Series Name: Annual growth rate of real GDP per capita (percent)Series Code: NY_GDP_PCAPRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 8.1.1: Annual growth rate of real GDP per capitaTarget 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countriesGoal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

This layer is part of SDGs Today. Please see sdgstoday.orgGross Domestic Product (GDP) is one of the most commonly used measures for tracking national accounts and economic activity. Tracking growth over time can provide insights into the growth or decline of a nation’s economic activities following global/national events, policy changes, and other large-scale phenomena.The OECD's quarterly national accounts (QNA) dataset presents GDP growth data collected from all the OECD member countries and some other major economies on the basis of a standardised questionnaire. It contains a wide selection of generally seasonally adjusted quarterly series most widely used for economic analysis from 1960 or whenever available. These indicators include measures such as GDP expenditure/output and industry-based employment rates. All available OECD QNA measurements are made available to the public here.For more information, contact STAT.Contact@oecd.org.

This map is part of SDGs Today. Please see sdgstoday.orgGross Domestic Product (GDP) is one of the most commonly used measures for tracking national accounts and economic activity. Tracking growth over time can provide insights into the growth or decline of a nation’s economic activities following global/national events, policy changes, and other large-scale phenomena.The OECD's quarterly national accounts (QNA) dataset presents GDP growth data collected from all the OECD member countries and some other major economies on the basis of a standardised questionnaire. It contains a wide selection of generally seasonally adjusted quarterly series most widely used for economic analysis from 1960 or whenever available. These indicators include measures such as GDP expenditure/output and industry-based employment rates. All available OECD QNA measurements are made available to the public here.For more information, contact STAT.Contact@oecd.org.

Some recent papers have concluded that authoritarian regimes have faster economic growth than democracies. These supposed growth benefits of autocracies are estimated using data sets in which growth rates rely heavily on data reported by each government. Governments have incentives to exaggerate their economic growth figures, however, and authoritarian regimes may have fewer limitations than democracies on their ability to do so. This paper argues that growth data submitted to international agencies are overstated by authoritarian regimes compared to democracies. If true, it calls into question the estimated relationship between government type and economic growth found in the literature. To measure the degree to which each government's official growth statistics are overstated, the economic growth rates reported in the World Bank's World Development Indicators are compared to a new measure of economic growth based on satellite imaging of nighttime lights. This comparison reveals whether or not dictators exaggerate their true growth rates and by how much. Annual GDP growth rates are estimated to be overstated by 0.5–1.5 percentage points in the statistics that dictatorships report to the World Bank.

Content

St. Louis Fed’s Economic News Index (ENI) uses economic content from key monthly economic data releases to forecast the growth of real GDP during that quarter. In general, the most-current observation is revised multiple times throughout the quarter. The final forecasted value (before the BEA’s release of the advance estimate of GDP) is the static, historical value for that quarter. For more information, see Grover, Sean P.; Kliesen, Kevin L.; and McCracken, Michael W. “A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth" (https://research.stlouisfed.org/publications/review/2016/12/05/a-macroeconomic-news-index-for-constructing-nowcasts-of-u-s-real-gross-domestic-product-growth/ )

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2013-04-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ferdinand Stöhr on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The GDP per capita growth resilience dataset for countries along the Belt and Road is a comprehensive reflection of the level of GDP per capita growth resilience of each country. The GDP per capita growth resilience dataset was prepared with reference to the World Bank's statistical database, using year-on-year data on GDP per capita (constant 2010 US dollars) for countries along the Belt and Road from 2000 to 2019, and based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator. Through a comprehensive diagnostic, a product on GDP per capita growth resilience was prepared. "The GDP per capita growth resilience dataset for countries along the Belt and Road is an important reference for analysing and comparing the current GDP per capita growth resilience of each country.

Gross Domestic Product (GDP) is the monetary value of all finished goods and services made within a country during a specific period. GDP provides an economic snapshot of a country, used to estimate the size of an economy and growth rate. This dataset contains the GDP based on Purchasing Power Parity (PPP).

GDP comparisons using PPP are arguably more useful than those using nominal GDP when assessing a nation's domestic market because PPP takes into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates which may distort the real differences in per capita income

Acknowledgement

Thanks to World Databank

Context

This dataset contains annual GDP growth for each country.

Content

This dataset contains data about 264 countries. There is some missing data for several countries. Format of data: .csv

Column names and description: - "Country Name" - name of country - "Country Code" - code of country (3 letters) - "Indicator Name" - all fields filled with 'GDP (current US$)' - "Indicator Code" - all fields contains 'NY.GDP.MKTP.CD' value - Colums for each year (1960 - 2020)

Acknowledgements

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. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.

World Bank national accounts data, and OECD National Accounts data files. Source of data: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG

The Gross Domestic Product per capita in the United States was last recorded at 66682.61 US dollars in 2024. The GDP per Capita in the United States is equivalent to 528 percent of the world's average. This dataset provides - United States GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The United Nations Conference on Trade and Development (UNCTAD) Digital Economy Database is a specialized data repository that provides global, regional, and country-level statistics and indicators on the digital economy, particularly in developing countries. It supports analysis and policymaking around e-commerce, digital trade, ICT infrastructure, and the broader digital transformation. Data sets include International merchandise trade, International trade in services, Foreign direct investment (FDI), Economic trends, Commodities, Maritime transport, Digital economy, and Population and labor force. Key tools include the UNCTADstat database, Country Profiles, and Nowcasts for real-time global trade and economic growth estimates. UNCTAD's datasets are widely used by policymakers, researchers, and organizations to analyze global trade dynamics, assess development progress, and formulate evidence-based policies.

This collection includes only a subset of indicators from the source dataset.

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries. This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels. Data for years 2020 to 2026 are stored in the corresponding repositories: covid: 10.5281/zenodo.5713825 counterfactual: 10.5281/zenodo.5713839 Tables are generated using the SPIN method, based on the RMRIO tables for the year 2015, GDP, imports and exports data from the International Financial Statistics (IFS) and the World Economic Outlooks (WEO) of October 2019 and April 2021. From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist. The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels. The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts: SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data. SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset. figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries. All tables are labelled in 2015 US$ and valued in basic prices.

On October 29, 1929, the U.S. experienced the most devastating stock market crash in it's history. The Wall Street Crash of 1929 set in motion the Great Depression, which lasted for twelve years and affected virtually all industrialized countries. In the United States, GDP fell to it's lowest recorded level of just 57 billion U.S dollars in 1933, before rising again shortly before the Second World War. After the war, GDP fluctuated, but it increased gradually until the Great Recession in 2008. Real GDP Real GDP allows us to compare GDP over time, by adjusting all figures for inflation. In this case, all numbers have been adjusted to the value of the US dollar in FY2012. While GDP rose every year between 1946 and 2008, when this is adjusted for inflation it can see that the real GDP dropped at least once in every decade except the 1960s and 2010s. The Great Recession Apart from the Great Depression, and immediately after WWII, there have been two times where both GDP and real GDP dropped together. The first was during the Great Recession, which lasted from December 2007 until June 2009 in the US, although its impact was felt for years after this. After the collapse of the financial sector in the US, the government famously bailed out some of the country's largest banking and lending institutions. Since recovery began in late 2009, US GDP has grown year-on-year, and reached 21.4 trillion dollars in 2019. The coronavirus pandemic and the associated lockdowns then saw GDP fall again, for the first time in a decade. As economic recovery from the pandemic has been compounded by supply chain issues, inflation, and rising global geopolitical instability, it remains to be seen what the future holds for the U.S. economy.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

Structural Change is the phenomenon witnessed by almost all the developing and the developed states in their journey towards a higher sustained economic growth. The rise in per capita income and the structural change are complementary to each other. A rise in per capita income reinforces structural change and structural change leads to the rise in the productive efficiency stimulating rise in the per capita income of the people. The paper analyses this phenomenon in the state of Bihar which has, of late, started ta witness a higher growth trajectory in its economy. Sourcing the data from the archival records of the various government agencies, the study found that the state of Bihar has mirrored the structural change being experienced by the economy of India. The share of the primary sector in the state's GSDP has drastically declined and is replaced by the services sector. This phenomenon is particularly ascribed to the rising share of trade, communications and the government led public spending in the construction sector. However, the state is not experiencing a balanced growth pattern in its economy; some districts have become more developed relative to the other districts. It has also been seen that due to the low economic base of the state, we cannot expect that the state's socio-economic indicators will converge sooner to the_ more developed states of India.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

GDPNow is a nowcasting model for gross domestic product (GDP) growth that synthesizes the bridge equation approach relating GDP subcomponents to monthly source data with factor model and Bayesian vector autoregression approaches. The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the US Bureau of Economic Analysis.

The Federal Reserve Bank of Atlanta's GDPNow release complements the quarterly GDP release from the Bureau of Economic Analysis (BEA). The Atlanta Fed recalculates and updates their GDPNow forecasts (called "nowcasts") throughout the quarter as new data are released, up until the BEA releases its "advance estimate" of GDP for that quarter. The St. Louis Fed constructs a quarterly time series for this dataset, in which both historical and current observations values are combined. In general, the most-current observation is revised multiple times throughout the quarter. The final forecasted value (before the BEA's release of the advance estimate of GDP) is the static, historical value for that quarter.

For futher information visit the Federal Reserve Bank of Atlanta (https://www.frbatlanta.org/cqer/research/gdpnow.aspx?panel=1).

Sao Tome and Principe ST: GDP: Growth:(GDP) Gross Domestic Productper Capita data was reported at 1.629 % in 2017. This records a decrease from the previous number of 1.909 % for 2016. Sao Tome and Principe ST: GDP: Growth:(GDP) Gross Domestic Productper Capita data is updated yearly, averaging 1.979 % from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 6.583 % in 2006 and a record low of 0.068 % in 2002. Sao Tome and Principe ST: GDP: Growth:(GDP) Gross Domestic Productper Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sao Tome and Principe – Table ST.World Bank: Gross Domestic Product: Annual Growth Rate. Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP per capita is gross domestic product divided by midyear population. 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.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average;

This dataset contains three CSV files documenting the relationship between log GDP and infant mortality rates for 30 countries. The data has been compiled to analyze the impact of economic status on child mortality rates. Each file includes relevant variables for conducting cross-national research on this topic.Files:Country_LogGDP.csvThis file contains the log-transformed GDP data for 30 countries.Variables:Country: The name of the country.Year: The year of the observation.LogGDP: The log-transformed value of the country's GDP for the corresponding year.Infant_Mortality.csvThis file provides the infant mortality rate data (number of infant deaths per 1,000 live births) for the same 30 countries.Variables:Country: The name of the country.Year: The year of the observation.Infant_Mortality: The infant mortality rate for the corresponding year.Average_LogGDP_InfantMortality.csvThis file contains the average log GDP and infant mortality rates for the 30 countries.Variables:Country: The name of the country.Average_LogGDP: The average log-transformed GDP for each country over the time period.Average_Infant_Mortality: The average infant mortality rate for each country over the time period.

Explore data on Saudi Arabia's growth rate of expenditure on GDP at current prices . Analyze Gross Fixed Capital Formation, Government expenditures, Imports, Exports, and more to understand the country's economic growth trends.Follow data.kapsarc.org for timely data to advance energy economics research.Important notes:2022,2023,2024: Preliminary Data.The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.Data were revised from 1970 to 2009