniston, AL, real, industry, GDP, and USA.

Anniston-Oxford, AL - Total Quantity Indexes for Real GDP for Anniston-Oxford-Jacksonville, AL (MSA) was 112.26300 Index 2009=100 in January of 2023, according to the United States Federal Reserve. Historically, Anniston-Oxford, AL - Total Quantity Indexes for Real GDP for Anniston-Oxford-Jacksonville, AL (MSA) reached a record high of 112.26300 in January of 2023 and a record low of 83.91300 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Anniston-Oxford, AL - Total Quantity Indexes for Real GDP for Anniston-Oxford-Jacksonville, AL (MSA) - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Gross Domestic Product: Private Goods-Producing Industries in Oxford County, ME (GDPGOODS23017) from 2001 to 2023 about Oxford County, ME; goods-producing; ME; private; industry; GDP; and USA.

Graph and download economic data for Real Gross Domestic Product: Government and Government Enterprises in Oxford County, ME (REALGDPGOVT23017) from 2001 to 2023 about Oxford County, ME; ME; enterprises; government; real; GDP; and USA.

Anniston-Oxford, AL - Total Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) was 5891.05900 Mil. of $ in January of 2023, according to the United States Federal Reserve. Historically, Anniston-Oxford, AL - Total Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) reached a record high of 5891.05900 in January of 2023 and a record low of 2745.81300 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Anniston-Oxford, AL - Total Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Gross Domestic Product: Private Services-Providing Industries in Oxford County, ME (GDPSERV23017) from 2001 to 2023 about Oxford County, ME; services-providing; ME; private; industry; GDP; and USA.

Anniston-Oxford, AL - Total Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) was 4764.87100 Mil. of Chn. 2009 $ in January of 2023, according to the United States Federal Reserve. Historically, Anniston-Oxford, AL - Total Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) reached a record high of 4764.87100 in January of 2023 and a record low of 3487.09400 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Anniston-Oxford, AL - Total Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) - last updated from the United States Federal Reserve on November of 2025.

Anniston-Oxford, AL - Total Per Capita Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) (DISCONTINUED) was 29803.00000 $ in January of 2017, according to the United States Federal Reserve. Historically, Anniston-Oxford, AL - Total Per Capita Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) (DISCONTINUED) reached a record high of 33965.00000 in January of 2007 and a record low of 28972.00000 in January of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for Anniston-Oxford, AL - Total Per Capita Real Gross Domestic Product for Anniston-Oxford-Jacksonville, AL (MSA) (DISCONTINUED) - last updated from the United States Federal Reserve on October of 2025.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

In 2023, the gross domestic product per capita in London was 63,618 British pounds, compared with 37,135 pounds per capita for the United Kingdom as a whole. Apart from London, the only other region of the UK that had a greater GDP per capita than the UK average was South East England, at 38,004 pounds per capita. By contrast, North East England had the lowest GDP per capita among UK regions, at 26,347 pounds. Regional imbalance in the UK economy? London's overall GDP in 2022 was over 508 billion British pounds, which accounted for almost a quarter of the overall GDP of the United Kingdom. South East England had the second-largest regional economy in the country, with a GDP of almost 341.7 billion British pounds. Furthermore, these two regions were the only ones that had higher levels of productivity (as measured by output per hour worked) than the UK average. While recent governments have recognized regional inequality as a major challenge facing the country, it may take several years for any initiatives to bear fruit. The creation of regional metro mayors across England is one of the earliest attempts at giving regions and cities in particular more power over spending in their regions than they currently have. UK economy growth slow in late 2024 After ending 2023 with two quarters of negative growth, the UK economy grew at the reasonable rate of 0.8 percent and 0.4 percent in the first and second quarters of the year. This was, however, followed by zero growth in the third quarter, and by just 0.1 percent in the last quarter of the year. Other economic indicators, such as the inflation rate, fell within the expected range in 2024, but have started to rise again, with a rate of three percent recorded in January 2025. While unemployment has witnessed a slight uptick since 2022, it is still at quite low levels compared with previous years.

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

Correlations between border restrictions and outcomes by jurisdiction type (only for the 159 (83%) of jurisdictions that enacted the highest level of border restrictions, i.e., Oxford Stringency Index ‘level 4’).

This paper examines the role of economic shrinking in the process of long term economic growth over the last millennia, the last two centuries, and the last 70 years. The paper's main conclusion is that economic shrinking, both the rate at which economies shrink when they shrink and the frequency that they shrink (i.e., real per capita GDP declines) is a more important determinant of economic growth over the long term than the rate of growth when economies grow. In fact, economies in the developed world actually grow more slowly when they grow than poorer economies.Several possible reasons for the decline in shrinking and the associated increase in economic stability are considered and found wanting as explanations: structural change, demography, technological change, and stabilization policy. The paper concludes that the ultimate source of the reduction in shrinking is institutions.

GDP 2015 (purchasing power parity)

Abstract:

A gridded data set of Gross domestic product (purchasing power parity) produced by Kummu, M. et al. Gridded global dataset for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5:180004 doi: 10.1038/sdata.2018.4 (2018). ERGO has extracted the MOOD extent for this dataset.

File naming scheme:

full description in document kummuetal2018sdata20184.pdf.

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716

Source:

Data obtained from GDP 2015 (purchasing power parity, full description in document kummuetal2018sdata20184.pdf).

Software used:ArcMap 10.8

License: CC-BY-SA 4.0

Processed by:ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

This dataset contains historical Real Gross Domestic Product (GDP) and Growth Rates of GDP for Baseline Countries/Regions (in billions of 2010 dollars). from multiple sources.1. World Bank World Development Indicators2. International Monetary Fund of the IMF3. IHS Global Insight4. Oxford Economic Forecasting

The country layer captures the network of ports and routes that facilitate a country’s maritime trade. The includes all the exporting (either in country or in transport connected country), transhipment, and importing (at ports of bilateral trade partner) ports that facilitate a country’s maritime exports, and the importing (either in country or in transport connected country), transhipment, and exporting (at ports of bilateral trade partner) ports that facilitate for a country’s maritime imports. The Oxford Maritime Transport (OxMarTrans) model predicts the allocation of maritime trade flows (based on bilateral trade data) on the maritime transport network, including the port and route taken, to determine the dependency between ports and trade flow. In other words, it captures how trade between origin and destination is most likely being shipped across the global maritime transport network, including the port used for exporting, transhipment (if required) and importing. The resulting network consist of >2.1 million unique port-country pair combinations across >25,000 unique country pairs and 13 economic sectors, which capture what share of maritime trade between two countries going through specific ports and on specific routes. The base year considered is 2022.The 13 economic sectors are based on the International Convention on the Harmonized Commodity Description and Coding System (HS Convention) and align to the 21 HS Sections as per the table below.NameHS SectionAnimal & Animal Products1Vegetable Products2Prepared Foodstuffs & Beverages3+4Mineral Products5Chemical & Allied Industries6Plastics, Rubber, Leather7+8Wood & Wood Products9+10Textiles & Footwear11+12Stone & Glass13+14Metals15Machinery & Electrical Equipment16+18Vehicles & Equipment17Miscellaneous19+20+21The spillover simulator shows the amount of country’s imports or exports that is at-risk of being affected because of a disruption at a selected port in value terms. This could be because the port was exporting/importing a country’s imports/exports, or because of transhipments. This impacts can be analysed in aggregate terms (across all commodity sectors) or per commodity sector. In addition, the amount of trade at-risk is expressed as a fraction of an economy’s Gross Domestic Product (GDP) to capture how impactful trade disruptions are in relative terms for respective country economies. It should be noted that in most cases trade is not directly lost, but merely delayed in case of smaller disruptions (<7 days). However, in case of larger disruptions (>30 days) and limited possibilities to reroute goods, trade bottlenecks could occur, potentially resulting in severe supply shortages. GDP data comes from the October 2023 WEO (NGDPD = Gross domestic product, current prices; Year 2022).Source: Verschuur, J., Koks, E.E. & Hall, J.W. Ports’ criticality in international trade and global supply-chains. Nat Commun 13, 4351 (2022). https://doi.org/10.1038/s41467-022-32070-0Variables:from_portid = port id. Full list of ports can be found here.from_portname = port name. from_country = country the port resides in.from_iso3 = ISO 3-letter country code of the port.to_country = country for which we compute trade that is at-risk of being affected because of a disruption at from_portname.to_iso3 = ISO 3-letter country code of the country for which we compute trade that is at-risk of being affected because of a disruption at from_portname.industry = one of the following: - Animal & Animal Products- Vegetable Products- Prepared Foodstuffs & Beverages- Mineral Products- Chemical & Allied Industries- Plastics, Rubber, Leather- Wood & Wood Products- Textiles & Footwear- Stone & Glass - Metals- Machinery & Electrical Equipment- Vehicles & Equipment- Miscellaneous- Total (sum of all of the above)hs_section = corresponding HS section(s)unit = all values are expressed in US Dollars.scale = all values are expressed in units.daily_export_value_at_risk = daily to_country’s exports that is at-risk of being affected because of a disruption at from_portname.daily_import_value_at_risk = daily to_country’s imports that is at-risk of being affected because of a disruption at from_portname.

Regression analysis of jurisdictions reaching level 4 border restrictions and outcomes (excess mortality and GDP growth).

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Oxford's (OXB) Genomic Future: What Lies Ahead?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Multivariable regression analysis for island jurisdictions comparing border restrictions and outcomes (excess mortality and GDP growth).