General Household Survey 2015
South Africa, 2015
Statistics South Africa
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January 25, 2017
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Identification
IDNO
ZAF_2015_GHS_v01_M
Title
General Household Survey 2015
Countries
| Name | Code |
|---|---|
| South Africa | zaf |
Study notes
The GHS is an annual household survey conducted by Stats SA since 2002. The survey replaced the October Household Survey (OHS) which was introduced in 1993 and was terminated in 1999.
The survey is an omnibus household-based instrument aimed at determining the progress of development in the country. It measures, on a regular basis, the performance of programmes as well
as the quality of service delivery in a number of key service sectors in the country. The GHS covers six broad areas, namely education, health and social development, housing,
household access to services and facilities, food security, and agriculture. This report has three main objectives: firstly, to present the key findings of GHS 2015. Secondly, it provides trends across a fourteen year period, i.e. since the GHS was introduced in 2002; and
thirdly, it provides a more in-depth analysis of 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 (P0318.2).
The survey is an omnibus household-based instrument aimed at determining the progress of development in the country. It measures, on a regular basis, the performance of programmes as well
as the quality of service delivery in a number of key service sectors in the country. The GHS covers six broad areas, namely education, health and social development, housing,
household access to services and facilities, food security, and agriculture. This report has three main objectives: firstly, to present the key findings of GHS 2015. Secondly, it provides trends across a fourteen year period, i.e. since the GHS was introduced in 2002; and
thirdly, it provides a more in-depth analysis of 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 (P0318.2).
Kind of data
Sample survey data [ssd]
Version
Version
v1.0: Edited, anonymised dataset for public distribution
DDI obtained from DataFrist, UCT, South Africa: ddi-zaf-datafirst-ghs-2015-v1
DDI obtained from DataFrist, UCT, South Africa: ddi-zaf-datafirst-ghs-2015-v1
Version date
2016-09-20
Version notes
Version 1 of the General Household Survey 2015 was acquired from the Statistics South Africa on the 7th of June 2016. Version 1.1 is this dataset, with value and variable labels added by DataFirst.
Scope
Topics
| Topic | Vocab | metadata.study_desc.study_info.topics.uri |
|---|---|---|
| employment [3.1] | CESSDA | http://www.nesstar.org/rdf/common |
| unemployment [3.5] | CESSDA | http://www.nesstar.org/rdf/common |
| LABOUR AND EMPLOYMENT [3] | CESSDA | http://www.nesstar.org/rdf/common |
| DEMOGRAPHY AND POPULATION [14] | CESSDA | http://www.nesstar.org/rdf/common |
Coverage
Geographic coverage
The General Household Survey 2015 had national coverage.The lowest level of geographic aggregation for this dataset is Province and Metro.
Geographic unit
The lowest level of geographic aggregation for the data is Province and, within the Provinces, at the level of Metropolitan and Non-Metropolitan Area. StatsSA used a new master sample from GHS 2015. This is weighted to metro level and data is provided at this level (data for earlier years is only reliable at the level of Province).
Unit of analysis
The units of anaylsis for the General Household Survey 2015 are individuals and households.
Universe
The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers’ hostels. The survey does not cover other collective living quarters such as students’ hostels, old-age homes, hospitals, prisons and military barracks, and is therefore
only representative of non-institutionalised and non-military persons or households in South Africa.
only representative of non-institutionalised and non-military persons or households in South Africa.
Producers and sponsors
Authoring entity
| Name | Affiliation |
|---|---|
| Statistics South Africa | Government of South Africa |
Sampling
Sampling procedure
The General Household Survey (GHS) uses the Master Sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household-based surveys having design requirements that are reasonably compatible with the GHS.
The GHS 2015 collection was based on the 2013 Master Sample.
This Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs).
The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country and had other information that is crucial for stratification and creation of PSUs.
There are 3 324 primary sampling units (PSUs) in the Master Sample with an expected sample of approximately 33 000 dwelling units (DUs).
The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs).
The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates.
The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels.
Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms.
This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.
The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.
Caution must be exercised when interpreting the results of the GHS at low levels of disaggregation. The sample and reporting are based on the provincial boundaries as defined in December/January 2006.
These new boundaries resulted in minor changes to the boundaries of some provinces, especially Gauteng, North West, Mpumalanga, Limpopo, Eastern Cape and Western Cape.
In previous reports the sample was based on the provincial boundaries as defined in 2001, and there will therefore be slight comparative differences in terms of provincial boundary definitions.
Details of the sampling proceedure can be found in Report No. P0318 available from Statistics Souoth Africa and attached to this Survey as an external resource.
The GHS 2015 collection was based on the 2013 Master Sample.
This Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs).
The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country and had other information that is crucial for stratification and creation of PSUs.
There are 3 324 primary sampling units (PSUs) in the Master Sample with an expected sample of approximately 33 000 dwelling units (DUs).
The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs).
The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates.
The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels.
Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms.
This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.
The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.
Caution must be exercised when interpreting the results of the GHS at low levels of disaggregation. The sample and reporting are based on the provincial boundaries as defined in December/January 2006.
These new boundaries resulted in minor changes to the boundaries of some provinces, especially Gauteng, North West, Mpumalanga, Limpopo, Eastern Cape and Western Cape.
In previous reports the sample was based on the provincial boundaries as defined in 2001, and there will therefore be slight comparative differences in terms of provincial boundary definitions.
Details of the sampling proceedure can be found in Report No. P0318 available from Statistics Souoth Africa and attached to this Survey as an external resource.
Response rate
Province / Metropolitan Area Response Rates
National 90,48
Western Cape 91,67
Non Metro 93,17
City of Cape Town 91,03
Eastern Cape 94,77
Non Metro 96,66
Buffalo City 92,54
Nelson Mandela Bay 89,52
Northern Cape 95,00
Free State 95,00
Non Metro 95,37
Mangaung 94,07
KwaZulu-Natal 95,23
Non Metro 96,58
eThekwini 92,87
North West 94,99
Gauteng 78,01
Non Metro 93,62
Ekurhuleni 81,76
City of Johannesburg 71,11
City of Tshwane 75,47
Mpumalanga 97,24
Limpopo 98,83
National 90,48
Western Cape 91,67
Non Metro 93,17
City of Cape Town 91,03
Eastern Cape 94,77
Non Metro 96,66
Buffalo City 92,54
Nelson Mandela Bay 89,52
Northern Cape 95,00
Free State 95,00
Non Metro 95,37
Mangaung 94,07
KwaZulu-Natal 95,23
Non Metro 96,58
eThekwini 92,87
North West 94,99
Gauteng 78,01
Non Metro 93,62
Ekurhuleni 81,76
City of Johannesburg 71,11
City of Tshwane 75,47
Mpumalanga 97,24
Limpopo 98,83
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 (of Stats SA) 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 and Race (i.e. population group).
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
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 (of Stats SA) 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 and Race (i.e. population group).
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 collection
| Start | End |
|---|---|
| 2015-01 | 2015-12 |
Mode of data collection
Face-to-face [f2f]
Questionnaires
A single survey was adminsitered for each household.
Teh Questionnaire comprises the following main sections:
A: Particulars of the dwelling
B: Households at the selected dwelling unit
C: Field staff
D: Survey period
E: Response details
Section 1: Household Specific Functioning
Section 2: Health and General Functioning
Section 3: Social Security and Religion
Section 4: Economic Activities
Section 5: General Household Information and Service Delivery
Section 6: Communication and Transport
Section 7: Health, welfare and Food Security
Section 8: Household Livelihoods
Section 9: Mortality in the last 12 months
Section 10: Interviewer summary section
Teh Questionnaire comprises the following main sections:
A: Particulars of the dwelling
B: Households at the selected dwelling unit
C: Field staff
D: Survey period
E: Response details
Section 1: Household Specific Functioning
Section 2: Health and General Functioning
Section 3: Social Security and Religion
Section 4: Economic Activities
Section 5: General Household Information and Service Delivery
Section 6: Communication and Transport
Section 7: Health, welfare and Food Security
Section 8: Household Livelihoods
Section 9: Mortality in the last 12 months
Section 10: Interviewer summary section
Data access
Access authorities
| Name | Affiliation | URI | |
|---|---|---|---|
| DataFirst | University of Cape Town | support@data1st.org | http://www.support.datafirst.uct.ac.za |
Access conditions
Public use data, available to all.
Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source of the data; that it is specified that the application and/or analysis is the result of the user's independent
processing of the data; and that neither the basic data nor any reprocessed version or application thereof may be sold or offered for sale in any form whatsoever without prior permission from Stats SA.
Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source of the data; that it is specified that the application and/or analysis is the result of the user's independent
processing of the data; and that neither the basic data nor any reprocessed version or application thereof may be sold or offered for sale in any form whatsoever without prior permission from Stats SA.
Citation requirements
Statistics South Africa. General Household Survey 2015 [dataset]. Version 1.1. Pretoria: Statistics South Africa [producer], 2016. Cape Town: DataFirst [distributor], 2016.
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.
Copyrights
Copyright 2015, Statistics South Africa
Contacts
Contact(s)
| Name | Affiliation | URI | |
|---|---|---|---|
| DataFirst Helpdesk | University of Cape Town | support@data1st.org | http://support.data1st.org/ |
Metadata production
IDNo
ddi-zaf-datafirst-ghs-2013-v1
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | Metadata Producer |
| Development Data Group | World Bank | Metadata Producer |
Production date
2016-06-08
Version
Version 1
UCT
Version 2
Edited by the World Bank, DECDG - variable level metadata added (literal questions, interviewer instructions, universe, derivations)
UCT
Version 2
Edited by the World Bank, DECDG - variable level metadata added (literal questions, interviewer instructions, universe, derivations)