In 2021, Southern Africa's richest ** percent held around ** percent of the total wealth. Furthermore, the richest one percent in the region held over ** percent. The other African regions had a slightly smaller share of wealth with the wealthiest people. For instance, in West Africa, the richest ** percent held close to ** percent of the wealth, while the richest one percent held ** percent. On the other hand. The poorest ** percent in all the regions held lower than ***** percent of the wealth.

South Africa Net Wealth data was reported at 19,827.000 ZAR bn in 2024. This records an increase from the previous number of 17,969.000 ZAR bn for 2023. South Africa Net Wealth data is updated yearly, averaging 1,987.000 ZAR bn from Dec 1975 (Median) to 2024, with 50 observations. The data reached an all-time high of 19,827.000 ZAR bn in 2024 and a record low of 97.000 ZAR bn in 1975. South Africa Net Wealth data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.H001: Household Balance Sheet.

South Africa Net Wealth Including Durable Consumer Goods data was reported at 21,126.000 ZAR bn in 2024. This records an increase from the previous number of 19,194.000 ZAR bn for 2023. South Africa Net Wealth Including Durable Consumer Goods data is updated yearly, averaging 2,175.000 ZAR bn from Dec 1975 (Median) to 2024, with 50 observations. The data reached an all-time high of 21,126.000 ZAR bn in 2024 and a record low of 104.000 ZAR bn in 1975. South Africa Net Wealth Including Durable Consumer Goods data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.H001: Household Balance Sheet.

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.

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]

South Africa Non Financial Corporations: Less Current Taxes on Income and Wealth data was reported at 195,590.000 ZAR mn in 2017. This records an increase from the previous number of 175,037.000 ZAR mn for 2016. South Africa Non Financial Corporations: Less Current Taxes on Income and Wealth data is updated yearly, averaging 97,621.000 ZAR mn from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 195,590.000 ZAR mn in 2017 and a record low of 9,304.000 ZAR mn in 1995. South Africa Non Financial Corporations: Less Current Taxes on Income and Wealth data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.A042: SNA 2008: Production and Distribution Account: Non Financial Corporations .

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In 2024, most respondents in a survey conducted in South Africa cited that the best way for the government to create wealth for people is to make it easier for them to start small businesses, with a share of ** percent. Reducing taxes and increasing social grants followed, with a share of ** percent and ** percent, respectively.

South Africa was home to the highest number of millionaires in Africa as of 2023. The country had ****** high net worth individuals (HNWIs), corresponding to roughly ********* of the total number of millionaires on the continent. Second, in rank, Egypt counted ****** HNWIs. According to the source, approximately ******* HNWIs lived in Africa, each with *** million U.S. dollars or more net assets, excluding government funds. The wealth value refers to assets such as cash, properties, and business interests held by individuals living in a country with fewer liabilities. The rich in Africa Compared to 2020, the number of African millionaires increased by nearly **** percent. This means that ****** people joined the group of individuals with minimum net assets of *** million U.S. dollars. The number of centi- and multimillionaires has increased as well. In 2022, the Nigerian Aliko Dangote held the title of the wealthiest person in Africa. Founder and chairman of Dangote Cement, the largest cement producer in the whole African continent, the billionaire also owns salt and sugar manufacturing companies. His net worth is estimated at nearly ** billion U.S. dollars. Trillions of U.S. dollars in riches Total private wealth in Africa amounted to *** trillion U.S. dollars in 2021, a slight increase from 2020. That year, the coronavirus (COVID-19) pandemic had led to job losses, drops in salaries, and the closure of many local businesses. Compared to other African countries, South Africa concentrated the largest private wealth. Egypt, Nigeria, Morocco, and Kenya completed the leading wealth markets. The five nations accounted for over ** percent of Africa’s total wealth in 2021.

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were:
Wealth creation in terms of income and expenditure dynamics and asset endowments;
Demographic dynamics as these relate to household composition and migration;
Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services

Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample.
Wave 2 of NIDS re-interviewed respondents interviewed in Wave 1, gathering information on developments in their lives since they were interviewed in 2008.
Wave 3 of the survey took place between April and December 2012 and re-interviewed respondents from Waves 1 and 2.

South Africa Households and NPISH: Less Current Taxes on Income and Wealth data was reported at 465,800.000 ZAR mn in 2017. This records an increase from the previous number of 429,215.000 ZAR mn for 2016. South Africa Households and NPISH: Less Current Taxes on Income and Wealth data is updated yearly, averaging 3,997.000 ZAR mn from Dec 1946 to 2017, with 72 observations. The data reached an all-time high of 465,800.000 ZAR mn in 2017 and a record low of 60.000 ZAR mn in 1946. South Africa Households and NPISH: Less Current Taxes on Income and Wealth data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.A043: SNA 2008: Production and Distribution Account: Households and NPISH .

Objectives: The present study examined the prevalence and patterns of non-communicable disease multimorbidity by wealth quintile among adults in South Africa.Methods: The South African National Income Dynamics Study Wave 5 was conducted in 2017 to examine the livelihoods of individuals and households. We analysed data in people aged 15 years and older (N = 27,042), including self-reported diagnosis of diabetes, stroke, heart disease and anthropometric measurements. Logistic regression and latent class analysis were used to analyse factors associated with multimorbidity and common disease patterns.Results: Multimorbidity was present in 2.7% of participants. Multimorbidity was associated with increasing age, belonging to the wealthiest quintile group, increasing body mass index and being a current smoker. Having secondary education was protective against multimorbidity. Three disease classes of multimorbidity were identified: Diabetes and Hypertension; Heart Disease and Hypertension; and Stroke and Hypertension.Conclusion: Urgent reforms are required to improve health systems responsiveness to mitigate inequity in multimorbidity patterns in the adult population of South Africa as a result of income inequality.

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.

South Africa ZA: Coal Rents: % of GDP data was reported at 1.604 % in 2016. This records an increase from the previous number of 1.331 % for 2015. South Africa ZA: Coal Rents: % of GDP data is updated yearly, averaging 2.493 % from Dec 1971 (Median) to 2016, with 46 observations. The data reached an all-time high of 7.850 % in 2008 and a record low of 1.000 % in 1999. South Africa ZA: Coal Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

ZA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 0.165 % in 2010. This records an increase from the previous number of 0.164 % for 2000. ZA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 0.164 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.165 % in 2010 and a record low of 0.159 % in 1990. ZA: Population Living in Areas Where Elevation is Below 5 Meters: % of Total 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: Land Use, Protected Areas and National Wealth. Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

South Africa ZA: Total Natural Resources Rents: % of GDP data was reported at 4.698 % in 2016. This records an increase from the previous number of 4.319 % for 2015. South Africa ZA: Total Natural Resources Rents: % of GDP data is updated yearly, averaging 5.173 % from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 14.575 % in 1980 and a record low of 1.916 % in 1970. South Africa ZA: Total Natural Resources Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted 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

Units of analysis in the Financial Diaries Study 2003-2004 include households and individuals

Kind of data

Sample survey data [ssd]

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]

South Africa ZA: Forest Rents: % of GDP data was reported at 0.724 % in 2016. This records a decrease from the previous number of 0.743 % for 2015. South Africa ZA: Forest Rents: % of GDP data is updated yearly, averaging 0.841 % from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 1.221 % in 2002 and a record low of 0.487 % in 2011. South Africa ZA: Forest Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Forest rents are roundwood harvest times the product of regional prices and a regional rental rate.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted Average;

As of January 2024, Johann Rupert and his family are the richest people in South Africa with a net worth of 11.1 billion U.S. dollars. The Rupert family are ranked at 216 globally and are the second richest people in Africa after Nigerian billionaire, Aliko Dangote, reclaimed the title. Rupert's net worth dropped by seven million U.S. dollars from 2023, mainly due to a decline in the market value of luxury goods company Richemont, where he owns an estimated 9.14 percent stake. Nicky Oppenheimer and his family placed as the second richest in South Africa, with a net worth of 9.5 billion U.S. dollars and ranking at 276 worldwide. Their net worth source was mostly founded via the diamond market. They were followed by Koos Bekker, the chairman of media group Naspers, with 3.1 billion U.S. dollars who placed 1,133 globally. Patrice Motsepe, the first black African on the Forbes list and founder of African Rainbow Minerals, ranked 1,140 out of the global billionaires list, with a net worth of three billion U.S. dollars. Where does the wealth reside in the continent? The three largest economies on the continent in terms of Gross Domestic Product (GDP), namely Nigeria, Egypt, and South Africa saw the highest concentration of private wealth, with South Africa ranking first when it came to private wealth. In fact, out of Africa’s 20 wealthiest families and individuals, 14 of them were from these economies. Since 2010, the number of high net worth individuals on the continent fluctuated peaking at 148 individuals in 2017 and reaching its lowest in 2020 at 125. High net worth individuals are people whose net assets exceed one million U.S. dollars. On the other hand, South Africa suffered from severe income inequality ranking as the most unequal country in the world with a Gini index of 0.63 points.

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments; Access to cash transfers and social services Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample. In 2010/2011 Wave 2 of NIDS re-interviewed these people, gathering information on developments in their lives since they were interviewed first in 2008. As such, the comparison of Wave 1 and Wave 2 information provides a detailed picture of how South Africans have fared over two years of very difficult socio-economic circumstances.