General Household Survey 2012
South Africa, 2012
Get MicrodataIdentification
ZAF_2012_GHS_v01_M
General Household Survey 2012
| Name | Country code |
|---|---|
| South Africa | ZAF |
Other Household Survey [hh/oth]
Version 01 (June 2017) Identical to DataFirst DDI: zaf-statssa-ghs-2012-v2 except the ID and DDI fields edited to World Bank IDs.
The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, employment, health, housing and household access to services.
Sample survey data [ssd]
The units of anaylsis for the General Household Survey 2012 are individuals and households.
Version
v2: Edited, anonymised dataset for public distribution
2013
Version 1 of the General Household Survey 2012 was downloaded from the Statistics South Africa website on the 30th of September 2013.
This version, version 2, of the General Household Survey 2012 was re-issued by Statistics SA and was dowloaded from their website on the 15th of October 2013.
In version 2 of the GHS 2012, four variables in the house file have been corrected. These variables had errors which occurred during the data preparation phase and were caused by malfunctioning logical edit routines. The variable are:
Totmhinc (pensions and remittances were not taken into account in the calculation of the total monthly household income and this was corrected in version 2)
Q412agrant
Q412anon
Q412bmain
Please note that DataFirst provides versioning at dataset and file level. Revised files have new version numbers. Files that are not revised retain their original version numbers. Any changes to files will result in the dataset having a new version number. Thus version numbers of files within a dataset may not match
The 2008 - 2013 sample was designed to report at Provincial and Metro level (not DC level). However, StatsSA did not take the absence population estimates at metro level into account when weighting the data and therefore this data is not reliable at Metro level. StatsSA's new master sample used from 2015 onwards will weight down to metro level and the data will be provided at that level.
Scope
The scope of the General Household Survey 2012 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, disability, access to social services, mortality.
| 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
The General Household Survey 2012 had national coverage.
The lowest level of geographic aggregation for the data is province. The data also includes four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).
The lowest level of geographic aggregations covered by the General Household Survey 2011 is Province.
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 |
|---|
| Statistics South Africa |
Sampling
The sample design for the GHS 2012 was based on a master sample (MS) that was originally designed for the Quarterly Labour Force Survey (QLFS) and was used for the first time for the GHS in 2008. This master sample is shared by the QLFS, GHS, Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Surveys (IES).
The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of primary sampling units (PSUs) from within strata, and systematic sampling of dwelling units (DUs) from the sampled PSUs. A self-weighting design at provincial level was used and MS stratification was divided into two
levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification; household size, education, occupancy status, gender, industry and income.
Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used:
• Where possible, PSU sizes were kept between 100 and 500 DUs;
• EAs with fewer than 25 DUs were excluded;
• EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type;
• Virtual splits were applied to large PSUs: 500 to 999 split into two; 1 000 to 1 499 split into three; and 1 500 plus split into four PSUs; and
• Informal PSUs were segmented.
A randomised-probability-proportional-to-size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.
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. These were adjusted for four factors: Informal PSUs, Growth PSUs, Sample Stabilisation, and Non-responding Units.
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 older. The provincial level age groups are 0–14, 15–34, 35–64; and 65 years and older. The calibrated weights were constructed in such away 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 imputation was done to retain these records.
Data collection
| Start | End |
|---|---|
| 2012-07 | 2012-09 |
Data appraisal
Please note that DataFirst provides versioning at dataset and file level. Revised files have new version numbers. Files that are not revised retain their original version numbers. Any changes to files will result in the dataset having a new version number. Thus version numbers of files within a dataset may not match
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst | University of Cape Town | http://www.datafirst.uct.ac.za | support@data1st.org |
The GHS 2012 dataset is a licensed dataset, accessible under conditions.
Statistics South Africa. General Household Survey 2012 [dataset]. Version 2. Pretoria. Statistics South Africa [producer], 2013. Cape Town. DataFirst [distributor], 2013.
Disclaimer and copyrights
The use of any data is subject to acknowledgement of Stats SA as the supplier and owner of copyright. Statistics South Africa (Stats SA) will not be liable for any damages or losses, except to the extent that such losses or damages are attributable to a breach by Stats SA of its obligations in terms of an existing agreement or to the negligence or wilful act or omissions of the Stats SA, its servants or agents, arising out of the supply of data and or digital products in terms of that agreement. The user indemnifies Stats SA against any claims of whatsoever nature (including legal costs) by third parties arising from the reformatting, restructuring, reprocessing and/or addition of the data, by the user.
Copyright 2012, Statistics South Africa
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst Helpdesk | University of Cape Town | support@data1st.org | http://support.data1st.org/ |