In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

Seychelles had the largest Gross Domestic Product (GDP) per capita in Africa as of 2024. The value amounted to 21,630 U.S. dollars. Mauritius followed with around 12,330 U.S. dollars, whereas Gabon registered 8,840 U.S. dollars. GDP per capita is calculated by dividing a country’s GDP by its population, meaning that some of the largest economies are not ranked within the leading ten. Impact of COVID-19 on North Africa’s GDP When looking at the GDP growth rate in Africa in 2024, Libya had the largest estimated growth in Northern Africa, a value of 7.8 percent compared to the previous year. Niger and Senegal were at the top of the list with rates of 10.4 percent and 8.3 percent, respectively. During the COVID-19 pandemic, the impact on the economy was severe. The growth of the North African real GDP was estimated at minus 1.1 percent in 2020. However, estimations for 2022 looked much brighter, as it was set that the region would see a GDP growth of six percent, compared to four percent in 2021.
Contribution of Tourism Various countries in Africa are dependent on tourism, contributing to the economy. In 2023, travel and tourism were estimated to contribute 182.6 billion U.S. dollars, a clear increase from 96.5 in 2020 following COVID-19. As of 2024, South Africa, Mauritius, and Egypt led tourism in the continent according to the Travel & Tourism Development Index.

Guyana was the South American country 20360the highest gross national income per capita, with 20,360 U.S. dollars per person in 2023. Uruguay ranked second, registering a GNI of 19,530 U.S. dollars per person, based on current prices. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Which are the largest Latin American economies? Based on annual gross domestic product, which is the total amount of goods and services produced in a country per year, Brazil leads the regional ranking, followed by Mexico, Argentina, and Chile. Many Caribbean countries and territories hold the highest GDP per capita in this region, measurement that reflects how GDP would be divided if it was perfectly equally distributed among the population. GNI per capita is, however, a more exact calculation of wealth than GDP per capita, as it takes into consideration taxes paid and income receipts from abroad. How much inequality is there in Latin America? In many Latin American countries, more than half the total wealth created in their economies is held by the richest 20 percent of the population. When a small share of the population concentrates most of the wealth, millions of people don't have enough to make ends meet. For instance, in Brazil, about 5.32 percent of the population lives on less than 3.2 U.S. dollars per day.

About Dataset

Description This dataset contains detailed rankings and indicators from the 2023 Legatum Prosperity Index, assessing and ranking countries based on various dimensions of prosperity and development. The indicators cover aspects such as:

• Country: The name of the country.
• AveragScore: The overall average score of the country across all indicators.
• SafetySecurity: Freedom from conflict, terrorism, and crime.
• PersonelFreedom: Rights to speech, assembly, and individual autonomy.
• Governance: Quality of democracy, rule of law, and government effectiveness.
• SocialCapital: Strength of personal relationships and civic engagement.
• InvestmentEnvironment: Conditions for private investment and credit access.
• EnterpriseConditions: Business environment and market competition.
• MarketAccessInfrastructure: Ease of trade and quality of infrastructure.
• EconomicQuality: Macroeconomic stability and employment quality.
• LivingConditions: Standard of living and access to basic services.
• Health: Population health and healthcare access.
• Education: Quality and accessibility of education.
• NaturalEnvironment: Environmental quality and sustainability.

Source

The data is sourced from the 2023 Legatum Prosperity Index available at prosperity.com/rankings.

License

This dataset is shared under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). You are free to use, share, and adapt the data, provided that you attribute the source and share any derived works under the same license.

Acknowledgements

We acknowledge the Legatum Institute for compiling and providing the data used in this dataset. For more information on the methodology and detailed country reports, please visit the Legatum Prosperity Index website.

Usage

This dataset can be used for research, analysis, and educational purposes to understand the different dimensions of prosperity and development across countries in 2023. It is a valuable resource for policymakers, researchers, and anyone interested in global development metrics. Additionally, clustering analysis can be performed to group countries based on their development levels, providing insights into regional similarities and differences.

Keywords

Global Prosperity, Country Development, Safety, Governance, Health, Education, Economic Quality, 2023 Rankings, Legatum Prosperity Index, Clustering, Tabular, Social Science, Economics, Advanced

This data collection contains information about selected interactions between major powers, such as the United States, the former Soviet Union, the People's Republic of China, and Eastern European countries, and less developed countries for the years 1959, 1961, 1963, and 1965. The variables measuring the interactions include indicators of economic, political, and educational influence of the major powers on the less developed countries, such as the proportions of exports to and imports from the major powers, economic aid received from the major powers, the number of students from the less developed countries enrolled in educational institutions of the more developed countries, diplomatic recognition extended to the major powers by the less developed countries, news services of the major powers in the less developed countries, and the relative geographic distance between each of the less developed countries and the more developed countries. Also included are variables describing characteristics of the less developed countries, such as population and description of the Communist Party in each country. Additional variables provide information on the date of admission of each country to the United Nations, the degree of freedom of the press, and Communist Party membership.

South Africa's GDP was estimated at just over 403 billion U.S. dollars in 2024, the highest in Africa. Egypt followed, with a GDP worth around 380 billion U.S. dollars, and ranked as the second-highest on the continent. Algeria ranked third, with about 260 billion U.S. dollars. These African economies are among some of the fastest-growing economies worldwide. Dependency on oil For some African countries, the oil industry represents an enormous source of income. In Nigeria, oil generates over five percent of the country’s GDP in the third quarter of 2023. However, economies such as the Libyan, Algerian, or Angolan are even much more dependent on the oil sector. In Libya, for instance, oil rents account for over 40 percent of the GDP. Indeed, Libya is one of the economies most dependent on oil worldwide. Similarly, oil represents for some of Africa’s largest economies a substantial source of export value. The giants do not make the ranking Most of Africa’s largest economies do not appear in the leading ten African countries for GDP per capita. The GDP per capita is calculated by dividing a country’s GDP by its population. Therefore, a populated country with a low total GDP will have a low GDP per capita, while a small rich nation has a high GDP per capita. For instance, South Africa has Africa’s highest GDP, but also counts the sixth-largest population, so wealth has to be divided into its big population. The GDP per capita also indicates how a country’s wealth reaches each of its citizens. In Africa, Seychelles has the greatest GDP per capita.

Graph and download economic data for Gross Domestic Product Per Capita for Least Developed Countries (NYGDPPCAPCDLDC) from 1960 to 2023 about per capita and GDP.

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.

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

Graph and download economic data for Population Growth for Least Developed Countries (SPPOPGROWLDC) from 1961 to 2024 about population and rate.

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

Country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. Country rankings may be compared or analysed separately. Indicators offered for each country: H Index, Documents, Citations, Citation per Document and Citable Documents.

According to preliminary data, the agricultural sector contributed around 6.8 percent to the gross domestic product (GDP) of China in 2024, whereas 36.5 percent of the economic value added originated from the industrial sector and 54.6 percent from the service sector, respectively. The total GDP of China at current prices amounted to approximately 134.91 trillion yuan in 2024. Economic development in China The gross domestic product (GDP) serves as a primary indicator to measure the economic performance of a country or a region. It is generally defined as the monetary value of all finished goods and services produced within a country in a specific period of time. It includes all of private and public spending, government spending, investments, and net exports which are calculated as total exports minus imports. In other words, GDP represents the size of the economy.With its national economy growing at an exceptional annual growth rate of above nine percent for three decades in succession, China had become the worlds’ second largest economy by 2010, surpassing all other economies but the United States. Even though China's GDP growth has cooled down in recent years, its economy still expanded at roughly two times the pace of the United States in 2024. Breakdown of GDP in China When compared to other developed countries, the proportions of agriculture and industry in China's GDP are significantly higher. Even though agriculture is a major industry in the United States, it only accounted for about one percent of the economy in 2023. While the service sector contributed to more than 70 percent of the economy in most developed countries, it's share was considerably lower in China. This was not only due to China's lower development level, but also to the country’s focus on manufacturing and export. However, as the future limitations of this growth model become more and more apparent, China is trying to shift it's economic focus to the high-tech and service sectors. Accordingly, growth rates of the service sector have been considerably higher than in industry and agriculture in the years before the spread of the coronavirus pandemic.

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

South Africa concentrated the largest amount of private wealth in Africa as of 2021, some 651 billion U.S. dollars. Egypt, Nigeria, Morocco, and Kenya followed, establishing the five wealthier markets in the continent. The wealth value referred to assets, such as cash, properties, and business interests, held by individuals living in each country, with liabilities discounted. Overall, Africa counted in the same year approximately 136,000 high net worth individuals (HNWIs), each with net assets of one million U.S. dollars or more.

 COVID-19 and wealth constraints  

Africa held 2.1 trillion U.S. dollars of total private wealth in 2021. The amount slightly increased in comparison to the previous year, when the coronavirus (COVID-19) pandemic led to job losses, drops in salaries, and the closure of many local businesses. However, compared to 2011, total private wealth in Africa declined 4.5 percent, constrained by poor performances in Angola, Egypt, and Nigeria. By 2031, however, the private wealth is expected to rise nearly 40 percent in the continent.

 The richest in Africa 

Besides 125 thousand millionaires, Africa counted 6,700 multimillionaires and 305 centimillionaires as of December 2021. Furthermore, there were 21 billionaires in the African continent, each with a wealth of one billion U.S. dollars and more. The richest person in Africa is the Nigerian Aliko Dangote. The billionaire is the founder and chairman of Dangote Cement, the largest cement producer on the whole continent. He also owns salt and sugar manufacturing companies.

The United Nations Committee for Development Policy uses three criteria to identify countries as least developed and reviews the list of LDCs every three years.

This study examines the determinants influencing the likelihood of Sub-Saharan African (SSA) countries seeking assistance from the International Monetary Fund (IMF). The IMF, as a global institution, aims to promote sustainable growth and prosperity among its member countries by supporting economic strategies that foster financial stability and collaboration in monetary affairs. Utilising panel-probit regression, this study analyses data from thirty-nine SSA countries spanning from 2000 to 2022, focusing on twelve factors: Current Account Balance (CAB), inflation, corruption, General Government Net Lending and Borrowing (GGNLB), General Government Gross Debt (GGGD), Gross Domestic Product Growth (GDPG), United Nations Security Council (UNSC) involvement, regime types (Closed Autocracy, Electoral Democracy, Electoral Autocracy, Liberal Democracy) and China Loan. The results indicate that corruption and GDP growth rate have the most significant influence on the likelihood of SSA countries seeking IMF assistance. Conversely, factors such as CAB, UNSC involvement, LD and inflation show inconsequential effects. Notable, countries like Sudan, Burundi, and Guinea consistently rank high in seeking IMF assistance over various time frames within the observed period. Sudan emerges with a probability of more than 44% in seeking IMF assistance, holding the highest ranking. Study emphasises the importance of understanding SSA region rankings and the variability of variables for policymakers, investors, and international organisations to effectively address economic challenges and provide financial assistance.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

A key challenge for a future climate change agreement is allocating emissions targets for individual developed countries that are perceived as equitable given differing national circumstances. Many economics-based frameworks for evaluating future targets use as a key criterion for individual country targets the notion that mitigation measures should result in similar costs (specifically, that the required mitigation actions relative to baseline emissions result in a similar percentage reduction of individual countries’ GDP in the target year or period). Such an economic criterion provides a transparent and objective basis for comparison, but it does not necessarily mean that comparable targets for individual countries are also equitable. A set of thought experiments demonstrates that such an approach indeed does not reflect equity between countries. This is because future business-as-usual emissions, against which the costs of mitigation are assessed, depend on past policy choices and mitigation pathways. An approach that sets future emissions targets at a specific date based on comparable costs, without regard to past policy choices and commitments, would penalise countries that have taken early action and provides a disincentive for taking strong domestic mitigation actions in future. This analysis suggests that the choice of ‘business-as-usual’ emissions against which the future costs of mitigation are assessed needs to receive more attention if economic comparability is intended to also reflect equity of emissions targets over time.

Series Name: Average tariff applied by developed countries most-favored nation status by type of product (percent)Series Code: TM_TAX_DMFNRelease 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 17.12.1: Weighted average tariffs faced by developing countries, least developed countries and small island developing StatesTarget 17.12: Realize timely implementation of duty-free and quota-free market access on a lasting basis for all least developed countries, consistent with World Trade Organization decisions, including by ensuring that preferential rules of origin applicable to imports from least developed countries are transparent and simple, and contribute to facilitating market accessGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/