Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q4 2024 about per capita, real, GDP, and USA.

The Gross Domestic Product per capita in Iran was last recorded at 5667.53 US dollars in 2023. The GDP per Capita in Iran is equivalent to 45 percent of the world's average. This dataset provides - Iran GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product per capita in St Lucia was last recorded at 11440.06 US dollars in 2023. The GDP per Capita in St Lucia is equivalent to 91 percent of the world's average. St Lucia GDP per capita - values, historical data, forecasts and news - updated on March of 2025.

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

The Gross Domestic Product per capita in Netherlands was last recorded at 51305.63 US dollars in 2023. The GDP per Capita in Netherlands is equivalent to 406 percent of the world's average. This dataset provides the latest reported value for - Netherlands GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

011 -- Gross domestic product per capita by major region 2000-2016*

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

"The resilience of the domestic economic systems of the countries along the Belt and Road reflects the level of resilience of the domestic economic systems of each country, and the higher the value of the data, the stronger the resilience of the domestic economic systems of the countries along the Belt and Road. The resilience of domestic economic systems includes macroeconomic development resilience, industrial and service sector development resilience, and the data products are prepared with reference to the World Bank statistical database, using GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by GDP deflator, and gross savings as measured by GDP deflator for countries along the Belt and Road from 2000 to 2019. The resilience products of the domestic economic system are prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator, using year-on-year data of six indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, gross savings as a percentage of GDP, industrial value added as a percentage of GDP, and service value added as a percentage of GDP. "The resilience dataset of the domestic economic systems of the countries along the Belt and Road is an important reference for analysing and comparing the resilience of the domestic economic systems of various countries.

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

The statistic shows the gross domestic product (GDP) per capita in India from 1987 to 2029. In 2020, the estimated gross domestic product per capita in India amounted to about 1,915.55 U.S. dollars. See figures on India's economic growth here. For comparison, per capita GDP in China had reached about 6,995.25 U.S. dollars in 2013.

India's economic progress

India’s progress as a country over the past decade can be attributed to a global dependency on cheaper production of goods and services from developed countries around the world. India’s economy is built upon its agriculture, manufacturing and services sector, which, along with its drastic rise in population and demand for employment, led to a significant increase of the nation’s GDP per capita. Despite experiencing rather momentous economic gains since the mid 2000s, the Indian economy stagnated around 2012, with a decrease in general growth as well as the value of its currency. Residents and consumers in India have recently shown pessimism regarding the future of the Indian economy as well as their own financial situation, and with the recent economic standstill, consumer confidence in the country could potentially lower in the near future.

Typical Indian exports consist of agricultural products, jewelry, chemicals and ores. Imports consist primarily of crude oil, gold and precious stones, used primarily in the manufacturing of jewelry. As a result, India has seen a rather highly increased demand of several gems in order to boost their jewelry industry and in general their exports. Although India does not export an extensive amount of goods, especially when considering the stature of the country, India has remained as one of the world’s largest exporters.

Dataset in excel of main macroeconomic indicators growth from 2017 to 2021 for near 200 countries and according to IMF data. It allows us to quickly assess the impact of the COVID19 in the global economic

It includes: real GDP growth, GDP per capita, inflation, unemployment rate, general government net lending /borrowing.

This paper explores the relationship between the relative size of the Small and Medium Enterprise (SME) sector, economic growth, and poverty alleviation using a new database on the share of SME labor in the total manufacturing labor force. Using a sample of 45 countries, we find a strong, positive association between the importance of SMEs and GDP per capita growth. The data do not, however, confidently support the conclusions that SMEs exert a causal impact on growth. Furthermore, we find no evidence that SMEs alleviate poverty or decrease income inequality. Universe: Small and Medium Enterprise sector of 45 countries

Introduction

This study reproduces the results of the article Relationship of gender differences in preferences to economic development and gender equality (DOI: 10.1126/science.aas9899) and partially its supplementary material.

The code for the analysis can be found at the following GitHub page: https://github.com/scerioli/Global-Preferences-Survey

Preparation of the data

Data Collection, Cleaning, and Standardization

The data used in the Falk & Hermle 2018 is not fully available because of two reasons:

1. Data paywall: Some part of the data is not available for free. It requires to pay a fee to the Gallup to access them. This is the case for the additional data set that is used in the article, for instance, the one that contains the education level and the household income quintile. Check the website of the briq - Institute on Behavior & Inequality for more information on it.

2. Data used in study is not available online: This is what happened for the LogGDP p/c calculated in 2005 US dollars (which is not directly available online). We decided to calculate the LogGDP p/c in 2010 US dollars because it was easily available, which should not change the main findings of the article.

Global Preferences Survey

This data is protected by copyright and cannot be given to third parties.

To download the GPS data set, go to the website of the Global Preferences Survey in the section "downloads". There, choose the "Dataset" form and after filling it, we can download the data set.

Hint: The organisation can be also "private".

The following two relevant papers have to be also cited in all publications that make use of or refer in any kind to GPS dataset:

• Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., & Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics, 133 (4), 1645–1692.

• Falk, A., Becker, A., Dohmen, T. J., Huffman, D., & Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper No. 9674.

GDP per capita

From the website of the World Bank, one can access the data about the GDP per capita on a certain set of years. We took the GDP per capita (constant 2010 US$), made an average of the data from 2003 until 2012 for all the available countries, and matched the names of the countries with the ones from the GPS data set.

Gender Equality Index

The Gender Equality Index is composed of four main data sets.

• Time since women’s suffrage: Taken from the Inter-Parliamentary Union Website. We prepared the data in the following way. For several countries more than one date where provided (for example, the right to be elected and the right to vote). We use the last date when both vote and stand for election right were granted, with no other restrictions commented. Some counties were a colony or within union of the countries (for instance, Kazakhstan in Soviet Union). For these countries, the rights to vote and be elected might be technically granted two times within union and as independent state. In this case we kept the first date. It was difficult to decide on South Africa because its history shows the racism part very entangled with women's rights. We kept the latest date when also Black women could vote. For Nigeria, considered the distinctions between North and South, we decided to keep only the North data because, again, it was showing the completeness of the country and it was the last date. Note: USA data doesn't take into account that also up to 1964 black women couldn't vote (in general, Blacks couldn't vote up to that year). We didn’t keep this date, because it was not explicitly mentioned in the original data set. This is in contrast with other choices made, but it is important to reproduce exactly the results of the publication, and the USA is often easy to spot on the plots.

• UN Gender Inequality Index: Taken from the Human Development Report 2015. We kept only the table called "Gender Inequality Index".

• WEF Global Gender Gap: WEF Global Gender Gap Index Taken from the World Economic Forum Global Gender Gap Report 2015. For countries where data were missing, data was added from the World Economic Forum Global Gender Gap Report 2006. We modified some of the country names directly in the csv file, that is why we provide it as an input file.

• Ratio of female and male labour force participation: Average International Labour Organization estimates from 2003 to 2012 taken from the World Bank database (http://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). Values were inverted to create an index of equality. We took the average for the period between 2004 and 2013.

In our extended analysis, we also involved the following index:

• United Nations Development Programme Gender Development Index taken from Human Development Reports 2020. Note that we have downloaded the two tables of the Human Development Index for males and females, and used the ratio of the two as a GDI index, as described in the report.

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

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

Georgia Main Aggregates: Gross National Income per Capita: USD data was reported at 1,045.519 USD in Jun 2018. This records an increase from the previous number of 939.952 USD for Mar 2018. Georgia Main Aggregates: Gross National Income per Capita: USD data is updated quarterly, averaging 612.683 USD from Mar 1996 (Median) to Jun 2018, with 90 observations. The data reached an all-time high of 1,223.330 USD in Dec 2014 and a record low of 128.798 USD in Mar 1999. Georgia Main Aggregates: Gross National Income per Capita: USD data remains active status in CEIC and is reported by National Statistics Office of Georgia. The data is categorized under Global Database’s Georgia – Table GE.A033: GDP: Main Aggregates.

The Gross Domestic Product per capita in Egypt was last recorded at 16960.57 US dollars in 2023, when adjusted by purchasing power parity (PPP). The GDP per Capita, in Egypt, when adjusted by Purchasing Power Parity is equivalent to 95 percent of the world's average. This dataset provides - Egypt GDP per capita PPP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Brazil Gross Domestic Product per Capita: South: Santa Catarina: Major Gercino data was reported at 13,537.010 BRL in 2016. This records an increase from the previous number of 12,221.110 BRL for 2015. Brazil Gross Domestic Product per Capita: South: Santa Catarina: Major Gercino data is updated yearly, averaging 12,221.110 BRL from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 13,537.010 BRL in 2016 and a record low of 10,376.020 BRL in 2013. Brazil Gross Domestic Product per Capita: South: Santa Catarina: Major Gercino data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AF024: SNA 2008: Gross Domestic Product per Capita: by Municipality: South: Santa Catarina.

Given both corruption's and bureaucratic inefficiency's importance for development and good governance, understanding their causes is paramount. This paper argues that majority state ownership of most the most important economic sectors of a country results in higher levels of corruption and inefficiency. When political and managerial elites both own and manage the country's most important economic resources, they have greater incentives for corrupt or inefficient behavior. These elites use national resources at their disposal more for short-term personal and political goals than for long-term economic ones. This paper tests this hypothesis on a relatively underused, but often cited, data set from the 1980s. Using a cross-national, regression analysis, this paper finds that the best predictors a country's level of corruption or bureaucratic inefficiency are these: majority state ownership of significant economic sectors, levels of GDP per capita, levels of government spending, and levels of democracy. Other factors, such as common law heritage, percent of population that is Protestant, federalism, economic freedoms, or mineral/ oil exporting, were not consistent, significant predictors of either bureaucratic inefficiency or corruption. We also argue that Tobit may be the best estimation procedure for these data.