The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities.
Kind of data
Sample survey data [ssd]
v1.2 Edited, anonymised dataset for public distribution
Version 1.0 of the General Household Survey 2005 was acquired from Statistics South Africa in 2006. A new version of this dataset was released in 2010. This version (our version 1.1) was reweighted to reflect (a) the findings of the Community Survey 2007 and new HIV/AIDS and mortality data, and (b) the adjusted provincial boundaries that came into effect in December 2006.
This version, 1.2, downloaded 11 August, 2011, includes the new weights for the GHS 2002-2008 released by Statistics South Africa at the same time as the GHS 2009 (6 May 2010).
From 2005 the "Stratum" variable, indicating rural and urban areas for each province, is no longer included in the GHS dataset.
in-job training [3.2]
labour relations/conflict [3.3]
working conditions [3.6]
LABOUR AND EMPLOYMENT 
TRADE, INDUSTRY AND MARKETS 
DEMOGRAPHY AND POPULATION 
The scope of the General Household Survey 2005 was national coverage.
The lowest level of geographic aggregations covered by the General Household Survey 2005 is District Council. This level was introduced with the 2005 dataset.
The "Stratum" variable, indicating rural and urban areas by Province, which was included in the GHS datasets from 2002 to 2004, is no longer provided in the GHS dataset, from GHS 2005.
Unit of analysis
The units of anaylsis for the General Household Survey 2005 are individuals and households.
The survey covered 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 students' hostels, old age homes, hospitals, prisons and military barracks.
Producers and sponsors
Statistics South Africa
For the GHS 2005 a multi-stage stratified sample was drawn using probability proportional to size principles. The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of Census 2001. As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample was allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated.
The master sample is divided into five independent clusters. In order to avoid respondent fatigue, the Labour Force Survey (LFS) is a rotating panel survey that is conducted twice yearly, whereas the GHS sample uses different clusters.
87,5% of the expected 32 146 interviews were successfully completed and positive responses were obtained. It was not possible to complete interviews in 3,8 % of the sampled dwelling units. An additional 8,3% of all interviews were not conducted for various reasons, for instance the sampled dwelling units had become vacant or had changed status (e.g. they were used as shops or small businesses at the time of the enumeration but were originally listed as dwelling units).
A two-stage theoretical weighting procedure was done on the GHS 2005. In the first stage primary sampling units (PSU) are selected with probability proportional to size (PPS) from the census population.
Because there were undercounts in some PSUs (because households could not be traced or because of refusals to answer), the weight of each such PSU was adjusted upwards by a factor of nHH/n*HH where nHH was the number of households which should have been interviewed and n*HH was the number of households actually reached. Then all household weights were adjusted upwards by a further factor equal to the estimated population at the time of the GHS 2005 survey divided by the 1996 Census population estimate, to account for population growth between the 1996 Census (from which the master sample was drawn) and the date of the survey. These doubly adjusted weights are reported as the household weights in the data set. The person weights are derived by further adjusting the household weights in order to reproduce the marginal totals of the estimated population at the time of the 2005 GHS by gender, population group, province and age group. A SAS macro called CALMAR was used for this purpose.
The population estimate was derived by a 'bottom up' (cohort-by-cohort) exponential extrapolation from the 1996 and 2001 censuses. Such an estimate is quite reliable for the total population and the gender, population group and provincial subtotals. It is less reliable for the age distribution. Improved population estimates will become available when Statistics South Africa completes its short-term population projection model. The weights in this and other surveys may be modified in the light of model estimates.
In May 2010 Statistics SA revised the population model to produce mid-year population estimates during 2008 in the light of the findings of the Community Survey 2007 and new HIV/AIDS and mortality data. The new data have been used to adjust the benchmarking for all previous datasets. Weighting and benchmarking were also adjusted for the provincial boundaries that came into effect in December 2006. The new weights mean that the data for the GHS 2002 to GHS 2009 are now comparable. The General Household Survey 2005 data files (version 1.2) contain the new weights.
As a result of new statistical programs used for weighting, which discards records with unspecified values for the benchmarking variables, namely age, sex and population group, it became necessary to impute missing values for these variables. A combination of logical and hot deck imputation methods were used to impute the demographic variables of the whole GHS series from 2002-2009.
A new weighting system was also introduced for the household files as part of the revision process. This was based on household estimates that were developed using the headship ratio methodology. The databases of Census 1996, Census 2001, Community Survey 2007 and the Labour Force Survey 2003, Labour Force Survey 2005, and Quarterly Labour Force (Quarter 3) of 2009 were used to analyse 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.
Missing values and unknown values were excluded from totals used as denominators for the calculation of percentages, unless otherwise specified. Frequency values have been rounded off to the nearest thousand. Population totals in all tables reflect the population and sub-populations as calculated with SAS and rounded off. This will not always correspond exactly with the sum of the preceding rows because all numbers are rounded off to the nearest thousand.
Dates of collection
Mode of data collection
The GHS 2005 questionnaire collected data on:
Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, 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, disability, access to social services, mortality.
Women's characteristics: fertility
Estimation and use of standard error
The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.
University of Cape Town
The GHS 2005 dataset is a licensed dataset, accessible under conditions.
Publications based on datasets distributed by DataFirst should acknowledge relevant sources by means of bibliographic citations. To ensure that such source attributions are captured for social science bibliographic utilities, citations must appear in footnotes or in the reference section of publications. The bibliographic citation for this dataset is:
General Household Survey 2005 [microdata files]. Pretoria: Statistics South Africa [producer], 2010. Cape Town: DataFirst [distributor],2011.
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University of Cape Town
University of Cape Town
Version 1.2 - Adapted for use by the World Bank Microdata Library - changed study ID to match Microdata Library Standard