General Household Survey 2019
South Africa, 2019
Get MicrodataIdentification
ZAF_2019_GHS_v01_M
General Household Survey 2019
| Name | Country code |
|---|---|
| South Africa | ZAF |
Other Household Survey [hh/oth]
Statistics South Africa. General Household Survey 2019 [dataset]. Version 1. Pretoria: Statistics SA [producer], 2019. Cape Town: DataFirst [distributor], 2019.
Note: The questionnaire for the GHS series changed in 2019 and the variables were also renamed. See the document ghs-2019-variables-renamed for a correspondence between the old names and the new ones.
Sample survey data [ssd]
Households and individuals
Version
Version 01: Edited, anonymised dataset for public distribution
2019
Version 1 was originally downloaded from Stats SA in February 2020.
Scope
The scope of the General Household Survey includes:
- Household characteristics: Dwelling type, home ownership, access to water and sanitation, access to services, transport, household assets, land ownership, agricultural production
- Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, fertility, mortality, disability, access to social services
| Topic | Vocabulary | URI |
|---|---|---|
| employment [3.1] | CESSDA | http://www.nesstar.org/rdf/common |
| unemployment [3.5] | CESSDA | http://www.nesstar.org/rdf/common |
| LABOUR AND EMPLOYMENT [3] | CESSDA | http://www.nesstar.org/rdf/common |
| DEMOGRAPHY AND POPULATION [14] | CESSDA | http://www.nesstar.org/rdf/common |
Coverage
National coverage
The lowest level of geographic aggregation for the data is Province (and metropolitan municipality, where this applies).
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.
Producers and sponsors
| Name | Affiliation |
|---|---|
| Statistics South Africa | Government of South Africa |
| Name |
|---|
| Government of South Africa |
Sampling
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 3324 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).
The sample weights were constructed to account for the following: the original selection probabilities (design weights), adjustments for PSUs that were sub-sampled or segmented, excluded population from the sampling frame, non-response, weight trimming, and benchmarking to known population estimates from the Demographic Analysis Division within Stats SA.
The sampling weights for the data collected from the sampled households were constructed so that the responses could be properly expanded to represent the entire civilian population of South Africa. The design weights, which are the inverse sampling rate (ISR) for the province, are assigned to each of the households in a province.
Mid-year population estimates produced by the Demographic Analysis Division were used for benchmarking. The final survey weights were constructed using regression estimation to calibrate to national level population estimates cross-classified by 5-year age groups, gender and race, and provincial population estimates by broad age groups. The 5-year age groups are: 0–4, 5–9, 10–14, 55–59, 60–64; and 65 and over. The provincial level age groups are 0–14, 15–34, 35–64; and 65 years and over. The calibrated weights were constructed such that all persons in a household would have the same final weight.
The Statistics Canada software StatMx was used for constructing calibration weights. The population controls at national and provincial level were used for the cells defined by cross-classification of Age by Gender by Race. Records for which the age, population group or sex had item non-response could not be weighted and were therefore excluded from the dataset. No additional imputation was done to retain these records.
Household estimates that were developed using the UN headship ratio methodology were used to weight household files. The databases of Census 1996, Census 2001, Community Survey 2007 Census 2011 were used to analyze trends and develop models to predict the number of households for each year. The weighting system was based on tables for the expected distribution of household heads for specific age categories, per population group and province.
Survey instrument
Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.
Data collection
| Start | End |
|---|---|
| 2019-01 | 2019-12 |
Data appraisal
The questionnaire for the GHS series changed in 2019 and the variables were also renamed. See the document ghs-2019-variables-renamed for a correspondence between the old names and the new ones.
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst | University of Cape Town | www.support.datafirst.org | support@data1st.org |
Public use files, available to all
Statistics South Africa. General Household Survey 2019 [dataset]. Version 1. Pretoria: Statistics SA [producer], 2019. Cape Town: DataFirst [distributor], 2019.
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | |
|---|---|---|
| DataFirst Helpdesk | University of Cape Town | support@data1st.org |
Metadata production
DDI_ZAF_2019_GHS_v01_M
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | Metadata Producer |
| Development Economics Data Group | The World Bank | Metadata adapted for Microdata Library |
2021-02-17
Metadata version
Version 01: This metadata was downloaded from DataFirst website(https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/central). The following two metadata information has been edited – Document and Survey ID.