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.

Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

The Gross Domestic Product per capita in Mexico was last recorded at 10313.49 US dollars in 2024. The GDP per Capita in Mexico is equivalent to 82 percent of the world's average. This dataset provides - Mexico GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product per capita in Singapore was last recorded at 67706.83 US dollars in 2024. The GDP per Capita in Singapore is equivalent to 536 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 Gross Domestic Product per capita in Thailand was last recorded at 6573.44 US dollars in 2024. The GDP per Capita in Thailand is equivalent to 52 percent of the world's average. This dataset provides - Thailand GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. 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. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.

Analysis of ‘Gross domestic product (GDP) per inhabitant by region’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/846d9f94-854e-5016-a0f1-f7d0d33cfa2f on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Definition: Economic performance varies widely within North Rhine-Westphalia. Gross domestic product (GDP) per inhabitant is an indicator of regional economic power, which makes it possible to illustrate regional differences. GDP per inhabitant is shown in current prices, i.e. in the current year’s prices.

Note: A general audit was carried out in 2014. This mainly served to introduce the new European System of Accounts (ESA 2010) across Europe. ESA 2010 is based on the global new System of National Accounts (SNA 2008) and replaced the previous ESA 1995.

Data source:
Working Group on National Accounts of the Länder

--- Original source retains full ownership of the source dataset ---

The Gross Domestic Product per capita in France was last recorded at 39441.26 US dollars in 2024. The GDP per Capita in France is equivalent to 312 percent of the world's average. This dataset provides the latest reported value for - France GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.

Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.

The Gross Domestic Product per capita in Nigeria was last recorded at 2447.64 US dollars in 2024. The GDP per Capita in Nigeria is equivalent to 19 percent of the world's average. This dataset provides the latest reported value for - Nigeria GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset was assembled for the purpose of demonstrating a simple linear regression. The table compares GDP per Capita to Life Satisfaction; definitions of both are below. The goal is to explore the relationship between money and happines

Life Satisfaction Provided by Organization for Economic Co-Operation and Development The indicator considers people's evaluation of their life as a whole. It is a weighted-sum of different response categories based on people's rates of their current life relative to the best and worst possible lives for them on a scale from 0 to 10, using the Cantril Ladder (known also as the "Self-Anchoring Striving Scale").

GDP per Capita for 2015 Provided by International Monetary Fund. The gross domestic product of a nation divided by population of that nation.

This dataset provides economic indicators for FUAs of more than 250 000 inhabitants, including GDP, GDP per capita, jobs and labour productivity.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   When economic statistics are unavailable at a more granular level than the FUA (e.g. municipal level), indicators are estimated by adjusting regional (OECD TL2 and TL3 regions) values to FUA boundaries, based on the population distribution in each region. Regional values (GDP and jobs) in TL3 regions are used as data inputs and combined with gridded population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA boundaries are intersected with TL3 borders to compute the share of the regional population that lives within FUAs in each region. This share is then applied to the variable of interest (e.g. GDP) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   When a more granular level is available, data is aggregated for each FUA. For example in the United States, GDP estimates are available at the county-level (<a href=https://www.bea.gov/data/employment/employment-county-metro-and-other-areas>US Bureau of Economic Analysis</a>), and then aggregated by FUA.
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href="http://data-explorer.oecd.org/s/1e5">Economy - FUAs</a>), <a href=http://oe.cd/geostats>http://oe.cd/geostats</a></p>

   <h3>Further information</h3>
   <ul> 
   <li> <a href=https://localdataportal.oecd.org/>OECD Local Data Portal </a> </li>
   <li> <a href=https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2024_f42db3bf-en.html/>OECD Regions and Cities at a Glance </a> </li>
   </ul>
   <p align="justify">For questions and/or comments, please email <a href="mailto:CitiesStat@oecd.org">CitiesStat@oecd.org</a>

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

The Gross Domestic Product per capita in India was last recorded at 2396.71 US dollars in 2024. The GDP per Capita in India is equivalent to 19 percent of the world's average. This dataset provides - India GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains data on expenditure per full-time equivalent student and per full-time equivalent student as a percentage of GPD per capita. The default table displays data for 2021 in current USD PPP and as a percentage of GDP per capita, from all expenditure sources, and unfiltered by type of expenditure. The selection can be changed to display data: by year, by source of expenditure, by destination of expenditure and by type of expenditure. Please note that the filters are inter related, meaning that selection in one category may impact the possibility to select options in another category.

For more information, please consult Education at a Glance 2024 and the OECD Handbook for Internationally Comparative Education Statistics: Concepts, Standards, Definitions and Classifications. Additional details regarding the methodology used, references to the sources, and specific notes for each country can be found in Education at a Glance 2024 Sources, Methodologies and Technical Notes.

The Gross Domestic Product per capita in Kenya was last recorded at 1853.09 US dollars in 2024. The GDP per Capita in Kenya is equivalent to 15 percent of the world's average. This dataset provides the latest reported value for - Kenya GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

The description of the variables included in the data set are explained at below: 1. The dataset covers 106 countries and the period between 2009 and 2020. 2. Economic growth: The four-year average growth rate of real GDP per capita (constant 2015 $) Source: The World Development Indicators 3. The Environmental Performance Index : The four-year average EPI index Source: Socioeconomic Data and Application Center (SEDAC) 4. Gross fixed capital formation : The four-year average gross fixed capital formation (% GDP) Source: The World Development Indicators 5. Tourism development: The four-year average of number of international tourist arrivals per active population (15+) Source: The World Development Indicators 6. Initial real GDP per capita: Natural Logarithmic form of the real GDP per capita at the beginning of each period (constant 2015 $) Source: The World Development Indicators 7. Fertility: Logarithmic form of total births per woman at the beginning of each period Source: The World Development Indicators 8. Life Expectancy: Initial logarithmic form of life expectancy at birth. Source: Human Development Reports (UNDP) 9. Government Expenditures : The average proportion of general government final consumption expenditure (% GDP )Source: The World Development Indicators 10. Trade Openness: Average sum of exports and imports (% GDP) Source: The World Development Indicators 11. Inflation : The average annual percentage change in the consumer price index during each period Source: The World Development Indicators 12. Mean Years of Schooling: The four-year average number of years of education received by people ages 25 and older Source: Human Development Reports (UNDP)

This indicator provides values for gross domestic product (GDP) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons. Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.