ZAF_2022_QLFS-Q1_v01_M
Quarterly Labour Force Survey 2022
Quarter 1
Name | Country code |
---|---|
South Africa | ZAF |
Labor Force Survey [hh/lfs]
Sample survey data [ssd]
Individuals
Version 01: Edited, anonymised dataset for public distribution
2022
This version was retrieved by DataFirst on the 8th of June 2022.
For Q1 2022 Statistics South Africa continued to use Computer Assisted Telephone Interviewing, in an attempt to reduce the spread of COVID-19. This meant that households without telephones were out of scope. See the statistical release for more information.
INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.
From 2010 the income data collected by South Africa's Quarterly Labour Force Survey is no longer provided in the QLFS dataset (except for a brief return in QLFS 2010 Q3 which may be an error). Possibly because the data is unreliable at the level of the quarter, Statistics South Africa now provides the income data from the QLFS in an annualised dataset called Labour Market Dynamics in South Africa (LMDSA). The datasets for LMDSA are available from DataFirst's website.
National coverage
Provincial and metropolitan level, as well as by geographic type.
The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.
Name | Affiliation |
---|---|
Statistics South Africa | Government of South Africa |
The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33000 dwelling units.
The sample for the QLFS 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.
For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.
The sample weights were constructed in order to account for the following: the original selection probabilities (design weights); adjustments for PSUs that were sub-sampled or segmented; excluded population from the sampling frame (EAs with insufficient DUs); non-response; weight trimming; benchmarking to known population estimates from the Demographic Analysis Chief Directorate within Stats SA; and raking to bias-adjusted control estimates from a bias-adjustment procedure to compensate for the non-coverage bias in the sample due to only observing those households that can be contacted by telephone.
The survey questionnaire consists of the following sections:
Start | End |
---|---|
2022-01 | 2022-03 |
COVID 19 Affected data collection for QLFS 2022 Q1. Please see the sampling section and the statistical release for more on this.
Name | Affiliation | URL | |
---|---|---|---|
DataFirst | University of Cape Town | support.data1st.org | support@data1st.org |
Public access data for use under a Creative Commons CC-BY (Attribution-only) License
Statistics South Africa. Quarterly Labour Force Survey 2022: Q1 [dataset]. Version 1. Pretoria: Statistics South Africa [producer], 2022. Cape Town: DataFirst [distributor], 2022. DOI: https://doi.org/10.25828/xra9-9483
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Name | Affiliation | |
---|---|---|
DataFirst Support | University of Cape Town | support@data1st.org |
DDI_ZAF_2022_QLFS-Q1_v01_M
Name | Affiliation | Role |
---|---|---|
DataFirst | University of Cape Town | Metadata producer |
Development Economics Data Group | The World Bank | Metadata adapted for Microdata Library |
2022-06-08
Version 01: This metadata was downloaded from the DataFirst website (https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/central). The following two metadata fields were edited - Document and Survey ID.
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