• Data Repository
  • Collections
  • Citations
  • Metadata Dashboard
  • Contact us
  • Login
    Login
    Home / Data Site / LFS-QLFS-LMDSA / ZAF-STATSSA-QLFS-2009-Q3-V2.1
LFS-QLFS-LMDSA

Quarterly Labour Force Survey 2009, Quarter 3

South Africa, 2009
Get Microdata
Reference ID
zaf-statssa-qlfs-2009-q3-v2.1
Producer(s)
Statistics South Africa
Collections
South African Labour Force Survey Data
Metadata
Documentation in PDF DDI/XML JSON
Created on
Jul 05, 2012
Last modified
Jul 01, 2020
Page views
54593
Downloads
4514
  • Dataset Description
  • Data Description
  • Downloads
  • Get Microdata
  • Related Publications
  • Identification
  • Version
  • Scope
  • Coverage
  • Producers and sponsors
  • Sampling
  • Data collection
  • access_policy
  • Contacts
  • Metadata production
  • Identification

    Survey ID number
    zaf-statssa-qlfs-2009-q3-v2.1
    Title
    Quarterly Labour Force Survey 2009
    Subtitle
    Quarter 3
    Country
    Name Country code
    South Africa zaf
    Study type
    Labor Force Survey [hh/lfs]
    Series Information
    Statistics South Africa. Quarterly Labour Force Survey 2009: Q3 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2009. Cape Town: DataFirst [distributor], 2012. DOI: https://doi.org/10.25828/90dg-gk64
    Abstract
    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
    Individuals

    Version

    Version Description
    v2.1: Edited, anonymised dataset for public distribution
    Version Date
    2012-10-29
    Version Notes
    Version 2.0 of the QLFS 2009 Q3 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 2009 Q3 the "Occup" variable is coded 888, with the associated label "Not applicable", for 67,722 observations. In the newer version this category of responses is assigned the code 0 and is not labelled (as it was in the previous version) for the same 67,722 observations. This recoding process has been applied to a large number of categorical variables in the datafile. A few other categorical variables have been recoded in a similar vein but as different (non-zero) values. For example, values of 888 for "Q4212TOTALHRS" have been redefined as having the value 88.

    Second, 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.

    Scope

    Notes

    INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.

    Keywords
    Employment in-job training labour relations/conflict retirement unemployment working conditions labour and employment trade, industry and markets demography and population.

    Coverage

    Geographic Coverage
    National coverage
    Geographic Unit
    Provincial and metropolitan level
    Universe
    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

    Primary investigators
    Name
    Statistics South Africa

    Sampling

    Sampling Procedure
    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.

    Data collection

    Dates of Data Collection
    Start End
    2009-07 2009-09

    access_policy

    Access authority
    Name Affiliation URL Email
    DataFirst University of Cape Town support@data1st.org
    Access conditions
    Public use files, accessible to all
    Citation requirements
    Statistics South Africa. Quarterly Labour Force Survey 2009: Q3 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2009. Cape Town: DataFirst [distributor], 2012. DOI: https://doi.org/10.25828/90dg-gk64

    Contacts

    Contacts
    Name Affiliation Email URL
    DataFirst Helpdesk University of Cape Town support@data1st.org

    Metadata production

    DDI Document ID
    ddi-zaf-datafirst-qlfs-2009-q3-v3
    Producers
    Name Affiliation Role
    DataFirst University of Cape Town DDI Producer
    Date of Metadata Production
    2020-03-30

    Metadata version

    DDI Document version
    Version 4
    Back to Catalog
    DataFirst

    © DataFirst, All Rights Reserved.