General Household Survey 2002
South Africa, 2002
Statistics South Africa
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May 05, 2014
Last modified
May 05, 2014
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Identification
Survey ID Number
ZAF_2002_GHS_v01_M
Title
General Household Survey 2002
Country
| Name | Country code |
|---|---|
| South Africa | zaf |
Study type
Other Household Survey [hh/oth]
Abstract
The main purpose of the GHS is to measure the level of development and performance of various government programmes and projects in South Africa. The data provides national indicators on various living conditions such as access to services and facilities, and education and health, for 2002.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The units of anaylsis for the General Household Survey 2002 are individuals and households.
Version
Version Description
v1.1 Edited, anonymised dataset for public distribution
Version Notes
Version 1.0 of the General Household Survey 2002 was acquired from Statistics South Africa in 2004. This version, version 1.1 of the dataset was downloaded from Statistics South Africa, website on the 11th of August 2011.
Version 1.1 includes the new weights released for the GHS 2002-2008 released at the same time as the GHS 2009 (6 May 2010).
Version 1.0 was released without birth data or data on children. On request, Statistics SA provided DataFirst with these data files.
The Child data file has incomplete data for the variable on children still alive (q315aliv) but these additional files have been made available to the public for analysis.
Version 1.1 includes the new weights released for the GHS 2002-2008 released at the same time as the GHS 2009 (6 May 2010).
Version 1.0 was released without birth data or data on children. On request, Statistics SA provided DataFirst with these data files.
The Child data file has incomplete data for the variable on children still alive (q315aliv) but these additional files have been made available to the public for analysis.
Scope
Notes
The scope of the General Household Survey 2002 includes:
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
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
Topics
| Topic | Vocabulary | URI |
|---|---|---|
| employment [3.1] | CESSDA | http://www.nesstar.org/rdf/common |
| labour relations/conflict [3.3] | CESSDA | http://www.nesstar.org/rdf/common |
| retirement [3.4] | CESSDA | http://www.nesstar.org/rdf/common |
| unemployment [3.5] | CESSDA | http://www.nesstar.org/rdf/common |
| working conditions [3.6] | CESSDA | http://www.nesstar.org/rdf/common |
| LABOUR AND EMPLOYMENT [3] | CESSDA | http://www.nesstar.org/rdf/common |
| TRADE, INDUSTRY AND MARKETS [2] | CESSDA | http://www.nesstar.org/rdf/common |
| DEMOGRAPHY AND POPULATION [14] | CESSDA | http://www.nesstar.org/rdf/common |
| income, property and investment/saving [1.5] | CESSDA | http://www.nesstar.org/rdf/common |
| crime [5.1] | CESSDA | http://www.nesstar.org/rdf/common |
| EDUCATION [6] | CESSDA | http://www.nesstar.org/rdf/common |
| childbearing, family planning and abortion [8.2] | CESSDA | http://www.nesstar.org/rdf/common |
| general health [8.4] | CESSDA | http://www.nesstar.org/rdf/common |
| nutrition [8.7] | CESSDA | http://www.nesstar.org/rdf/common |
| specific diseases and medical conditions [8.9] | CESSDA | http://www.nesstar.org/rdf/common |
| TRANSPORT, TRAVEL AND MOBILITY [11] | CESSDA | http://www.nesstar.org/rdf/common |
| fertility [14.2] | CESSDA | http://www.nesstar.org/rdf/common |
| specific social services: use and provision [15.3] | CESSDA | http://www.nesstar.org/rdf/common |
Coverage
Geographic Coverage
The scope of the General Household Survey 2002 was national coverage.
Geographic Unit
The lowest level of geographic aggregations covered by the General Household Survey 2001 is province.
Universe
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
Primary investigators
| Name |
|---|
| Statistics South Africa |
Sampling
Sampling Procedure
For the General Household Survey 2002 a multi-stage stratified sample was drawn using probability proportional to size principles. The first stage was stratification by province, then by type of area within each province. Primary sampling units (PSUs) were then selected proportionally within each stratum (urban or non-urban) in all provinces. Altogether 3000 PSUs were selected. Within each PSU ten dwelling units were selected systematically for enumeration.
The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its surveys. The master sample was drawn from the database of enumeration areas (EAs) which was established during the demarcation phase of census 1996. As part of the master sample, small EAs consisting of fewer than 100 dwelling units are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 dwelling units, 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. Independent samples were drawn from each stratum within each province. The smaller provinces were given a disproportionately larger number of PSUs than the bigger provinces.
The master sample was divided into five independent clusters. In order to avoid respondent fatigue, the sample for GHS was drawn from a different cluster from the two clusters already being used for the LFS, which is a twice-yearly rotating panel survey. Altogether 30 000 dwelling units (including units in hostels) were visited for the GHS 2002.
The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its surveys. The master sample was drawn from the database of enumeration areas (EAs) which was established during the demarcation phase of census 1996. As part of the master sample, small EAs consisting of fewer than 100 dwelling units are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 dwelling units, 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. Independent samples were drawn from each stratum within each province. The smaller provinces were given a disproportionately larger number of PSUs than the bigger provinces.
The master sample was divided into five independent clusters. In order to avoid respondent fatigue, the sample for GHS was drawn from a different cluster from the two clusters already being used for the LFS, which is a twice-yearly rotating panel survey. Altogether 30 000 dwelling units (including units in hostels) were visited for the GHS 2002.
Weighting
Weighting the GHS of July/August 2002:
A two-stage theoretical weighting procedure was done on the GHS 2002. 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 2002 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 2002 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.
GHS 2002 Data revisions
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. These data files (version 1.1) 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 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.
A two-stage theoretical weighting procedure was done on the GHS 2002. 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 2002 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 2002 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.
GHS 2002 Data revisions
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. These data files (version 1.1) 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 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.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2002-07 | 2002-07 |
Data Collection Mode
Face-to-face [f2f]
Data Collectors
| Name | Abbreviation |
|---|---|
| Statistics South Africa | SSA |
Questionnaires
Questionnaires
The questionnaire was designed taking into consideration the need to compare results of this survey to the one conducted in June 2001 in the 13 nodal areas identified as priority areas for the Integrated Rural Development Strategy (IRDS), namely, the Social Development Indicators Survey (SDIS). The questions in the GHS were similar to the ones used in the SDIS as proposed by representatives of departments in the social cluster of government responsible for implementation of the IRDS.
The GHS 2002 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
The GHS 2002 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
Data Appraisal
Estimates of Sampling Error
Estimation and use of standard error
The published results of the General Household Survey2002 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 may have varied by chance because only a sample of the population was included.
The published results of the General Household Survey2002 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 may have varied by chance because only a sample of the population was included.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst | University of Cape Town | info@data1st.org | http://www.datafirst.uct.ac.za |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Manager, DataFirst | University of Cape Town | info@data1st.org | http://www.datafirst.uct.ac.za |
Confidentiality
Access conditions
The GHS 2002 dataset is a licensed dataset, accessible under conditions.
Citation requirements
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 2002 [microdata file]. Pretoria: Statistics South Africa [producer], 2004. Cape Town: DataFirst [distributor],2010.
General Household Survey 2002 [microdata file]. Pretoria: Statistics South Africa [producer], 2004. Cape Town: DataFirst [distributor],2010.
Disclaimer and copyrights
Disclaimer
The information products and services of Statistics South Africa are protected in terms of the Copyright Act, 1978 (Act 98 of 1978). As the State President is the holder of State copyright, all organs of State enjoy unhindered use of the Department’s information products and services, without a need for further permission to copy in terms of that copyright. Where a copy of the information is made available to any third party outside the State, the third party must be made aware of the existence of State copyright and ownership of the information by the State. The State (through Statistics SA) retains the full ownership of its information, products and services at all times; access to information does not give ownership of the information to the client.
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.
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
Copyright 2004, Statistics South Africa.
Metadata production
DDI Document ID
DDI_ZAF_2002_GHS_v01_M
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | DDI Producer |
Date of Metadata Production
2011-10-10
DDI Document version
Version 1.2
This version has additional metadata and variable descriptions added.
Version 1.3 - Adapted for use by the World Bank Microdata Library - changed study ID to match Microdata Library Standard
This version has additional metadata and variable descriptions added.
Version 1.3 - Adapted for use by the World Bank Microdata Library - changed study ID to match Microdata Library Standard