Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33 000 dwelling units.

The sample for the QLFS 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.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

Mode of data collection

Face-to-Face and Computer Assisted Personal and Telephone Interview

Data on hostel standards of living in South Africa. Source: StatsSA Superweb, 2018 http://superweb.statssa.gov.za/webapi/jsf/login.xhtml?invalidSession=true&reason=Session+not+established

Data presented as a spreadsheet; Provides Electricity generated and available for distribution in South Africa since 2002.Linage: The data presented is extracted from Statistics South Africa (Stats SA) Electricity generated and available for distribution trends as published on https://www.statssa.gov.za/Publication date: 5/6/2025data source: Electricity generated and available for distribution(202503), Stats SA, published 6 May 2025Contact person: Elize van der Berg, Department of the Premier, Elize.VanDerBerg@westerncape.gov.za

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS 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.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Personal Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

Analysis unit

Woman, Birth, Child, Birth, Man, Household Member

Universe

Women age 15-49, Births, Children age 0-4, Men age 15-59, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: National Department of Health (NDoH) [South Africa], Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF.

SAMPLE UNIT: Woman SAMPLE SIZE: 8514

SAMPLE UNIT: Birth SAMPLE SIZE: 14144

SAMPLE UNIT: Child SAMPLE SIZE: 3548

SAMPLE UNIT: Man SAMPLE SIZE: 3618

SAMPLE UNIT: Member SAMPLE SIZE: 38850

Mode of data collection

Face-to-face [f2f]

Abstract

This dataset contains statistics on deaths in South Africa in 2012. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2012 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2012 and registered at the Department of Home Affairs in South Africa.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with two instruments: the death register and the medical certificate in respect of death.

Description: Data presented as a spreadsheet; Provides Population estimates by age for South Africa since 2002.Linage: The data presented is extracted from Statistics South Africa (Stats SA) Mid-year population estimates (MYPE) trends as published on https://www.statssa.gov.za/Publication Date: 15 July 2022Contact Person: Elize van der Berg, Department of the Premier, Elize.VanDerBerg@westerncape.gov.za

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage.

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm).

There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

For more information see the statistical release that accompanies the data.

Mode of data collection

Face-to-face [f2f]

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2020. The mortality and causes of death dataset is part of a regular series published by Stats SA, based on data collected through the civil registration system. This dataset is the most recent cumulative round in the series which began with the separately available dataset Recorded Deaths 1996.

The main objective of this dataset is to outline emerging trends and differentials in mortality by selected socio-demographic and geographic characteristics for deaths that occurred in the registered year and over time. Reliable mortality statistics, are the cornerstone of national health information systems, and are necessary for population health assessment, health policy and service planning; and programme evaluation. They are essential for studying the occurrence and distribution of health-related events, their determinants and management of related health problems. These data are particularly critical for monitoring the Sustainable Development Goals (SDGs) and Agenda 2063 which share the same goal for a high standard of living and quality of life, sound health and well-being for all and at all ages. Mortality statistics are also required for assessing the impact of non-communicable diseases (NCD's), emerging infectious diseases, injuries and natural disasters.

Geographic coverage

The survey has national coverage.

Analysis unit

Individuals

Universe

This dataset is based on information on mortality and causes of death from the South African civil registration system. It covers all death notification forms from the Department of Home Affairs for deaths that occurred in 1997-2020, that reached Stats SA during the 2021/2022 processing phase.

Kind of data

Administrative records

Mode of data collection

Other

Research instrument

The registration of deaths is captured using two instruments: form BI-1663 and form DHA-1663 (Notification/Register of death/stillbirth).

Data appraisal

This cumulative dataset is part of a regular series published by Stats SA and includes all previous rounds in the series (excluding Recorded Deaths 1996). Stats SA only includes one variable to classify the occupation group of the deceased (OccupationGrp) in the current round (1997-2020). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classifcation (Industry) in all previous rounds. Therefore, DataFirst has made the 1997-2015 cumulative round available as a separately downloadable dataset which includes both occupation group and industry classification of the deceased spanning the years 1997-2015.

Geographic coverage

National

Analysis unit

Units of analysis in the survey were households and individuals

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

This dataset includes imputation for missing data in key variables in the ten percent sample of the 2001 South African Census. Researchers at the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford used sequential multiple regression techniques to impute income, education, age, gender, population group, occupation and employment status in the dataset. The main focus of the work was to impute income where it was missing or recorded as zero. The imputed results are similar to previous imputation work on the 2001 South African Census, including the single ‘hot-deck’ imputation carried out by Statistics South Africa.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Sources: Stats SA (www.statssa.gov.za), *SAPS (www.saps.gov.za).

Description: Data presented as a spreadsheet; Provides Population estimates by population group, gender and age for South Africa since 2002.Linage: The data presented is extracted from Statistics South Africa (Stats SA) Mid-year population estimates (MYPE) trends as published on https://www.statssa.gov.za/Publication Date: 15 July 2022Contact person: Elize van der Berg, Department of the Premier, Elize.VanDerBerg@westerncape.gov.za

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Twice a year: February and August

Sampling procedure

Sample size:

Abstract

The data comes from the Electricity, Gas and Water Supply Iindustry Large Sample Survey by StatsSA which aims to provide financial, production, employment and related information for the electricity, gas and water supply industry in South Africa. The survey was conducted irregularly. This survey is based on a complete enumeration of private and public enterprises contributing to the top 99,5% of the industry turnover and adjustment factors were applied to compensate for the units contributing to the bottom 0,5% of industry turnover. Results of the survey are mainly used within Stats SA for benchmarking the gross domestic product (GDP) and its components. These statistics are also used by government to make decisions on industry policies and plans, and in monitoring the performance and contribution of individual industries to the South African economy. The private sector uses the data to analyse comparative business and industry performance.

Geographic coverage

The survey has national coverage.

Analysis unit

Establishments

Kind of data

Time-Series

Mode of data collection

Face-to-Face, Internet and Telephone

Description: Data presented as a spreadsheet; Provides an overview of the labour force participation rate across all provinces and metros in South Africa since 2008. Linage: The data presented is extracted from Statistics South Africa (Stats SA) Quarterly Labour Force Survey (QLFS) trends as published on https://www.statssa.gov.za/ Publication Date: 01 April 2024 Contact Person: Elize van der Berg, Department of the Premier, Elize.VanDerBerg@westerncape.gov.za

Description: data presented as spreadsheet; Provides an overview of the official unemployment rate by narrow definition across all provinces and metros in South Africa since 2008.Linage: The data presented is extracted from Statistics South Africa (Stats SA) Quarterly Labour Force Survey (QLFS) trends as published on https://www.statssa.gov.za/Publication Date: 14 May 2024Data Sources: QLFS Trends 2008-2024Q1, Stats SA, published ‎14 ‎May ‎2024Contact Person: Elize van der Berg, Department of the Premier, Elize.VanDerBerg@westerncape.gov.za

Description: Data presented as a spreadsheet; Provides an overview of the labour absorption rate across all provinces and metros in South Africa since 2008.Linage: The data presented is extracted from Statistics South Africa (Stats SA) Quarterly Labour Force Survey (QLFS) trends as published on https://www.statssa.gov.za/Publication Date: 14 May 2024Data Sources: QLFS Trends 2008-2024Q1, Stats SA

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

Abstract

The Income and Expenditure Survey is conducted every five years in South Africa.The main purpose of the survey is to determine the average expenditure patterns of households in different areas of the country. This survey forms the basis for the determination of the “basket” of consumer goods and services used for the calculation of the Consumer Price Index. The IES is based on the sample for the rotating panel of the twice yearly Labour force Survey (LFS). The IES 2000 was conducted in October 2000.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the survey are households and individuals

Universe

The survey covered all household members

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]