Johannesburg was the wealthiest city in Africa as of 2021. South Africa's biggest city held *** billion U.S. dollars in private wealth, while Cape Town followed with *** billion U.S. dollars. The country led the ranking of wealthiest nations in Africa. The wealth value referred to assets such as cash, properties, and business interests held by individuals living in each country, less liabilities. Moreover, government funds were excluded.

This statistic shows the top 5 African cities in 2014 by number of residing billionaires. In 2014, ** billionaires were living in Lagos, Nigeria.

The price per square meter in a luxury apartment in Cape Town, South Africa, reached ***** U.S. dollars in 2018. It was double of the price measured in Umhlanga, also a city in South Africa, and second in the ranking. The index tracked the square meter price in selected prime apartments, measuring from *** to *** square meters, mainly in exclusive living complexes.

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.

As of January 2023, Aliko Dangote was the richest man in Africa. He had a net worth of around 13.5 billion U.S. dollars and ranked 128th worldwide. From Nigeria, he is the founder and chairman of the Dangote Group, a large conglomerate operating in several sectors including cement and sugar. The South African Johann Rupert and family followed as the second-richest people in Africa, with a net worth of 10.7 billion U.S. dollars.

Dangote Group continues to expand

Founded in 1981, the Dangote Group (Dangote Industries Limited) is among the largest conglomerates in Africa. Its main subsidiary, Dangote Cement Plc, is the main cement manufacturer on the African continent. The business went public in 2010 and is the largest company listed on the Nigerian Stock Exchange. In addition to the cement industry, the Group also manufactures and processes food products, such as sugar, flour, and salt. With Nigeria being the leading African country for oil production, Dangote expanded his business into the oil industry in recent years. For this purpose, the Group built Africa’s biggest oil refinery near Lagos, Nigeria.

Africa’s wealthiest countries

Wealth in Africa is concentrated in a few countries and, within those, in a few families. Counting the highest numbers of billionaires, South Africa, Egypt, and Nigeria are the wealthiest nations, having also the largest gross domestic products (GDPs) in Africa. These countries count the highest number of high-net-worth individuals (HNWIs), which amounts to over 39,000 in South Africa. Not surprisingly, Johannesburg and Cape Town have the highest concentration of private wealth in Africa. Moreover, South Africa has the highest wealth per capita after Mauritius.

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.

The average for 2024 based on 52 countries was 6829 U.S. dollars. The highest value was in the Seychelles: 29242 U.S. dollars and the lowest value was in Burundi: 836 U.S. dollars. The indicator is available from 1990 to 2024. Below is a chart for all countries where data are available.

Information on the top 200 wealthiest people in South Africa.

South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ data was reported at 73.578 % in 2017. This records a decrease from the previous number of 79.605 % for 2014. South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ data is updated yearly, averaging 73.578 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 79.605 % in 2014 and a record low of 63.004 % in 2011. South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ 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: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

South Africa ZA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population data was reported at 22.346 % in 2010. This records an increase from the previous number of 13.088 % for 2005. South Africa ZA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population data is updated yearly, averaging 17.717 % from Dec 2005 (Median) to 2010, with 2 observations. The data reached an all-time high of 22.346 % in 2010 and a record low of 13.088 % in 2005. South Africa ZA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population 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: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Abstract

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need. The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Geographic coverage

Langa in Cape Town, Diepsloot in Johannesburg and Lugangeni, a rural village in the Eastern Cape.

Analysis unit

Households and individuals

Universe

The survey covered households in the three geographic areas.

Kind of data

Sample survey data

Sampling procedure

To create the sampling frame for the Financial Diaries, the researchers echoed the method used in the Rutherford (2002) and Ruthven (2002), a participatory wealth ranking (PWR). Within South Africa, the participatory wealth ranking method is used by the Small Enterprise Foundation (SEF), a prominent NGO microlender based in the rural Limpopo Province. Simanowitz (1999) compared the PWR method to the Visual Indicator of Poverty (VIP) and found that the VIP test was seen to be at best 70% consistent with the PWR tests. At times one third of the list of households that were defined as the poorest by the VIP test was actually some of the richest according to the PWR. The PWR method was also implicitly assessed in van der Ruit, May and Roberts (2001) by comparing it to the Principle Components Analysis (PCA) used by CGAP as a means to assess client poverty. They found that three quarters of those defined as poor by the PCA were also defined as poor by the PWR. We closely followed the SEF manual to conduct our wealth rankings, and consulted with SEF on adapting the method to urban areas.

The first step is to consult with community leaders and ask how they would divide their community. Within each type of areas, representative neighbourhoods of about 100 households each were randomly chosen. Townships in South Africa are organised by street - with each street or zone having its own street committee. The street committees are meant to know everyone on their street and to serve as stewards of all activity within the street. Each street committee in each area was invited to a central meeting and asked to map their area and give a roster of household names. Following the mapping, each area was visited and the maps and rosters were checked by going door to door with the street committee.

Two references groups were then selected from the street committee and senior members of the community with between four and eight people in each reference group. Each reference group was first asked to indicate how they define a poor household versus those that are well off. This discussion had a dual purpose. First, it relayed information about what each community believes is rich or poor. Second, it started the reference group thinking about which households belong under which heading.

Following this discussion, each reference group then ranked each household in the neighbourhood according to their perceived wealth. The SEF methodology of wealth ranking is de-normalised in that reference groups are invited to put households into as many different wealth piles as they feel in appropriate. Only households that are known by both reference groups were kept in the sample.

The SEF guidelines were used to assign a score to each household in a particular pile. The scores were created by dividing 100 by the number of piles multiplied by the level of the pile. This means that if the poorest pile was number 1, then every household in the pile was assigned a score of 100, representing 100% poverty. If the wealthiest pile was pile number 6, then every household in that pile received a score of 16.7 and every household in pile 5 received a score of 33.3. An average score for both reference groups was taken for the distribution.

One way of assessing how good the results are is to analyse how consistent the rankings were between the two reference groups. According to the SEF methodology, a result is consistent if the scores between the two reference groups have no more than a 25 points difference. A result is inconsistent if the difference between the scores is between 26 and 50 points while a result is unreliable is the difference between the scores is above 50 points. SEF uses both consistent and inconsistent rankings, as long as they use the average across two reference groups - this would mean that 91% of the sample could be used. However, because only used two reference groups were used, only the consistent household for the final sample selection was considered.

To test this further,the number of times that the reference groups put a household in the exact same category was counted. The extent of agreement at either end of the wealth spectrum between the two reference groups was also assessed. This result would be unbiased by how many categories the reference groups put households into.

Following the example used in India and Bangladesh, the sample was divided into three different wealth categories depending on the household's overall score. Making a distinction between three different categories of wealth allowed the following of a similar ranking of wealth to Bangladesh and India, but also it kept the sample from being over-stratified. A sample of 60 households each was then drawn randomly from each area. To draw the sample based on a proportion representation of each wealth ranking within the population would likely leave the sample lacking in wealthier households of some rankings to draw conclusions. Therefore the researchers drew equally from each ranking.

Mode of data collection

Face-to-face [f2f]

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.

South Africa ZA: Coverage: Social Insurance Programs: Richest Quintile: % of Population data was reported at 8.971 % in 2010. This records an increase from the previous number of 6.766 % for 2005. South Africa ZA: Coverage: Social Insurance Programs: Richest Quintile: % of Population data is updated yearly, averaging 7.868 % from Dec 2005 (Median) to 2010, with 2 observations. The data reached an all-time high of 8.971 % in 2010 and a record low of 6.766 % in 2005. South Africa ZA: Coverage: Social Insurance Programs: Richest Quintile: % of Population 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: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

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

Multilevel logistic regression models for individual and contextual level predictors of intimate partner violence in South Africa.

Individual & household-level characteristics of respondents.

Abstract

The Coronavirus Rapid Mobile Survey of Maternal and Child Health (CRAM-MATCH) was a rapid SMS (Short Message Service) survey conducted in South Africa conducted among pregnant women and mothers registered with the MomConnect mhealth platform in South Africa. This national survey was conducted in June (n=3140) with a follow up in July (n=2287). The survey collected data from pregnant women and new mothers in South Africa on how the Coronavirus pandemic has affected their health including their access to health care.

Geographic coverage

The survey data is nationally representative

Analysis unit

Individuals

Universe

The survey collected data from pregnant women and new mothers in South Africa.

Kind of data

Sample survey data

Sampling procedure

The sample was drawn from the Momconnect mhealth platform created by the South African National Department of Health (NDOH) in 2014. MomConnect is a mobile health (mHealth) solution created to improve and promote maternal health services in South Africa by providing pregnant mothers with free messaging facility and a helpdesk. The mobile health application also created a national pregnancy registry which has excellent coverage of pregnant women and new mothers. By 2017 more than half of the women attending public sector antenatal care services in South Africa were registered on the Momconnect platform. By 2019 there were over 2 million registered MomConnect users.

A self-weighted sample of 15 000 pregnant women and mothers with children under 12 months was drawn from the database of MomConnect users. The sample was stratified based on province, gestational age or age of their baby and their type of phone. The 15 000 women all received an invitation to join the SMS survey on the afternoon of 24 June 2020. They could respond by SMS with "JOIN" to participate in the survey, by SMSing "STOP" to not participate or to reply with "MORE" if they needed more information. Those who participated in the survey received R10 in airtime. The wave 1 survey was completed on June 30, 2020. The wave 2 survey invitation was sent on the 2nd of July 2020 and the survey ended on the 5th of July 2020.

Poverty Quintiles Two sets of poverty quintiles were created for respondents by constructing poverty quintiles for primary care public health facilities. The first poverty quintile measures the wealth quintile of the small area place where the facility that the respondent last visited is located. The second poverty quintile measures the average wealth quintile of the catchment area that the facility covers. Because of the focus on access to primary care and because the Momconnect moms' registrations are at their local primary care facility, only data related to public sector primary care facilities was extracted from the government database of facilities (clinics, community health centres and community day centres).

The richest 15% of areas was also excluded since these individuals are unlikely to make use of public facilities. This implies that the 'wealthiest' quintile only represents the wealthiest of the 85% poorest South Africans. Each small area place in Census was then linked to their closest public primary care facility, using the GIS codes in both the Census and the national facility database to create a catchment area for each facility.Poverty quintiles were created by deriving a measure of living standards and wealth measures via Principal Component Analysis (PCA), using data on employment status, education level, earnings, household size, and cell phone and car ownership of the residents of the area collected during the 2011 census. PCA was used to calculate wealth scores and these were aggregated over the entire catchment area, weighted by the population size of each Small Area place in the Census 2011. The sample of respondents was matched to these poverty quintiles via the Momconnect facility identifier, which captures the facility where the mother was registered.

Mode of data collection

Mobile Phone [mp]

Research instrument

Two questionnaires were used, one for the Wave 1 Survey and another for the Wave 2 Survey.

Response rate

Assuming a response rate of 20%, from the targeted sample of 15 000 women, the project aimed to achieve a survey sample of 3000 and realised a sample of 3140 for wave 1 and thus had an effective response rate of 21%. Of the 3140 individuals who responded to wave 1, 2287 also responded in wave 2. The attrition rate between wave 1 and wave 2 was thus about 27%.

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.

Human Immunodeficiency Virus (HIV) is one of the critical global health issues, posing severe risks due to its ability to weaken the immune system progressively. Without a cure or effective vaccine, HIV remains a serious health threat in developing countries, especially in South Asia, sub-Saharan Africa, and countries such as India. This study explores the socio-economic and demographic determinants of comprehensive knowledge of HIV among Indian men aged 15–54 years. The study used descriptive statistics and binary logistic regression models to examine the predictors of comprehensive knowledge of HIV among men using the latest round of the National Family Health Survey data, 2019−21 (NFHS-5). Results indicate that comprehensive knowledge of HIV was more prevalent among non-adolescents and was positively associated with being unmarried, educated, wealthier, and residing in urban areas. Logistic regression models revealed that men with higher education were nearly three times more likely to have comprehensive knowledge of HIV than those without formal education. Furthermore, men with full mass media exposure, residing in the Western and North-Eastern regions of India, working in the service sector, and belonging to the richest wealth quintile were significantly more likely to possess comprehensive knowledge of HIV. These findings highlight the importance of targeted interventions focusing on education, economic empowerment, and media outreach to address disparities in HIV awareness among men across different socio-economic and demographic backgrounds in India.