As of 2019, approximately 10.68 million out of South Africa's 17.16 million households drew their income from regular salaries, wages or commissions. 7.9 million households received social grants paid by the government for citizens in need of state support.

South Africa Number of Households: Black African: by Income: Other Income Sources data was reported at 287.000 Unit th in 2017. This records an increase from the previous number of 267.000 Unit th for 2016. South Africa Number of Households: Black African: by Income: Other Income Sources data is updated yearly, averaging 261.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 287.000 Unit th in 2017 and a record low of 191.000 Unit th in 2010. South Africa Number of Households: Black African: by Income: Other Income Sources data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

Net primary income (Net income from abroad) (current US$) in South Africa was reported at --5166056341 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Net income from abroad - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

South Africa Number of Households: Black African: Female: by Income: Other Income Sources data was reported at 119.000 Unit th in 2017. This records an increase from the previous number of 113.000 Unit th for 2016. South Africa Number of Households: Black African: Female: by Income: Other Income Sources data is updated yearly, averaging 107.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 122.000 Unit th in 2015 and a record low of 75.000 Unit th in 2010. South Africa Number of Households: Black African: Female: by Income: Other Income Sources data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

South Africa Number of Households: by Income: Other Income Sources data was reported at 476.000 Unit th in 2017. This records an increase from the previous number of 460.000 Unit th for 2016. South Africa Number of Households: by Income: Other Income Sources data is updated yearly, averaging 438.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 476.000 Unit th in 2017 and a record low of 362.000 Unit th in 2010. South Africa Number of Households: by Income: Other Income Sources data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

Adjusted net national income per capita (annual % growth) in South Africa was reported at 5.2024 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Adjusted net national income per capita (annual % growth) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

South Africa's GDP was estimated at just over 403 billion U.S. dollars in 2024, the highest in Africa. Egypt followed, with a GDP worth around 380 billion U.S. dollars, and ranked as the second-highest on the continent. Algeria ranked third, with about 260 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.

Abstract

The Income and Expenditure Survey 2005-2006 conducted by Statistics South Africa (Stats SA) between September 2005 and August 2006. The survey was designed to collect data on items and services acquired by South African households, sources of household income (monetary or in-kind) and household expenditure within a given reference period. Studies of this nature play an important role in evaluating changes in consumption patterns and income distribution. The data collected provides input to the goods and services for inclusion in the Consumer Price Index (CPI) basket of goods and services.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey are households and individuals

Universe

The survey covered all de jure household members

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

The 2005-2006 IES was designed to collect information on items and services acquired by South African households, various sources of income acquired by participating households (monetary or in-kind) and details as to how they spent this income (on expenditure, remittances, etc.). This was accomplished by collecting details of all expenditure by a participating household and all acquisitions of goods and services for the household’s own consumption within a given reference period. Studies of this nature play an important role in evaluating changes in consumption patterns, levels of income and income distribution. The results of the survey serve as an input into identifying the goods and services that should be included in the Consumer Price Index (CPI) basket of goods and services.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Consumption expenditure items/services

Universe

The IES 2005/2006 included all domestic households, holiday homes and all households in workers’ residences such as mining hostels and dormitories for workers. It did not include institutions such as hospitals, prisons, old-age homes, student hostels and dormitories for scholars. Also excluded were boarding houses, hotels, lodges and guest houses.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design • A newly designed Master Sample, consisting of 3 000 Primary Sampling Units (PSUs), based on the 2001 Population Census Enumeration Areas, was used as the sampling frame. The Master Sample is used for all household surveys conducted by Statistics South Africa (Stats SA). • The 3 000 primary sampling units (PSUs) from the Master Sample were representatively divided into four quarterly allocations of 750 each. • Within each quarterly allocation, a random sample of 250 PSUs was selected every month. • Eight dwelling units were systematically selected from each of the sampled PSUs for fieldwork. In total, 24 000 dwelling units were covered during the twelve months of data collection for the IES 2005/2006. This process ensured that the sample was evenly spread over the twelve months, while it remained nationally representative in each quarter.

Mode of data collection

Face-to-face [f2f]

Response rate

From the 24 000 dwelling units sampled across South Africa, 25 192 households were identified. Out of these, there was a sample realisation of 22 617 households, with the remaining 2 575 households were classified as out of scope due to a number of reasons, such as listing error, vacant dwelling, unoccupied dwelling, etc.

South Africa Number of Households: Others: by Income: Other Income Sources data was reported at 190.000 Unit th in 2017. This records a decrease from the previous number of 193.000 Unit th for 2016. South Africa Number of Households: Others: by Income: Other Income Sources data is updated yearly, averaging 178.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 193.000 Unit th in 2016 and a record low of 150.000 Unit th in 2009. South Africa Number of Households: Others: by Income: Other Income Sources data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

Adjusted net national income per capita (current US$) in South Africa was reported at 5521 USD in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Adjusted net national income per capita - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in South Gorin. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In South Gorin, the median household income for the households where the householder is White decreased by $3,706(9.07%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $40,862 in 2011 and $37,156 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in South Gorin, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/south-gorin-mo-median-household-income-by-race-trends.jpeg" alt="South Gorin, MO median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in South Gorin.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Gorin median household income by race. You can refer the same here

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

This layer shows the purchasing power per capita in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 62,579 South African rand. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African rand (ZAR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita by various categoriesCount of households by income quintilesThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

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.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey are households

Universe

The survey covered private dwellings, workers' hostels, residential hotels, and nurses' and doctors' quarters, but excluded hospitals and clinics, hotels and guest houses, prisons, schools and student hostels and old-age homes.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the IES 2010/2011 was obtained from Statistics South Africa’s Master Sample (MS) based on the 2001 Population Census enumeration areas (EAs). The scope of the Master Sample (MS) is national coverage of all households in South Africa and the target population consists of all qualifying persons and households in the country. In summary, it has been designed to cover all households living in private dwelling units and workers living in workers’ quarters in the country. The IES 2010/2011 sample is based on an extended sample of 3 254 PSUs, which consists of the 3 080 PSUs in the Master Sample and a supplement of 174 urban PSUs selected from the PSU frame. The IES sample file contained 31 419 sampled dwelling units (DUs). The 31 419 sampled DUs consist of 31 007 DUs sampled from the 3 080 design PSUs in the Master Sample and 412 DUs from the supplemented 174 urban PSUs. In the case of multiple households at a sampled DU, all households in the DU were included.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were four modules in the household questionnaire with eighteen subsections. The first module collected general household data and data on household members. Modules 2 to 4 collected data on consumption expenditure, household finances and income. The diary was a booklet in which the respondent recorded weekly expenditure data. A household completed a different diary for each week of the survey period.

Response rate

From the 31 419 dwelling units sampled across South Africa, 33 420 households were identified. Out of these, there was a sample realisation of 27 665 (82,8%) households, with the remaining 5 755 (17,2%) households being classified as out of scope.

This map shows the purchasing power per capita in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 62,579 South African rand. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African rand (ZAR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita by various categoriesCount of households by income quintilesThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Taxes on income, profits and capital gains (% of revenue) in South Africa was reported at 49.03 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Taxes on income, profits and capital gains (% of revenue) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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.

Abstract

The 1993 Project for Statistics on Living Standards and Development was an integrated household survey similar in design to a World Bank Living Standards Measurement Survey. The survey collected data on the socio-economic condition of households. Households in Kwazulu-Natal province were re-surveyed from March to June 1998 for the Kwazulu-Natal Income Dynamics Study. Combining these two survey datasets has yielded a panel (or longitudinal) dataset in which the same individuals and households have been interviewed at two points in time, 1993 and 1998. These are the first two waves of the KIDS panel study.

The institutions collaborating in the KIDS study include the University of KwaZulu-Natal (UKZN), the University of Wisconsin-Madison and the International Food Policy Research Institute (IFPRI).

Geographic coverage

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

Analysis unit

Households and individuals

Universe

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

Kind of data

Sample survey data

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. Due to the geographic concentration of African and Indian households, KIDS-unlike the PSLSD-limits its scope to African and Indian households. In the KwaZulu-Natal province, Africans represent 85 percent of the population and Indians represent 12 percent. Compared with their representation nationally, White and Coloured people are underrepresented in KwaZulu-Natal. Effectively, the numbers of White and Coloureds in the KwaZulu-Natal sample are too small, and too geographically concentrated in a few clusters, to permit meaningful inference. The KIDS study has thus been limited to the first two population groups.

PSLSD was a survey of households. However, households are a complicated object to define, particularly in longitudinal studies. To transform KIDS from a single-round household survey into a longitudinal household panel study required a redefinition of the sampling unit. In 1998, a decision was made to follow the core household members with the intention of capturing the major decision makers within the household. A household member is a core person if he/she satisfied any of the following criteria (the self-declared head of household from the 1993 survey):

• Spouse/partner of the self-declared head of household (from the 1993 survey)
• Lives in a three generation household and all of the following are true:
• Child of the self-declared household head, son/daughter-in-law of the household head, or niece/nephew of self-declared head
• At least 30 years old
• Has at least one child living in household
• Spouse/partner of person satisfying criteria

Thus all heads of households and spouses of heads are automatically classified as core and in some three-generation households, adult children are also included in this cateogry. In this way, we can see the 1993 survey as the baseline information for a random sample of dynasties. The efforts of the 1998 and 2004 surveyors to find the location of the 1993 core members can then be seen as a way to keep track of the 1993 dynasties.

Mode of data collection

Face-to-face [f2f]

Research instrument

KIDS re-interviews the KwaZulu-Natal (KZN) sample of the 1993 nationwide survey known as the Project for Statistics on Living Standards and Development (PSLSD.) The original project was financed by the World Bank and had the characteristics of the Living Standard Measurement Surveys. Reflecting their origin, all three waves of fieldwork for KIDS-1993, 1998, and 2004-collected information on household composition, expenditure on food and on other durable and non-durable goods, education, health, agricultural production, employment, and additional sources of labor and non-labor income. To ensure comparability, the 1998 and 2004 questionnaires largely followed the 1993 version of the questionnaire, however, a few modules have been added and removed. For example, the 1998 survey added sections on assets to marriage, economic shocks, and social capital and trust.

ABSTRACT: The 1995 Income and Expenditure Survey questionnaire contains questions about all sources of household income. It also covers the purchase of a wide variety of products and services, including new items such as cellular telephones. Demographic variables include: Age, gender, population group, profession.