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.

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.

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.

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

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 recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

Mauritius concentrated the highest private wealth per capita in Africa in 2021: ****** U.S. dollars. South Africa followed, with a wealth amount of ****** U.S. dollars per capital. Overall, total private wealth on the continent amounted to *** trillion U.S. dollars that year.

South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.550 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.550 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: 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 South Africa – Table ZA.World Bank: 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.

Full dataset for replicating the study on e-governance in Africa. All original data can be retrieved from databases of the United Nations: https://publicadministration.un.org/egovkb/Data-Center, and the World Bank: https://databank.worldbank.org/source/world-development-indicators. Abstract E-governance is considered one of the most important factors in delivering and administering public services in modern societies. However, data show that many African countries are currently lagging behind countries in other parts of the world. This manuscript investigates how various factors, including economic prosperity, government effectiveness, and infrastructural support, contribute to the growth and effectiveness of e-governance initiatives in 54 African countries. We specifically analyze the influence of three factors: economic prosperity (measured by GDP per capita), political competence (measured by government effectiveness), and infrastructural or technological support (measured by access to electricity). Panel data covering a 5-year period were retrieved from databases of the United Nations and World Bank, and a multiple linear regression analysis was used to analyze the data. We found that the three factors influenced e-governance to varying degrees. However, while infrastructural support and political competence were statistically significant, economic prosperity was not.

South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -1.230 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -1.230 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: Survey Mean Consumption or Income per Capita: Total 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 South Africa – Table ZA.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total 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 dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2024, Niger's real GDP is estimated to grow by 10.4 percent compared to the previous year. During 2023, the GDP is estimated to have increased by only 1.4 percent, nevertheless a positive trend. The country's real GDP is forecast to continue growing but at a slower pace. Between 2025 and 2029, it is expected to grow annually by roughly six percent. Furthermore, the GDPs of Senegal, Libya, and Rwanda might increase by around 8.3 percent, 7.8 percent, and 6.9 percent during 2024, respectively. Niger: A dependence on agriculture A large portion of Niger's economy comes from agriculture. In 2022, agriculture accounted for almost 40 percent of the GDP. Niger is not the only country in Africa where agriculture plays a crucial role. For example, agriculture made up nearly 60 percent of Sierra Leone’s GDP in 2022. Such dependence could mean that any disruptions in the agricultural products market could have significant effects on the country's GDP. Sub-Saharan Africa's economy will be among the fastest-growing regions worldwide Three African countries have significantly larger economies, namely, Nigeria, South Africa, and Egypt. As of 2022, these countries' GDP stood at nearly 477.4 billion, 475.2 billion, and 405.7 billion U.S. dollars. Furthermore, it is anticipated that Sub-Saharan Africa's GDP growth in 2026 will rank as the second-fastest growing economic region in the world after the ASEAN-5 countries, with a growth rate of approximately four percent. In contrast, economic areas such as the European Union are forecast to grow at only about 1.5 percent in the same year.

The novel coronavirus pandemic has unsettled the political, economic and social structures of the world. Yet, in the context of global economies in recession, opportunities also abound for many countries, including in Africa, to pursue new directions in governance and management. For instance, the pandemic may be closing gaps between the so-called developed and the developing worlds, thereby giving African countries some geopolitical and economic leverage, both in terms of international alliances and managing fiscal challenges. This project, using the case of Sierra Leonne, focuses on how African countries can chart new paths is their management and governance of foreign aid. The project investigates how aid-funded projects are implemented in Africa using the yardstick of the World Bank’s International Good Governance Standard and, in the process, answers the question of how African countries can alternatively and efficiently administer and manage foreign aid-funded projects? This question is important because Sub-Saharan Africa is one of the world’s most aided regions. Aid as a percentage of Gross Domestic Product (GDP) in the region has averaged around 5% for much of the past two decades. Aid has reached nearly 10% at times and still equals nearly 6% of the region’s GDP. Yet, the growth records of nearly all African countries have thus far been unsatisfactory compared with the amount of aid funds received. The case of high aid flows into African economies, on one hand, and evidence of abysmal growth outcomes, on the other, have led to questions about the usefulness of foreign aid. At a time when Africa’s traditional donor countries are biting the dust, due to the pandemic, these questions become even more crucial. The project calls for a rethink of Africa’s economic management practices to meet the needs of present times.

This study investigates the spatial effects of armed conflict on Sub-Saharan Africa’s (SSA) economic growth, focusing on Central Africa, East Africa, and West Africa. Utilizing Spatial Durblin Model (SDM), the analysis reveals significant spatial effects of armed conflict intensity, indicating that conflict in neighboring countries influences conflict levels within a focal country. The study finds a weak or inconclusive relationship between GDP per capita (GDPpc) and conflict intensity, with East Africa showing a significant negative association, suggesting that higher economic prosperity in neighboring countries may mitigate conflict. Conversely, higher corruption levels in Central and West Africa are positively associated with increased conflict intensity, highlighting corruption’s destabilizing influence. Spatial lag SDM results suggest potential benefits of regional economic cooperation in reducing conflict intensity. Moreover, significant positive spatial autocorrelation underscores the interconnected nature of conflict within SSA, with West Africa exhibiting more pronounced spatial spillover effect. Findings from Spatial Autoregressive (SAR) models confirm the weak association between GDPpc and conflict intensity but emphasize the consistent positive association between corruption and conflict intensity. Additionally, the Spatial Error Model (SEM) reaffirms corruption’s detrimental impact on governance and stability. Additionally, the hypothesis of a significant difference in the effect of armed conflict across different SSA subregions is supported, with Central Africa experiencing the strongest negative impact on economic growth, followed by East and West Africa. The study highlights substantial regional heterogeneity in the economic consequences of armed conflict, emphasizing the need for regionally tailored policy interventions to address conflict-related economic disruptions in SSA.

Using national income and expenditure distribution data from 119 countries, the authors decompose total income inequality between the individuals in the world, by continent and by "region" (countries grouped by income level). They use a Gini decomposition that allows for an exact breakdown (without a residual term) of the overall Gini by recipients. Looking first at income inequality in income between countries is more important than inequality within countries. Africa, Latin America, and Western Europe and North America are quite homogeneous continent, with small differences between countries (so that most of the inequality on these continents is explained by inequality within countries). Next the authors divide the world into three groups: the rich G7 countries (and those with similar income levels), the less developed countries (those with per capita income less than or equal to Brazil's), and the middle-income countries (those with per capita income between Brazil's and Italy's). They find little overlap between such groups - very few people in developing countries have incomes in the range of those in the rich countries.

South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data was reported at 11.110 Intl $/Day in 2014. This records a decrease from the previous number of 11.800 Intl $/Day for 2010. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data is updated yearly, averaging 11.455 Intl $/Day from Dec 2010 (Median) to 2014, with 2 observations. The data reached an all-time high of 11.800 Intl $/Day in 2010 and a record low of 11.110 Intl $/Day in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Poverty. Mean consumption or income per capita (2011 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.; ; 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 choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in PovcalNet. The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

As of February 2025, Morocco had an internet penetration of over 92 percent, making it the country with the highest internet penetration in Africa. Libya ranked second, with 88.5 percent, followed by Seychelles with over 87 percent. On the other hand, The Central African Republic, Chad, and Burundi had the lowest prevalence of internet among their population. Varying but growing levels of internet adoption Although internet usage varies significantly across African countries, the overall number of internet users on the continent jumped to around 646 million from close to 181 million in 2014. Of those, almost a third lived in Nigeria and Egypt only, two of the three most populous countries on the continent. Furthermore, internet users are expected to surge, reaching over 1.1 billion users by 2029. Mobile devices dominate web traffic Most internet adoptions on the continent occurred recently. This is among the reasons mobile phones increasingly play a significant role in connecting African populations. As of early January 2024, around 74 percent of the web traffic in Africa was via mobile phones, over 14 percentage points higher than the world average. Furthermore, almost all African countries have a higher web usage on mobile devices compared to other devices, with rates as high as 92 percent in Sudan. This is partly due to mobile connections being cheaper and not requiring the infrastructure needed for traditional desktop PCs with fixed-line internet connections.

Background: Around 80% of the African population lives in urban areas, and a rapid urbanization is observed in almost all countries. Urban poverty has been linked to several sexual and reproductive health risks, including high levels of unintended pregnancies. We aim to investigate wealth inequalities in demand for family planning satisfied with modern methods (mDFPS) among women living in urban areas from African countries.Methods: We used data from 43 national health surveys carried out since 2010 to assess wealth inequalities in mDFPS. mDFPS and the share of modern contraceptive use were stratified by groups of household wealth. We also assessed the ecological relationship between the proportion of urban population living in informal settlements and both mDFPS and inequalities in coverage.Results: mDFPS among urban women ranged from 27% (95% CI: 23–31%) in Chad to 87% (95% CI: 84–89%) in Eswatini. We found significant inequalities in mDFPS with lower coverage among the poorest women in most countries. In North Africa, inequalities in mDFPS were identified only in Sudan, where coverage ranged between 7% (95% CI: 3–15%) among the poorest and 52% (95% CI: 49–56%) among the wealthiest. The largest gap in the Eastern and Southern African was found in Angola; 6% (95% CI: 3–11%) among the poorest and 46% (95% CI: 41–51%) among the wealthiest. In West and Central Africa, large gaps were found for almost all countries, especially in Central African Republic, where mDFPS was 11% (95% CI: 7–18%) among the poorest and 47% (95% CI: 41–53%) among the wealthiest. Inequalities by type of method were also observed for urban poor, with an overall pattern of lower use of long-acting and permanent methods. Our ecological analyses showed that the higher the proportion of the population living in informal settlements, the lower the mDFPS and the higher the inequalities.Conclusion: Our results rise the need for more focus on the urban-poorer women by public policies and programs. Future interventions developed by national governments and international organizations should consider the interconnection between urbanization, poverty, and reproductive health.

Future impacts of climate change on marine fisheries have the potential to negatively influence a wide range of socio-economic factors, including food security, livelihoods and public health, and even to reshape development trajectories and spark transboundary conflict. Yet there is considerable variability in the vulnerability of countries around the world to these effects. We calculate a vulnerability index of 147 countries by drawing on the most recent data related to the impacts of climate change on marine fisheries. Building on the Intergovernmental Panel on Climate Change framework for vulnerability, we first construct aggregate indices for exposure, sensitivity and adaptive capacity using 12 primary variables. Seven out of the ten most vulnerable countries on the resulting index are Small Island Developing States, and the top quartile of the index includes countries located in Africa (17), Asia (7), North America and the Caribbean (4) and Oceania (8). More than 87% of least developed countries are found within the top half of the vulnerability index, while the bottom half includes all but one of the Organization for Economic Co-operation and Development member states. This is primarily due to the tremendous variation in countries’ adaptive capacity, as no such trends are evident from the exposure or sensitivity indices. A negative correlation exists between vulnerability and per capita carbon emissions, and the clustering of states at different levels of development across the vulnerability index suggests growing barriers to meeting global commitments to reducing inequality, promoting human well-being and ensuring sustainable cities and communities. The index provides a useful tool for prioritizing the allocation of climate finance, as well as activities aimed at capacity building and the transfer of marine technology.

The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government.

Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized.

Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers.

Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.

Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis.