General Household Survey 2012
South Africa, 2012
Reference ID
ZAF_2012_GHS_v01_M_ILO
Producer(s)
Statistics South Africa
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Mar 29, 2017
Last modified
Mar 29, 2017
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Identification
Survey ID Number
ZAF_2012_GHS_v01_M_ILO
Title
General Household Survey 2012
Country
| Name | Country code |
|---|---|
| South Africa | ZAF |
Series Name
Other Household Survey [hh/oth]
Series Information
The GHS is a household survey that has been performed annually by Stats SA since 2002. The survey was introduced to address a need identified by the Government of South Africa to determine the level of development in the country and to measure, on a regular basis, the performance of programmes and projects that were implemented to address these needs. The survey is specifically designed to measure multiple facets of the living conditions of South African households, as well as the quality of service delivery in a number of key service sectors. The GHS covers six broad areas, namely education, health and social development, housing, household access to services and facilities, food security, and agriculture.
Abstract
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. This report has two main objectives: firstly, to present the key findings of the GHS 2012 in the context of the trends that were measured since the first GHS was conducted in 2002; and secondly, to provide a more in-depth analysis of the detailed questions related to selected service delivery issues. As with previous reports, this report will not include tables with specific indicators measured, as these will be included in a more comprehensive publication of development indicators, entitled Selected development indicators.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The units of anaylsis for the General Household Survey 2012 are individuals and households.
Version
Version number
Version 01
Scope
Study notes
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.
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 Classification
| Topic | Vocabulary |
|---|---|
| Agriculture & Rural Development | ILO |
| Education | ILO |
| Health | ILO |
| Household Income | ILO |
| Transport | ILO |
| Water | ILO |
| Employment | ILO |
| Unemployment | ILO |
| Gender | ILO |
| Disability | ILO |
Coverage
Geographic 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).
Geographic Unit
The lowest level of geographic aggregations covered by the General Household Survey 2011 is Province.
Universe
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
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| Statistics South Africa | South African Government |
Sampling
Sampling Procedure
A multi-stage design was used, which is based on a stratified design with probability proportional to size selection of primary sampling units (PSUs) at the first stage and sampling of dwelling units (DUs) with systematic sampling at the second stage. After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2001 data (secondary stratification). Survey officers employed and trained by Stats SA visited all the sampled dwelling units in each of the nine provinces. During the first phase of the survey, sampled dwelling units were visited and informed about the coming survey as part of the publicity campaign. The actual interviews took place four weeks later. A total of 25 330 households (including multiple households) were successfully interviewed during face-to-face interviews.
Deviations from the Sample Design
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 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.
Response Rate
Response rates per province, 2012
Province Per cent
Western Cape 94,4
Eastern Cape 98,6
Northern Cape 95,2
Free State 95,4
KwaZulu-Natal 99,4
North West 96,8
Gauteng 81,4
Mpumalanga 95,1
Limpopo 98,4
South Africa 94,1
The national response rate for the survey was 94,2%. The highest response rate (99,4%) was recorded in KwaZulu-Natal and the lowest in Gauteng (81,4%).
Province Per cent
Western Cape 94,4
Eastern Cape 98,6
Northern Cape 95,2
Free State 95,4
KwaZulu-Natal 99,4
North West 96,8
Gauteng 81,4
Mpumalanga 95,1
Limpopo 98,4
South Africa 94,1
The national response rate for the survey was 94,2%. The highest response rate (99,4%) was recorded in KwaZulu-Natal and the lowest in Gauteng (81,4%).
Weighting
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.
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
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2012 | 2012 |
Time periods (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2012-07 | 2012-09 |
Mode of data collection
Face-to-face [f2f]
Characteristics of Data Collection Situation - Notes on data collection
Unlike some previous years when data were collected only during July, the data collection since GHS 2009 was spread over a three-month period.
Questionnaires
Type of Research Instrument
The details of the questions included in the GHS questionnaire are covered in four sections, each focusing on a particular aspect. Depending on the need for additional information, the questionnaire is adapted on an annual basis. New sections may be introduced on a
specific topic for which information is needed or additional questions may be added to existing sections. Likewise, questions that are no longer necessary may be removed. The GHS questionnaire has undergone some revisions over time. These changes were primarily the result of shifts in focus of government programmes over time. The 2002–2004 questionnaires were very similar. Changes made to the GHS 2005 questionnaire included additional questions in the education section with a total of 179 questions. Between 2006 and 2008, the questionnaire remained virtually unchanged. For GHS 2009, extensive stakeholder consultation took place during which the questionnaire was reviewed to be more in line with the monitoring and evaluation frameworks of the various government departments.
Particular sections that were modified substantially during the review were the sections on education, social development, housing, agriculture, and food security. Even though the number of sections and pages in the questionnaire remained the same, questions in the
GHS 2009 were increased from 166 to 185 between 2006 and 2008. Following the introduction of a dedicated survey on Domestic Tourism, the section on tourism was dropped for GHS 2010. Due to a further rotation of questions, particularly the addition of a module on energy, the GHS 2012 questionnaire contained 182 questions.
Contents of the GHS 2012 questionnaire
Section Number of Details of each section
questions
Cover page Household information, response details, field staff information, result codes, etc.
Flap 6 Demographic information (name, sex, age, population group, etc.)
Section 1 55 Biographical information (education, health, disability, welfare)
Section 2 16 Economic activities
Section 3 78 Household information (type of dwelling, ownership of dwelling, electricity, water and sanitation, environmental issues, services, transport, etc.)
Section 4 27 Food security, income and expenditure (food supply, agriculture, expenditure, etc.)
All sections 182 Comprehensive coverage of living conditions and service delivery
specific topic for which information is needed or additional questions may be added to existing sections. Likewise, questions that are no longer necessary may be removed. The GHS questionnaire has undergone some revisions over time. These changes were primarily the result of shifts in focus of government programmes over time. The 2002–2004 questionnaires were very similar. Changes made to the GHS 2005 questionnaire included additional questions in the education section with a total of 179 questions. Between 2006 and 2008, the questionnaire remained virtually unchanged. For GHS 2009, extensive stakeholder consultation took place during which the questionnaire was reviewed to be more in line with the monitoring and evaluation frameworks of the various government departments.
Particular sections that were modified substantially during the review were the sections on education, social development, housing, agriculture, and food security. Even though the number of sections and pages in the questionnaire remained the same, questions in the
GHS 2009 were increased from 166 to 185 between 2006 and 2008. Following the introduction of a dedicated survey on Domestic Tourism, the section on tourism was dropped for GHS 2010. Due to a further rotation of questions, particularly the addition of a module on energy, the GHS 2012 questionnaire contained 182 questions.
Contents of the GHS 2012 questionnaire
Section Number of Details of each section
questions
Cover page Household information, response details, field staff information, result codes, etc.
Flap 6 Demographic information (name, sex, age, population group, etc.)
Section 1 55 Biographical information (education, health, disability, welfare)
Section 2 16 Economic activities
Section 3 78 Household information (type of dwelling, ownership of dwelling, electricity, water and sanitation, environmental issues, services, transport, etc.)
Section 4 27 Food security, income and expenditure (food supply, agriculture, expenditure, etc.)
All sections 182 Comprehensive coverage of living conditions and service delivery
Data Processing
Cleaning Operations
Historically the GHS used a conservative and hands-off approach to editing. Manual editing, and little if any imputation was done. The focus of the editing process was on clearing skip violations and ensuring that each variable only contains valid values. Very few limits to valid values were set and data were largely released as it was received from the field. With GHS 2009, Stats SA introduced an automated editing and imputation system that was continued for GHS 2010–2012. The challenge was to remain true, as much as possible, to the conservative approach used prior to GHS 2009 and yet, at the same time, to develop a standard set of rules to be used during editing which could be applied consistently across time. When testing for skip violations and doing automated editing, the following general rules are applied in cases where one question follows the filter question and the skip is violated:
• If the filter question had a missing value, the filter is allocated the value that corresponds with the subsequent question which had a valid value.
• If the values of the filter question and subsequent question are inconsistent, the filter question’s value is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated, the question subsequent to the filter question is dealt with by either setting it to missing and imputing or, if that fails, printing a message of edit failure for further investigation, decision-making and manual editing.
In cases where skip violations take place for questions where multiple questions follow the filter question, the rules used are as follows:
• If the filter question has a missing value, the filter is allocated the value that corresponds with the value expected given the completion of the remainder of the question set.
• If the filter question and the values of subsequent questions values were inconsistent, a counter is set to see what proportion of the subsequent questions have been completed. If more than 50% of the subsequent questions have been completed, the filter question’s value is modified to correspond with the fact that the rest of the questions in the set were completed. If less than 50% of the subsequent questions in the set were completed, the value of the filter question is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the questions in the set that follows
the filter question are set to missing.
When dealing with internal inconsistencies, as much as possible was done using logical imputation, i.e. information from other questions is compared with the inconsistent information. If other evidence is found to back up either of the two inconsistent viewpoints, the inconsistency is resolved accordingly. If the internal consistency remains, the question subsequent to the filter question is dealt with by either setting it to missing and imputing its value or printing a message of edit failure for further investigation, decision-making and manual editing.
• If the filter question had a missing value, the filter is allocated the value that corresponds with the subsequent question which had a valid value.
• If the values of the filter question and subsequent question are inconsistent, the filter question’s value is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated, the question subsequent to the filter question is dealt with by either setting it to missing and imputing or, if that fails, printing a message of edit failure for further investigation, decision-making and manual editing.
In cases where skip violations take place for questions where multiple questions follow the filter question, the rules used are as follows:
• If the filter question has a missing value, the filter is allocated the value that corresponds with the value expected given the completion of the remainder of the question set.
• If the filter question and the values of subsequent questions values were inconsistent, a counter is set to see what proportion of the subsequent questions have been completed. If more than 50% of the subsequent questions have been completed, the filter question’s value is modified to correspond with the fact that the rest of the questions in the set were completed. If less than 50% of the subsequent questions in the set were completed, the value of the filter question is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the questions in the set that follows
the filter question are set to missing.
When dealing with internal inconsistencies, as much as possible was done using logical imputation, i.e. information from other questions is compared with the inconsistent information. If other evidence is found to back up either of the two inconsistent viewpoints, the inconsistency is resolved accordingly. If the internal consistency remains, the question subsequent to the filter question is dealt with by either setting it to missing and imputing its value or printing a message of edit failure for further investigation, decision-making and manual editing.
Data Appraisal
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
Contact
| Name | Affiliation | URI | |
|---|---|---|---|
| Statistics South Africa | South African Government | distribution@statssa.gov.za | Link |
Conditions
The GHS 2012 dataset is a licensed dataset, accessible under conditions.
Citation requirement
Statistics South Africa. General Household Survey 2012 [dataset]. Version 2. Pretoria. Statistics South Africa [producer], 2013. Cape Town. DataFirst [distributor], 2013.
Contact
| Name | Affiliation | URI | |
|---|---|---|---|
| Statistics South Africa | South African Government | distribution@statssa.gov.za | Link |
Disclaimer and copyrights
Disclaimer
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 2012, Statistics South Africa
Metadata production
Document ID
DDI_ZAF_2012_GHS_v01_M_ILO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Department of Statistics | ILO | International Labour Organization | Producer of DDI |
Date of Production
2017-03-28