The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
v1.0: Edited, anonymised dataset for public distribution
This version was downloaded by DataFirst on the 2nd of March 2020.
INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.
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.
Producers and sponsors
Statistics South Africa
The Quarterly Labour Force Survey (QLFS) uses the Master Sample frame that 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 QLFS. The 2013 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) reflects 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 QLFS estimates.
The Master Sample is designed to be representative at the 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. It is divided equally into four subgroups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4), and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.
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. Thus, sampled dwellings are expected to remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for two quarters (as an example) and a new household moves in, the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).
The sample weights were constructed in order to account for the following: the original selection probabilities (design weights), adjustments for PSUs that were subsampled or segmented, excluded population from the sampling frame, non-response, weight trimming, and benchmarking to known population estimates from the Demographic Analysis Division within Stats SA.
The eligible households in the sampled dwellings can be divided into two response categories: respondents and non-respondents. Weight adjustment is applied to account for the non-respondent households (e.g. refusal, no contact, etc.). The adjustment for total non-response was computed at two levels of non-response: PSU non-response and household non-response.
In the final step of constructing the sample weights, all individuals within a household are assigned the same adjusted base weight. The adjusted base weights are calibrated such that the aggregate totals will match with independently derived population estimates (from the Stats SA Demographic Analysis Division) for various age, race and gender groups at national level and individual metropolitan and non-metropolitan area levels within the provinces. The calibrated weights are constructed using the constraint that each person within the same household should have the same calibrated weight, with a lower bound on the calibrated weights set at 50.
Dates of Data Collection
Data Collection Mode
The survey questionnaire consists of five section:
Section 1: Biographical information (marital status, language, migration, education, training, literacy, etc.)
Section 2: Economic activities for persons aged 15 years and older
Section 3: Unemployment and economic inactivity for persons aged 15 years and older
Section 4: Main work activities in the last week for persons aged 15 years and older
Section 5: Earnings in the main job for employees, employers and own-account workers aged 15 years and older
In general, imputation is used for item non-response (i.e. blanks within the questionnaire) and edit failures (i.e. invalid or inconsistent responses).
Statistics South Africa. Quarterly Labour Force Survey 2019: Q4 [dataset]. Version 1.0. Pretoria: Statistics South Africa [producer], 2019. Cape Town: DataFirst [distributor], 2020. DOI: https://doi.org/10.25828/28jb-6t54