Quarterly Labour Force Survey 2008, Quarter 2
South Africa, 2008
Statistics South Africa
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July 01, 2020
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July 01, 2020
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Identification
IDNO
zaf-statssa-qlfs-2008-q2-v2.1
Title
Quarterly Labour Force Survey 2008, Quarter 2
Subtitle
Quarter 2
Country
| Name | Country code |
|---|---|
| South Africa | zaf |
Study type
Labor Force Survey [hh/lfs]
Series Information
Statistics South Africa. Quarterly Labour Force Survey 2008: Q2 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2008. Cape Town: DataFirst [distributor], 2012. DOI: https://doi.org/10.25828/z2kz-sw73
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
2008-08-28
Version Notes
Version 2.0 of the QLFS 2008 Q2 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 Q2 the "Q319aODDJOBS" variable is coded 8, with the associated label "Not applicable", for 55,106 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 55,106 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 Q26bTIME have been redefined as having the value 888, while values of 88 for Previndus and Prevoccup have been recoded as 10 (in addition to some observations being recoded as zero). In the latter case,observations that were previously lumped together in a category were split into two separate ones for the two variables mentioned.
Second, there is an apparent difference between the definitions of underemployment ("underempl") between versions. Note that this variable was also subjected to the abovementioned recoding procedure. 2837 observations shift status from "underemployed" to "not underemployed" between versions. Encouragingly, the "not applicable" coded observations are consistent.
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):
Underemployment (underempl) (@233 1.)
Derived variable:
If hours usually work is less than 35 or total hours usually work is less than 35 and if additional hours that could have been worked is between 0 and 3 and if available to start work in the next four weeks should extra work become available then that is underemployment.
with the relevant variables named as follows:
Q418HRSWRK (Hours usually worked, Question 4.18)
Q420TOTALHRS (Total hours usually work, Question 4.19)
Q422MOREHRS (Like to be able to work more hours, Question 4.22)
Q425STARTXWRK (Able to start extra work, Question 4.25)
The above definition provided could have a number of possible interpretations. This complicated the process of checking for the source of the between version discrepancy. One plausible interpretation could be that workers were defined as unemployed if they worked fewer than 35 hours per week in one job (Q418HRSWRK < 35) or less than a total of 35 hours a week on more than one job (Q420TOTALHRS < 35). Simultaneously. they must also have expressed some interest in working more (1 <= Q422MOREHRS <= 3) and confirmed that they were available to work more (Q425STARTXWRK == 1) to fulfil all criterion and be defined as underemployed. Note that this definition (in terms of the numerical ranges specified) would only apply to the original version, as the recoding of missing, non-applicable or unspecified variable values as 0 would alter the listed mathematical inequalities that comprise the logical tests assigning status to observations.
This definition does not produce results that agree exactly with Stats SA's derived version of "underempl" in either version of the datafile. In the older version, entries instead appear to be erroneously assigned into the underemployed category largely on the basis of their answer for Q422MOREHRS and Q425STARTXWRK. More specifically, having values in the ranges defined for these two variables is a necessary but sufficient condition for being assigned into the underemployed category. In a few exceptional cases (n = 16), respondents that answered in the affirmative to both Q422MOREHRS and Q425STARTXWRK were not defined as underemployed despite having values for Q420TOTALHRS and Q418HRSWRK on either side of the ostensible cutoff, 35 hours.
This issue appears mostly fixed in the newer version, which has a definition far more closely aligned to the one detailed above. There are two exceptional observations which fulfil the above criteria, but are not defined as underemployed by Stats SA. It is unclear as to why these two respondents are not defined as underemployed, as all of the other underemployed observations defined by DataFirst using the above criteria map perfectly onto the Stats SA underemployment variable. Users looking to check this themselves are advised that the redefinition of the "not applicable" category to zero valued entries for the Q418HRSWRK and Q420TOTALHRS variables must be taken into account when generating their versions of the underemployment variable.
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.
Second, there is an apparent difference between the definitions of underemployment ("underempl") between versions. Note that this variable was also subjected to the abovementioned recoding procedure. 2837 observations shift status from "underemployed" to "not underemployed" between versions. Encouragingly, the "not applicable" coded observations are consistent.
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):
Underemployment (underempl) (@233 1.)
Derived variable:
If hours usually work is less than 35 or total hours usually work is less than 35 and if additional hours that could have been worked is between 0 and 3 and if available to start work in the next four weeks should extra work become available then that is underemployment.
with the relevant variables named as follows:
Q418HRSWRK (Hours usually worked, Question 4.18)
Q420TOTALHRS (Total hours usually work, Question 4.19)
Q422MOREHRS (Like to be able to work more hours, Question 4.22)
Q425STARTXWRK (Able to start extra work, Question 4.25)
The above definition provided could have a number of possible interpretations. This complicated the process of checking for the source of the between version discrepancy. One plausible interpretation could be that workers were defined as unemployed if they worked fewer than 35 hours per week in one job (Q418HRSWRK < 35) or less than a total of 35 hours a week on more than one job (Q420TOTALHRS < 35). Simultaneously. they must also have expressed some interest in working more (1 <= Q422MOREHRS <= 3) and confirmed that they were available to work more (Q425STARTXWRK == 1) to fulfil all criterion and be defined as underemployed. Note that this definition (in terms of the numerical ranges specified) would only apply to the original version, as the recoding of missing, non-applicable or unspecified variable values as 0 would alter the listed mathematical inequalities that comprise the logical tests assigning status to observations.
This definition does not produce results that agree exactly with Stats SA's derived version of "underempl" in either version of the datafile. In the older version, entries instead appear to be erroneously assigned into the underemployed category largely on the basis of their answer for Q422MOREHRS and Q425STARTXWRK. More specifically, having values in the ranges defined for these two variables is a necessary but sufficient condition for being assigned into the underemployed category. In a few exceptional cases (n = 16), respondents that answered in the affirmative to both Q422MOREHRS and Q425STARTXWRK were not defined as underemployed despite having values for Q420TOTALHRS and Q418HRSWRK on either side of the ostensible cutoff, 35 hours.
This issue appears mostly fixed in the newer version, which has a definition far more closely aligned to the one detailed above. There are two exceptional observations which fulfil the above criteria, but are not defined as underemployed by Stats SA. It is unclear as to why these two respondents are not defined as underemployed, as all of the other underemployed observations defined by DataFirst using the above criteria map perfectly onto the Stats SA underemployment variable. Users looking to check this themselves are advised that the redefinition of the "not applicable" category to zero valued entries for the Q418HRSWRK and Q420TOTALHRS variables must be taken into account when generating their versions of the underemployment variable.
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.
Scope
Notes
INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.
Keywords
| Keyword |
|---|
| 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 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.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2008-04 | 2008-06 |
Data Collection Mode
Face-to-face [f2f]
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst | University of Cape Town | info@data1st.org | http://www.datafirst.uct.ac.za |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst Helpdesk | University of Cape Town | support@data1st.org | http://support.data1st.org/ |
Access conditions
Public use files, accessible to all
Citation requirements
Statistics South Africa. Quarterly Labour Force Survey 2008: Q2 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2008. Cape Town: DataFirst [distributor], 2012. DOI: https://doi.org/10.25828/z2kz-sw73
Metadata production
DDI Document ID
ddi-zaf-datafirst-qlfs-2008-q2-v3
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | DDI Producer |
Date of Metadata Production
2020-03-30
DDI Document version
Version 4