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.

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.

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.

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.

Certainly! Let's expand on the hypothetical scenario for Noviland in 2023:

In the year 2023, Noviland, with the country code NVL, experienced a dynamic economic landscape with various indicators reflecting its economic health. Notably, the country witnessed a significant inflow of personal remittances, constituting 8.5% of its Gross Domestic Product (GDP). This influx of funds from individuals living abroad played a crucial role in contributing to the overall economic activity within Noviland.

On the employment front, the nation displayed a commendable performance, as the unemployment rate stood at a modest 4.2% of the total labor force. This relatively low unemployment rate suggests a labor market where individuals actively seeking employment found opportunities, contributing to the overall stability of the workforce.

In terms of economic output, Noviland's GDP reached an impressive $150 billion in current US dollars. This figure represents the total value of goods and services produced within the country's borders during the specified year. The annual GDP growth rate was a robust 3.8%, indicating positive momentum in the country's economic expansion.

It's noteworthy that alternative data sources provide slightly different figures for Noviland's economic metrics. According to an alternative calculation or data set, the GDP is reported as $155 billion, with a slightly higher growth rate of 4.1%. These variations highlight the importance of considering multiple sources and methodologies when analyzing economic statistics.

In summary, Noviland's economy in 2023 showcased resilience, with substantial remittance inflows, a stable labor market, and strong economic growth. The alternative data sources underscore the complexity of economic analysis and the importance of a comprehensive understanding of various factors influencing a nation's economic performance.

State of Palestine (West Bank and Gaza) PS: GDP: PPP data was reported at 22,886.737 Intl $ mn in 2017. This records a decrease from the previous number of 23,257.194 Intl $ mn for 2016. State of Palestine (West Bank and Gaza) PS: GDP: PPP data is updated yearly, averaging 12,787.924 Intl $ mn from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 23,257.194 Intl $ mn in 2016 and a record low of 4,602.563 Intl $ mn in 1994. State of Palestine (West Bank and Gaza) PS: GDP: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

Turkmenistan TM: GDP: Market Price: Linked Series data was reported at 148,244.000 TMM mn in 2017. This records an increase from the previous number of 126,629.600 TMM mn for 2016. Turkmenistan TM: GDP: Market Price: Linked Series data is updated yearly, averaging 13,261.408 TMM mn from Dec 1989 (Median) to 2017, with 29 observations. The data reached an all-time high of 148,244.000 TMM mn in 2017 and a record low of 0.005 TMM mn in 1989. Turkmenistan TM: GDP: Market Price: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkmenistan – Table TM.World Bank: Gross Domestic Product: Nominal. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. Data are in current local currency.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

The Gross Domestic Product per capita in Switzerland was last recorded at 89555.56 US dollars in 2023. The GDP per Capita in Switzerland is equivalent to 709 percent of the world's average. This dataset provides the latest reported value for - Switzerland GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product per capita in Singapore was last recorded at 65422.46 US dollars in 2023. The GDP per Capita in Singapore is equivalent to 518 percent of the world's average. This dataset provides - Singapore GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The International Macroeconomic Data Set provides data from 1969 through 2030 for real (adjusted for inflation) gross domestic product (GDP), population, real exchange rates, and other variables for the 190 countries and 34 regions that are most important for U.S. agricultural trade. The data presented here are a key component of the USDA Baseline projections process, and can be used as a benchmark for analyzing the impacts of U.S. and global macroeconomic shocks.

Administrative unitsRepresents the administrative units used for GDP per capita (PPP) and HDI data products. National administrative units have id 1-999, sub-national ones 1001-admin_areas_GDP_HDI.ncGDP_per_capita_PPP_1990_2015The GDP per capita (PPP) dataset represents average gross domestic production per capita in a given administrative area unit. GDP is given in 2011 international US dollars. Gap-filled sub-national data were used, supplemented by national data where necessary. Datagaps were filled by using national temporal pattern. Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_1990_2015_5arcminThis global dataset represents the gross domestic production (GDP) of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE...

Data from 1st of June 2022. For most recent GDP data, consult dataset nama_10_gdp. Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that the index, calculated from PPS figures and expressed with respect to EU27_2020 = 100, is intended for cross-country comparisons rather than for temporal comparisons."

France FR: GDP: PPP data was reported at 2,876,059.993 Intl $ mn in 2017. This records an increase from the previous number of 2,765,185.411 Intl $ mn for 2016. France FR: GDP: PPP data is updated yearly, averaging 1,792,076.091 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 2,876,059.993 Intl $ mn in 2017 and a record low of 1,027,853.767 Intl $ mn in 1990. France FR: GDP: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis. Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low EF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

The dataset reports annual estimates for primary energy per capita and GDP per capita for 185 countries for 1950 through 2014. The data allows investigating long-term joint evolution of economic activity and energy demand, which is important for both understanding the past energy needs of economic development, and forming useful baselines for scenario development, especially for integrated assessment modeling around climate change mitigation. Other commonly used datasets only go back to 1971 (International Energy Agency) for worldwide coverage and so extending the data back to 1950 allows analyzing a longer time period than before. The dataset also includes more individual country time series than IEA data thanks to data from the UN.

This dataset contains homomorphically encrypted data derived from the Agra Fintech World Happiness Index and Inflation Dataset. It is specifically prepared for use in data-unaware learning processes, enabling privacy-preserving analysis without exposing the original data.

About Original Dataset - Context

Happiness and well-being are essential indicators of societal progress, often influenced by economic conditions such as GDP and inflation. This dataset combines data from the World Happiness Index (WHI) and inflation metrics to explore the relationship between economic stability and happiness levels across 148 countries from 2015 to 2023. By analyzing key economic indicators alongside social well-being factors, this dataset provides insights into global prosperity trends.

• Content

This dataset is provided in CSV format and includes 16 columns, covering both happiness-related features and economic indicators such as GDP per capita, inflation rates, and corruption perception. The main columns include:

Happiness Score & Rank (World Happiness Index ranking per country) Economic Indicators (GDP per capita, inflation metrics) Social Factors (Freedom, Social Support, Generosity) Geographical Information (Country & Continent)

• Acknowledgements

The dataset is created using publicly available data from World Happiness Report, Gallup World Poll, and the World Bank. It has been structured for research, machine learning, and policy analysis purposes.

• Inspiration

How do economic factors like inflation, GDP, and corruption affect happiness? Can we predict a country's happiness score based on economic conditions? This dataset allows you to analyze these relationships and build models to predict well-being trends worldwide.

Greece GR: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data was reported at 8.866 % in 2017. This records an increase from the previous number of 8.600 % for 2016. Greece GR: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 8.600 % from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 10.984 % in 1995 and a record low of 7.236 % in 2010. Greece GR: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Gross Domestic Product: Share of GDP. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average; Note: Data for OECD countries are based on ISIC, revision 4.

Tonga TO: GDP: Market Price: Linked Series data was reported at 941.800 TOP mn in 2017. This records an increase from the previous number of 889.500 TOP mn for 2016. Tonga TO: GDP: Market Price: Linked Series data is updated yearly, averaging 444.684 TOP mn from Dec 1989 (Median) to 2017, with 29 observations. The data reached an all-time high of 941.800 TOP mn in 2017 and a record low of 133.835 TOP mn in 1989. Tonga TO: GDP: Market Price: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tonga – Table TO.World Bank: Gross Domestic Product: Nominal. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. Data are in current local currency.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

Trinidad and Tobago TT: GDP: % of Manufacturing: Medium and High Tech Industry data was reported at 39.598 % in 2015. This stayed constant from the previous number of 39.598 % for 2014. Trinidad and Tobago TT: GDP: % of Manufacturing: Medium and High Tech Industry data is updated yearly, averaging 37.496 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 40.153 % in 1995 and a record low of 20.698 % in 1992. Trinidad and Tobago TT: GDP: % of Manufacturing: Medium and High Tech Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank: Gross Domestic Product: Share of GDP. The proportion of medium and high-tech industry value added in total value added of manufacturing; ; United Nations Industrial Development Organization (UNIDO), Competitive Industrial Performance (CIP) database; ;