As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

As of 2022, 8.1 million women lived in Gauteng, the most populated province in South Africa. KwaZulu-Natal, Western Cape, and Eastern Cape followed as the provinces with the largest number of women, reaching six, 3.7, and 3.5 million, respectively.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

Abstract

The 1970 South African Population Census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, migration, family and employment details.

Geographic coverage

National coverage of the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal, and the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, Kwandebele, Transkei and Bophuthatswana.

Analysis unit

The units of analysis for the South African Census 1970 were households and individuals

Universe

The South African population census of 1970 covered all de jure household members (usual residents) of South Africa and the "national states".

The Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 1970 Census was a full count for Whites, Coloureds and Asians, and a 5% sample for Blacks (Africans)

Sampling deviation

The country was divided into 400 census districts for the 1970 Census. In most cases the boundaries of the census districts corresponded with those of the magisterial districts. However, in some cases the boundaries did not correspond, particularly in the areas in and around the "National States". This was to facilitate the administration of the census and to make it easier to exclude figures of the "National states" from provincial totals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1970 Population Census of the Republic of South Africa questionnaires were: Form 01, to be completed by "Whites, Coloured and Asiatics" Form 02, to be completed by "Bantu" Form 03, for families, households and dwellings

Form 01 collected data on relationship to household head, population group, sex, age, marital status, place of birth, and citizenship, as well as usual place of residence, home language, religion, level of education and income. Employment data collected included occupation, employment status and industry type.

Form 02 collected data for African South Africans on relationship to household head, sex, age, marital status, fertility, place of birth, home language and literacy, religion and level of education. Employment data collected included occupation, employment status and industry type.

Form 03 collected household data, including data on dwelling type, building material of dwelling walls, number of rooms and age of the dwelling. Data on home ownership. Data was also collected on the number and sex of household members and their relationship to the household head. Data on household heads included their population group, age and marital status. Income data was also collected, for husbands and wives. Data on home ownership, household size and domestic workers was also collected, but for Urban households only.

Abstract

The 1998 South Africa Demographic and Health Survey (SADHS) is the first study of its kind to be conducted in South Africa and heralds a new era of reliable and relevant information in South Africa. The SADHS, a nation-wide survey has collected information on key maternal and child health indicators, and in a first for international demographic and health surveys, the South African survey contains data on the health and disease patterns in adults.

Plans to conduct the South Africa Demographic and Health Survey go as far back as 1995, when the Department of Health National Health Information Systems of South Africa (NHIS/SA) committee, recognised serious gaps in information required for health service planning and monitoring.

Fieldwork was conducted between late January and September 1998, during which time 12,247 households were visited, 17,500 people throughout nine provinces were interviewed and 175 interviewers were trained to interview in 11 languages.

The aim of the 1998 South Africa Demographic and Health Survey (SADHS) was to collect data as part of the National Health Information System of South Africa (NHIS/SA). The survey results are intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health services in the country. A variety of demographic and health indicators were collected in order to achieve the following general objectives:

(i) To contribute to the information base for health and population development programme management through accurate and timely data on a range of demographic and health indicators. (ii) To provide baseline data for monitoring programmes and future planning. (iii) To build research and research management capacity in large-scale national demographic and health surveys.

The primary objective of the SADHS is to provide up-to-date information on: - basic demographic rates, particularly fertility and childhood mortality levels, - awareness and use of contraceptive methods, - breastfeeding practices, - maternal and child health, - awareness of HIV/AIDS, - chronic health conditions among adults, - lifestyles that affect the health status of adults, and - anthropometric indicators.

Geographic coverage

It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces.

Analysis unit

• Household
• Women age 15-49
• Men age 15 and above

Universe

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country.

Kind of data

Sample survey data

Sampling procedure

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country. The design for the SADHS called for a representative probability sample of approximately 12,000 completed individual interviews with women between the ages of 15 and 49. It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces. As far as possible, estimates were to be produced for the four South African population groups. Also, in the Eastern Cape province, estimates of selected indicators were required for each of the five health regions.

In addition to the main survey of households and women 15-49 that followed the DHS model, an adult health module was administered to a sample of adults aged 15 and over in half of the households selected for the main survey. The adult health module collected information on oral health, occupational hazard and chronic diseases of lifestyle.

SAMPLING FRAME

The sampling frame for the SADHS was the list of approximately 86,000 enumeration areas (EAs) created by Central Statistics (now Statistics South Africa, SSA) for the Census conducted in October 1996. The EAs, ranged from about 100 to 250 households, and were stratified by province, urban and non-urban residence and by EA type. The number of households in the EA served as a measure of size of the EA.

CHARACTERISTICS OF THE SADHS SAMPLE

The sample for the SADHS was selected in two stages. Due to confidentiality of the census data, the sampling was carried out by experts at the CSS according to specifications developed by members of the SADHS team. Within each stratum a two stage sample was selected. The primary sampling units (PSUs), corresponded to the EAs and will be selected with probability proportional to size (PPS), the size being the number of households residing in the EA, or where this was not available, the number of census visiting points in the EA. This led to 972 PSUs being selected for the SADHS (690 in urban areas and 282 in non-urban areas. Where provided by SSA, the lists of visiting points together with the households found in these visiting points, or alternatively a map of the EA which showed the households, was used as the frame for second-stage sampling to select the households to be visited by the SADHS interviewing teams during the main survey fieldwork. This sampling was carried out by the MRC behalf of the SADHS working group. If a list of visiting points or a map was not available from SSA, then the survey team took a systematic sample of visiting points in the field. In an urban EA ten visiting points were sampled, while in a non-urban EA twenty visiting points were sampled. The survey team then interviewed the household in the selected visiting point. If there were two households in the selected visiting point, both households were interviewed. If there were three or more households, then the team randomly selected one household for interview. In each selected household, a household questionnaire was administered; all women between the ages of 15 and 49 were identified and interviewed with a woman questionnaire. In half of the selected households (identified by the SADHS working group), all adults over 15 years of age were also identified and interviewed with an adult health questionnaire.

SAMPLE ALLOCATION

Except for Eastern Cape, the provinces were stratified by urban and non-urban areas, for a total of 16 sampling strata. Eastern Cape was stratified by the five health regions and urban and non-urban within each region, for a total of 10 sampling strata. There were thus 26 strata in total.

Originally, it was decided that a sample of 9,000 women 15-49 with complete interviews allocated equally to the nine provinces would be adequate to provide estimates for each province separately; results of other demographic and health surveys have shown that a minimum sample of 1,000 women is required in order to obtain estimates of fertility and childhood mortality rates at an acceptable level of sampling errors. Since one of the objectives of the SADHS was to also provide separate estimates for each of the four population groups, this allocation of 1,000 women per province would not provide enough cases for the Asian population group since they represent only 2.6 percent of the population (according to the results of the 1994 October Household Survey conducted by SSA). The decision was taken to add an additional sample of 1,000 women to the urban areas of KwaZulu-Natal and Gauteng to try to capture as many Asian women as possible as Asians are found mostly in these areas. A more specific sampling scheme to obtain an exact number of Asian women was not possible for two reasons: the population distribution by population group was not yet available from the 1996 census and the sampling frame of EAs cannot be stratified by population group according to SSA as the old system of identifying EAs by population group has been abolished.

An additional sample of 2,000 women was added to Eastern Cape at the request of the Eastern Cape province who funded this additional sample. In Eastern Cape, results by urban and non-urban areas can be given. Results of selected indicators such as contraceptive knowledge and use can also be produced separately for each of the five health regions but not for urban/non-urban within health region.

Result shows the allocation of the target sample of 12,000 women by province and by urban/nonurban residence. Within each province, the sample is allocated proportionately to the urban/non-urban areas.

In the above allocation, the urban areas of KwaZulu-Natal have been oversampled by about 57 percent while those of Gauteng have been oversampled by less than 1 percent. For comparison purposes, it shows a proportional allocation of the 12,000 women to the nine provinces that would result in a completely self-weighting sample but does not allow for reliable estimates for at least four provinces (Northern Cape, Free State, Mpumalanga and North-West).

The number of households to be selected for each stratum was calculated as follows:

• According to the 1994 October Household Survey, the estimated number of women 15-49 per households is 1.2. The overall response rate was assumed to be 80 percent, i.e., of the households selected for the survey only 90 percent would be successfully interviewed, and of the women identified in the households with completed interviews, only 90 percent would have a complete woman questionnaire. Using these two parameters in the above equation, we would expect to select approximately 12,500 households in order to yield the target sample of women.

-

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.

Analysis unit

Persons, households, and dwellings 19 districts in the Eastern Cape province are not organized into households -- 1.3% of file

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: An occupied dwelling was a premises (visiting point or physical address) that was inhabited by one or more households on census night. An occupied dwelling may have been a house, room, flat or apartment, shack, hut, tent, caravan, houseboat, shop, school, etc. - Households: A household consists of a person, or a group of persons, who occupy a common dwelling (or part of it) for at least four days a week and who provide themselves jointly with food and other essentials for living. In other words, they live together as a unit. People who occupy the same dwelling, but who do not share food or other essentials, were enumerated as separate households. For example, people who shared a dwelling, but who bought food and ate separately, were counted as separate households. - Group quarters: A special dwelling is one which is not privately occupied by a household. It is usually an institution such as a prison, hotel, hostel, home for the aged, etc. Also hostels: a collective form of accommodation specifically built during the apartheid era for mine, factory, power station, municipal or other employees.

Universe

Every person present in South Africa on Census Night, October 9-10, 1996, should have been enumerated.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Statistics South Africa

SAMPLE SIZE (person records): 3621164.

SAMPLE DESIGN: The household was basically drawn as a 10% systematic sample of households from the census household file, stratified as specified below. The 10% person level sample was obtained by including all persons in these households plus the persons drawn in independent 10% systematic samples of all persons in special institutions and hostels. NOTE: 19 districts in the Eastern Cape province are not organized into households, because of an error in the original data file. 1.3% of the sample is affected.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were five different questionnaires that were used: 1) A household questionnaire which was completed in each household in the country. This questionnaire included information on each individual in the household, for example age and gender, as well as on the household as a whole, for example access to electricity and tap water. 2) An individual questionnaire, which was completed by individuals living on their own, for example those living in hostels or compounds. 3) A summary questionnaire for hostels. 4) A questionnaire for institutions, for example prisons, tourist hotels or homes for the aged. 5) A questionnaire for the homeless.

Abstract

The 1970 South African Population Census collected data on dwellings and individuals' demographic, migration, family and employment details.

Geographic coverage

National coverage of the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal, and the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, Kwandebele, Transkei and Bophuthatswana.

Analysis unit

The units of analysis for the South African Census 1970 were households and individuals

Universe

The South African population census of 1970 covered all de jure household members (usual residents) of South Africa and the "national states".

The Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 1970 Census was a full count for Whites, Coloureds and Asians, and a 5% sample for Blacks (Africans)

Sampling deviation

The country was divided into 400 census districts for the 1970 Census. In most cases the boundaries of the census districts corresponded with those of the magisterial districts. However, in some cases the boundaries did not correspond, particularly in the areas in and around the "National States". This was to facilitate the administration of the census and to make it easier to exclude figures of the "National states" from provincial totals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1970 Population Census of the Republic of South Africa questionnaires were: Form 01, to be completed by "Whites, Coloured and Asiatics" Form 02, to be completed by "Bantu" Form 03, for families, households and dwellings

Form 01 collected data on relationship to household head, population group, sex, age, marital status, place of birth, and citizenship, as well as usual place of residence, home language, religion, level of education and income. Employment data collected included occupation, employment status and industry type.

Form 02 collected data for African South Africans on relationship to household head, sex, age, marital status, fertility, place of birth, home language and literacy, religion and level of education. Employment data collected included occupation, employment status and industry type.

Form 03 collected household data, including data on dwelling type, building material of dwelling walls, number of rooms and age of the dwelling. Data on home ownership. Data was also collected on the number and sex of household members and their relationship to the household head. Data on household heads included their population group, age and marital status. Income data was also collected, for husbands and wives. Data on home ownership, household size and domestic workers was also collected, but for Urban households only.

Abstract

The main purpose of the GHS is to measure the level of development and performance of various government programmes and projects in South Africa. The data provides national indicators on various living conditions such as access to services and facilities, and education and health, for 2002.

Geographic coverage

The scope of the General Household Survey 2002 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2002 are individuals and households.

Universe

The survey covered 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 students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the General Household Survey 2002 a multi-stage stratified sample was drawn using probability proportional to size principles. The first stage was stratification by province, then by type of area within each province. Primary sampling units (PSUs) were then selected proportionally within each stratum (urban or non-urban) in all provinces. Altogether 3000 PSUs were selected. Within each PSU ten dwelling units were selected systematically for enumeration.

The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its surveys. The master sample was drawn from the database of enumeration areas (EAs) which was established during the demarcation phase of census 1996. As part of the master sample, small EAs consisting of fewer than 100 dwelling units are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 dwelling units, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and, within each province, by urban and non-urban areas. Independent samples were drawn from each stratum within each province. The smaller provinces were given a disproportionately larger number of PSUs than the bigger provinces.

The master sample was divided into five independent clusters. In order to avoid respondent fatigue, the sample for GHS was drawn from a different cluster from the two clusters already being used for the LFS, which is a twice-yearly rotating panel survey. Altogether 30 000 dwelling units (including units in hostels) were visited for the GHS 2002.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed taking into consideration the need to compare results of this survey to the one conducted in June 2001 in the 13 nodal areas identified as priority areas for the Integrated Rural Development Strategy (IRDS), namely, the Social Development Indicators Survey (SDIS). The questions in the GHS were similar to the ones used in the SDIS as proposed by representatives of departments in the social cluster of government responsible for implementation of the IRDS.

The GHS 2002 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Sampling error estimates

Estimation and use of standard error

The published results of the General Household Survey2002 are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate may have varied by chance because only a sample of the population was included.

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

South Africa is among the world’s top eight tuberculosis (TB) burden countries, and despite a focus on HIV-TB co-infection, most of the population living with TB are not HIV co-infected. The disease is endemic across the country, with 80–90% exposure by adulthood. We investigated epidemiological risk factors for (TB) in the Northern Cape Province, South Africa: an understudied TB endemic region with extreme TB incidence (926/100,000). We leveraged the population’s high TB incidence and community transmission to design a case-control study with similar mechanisms of exposure between the groups. We recruited 1,126 participants with suspected TB from 12 community health clinics and generated a cohort of 774 individuals (cases = 374, controls = 400) after implementing our enrollment criteria. All participants were GeneXpert Ultra tested for active TB by a local clinic. We assessed important risk factors for active TB using logistic regression and random forest modeling. We find that factors commonly identified in other global populations tend to replicate in our study, e.g. male gender and residence in a town had significant effects on TB risk (OR: 3.02 [95% CI: 2.30–4.71]; OR: 3.20 [95% CI: 2.26–4.55]). We also tested for demographic factors that may uniquely reflect historical changes in health conditions in South Africa. We find that socioeconomic status (SES) significantly interacts with an individual’s age (p = 0.0005) indicating that protective effect of higher SES changed across age cohorts. We further find that being born in a rural area and moving to a town strongly increases TB risk, while town birthplace and current rural residence is protective. These interaction effects reflect rapid demographic changes, specifically SES over recent generations and mobility, in South Africa. Our models show that such risk factors combined explain 19–21% of the variance (r2) in TB case/control status.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

Abstract

The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities.

Geographic coverage

The scope of the General Household Survey 2005 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2005 are individuals and households.

Universe

The survey covered 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 students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the GHS 2005 a multi-stage stratified sample was drawn using probability proportional to size principles. The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of Census 2001. As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample was allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated.

The master sample is divided into five independent clusters. In order to avoid respondent fatigue, the Labour Force Survey (LFS) is a rotating panel survey that is conducted twice yearly, whereas the GHS sample uses different clusters.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2005 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

87,5% of the expected 32 146 interviews were successfully completed and positive responses were obtained. It was not possible to complete interviews in 3,8 % of the sampled dwelling units. An additional 8,3% of all interviews were not conducted for various reasons, for instance the sampled dwelling units had become vacant or had changed status (e.g. they were used as shops or small businesses at the time of the enumeration but were originally listed as dwelling units).

Sampling error estimates

Estimation and use of standard error The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.

Abstract

The South African Population Research Infrastructure Network (SAPRIN) is a national research infrastructure funded through the Department of Science and Technology and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise the legacy longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 116 000 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 100 000; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 125 000.

SAPRIN data are processed for longitudinal analysis by organising the demographic data into residence episodes at a geographical location, and membership episodes within a household. Start events include enumeration, birth, in-migration and relocating into a household from within the study population; exit events include death (by cause), out-migration, and relocating to another location in the study population. Variables routinely updated at individual level include health care utilisation, marital status, labour status, education status, as well as recording household asset status. Anticipated outcomes of SAPRIN include: (i) regular releases of up-to-date, longitudinal data, representative of South Africa’s fast-changing poorer communities for research, interpretation and calibration of national datasets; (ii) national statistics triangulation, whereby longitudinal SAPRIN data are triangulated with National Census data for calibration of national statistics and studying the mechanisms driving the national statistics; (iii) An interdisciplinary research platform for conducting observational and interventional research at population level; (iv) policy engagement to provide evidence to underpin policy-making for cost evaluation and targeting intervention programmes, thereby improving the accuracy and efficiency of pro-poor, health and wellbeing interventions; (v) scientific education through training at related universities; and (vi) community engagement, whereby coordinated engagement with communities will enable two-way learning between researchers and community members, and enabling research site communities and service providers to have access to and make effective use of research results.

Geographic coverage

The Agincourt HDSS covers an area of approximately 420km2 and is located in Bushbuckridge District, Mpumalanga in the rural north-east of South Africa close to the Mozambique border. DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 km from Polokwane, the capital city of Limpopo Province and 15-50 km from the University of Limpopo (Turfloop Campus). The site covers an area of approximately 200 km2. AHRI is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the KwaMsane township strandles the highway) and in the north by the Inyalazi river for portions of the boundary. The area is 438km2.

Analysis unit

Exposure episodes

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of the household will be enumerated. A resident household member is an individual that intends to sleep the majority of time at the dwelling occupied by the household over a four-month period. Households will include resident and non-resident members. An individual is a non-resident member if they have close ties to the household, but do not physically reside with the household most of the time. They can also be called temporary migrants and they are enumerated within the household list. Because household membership is not tied to physical residency, an individual may be a member of more than one household.

Kind of data

Event/transaction data

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance areas.

Abstract

WHO implemented a World Health Survey to collect comprehensive baseline data on the health of populations and on the outcomes associated with investment in health systemsThe Survey Programme was developed in individual countries through consultation with policymakers and participants in routine HIS in these countries. The overall aims of the survey are to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness, and gather information on modes and extents of payment for health services.

Geographic coverage

The survey had national coverage. The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

Analysis unit

Households and individuals

Universe

The target population includes any adul aged 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which increases sample variance and effectively reduces our estimating power. WHO requires an absolute maximum of 50 respondents per PSU, and ideally would suggest 20-30. This means that for a sample size of 5000 respondents, 100- 200 PSU clusters should be taken into the sample. Calculating that, roughly, one fifth of the total number of PSU clusters in a country will be randomly selected into the survey sample, the sampling frame should consist of 500-1000 PSU clusters.

PROBABILITY SAMPLING

Probability sampling means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. Non-probability methods of sampling such as quota or convenience sampling and random walk, may introduce bias into the survey, will throw survey findings into question, and are not accepted by WHO.

The probability of selection into the survey sample for each cluster will be proportional to its relative size. Systematic Sampling Systematic sampling is the ordered sampling at fixed intervals from a list, starting from a randomly chosen point. Typically, systematic sampling is not used at the first stage of sampling (selection of PSUs) because it renders the estimation of sampling error difficult.

Systematic sampling is recommended at the SSU, TSU, and household selection stages of sampling. Systematic sampling may be linear or circular.

SELECTION OF HOUSEHOLDS

The Household is a device used to get at the individual. The household is the sampling unit while the individual is the observational unit. While it would be preferable to randomly select from a list of all eligible persons in a country, such lists, with a few exceptions, are not available, so we must employ a final cluster, the household, to get at our observational units.

Households will be selected from lists of dwelling units. Non-probabilistic methods of household selection such as the random walk are not acceptable. Such lists are typically available from population registries, household listings, voter lists and census list. As it is essential to include all households in the sampling frame, an assessment of the methodology employed to select households must be made: - How much has the population changed since these lists were made? - Completeness of coverage. Are there unregistered populations (e.g. slums) - Population shifts - Changes in Registry

QUALITY

Almost all lists will suffer from routine problems. WHO recommends that survey institutions manually enumerate all the households in

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. In 2005, Stats SA undertook a major revision of the Labour Force Survey (LFS) which had been conducted twice per year since 2000. This revision resulted in changes to the survey methodology, the survey questionnaire, the frequency of data collection and data releases, and the survey data capture and processing systems. The redesigned labour market survey, the QLFS, is now the principal vehicle for collecting labour market information on a quarterly basis. This report is the third annual report on the labour market in South Africa produced by Stats SA. The analysis is based on annual labour market data from 2005 to 2010. The report also includes a statistical appendix with historical data dating back to 2005 on an annual basis.

Objective The objective of this report is two-fold: first, to present annual labour market data backcast to 2005, and second, to analyse important aspects of the labour market in South Africa over the past five years.

Data sources Labour Force Survey – 2005 to 2007 (March and September each year) QLFS – 2008 to 2010 (Quarters 1 to 4)

Geographic coverage

the nine provinces of South Africa

Analysis unit

Individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Labour Force Survey and the Quarterly Labour Force Survey are based on a Master Sample and there have been three of them so far. The design of each is outlined below.

1999 Master Sample For the LFSs of February 2000 to March 2004, a rotating panel sample design was used to allow for measurement of change in people's employment situation over time. The same dwellings were visited on, at most, five different occasions. After this, new dwelling units were included for interviewing from the same PSU in the master sample. This means a rotation of 20% of dwelling units each time. The database of enumerator areas (EAs) established during the demarcation phase of Census '96 constituted the sampling frame for selecting EAs for the LFS. Small EAs consisting of fewer than 100 dwelling units were combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 dwelling units, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involved explicit stratification by province and within each province, by urban and non-urban areas (Census 1996 definitions). Independent samples of PSUs were drawn for each stratum within each province. The smaller provinces were given a disproportionately large number of PSUs compared to the bigger provinces. Simple random sampling was applied to select 10 dwelling units to visit in each PSU as ultimate sampling units. If more than one household was found in the same dwelling unit all such households were interviewed.

2004 Master Sample The 2004 Master Sample was used in the LFSs of September 2004 to September 2007. Enumeration Areas (EAs) that had a household count of less than twenty-five were omitted from the census frame that was used to draw the sample of PSUs for the Master Sample. Other omissions from the frame included all institution EAs except workers' hostels, convents and monasteries. EAs in the census database that were found to have less than sixty dwelling units during listing were pooled. This Master Sample was a multi-stage stratified sample. The overall sample size of PSUs was 3 000. The explicit strata were the 53 district councils. The 3 000 PSUs were allocated to these strata using the power allocation method. The PSUs were then sampled using probability proportional to size principles. The measure of size used was the number of households in a PSU as counted in the census. The sampled PSUs were listed with the dwelling unit as the listing unit. From these listings systematic samples of dwelling units per PSU were drawn. These samples of dwelling units formed clusters. The size of the clusters differed depending on the specific survey requirements. The LFS used one of the clusters that contained ten dwelling units.

Current Master Sample The Quarterly Labour Force Survey (QLFS) frame has been developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a master sample of primary sampling units (PSUs) which comprises EAs that are drawn from across the country.

The sample is designed to be representative at provincial level and within provinces at metro/nonmetro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four subgroups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the redesigned Labour Force Survey (i.e. the QLFS) is based on a stratified twostage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Sample rotation Each quarter, a ¼ of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings will remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, say two quarters and a new household moves in then the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (unoccupied).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire of QLFS is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

Eastern Cape had the largest share of women in the total population in South Africa. In 2022, 52.7 percent of the people in the province were women. Limpopo and KwaZulu-Natal followed closely, with a share of 52.5 and 52.1 percent, respectively. In absolute terms, Gauteng had the largest number of women residing there, at 8.1 million.

Abstract

The ‘South African Population Research Infrastructure Network’ (SAPRIN) is a national research infrastructure funded through the Department of Science and Innovation and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise and share the longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 113 113 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 38 479; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 139 250.

This dataset represents a snapshot of the continually evolving data in the underlying longitudinal databases maintained by the SAPRIN nodes. In these databases the rightmost extend of the individual's surveillance episode is indicated by the data collection date of the last time the individual's membership of a household under surveillance has been confirmed. Each dataset has a right censor date (31 December 2017 for the current version of the dataset) and individual surveillance episodes are terminated at that point if the individual is still under surveillance beyond the cut-off date.

Each individual surveillance episode is associated with a physical location, for internal residency episodes it is the actual place of residence of the individual, for external residence episodes (periods of temporary migration) it is the place of residence of the individual's household. If an individual change their place of residency from one location within the surveillance area to another location still within the surveillance area, the episode at the original location is terminated with a location exit event, and a new episode starts with a location entry event at the destination location. It is also possible for the household the individual is a member of, to change their place of residency in the surveillance area, whilst the individual is externally resident (is a temporary migrant), in which case the individual's external resident episode will also be split with a location exit-entry pair of events.

At every household visit written consent is obtained from the household respondent for continued participation in the surveillance and such consent can be withdrawn. When this happens all household members' surveillance episodes are terminated with a refusal event. It is possible for households to again provide consent to participate in the surveillance after some time, in such cases surveillance events are restarted with a permission event.

As mentioned previously, surveillance episodes are continually extended by the last data collection event if the individual remains under surveillance. In certain cases, individuals may be lost to follow-up and surveillance episodes where the date of last data collection is more than one year prior to the right censor data are terminated as lost to follow up at that last data collection date. Individuals with data collection dates within a year of the right censor date is considered still to be under surveillance up to this last data collection date.

Each surveillance episode contains the identifier of the household the individual is a member of during that episode. Under relatively rare circumstances it is possible for an individual to change household membership whilst still resident at the same location, or to change membership whilst externally resident, in these cases the surveillance episode will be split with a pair of membership end and membership start events. More commonly membership start and end events coincide with location exit and entry events or in- and out-migration events. Memberships also obviously start at birth or enumeration and end at death, refusal to participate or lost to follow-up.

In about half of the cases, individuals have a single episode from first enumeration, birth or in-migration, to their eventual death, out-migration or currently still under surveillance. In the remaining cases, individuals transition from internal residency to external residency via out-migration, or from one location to another via internal migration with a location exit and entry event, or some other rarer form of transition involving membership change, refusal or lost to follow-up. Usually these series of surveillance episodes are continuous in time, with no gaps between episodes, but gaps can form, e.g. when an individual out-migrates and end membership with the household and so is no longer under surveillance, only to return via in-migration at some future date and take up membership with same or different household.

The SAPRIN Individual Surveillance Episodes 2020 Datasets consists of three types of the Demographic surveillance datasets: 1.SAPRIN Individual Surveillance Episodes 2020: Basic Dataset. This dataset contains only the internal and external residency episodes for an individual. 2.SAPRIN Individual Surveillance Episodes 2020: Age-Year-Delivery Dataset. This dataset splits the basic surveillance episodes at calendar year end and at the date when the age in years (birth-day) of an individual changes. In the case of women who have given births, episodes are split at the time of delivery as well. 3.SAPRIN Individual Surveillance Episodes 2020: Detailed Dataset. This dataset adds to the dataset 2 time-varying attributes such as education, employment, marital status and socio-economic status.

Geographic coverage

The South African Population Research Infrastructure Network (SAPRIN) currently represents a network of three Health and Demographic Surveillance System (HDSS) nodes located in rural South Africa, namely: 1) MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, which has collected data since 1993. The nodal website is: http://www.agincourt.co.za; 2) the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, which has collected data since 1996.The nodal website is: N/A; 3) and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, which has collected data since 2000.The nodal website is: http://www.ahri.org.

The Agincourt HDSS covers a surveillance area of approximately 420 square kilometres and is located in the Bushbuckridge District, Mpumalanga in the rural northeast of South Africa close to the Mozambique border. At baseline in 1992, 57 600 people were recorded in 8900 households in 20 villages; by 2006, the population had increased to about 70 000 people in 11 700 households. As of December 2017, there were 113 113 people under surveillance of whom 28% were not resident within the surveillance area, with a total of about 2m person years of observation. 33% of the population is under 15 years old. The population is almost exclusively Shangaan-speaking.The Agincourt HDSS has population density of over 200 persons per square kilometre. The Agincourt HDSS extends between latitudes 24° 50´ and 24° 56´S and longitudes 31°08´ and 31°´ 25´ E. The altitude is about 400-600m above sea level.

DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 kilometres from Polokwane, the capital city of Limpopo Province and 15-50 kilometres from the University of Limpopo. The site covers an area of approximately 400 square kilometres . The initial total population observed was about 8 000 but the field site was expanded in 2010. As of December 2017, there were 38 479 people under surveillance, of whom 22% were not resident within the surveillance area, with about 400,000 person years of observation. 30% of the population is under 15 years old. The population is predominantly Sotho speaking. Most households have electricity. Some households have piped water either inside the house or in their yards, but most fetch water from taps situated at strategic points in the villages. Most households have a pit latrine in their yards. The area lies between latitudes and 23°65´ and 23°90´S and longitudes 29°65´ and 29°85´E. The HDSS is located on a high plateau area (approximately 1250 m above sea level) where communities typically consist of households clustered in villages, with access to local land for small-scale food production.

Africa Health Research Institute (AHRI) is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the Kwamsane township stradles the highway) and in the north by the Inyalazi river for portions of the boundary. The surveillance area is approximately 850 square kilometres. As of December 2017, there were 139 250 people under surveillance of whom 28% were not resident within the surveillance area, with about 1.7m person years of observation. 32% of the population is under 15 years old. The population is almost exclusively Zulu-speaking. The surveillance area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20-3000 people per square kilometre). The area lies between latitudes -28°24' and 28°20'N and longitudes 32°10' and 31°58'E.

Analysis unit

Households and individuals

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of

Description: The study makes assessments of the state of young people in South Africa in relation to education, economic and civic participation, and health and wellbeing. The study consisted of a literature review, secondary data analysis and a nationally representative survey of 3 541 young people aged 18-35 years. Abstract: The study makes assessments of the state of young people in South Africa in relation to education, economic and civic participation, and health and well-being. The study consisted of a literature review, secondary data analysis and a nationally representative survey of 3 541 young people aged 18-35 years. The Status of the Youth Report (SYR) 2003-2004 was commissioned by the Umsobomvu Youth Fund as a background document against which to make future, regular assessments of the state of young people in South Africa. The study had two components, first, a review of existing literature and available secondary data sources and second, a nationally representative survey of young people between the ages of 18 and 35 years of age, which is documented in this data set. The topics covered included in the data set included education, labour market participation, health and disability, crime and violence, and social integration. Face-to-face interview Young people residing in South African households between ages 18 and 35. A self-weighting sample was designed, based on the most recent available data from Statistics South Africa. Thus, the major reporting domains of the sample were drawn so that they are proportional to that of Census 2001. Households (primary sampling units) were selected to render a national sample of 3 500 young people, representative of population group and province. The Census 2001 enumeration areas (EAs) selected by the HSRC's Surveys, Analysis, Modelling and Mapping Unit were associated with their different municipalities and plotted on a national map. Route maps were prepared to identify each primary sample unit in the sample design within each district and in each province. These maps served as a guide for the survey team into the correct enumeration area and to the selected households within each area. The original sample design was 3 500.The fieldwork company targeted 3 600, so as to allow for refusals and incomplete questionnaires. Response rate to the targeted sample of 3 541 is 98.36 percent. Minority population groups were slightly over-sampled in the study but, as this is so slight, it does not change the self-weighting nature of the sample.