🧾 About Dataset

🌍 Global GDP by Country — 2024 Edition

📖 Overview

The Global GDP by Country (2024) dataset provides an up-to-date snapshot of worldwide economic performance, summarizing each country’s nominal GDP, growth rate, population, and global economic contribution.

This dataset is ideal for economic analysis, data visualization, policy modeling, and machine learning applications related to global development and financial forecasting.

📊 Dataset Information

• Total Records: 181 countries
• Time Period: 2024 (latest available global data)
• Geographic Coverage: Worldwide
• File Format: CSV
• File Size: ~10 KB
• Missing Values: None (100% complete dataset)

🎯 Target Use-Cases:
- Economic growth trend analysis
- GDP-based country clustering
- Per capita wealth comparison
- Share of world economy visualization

🧩 Key Features

📈 Statistical Summary

Population Overview

• Mean Population: 43.6 million
• Standard Deviation: 155.5 million
• Minimum Population: 9,816 (small island nations)
• Median Population: 9.1 million
• Maximum Population: 1.43 billion (China)

🌟 Highlights

💰 Top Economies (Nominal GDP):
United States, China, Japan, Germany, India

📈 Fastest Growing Economies:
India, Bangladesh, Vietnam, and Rwanda

🌐 Global Insights:
- The dataset covers 181 countries representing 100% of global GDP.
- Suitable for data visualization dashboards, AI-driven economic forecasting, and educational research.

💡 Example Use-Cases

• Build a choropleth map showing GDP distribution across continents.
• Train a regression model to predict GDP per capita based on population and growth.
• Compare economic inequality using population vs GDP share.

📚 Dataset Citation

Source: Worldometers — GDP by Country (2024)
Dataset compiled and cleaned by: Asadullah Shehbaz
For open research and data analysis.

CONTENT

This dataset contains data on key indicators of world's top 6 Economies (by GDP) which includes USA, China, Japan, Germany, United Kingdom, India between the time interval of 30 years from 1990 to 2020. Data scraped from World Bank Data website and processed using Python Pandas library. This dataset could be used to do Time Series Analysis and Forecasting.

Code notebook:

https://deepnote.com/workspace/charan-chandrasekaran-9b7f-9e1375d3-f150-44ca-a9fb-feb08a1e8585/project/Data-extraction-from-World-bank-data-on-Top-6-Economies-2cdf8112-d412-4044-a58e-5e464804e9b6

INDICATORS

1. GDP (current US$)
2. GDP, PPP (current international $)
3. GDP per capita (current US$)
4. GDP growth (annual %)
5. Imports of goods and services (% of GDP)
6. Exports of goods and services (% of GDP)
7. Central government debt, total (% of GDP)
8. Total reserves (includes gold, current US$)
9. Unemployment, total (% of total labor force) (modelled ILO estimate)
10. Inflation, consumer prices (annual %)
11. Personal remittances, received (% of GDP)
12. Population, total
13. Population growth (annual %)
14. Life expectancy at birth, total (years)
15. Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)

SOURCE

The World Bank : https://data.worldbank.org/country

U.S. Public Debt vs. GDP

Trends and Comparisons

By Charlie Hutcheson [source]

About this dataset

This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.

The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.

By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time

Research Ideas

• Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
• Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
• Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

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.

CONTENT

The figures are based on GDP (Nominal) and sector composition ratios provided by the CIA World Fact Book. Agriculture includes farming, fishing, and forestry. Industry includes mining, manufacturing, energy production, and construction. Services cover government activities, communications, transportation, finance, and all other private economic activities that do not produce material goods.

CONTEXT

• Agriculture Sector : Agriculture Sector contributes 6.4 percent of total world's economic production. Total production of sector is $5,084,800 million. China is the largest contributer followed by India. China and India accounts for 19.49 and 7.39 percent of total global agricultural output. World's largest economy United States is at third place. Next in line come Brazil and Indonesia

• **Industry Sector : **With GDP of $23,835 billion, Industry Sector holds a share of 30% of total GDP nominal. China is the largest contributor followed by US. Japan is at 3rd and Germany is at 4th place. These four countries contributes 45.84 of total global industrial output.

• Services Sector : Services sector is the largest sector of the world as 63 percent of total global wealth comes from services sector. United States is the largest producer of services sector with around 15.53 trillion USD. Services sector is the leading sector in 201 countries/economies. 30 countries receive more than 80 percent of their GDP from services sector. Chad has lowest 27% contribution by services sector in its economy.

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

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.

📝 Description

This dataset contains over 400,000 macroeconomic events collected from global sources across more than 90 countries and regions, covering years 2020–2025. It mirrors professional economic calendars used by traders, economists, and analysts to track key economic indicators that move financial markets.

Each event includes its scheduled release time, geographical zone, currency, importance level, and actual, forecast, and previous values when available.

You can use this dataset for:

• 📈 Economic forecasting and sentiment analysis
• 💹 Financial market event-driven modeling
• 🧠 Machine learning applications on macroeconomic signals
• 🌍 Comparative studies of country-level indicators

📊 Dataset Structure

🌐 Coverage

• Zones: 90+ economies — including United States, Euro Zone, China, Japan, United Kingdom, India, Brazil, Australia, Türkiye, South Africa, and more.
• Currencies: USD, EUR, JPY, GBP, CNY, INR, and 50+ others.
• Time Period: ~2000 to 2025 (depending on data availability).
• Events Count: 409,234 records.

⚙️ Data Quality Notes

• Missing values in currency, importance, or actual columns occur mainly for minor or regional events.
• Times are reported as given in the source; some may represent local times.
• The dataset is cleaned and standardized for country and currency fields but can be further enriched with ISO country codes or UTC timestamps.

🧠 Example Use Cases

1. Market Volatility Forecasting: Use event importance and actual-vs-forecast deviations to predict short-term asset volatility.
2. Macroeconomic Trends: Track inflation or employment releases over time by country or region.
3. Event Sentiment Modeling: NLP on the event column for topic clustering (e.g., inflation vs. housing).
4. Calendar Effect Studies: Combine with asset price data (SPX, EURUSD, etc.) to measure event-driven reactions.

📦 File Info

• economic_calendar.csv

  • 409,234 rows
  • 10 columns
  • Size: ~31 MB

🏁 Tags

economics, macroeconomics, finance, forex, stock-market, forecasting, time-series, machine-learning, econometrics

🔗 Suggested License

If it’s scraped or aggregated from public calendars (like Investing.com), use: CC BY-NC-SA 4.0 — Attribution-NonCommercial-ShareAlike.

The Gross Domestic Product (GDP) of countries and regions from 1960 to 2020 provides a comprehensive view of economic development over six decades. GDP measures the total value of goods and services produced in a country or region over a specific period and is an important indicator of economic health and growth. Below is a summary of the GDP trends for major regions and selected countries:

Global Overview (1960-2020) 1- 1960s-1980s: During this period, many developed economies such as the United States, Japan, and Western European countries experienced robust economic growth. This was a time of post-World War II reconstruction, technological advancement, and increasing globalization.

2- 1990s-2000s: The fall of the Soviet Union in the early 1990s marked a shift in global economic dynamics. Many former Soviet states and Eastern European countries transitioned to market economies. Asia, particularly China and India, began to emerge as major economic players due to economic reforms and rapid industrialization.

3- 2010s-2020: The 2010s were marked by steady growth in advanced economies, while emerging markets such as China, India, Brazil, and others became significant contributors to global GDP. However, the COVID-19 pandemic in 2020 led to a severe global economic downturn.

The dataset uses multi-source datasets (TC, remote sensing, meteorological, vector, and socioeconomic data) from various domestic and international data platforms and institutions .The TC best track data was obtained at the Japan Meteorological Agency (JMA) (http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html) and Japan typhoon Digital Center (http://agora.ex.nii.ac.jp/digital-typhoon ).Tropical cyclone information for past tropical cyclones includes the position, central pressure, moving velocity, duration time ,moving distance and intensity of each TC from 1951-2018 for every six hour. Remote sensing data include digital elevation model (DEM), normalized difference vegetation index (NDVI), land use, and land cover. Meteorological data include the wind speed and total precipitation.Total precipitation and wind speed is in the period of 2010-2018 , collected respectively from China Meterological Administration(http://data.cma.cn),.Socioeconomic data include the population density, GDP, and historical disaster loss.The data of population density, GDP and vegetation index were obtained from spatial grid datasets of the Chinese population, GDP and vegetation index based on 1km spatial resolution.Land use and cover data was from multi period land use and cover remote sensing datasets in China (CNLUCC), and all these datasets is available at Resource and Environment data Cloud Platform provided by Institute of geography, resources and environment, Chinese Academy of Sciences(http://www.resdc.cn). DEM data was deprived from Aster GDEM v2 at 30 m resolution, available at United States Geological Survey (USGS Earth Explorer site in Center for Earth Observation.The indicator of Slope is calculated from the DEM data..Vector data include road networks, railway networks, water networks, coastlines and point of interest (POI) data such as the medical, public, and educational infrastructure and charitable organizations in the cities of Guangdong.The Indicator of coastline,river density ,railway were deprived from 1: 1 million National basic geographic information dataset (2017 version)in National Basic Geographic Information Center.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

Explore annual GDP growth rates for various countries with this dataset. Analyze trends and patterns related to GDP growth to make informed decisions. Click here for more information!

GDP growth (annual %), GDP, Growth Rates

Kenya, Spain, Syrian Arab Republic, Bosnia and Herzegovina, El Salvador, Italy, Sint Maarten (Dutch part), Comoros, Kosovo, Argentina, Bulgaria, Guinea-Bissau, Slovenia, Guinea, Belize, Low income, Lower middle income, New Caledonia, St. Kitts and Nevis, Benin, World, Kyrgyz Republic, United Arab Emirates, Ethiopia, Burundi, Korea, Rep., Low & middle income, Euro area, Libya, Luxembourg, Namibia, Kiribati, India, Burkina Faso, East Asia & Pacific (excluding high income), Tajikistan, Lao PDR, Equatorial Guinea, Niger, Liechtenstein, Palau, Hong Kong SAR, China, Switzerland, Tonga, Qatar, Turkiye, Middle East & North Africa (excluding high income), Indonesia, Iraq, Fiji, Central Europe and the Baltics, Isle of Man, Costa Rica, Finland, Small states, Singapore, Slovak Republic, Netherlands, Turks and Caicos Islands, Europe & Central Asia (IDA & IBRD countries), Japan, Bhutan, Belgium, Australia, Denmark, Heavily indebted poor countries (HIPC), Middle East & North Africa (IDA & IBRD countries), Uzbekistan, Pacific island small states, Mongolia, Gabon, St. Vincent and the Grenadines, Ukraine, Venezuela, RB, Latvia, Macao SAR, China, Vietnam, Arab World, Myanmar, Latin America & Caribbean (excluding high income), Haiti, Micronesia, Fed. Sts., Nicaragua, Panama, San Marino, Gambia, The, Guatemala, IDA & IBRD total, Azerbaijan, Chad, Zimbabwe, Mali, Bolivia, Grenada, Mexico, East Asia & Pacific (IDA & IBRD countries), Timor-Leste, Dominica, Peru, Malawi, Trinidad and Tobago, Nauru, Monaco, Tuvalu, Egypt, Arab Rep., Virgin Islands (U.S.), Sao Tome and Principe, Cabo Verde, IDA only, Mozambique, Oman, Yemen, Rep., Albania, New Zealand, Latin America & Caribbean, Rwanda, Cameroon, Lesotho, Solomon Islands, Germany, Bangladesh, Papua New Guinea, Maldives, Moldova, Antigua and Barbuda, Congo, Dem. Rep., Romania, Portugal, Africa Western and Central, Mauritius, France, Uruguay, Tanzania, Colombia, South Asia (IDA & IBRD), Honduras, South Sudan, Sudan, Cuba, Least developed countries: UN classification, South Asia, Tunisia, Guyana, Nepal, Barbados, Brunei Darussalam, United States, Canada, Lebanon, Africa Eastern and Southern, Sub-Saharan Africa (excluding high income), Angola, Bahamas, The, Fragile and conflict affected situations, Malta, Middle East & North Africa, Turkmenistan, Cote d'Ivoire, Northern Mariana Islands, Thailand, Seychelles, North Macedonia, Afghanistan, Russian Federation, IBRD only, Iran, Islamic Rep., Malaysia, Djibouti, Europe & Central Asia (excluding high income), Norway, Dominican Republic, French Polynesia, Jordan, Nigeria, Lithuania, Estonia, Eswatini, Vanuatu, Late-demographic dividend, St. Lucia, Cambodia, Curacao, Kuwait, Belarus, American Samoa, Bahrain, Somalia, Pre-demographic dividend, Ghana, Sierra Leone, Jamaica, Ecuador, European Union, Post-demographic dividend, Brazil, Central African Republic, Chile, Puerto Rico, Pakistan, Uganda, United Kingdom, IDA total, Marshall Islands, Czechia, Channel Islands, Poland, Togo, Latin America & the Caribbean (IDA & IBRD countries), Sweden, Iceland, Armenia, Georgia, Montenegro, Europe & Central Asia, Hungary, IDA blend, Sub-Saharan Africa (IDA & IBRD countries), Paraguay, Zambia, Andorra, OECD members, Bermuda, Early-demographic dividend, Croatia, Upper middle income, Algeria, Samoa, Eritrea, Suriname, Mauritania, Guam, China, Sri Lanka, Congo, Rep., Liberia, Greece, Botswana, East Asia & Pacific, West Bank and Gaza, Philippines, Cayman Islands, Saudi Arabia, South Africa, High income, Serbia, Caribbean small states, Greenland, Cyprus, Aruba, Ireland, Israel, Kazakhstan, Morocco, Madagascar, Other small states, Sub-Saharan Africa, Senegal, Middle income, Austria, North America Follow data.kapsarc.org for timely data to advance energy economics research.

This dataset contains panel data for a sample of 15 countries (Australia, Austria, Canada, China, Denmark, France, Germany, Israel, Italy, Japan, Republic of Korea, Spain, Sweden, Switzerland and United States) over the period 2006-2015. The series used are available for a small number of developed countries and for a relatively short time period. Solar PV module prices, imports of solar PV panels and public budget for R&D in PV are in real terms and were obtained by dividing them by the United States GDP deflator. The series are obtained from five main sources. Imports value of solar PV panels series are taken from Commodity Trade Statistics database (COMTRADE). PV panels (cells and modules) are a part of the category HS 854140, "Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes". Solar PV module prices, cumulative installed PV capacity and public budget for R&D in PV series are constructed from the PVPS report Trends in Photovoltaic Applications of the International Energy Agency (IEA). Population density, political stability index, renewable energy consumption and per capita carbon dioxide emissions series are all obtained from the World Bank (WB). Real GDP per capita series is taken from Federal Reserve Bank of St. Louis (FRED). Technological development in PV and crude oil import price series are drawn from the Organisation for Economic Co-operation and Development (OECD) database. Since crude oil import price series are not available for China and Israel, we use the West Texas Intermediate spot crude oil price as a proxy. The dummy for presence of feed-in tariff is constructed from the OECD database.

This dataset contains estimates of the socio-economic status (SES) for six countries, including Egypt, Turkey, the United States, the United Kingdom, Japan, and China. The dataset covers the period from 1880 to 2010 and includes SES measures such as average income and educational ranking for each country. The SES scores are reported as percentile rankings ranging from 1-99. The dataset was collected from various sources and compiled into a single dataset for easy analysis and comparison of these six countries' socio-economic development over time. This dataset can be used for research and analysis purposes in various fields, including economics, sociology, and political science.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

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.