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.

As of January 2024, Johann Rupert and his family are the richest people in South Africa with a net worth of 9.6 billion U.S. dollars. The Rupert family are ranked at 224 globally and are the second richest people in Africa after Nigerian billionaire, Aliko Dangote, reclaimed the title. Rupert's net worth dropped by 2.2 billion 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 9.4 billion U.S. dollars and ranking at 232 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 2.6 billion U.S. dollars who placed 1,202 globally. Patrice Motsepe, the first black African on the Forbes list and founder of African Rainbow Minerals, ranked 1,208 out of the global billionaires list, with a net worth of 2.6 billion U.S. dollars.

Where does the wealth reside in the continent?

The three largest economies in the continent in terms of Gross Domestic Product (GDP), namely Nigeria, Egypt, and South Africa saw the highest concentration of private wealth in the continent, 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 in 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 coefficient of 62.73 percent.

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.

Abstract

The information collected in household surveys, such as this one, is used to describe and understand the living conditions and experiences of South Africans. Often, however, different surveys use different sample areas and interview different households, making it difficult to know whether the living standards or circumstances of particular households have improved. The aim of this survey is to determine whether or not there have been any changes in the socio-economic conditions of those households interviewed in 1993. This information will be used to understand the dynamics of household behaviour over time.

Geographic coverage

The survey covered households in KwaZulu-Natal Province, on the east coast of South Africa

Analysis unit

Units of analysis in the Kwazulu Natal Income Dynamics Study 1993-1998 are households and individuals

Universe

The Kwazulu Natal Income Dynamics Study 1993-1998 covered all household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1993 sample was selected using a two-stage self-weighting design. In the first stage, clusters were chosen with probability proportional to size from census enumerator subdistricts (ESD) or approximate equivalents where an ESD was not available. In the second stage, all households in each chosen cluster were enumerated and a random sample of them selected. (See PSLSD, 1994, for further details.)

In 1993, the KwaZulu-Natal portion of the PSLSD sample was designed to be representative at the provincial level, conditional on the accuracy of the 1991 census and other information used for the sampling frame, and contained households of all races. It was decided not to re-survey the small number of white and coloured households in 1998, however. While there were minor advantages to retaining these groups, the relatively small number of households in each group (112 white households and 53 coloured) would have precluded most comparative ethnic analyses. Moreover, the households in these ethnic groups were entirely located in a small number of clusters (due to the general lack of spatial integration of the population), undermining their representativeness. As a result, the 1998 sample includes only African and Indian households.

Mode of data collection

Face-to-face [f2f]

Abstract

In 2008, the South African Presidency embarked on an intensive effort to track changes in the well-being of South Africans by closely following about 28 000 people - young and old, rich and poor - over a period of years. This was undertaken through initiating the National Income Dynamics Study (NIDS). The NIDS survey is the first national panel study to document the dynamic structure of a sample of household members in South Africa and changes in their incomes, expenditures, assets, access to services, education, health, and other dimensions of well-being. A key feature of the panel study is its ability to follow people as they move out of their original 7 305 households. In doing this, the movement of household members as they leave and/or return to the household or set up their own households will be adequately captured in subsequent waves of this panel study.

The first “baseline” wave of NIDS was conducted by the Southern Africa Labour and Development Research Unit (SALDRU) based at the University of Cape Town's School of Economics. The first wave of fieldwork commenced in February 2008, and data and report released in July 2009. The design of NIDS envisaged data collection every two years.

Elsewhere in the world such surveys have been invaluable in promoting understanding of who is making progress in a society and who is not and, importantly, what factors are driving these dynamics. In addition, panel data is invaluable for the purposes of evaluating and monitoring the efficacy of social policies and programmes. This is because the panel allows researchers and policy analysts to see how households and individuals are impacted when they become eligible for these programmes.

Geographic coverage

The NIDS 2008 covered the whole of South Africa. The lowest level of geographic aggregation for the data is district municipality.

Analysis unit

The units of analysis in the NIDS 2008 survey are individuals and households.

Universe

The target population for NIDS 2008 was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified, two-stage cluster sample design was employed in sampling the households to be included in the base wave. In the first stage, 400 Primary Sampling Units (PSUs) were selected from Stats SA's 2003 Master Sample of 3000 PSUs. This Master Sample was the sample used by Stats SA for its Labour Force Surveys and General Household Surveys between 2004 and 2007 and for the 2005/06 Income and Expenditure Survey. Each of these surveys was conducted on non-overlapping samples drawn within each PSU.

The sample of PSUs for NIDS is a subset of the Master Sample. The explicit strata in the Master Sample are the 53 district councils (DCs). The sample was proportionally allocated to the strata based on the Master Sample DC PSU allocation and 400 PSUs were randomly selected within strata. It should be noted that the sample was not designed to be representative at provincial level, implying that analysis of the results at province level is not recommended.

Sample of dwelling units

At the time that the Master Sample was compiled, 8 non-overlapping samples of dwelling units were systematically drawn within each PSU. Each of these samples is called a "cluster" by Stats SA. These clusters were then allocated to the various household surveys that were conducted by Stats SA between 2004 and 2007. However, two clusters in each PSU were never used by Stats SA and these were allocated to NIDS.

It was sometimes necessary to re-list a PSU when the situation on the ground had drastically changed to an extent that the information recorded on the listing books no longer reflected the situation on the ground. In these cases, the PSU was re-listed and a new sample of dwelling units selected. However, the downside of re-listing a PSU is that the chance of sample overlap with dwelling units that are in other surveys is increased. The extent of this overlap cannot be quantified as the lists are no longer comparable. There is anecdotal evidence that sample overlap might have occurred in some PSUs.

Individual respondent selection

Fieldworkers were instructed to interview all households living at the selected address/dwelling unit. If they found that the dwelling unit was vacant or the dwelling no longer existed they were not permitted to substitute the dwelling unit but recorded this information on the household control sheet.

The household control sheet is a two page form. This form was completed for every dwelling unit that was selected in the study, regardless of whether or not a successful interview was conducted. Where more than one household resided at the selected dwelling unit, a separate household control sheet was completed for every household and they were treated in the data as separate units. In order to qualify as separate households they should not share resources or food. Lodgers and live-in domestic workers were considered separate households.

All resident household members at selected dwelling units were included in the NIDS panel, providing that at least one person in the household agreed to participate in the study. The household roster in the household questionnaire was used to identify potential participants in the study. Firstly, respondents were asked to list all individuals that have lived under this "roof" or within the same compound/homestead at least 15 days during the last 12 months OR who arrived in the last 15 days and this was now their usual residence. In addition the persons listed should share food from a common 'pot' and share resources from a common resource pool. All those listed on the household roster are considered household members.

All resident household members became NIDS sample members. In addition, non-resident members that were "out of scope" at the time of the survey also became NIDS sample members. Out-of-scope household members were those living in insititutions (such as boarding school hostels, halls of residence, prisons or hospitals) which were not part of the sampling frame. These individuals had a zero probability of selection at their usual place of residence and were thus included in the NIDS sample as part of the household that had listed them as non-resident members. These two groups constitute the permanent sample members (PSMs) and should have had an individual questionnaire (adult, child or proxy) completed for them. These individuals are PSMs even if they refused to be interviewed in the base wave.

An initial sample of 9600 dwelling units was drawn with the expectation of realizing 8000 successful interviews. However, during the initial round of fieldwork for Wave 1 we did not achieve the target number of households. Therefore we went back to the field to attempt to overturn refusals in 48 PSUs and to visit 24 new dwelling units in 32 of these areas. Stats SA drew an additional 24 dwelling units from their Master Sample in predominantly White and Asian PSUs in order to improve representation of these population groups in the data.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were administered for the National Income Dynamics Study 2008:

HOUSEHOLD QUESTIONNAIRE: This covered household characteristics, household roster, mortality history, living standards, expenditure, consumption, negative events, positive events, agriculture ADULT QUESTIONNAIRE: This was administered to all people in sampled households who were 15-years old or older on the day of the interview. The Adult Questionnaire collected data on demographics, education, labour market participation, income, health, well-being, numeracy and anthropometric measurements CHILD QUESTIONNAIRE: This asked questions of household members who were 14-years old or younger, and covered education, health, family support, grants and numeracy and anthropometric data PROXY QUESTIONNAIRE: These were completed where possible for adults who were unavailable or unable to answer their own adult questionnaire

Cleaning operations

Initially the intention was that data capture would be done in-house. However, by early March 2008 it became evident that data capture was proceeding too slowly and Citizen Surveys was awarded the tender for the work.All questionnaires were double captured and anomolies reconciled. Regular data dumps enabled the checking of captured data against hard copies of the questionnaires.

Response rate

Response rates in phase 1 of Wave 1 of the NIDS survey were disappointing and phase 2 was embarked upon to realise a more acceptable base wave sample. A detailed analysis of household level and individual level response rates follows. Item non-response rates are not addressed here. Such non-response is flagged in the data and is appropriately discussed in the context of specific analyses in the Discussion Paper series.

Household response rates were calculated using the number of visited dwelling units as the denominator and the number of participating households as the numerator. In the instances where response rates are given by race the predominant race group of the PSU is assigned to all households in that PSU. This is done because, by definition, non participating households were not interviewed and we did not gather information about the race of their members from the questionnaires.

Every effort was made to correctly

Description: Topics covered in the questionnaire are: work and unemployment, respondent characteristics, household characteristics, personal and household income variables. The data set for dissemination contains 2885 cases and 262 variables. Abstract: Under the auspices of the Department and Training (DHET), the Human Sciences Research Council (HSRC) and a consortium of partners are undertaking the Labour Market Intelligence Partnership Research Project (LMIP) in order to address the need for an improved system of labour market analysis and planning in South Africa. At the centre of skills planning is need for quality and reliable data on the South African labour market information. Theme 1 of LMIP entitled "Establishing a foundation for labour market information systems in South Africa" acknowledges the need to improve the quality as well as quantity of the current labour market information for effective skills planning. It is against this background that the current project - Survey of attitudes towards employment and unemployment related issues will be conducted to: Determine attitudes of South Africans towards the labour market. Develop a systemic and methodology sound infrastructure for studying changing work attitudes, values and behaviour patterns of agents in and out the labour market. The broad aim of this study is to determine attitudes of South Africans towards the labour market. This entails: Investigating the nature and distribution of work orientations and work values of South Africans; Exploring the public's attitudes towards the state of unemployment and barriers; To explore the public's assessment of the relevance of post school education and some specific skills in the workplace; To get in depth insights about the perceived effective job search strategies; To investigate the satisfaction levels of those in employment, and subjective evaluation of various aspects of their work. Data for the study will be collected through the South African Social Attitudes Survey. The South African Social Attitudes Survey (SASAS) is the Human Science's survey which has been collecting data on South Africa attitudes, beliefs and behaviour patterns annually since 2003. The Education and Skills Development Research Programme included a module into the SASAS study which looked at five 1) Work values and ethics, 2) perceived barriers to employment, 3) perceived skills and competencies required in the labour market, 4) job search strategies, and 5) subjective evaluation of different aspects of work. Face-to-face interview National Population: Adults (aged 16 and older) SASAS has been designed to yield a representative sample of 3500 adult South African citizens aged 16 and older (with no upper age limit), in households geographically spread across the country's nine provinces. The sampling frame used for the survey was based on the 2011 census and a set of small area layers (SALs). Estimates of the population numbers for various categories of the census variables were obtained per SAL. In this sampling frame special institutions (such as hospitals, military camps, old age homes, schools and university hostels), recreational areas, industrial areas and vacant SALs were excluded prior to the drawing of the sample. Small area layers (SALs) were used as primary sampling units and the estimated number of dwelling units (taken as visiting points) in the SALs as secondary sampling units. In the first sampling stage the primary sampling units (SALs) were drawn with probability proportional to size, using the estimated number of dwelling units in an SAL as measure of size. The dwelling units as secondary sampling units were defined as "separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc." In the second sampling stage a predetermined number of individual dwelling units (or visiting points) were drawn with equal probability in each of the drawn dwelling units. Finally, in the third sampling stage a person was drawn with equal probability from all 16 year and older persons in the drawn dwelling units. Three explicit stratification variables were used, namely province, geographic type and majority population group. As stated earlier, within each stratum, the allocated number of primary sampling units (which could differ between different strata) was drawn using proportional to size probability sampling with the estimated number of dwelling units in the primary sampling units as measure of size. In each of these drawn primary sampling units, seven dwelling units were drawn. This resulted in a sample of 3500 individuals. A list of the 500 drawn SALs were given to geographic information specialists (GIS) and maps were then created for each of the 500 areas, indicating certain navigational beacons such as schools, roads churches etc. Selection of individuals: For each of the SASAS samples interviewers visited each visiting point drawn in the SALs (PSU) and listed all eligible persons for inclusion in the sample, that is all persons currently aged 16 years or older and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

Abstract

The Survey of Activities of Young People was conducted by Statistics South Africa and commissioned by the Department of Labour, primarily to gather information necessary for formulating an effective programme of action to address the issue of harmful work done by children in South Africa. Technical assistance for the survey was provided by the International Labour Organisation (ILO) and a consultant appointed by the Department of Labour. Stats SA also worked with an advisory committee, consisting of representatives from national government departments most directly concerned with child labour (the Departments of Labour,Welfare,Education and Health), non-governmental organisations, and the United Nations Children's Fund (Unicef).

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The sample frame was based on the 1996 Population Census Enumerator Areas (EA) and the number of households counted in 1996 Population Census. The sampled population excluded all prisoners in prison, patients in hospitals, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households. Coverage rules for the survey were that all children of usual residents were to be included even if they were not present. This means that most boarding school pupils were included in their parents’ household. The 16 EA types from the 1996 Population Census were condensed into four area types. The four area types were Formal Urban, Informal Urban, Tribal, and Commercial Farms. A decision was made to drop the Institution type EAs.

The EAs were stratified by province, and within a province by the four area types defined above. The sample size (6110 households) was disproportionately allocated to strata by using the square root method. Within the strata the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). PSUs consisted of ONE or more EAs of size 100 households to ensure sufficient numbers for screening. Statistics SA was advised by child labour experts that there was a likelihood of high rates of child labour in the Urban Informal and Rural Farm areas. The sample allocation to Rural Commercial Farms was therefore increased to a minimum of 20 PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33000 dwelling units.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Biographical information (marital status, education, etc.) - Economic activities in the last week for persons aged 15 years and older - Unemployment and economic inactivity for persons aged 15 years and above - Main work activity in the last week for persons aged 15 years and above - Earnings in the main job for employees, employers and own-account workers aged 15 years and above

From 2010 the income data collected by South Africa's Quarterly Labour Force Survey is no longer provided in the QLFS dataset (except for a brief return in QLFS 2010 Q3 which may be an error). Possibly because the data is unreliable at the level of the quarter, Statistics South Africa now provides the income data from the QLFS in an annualised dataset called Labour Market Dynamics in South Africa (LMDSA). The datasets for LMDSA are available from DataFirst's website.

Description: Topics covered in the questionnaire are: democracy and governance, national identity and pride, intergroup relations, immigrant related attitudes and behaviour, education, moral issues, fatherhood, personal wellbeing index, poverty, taxation, crime and safety, batho pele, voting, respondent characteristics, household characteristics, personal and household income. Of the targeted population of 3500, 2885 responses (82.4%) was realized. The data set for dissemination contains 2885 cases and 484 variables. Abstract: The primary objective of the South African Social Attitudes Survey (SASAS) is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa. In meeting this objective, the HSRC is carefully and consistently monitoring and providing insight into changes in attitudes among various socio-demographic groupings. SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how it's changing political and institutional structures interact over time with changing social attitudes and values. The survey is conducted annually and the 2018 survey is the sixteenth wave in the series. The core module will remain constant for subsequent annual SASAS surveys with the aim of monitoring change and continuity in a variety of socio-economic and socio-political variables. In addition, a number of themes will be accommodated in rotation. The rotating element of the survey consists of two or more topic-specific modules in each round of interviewing and is directed at measuring a range of policy and academic concerns and issues that require more detailed examination at a specific point in time than the multi-topic core module would permit. This dataset focuses specifically on democracy and governance, national identity and pride, intergroup relations, immigrant related attitudes and behaviour, education, moral issues, personal wellbeing index, crime and security, poverty, batho pele, voting, respondent characteristics, household characteristics, personal and household income variables. Face-to-face interview National Population: Adults (aged 16 and older). SASAS has been designed to yield a representative sample of 3500 adult South African citizens aged 16 and older (with no upper age limit), in households geographically spread across the country's nine provinces. The sampling frame used for the survey was based on the 2011 census and a set of small area layers (SALs). Estimates of the population numbers for various categories of the census variables were obtained per SAL. In this sampling frame special institutions (such as hospitals, military camps, old age homes, schools and university hostels), recreational areas, industrial areas and vacant SALs were excluded prior to the drawing of the sample. Small area layers (SALs) were used as primary sampling units and the estimated number of dwelling units (taken as visiting points) in the SALs as secondary sampling units. In the first sampling stage the primary sampling units (SALs) were drawn with probability proportional to size, using the estimated number of dwelling units in an SAL as measure of size. The dwelling units as secondary sampling units were defined as separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc. In the second sampling stage a predetermined number of individual dwelling units (or visiting points) were drawn with equal probability in each of the drawn dwelling units. Finally, in the third sampling stage a person was drawn with equal probability from all 16 year and older persons in the drawn dwelling units. Three explicit stratification variables were used, namely province, geographic type and majority population group. As stated earlier, within each stratum, the allocated number of primary sampling units (which could differ between different strata) was drawn using proportional to size probability sampling with the estimated number of dwelling units in the primary sampling units as measure of size. In each of these drawn primary sampling units, seven dwelling units were drawn. This resulted in a sample of 2885 individuals. A list of the 500 drawn SALs were given to geographic information specialists and maps were then created for each of the 500 areas, indicating certain navigational beacons such as schools, roads churches etc. Selection of individuals: For each of the SASAS samples interviewers visited each visiting point drawn in the SALs (PSU) and listed all eligible persons for inclusion in the sample, that is all persons currently aged 16 years or older and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

Abstract

The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa. It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

All editions of the LFS have been updated (some more than once) since their release. These version changes are detailed in a document available from DataFirst (in the "external documents" section titled "LFS 2000-2008 Collated Version Notes on the South African LFS").

Geographic coverage

National Coverage

Analysis unit

Dwelling units (households) and individuals

Universe

The LFS sample covers the non-institutional population except for workers' hostels. However, persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The LFS is a twice-yearly rotating panel household survey. A rotating panel sample involves visiting the same dwelling units on a number of occasions (in this instance, five at most), and replacing a proportion of these dwelling units each round (in this instance 20%). New dwelling units are added to the sample to replace those that are taken out. This February 2000 dataset represents the pilot study for the LFS. A sample of 10 000 households was drawn in 1 574 enumerator areas (EAs) (that is 10 households in each of the 426 non-urban EAs and 5 households in each of the 1 148 urban EAs). A two-stage sampling procedure was applied and the sample was stratified, clustered and selected to meet the requirements of probability sampling.

The sample was based on the 1996 Population Census enumerator areas and the estimated number of households from the 1996 Population Census. The sampled population excluded all prisoners in prisons, patients in hospitals, people residing in boarding houses and hotels (whether temporary or semi-permanent). The sample was explicitly stratified by province and area type (urban/rural). Within each explicit stratum the EAs were further stratified by simply arranging them in geographical order by District Council, Magisterial District and, within the magisterial district, by average household income (for formal urban areas and hostels). The allocated number of EAs was systematically selected with probability proportional to size in each stratum. The measure of size was the estimated number of households in each EA. A systematic sample of 10 households in non-urban and 5 households in urban areas was then drawn.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collected includes data on households and person data (via the Flap and Section 1 of the questionnaire), worker data on persons 15-65 years (Sections 2, 3, 4 and 5). worker data collected includes labour market data, including employment in both the formal and informal sectors, and data on unemployment. Most questions in the Labour Force Survey questionnaire are pre-coded, i.e. there is a set number of choices from which one or more must be selected. Post-coding was done for open-ended questions.

Abstract

South Africa's first Living Conditions Survey (LCS) was conducted by Statistics South Africa over a period of one year between 13 October 2014 and 25 October 2015. The main aim of this survey is to provide data that will contribute to a better understanding of living conditions and poverty in South Africa for monitoring levels of poverty over time. Data was collected from 27 527 households across the country. The survey used a combination of the diary and recall methods. Households were asked to record their daily acquisitions in diaries provided by Statistics SA for a period of a month. The survey also employed a household questionnaire to collect data on household expenditure, subjective poverty, and income.

Geographic coverage

The survey had national coverage.

Analysis unit

Households and individuals

Universe

The sample for the survey included all domestic households, holiday homes and all households in workers' residences, such as mining hostels and dormitories for workers, but excludes institutions such as hospitals, prisons, old-age homes, student hostels, and dormitories for scholars, boarding houses, hotels, lodges and guesthouses.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Survey 2014-2015 sample was based on the LCS 2008-2009 master sample of 3 080 PSUs. However, there were 40 PSUs with no DU sample, thus the sample of 30 818 DUs was selected from only 3 040 PSUs. Amongst the PSUs with no DU sample, 25 PSUs were non-respondent because 19 PSUs were not captured on the dwelling frame, and 6 PSUs had an insufficient DU count. The remaining 15 PSUs were vacant and therefore out-of-scope. Among the PSUs with a DU sample, 2 974 PSUs were respondent, 50 PSUs were non-respondent and 16 PSUs were out-of-scope. The scope of the Master Sample (MS) is national coverage of all households in South Africa. It was designed to cover all households living in private dwelling units and workers living in workers' quarters in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Living Conditions Survey 2014-2015 used three data collection instruments, namely a household questionnaire, a weekly diary, and the summary questionnaire. The household questionnaire was a booklet of questions administered to respondents during the course of the survey month. The weekly diary was a booklet that was left with the responding household to track all acquisitions made by the household during the survey month. The household (after being trained by the Interviewer) was responsible for recording all their daily acquisitions, as well as information about where they purchased the item and the purpose of the item. A household completed a different diary for each of the four weeks of the survey month. Interviewers then assigned codes for the classification of individual consumption according to purpose (COICOP) to items recorded in the weekly diary, using a code list provided to them.

Data appraisal

Anthropometric data collected during the survey are not included in the dataset.

Abstract

The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa. It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

All editions of the LFS have been updated (some more than once) since their release. These version changes are detailed in a document available from DataFirst (in the "external documents" section titled "LFS 2000-2008 Collated Version Notes on the South African LFS").

Analysis unit

Households (dwellings) and individuals

Universe

The LFS sample covers the non-institutional population except for workers' hostels. However, persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The LFS is a twice-yearly rotating panel household survey. A rotating panel sample involves visiting the same dwelling units on a number of occasions (in this instance, five at most), and replacing a proportion of these dwelling units each round. New dwelling units are added to the sample to replace those that are taken out. The pilot round of LFS fieldwork took place in February 2000, based on a probability sample of 10 000 dwelling units. This survey took place six months later, using a larger probability sample of 30,000 dwelling units. Among the 10,000 households visited in February, approximately 40% were re-visited in September 2000. The fieldworkers had some difficulty in identifying certain dwelling units in the sample, particularly in those areas where there are no addresses.

The Master Sample is based on the 1996 Population Census of enumeration areas (EA) and the estimated number of dwelling units from the 1996 Population Census. All 3000 PSUs included in the Master Sample were used in the Labour Force Survey. A PSU is either one EA or several EAs when the number of dwelling units in the base or originally selected EA was found to have less than 100 dwelling units. Each EA had to have approximately 150 dwelling units but it was discovered that many contained less. Thus, in some cases, it has been found necessary to add EAs to the original (census) EA to ensure that the minimum requirement of 100 dwellings, in the first stage of forming the PSUs, was met. The size of the PSUs in the Master Sample varied from 100 to 2445 dwelling units. Special dwellings such as prisons, hospitals, boarding houses, hotels, guest houses (whether catering or self-catering), schools and churches were excluded from the sample.

Explicit stratification of the PSUs was done by province and area type (urban/rural). Within each explicit stratum, the PSUs were implicitly stratified by District Council, Magisterial District and, within the magisterial district, by average household income (for formal urban areas and hostels) or EA. The allocated number of EAs was systematically selected with "probability proportional to size" in each stratum. Once the PSUs included in the sample were known, their boundaries had to be identified on the ground. After boundary identification, the next stage was to list accurately all the dwelling units in the PSUs.

The second stage of the sample selection was to draw from the dwelling units listing whereby a systematic sample of 10 dwelling units was drawn from each PSU. As a result, approximately 30,000 households (units) were interviewed. However, if there was growth of more than 20% in a PSU, then the sample size was increased systematically according to the proportion of growth in the PSU.

The first pilot round of LFS fieldwork took place in February 2000, based on a probability sample of 10 000 dwelling units. The sample was increased to 30 000 dwelling units in September 2000. Both of these surveys were published as discussion documents. The third round took place in February 2001, using the same 30 000 dwelling units. The fourth round of the LFS, which took place in September 2001 drew a new sample of 30 000 dwelling units were visited. Rotation of 20% of this commenced with the fifth round being conducted (February/March 2002)

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.

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.

Abstract

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.

Completed and non-response interviews in the NIDS Data: The NIDS datasets contain both completed and non-response interviews (e.g. Refusals). It is recommended that researchers limit their research to completed interviews to avoid item non-response from non-response interviews. The completed interviews can be identified by making use of the wx'_y'_outcome variables, where x' represents the wave andy' represents the relevant data file/outcome type indicator. These outcome variables can be found in each of the following data files, Adult, Child, Proxy, HHQuestionnaire and Link File. The only exception to this is Wave 1 where no outcome variable exists. This is because at a household level, all of the interviews are completed. However this does not apply at an individual level where non-response interviews can be identified by making use of the "Reason for refusal" variables, namely w1_a_refexpl or w1_c_refexpl in the Adult and Child data files respectively.

Geographic coverage

The NIDS data is nationally representative. The survey began in 2008 with a nationally representative sample of over 28,000 individuals in 7,300 households across the country. The survey is repeated every two years with these same household members, who are called Continuing Sample Members (CSMs). The survey is designed to follow people who are CSMs, wherever they may be in SA at the time of interview. The NIDS data is therefore, by design, not representative provincially or at a lower level of geography (e.g. District Council).

Analysis unit

Households and individuals

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

As in Wave 1 four types of questionnaires were administered in Wave 2:

Household questionnaire: One household questionnaire was completed per household by the oldest woman in the household or another person knowledgeable about household affairs and particularly household spending. Household questionnaires took approximately 45 minutes in non-agricultural households and 70 minutes in agricultural households to complete. Individual Adult questionnaire: The Adult questionnaire was applied to all present Continuing Sample Members and other household member's resident in their households that are aged 15 years or over. This questionnaire took an average of 45 minutes per adult to complete. Individual Proxy Questionnaire: Should an individual qualifying for an Adult questionnaire not be present then a Proxy Questionnaire (a much reduced Adult Questionnaire using third party referencing in the questioning) was taken on their behalf with a present resident adult. On average a Proxy questionnaire took 20 minutes. Proxy Questionnaires were also asked for CSMs who had moved out of scope (out of South Africa or to a non-accessible institution such as prison), except if the whole household moved out of scope, and could therefore not be tracked or interviewed directly. Child questionnaire: This questionnaire collected information about all Continuing Sample Members and residents in their household younger than 15. Information about the child was gathered from the care-giver of the child. The questionnaire focused on the child's educational history, education, anthropometrics and access to grants. This questionnaire took an average of 20 minutes per child to complete.

Phase Two of Wave 2: In June 2011 NIDS commissioned a Phase Two of Wave 2 as a Non-Response Follow-Up from Phase 1 of Wave 2. Household included in this subsample where those that refused and those that could not be located or tracked in Phase 1. Out of a total of 1064 households attempted, an additional 389 households were successfully interviewed in Phase Two.

Questionnaire Differences between W2 Phase 1 & W2 Phase2 There are two important methodological differences between Phase 1 and Phase 2: 1. Not all sections of the original Wave 2 questionnaires were asked. This reduced respondent burden and the time required for fieldworker training. Questions NOT asked in Phase 2 are indicated with the non-response code “-2”. Core modules such as household composition and income were still asked. Consult the Wave 2 Phase 2 questionnaires for more details of these differences. 2. Movers out of Phase 2 dwelling units were not tracked further. Address information was collected for this sub-sample and they will be tracked as part of the Wave 3 fieldwork exercise. These individuals are classified as “Not tracked” in the Wave 2 dataset.

As of 2024, an individual living in South Africa with less than 1,109 South African rand (roughly 62.14 U.S. dollars) per month was considered poor. Furthermore, individuals having 796 South African rand (approximately 44.60 U.S. dollars) a month available for food were living below the poverty line according to South African national standards. Absolute poverty National poverty lines are affected by changes in the patterns of household consumers and fluctuations in prices of services and goods. They are calculated based on the consumer price indices (CPI) of both food and non-food items separately. The national poverty line is not the only applicable threshold. For instance,13.2 million people in South Africa were living under 2.15 U.S. dollars, which is the international absolute poverty threshold defined by the World Bank. Most unequal in the globe A prominent aspect of South Africa’s poverty is related to extreme income inequality. The country has the highest income Gini index globally at 63 percent as of 2023. One of the crucial obstacles to combating poverty and inequality in the country is linked to job availability. In fact, youth unemployment was as high as 49.14 percent in 2023.

Abstract

The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, health and social development, housing, household access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey 2014 had national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data

Sampling procedure

The sample design for the GHS 2014 was based on a master sample (MS) that was originally designed for the Quarterly Labour Force Survey (QLFS) and was used for the first time for the GHS in 2008. This master sample is shared by the QLFS, GHS, Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Survey (IES).

The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of primary sampling units (PSUs) from within strata, and systematic sampling of dwelling units (DUs) from the sampled PSUs. A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire

Data appraisal

GHS 2009-2015: The variable on land size in the General Household Survey questionnaire for 2009-2015 should be used with caution. The data comes from questions on the households' agricultural activities in Section 8 of the GHS questionnaire: Household Livelihoods: Agricultural Activities. Question 8.8b asks: “Approximately how big is the land that the household use for production? Estimate total area if more than one piece.” One of the response category is worded as: 1 = Less than 500m2 (approximately one soccer field) However, a soccer field is 5000 m2, not 500, therefore response category 1 is incorrect. The correct category option should be 5000 sqm. This response option is correct for GHS 2002-2008 and was flagged and corrected by Statistics SA in the GHS 2016.

GHS 2014-2018 and from GHS 2021 onwards: The person data file in has two Employment Status variables in the GHS. According to the Statistics SA documentation, both these variables are derived from the variables LAB1 and LAB12. But the value “Unspecified” in the variable employ_Status1 becomes “Not Economically Active” in the variable employ_Status2. There is no clear explanation for this change. Statistics SA has been contacted for further information on these variables.

South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 63.000 % in 2014. This records a decrease from the previous number of 63.400 % for 2010. South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 63.000 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 64.800 % in 2005 and a record low of 57.800 % in 2000. South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate 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. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

By 2030, the middle-class population in Asia-Pacific is expected to increase from **** billion people in 2015 to **** billion people. In comparison, the middle-class population of sub-Saharan Africa is expected to increase from *** million in 2015 to *** million in 2030. Worldwide wealth While the middle-class has been on the rise, there is still a huge disparity in global wealth and income. The United States had the highest number of individuals belonging to the top one percent of wealth holders, and the value of global wealth is only expected to increase over the coming years. Around ** percent of the world’s population had assets valued at less than 10,000 U.S. dollars, while less than *** percent had assets of more than one million U.S. dollars. Asia had the highest percentage of investable assets in the world in 2018, whereas Oceania had the highest percentage of non-investable assets. The middle-class The middle class is the group of people whose income falls in the middle of the scale. China accounted for over half of the global population for middle-class wealth in 2017. In the United States, the debate about the middle class “disappearing” has been a popular topic due to the increase in wealth among the top billionaires in the nation. Due to this, there have been arguments to increase taxes on the rich to help support the middle class.