Core Inflation Rate MoM in South Africa increased to 0.30 percent in June from 0 percent in May of 2025. This dataset includes a chart with historical data for South Africa Core Inflation Rate MoM.

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The 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/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area 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 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

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

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an

Core Consumer Prices in South Africa increased to 102.20 points in June from 101.90 points in May of 2025. This dataset provides the latest reported value for - South Africa Core Consumer Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Lithic technologies have been used to trace dispersals of early human populations within and beyond Africa. Convergence in lithic systems has the potential to confound such interpretations, implying connections between unrelated groups. Due to their reductive nature, stone artefacts are unusually prone to this chance appearance of similar forms in unrelated populations. Here we present data from the South African Middle Stone Age sites Uitpanskraal 7 and Mertenhof suggesting that Nubian core reduction systems associated with Late Pleistocene populations in North Africa and potentially with early human migrations out of Africa in MIS 5 also occur in southern Africa during early MIS 3 and with no clear connection to the North African occurrence. The timing and spatial distribution of their appearance in southern and northern Africa implies technological convergence, rather than diffusion or dispersal. While lithic technologies can be a critical guide to human population flux, their utility in tracing early human dispersals at large spatial and temporal scales with stone artefact types remains questionable.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Benthic and planktic foraminifera counts from three vibracores within Concession 20D on the Orange Shelf of western South Africa. The vibracores from which foraminifera were subsampled include inner shelf core 12820D, outer shelf core 14820D, and outer shelf core 14620D. Counts are presented as absolute abundances and were counted to a 300-300 benthic-planktic threshold. Where this threshold could not be met, the total amount of benthic and planktic foraminifera for all sampled sediment was noted.

Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database (http://dx.doi.org/10.7910/DVN/MZLXVQ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, Round 6 (2014-2015) 36 countries, Round 7 (2016-2018) 34 countries, and Round 8 (2019-2021). The survey covered 39 countries in Round 9 (2021-2023).

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens of South Africa who are 18 years and older

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

South Africa - Sample size: 1,582 - Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample - Stratification: Region and urban-rural location - Stages: PSUs (from strata), start points, households, respondents - PSU selection: Probability Proportionate to Population Size (PPPS) - Cluster size: 8 households per PSU - Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval - Respondent selection: Gender quota filled by alternating interviews between men and women; respondents of appropriate gender listed, after which computer randomly selects individual - Weighting: Weighted to account for individual selection probabilities

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 9 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data.

The questionnaire consists of three parts: 1. Part 1 captures the steps for selecting households and respondents, and includes the introduction to the respondent and (pp.1-4). This section should be filled in by the Fieldworker. 2. Part 2 covers the core attitudinal and demographic questions that are asked by the Fieldworker and answered by the Respondent (Q1 – Q100). 3. Part 3 includes contextual questions about the setting and atmosphere of the interview, and collects information on the Fieldworker. This section is completed by the Fieldworker (Q101 – Q123).

Response rate

Response rate was 85%.

Sampling error estimates

The sample size yields country-level results with a margin of error of +/-2.5 percentage points at a 95% confidence level.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

Abstract

The primary objective of SASAS is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa to be able to carefully and consistently monitor and explain changes in attitudes amongst various socio-demographic groupings. The SASAS explores a wide range of value changes, including the distribution and shape of racial attitudes and aspirations, attitudes towards democratic and constitutional issues, and the redistribution of resources and power. Moreover, there is also an explicit interest in mapping changing attitudes towards some of the moral issues that confront and are fiercely debated in South Africa, such as gender issues, AIDS, crime and punishment, governance, and service delivery. The SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how its changing political and institutional structures interact over time with changing social attitudes and values.

Geographic coverage

The survey has national coverage

Analysis unit

The units of analysis in the study are households and individuals

Universe

The population under investigation includes adults aged 16 and older in private households in South Africa

Kind of data

Sample survey data [ssd]

Sampling procedure

The South African Social Attitudes Survey (SASAS) is a nationally representative survey series that has been conducted on an annual basis by the Human Sciences Research Council's (HSRC) since 2003. The survey has been designed to yield a representative sample of adults aged 16 years and older. The sampling frame for the survey is the HSRC's second Master Sample, which was designed in 2007 and consists of 1 000 primary sampling units (PSUs). The 2001 population census enumerator areas (EAs) were used as PSUs.

These PSUs (EAs) were drawn, with probability proportional to size, from a sampling frame created by Professor David Stoker containing all 80,787 of the 2001 EAs. This sampling frame uses the estimated number of dwelling units (DUs) in an EA (PSU) as a measure of size. The sampling frame was annually updated to coincide with StatsSA's mid-year population estimates in respect of the following variables: province, gender, population group, and age group. In updating the 2007 version of this sampling frame, additional use was made of (a) the GeoTerraImage (GTI) residential structure count in all metropolitan EAs in 2004/2006 and (b) the ESKOM counts of dwelling units in all cities, towns, townships and villages.

The HSRC's second master sample excludes special institutions (such as hospitals, military camps, old age homes, school and university hostels), recreational areas, industrial areas, vacant EAs as well as the 1000 EAs included in the first HSRC's master sample (2003-2006). It therefore focuses on dwelling units or visiting points as secondary sampling units (SSUs), which have been defined as 'separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc.'.

For the 2007 SASAS round of interviewing, a sub-sample of 500 PSUs was drawn from the HSRC's 2nd Master Sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. Within each stratum, the allocated number of PSUs was drawn using probability proportional to size sampling technique with the estimated number of dwelling units in the PSU as measure of size. In each of these drawn PSUs, 14 dwelling units were selected and systematically grouped into two sub-samples of seven, each corresponding to the two SASAS questionnaire versions.

Selection of individuals

Interviewers called at each visiting point selected from the 2nd HSRC master sample and listed all those eligible for inclusion in the sample, that is, all persons currently aged 16 or over and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

Mode of data collection

Face-to-face [f2f]

Research instrument

To accommodate the wide variety of topics included in the survey, two questionnaires were administered simultaneously. Apart from the standard set of demographic and background variables, each version of the questionnaire contained a harmonised core module.

The questions contained in the core modules of the two SASAS questionnaires (demographics and core thematic issues) were asked of 7000 respondents, while the remaining rotating modules were asked of a half sample of approximately 3500 respondents each.

The core module remains constant for with the aim of monitoring change and continuity in a variety of socio-economic and socio-political variables. In addition, a number of themes are accommodated in rotation. The rotating element of the survey consists of two or more topic-specific modules in each round of interviewing and is directed at measuring a range of policy and academic concerns and issues that require more detailed examination at a specific point in time than the multi-topic core module would permit.

Topics included in the questionnaires are: democracy, national identity, public services, moral issues, crime, voting, demographics and other classificatory variables.

Rotating modules are: child poverty, poverty, household expenditure, climate change / global warming, Soccer World Cup, service delivery, Batho Pele principles and smoking and tobacco behaviour.

International Social Survey Programme. (ISSP web page:www.issp.org/)

The International Social Survey Programme (ISSP) is run by a group of research organisations, each of which undertakes to field annually an agreed module of questions on a chosen topic area. SASAS 2003 represents the formalisation of South Africa's inclusion in the ISSP, the intention being to include the module in one of the SASAS questionnaires in each round of interviewing. Each module is chosen for repetition at intervals to allow comparisons both between countries (membership currently stands at 48) and over time. In 2007, the chosen subject was the leisure time and sport and the module was carried in version two of the questionnaire (Qs.1-60). This data can be accessed through the ISSP data portal (see link above).

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.

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Abstract

This dataset is the election survey conducted in South Africa by Robert Mattes of the Democracy in Africa Research Unit at the University of Cape Town in 2015. The survey collects data using standard questions from two international election studies, the Comparative National Elections Survey (CNEP), and the Comparative Study of Electoral Systems (CSES). The Comparative National Elections Survey is coordinated by the Mershon Center for International Security Studies at Ohio State University (https://u.osu.edu/cnep/). The Comparative Study of Electoral Systems is a collaborative program of research among election study teams from around the world, run by the Center for Political Studies and GESIS, Leibniz Institute for the Social Sciences, in Germany, and the University of Michigan in the US (http://www.cses.org/). The South African study includes additional questions. The study and the earlier 2004 CNEP for South Africa are part of a series of South African surveys conducted by DARU, called the South African National Election Study.

Geographic coverage

This survey has national coverage.

Analysis unit

Individuals

Universe

The universe of the study is citizens in South Africa.

Kind of data

Sample survey data

Sampling procedure

The survey used a random, nationally representative, stratified, area probability cluster sample. Primary sampling units were census enumerator areas (EAs) selected as a random sample, with probability proportionate to population size. All EAs were stratified by 1) Province, 2) Urban/Rural and 3) Race. Within each EA, a skip interval of 10 dwellings to select a household was used. That is, walking in a designated direction away from the start point, selecting the 10th household for the first interview, counting dwellings on both the right and the left (and starting with those on the right if they are opposite each other). Once the household was chosen, the interviewer randomly selected an individual respondent within the household to be interviewed (altering gender quota). The total number of household in the sample was 1300.

Sampling deviation

If the household was vacant, if the household refused to participate, if the selected person refused to be interviewed, or if the selected respondent is not available after two callbacks, interviewers were instructed to move to the next house in the walk pattern (i.e. every tenth house). They were not permitted to substitute within a household

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey collected data using core questionnaires from both the Comparative National Elections Project and the Comparative Study of Electoral Systems, as well as a separate questionnaire developed by DARU.

Cleaning operations

The data was checked and cleaned by the original team at the Democracy in Africa Research Unit. DataFirst undertook further cleaning on the data, including the consolidation of data documentation.

Response rate

The response rate for the survey that the CSES Module appeared in was 34%.

Abstract

Stastistics South Africa (Stats SA), in partnership with the South African Medical Research Council (SAMRC), conducted the South Africa Demographic and Health Survey 2016 (SADHS 2016) at the request of the National Department of Health (NDoH). Technical assistance was provided through The DHS Program. Timely information about the health of the nation is essential for monitoring and evaluation. Survey data collection took place from 27 June 2016 to 4 November 2016.

The primary objective of the 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 coverage

Analysis unit

Household Woman Man Children

Universe

the survey covered All household members, all Wome age 15 years and olde, Men age 15 years and older and children

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.

A listing operation was carried out in all selected PSUs from January to March 2016, and the updated lists of DUs served as a sampling frame for the selection of DUs in the second stage. In the second stage of selection, a fixed number of 20 DUs per cluster were selected with systematic selection from the created listing. All households in a selected DU were eligible for interviews.

Some of the selected PSUs were informal, unstructured settlements with no clear identifications of DUs. To ensure listing coverage within each informal, unstructured PSU selected, segmentation was carried out, with the PSU divided into multiple segments of about 20 DUs each. Only one segment was selected at random for the survey; in segments with 20 DUs or fewer, all DUs in the segment were eligible for the survey. In segments with more than 20 DUs, 20 DUs were randomly selected and were eligible for the survey. A cluster in the SADHS 2016 is therefore either a PSU or a segment of a PSU.

In half of selected DUs, all households were eligible for interviews with the Household Questionnaire, and all women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible for interviews with a standard individual questionnaire. Within this subsample, households in every other DU were eligible to have their salt tested for the presence of iodine.

In the remaining half of DUs, all households were eligible for interviews with the Household Questionnaire, and all women and men age 15 and older who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible for individual interviews and for biomarker collection. Women age 15-49 and men age 15-59 were eligible for the standard individual questionnaire, as well as a South Africa-specific module on adult health; women age 50 and older and men age 60 and older were eligible for a few sections of the individual questionnaire and the adult health module. In addition, children age 0-59 months were eligible for biomarker collection.

Finally, in all households in selected DUs, one woman age 18 and older was selected for a module on domestic violence. In addition, for each child age 0-5 whose biological mother did not live in the household, a guardian was eligible to complete the Caregiver’s Questionnaire.

Mode of data collection

Computer Assisted Personal Interview [capi]

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.

Appropriate analysis weights were calculated, taking the design probabilities and the response rate into account. Standard methods of analysis (Rutstein and Rojas 2006) were applied involving conversion of all dates to century month codes to facilitate calculation of ages at the time of different life events.

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.

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