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
v2.1: Edited, anonymised dataset for public distribution
This version of the QLFS 2008 Q4 was downloaded from the Statistics South Africa (Stats SA) website by DataFirst in January 2012. This version differs in a number of ways from the version that was obtained by DataFirst (from Stats SA) at some undeteremined time prior. The first of these differences is the way in which observations that fit into "unspecified", “not applicable” or "missing" type categories are coded for certain variables. For example, in the older version of the QLFS 2008 Q4 the "Q418HRSWRK" variable is coded 888, with the associated label "Not applicable", for 68,267 observations. In the newest version this category of responses is assigned the code 0 and is not labelled (as it was in the previous version) for the same 68,267 observations. This recoding process has been applied to a large number of categorical variables in the datafile. A few other categorical variables have instead been recoded in a similar vein but as different (non-zero) values. For example, values of 88 for "Q420FIRSTHRSWRK" have been redefined as having the value 888 for the same observations in the new version of the datafile.
Second, there is a substantive change for a single observation (for a single variable, "Q420TOTALHRSWRK"). This observation has changed values from 888 to 109 for the variable "Q420TOTALHRSWRK" only. The reason for this change is hitherto undocumented (to DataFirst's knowledge).
The metadata accompanying the release of the QLFS 2008 Q2 is somewhat ambiguous with its description of the variable derivation, which is supposedly constructed based on the following criteria (taken directly from the Stats SA metadata):
Third, a number of extra variables were introduced in the later version. It is unclear why these are not present in the older version of the datafile as they are detailed in metadata that was released at the same time as the original data:
1) "Geo_type" - Geography type (e.g. urban formal, rural informal, etc.)
2) "Hrswrk" - Hourse worked. A derived variable that was probably aimed at getting around problems created by the recoding of the hours worked variables used in the derivation of the underemployment variable
3) "Metro_code" - Metropolitan area code (e.g. Cape Town, eThekwini, Johannesburg, etc.)
4) "Status_Exp" - Expanded unemployment status.
5) "Stratum" - 6 digit number representing stratum formed during master sample 2006 where digit 1 represents province, based on 2005 provincial boundaries, digits 2-3 represent the metro/non-metro area and digit 4 confers geography type.
Finally, the two versions have different weights. To DataFirst's knowledge, the weighting changes are not clearly documented by Stats SA. The most likely explanation for the difference between the two sets of weights is that the newer version is calibrated to an updated set of mid-year population estimates. Users are advised to remain aware of these slight calibration differences when employing weights.
A new version of this dataset was released in 2014. The new version (our version 2.1) was reweighted to reflect the new population benchmarks from Census 2011.
This version, version 2.1 includes the new weights for the QLFS 2008-2013 series.
INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.
trade, industry and markets
demography and population.
Provincial and metropolitan level
The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are 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 QLFS frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.
The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a Master Sample of Primary Sampling Units (PSUs) which comprises of EAs that are drawn from across the country.
The sample is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.
The current sample size is 3 080 PSUs. It is divided equally into four sub-groups 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 to four 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 primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.
Statistics South Africa. Quarterly Labour Force Survey 2008: Q4 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2009. Cape Town: DataFirst [distributor], 2012. DOI: https://doi.org/10.25828/kn41-fv16