Abstract

The Beijing Platform for Action which emerged from the 1995 Fourth United Nations World Conference on Women called for the development of 'suitable statistical means to recognise and make visible the full extent of the work of women and all their contributions to the national economy, including their contribution in the unremunerated and domestic sectors'. During 2000, Statistics South Africa (Stats SA) conducted the fieldwork for the first national time use study in the country. The aim of the survey was to provide information on the way in which different individuals in South Africa spend their time. Such information contributes to greater understanding of policymakers on the economic and social well-being of different societal groups. In particular, the study was intended to provide new information on the division of both paid and unpaid labour between women and men, and greater insight into less well understood productive activities such as subsistence work,casual work and work in the informal sector.

The survey thus had dual objectives: (1) improvement of concepts, methodology and measurement of all types of work and work-related activity, and (2) the feeding of information into better policy-making, with a particular focus on gender equity.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis for the survey include households and individuals

Universe

The survey covered household members in South Africa, ten years old and above

Kind of data

Sample survey data [ssd]

Sampling procedure

The time use study sample frame was based on the frame prepared for the 1999 Survey of activities of young people (SAYP). This sample frame was based on the 1996 population census enumerator areas (EAs) and the number of households counted in the 1996 population census. The sampled population excluded all prisoners in prison, patients in hospital, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. The 16 EA types from the 1996 Population Census were condensed into four area types, or strata. The four strata were formal urban, informal urban, non-commercial farming rural, and commercial farming areas. Institution type EAs were excluded from the sample.

The EAs were explicitly stratified by province, and within a province by the four strata. The sample size (10 800 dwelling units, with 3 600 units in each of the three tranches) was disproportionately allocated to the explicit strata 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). Primary sampling units (PSUs) consisted of an EA of at least 100 dwelling units. Where an EA contained less than 100 dwelling units, EAs were pooled (using Kish's method of pooling) to meet this requirement. Most EAs had fewer than 100 dwelling units. The dwelling unit was taken as the ultimate sampling unit (USU).

Firstly, a two stage sampling procedure was applied. The allocated number of PSUs was systematically selected with probability proportional to size in each explicit stratum (with the measure of size being the number of dwelling units in a PSU). In each PSU, a systematic sample of 12 households was drawn.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the time use survey was comprised of three sections. Section one covered details of the household. Section two covered demographic details of the first person selected as a respondent in that household. Section three consisted of a Background and methodology diary in which to record the activities performed by the first person selected during the 24 hours between 4 am on the day preceding the interview and 4 am on the day of the interview. Sections four and five were for the second selected person in the household but were otherwise identical to sections two and three respectively.

The household and demographic sections of the questionnaire contained many of the standard questions of Stats SA household surveys. This was done so as to facilitate comparison across surveys. These sections also contained some additional questions on issues that would be likely to affect time use. For the household section, for example, there were questions on access to household aids such as washing machines and vacuum cleaners. In the demographic section there were questions about the presence of the respondent's young children in the household.

The diary, which forms the core instrument of a time use study, was divided into half-hour slots. Respondents were asked an open-ended question as to the activities performed during a given half-hour. These activities were then post-coded by the fieldworker according to the activity classification system (see below). The respondent could report up to three activities for each time slot. Where there was more than one activity reported for a half hour, the respondent was asked whether these activities were conducted simultaneously, or one after the other. For each recorded activity, the questionnaire also included two location codes. The first code provides for eight broadly defined locations plus the mobile activity of travel. Where the location of a particular activity could be classified as more than one of the given options, the option highest on the list took precedence. For example, a domestic worker was classified as working in someone else's dwelling rather than in a workplace. The second code distinguished between interior (inside) and exterior (outside) for the eight broadly-defined locations, and distinguished the mode of travel for all travel activity.

Cleaning operations

The data from the diary were captured in Sybase at Stats SA head office through a custom-designed data capture programme. The programme contained some in-built checks. Further checks were done manually prior to and after capture. The data were subsequently downloaded into SAS format, and the SAS programme was used for analysis.

As of 2023/2024, the highest number of commercial crime offenses recorded by the South African police was in the Midrand location, which is located in the province of Gauteng. The number of offenses in this station amounted to over 1,700 offenses. Sandton in Gauteng came in second, with 1,620 offenses.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Any structure intended or used for human habitation - Households: A household is a group of persons who live together and provide themselves jointly with food or other essentials for living, or a single person who lives alone. - Group quarters: Living quarters where certain facilities are shared by groups of individuals or households. They can be divided into: (a) hotels, motels, guesthouses, etc.; (b) workers' hostels and students' residences; and (c) institutions.

Universe

All persons present in the country on the night of 9-10 October, 2011

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics South Africa

SAMPLE DESIGN: Systematic stratified sample. 1 in 10 sample of household records classified as housing units or converted households, and an independent 1 in 10 sample of persons who resided in other living quarters. Local municipalities were the primary strata and demographic characteristics of persons within the household were used a secondary strata.

SAMPLE UNIT: household

SAMPLE FRACTION: 8.50%

SAMPLE SIZE (person records): 4,418,594

Mode of data collection

Face-to-face [f2f]

Research instrument

Three separate questionnaires were used to enumerate the household population (form A), transient individuals and individuals staying in hotels on census night (form B), and the institutional population (form C).

Abstract

The Time Use Survey (TUS) is a household-based survey that measures and analyses the time spent by women and men, girls and boys, the rich and the poor, on different activities over a specified period. Statistics South Africa (Stats SA) conducts time use surveys using the 'yesterday' diary approach. A 'yesterday' diary is one in which the respondent is asked what they did for each period in the 24 hours of a day preceding the survey interview. Unlike data from other surveys, time use data reflects what activities are performed, how they are performed and how long it takes to perform such activities. Such activities include paid work, unpaid work, volunteer work, domestic work, leisure and personal activities.

Stats SA conducted the first TUS in 2000 and the second one in 2010. The TUS aims to provide information on the division of both paid and unpaid labour between women and men, shed light on the reproductive and leisure activities of household members, and provide information about less well-understood productive activities such as subsistence work, casual work and work in the informal sector. Therefore, TUS surveys can be used for gender policy analysis in relation to employment and unemployment, services for children, the elderly and people with disabilities, and provision of basic household services such as electricity and water that obviate the need for manual collection of fuel and water for household use.

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The TUS sample covered the non-institutional population aged 10 years and above excluding those living in worker hostels - thus representing an estimated 39,9 million people.

Kind of data

Sample survey data

Sampling procedure

The TUS 2000 sample frame was based on the frame prepared for the 1999 Survey of activities of young people (SAYP). This sample frame was based on the 1996 population census enumerator areas (EAs) and the number of households counted in the 1996 population census. The sampled population excluded all prisoners in prison, patients in hospital, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. The 16 EA types from the 1996 Population Census were condensed into four area types, or strata. The four strata were formal urban, informal urban, non-commercial farming rural, and commercial farming areas. Institution type EAs were excluded from the sample.

The sample is based on a stratified two-stage 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. The EAs were explicitly stratified by province, and within a province by the four strata. The sample size (10 800 dwelling units, with 3 600 units in each of the three tranches) was disproportionately allocated to the explicit strata 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 an EA of at least 100 dwelling units. Where an EA contained less than 100 dwelling units, EAs were pooled (using Kish's method of pooling) to meet this requirement. Most EAs had fewer than 100 dwelling units. The dwelling unit was taken as the ultimate sampling unit (USU).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the TUS is comprised of five sections:

Section 1 - details of all household members Section 2 - demographic details of the first person selected (respondent one) in each household Section 3 - recorded activities performed by respondent one in each household (diary) Section 4 - demographic details of the second person selected (respondent two) in each household Section 5 - recorded activities performed by respondent two in each household (diary)

The diary was divided into half-hour slots. Respondents were asked an open-ended question on the activities performed during each half-hour period. These activities were then post-coded by the fieldworker according to the activity classification system. The respondent could report up to three activities for each time slot. Where there was more than one activity reported for a half hour, the respondent was asked whether these activities were conducted simultaneously, or one after the other.

The sections of the questionnaire for household and demographic data collection also contained additional questions on issues likely to affect time use. For example questions on access to household appliances owned. The questionnaire includes two location codes for each recorded activity. The first code provides for eight broadly-defined locations plus the mobile activity of travel. Where the location of a particular activity could be classified as more than one of the given options, the option highest on the list took precedence. The second code distinguished whether the activity was done inside or outside for the eight broadly-defined locations, and distinguished the mode of travel for all travel activity.

As of 2023/2024, Mitchells Plain, located in the South African province of the Western Cape, recorded the highest number of drug-related crimes in the country, with some 4,900 crimes. Kraaifontein, also located in the Western Cape region, ranked second with around 3,200 cases.

Abstract

The Time Use Survey (TUS) is a household-based survey that measures and analyses the time spent by women and men, girls and boys, the rich and the poor, on different activities over a specified period. Statistics South Africa (Stats SA) conducts time use surveys using the 'yesterday' diary approach. A 'yesterday' diary is one in which the respondent is asked what they did for each period in the 24 hours of a day preceding the survey interview. Unlike data from other surveys, time use data reflects what activities are performed, how they are performed and how long it takes to perform such activities. Such activities include paid work, unpaid work, volunteer work, domestic work, leisure and personal activities.

Stats SA conducted the first TUS in 2000 and the second one in 2010. The TUS aims to provide information on the division of both paid and unpaid labour between women and men, shed light on the reproductive and leisure activities of household members, and provide information about less well-understood productive activities such as subsistence work, casual work and work in the informal sector. Therefore, TUS surveys can be used for gender policy analysis in relation to employment and unemployment, services for children, the elderly and people with disabilities, and provision of basic household services such as electricity and water that obviate the need for manual collection of fuel and water for household use.

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The TUS sample covered the non-institutional population aged 10 years and above excluding those living in worker hostels - thus representing an estimated 39,9 million people.

Kind of data

Sample survey data

Sampling procedure

The Time Use Survey (TUS) utilised the frame developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size of the survey. The sample size for the TUS is roughly 30 000 dwellings.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/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, rural formal and tribal areas.

The current sample size is 3 080 PSUs 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 TUS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the TUS is comprised of five sections:

Section 1 - details of all household members Section 2 - demographic details of the two selected individuals in a household Section 3 - economic activities of the two selected individuals in a household Section 4 - main work activity of the two selected individuals in a household Section 5 - recorded activities performed by the selected person in a household (diary)

The diary was divided into half-hour slots. Respondents were asked an open-ended question as to the activities performed during a given half-hour. These activities were then post-coded by the fieldworker according to the activity classification system.The respondent could report up to three activities for each time slot. Where there was more than one activity reported for a half hour, the respondent was asked whether these activities were done simultaneously, or one after the other.

The questionnaire includes two location codes for each recorded activity. The first code provides for eight broadly-defined locations plus the mobile activity of travel. Where the location of a particular activity could be classified as more than one of the given options, the option highest on the list took precedence. The second code distinguished whether the activity was done inside or outside for the eight broadly-defined locations, and distinguished the mode of travel for all travel activity.

Data appraisal

Question 1.4 in the questionnaire for the Time Use Survey 2010 is about the travel distance for wood/dung collection. It has six response options. Option 6 (Not Applicable) would be for the households not relying on wood/dung for fuel. However, the corresponding variable in the data file "Q14FarWood" has seven choices, including 'Other', which has quite significant number of responses (720 households). And these 'Other' households still have responses for Q1.5 about 'who's collecting wood/dung'. So they cannot relate to missing responses. It is not clear what this "Other" response category is and Statistics SA has been approached for further information.

United States AHE: sa: PW: EH: Offices of Specialty Therapists data was reported at 33.440 USD in Mar 2025. This records a decrease from the previous number of 33.480 USD for Feb 2025. United States AHE: sa: PW: EH: Offices of Specialty Therapists data is updated monthly, averaging 21.160 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 33.480 USD in Feb 2025 and a record low of 10.930 USD in Jan 1990. United States AHE: sa: PW: EH: Offices of Specialty Therapists data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

United States AHE: sa: PW: EH: Offices of Other Health Practitioners data was reported at 31.050 USD in Mar 2025. This records an increase from the previous number of 30.980 USD for Feb 2025. United States AHE: sa: PW: EH: Offices of Other Health Practitioners data is updated monthly, averaging 18.340 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 31.050 USD in Mar 2025 and a record low of 9.460 USD in Jan 1990. United States AHE: sa: PW: EH: Offices of Other Health Practitioners data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Abstract

The primary objective of the South Africa Demographic and Health Survey (SADHS) 2016 is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the SADHS 2016 collected information on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of contraceptives; breastfeeding practices; nutrition; childhood and maternal mortality; maternal health, including antenatal and postnatal care; key aspects of child health, including immunisation coverage and prevalence and treatment of acute respiratory infection (ARI), fever, and diarrhoea; potential exposure to the risk of HIV infection; coverage of HIV counselling and testing (HCT); and physical and sexual violence against women. Another critical objective of the SADHS 2016 is to provide estimates of health and behaviour indicators for adults age 15 and older, including use of tobacco, alcohol, and codeine-containing medications. In addition, the SADHS 2016 provides estimates of the prevalence of anaemia among children age 6-59 months and adults age 15 and older, and the prevalence of hypertension, anaemia, high HbA1c levels (an indicator of diabetes), and HIV among adults age 15 and older.

The information collected through the SADHS 2016 is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the SADHS 2016 is the Statistics South Africa Master Sample Frame (MSF), which was created using Census 2011 enumeration areas (EAs). In the MSF, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighbouring EAs were pooled together to form new PSUs, and large EAs were split into conceptual PSUs. The frame contains information about the geographic type (urban, traditional, or farm) and the estimated number of residential dwelling units (DUs) in each PSU. The sampling convention used by Stats SA is DUs. One or more households may be located in any given DU; recent surveys have found 1.03 households per DU on average.

Administratively, South Africa is divided into nine provinces. The sample for the SADHS 2016 was designed to provide estimates of key indicators for the country as a whole, for urban and non-urban areas separately, and for each of the nine provinces in South Africa. To ensure that the survey precision is comparable across provinces, PSUs were allocated by a power allocation rather than a proportional allocation. Each province was stratified into urban, farm, and traditional areas, yielding 26 sampling strata.

The SADHS 2016 followed a stratified two-stage sample design with a probability proportional to size sampling of PSUs at the first stage and systematic sampling of DUs at the second stage. The Census 2011 DU count was used as the PSU measure of size. A total of 750 PSUs were selected from the 26 sampling strata, yielding 468 selected PSUs in urban areas, 224 PSUs in traditional areas, and 58 PSUs in farm areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used in the SADHS 2016: the Household Questionnaire, the individual Woman’s Questionnaire, the individual Man’s Questionnaire, the Caregiver’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to South Africa. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the questionnaires in English, the questionnaires were translated into South Africa’s 10 other official languages. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files for the SADHS 2016 were transferred via the IFSS to the Stats SA head office in Pretoria, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by a core group of four people; secondary editing was completed by 11 people. All persons involved in data processing took part in the main fieldwork training, and they were supervised by senior staff from Stats SA with support from ICF. Data editing was accomplished using CSPro software. Secondary editing was initiated in October 2016 and completed in February 2017. Checking inconsistencies in dates of immunisations was aided by the digital images of the immunisation page of the Road-to-Health booklet that had been collected on the tablet by fieldworkers at the time of the interview for that purpose.

Response rate

A total of 15,292 households were selected for the sample, of which 13,288 were occupied. Of the occupied households, 11,083 were successfully interviewed, yielding a response rate of 83%.

In the interviewed households, 9,878 eligible women age 15-49 were identified for individual interviews; interviews were completed with 8,514 women, yielding a response rate of 86%. In the subsample of households selected for the male survey, 4,952 eligible men age 15-59 were identified and 3,618 were successfully interviewed, yielding a response rate of 73%. In this same subsample, 12,717 eligible adults age 15 and older were identified and 10,336 were successfully interviewed with the adult health module, yielding a response rate of 81%. Response rates were consistently lower in urban areas than in nonurban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the SADHS 2016 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the SADHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SADHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings

See details of the data quality tables in Appendix C of the survey final report.

In 2020, the box office revenue generated in Nigeria decreased by 73 percent compared to 2019. Similarly, South Africa's box office dropped by 82 percent. Due to the coronavirus (COVID-19) pandemic, cinemas had to close. In Nigeria, they reopened in September 2020. Similarly, in South Africa movie theaters were closed for five months, after the country introduced a hard lockdown in March 2020.

Abstract

In 2009, Statistics South Africa (Stats SA) conducted a study to evaluate the state of agricultural statistics in the country. The research sought to evaluate the quality, quantity (depth and breadth) and frequency of agricultural statistics as provided in the country at the time. The research revealed, among others, that agricultural statistics, at the time, fell short in terms of the specified aspects. Critically, regarding quantity, the country lacked information on smallholder and subsistence agriculture. In addition, the agricultural sector lacked a comprehensive frame (farmer list) that covered all agricultural activities in the country as the current census of commercial agriculture was partially covering the sector. A decision was reached in 2010 to include three questions related to agriculture in the Population Census 2011 (Census 2011) questionnaire. The main objective was to identify all households involved in agriculture in the country in order to plan a frame for a proper agricultural census. The list of households engaged in agriculture generated from the above exercise of Census 2011 will complement the current tax based frame sourced from the South African Revenue Service (SARS) to develop a complete frame of all agricultural activities in the country. The data presented in this report is obtained from Census 2011 and provide useful insights on the geographic sphere. Specifically, the information presented is a result of the three agricultural questions, which were included in the population questionnaire. This information is critical for the measurement of the food security of the country at both national and household levels.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the collection of census data was a "farming enterprise", defined as "a legal unit or a combination of legal units that includes and directly controls all functions necessary to carry out its production activities".

Kind of data

Sample survey data [ssd]

Sampling procedure

i. Methodological modality for conducting the census The CCA 2007 used the classical approach.

ii. Frame The main source of the frame was the business register, which contains all businesses undertaking agricultural activities registered for VAT with the South African Revenue Service (the Tax Office).

iii. Complete and/or sample enumeration methods The 2007 CCA was conducted on the basis of a complete enumeration of farming enterprises.

Mode of data collection

Face-to-face [f2f]

Research instrument

The CCA used a single questionnaire for census data collection on:

· Ownership of farm · Particulars of the farming unit · Land-use during the reporting period · Field crops, horticultural products and forestry · Animals, animal products · Other income · Employment · Current expenditure · Purchased livestock, poultry and additional products · Market value of assets and capital expenditure during the financial year · Losses and expenditure due to theft, disaster, accidents and violent crimes · Farming debt · Agricultural services · Inventory · Balance sheet · Ocean (marine) fishing

The census questionnaire covered 14 of the 16 core items recommended for the WCA 2010. Core items not covered were (i) "Household size"; and (ii) "Main purpose of production of the holding".

Cleaning operations

1. DATA PROCESSING AND ARCHIVING Manual data entry was used for the census questionnaires. Data entry application with consistency checks and skip patterns was applied. Ratio imputation was used for both item and unit non-response.

2. CENSUS DATA QUALITY Several steps were put in place to ensure the quality of census results, for example: careful design of the questionnaire and its testing in pilot studies; preparation of training manuals and training of enumerators; equipping the capturing system with warnings and consistency checks. Comparisons were made with the frame and with the estimates of the 2002 CoCA, and with the estimates from the annual agriculture and related services survey, as well as with various sources that reported on the sector.

Employment, unemployment and economic inactivity levels and rates by age group, UK, rolling three-monthly figures, seasonally adjusted. Labour Force Survey. These are official statistics in development.

Geographic coverage

National Coverage

Universe

The target population is all births recorded on the NPR between 1998 and 2010 for South African citizens and permanent residents, regardless of which year the birth occurred. All births that occurred in South Africa with parents being non-South African citizens or not permanent residents were excluded.

Sampling procedure

The registration of births in South Africa is governed by the Births and Deaths Registration Act, 1992 (Act No. 51 of 1992), as amended, and is administered by the Department of Home Affairs (DHA) using Form DHA-24 (Notice of birth), which recently replaced Form BI-24 that was previously used. Notice of the birth must be given by one of the parents or; if neither parent is available to do so, the person having charge of the child or a person requested by the parents to do so. The person requested to register the birth must have a written mandate from the child's parents which must also include the reasons why neither of the parents is in a position to register the birth. The birth of a child outside the country; where at least one parent is a South African citizen; can be registered at any South African Mission abroad.Documentary proof in the form of a birth certificate of the foreign country must accompany the Notice of Birth.

The Act states that a child must be registered within 30 days of birth. Where the notice of a birth is given after the expiration of 30 days from the date of the birth, the Director-General may demand that reasons for the late notice be furnished and that the fingerprints be taken of the person whose notice of birth is given. Where the notice of a birth is given for a person aged 15 years and older, the birth shall be registered if it complies with the prescribed requirements for a late registration of birth.

Following the registration of a birth, a birth certificate is issued by the DHA. Citizens and permanent residents receive computer-printed abridged birth certificates and non-citizens receive handwritten certificates. The information of South African citizens and permanent residents is captured on the National Population Register (NPR).

The following persons and particulars are eligible to be included on the NPR:

• All children born of South African citizens and permanent residents when the notice of the birth is given within one year after the birth of the child.

• All children born of South African citizens and permanent residents when the notice of the birth is given one year after the birth of the child; together with the prescribed requirement for a late registration of birth.

• All South African citizens and permanent residents who, upon attainment of the age of 16, applied for and were granted identification cards (or books).

• All South African citizens and permanent residents who die at any age after birth.

• All South African citizens and permanent residents who depart permanently from South Africa.

The DHA captures information on places based on magisterial districts using the twelfth edition of the Standard Code List of Areas (Central Statistics Services, 1995). Stats SA then recodes the magisterial districts into district councils (DCs), metropolitan areas (metros) and provinces based on the 2011 municipal boundaries. The data sets for 1998 to 2010 have all been recoded according to the 2011 municipal boundaries.

It should be noted that the distribution of births by DCs, metros and provinces are approximate figures; as there was no perfect match of magisterial districts for all DCs, metros and provinces since some magisterial districts are situated in more than one DC, metro or province. Such magisterial districts were allocated to the district council where the majority of the land area falls (see the folder on maps). The only exception was with Nigel in Gauteng province. The majority of the land area of Nigel magisterial district is in Sedibeng district council (which is mainly farm areas and therefore sparsely populated) while the majority of the population lives in Ekurhuleni metropolitan area. As such, Nigel was classified to Ekurhuleni and not Sedibeng.

Magisterial district of birth refers to the district of birth occurrence for births registered before 15 years of age. For those that were registered from 15 years of age, district refers to the district of birth registration. Furthermore, from 2009, the processing of late birth registrations from age 15 were centralised at the DHA head office in Pretoria. As such, the late birth registrations processed in Pretoria from 15 years have a district code of Pretoria; even if they occurred in other areas. There were a few exceptional cases which were registered in Pretoria; but were not captured using the Pretoria code.

Mode of data collection

Other [oth]

Research instrument

NOTICE OF BIRTH - [Births and Deaths Registration Act 51 of 1992]

A. DETAILS OF THE CHILD

B. DETAILS OF FATHER (PARENT A)

C. DETAILS OF MOTHER (PARENT B)

D. ACKNOWLEDGEMENT OF PATERNITY OF A CHILD BORN OUT OF WEDLOCK

E. DETAILS OF THE LEGAL GUARDIAN/SOCIAL WORKER*

F. DECLARATION

G. FOR OFFICIAL USE ONLY - OFFICE OF ORIGIN

Cleaning operations

Data capturing of information on births is done by DHA officials. The data is captured directly onto the Population Register Database at Nucleus Bureau. These transactions are used to update the database of the NPR and the population register database. As soon as the DHA has captured the data; the data is made available on the mainframe. The data is then downloaded via ftp; or collected from the State Information Technology Agency (SITA) written on a CD by Stats SA. For the purpose of producing vital statistics, the following system is followed: all the civil transactions carried out at all DHA offices are written onto a cassette every day. At the end of every month, a combined set of cassettes is created containing all the transactions done for the month. These transactions are downloaded and the birth transactions are extracted for processing at Stats SA. The year in which the births are registered is the registration year. Using this information, Stats SA provides a breakdown of the registered births according to the year in which the births occurred.

While birth information sent to Stats SA is the same as that in the population register, there is a difference in the format between the two. On one hand, Stats SA’s data are based on births registered during the year (registration-based), while on the other hand, entries in the population register reflect the date of birth.

Data appraisal

Users are cautioned on the following limitations of the data:

• Father’s age had a high percentage of cases where information was unspecified or unknown for all the years.
• Data for 1998 and 1999 have incorrect information on month of birth, which could not be resolved.

Note: - Unknown : refers to cases where the answer provided is not correct or not possible given the options available. - Unspecified: refers to cases where no response was given.

Provides the number and value of dwelling units approved by sector (public/private) and by state, number and value of new houses, new other residential dwelling units approved by type of building, and the number and value of non-residential building jobs approved by type of building (i.e. by function such as 'retail and wholesale trade', 'offices') and value ranges. State data includes the number of private sector houses approved; number and value of new other residential dwellings by type of building such as flats, units or apartments in a building of one or two storeys; number and value of non-residential building jobs by type of building and sector; and for Greater Capital City Statistical Areas, the total number of dwelling units approved broken down by Houses, Dwellings Excluding Houses and Total Dwelling Units. Seasonally adjusted and trend estimates by state are included for the number of dwelling units and value of building approved. The quarterly value of building approved is shown in chain volume measure terms. Small geographic area data cubes are presented for Statistical Areas Level 2 and Local Government Areas. Small area data cubes will be released in an "Additional information" release five business days after the main publication.

Abstract

Marriage data: In South Africa Civil Marriages are administered through the Marriage Act, 1961 (Act No. 25 of 1961) as amended, and its associated regulations. Customary marriages are governed by the Recognition of Customary Marriages Act, 1998 (Act No. 120 of 1998) which came into effect on 15 November 2000. Civil unions (relationships between same-sex couples that are legally recognized by a state authority) are covered by the Civil Union Act, 2006 (Act No. 17 of 2006) which came into operation on 30 November 2006.

The South African Department of Home Affairs is responsible for the administration of marriages in South Africa, under these laws. After the ceremony of a marriage or a civil union, the marriage officer submits the data to the nearest office of the Department of Home Affairs (DHS), where the marriage / civil union details for citizens and permanent residents are recorded in the National Population Register (NPR). Statistics South Africa obtains data on marriages and civil unions from DHA through the State Information Technology Agency (SITA) for this dataset.

NOTE: In customary marriages, the two spouses and their witnesses present themselves at a DHA office in order to register a customary marriage. Therefore the province of registration is not necessarily the province of the place of usual residence of the couple since the registration of the marriage can take place in any DHA office.

Divorce data: The dissolution of registered marriages and civil unions is governed by the Divorce Act, 1979 as amended, and its associated regulations (Act No.70 of 1979) and the Jurisdiction of Regional Courts Amendment Act, 2008 (Act No. 32 of 2008) as amended which came into effect on 9 August 2010. The South African Department of Justice and Constitutional Development (DJCD) is responsible for managing divorces under these Acts. Statistics South Africa obtains the divorce data from the DJCD for this dataset.

NOTE: The data includes divorce applications that were concluded in 2012, that is, that were finalised and issued with decrees of divorce in 2012 by DJCD.

Geographic coverage

The data has national coverage.

Analysis unit

Individuals

Universe

The data covers all civil marriages that were recoreded by the Department of Home Affairs and all divorce applications that were granted by the Department of Justice and Constitutional Development in 2012 in South Africa.

Kind of data

Administrative records

Mode of data collection

Other

Data appraisal

Geography is problematic in this dataset as not all the data files have geographic data. The Civil Marriages and Civil Unions data files include a Province of Registration variable but the Customary Marriages data file does not. There is also no geographical data in the Divorces file. As this data file includes divorce data from only a subset of divorce courts, this lack of geographical information compromises its usability.

Economic inactivity (aged 16 to 64 years ) by reason (seasonally adjusted). These estimates are sourced from the Labour Force Survey, a survey of households. These are official statistics in development.

Actual weekly hours worked including by sex, full-time, part-time and second jobs, UK, rolling three-monthly figures published monthly, seasonally adjusted. Labour Force Survey. These are official statistics in development.

The population share with internet access in South Africa was forecast to continuously increase between 2024 and 2029 by in total 18.8 percentage points. After the ninth consecutive increasing year, the internet penetration is estimated to reach 98 percent and therefore a new peak in 2029. Notably, the population share with internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via any means. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

AHE: sa: PW: EH: Offices of Chiropractors data was reported at 27.630 USD in Mar 2025. This records an increase from the previous number of 27.100 USD for Feb 2025. AHE: sa: PW: EH: Offices of Chiropractors data is updated monthly, averaging 13.820 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 27.630 USD in Mar 2025 and a record low of 7.920 USD in Jan 1990. AHE: sa: PW: EH: Offices of Chiropractors data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.