General Household Survey 2003
South Africa, 2003
Created on
Apr 25, 2019
Last modified
Apr 25, 2019
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2867
Identification
Survey ID Number
ZAF_2003_GHS_v01_M
Title
General Household Survey 2003
Country
| Name | Country code |
|---|---|
| South Africa | zaf |
Study type
Other Household Survey [hh/oth]
Abstract
Stats SA conducted the October Household Survey (OHS) annually from 1994 to 1999, based on a probability sample of a large number of households ranging from 16 000 to 30 000 households each year (depending on availability of funding). This survey was discontinued in 1999 due to the reprioritisation of surveys in the face of financial constraints. February 2000 saw the birth of the Labour Force Survey (LFS), which is a biannual survey conducted by Stats SA in March and September of each year. The LFS covers some areas previously covered by the OHS, but not all, since it is a specialised survey principally designed to measure the dynamics in the labour market. The September LFS each year does include a section designed to measure social indicators such as access to infrastructure, but again this section does not go into as much depth as the OHS used to. A need was therefore identified by our users for a regular survey designed specifically to measure the level of development and the performance of government programmes and projects. The General Household Survey (GHS) was developed for this purpose. While the survey replaces the October Household Survey (OHS), the indicators measured in the 13 nodal areas identified for the Integrated Rural Development Strategy (IRSD) formed the basis for the subject matter of the survey. The first round of the GHS was conducted in July 2002 and the second round in July 2003.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The units of anaylsis for the General Household Survey 2003 are individuals and households.
Version
Version Description
v1.2 Edited, anonymised dataset for public distribution
Version Date
2004
Version Notes
Version 1.0 of the General Household Survey 2003 was acquired from Statistics South Africa in 2004. 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 on 20 September, 2011, includes the new weights released for the GHS 2002-2008 released at the same time as the GHS 2009 (6 May 2010).
Version 1.2 of the household file of the General Household Survey 2003 includes data for the variable “Q450Fiel”, which was missing in version 1.1. In previous versions of this dataset, in the variable “Q450Orch” values for “Unspecified” and “Not applicable” were combined.These have been separated in this version of the dataset.
This version, 1.2, downloaded on 20 September, 2011, includes the new weights released for the GHS 2002-2008 released at the same time as the GHS 2009 (6 May 2010).
Version 1.2 of the household file of the General Household Survey 2003 includes data for the variable “Q450Fiel”, which was missing in version 1.1. In previous versions of this dataset, in the variable “Q450Orch” values for “Unspecified” and “Not applicable” were combined.These have been separated in this version of the dataset.
Scope
Notes
The scope of the General Household Survey 2003 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 |
| in-job training [3.2] | 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 |
Keywords
| Keyword |
|---|
| services |
| goverment policy |
| delivery |
| goverment performance |
Coverage
Geographic Coverage
The scope of the General Household Survey 2003 was national coverage.
Geographic Unit
The lowest level of geographic aggregations covered by the General Household Survey 2003 is province. The variable "Stratum" stratifies the data by urban and non-urban categories for each 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 |
Producers
| Name |
|---|
| Statistics South Africa (http://www.statssa.gov.za) |
Sampling
Sampling Procedure
For the GHS 2003 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 1996. 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 LFS is a rotating panel survey which is conducted twice yearly), the GHS sample uses a different cluster from the LFS clusters.
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 1996. 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 LFS is a rotating panel survey which is conducted twice yearly), the GHS sample uses a different cluster from the LFS clusters.
Response Rate
Response codes Number of responses %
Completed 26 469 84.7
Non-contact 897 2.9
Refusal 645 2.1
Partly completed 18 0.1
Unusable information 1 0.0
Vacant 1 510 4.8
Listing error 246 0.8
Other 1 447 4.6
Total 31 233 100.0
Completed 26 469 84.7
Non-contact 897 2.9
Refusal 645 2.1
Partly completed 18 0.1
Unusable information 1 0.0
Vacant 1 510 4.8
Listing error 246 0.8
Other 1 447 4.6
Total 31 233 100.0
Weighting
A two-stage theoretical weighting procedure was done on the GHS 2003. 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 2003 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 2003 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.
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. The General Household Survey 2003 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.
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 2003 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 2003 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.
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. The General Household Survey 2003 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.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2003-07 | 2003-07 |
Data Collection Mode
Face-to-face [f2f]
Data Collectors
| Name | Abbreviation |
|---|---|
| Statistics South Africa | SSA |
Questionnaires
Questionnaires
The GHS 2003 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
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
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 2003 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 2003 [microdata files]. Pretoria: Statistics South Africa [producer], 2010. Cape Town: DataFirst [distributor],2011.
General Household Survey 2003 [microdata files]. Pretoria: Statistics South Africa [producer], 2010. Cape Town: DataFirst [distributor],2011.
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_2003_GHS_v01_M
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | DDI Producer |
Date of Metadata Production
2011-09-12
DDI Document version
Version 1.1
Version 1.2 - Adapted for use by the World Bank Microdata Library - changed study ID to match Microdata Library Standard
Version 1.2 - Adapted for use by the World Bank Microdata Library - changed study ID to match Microdata Library Standard