Quarterly Labour Force Survey 2012, Second Quarter
Labor Force Survey [hh/lfs]
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
v2.0: Edited, anonymised dataset for public distribution
This version of the QLFS 2012 Q2 was downloaded from the Statistics South Africa (Stats SA) website in April 2014 as a revision to the version previously downloaded in July 2012.
The two versions have different weights. Stats SA updated the QLFS results (2008-2013) to reflect the new population benchmarks from Census 2011. Although the weighting changes are not clearly documented by Stats SA, users are advised to remain aware of these slight calibration differences when employing weights.
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 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.
Response rates by province
Province Apr–Jun 2012 (Percent)
Western Cape 86.4
Eastern Cape 99.2
Northern Cape 89.0
Free State 96.9
North West 93.4
South Africa 92.6
Stats SA updated the QLFS results (2008-2013) to reflect the new population benchmarks from Census 2011. Although the weighting changes are not clearly documented by Stats SA, users are advised to remain aware of these slight calibration differences between the previous version and the current (revised) data version when employing weights.
The sampling weights for the data collected from the sampled households are constructed in such a manner that the responses could be properly expanded to represent the entire civilian population of South Africa. The weights are the result of calculations involving several factors, including original selection probabilities, adjustment for non-response, and benchmarking to known population estimates from the Demographic division of Stats SA.
The final survey weights are constructed using regression estimation to calibrate to the known population counts at the national level population estimates (which are supplied by the Demography division) 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, etc., and 65 years and above. The provincial level age groups are: 0–14, 15–34, 35–64, and 65 years and over. The calibrated weights are constructed such that all persons in a household would have the same final weight.
Dates of Data Collection
Data Collection Mode
Statistics South Africa
Estimates of Sampling Error
Since estimates are based on sample data, they differ from figures that would have been obtained from complete enumeration of the population using the same instrument. Results are subject to both sampling and non-sampling errors. Non-sampling errors include biases from inaccurate reporting, processing, and tabulation, etc., as well as errors from non-response and incomplete reporting. These types of errors cannot be measured readily. However, to some extent, non-sampling errors can be minimised through the procedures used for data collection, editing, quality control, and non-response adjustment. The variances of the survey estimates are used to measure sampling errors.
Use of the dataset must be acknowledged using a citation which would include:
- Identification of the Primary Investigator,
- Title of the survey (including country, acronym and year of implementation),
- Survey reference number,
- Source and date of download.
Statistics South Africa. South Africa Quarterly Labour Force Survey (QLFS-Q2) 2012, Second Quarter 2012. Ref. ZAF_2012_QLFS-Q2_v02_M. Dataset downloaded from [url] on [date].
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.
Copyright, Statistics South Africa
DDI Document ID
University of Cape Town
Date of Metadata Production
DDI Document version
Version 02 (April 2014): This version is identical to Version 01, with revisions to data.