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.

In the fourth quarter of 2023, the labor force participation rate among Black South Africans reached over 59 percent, marking a year-on-year change of two percent compared to the fourth quarter of 2022. However, the participation rate of the labor force among Indian/Asian South Africans dropped from about 61 percent in the previous year to 58 percent.

In 2022, women in South Africa represented 51.1 percent of the population. The majority of them were White South African, reaching 51.7 percent of the population. On the other hand, Indian/Asian women had a share of 48.9 percent.

Abstract

The 2003 South African Demographic and Health Survey is the second national health survey to be conducted by the Department of Health, following the first in 1998. Compared with the first survey, the new survey has more extensive questions around sexual behaviour and for the first time included such questions to a sample of men. Anthropometric measurements were taken on children under five years, and the adult health module has been enhanced with questions relating to physical activity and micro-nutrient intake, important risk factors associated with chronic diseases. The 2003 SADHS has introduced a chapter reporting on the health, health service utilisation and living conditions of South Africa's older population (60 years or older) and how they have changed since 1998. This has been introduced because this component of the population is growing at a much higher rate than the other age groups. The chapter on adolescent health in 1998 focussed on health risk-taking behaviours of people aged 15-19 years. The chapter has been extended in the 2003 SADHS to include indicators of sexual behaviour of youth aged 15-24 years.

A total of 10 214 households were targeted for inclusion in the survey and 7 756 were interviewed, reflecting an 85 percent response rate. The survey comprised a household schedule to capture basic information about all the members of the household, comprehensive questionnaires to all women aged 15-49, as well as anthropometry of all children five years and younger. In every second household, interviews of all men 15-59 were conducted and in the alternate households, interviews and measurements of all adults 15 years and older were done including heights, weights, waist circumference, blood pressure and peak pulmonary flow. The overall response rate was 75 percent for women, 67 percent for men, 71 percent for adults, and 84 percent for children. This is slightly lower than the overall response rate for the 1998 SADHS, but varied substantially between provinces with a particularly low response rate in the Western Cape.

OBJECTIVES

In 1995 the National Health Information System of South Africa (NHIS/SA) committee identified the need for improved health information for planning services and monitoring programmes. The first South African Demographic and Health Survey (SADHS) was planned and implemented in 1998. At the time of the survey it was agreed that the survey had to be conducted every five years to enable the Department of Health to monitor trends in health services.

Information on a variety of demographic and health indicators were collected. The results of these surveys are intended to assist policy makers and programme managers in evaluating and designing programmes and strategies for improving health services in the country. In addition to the aspects covered in the 1998 SADHS, information on the following additional aspects was included in the 2003 SADHS: - Information on children living in households where the biological mother is not staying in the household i.e. mother is dead, etc.
- Child anthropometric data
- Information on reproductive health and sexual behaviour of men
- Information on malaria
- Information on pensions/grants received by members of the household.

The primary objective of the 2003 SADHS was to provide up-to-date information on: - Characteristics of households and respondents - Fertility - Contraception and fertility preferences - Sexual behaviour, HIV and AIDS - Infant and child mortality - Maternal and child health - Infant and child feeding - Adolescent health - Mortality and morbidity in adults - Utilisation of health services - Adult health: hypertension, chronic pulmonary disease and Asthma - Risk factors for chronic diseases - Oral health - Health of older persons

STUDY LIMITATIONS AND RECOMMENDATIONS

Comparison of the socio-demographic characteristics of the sample with the 2001 Population Census shows an over-representation of urban areas and the African population group, and an under-representation of whites and Indian females. It also highlights many anomalies in the ages of the sample respondents, indicating problems in the quality of the data of the 2003 survey. Careful analysis has therefore been required to distinguish the findings that can be considered more robust and can be used for decision making. This has involved considering the internal consistency in the data, and the extent to which the results are consistent with other studies.

Some of the key demographic and adult health indicators show signs of data quality problems. In particular, the prevalence of hypertension, and the related indicators of quality of care are clearly problematic and difficult to interpret. In addition, the fertility levels and the child mortality estimates are not consistent with other data sources. The data problems appear to arise from poor fieldwork, suggesting that there was inadequate training, supervision and quality control during the implementation of the survey. It is imperative that the next SADHS is implemented with stronger quality control mechanisms in place. Moreover, consideration should be given to the frequency of future surveys. It is possible that the SADHS has become overloaded - with a complex implementation required in the field. Thus it may be appropriate to consider a more frequent survey with a rotation of modules as has been suggested by the WHO.

Geographic coverage

The SADHS sample was designed to be a nationally representative probability sample of approximately 10000 households. The country was stratified into the nine provinces and each province was further stratified into urban and non-urban areas.

Analysis unit

• Household
• Women age 15-49
• Men age 15-59
• Children under six years

Universe

The population covered by the 2003 SADHS is defined as the universe of all women age 15-49, all men 15-59 in South Africa.

Kind of data

Sample survey data

Sampling procedure

The SADHS sample was designed to be a nationally representative probability sample of approximately 10000 households. The country was stratified into the nine provinces and each province was further stratified into urban and non-urban areas.

The sampling frame for the SADHS was provided by Statistics South Africa (Stats SA) based on the enumeration areas (EAs) list of approximately 86000 EAs created during the 2001 census. Since the Indian population constitutes a very small fraction of the South African population, the Census 2001 EAs were stratified into Indian and non-Indian. An EA was classified as Indian if the proportion of persons who classified themselves as Indian during Census 2001 enumeration in that EA was 80 percent or more, otherwise it was classified as Non-Indian. Within the Indian stratum, EAs were sorted descending by the proportion of persons classified as Indian. It should be noted that some provinces and non-urban areas have a very small proportion of the Indian population hence the Indian stratum could not be further stratified by province or urban/non-urban. A sample of 1000 households was allocated to the stratum. Probability proportional to size (PPS) systematic sampling was used to sample EAs and the proportion of Indian persons in an EA was the measure of size. The non-Indian stratum was stratified explicitly by province and within province by the four geo types, i.e. urban formal, urban informal, rural formal and tribal. Each province was allocated a sample of 1000 households and within province the sample was proportionally allocated to the secondary strata, i.e. geo type. For both the Indian and Non-Indian strata the sample take of households within an EA was sixteen households. The number of visited households in an EA as recorded in the Census 2001, 09 Books was used as the measure of size (MOS) in the Non-Indian stratum.

The second stage of selection involved the systematic sampling of households/stands from the selected EAs. Funds were insufficient to allow implementation of a household listing operation in selected EAs. Fortunately, most of the country is covered by aerial photographs, which Statistics SA has used to create EA-specific photos. Using these photos, ASRC identified the global positioning system (GPS) coordinates of all the stands located within the boundaries of the selected EAs and selected 16 in each EA, for a total of 10080 selected. The GPS coordinates provided a means of uniquely identifying the selected stand. As a result of the differing sample proportions, the SADHS sample is not self-weighting at the national level and weighting factors have been applied to the data in this report.

A total of 630 Primary Sampling Units (PSUs) were selected for the 2003 SADHS (368 in urban areas and 262 in non-urban areas). This resulted in a total of 10214 households being selected throughout the country1. Every second household was selected for the adult health survey. In this second household, in addition to interviewing all women aged 15-49, all adults aged 15 and over were eligible to be interviewed with the adult health questionnaire. In every alternate household selected for the survey, not interviewed with the adult health questionnaire, all men aged 15-59 years were also eligible to be interviewed. It was expected that the sample would yield interviews with approximately 10000 households, 12500 women aged 15-49, 5000 adults and 5000 men.

Mode of data collection

Face-to-face

Research instrument

The survey utilised five questionnaires: a Household Questionnaire, a Women's Questionnaire, a Men's Questionnaire, an Adult Health Questionnaire and an Additional Children Questionnaire. The contents of the

India IHIS: Percentage of Hotel Guest Arrivals: Independent Hotels: South Africa data was reported at 2.400 % in 2018. This records a decrease from the previous number of 2.700 % for 2017. India IHIS: Percentage of Hotel Guest Arrivals: Independent Hotels: South Africa data is updated yearly, averaging 2.300 % from Mar 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 2.700 % in 2017 and a record low of 1.700 % in 2005. India IHIS: Percentage of Hotel Guest Arrivals: Independent Hotels: South Africa data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHB015: Indian Hotel Industry Survey: Percentage of Hotel Guest Arrivals: by Major Countries.

India IHIS: Percentage of Hotel Guest Arrivals: Two-Star: South Africa data was reported at 2.500 % in 2017. This records an increase from the previous number of 2.400 % for 2016. India IHIS: Percentage of Hotel Guest Arrivals: Two-Star: South Africa data is updated yearly, averaging 2.050 % from Mar 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 2.800 % in 2010 and a record low of 1.200 % in 2004. India IHIS: Percentage of Hotel Guest Arrivals: Two-Star: South Africa data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHB015: Indian Hotel Industry Survey: Percentage of Hotel Guest Arrivals: by Major Countries.

South Africa Imports from India of Woven cotton fabrics, not under 85% content, weight not over 200 g/m2 was US$4.78 Million during 2024, according to the United Nations COMTRADE database on international trade.

Time series data for the statistic Imports_India_from_South_Africa. Indicator Definition:Goods, Value of Imports, Cost, Insurance, Freight (CIF), US DollarsThe indicator "Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars" stands at 0.6426 Billion as of 05/31/2025. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -37.83 percent compared to the value the year prior.The 1 year change in percent is -37.83.The 3 year change in percent is -57.29.The 5 year change in percent is 177.82.The 10 year change in percent is 42.51.The Serie's long term average value is 0.406 Billion. It's latest available value, on 05/31/2025, is 58.38 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 07/31/1991, to it's latest available value, on 05/31/2025, is +146,296.28%.The Serie's change in percent from it's maximum value, on 10/31/2023, to it's latest available value, on 05/31/2025, is -59.28%.

In 2021, Black South Africans were the largest group in the country accessing the internet via a smartphone. Some ** percent of Black South Africans used a smartphone to go online, while the white population followed with nearly ** percent. Indian/Asian individuals, on the other hand, were the population group with the smallest share of internet access via a smartphone. In 2022, Black South Africans were the largest population group in the country, followed by the colored and white populations.

About this dataset

Context

The dataset tabulates the population of South Tucson by race. It includes the population of South Tucson across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of South Tucson across relevant racial categories.

Key observations

The percent distribution of South Tucson population by race (across all racial categories recognized by the U.S. Census Bureau): 45.89% are white, 3.40% are Black or African American, 19.39% are American Indian and Alaska Native, 0.13% are Asian, 16.52% are some other race and 14.67% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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: This column displays the racial categories (excluding ethnicity) for the South Tucson
• Population: The population of the racial category (excluding ethnicity) in the South Tucson is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of South Tucson total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Tucson Population by Race & Ethnicity. You can refer the same here

Abstract

The KwaZulu-Natal Income Dynamics Study (KIDS) is a panel study that follows a random sample of households who lived in the South African province of KwaZulu-Natal (KZN) in 1993. These households and those who have split off from them were interviewed again in 1998 and 2004. This document summarizes the main features of the third wave of KIDS conducted in 2004.

Geographic coverage

The Province of Kwazulu-Natal

Analysis unit

individuals, communities

Universe

The sample covered on African and Indian Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

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 - A 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 - Have at least one child living in household - Spouse/partner of person satisfying criterion.

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.

In 2004, due to the aging of the core members and the high prevalence of HIV/AIDS in South Africa, the study was extended in a complementary way to track and interview the households of the children of the core or the next generation. These are sons and daughters of core members older than 18, who have established a "new" household since 1993 (labeled as "K"). By establishing a new household we mean that these children are now living away from their own parents with their own children, or with the children of their partner. Using the next generation to keep track of family "dynasties" provides a way of refreshing the panel and establishing a generational transition. In addition, due to our interest in the impact on children of the HIV/AIDS epidemic, the 2004 wave followed foster children to their new households. This group is defined as children aged less than 18 years old of core and next generation household members who no longer live with their parents i.e. no longer live in core or next generation households (labeled as "N"). As described in Appendix A, different questionnaire modules were administered in the core, next generation, and foster child households.

As the goal of the 2004 wave of KIDS was to find and interview the households of the children of the core and the foster children in addition to those of the regular core members, we had three ways in which we could contact the 1993 dynasties. In 1998, almost 84% of the 1993 dynasties were found as documented by May et al. (2000). From the 1132 dynasties interviewed in 1998, the 2004 wave found 841, yielding a response rate of 74%. Most of these dynasties were still composed of the original core members (760) however some of them were represented by the next generation of household members (K) or foster children (N).

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire containing the following sections:

Household Roster Household Services Food Spending and Consumption Non-Food Spending and Assets Remittances Household Income from Non-Employment Sources Economic Shocks, Agriculture Employment Health Social Capital and Trust Children Tests of Learning and Anthropometry

Response rate

In 1998, almost 84% of the 1993 dynasties were found as documented by May et al. (2000). From the 1132 dynasties interviewed in 1998, the 2004 wave found 841, yielding a response rate of 74%. Most of these dynasties were still composed of the original core members (760) however some of them were represented by the next generation of household members (K) or foster children (N).

South Africa Imports from India of Woven cotton fabrics, not under 85% content, weight over 200 g/m2 was US$1.13 Million during 2024, according to the United Nations COMTRADE database on international trade.

India Imports from South Africa of Natural Borates and Concentrates , Natural Boric Acid was US$99.39 Thousand during 2011, according to the United Nations COMTRADE database on international trade. India Imports from South Africa of Natural Borates and Concentrates , Natural Boric Acid - data, historical chart and statistics - was last updated on November of 2025.

South Africa Exports of cotton yarn (cotton content less than 85%) to India was US$0 during 2016, according to the United Nations COMTRADE database on international trade. South Africa Exports of cotton yarn (cotton content less than 85%) to India - data, historical chart and statistics - was last updated on November of 2025.

South Africa Imports from India of Iron Oxides and Hydroxides, Earth Color was US$43.52 Thousand during 2024, according to the United Nations COMTRADE database on international trade. South Africa Imports from India of Iron Oxides and Hydroxides, Earth Color - data, historical chart and statistics - was last updated on November of 2025.

South Africa Imports from India of Natural Borates and Concentrates , Natural Boric Acid was US$17 during 2016, according to the United Nations COMTRADE database on international trade. South Africa Imports from India of Natural Borates and Concentrates , Natural Boric Acid - data, historical chart and statistics - was last updated on November of 2025.

South Africa Exports of woven cotton fabrics, under 85% content, over 200g/m2 to India was US$0 during 2021, according to the United Nations COMTRADE database on international trade. South Africa Exports of woven cotton fabrics, under 85% content, over 200g/m2 to India - data, historical chart and statistics - was last updated on December of 2025.

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.

India IHIS: Percentage of Hotel Guest Arrivals: More than 150 Rooms: South Africa data was reported at 1.100 % in 2018. This stayed constant from the previous number of 1.100 % for 2017. India IHIS: Percentage of Hotel Guest Arrivals: More than 150 Rooms: South Africa data is updated yearly, averaging 1.300 % from Mar 2000 (Median) to 2018, with 19 observations. The data reached an all-time high of 1.900 % in 2016 and a record low of 0.900 % in 2008. India IHIS: Percentage of Hotel Guest Arrivals: More than 150 Rooms: South Africa data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHB015: Indian Hotel Industry Survey: Percentage of Hotel Guest Arrivals: by Major Countries.

India IHIS: Percentage of Hotel Guest Arrivals: Heritage: South Africa data was reported at 1.000 % in 2018. This records an increase from the previous number of 0.700 % for 2017. India IHIS: Percentage of Hotel Guest Arrivals: Heritage: South Africa data is updated yearly, averaging 0.700 % from Mar 2000 (Median) to 2018, with 19 observations. The data reached an all-time high of 1.600 % in 2003 and a record low of 0.400 % in 2012. India IHIS: Percentage of Hotel Guest Arrivals: Heritage: South Africa data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHB015: Indian Hotel Industry Survey: Percentage of Hotel Guest Arrivals: by Major Countries.