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.

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

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.

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.

Global Prosperity Index 2023

Overview

Explore the 2023 Global Prosperity Index dataset, featuring comprehensive rankings of countries based on various dimensions of prosperity. This dataset includes scores across critical factors such as safety, governance, economic quality, health, and more.

Dataset Features

• country: Country name.
• code: ISO country code.
• region: Geographical region.
• political_regime: Type of political regime.
• safety_and_security: Score for the degree to which war, conflict, terror, and crime have destabilized security.
• personal_freedom: Score for progress towards legal rights, individual liberties, and social tolerance.
• governance: Score for the extent of checks on power, government effectiveness, and lack of corruption.
• social_capital: Score for the strength of personal/social relationships, institutional trust, social norms, and civic participation.
• investment_environment: Score for the protection and accessibility of investments.
• enterprise_conditions: Score for regulations enabling business start, competition, and expansion.
• infrastructure_and_market_access: Score for the quality of trade infrastructure and market conditions.
• economic_quality: Score for sustainable wealth generation and workforce engagement.
• living_conditions: Score for the quality of life, including material resources, shelter, basic services, and connectivity.
• health: Score for health outcomes, systems, illness/risk factors, and mortality rates.
• education: Score for enrolment, outcomes, and quality across education stages and adult skills.
• natural_environment: Score for aspects of the physical environment affecting daily life and future prosperity.
• average_score: Overall average score.

Key Insights

• Political Regime: Liberal democracies lead in prosperity.
• Safety and Security: High safety correlates with overall prosperity.
• Personal Freedom: Essential for well-being and innovation.
• Governance: Effective governance boosts social and economic quality.
• Investment Environment: Critical for enterprise and infrastructure.

Challenge Ideas

1. Regional Comparisons: Compare prosperity indicators across different regions. Analyze the strengths and weaknesses of each region and identify key factors that contribute to regional prosperity.

2. Policy Development: Use the dataset to develop policy recommendations for improving prosperity in specific countries. Focus on areas such as governance, health, and education to suggest actionable improvements.

3. Correlation Analysis: Investigate the relationships between different prosperity factors. Determine how elements like personal freedom and economic quality are interconnected and identify the most influential indicators for overall prosperity.

4. Research Projects: Leverage the dataset for academic and professional research.

   Tags

• Global Prosperity, Country Rankings, Governance, Economic Quality, Social Indicators, Health and Education.

References

• 2023 Prosperity Index
• Methodology
• Political Systems

Gross Domestic Product (GDP) is a measure of the total economic output of a country. It is the sum of all the goods and services produced within a country over a given period. The GDP of a country is an important indicator of its economic health and can be used to compare the economic performance of different countries.

According to the World Bank, the United States has the highest GDP of any country in the world, with a value of $23.3 trillion. The American economy is one of the most diversified and technologically advanced in the world which contributes to the US’s large GDP. China is the second-largest economy in the world, with a GDP of $17.7 trillion. Japan, Germany, India, the United Kingdom, and France round out the top seven, all with GDPs over $3 trillion.

On the other hand, there are countries with low GDPs. The country with the lowest GDP in the world is Nauru, with a value of $133.2 million. Palau, Marshall Islands, Federated States of Micronesia, and São Tomé and Príncipe are some other countries with low GDPs. These countries are typically characterized by limited natural resources, small populations, geographic isolation, and a heavy reliance on tourism or foreign aid.

It is important to note that GDP is not necessarily an accurate reflection of the economic well-being of a country’s citizens. While a high GDP indicates a large and productive economy, it does not necessarily mean that all citizens are equally prosperous. Countries with lower GDPs may also have a higher standard of living if income is distributed more equally among the population.

As of April 2025, South Africa's GDP was estimated at over 410 billion U.S. dollars, the highest in Africa. Egypt followed, with a GDP worth around 347 billion U.S. dollars, and ranked as the second-highest on the continent. Algeria ranked third, with nearly 269 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.

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.

Many may questions what the economy might have to do with the global poverty issue. I personally believe the economy is one of the main contributors to global poverty and inequalities. Supply and demand drives the economy, and in North America, our society is very materialistic. Although the economy does not revolve around just what we buy, it also consists of things such as oil, and other unsustainable resources that are widely used due to their accessibility. The GDP would be significantly less without trade to other countries. While this involves one global power house, in this case America, other countries are stuck in a position of fending on their own and struggle to compete. America takes advantage of this, especially the big companies that are American. A basic example of this would be Apple designing their phones in California, assembling them in China and the importing them all over the world. It is even deeper than that, because the materials from this phone are coming from developed countries for the most part. The company Apple, has a larger GDP than most countries. This shows how much is invested into our own goods but the people who build these products in developed countries are left with nothing. It shows how greed is the major reasons why global poverty has not ended yet, we extract and then move on.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

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

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.

The decades that followed the Second World War were among the most prosperous in modern history, and are referred to as the Golden Age of Capitalism in many countries. This period came to an end, however, with the 1973-1975 recession. Differences across the bloc Across the OECD member states, there was a significant drop in real GDP growth over the two decades, falling from an average of five percent annual growth in the 1960s to just 3.5 percent annually in most of the 1970s. Of all OECD countries shown here, Japan experienced the highest rate of real GDP growth in both decades, although it dropped from 11 to six percent between these years (Japan's real GDP growth was still higher in the 1970s than the other members' rates in the 1960s). Switzerland saw the largest relative decline over the two periods, with growth in the 1970s below one third of its growth rate in the 1960s. What caused the end of rapid growth? The Yom Kippur War between Israel and its Arab neighbors (primarily Egypt and Syria) resulted in the Arab oil-producing states placing an embargo on Israel's Western allies. This resulted in various energy and economic crises, compounded by other issues such as the end of the Bretton Woods financial system, which had far-reaching consequences for the OECD bloc. Additionally, the cost of agricultural goods and raw materials increased, and there was a very rare case of stagflation across most of the world's leading economies.

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.

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.

Greece GR: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -8.350 % in 2015. Greece GR: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -8.350 % from Dec 2015 (Median) to 2015, with 1 observations. Greece GR: 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 Greece – Table GR.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.

Series Name: Average tariff applied by developed countries most-favored nation status by type of product (percent)Series Code: TM_TAX_DMFNRelease Version: 2021.Q2.G.03 This dataset is 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/

The table shows the level of gross fixed capital formation, i.e. investment, as percent of GDP around the world. There are three values - for the last quarter, for the previous quarter, and for a year ago. These numbers are updated as soon as new data are released by the national authorities. Looking across countries, investment is about 20-25 percent of GDP, typically with greater values for less developed countries than for advanced economies. This is normal as those countries are in the process of industrialization that requires more investment.

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.

Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -2.590 % in 2021. Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -2.590 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -2.590 % in 2021 and a record low of -2.590 % in 2021. Colombia CO: 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 Colombia – Table CO.World Bank.WDI: Social: Poverty and Inequality. 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 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). 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 coverage and quality of the 2017 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 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (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.