Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

The Economic Research Service’s (ERS) International Macroeconomic Data Set provides annual historical and projected data for 181 countries that account for more than 99 percent of the global 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. The projections are calculated by the ERS Macroeconomic Team based on data compiled from the U.S. Government, international agencies’ projections, private forecast subscription services, and the USDA, Economic Research Service, Market and Trade Economics Division’s regional and country experts.Explore the International Macroeconomic for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.Follow data.kapsarc.org for timely data to advance energy economics research.Historical and projected real gross domestic product (GDP) and growth rates of GDP for baseline countries/regions (in billions of 2017 dollars) 1970-2034: Source: USDA, Economic Research Service (ERS) based on data from World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by USDA, ERS all converted to a 2017 base year.Historical and projected real GDP per capita and growth rates for baseline countries/regions (in billions of 2017 dollars) 1970-2034: Source: USDA, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.Historical and projected GDP deflator and growth rates of GDP deflator for baseline countries/regions (in billions of 2017 dollars) 1970-2034. Source: USDA, Economic Research Service (ERS) based on data from World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by USDA, ERS, all converted to a 2017 base year.Historical and projected real GDP shares and growth rates for baseline countries/regions (in billions of 2017 dollars) 1970-2034. Source: USDA, Economic Research Service, Macroeconomic Data Set, GDP table.Historical and projected real exchange rate and growth rates for baseline countries/regions (in billions of 2017 dollars) 1970-2034. Source: USDA, Economic Research Service (ERS), Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables.Historical and projected consumer price indices (CPI) for baseline countries/regions 1970-2034. Source: USDA, Economic Research Service (ERS) based on data from International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by USDA, ERS, all converted to a 2017 base year.Historical and projected population and growth rates for baseline countries/regions 1970-2034. Source: USDA, Economic Research Service (ERS) based on data from U.S. Department of Commerce, Bureau of the Census and USDA, ERS, International Data Base.

This dataset comprises global economic indicators including education budgets as a percentage of GDP from 1976 to 2023 and inflation rates across various regions and countries such as Aruba and Africa Eastern and Southern from 1961 to 2023. It provides insights into economic trends over several decades.

Economic Data Descriptions

GDP (current US$)

GDP (current US$) refers to the Gross Domestic Product measured in current United States dollars. It is a key economic indicator that represents the total monetary value of all goods and services produced within a country's borders in a specific time period, usually a year. This metric provides a comprehensive overview of a nation's economic activity and is widely used for comparing the economic performance of different countries. Calculated in current US dollars, GDP (current US$) takes into account inflation and exchange rate fluctuations, allowing for meaningful international comparisons of economic output and trends.

World Bank National Accounts Data

The World Bank National Accounts data refers to a comprehensive and reliable set of economic data compiled and maintained by the World Bank. These data files encompass a wide range of economic indicators, including GDP, for countries across the globe. The World Bank collects and analyzes national accounts data from various sources, such as government agencies and international organizations, to provide accurate and up-to-date information on economic activities, expenditures, and incomes within different countries. Researchers, policymakers, and analysts often rely on World Bank National Accounts data to assess economic performance, formulate policies, and conduct cross-country comparisons.

OECD National Accounts Data Files

The OECD (Organization for Economic Co-operation and Development) National Accounts data files contain detailed economic information about OECD member countries and other major economies. These files include comprehensive data on GDP, consumption, investment, government spending, and other economic indicators. OECD collects and publishes these data to facilitate in-depth analysis of economic trends, policy evaluations, and international comparisons. Researchers, economists, and policymakers utilize OECD National Accounts data files to gain insights into the economic health of individual countries, identify patterns, and make informed decisions based on robust and standardized economic data.

Slovenia SI: Government Expenditure on Education: Total: % of GDP data was reported at 5.365 % in 2021. This records a decrease from the previous number of 5.384 % for 2020. Slovenia SI: Government Expenditure on Education: Total: % of GDP data is updated yearly, averaging 5.365 % from Dec 1991 (Median) to 2021, with 27 observations. The data reached an all-time high of 6.130 % in 2000 and a record low of 4.076 % in 1991. Slovenia SI: Government Expenditure on Education: Total: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovenia – Table SI.World Bank.WDI: Social: Education Statistics. General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Median;

CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

Country Risk Assessment helps businesses to confidently evaluate global markets by incorporating country evaluation into strategic planning. Analysing trends over time to forecast and proactively plan for potential market shifts.

Country Risk Assessment is an estimate of the average credit risk of a country’s businesses. It is drawn up based on macroeconomic, financial and political data. It offers: - An indication of a country’s potential influence on businesses’ financial commitments. - Insight into the economic and political environment that could impact credit risk.

Dataset Structure and Content: Assessment Coverage: 20 sample companies with country risk evaluations Geographic Diversity: Multiple countries represented via ISO-3166 alpha2 country codes.

Risk Classification System: The dataset employs a standardized A-E rating scale to categorize country risk levels: A1: Very good macroeconomic outlook with stable political context and quality business climate (lowest default probability) A2: Good macroeconomic outlook with generally stable political environment A3: Satisfactory outlook with some potential shortcomings A4: Reasonable default probability with potential economic weaknesses B: Uncertain economic outlook with potential political tensions C: Very uncertain outlook with potential political instability D: Highly uncertain outlook with very unstable political context E: Extremely uncertain outlook with extremely difficult business conditions (highest default probability)

Application Context: This sample demonstrates how country risk assessments can be systematically documented and tracked over time. Each assessment includes comprehensive evaluations of the macroeconomic environment, political stability, and business climate factors that directly influence payment behavior and default probabilities. The dataset structure allows for both current and historical tracking, enabling trend analysis and comparative risk evaluation across different national markets. It serves as a representative example of how comprehensive country risk data can be organized and utilized for strategic business decision-making. Note: This is sample data intended to demonstrate the structure and capabilities of a country risk assessment system.

Learn More For a complete demonstration of our Country Risk Assessment capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/economic-insights to request additional information.

# Global Economy and Environmental Indicators (Clean Data, Interpole Data, and Log-Transformed Data)

Context

This dataset provides insights into globalization index (economic, social, and political), environmental quality, and economic indicators, log-transformed for analytical purposes. It covers a wide range of metrics such as carbon emissions, GDP, urbanization, and energy consumption, population. Researchers can utilize this dataset to analyze the relationship between economic development and environmental sustainability.

Sources and Inspiration

Primary Source: World Development Indicators (World Bank).

Inspiration: Understanding the IMPACT OF DIFFERENT DIMENSIONS OF GLOBALIZATION ON ENVIRONMENTAL QUALITY. This dataset simplifies complex patterns by providing log-transformed data, making it ideal for statistical applications.

Files Included:

The main dataset containing log-transformed variables. Size: 3,696 rows, 14 columns.

Column Descriptions (Global_132)

1. Country: Name of the country.
2. CountryCode: Numeric code representing the country.
3. Year: Year of the observation.
4. lnCO2: Log-transformed carbon dioxide emissions per capita (metric tons).
5. lnGI: Log-transformed governance index.
6. lnEcGI, lnSoGI, lnPoGI: Economic, social, and political governance indices, respectively.
7. lnGDP_pc: Log-transformed GDP per capita (constant 2015 USD).
8. lnGDP_pc2: Squared log-transformed GDP per capita.
9. lnEC: Log-transformed energy consumption per capita (kg oil equivalent).
10. lnPOP: Log-transformed population size.
11. lnURB: Log-transformed urbanization rate (percentage).
12. lnREC: Log-transformed renewable energy consumption.

Usage

Regression modeling. Time-series analysis. Exploratory data analysis on global environmental and economic patterns.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Content

Providing daily updates on global economic developments with coverage of high-income as well as developing countries. Data updates are provided for exchange rates, equity markets, and emerging market bond indices. Monthly data coverage (updated daily and populated upon availability) is provided for consumer prices, high-tech market indicators, industrial production, and merchandise trade.

Context

This Global Economic Monitor dataset provides worldwide economic updates. It covers exchange rates, equity markets, and emerging market bonds daily. Plus, get monthly insights on consumer prices, high-tech markets, industrial production, and trade trends.

Acknowledgment

The primary dataset was retrieved from the World Bank Group's Data Catalog. I would like to express our sincere appreciation to the World Bank for providing the core data used in this dataset.

©️ Image credit: Freepik

🧾 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.

EIU's Country Analysis provides award-winning forecasts, in-depth analysis and comprehensive data for nearly 200 global markets. This service helps organisations to understand political, policy and economic outlooks so that they can plan and operate effectively. Our experts assess global dynamics, offering a nuanced approach with detailed insights and rigorously researched, non-biased information.

Features include global and regional outlooks, daily insights and medium- to long-term country forecasts. We also cover industry analysis across 26 sectors, commodity forecasts, macroeconomic and industry data, proprietary ratings, and regulatory intelligence. Our analysis informs world-leading financial institutions, corporations, governments and academic institutions, helping them to evaluate opportunities, manage risks and anticipate disruptions.

In an increasingly interconnected global economy, understanding the economic landscape of different countries, regions, and income groups is vital for policymakers, researchers, and businesses. The World GDP (Gross Domestic Product) Dataset has been curated to provide comprehensive insights into the economic performance of countries worldwide, categorized by region and income group. This dataset is sourced from the World Bank Group, a renowned institution for global economic data.

Amidst growing skepticism towards globalization and rising digital trade, this study investigates the impact of Restrictions on Cross-Border Data Flows (RCDF) on Domestic Value Chains (DVCs) stability. As global value chains participation declines, the stability of DVCs—integral to internal economic dynamics—becomes crucial. This study situates within a framework exploring the role of innovation and RCDF in the increasingly interconnected global trade. Using a panel data fixed effect model, our analysis provides insights into the varying effects of RCDF on DVCs stability across countries with diverse economic structures and technological advancement levels. This approach allows for a nuanced understanding of the interplay between digital trade policies, value chain stability, and innovation. RCDF tend to disrupt DVCs by negatively impacting innovation, which necessitates proactive policy measures to mitigate these effects. In contrast, low-income countries experience a less detrimental impact; RCDF may even aid in integrating their DVCs into Global Value Chains, enhancing economic stability. It underscores the need for dynamic, adaptable policies and global collaboration to harmonize digital trade standards, thus offering guidance for policy-making in the context of an interconnected global economy.

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.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Journal of Political Economy FAQ - ResearchHelpDesk - The Journal of Political Economy is a monthly peer-reviewed academic journal published by the University of Chicago Press. Established by James Laurence Laughlin in 1892, it covers both theoretical and empirical economics. In the past, the journal published quarterly from its introduction through 1905, ten issues per volume from 1906 through 1921, and bimonthly from 1922 through 2019. The editor-in-chief is Magne Mogstad (University of Chicago). Abstract & Indexing Articles that appear in the Journal of Political Economy are indexed in the following abstracting and indexing services: Ulrich's Periodicals Directory (Print) Ulrichsweb (Online) J-Gate HINARI Association for Asian Studies Bibliography of Asian Studies (Online) Business Index CABI Abstracts on Hygiene and Communicable Diseases (Online) Agricultural Economics Database CAB Abstracts (Commonwealth Agricultural Bureaux) Dairy Science Abstracts (Online) Environmental Impact Global Health Leisure Tourism Database Nutrition and Food Sciences Database Rural Development Abstracts (Online) Soil Science Database Soils and Fertilizers (Online) Tropical Diseases Bulletin (Online) World Agricultural Economics and Rural Sociology Abstracts (Online) Clarivate Analytics Current Contents Social Sciences Citation Index Web of Science De Gruyter Saur Dietrich's Index Philosophicus IBZ - Internationale Bibliographie der Geistes- und Sozialwissenschaftlichen Zeitschriftenliteratur Internationale Bibliographie der Rezensionen Geistes- und Sozialwissenschaftlicher Literatur EBSCOhost America: History and Life ATLA Religion Database (American Theological Library Association) Biography Index: Past and Present (H.W. Wilson) Book Review Digest Plus (H.W. Wilson) Business Source Alumni Edition (Full Text) Business Source Complete (Full Text) Business Source Corporate (Full Text) Business Source Corporate Plus (Full Text) Business Source Elite (Full Text) Business Source Premier (Full Text) Business Source Ultimate (Full Text) Current Abstracts EBSCO MegaFILE (Full Text) EBSCO Periodicals Collection (Full Text) EconLit with Full Text (Full Text) ERIC (Education Resources Information Center) GeoRef Historical Abstracts (Online) Humanities & Social Sciences Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Source Humanities Source Ultimate Index to Legal Periodicals Retrospective: 1908-1981 (H.W. Wilson) Legal Source Library & Information Science Source MLA International Bibliography (Modern Language Association) OmniFile Full Text Mega (H.W. Wilson) Poetry & Short Story Reference Center Political Science Complete Public Affairs Index Readers' Guide Retrospective: 1890-1982 (H.W. Wilson) Russian Academy of Sciences Bibliographies Social Sciences Abstracts Social Sciences Full Text (H.W. Wilson) Social Sciences Index Retrospective: 1907-1983 (H.W. Wilson) SocINDEX SocINDEX with Full Text TOC Premier Women's Studies International Elsevier BV GEOBASE Scopus ERIC (Education Resources Information Center) ERIC (Education Resources Information Center) Gale Academic ASAP Academic OneFile Advanced Placement Government and Social Studies Book Review Index Plus Business & Company ProFile ASAP Business ASAP Business ASAP International Business Collection Business Insights: Essentials Business Insights: Global Business, Economics and Theory Collection Expanded Academic ASAP General Business File ASAP General OneFile General Reference Center Gold General Reference Centre International InfoTrac Custom InfoTrac Student Edition MLA International Bibliography (Modern Language Association) Popular Magazines US History Collection H.W. Wilson Social Sciences Index National Library of Medicine PubMed OCLC ArticleFirst Periodical Abstracts Sociological Abstracts (Online), Selective Ovid EconLit ERIC (Education Resources Information Center) GeoRef ProQuest ABI/INFORM Collection ABI/INFORM Global (American Business Information) ABI/INFORM Research (American Business Information) Business Premium Collection EconLit ERIC (Education Resources Information Center) GeoRef Health Management Database Health Research Premium Collection Hospital Premium Collection International Bibliography of the Social Sciences, Core MLA International Bibliography (Modern Language Association) PAIS Archive Professional ABI/INFORM Complete Professional ProQuest Central ProQuest 5000 ProQuest 5000 International ProQuest Central ProQuest Pharma Collection Research Library Social Science Database Social Science Premium Collection Sociological Abstracts (Online), Selective Worldwide Political Science Abstracts, Selective SCIMP (Selective Cooperative Index of Management Periodicals) Taylor & Francis Educational Research Abstracts Online Wiley-Blackwell Publishing Asia Asian - Pacific Economic Literature (Online)

Switzerland import data: Driving economic growth and employment through diverse imports, strategic partnerships, and sustainable practices.

ABOUT

The Global GDP Per Capita Dataset provides a comprehensive record of annual economic output per person across various countries and regions. It includes key economic indicators such as GDP per capita (adjusted for inflation and purchasing power parity), country codes, and yearly data points. This dataset is valuable for economists, researchers, policymakers, and analysts interested in studying economic growth, income distribution, and global development trends.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Provides annual GDP per capita data from 1990 to 2023
✅ Adjusted for inflation and purchasing power parity (PPP, constant 2021$)
✅ Sourced from the World Bank - World Development Indicators
✅ Useful for economic analysis, policy-making, and financial forecasting

This dataset serves as a crucial resource for understanding global economic trends, comparing living standards across nations, and making data-driven decisions in economic research and policy development.

FILE INFORMATION

The dataset consists of structured records related to GDP per capita, compiled from the World Bank’s World Development Indicators (WDI). Each file contains country-level economic data, including GDP per capita values in constant 2021 international dollars (PPP). This allows researchers, economists, and data analysts to study economic growth patterns and trends over time. The file type is CSV.

COLUMNS DESCRIPTION

• Entity – The country or region name.
• Code – The country code (ISO Alpha-3 format or OWID custom code) [NOTE : missing value if there is no ISO Alpha-3 code].
• Year – The year of the GDP observation (1990–2023).
• GDP per capita (PPP, 2021$) – The economic output per person, adjusted for inflation and cost of living.

This dataset provides valuable insights into economic trends over three decades, helping researchers analyze global income levels, economic development, and policy impacts.

This study illuminates the multifaceted influence of Chinese in Africa, driven by the imperative to understand the strategic and economic ramifications of this rapidly evolving relationship. Motivated by the critical role Africa plays in global geopolitics and resource dynamics, alongside Chinese expanding international influences, the research aims to quantitatively and psychologically assess the decision-making processes underpinning this engagement. Adopting a regret theory-based Multiple Criteria Decision Making (MCDM) framework, the study evaluates Chinese impact across 49 African countries from 2018 to 2022, employing six economic indicators to capture the breadth of Chinese activities. Through meticulous normalization, regret utility computation, and total gap analysis, the methodology affords a systematic ranking that reflects the varying degrees of Chinese economic influence. The findings uncover pronounced variances in the level of Chinese engagement across the continent, with countries like Nigeria and Egypt showcasing substantial influence convergence with the theoretical model of ideal economic partnership, whereas others like Cape Verde indicate minimal influence. Contributing to academic and practical discourse, this study not only provides a methodological blueprint for analyzing geopolitical influences but also offers insights that policymakers can leverage to optimize their engagement strategies with Chinese. It foregrounds the interplay between empirical economic data and behavioral economics within international relations research. The study acknowledges limitations, primarily in data availability, which may not capture the full scope of informal economic interactions. It proposes further research to enrich the understanding of the Chinese-Africa nexus through longitudinal studies, integration of qualitative data, and expansion of the analytical model to encompass broader socio-economic impacts and more diverse indicators.