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

In 2022, South African households had an average disposable income of over ****** South African rand (approximately ***** U.S. dollars). This was slightly higher than the previous year where the average disposable income was ****** South African rand (around ***** U.S. dollars). Within the observed period, the disposable income of households in the country was highest in 2018 at ****** South African rand (about ***** U.S. dollars), while it was lowest in 2004.

Key information about South Africa Monthly Earnings

• South Africa Monthly Earnings stood at 1,589 USD in May 2025, compared with the previous figure of 1,533 USD in Feb 2025
• South Africa Monthly Earnings data is updated quarterly, available from Nov 2004 to May 2025, with an average number of 1,430 USD
• The data reached the an all-time high of 1,877 USD in Aug 2011 and a record low of 968 USD in Feb 2009

*This indicator will be no longer available from March 2026, 4th onwards. CEIC has a plan to remove the non-standard frequency indicator after the standard frequency indicator has been added and be available on this table. CEIC converts Monthly Earnings into USD. Statistics South Africa provides Nominal Average Monthly Earnings in local currency based on 2016 Business Sampling Frame. Federal Reserve Board average market exchange rate is used for currency conversions. Monthly Earnings exclude Agriculture sector. Monthly Earnings prior to Q2 2015 are based on 2013 Business Sampling Frame and prior to Q2 2013 are based on 2009 Business Sampling Frame. Monthly Earnings are in quarterly frequency, ending in February, May, August, November of each year.


Further information about South Africa Monthly Earnings

• In the latest reports, South Africa Population reached 63 million people in Jun 2025
• Unemployment Rate of South Africa dropped to 32 % in Sep 2025
• The country's Labour Force Participation Rate dropped to 60 % in Sep 2025

The average monthly salary for South Africans who were employed in the formal non-agricultural sector was just over 26,800 South African rands (comparable to roughly 1,500 U.S. dollars) in November 2023, which represented a yearly increase of tw0 percent. During the period under review, the overall growth trend was positive, with the earnings increasing by 24.4 percent from 21,500 South African rands (approximately 1,180 U.S. dollars) in November 2018.    Minimum wage and highest-paid professions    Starting in March 2023, the minimum hourly wage in the country increased to 25.42 South African rands (comparable to 1.40 U.S. dollars), which represented an increase of 9.6 percent from 23.19 South African rands (1.27 U.S. dollars) per hour in the preceding year. On the other hand, professionals in executive and change management positions were paid the highest salaries in South Africa, with an average of 74,000 U.S. dollars yearly. Individuals with jobs in retail, trade, and craft followed, receiving an average of 66,000 U.S. dollars per annum.       Highest unemployment among Black South Africans In 2022, the unemployment rate in South Africa was nearly 30 percent following an increasing trend since 2008. The rate was highest among Black South Africans reaching as high as 36.8 percent in the second quarter of 2023. Moreover, Colored South Africans followed with around 22 percent, while white South Africans had a much lower unemployment rate of over 7 percent.

Abstract

The Income and Expenditure Survey is conducted every five years in South Africa.The main purpose of the survey is to determine the average expenditure patterns of households in different areas of the country. This survey forms the basis for the determination of the “basket” of consumer goods and services used for the calculation of the Consumer Price Index. The IES is based on the sample for the rotating panel of the twice yearly Labour force Survey (LFS). The IES 2000 was conducted in October 2000.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the survey are households and individuals

Universe

The survey covered all household members

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 23.500 % in 2014. This stayed constant from the previous number of 23.500 % for 2010. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 23.500 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 25.500 % in 2000 and a record low of 20.300 % in 2005. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % 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: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Since 1970, net national incomes per capita have grown in each region of the world. North America has experienced the largest increase, growing from nearly 4,500 U.S. dollars per capita in 1970 to $57,300 per capita in 2021. Europe and Central Asia follow behind North America, growing from 1,200 dollars per capita in 1970 to 22,000 in 2021. Other regions such as Sub-Saharan Africa, the Middle East and North Africa, Latin America and the Caribbean, and South Asia have not grown as high, but their growth is still significant, with net national incomes per capita in 2021 growing to between 10 and 20 times their 1970 levels.

South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -1.230 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -1.230 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.550 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.550 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Wages in South Africa increased to 29290 ZAR/Month in the second quarter of 2025 from 28289 ZAR/Month in the first quarter of 2025. This dataset provides - South Africa Total Quarterly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of 2024, Morocco had the highest average monthly salaries in Africa. Employees in the country earned around ***** U.S. dollars per month. South Africa and Tunisia followed, with average monthly salaries amounting to ***** U.S. dollars and ***** U.S. dollars, respectively.

Abstract

The October Household Survey is an annual survey based on a probability sample of a large number of households (ranging from 16 000 in 1996 through to 30 000 in 1997 and 1998, depending on the availability of funding). It covers a range of development indicators, including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered households and household members in the nine provinces of South Africa

Kind of data

Survey data

Sampling procedure

A sample of 30 000 households was drawn in 3 000 enumerator areas (EAs) (that is 10 households per enumerator area). 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 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) or EA. 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 was drawn.

Mode of data collection

Face-to-face

Research instrument

The data files in the October Household Survey 1999 correspond to the following sections in the questionnaire:

Person: Data from Section 1 and Section 4 Births: Data from Section 2 Children: Data from Section2 Worker: Data from Section 3 Migrant: Data from Section 5 House: Data from Section 6 Farming: Data from Section 7

Data appraisal

Researchers should note that the birth data in the OHS 1999 is not comparable with the birth data in OHS for the years 1994-1998 because the birth history question was phrased differently in 1999.

The question on birth history in the questionnaires for OHS 1996-1998 was: 2.1 How many children (live births) have you ever given birth to?

In the 1999 OHS questionnaire the question asked was: 2.1 How many children (live births) has …… given birth to in the last 12 months?

The 1999 data does not therefore include a full birth history, only births in the 12 months before the survey interview.

As of 2023, Rwanda had the lowest average monthly salary of employees in the world in terms of purchasing power parities (PPP), which takes the average cost of living in a country into account. Gambia had the second lowest average wages, with Ethiopia in third. Of the 20 countries with the lowest average salaries in the world, 17 were located in Africa. On the other hand, Luxembourg had the highest average monthly salaries of employees.

South Africa ZA: Price to Income Ratio: sa data was reported at 90.320 2015=100 in 2024. This records a decrease from the previous number of 92.422 2015=100 for 2023. South Africa ZA: Price to Income Ratio: sa data is updated yearly, averaging 98.221 2015=100 from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 132.663 2015=100 in 2007 and a record low of 71.323 2015=100 in 1997. South Africa ZA: Price to Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Africa – Table ZA.OECD.AHPI: House Price Index: Seasonally Adjusted: Non OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database.

Households Debt in South Africa increased to 62.50 percent of gross income in 2024 from 62.40 percent in 2023. This dataset provides - South Africa Households Debt To Income- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

A comprehensive survey was conducted by Central Statistical Service (later Statistics South Africa) in October 1995 in order to determine the income and expenditure of households in South Africa. This survey shows the earnings and spendings of South African households and the pattern of household consumption. The survey covered the metropolitan, urban and rural areas of South Africa. The main purpose of the survey was to determine the average expenditure patterns of households in the different areas concerned. This survey forms the basis for the determination of the "basket" of consumer goods and services used for the calculation of the Consumer Price Index.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey includes households

Universe

The 1995 IES differed from previous household surveys of its kind in South Africa, since it was a countrywide survey covering metro, urban and rural areas, rather than a more limited sub-set of households in 12 major metro/urban areas of the country previously referred to. By extending the sample to include the whole country, a clearer indication of the life circumstances of all South Africans in all parts of the country could be inferred.

Kind of data

Sample survey data [ssd]

Sampling procedure

Two surveys, namely the CSS's annual October household survey (OHS) and the IES were run concurrently during October 1995. Information for the IES was obtained, as far as possible, from the same 30 000 households that were visited for the 1995 OHS. Altogether, 3 000 enumerator areas (EAs) were drawn for the sample, and ten households were visited in each EA. The sample was stratified by race, province, urban and non-urban area. The 1991 population census was used as a frame for drawing the sample, including estimates of the size of the population in the formerly independent TBVC (Transkei-Bophuthatswana-Venda-Ciskei) states.

More details on the sampling frame and sampling procedure are given in the report on the 1995 OHS, Living in South Africa (CSS, 1996).

Mode of data collection

Face-to-face [f2f]

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]

Disposable Personal Income in South Africa increased to 4891661 ZAR Million in the second quarter of 2025 from 4821583 ZAR Million in the first quarter of 2025. This dataset provides - South Africa Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation for the NIDS data is district municipality.

Analysis unit

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

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.

Socio-economic characteristics of the households and participants in Seven Communities in East and West Africa (SevenCEWA), 2018.