Description: Topics covered in the questionnaire are: work and unemployment, respondent characteristics, household characteristics, personal and household income variables. The data set for dissemination contains 2885 cases and 262 variables. Abstract: Under the auspices of the Department and Training (DHET), the Human Sciences Research Council (HSRC) and a consortium of partners are undertaking the Labour Market Intelligence Partnership Research Project (LMIP) in order to address the need for an improved system of labour market analysis and planning in South Africa. At the centre of skills planning is need for quality and reliable data on the South African labour market information. Theme 1 of LMIP entitled "Establishing a foundation for labour market information systems in South Africa" acknowledges the need to improve the quality as well as quantity of the current labour market information for effective skills planning. It is against this background that the current project - Survey of attitudes towards employment and unemployment related issues will be conducted to: Determine attitudes of South Africans towards the labour market. Develop a systemic and methodology sound infrastructure for studying changing work attitudes, values and behaviour patterns of agents in and out the labour market. The broad aim of this study is to determine attitudes of South Africans towards the labour market. This entails: Investigating the nature and distribution of work orientations and work values of South Africans; Exploring the public's attitudes towards the state of unemployment and barriers; To explore the public's assessment of the relevance of post school education and some specific skills in the workplace; To get in depth insights about the perceived effective job search strategies; To investigate the satisfaction levels of those in employment, and subjective evaluation of various aspects of their work. Data for the study will be collected through the South African Social Attitudes Survey. The South African Social Attitudes Survey (SASAS) is the Human Science's survey which has been collecting data on South Africa attitudes, beliefs and behaviour patterns annually since 2003. The Education and Skills Development Research Programme included a module into the SASAS study which looked at five 1) Work values and ethics, 2) perceived barriers to employment, 3) perceived skills and competencies required in the labour market, 4) job search strategies, and 5) subjective evaluation of different aspects of work. Face-to-face interview National Population: Adults (aged 16 and older) SASAS has been designed to yield a representative sample of 3500 adult South African citizens aged 16 and older (with no upper age limit), in households geographically spread across the country's nine provinces. The sampling frame used for the survey was based on the 2011 census and a set of small area layers (SALs). Estimates of the population numbers for various categories of the census variables were obtained per SAL. In this sampling frame special institutions (such as hospitals, military camps, old age homes, schools and university hostels), recreational areas, industrial areas and vacant SALs were excluded prior to the drawing of the sample. Small area layers (SALs) were used as primary sampling units and the estimated number of dwelling units (taken as visiting points) in the SALs as secondary sampling units. In the first sampling stage the primary sampling units (SALs) were drawn with probability proportional to size, using the estimated number of dwelling units in an SAL as measure of size. The dwelling units as secondary sampling units were defined as "separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc." In the second sampling stage a predetermined number of individual dwelling units (or visiting points) were drawn with equal probability in each of the drawn dwelling units. Finally, in the third sampling stage a person was drawn with equal probability from all 16 year and older persons in the drawn dwelling units. Three explicit stratification variables were used, namely province, geographic type and majority population group. As stated earlier, within each stratum, the allocated number of primary sampling units (which could differ between different strata) was drawn using proportional to size probability sampling with the estimated number of dwelling units in the primary sampling units as measure of size. In each of these drawn primary sampling units, seven dwelling units were drawn. This resulted in a sample of 3500 individuals. A list of the 500 drawn SALs were given to geographic information specialists (GIS) and maps were then created for each of the 500 areas, indicating certain navigational beacons such as schools, roads churches etc. Selection of individuals: For each of the SASAS samples interviewers visited each visiting point drawn in the SALs (PSU) and listed all eligible persons for inclusion in the sample, that is all persons currently aged 16 years or older and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

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 and above who live in South Africa.

Geographic coverage

National Coverage

Analysis unit

Individuals, households

Universe

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, 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.

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Survey requirements and design :

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 requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings and these are divided equally into four rotation groups, i.e. 7 500 dwellings per rotation 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). Some of these EAs are small in terms of the number of households that were enumerated in them at the time of Census 2001. Stats SA's household-based surveys use a Master Sample which comprises of EAs that are drawn from across the country. For the purposes of the Master Sample the EAs that contained less than 25 households were excluded from the sampling frame, and those that contained between 25 and 99 households were combined with other EAs 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 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 that for example, that 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 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 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). Each quarter, ¼ 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.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consists of the following sections:

Section 1 - Biographical information (marital status, language, migration, education,training, literacy, etc. Section 2 - Economic activities Section 3 - Unemployment and economic inactivity Section 4 - Main work activities in the last week Section 5 - Earnings in the main job All sections - Comprehensive coverage of all aspects of the labour market

Cleaning operations

Data Processing

Introduction : 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 : The contents of the boxes containing questionnaires sent from the regional offices are verified when received at the DPC. The questionnaire barcodes captured in the provinces are captured again at the DPC to ensure that all questionnaires have been received.

Primary preparation : The purpose of primary preparation is to ensure that all questionnaires are correctly stacked and positioned prior to being guillotined.

Guillotining: The purpose of the guillotine process is to cut off the spines of the questionnaires in order to have pages separated for scanning.

Secondary preparation : The purpose of secondary preparation is to ensure that the questionnaires are correctly stacked and positioned for scanning. At the same time, quality assurance takes place on the work done during the primary preparation and guillotining processes.

Scanning : The purpose of scanning and recognition is to convert the questionnaires into an electronic format and Tagged Image File Format (TIFF) images.

Verification : The purpose of scanning verification is to manually correct un-interpretable characters, missing data and errors detected by validation rules.

Electronic coding: Industry and occupation codes are assigned using the electronic coding system which converts the respondents' industry and occupation descriptions into numeric codes based on Standard Industry Classification (SIC) and South African Standard Occupation Classification (SASCO). If the system fails to assign a code for either industry or occupation, the coding is assigned manually.

Automated editing and imputation : QLFS uses the editing and imputation module to ensure that output data is both clean and complete10. There are three basic components, called functions, in the Edit and Imputation Module:

Function A: Record acceptance Function B: Edit and imputation Function C: Clean up, derived variables and preparation for weighting Function A: Record acceptance

This function is divided into three phases:

First phase: Pre-function A : The first phase ensures that the records contain valid information in selected Cover Page questions required during edit and imputation and during the subsequent weighting and variance estimation. Any blanks or other errors that need to be corrected are done here before processing of the record can proceed.

Second phase: Function A record acceptance : The second phase ensures that there is enough demographic and labour market activity information to ensure that editing and imputation can be successfully completed.

Third phase: Post Function A clean up : This phase ensures that certain data are present where there is evidence that they should be. This for example, involves: • Ensuring that if there is written material in the job description questions then there are corresponding industry and occupation codes for them. • Ensuring that partial blanks or non-numeric characters that appear in questions where the Survey Officer is required to enter numbers are validated. • Ensuring that where there is written material in the space provided for "Other - specify" that the corresponding option is marked.

Function B: Edit and imputation : Having determined in Function A that the content of the record would support extensive editing and imputation, this function carries out those activities. Editing is the

All employers who have registered a vacancy on the ESSA system during the 2013 calendar year.

Description: The data set contains information from employers who have registered vacancies on the ESSA database and covers topics regarding the size and location of their industries, the profile of the work force, experience with ESSA and other recruitment channels used. The data set contains 265 cases and 94 variables. 265 of the 605 employers realised providing a 43.8% realisation. Abstract: As part of the Labour Market Intelligence Partnership (LMIP), the Human Sciences Research Council (HSRC) undertook projects aimed at identifying data held by government departments that would be relevant and useful for skills planning. One involved a high level audit of government databases that contain data on labour demand and supply. The latter LMIS' Data Audit project identified the Employment services of South Africa (ESSA) database under the administration of the Department of Labour's Public Employment Services (PES) branch as having potential to yield valuable labour market information because it is positioned in the critical space where matching of demand and supply takes place in the labour market. This argument was made in recognition that we should be aware of the development of new and useful data systems for incorporation in the skills planning mechanism. In this sense, the ESSA project was investigated with the expectation that in the following years it could mature. If ESSA becomes a larger intermediary, serving greater numbers of employers and work seekers, it can offer an increasingly more detailed picture of intermediation between employers and work seekers. We therefore need to know how enterprises and how work seekers perceive the ESSA as useful or not to their needs and how they interact with ESSA. What we learn through this research will feed into making improvements to the ESSA. The specific aim of this project is therefore to consider how firms' level of participation and the nature of that participation impacts on the value of the data in the ESSA data system. We also want to know more about the quality of data created via administrative processes in interaction with the enterprises. The main aim of the study is to investigate employer interaction with ESSA and to consider ESSA in the context of other recruitment channels used by employers. The greater the number of employers interacting with ESSA in terms of registering vacancies and recruiting from the ESSA database of work seekers, the larger the database records of these transactions will become. Thus rising levels of interaction between clients will make ESSA data richer and more detailed to be used as part of the system needed for skills planning decision making. For datasets to be used to support skills planning they must be of a minimum acceptable quality. Therefore, procedures for capturing registration of vacancies, registration of work seekers and the conclusion of agreements between parties must be a captured and maintained to highest possible levels of accuracy. The report can be accessed on the LMIP website: http://www.lmip.org.za/document/investigating-employer-interaction-employment-services-south-africa-essa

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 and above who live in South Africa. However, this report only covers labour market activities of persons aged 15 to 64 years.

Geographic coverage

National Coverage

Analysis unit

Individuals, Households

Universe

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.

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Mode of data collection

Face-to-face [f2f]

Research 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

Cleaning operations

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

Response rate

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

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.