General Household Survey 2013
South Africa, 2013
Get MicrodataIdentification
ZAF_2013_GHS_v01_EN_M_v01_A_OCS
General Household Survey 2013
| Name | Country code |
|---|---|
| South Africa | ZAF |
Other Household Survey [hh/oth]
The General Household Survey (GHS) has been used as an instrument to track the progress of development since 2002 when it was first introduced . It is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, health and social development, housing, household access to services and facilities, food security, and agriculture.
Sample survey data [ssd]
Households
Scope
The scope of the General Household Survey 2013 includes:
(a) HOUSEHOLD CHARACTERISTICS:
Dwelling type
Home ownership
Access to water and sanitation
Access to services
Transport
Household assets
Land ownership
Agricultural production
(b) INDIVIDUALS' CHARACTERISTICS:
Demographic characteristics
Relationship to household head
Marital status
Language
Education
Employment
Income
Health
Fertility
Disability
Access to social services
Mortality
| Topic | Vocabulary |
|---|---|
| Agriculture & Rural Development | FAO |
| Land (policy, resource management) | FAO |
| Financial Sector | FAO |
| Access to Finance | FAO |
| Water | FAO |
| Health | FAO |
| Population & Reproductive Health | FAO |
Coverage
National
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 |
Sampling
The sample design for the GHS 2013 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 Survey (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 splits 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 a way 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 levels 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 |
|---|---|
| 2013-10 | 2013-12 |
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. Changes to any of the data files will result in the dataset having a new version number. Thus, version numbers of files within a dataset may not match.
Data Access
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | https://microdata.worldbank.org/index.php/terms-of-use |
The GHS 2013 dataset is a licensed dataset, accessible under conditions.
Statistics South Africa. General Household Survey 2013 [dataset]. Version 1. Pretoria. Statistics South Africa [producer], 2014. Cape Town. DataFirst [distributor], 2014.
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.
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst Helpdesk | University of Cape Town | [email protected] | www.support.data1st.org/ |
Metadata production
DDI_ZAF_2013_GHS_v01_EN_M_v01_A_OCS_FAO
| Name | Affiliation | Role |
|---|---|---|
| Office of Chief Statistician | Food and Agriculture Organization | Adoption of metadata for FAM |
| DataFirst | University of Cape Town | Metadata Producer |
Metadata version
ZAF_2013_GHS_v01_EN_M_v01_A_OCS_v01