Quarterly Labour Force Survey 2013
South Africa, 2013
Identification
ZAF_2013_QLFS-Q1_v02_M
Quarterly Labour Force Survey 2013
First Quarter
| Name | Country code |
|---|---|
| South Africa | ZAF |
Labor Force Survey [hh/lfs]
The objective of the QLFS is to collect quarterly information about persons in the labour market, i.e., those who are employed; those who are unemployed and those who are not economically active.
This information will be published as core labour market indicators four weeks after the end of each quarter and an annual report and supplementary data will be published six months after the end of each calendar year.
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 and above who live in South Africa. However, this report only covers labour market activities of persons aged 15 to 64 years.
Sample survey data [ssd]
Individuals, Households
Version
Public Use Files
This version of the QLFS 2013 Q1 was downloaded from the Statistics South Africa (Stats SA) website in April 2014 as a revision to the version previously downloaded in June 2013.
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.
Scope
INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax.
Coverage
National Coverage
The QLFS sample covers the non-institutional population except for workers' hostels. However, persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, you would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would therefore be excluded.
Producers and sponsors
| Name |
|---|
| Statistics South Africa |
Sampling
The Labour Force Survey 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 requirements of the survey. The sample size for the QLFS is roughly 30 000 dwellings.
The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for Census 2001, the country was divided into 80 787 enumeration areas (EAs). Some of these EAs are small in terms of the number of households that were enumerated in them at the time of Census 2001. The Stats SA household-based surveys use a Master Sample of primary sampling units (PSUs) which comprises EAs that are drawn from across the country. For the purposes of the Master Sample, the EAs that contained fewer than 25 households were excluded from the sampling frame, and those that contained between 25 and 99 households were combined with other EAs of the same geographic type to form primary sampling units (PSUs). The number of EAs per PSU ranges between one and four. On the other hand, very large EAs represent two or more PSUs.
The sample is designed to be representative at provincial level and within provinces at 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 that, for example, within a metropolitan area the sample is designed to be representative at the different geography types that may exist within that metro.
The current sample size is 3 080 PSUs. It is equally divided 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 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 redesigned Labour Force Survey 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.
Sample rotation
The sampled PSUs have been assigned to 4 rotation groups, and dwellings selected from the PSUs assigned to rotation group '1' are rotated in the first quarter. Similarly, the dwellings selected from the PSUs assigned to rotation group '2' are rotated in the second quarter, and so on. Thus, each sampled dwelling will 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, say 2 quarters, and a new household moves in, then 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 (unoccupied).
At the end of each quarter, a quarter of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. A total of 3 080 PSUs were selected for the redesigned LFS, and 770 have been assigned to each of the four rotation groups.
Province Percentage (%)
Western Cape 89.9
Eastern Cape 99.3
Northern Cape 89.9
Free State 97.2
KwaZulu-Natal 97.3
North West 93.2
Gauteng 80.4
Mpumalanga 90.2
Limpopo 98.3
South Africa 92.2
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.
Weighting
The sampling weights for the data collected from the sampled households are constructed so 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 Analysis division of Stats SA.
The base weight is defined as the product of the provincial Inverse Sampling Rate (ISR) and the three adjustment factors, namely adjustment factor for informal PSUs, adjustment factor for subsampling of growth PSUs, and an adjustment factor to account for small EAs excluded from the sampling frame (i.e. EAs with fewer than 25 households).
Non-response adjustment
In general, imputation is used for item non-response (i.e. blanks within the questionnaire), and edit failure (i.e. invalid or inconsistent responses). The eligible households in the sampled dwellings can be divided into two response categories: respondents and non-respondents, and weight adjustment is applied to account for the non-respondent households (e.g. refusal, no contact, etc.). The sampled dwellings with no eligible households, e.g. foreigners only, or no households, (i.e. vacant dwellings), do not contribute to the survey.
The non-response adjusted weight is the product of the base weight with the non-response adjustment factor given above. If the PSU level non-response rate is too high, the non-response adjustment is applied at the VARUNIT level, where two VARUNITs have been created by grouping PSUs within strata. PSU level non-response adjustment is applied only if the corresponding adjustment factor is less than 1,5.
Final survey weights
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 Demographic Analysis division) cross-classified by 5-year age groups, gender and race, and provincial population estimates by broad age groups are used for calibration weighting. The 5-year age groups are: 0-4, 5-9, 10-14, 55-59, 60-64, and 65 and over. The provincial level age groups are: 0-14, 15-34, 35-64, and 65 years and over. The final weights are constructed in such a manner that all persons within a household would have the same weight.
Survey instrument
Quarterly Labour Force Survey Questionnaire 2013, Quarter 1
Cover
A. Particulars of the dwelling
B. Households at selected dwelling unit
C. Response details
SECTION 1 - This section covers particulars of each person in the household
SECTION 2 - This section covers economic activities in the last week for persons aged 15 years and above
SECTION 3 - This section covers unemployment and economic inactivity for persons aged 15 years and above
SECTION 4 - This section covers main work activity in the last week for persons aged 15 years and above
SECTION 5 - This section covers earnings in the main job for employees, employers and own-account workers aged 15 years and above
SECTION 6 - This section covers job creation programme or expanded public works programme in the last twelve months
Data collection
| Start | End |
|---|---|
| 2013-01 | 2013-03 |
| Name |
|---|
| Satistics South Africa |
To facilitate continuous data collection, training and fieldwork monitoring from the regional offices across the country, permanent field staff (332) were appointed to conduct the QLFS. In addition, 95 contract staff have been recruited to assist in areas such as listing and the capturing of publicity forms.
The appointment of a permanent fieldwork force for the QLFS marks an important break with the past practice of Stats SA regarding fieldwork for household-based surveys. For the first time, a household-based survey - the QLFS - will benefit from the continuity and institutional memory that develops through adopting this approach, which allows for: refresher training; performance monitoring and where necessary remedial action; and interviewer/respondent rapport to grow over the course of the four interviews.
Household members living in approximately 10 000 dwelling units in 1 025 PSUs are interviewed in each of the three months within a quarter. Key information from completed questionnaires is captured by data capturers in the regional office using the RTMS. This includes whether or not the interview was successful, thus allowing follow-up by senior staff in the event of refusals.
The QLFS data collection strategy is based on a '0110' approach. The first digit "0" represents the first week of any given month. During this week set-up interviews/publicity and listing maintenance is conducted. Data collection is conducted during the middle two weeks ("11") of each month (except in January and December). The last week ("0") is dedicated to completing the work allocation assigned for that month including the listing of growth areas in the sampled PSU identified during the first week. In summary, the "0"represents a non-data collection week and the "1" represents the two weeks of data collection.
Data processing
A detailed description of Data processing for teh QLFS is available in page 15 of the Quarterly Labour Force Guide.
The purpose of data processing is to ensure that the information collected from the sampled primary sampling units, dwelling units and households (i.e. the boxes containing QLFS questionnaires) are physically received, stored and processed. The aim is to produce a clean dataset that has all the information contained in the questionnaires. Except for the scanning system, all other elements of the data processing system were developed in-house.
One important innovation that is central to the smooth operation of the entire system is the development of barcodes that are linked to a unique number on each questionnaire. This information provides the link between the information recorded in the Master Sample database and other processes such as editing and imputation as well as weighting and variance estimation.
Processing phases
QLFS data processing is continuous, starting on the second week of every month. Data processing for each quarter must be completed by the first Friday of the subsequent month to ensure that the four-week deadline for publication of the QLFS results is met.
The phases listed below occur sequentially.
Receiving of questionnaires
Primary preparation
Guillotining
Scanning
Verification
Electronic coding
Automated editing and imputation
Data Access
Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source of the data; that it is specified that the application and/or analysis is the result of the user's independent processing of the data; and that neither the basic data nor any reprocessed version or application thereof may be sold or offered for sale in any form whatsoever without prior permission from Stats SA.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
Statistics South Africa. South Africa Quarterly Labour Force Survey (QLFS-Q1) 2013, First Quarter 2013. Ref. ZAF_2013_QLFS-Q1_v02_M. Dataset downloaded from [url] on [date].
Disclaimer and copyrights
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.
© Statistics South Africa, 2012
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| The World Bank Microdata Library | The World Bank | microdata@worldbank.org | http://microdata.worldbank.org |
Metadata production
DDI_ZAF_2013_QLFS-Q1_v02_M
| Name | Affiliation | Role |
|---|---|---|
| Development Data Group | World Bank | DDI Producer |
| Statistics South Africa | Metadata Producer |
2013-06-06
Metadata version
Version 02 (April 2014)
This version is identical to Version 01, with revisions to data.