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.

Context

This dataset provides country-level GDP (Gross Domestic Product) in current US dollars from 2000 to 2025, mapped to the seven classic continents (Asia, Africa, Europe, North America, South America, Australia, and Antarctica). It is designed to make global economic data easier to explore, compare, and visualize by combining both geographic and temporal dimensions.

GDP is one of the most widely used indicators to measure the size of an economy, its growth trends, and relative economic performance across regions.

Source

Data Provider: World Bank Open Data

Indicator Used: NY.GDP.MKTP.CD → GDP (current US$)

License: World Bank Dataset Terms of Use (aligned with CC BY 4.0)

Note: 2024–2025 values may be incomplete or missing for some countries, depending on World Bank publication updates.

Dataset Structure

Name of country → Country name

Continent → One of the 7 continents

2000–2025 → GDP values in current US$ (float, may contain missing values NaN)

Format: wide panel data (one row per country, one column per year).

Inspiration & Use Cases

This dataset was prepared to make economic analysis, visualization, and forecasting more accessible. It can be used for:

• Time-series forecasting (predicting GDP growth into the future)
• Cross-country comparisons (e.g., comparing GDP trends of India vs. USA vs. Brazil)
• Continent-level aggregation (summing GDP by continent per year)
• Data visualization (heatmaps, line charts, world choropleths)
• Machine Learning applications (e.g., clustering countries by GDP trajectory)

Citation

If you use this dataset, please cite:

Source: World Bank, World Development Indicators (NY.GDP.MKTP.CD). Licensed under the World Bank Terms of Use.

This dataset provides a comprehensive list of countries ranked by their nominal Gross Domestic Product (GDP). It includes key information such as the country's GDP value, ranking and year of measurement along with estimates done by IMF, World Bank and UN. You can utilize this dataset to analyze economic trends, compare country performance, and gain a deeper understanding of the global economic landscape. Please upvote if you find it useful.

This dataset provides a comprehensive view of global economic trends, combining multiple essential indicators for analysis and research. The data focuses on the period from 2020 to 2023 and includes two key components:

1. GDP Per Capita and Inflation (2020–2023)

Scope: Yearly GDP per capita (in USD) and inflation rates per countries over the four-year period.

1. Population (2023)

Scope: The total population of each country at the end of 2023.

The dataset is meticulously compiled from trusted sources:

GDP per capita and inflation data are sourced from the World Bank national accounts data and OECD National Accounts data files.

Population data is derived from the World Bank Data Catalog (Population Ranking).

Potential Applications

Analyze the impact of inflation on economic growth during and after the pandemic.

Examine relationships between GDP per capita and population size.

Compare economic indicators across countries and regions.

Key Features: Clean, structured, and ready-to-use format.

Country-level granularity for detailed comparisons.

Suitable for trend analysis, visualizations, and predictive modeling.

Licensing: This dataset is licensed under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, modify, and distribute the data for any purpose, including commercial use, as long as appropriate credit is given to the World Bank.

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.

Economies are ranked on their ease of doing business, from 1–186. A high ease of doing business ranking means the regulatory environment is more conducive to the starting and operation of a local firm. The rankings are determined by sorting the aggregate distance to frontier scores on 10 topics, each consisting of several indicators, giving equal weight to each topic. The rankings for all economies are benchmarked to June 2017. Quality/Lineage: The data is downloaded from the above link http://www.doingbusiness.org/rankings and manipulated only table format keeping the value same for all the countries as the requirement of the Strive database. The map is created based on the values of the country using rworldmap package in R.

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 PRIVATE DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Gross Domestic Product (GDP) in Iran was worth 436.91 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Iran represents 0.41 percent of the world economy. This dataset provides - Iran GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Vietnam was worth 476.39 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Vietnam represents 0.45 percent of the world economy. This dataset provides the latest reported value for - Vietnam GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Economies are ranked on their ease of doing business, from 1–186. A high ease of doing business ranking means the regulatory environment is more conducive to the starting and operation of a local firm. The rankings are determined by sorting the aggregate distance to frontier scores on 10 topics, each consisting of several indicators, giving equal weight to each topic. The rankings for all economies are benchmarked to June 2017. Quality/Lineage: The data is downloaded from the above link http://www.doingbusiness.org/rankings and manipulated only table format keeping the value same for all the countries as the requirement of the Strive database. The map is created based on the values of the country using rworldmap package in R.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GDP FROM AGRICULTURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Gross Domestic Product (GDP) in Australia was worth 1752.19 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Australia represents 1.65 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for GDP ANNUAL GROWTH RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides country-level happiness scores from the World Happiness Report, along with the key factors that influence overall well-being. It helps analyze how economic strength, social support, health, and freedom contribute to national happiness.

Column Description:

• Country – Name of the country.
• Region– Geographic region of the country.
• Happiness Rank – Country’s position based on happiness score.
• Happiness Score – Overall happiness rating.
• Standard Error– Measurement uncertainty of the score.
• Economy (GDP per Capita) – Economic performance of the country.
• Family – Level of social support available.
• Health (Life Expectancy)– Quality and length of healthy life.
• Freedom – Degree of personal freedom.
• Trust (Government Corruption) – Perception of corruption levels.
• Generosity – The Willingness of people to give me and support others.
• Dystopia Residual – Baseline value representing the possible life conditions.
• Year – Reporting year of the score.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Country Club, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Country Club did not include 7-person households. Across the different household sizes in Country Club the mean income is $77,320, and the standard deviation is $32,313. The coefficient of variation (CV) is 41.79%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $33,125. It then further increased to $41,750 for 6-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Country Club median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in State Center, IA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, State Center did not include 6, or 7-person households. Across the different household sizes in State Center the mean income is $91,897, and the standard deviation is $38,035. The coefficient of variation (CV) is 41.39%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $35,873. It then further increased to $107,551 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/state-center-ia-median-household-income-by-household-size.jpeg" alt="State Center, IA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Center median household income. You can refer the same here

Countries by Gross National Income (GNI)

Economic health by nation

About this dataset

Gross National Income (GNI) is a marker of the economic health of a nation - it encompasses a nation's GDP while also taking into account money flowing in and out of the country from foreign trade. This dataset provides GNI rankings for countries around the world, allowing for comparisons of economic health and growth. Explore how different nations fare in terms of GNI, and what this says about their overall economic stability!

How to use the dataset

The Gross National Income (GNI) of countries around the world is a measure of the economic health of a nation. It is a summation of a nation's GDP (Gross Domestic Product) plus the money flowing into and out of the country from foreign countries.

This dataset provides Rankings of countries by their GNI. The data is divided into two files: df_1.csv and df_2.csv. Both files contain the following columns:

No.: The number of the country. (Numeric)

Country: The name of the country. (String)

Research Ideas

• Measuring the economic health of a nation
• Comparing the GDP of different countries
• Determining the money flow into and out of a country

Acknowledgements

GNI data is sourced from wikipedia

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv

File: df_4.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | No. | The rank of the country based on GNI. (Numeric) | | Country | The name of the country. (String) | | GNI (Atlas method)[8] | The GNI of the country, in US dollars. (Numeric) | | GNI (Atlas method)[8].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GNI[9] | The GNI of the country, in US dollars. (Numeric) | | GNI[9].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GDP[10] | The GDP of the country, in US dollars. (Numeric) |

File: df_9.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) | | 1 | GNI (Integer) |

File: df_3.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) |

File: df_2.csv

File: df_6.csv | Column name | Description | |:--------------|:------------------------------------------------------------------| | Rank | The rank of the country based on GNI. (Numeric) | | 2021 | The GNI of the country in 2021. (Numeric) | | 2021.1 | The GNI of the country in 2021, adjusted for inflation. (Numeric) | | 2016 | The GNI of the country in 2016. (Numeric) | | 2016.1 | The GNI of the country in 2016, adjusted for inflation. (Numeric) | | 2014 | The GNI of the country in 2014. (Numeric) | | 2014.1 | The GNI of the country in 2014, adjusted for inflation. (Numeric) | | 2013 | The GNI of the country in 2013. (Numeric) | | 2013.1 | The GNI of the country in 2013, adjusted for inflation. (Numeric) | | 2012 | The GNI of the country in 2012. (Numeric) | | 2012.1 | The GNI of the country in 2012, adjusted for inflation. (Numeric) | | 2011 | The GNI of the country in 2011. (Numeric) | | 2011.1 | The GNI of the country in 2011, adjusted for inflation. (Numeric) | | 2010 | The GNI of the country in 2010. (Numeric) | | 2010.1 | The GNI of the country in 2010, adjusted for inflation. (Numeric) | | 2009 | The GNI of the country in 2009. (Numeric) | | 2009.1 | The GNI of the country in 2009, adjusted for inflation. (Numeric) | | 2008 | The GNI of the country in 2008. (Numeric) | | 2008.1 | The GNI of the country in 200...

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa).

It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level).

FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset supports FDI aggregates indicators available from the FDI in Figures.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates