Abstract

The survey was undertaken bi-annually from 1965-1985 by the South African Department of Manpower (now the South African Department of Labour) and the standard form used the department's own classification scheme. In 1996 the name changed to the Occupational Survey. In 1998 it was replaced by the Survey of Occupations by Race and Gender, but this was discontinued after the pilot study in 1998. The two series of Manpower Surveys are not always comparable. Data users need to read the data quality notes provided for further details on data comparability.

The Manpower Survey is a survey of enterprises in South Africa that collected industry and occupation data by gender and race for each enterprise. It covered both the private and public sector, but excluded workers in the informal sector and agricultural sector, and domestic workers in private households. Enterprise details for the survey sample were obtained from government sources, and the survey instrument was a form mailed to enterprise managers.

The dataset available from DataFirst is not at firm level, but is rather the industry and occupation data by gender and race available from the survey reports. This country-level data is from the Manpower Surveys conducted in 1965-1994, unearthed in a project to find and share historical South African microdata. The data was obtained with the assistance of Lucia Lotter, Anneke Jordaan and Marie-Lousie van Wyk from the Human Sciences Research Council's Research Use and Impact Assessment Department. The project was made possible by an exploratory grant obtained by Andrew Kerr and Martin Wittenberg of DataFirst from the Private Enterprise Development in Low-Income Countries (PEDL) research initiative. PEDL is a joint research initiative of the Centre for Economic Policy Research (CEPR) and the Uk Department For International Development (DFID). It aims to develop a research programme focusing on private-sector development in low-income countries.

Geographic coverage

The survey had national coverage, but excluded the "independent" " homelands" of Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Analysis unit

Individuals and institutions

Universe

The universe of the survey were enterprises in the formal non-agricultureal sector in South Africa

Kind of data

Sample survey data

Sampling procedure

The survey sample is based on lists of companies obtained from the databases of the Compensation Fund and Unemployment Insurance Fund of the South African Department of Labour) and the South African Tourism Board. During the time the surveys were conducted by the Department of labour (1965-1985), the sample of companies was 250,000. The survey was taken over by the Central Statistical Service (now Statistics South Africa) in 1987 who rationalised the sample to 12,800 companies in 1989, and later to 8500.

The sample excludes domestic workers in private household, and workers in the agricultural and informal sectors. The firms were classified into industries, based on the Standard Industrial Classification of all Economic Activities. Where these firms consisted of more than one establishment in more than one sector the firm was classified according to the sector in which it is predominantly engaged. Thus, although workers in the agricultural sector are not covered these may be included in firm data for those firms which include more than one establishment, and where one of the establishments is involved in agricultural production.

Entities in the "independent" " homelands" were excluded from the survey. These included Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The 1965-1985 questionnaire from the Department of Labour has 5 Sections: Section A: To be completed for all employees except artisans, apprentices and “Bantu” building workers Section B: To be completed for male artisans and apprentices only Section C: To be completed for women artisans and apprentices only Section D: To be completed for “Bantu” building workers only (“skilled Bantu building workers and learners registered in terms of the Bantu Building Workers' Act”) Section E: To be completed for all employees (total number of employees)

The 1987-1994 questionnaire from the Central Statistical Service has 4 Sections: Section 1: To be completed for all employees except artisans, apprentices Section 2: To be completed for artisans only (men and women) Section 3: To be completed for apprentices only (men and women) Section 4: To be completed for all employees (total number of employees)

The variable

Response rate

Since the questionnaire was completed by company managers, the response rate of the sample is very high (around 90 per cent)

Data appraisal

DATA RELIABILITY The Manpower survey enables investigations of long-term changes in the occupational and racial division of labour during the period 1965-1994. It is the only data source for this period that distinguishes artisans and apprentices from other manual workers, which allows analysis of these occupations over time. However, the data is not reliable at disaggregated levels because of the following:

(1) Both agriculture and the informal sector are excluded from the survey universe. These sectors are major employers in the South African economy. (2) Domestic workers in private households are also excluded from the sample. (3) The survey does not cover the unemployed and is therefore not representative of the economically active population. (4) Although this is an occupational survey, the information on occupations is derived from samples based on total employment within industries. (5) A new sample was drawn by the Central Statistical Service when they took over the Manpower Survey from the Department of Manpower in 1987, causing a break in the series.

QUESTIONNAIRES Finally, the variable

CODING OF INDUSTRIES The Standard Industrial Classification codelist used to code industries in the survey seems to have been the ISIC revision 2.The codelist is provided here with the data, and is also available from the United Nations Statistics Division's website, at http://unstats.un.org/unsd/cr/registry/regdnld.asp?Lg=1

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

National coverage

Analysis unit

Individuals

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.

Kind of data

Sample survey data

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). 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, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

Abstract

The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa. It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

All editions of the LFS have been updated (some more than once) since their release. These version changes are detailed in a document available from DataFirst (in the "external documents" section titled "LFS 2000-2008 Collated Version Notes on the South African LFS").

Geographic coverage

National Coverage

Analysis unit

Households (dwellings) and individuals

Universe

The LFS 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

Statistics South Africa uses a rotating panel methodology for the labour force survey. The rotating panel methodology involves visiting the same dwelling units on a number of occasions (in this instance, five at most). After the panel is established, a proportion of the dwelling units is replaced each round (in this instance, 20%). New dwelling units are added to the sample to replace those that are taken out.

Enumeration Areas (EAs) that had a household count of less than twenty-five were omitted from the census 2001 frame that was used to draw the sample of Primary Sampling Units (PSUs) for the new Master Sample. Other omissions from the Master Sample frame included all institution EAs except workers, hostels, convents and monasteries. EAs from census 2001 were pooled in two stages, before and after sampling. Before sampling the criterion that was used to pool EAs was that they should contain a minimum of one hundred households. However, during listing it was discovered that there were discrepancies between the information on the database and what was on the ground.

Therefore, in the second stage of pooling, EAs that were found to have less than sixty dwelling units during listing were pooled. The Master Sample is a multi-stage stratified sample. The overall sample size of PSUs was 3000. The explicit strata were the 53 district councils/metros (DCs). The 3000 PSUs were allocated to these DCs using the power allocation method. The PSUs were then sampled using probability proportional to size principles. The measure of size used was the number of households in a PSU as calculated in the census. The sampled PSUs were listed with the dwelling unit as the listing unit. From these listings systematic samples of dwelling units per PSU were drawn. These samples of dwelling units form clusters. The size of the clusters differs depending on the specific survey requirements. The LFS uses one of the clusters that contain ten dwelling units.

Mode of data collection

Face-to-face [f2f]

Abstract

The Manpower Survey is a survey of enterprises in South Africa that provides industry and occupation data by gender and race. It covered both the private and public sector, but excluded workers in the informal sector and agricultural sector, and domestic workers in private households. Enterprise details for the survey sample were obtained from government sources, and the survey instrument was a form mailed to enterprise managers.

The dataset available from DataFirst includes data from the surveys conducted in 1965-1994, unearthed in a project to find and share historical South African microdata. The data was obtained with the assistance of Lucia Lotter, Anneke Jordaan and Marie-Lousie van Wyk from the Human Sciences Research Council's Research Use and Impact Assessment Department. The project was made possible by an exploratory grant obtained by Andrew Kerr and Martin Wittenberg of DataFirst from the Private Enterprise Development in Low-Income Countries (PEDL) research initiative. PEDL is a joint research initiative of the Centre for Economic Policy Research (CEPR) and the Uk Department For International Development (DFID). It aims to develop a research programme focusing on private-sector development in low-income countries.

Geographic coverage

The survey had national coverage, but excluded the "independent" " homelands" of Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Analysis unit

Units of analysis in the survey include firms and individuals

Universe

The universe of the survey were enterprises in the formal non-agricultureal sector in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey sample is based on lists of companies obtained from the databases of the Compensation Fund and Unemployment Insurance Fund of the South African Department of Labour) and the South African Tourism Board. During the time the surveys were conducted by the Department of labour (1965-1985), the sample of companies was 250,000. The survey was taken over by the Central Statistical Service (now Statistics South Africa) in 1987 who rationalised the sample to 12,800 companies in 1989, and later to 8500.

The sample excludes domestic workers in private household, and workers in the agricultural and informal sectors. The firms were classified into industries, based on the Standard Industrial Classification of all Economic Activities. Where these firms consisted of more than one establishment in more than one sector the firm was classified according to the sector in which it is predominantly engaged. Thus, although workers in the agricultural sector are not covered these may be included in firm data for those firms which include more than one establishment, and where one of the establishments is involved in agricultural production.

Entities in the "independent" " homelands" were excluded from the survey. These included Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The 1965-1985 questionnaire from the Department of Labour has 5 Sections: Section A: To be completed for all employees except artisans, apprentices and “Bantu” building workers Section B: To be completed for male artisans and apprentices only Section C: To be completed for women artisans and apprentices only Section D: To be completed for “Bantu” building workers only (“skilled Bantu building workers and learners registered in terms of the Bantu Building Workers' Act”) Section E: To be completed for all employees (total number of employees)

The 1987-1994 questionnaire from the Central Statistical Service has 4 Sections: Section 1: To be completed for all employees except artisans, apprentices Section 2: To be completed for artisans only (men and women) Section 3: To be completed for apprentices only (men and women) Section 4: To be completed for all employees (total number of employees)

The variable

Response rate

Since the questionnaire was completed by company managers, the response rate of the sample is very high (around 90 percent)

Data appraisal

The Manpower survey enables investigations of long-term changes in the occupational and racial division of labour during the period 1965-1994. It is the only data source for this period that distinguishes artisans and apprentices from other manual workers, which allows analysis of these occupations over time. However, the data is not reliable at disaggregated levels because of the following:

(1) Both agriculture and the informal sector are excluded from the survey universe. These sectors are major employers in the South African economy. (2) Domestic workers in private households are also excluded from the sample. (3) The survey does not cover the unemployed and is therefore not representative of the economically active population. (4) Although this is an occupational survey, the information on occupations is derived from samples based on total employment within industries. (5) A new sample was drawn by the Central Statistical Service when they took over the Manpower Survey from the Department of Manpower in 1987, causing a break in the series.

Finally, the variable

Contributing family workers, male (% of male employment) (modeled ILO estimate) in South Africa was reported at 0.49494 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Contributing family workers; male (% of males employed) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Graph and download economic data for Infra-Annual Labor Statistics: Employees Total for South Africa (LFESEETTZAQ647N) from Q1 2008 to Q2 2025 about South Africa and employment.

Abstract

The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa. It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

All editions of the LFS have been updated (some more than once) since their release. These version changes are detailed in a document available from DataFirst (in the "external documents" section titled "LFS 2000-2008 Collated Version Notes on the South African LFS").

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The LFS 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

Statistics South Africa uses a rotating panel methodology for the labour force survey. The rotating panel methodology involves visiting the same dwelling units on a number of occasions (in this instance, five at most). After the panel is established, a proportion of the dwelling units is replaced each round (in this instance, 20%). New dwelling units are added to the sample to replace those that are taken out.

Enumeration Areas (EAs) that had a household count of less than twenty-five were omitted from the census 2001 frame that was used to draw the sample of Primary Sampling Units (PSUs) for the new Master Sample. Other omissions from the Master Sample frame included all institution EAs except workers, hostels, convents and monasteries. EAs from census 2001 were pooled in two stages, before and after sampling. Before sampling the criterion that was used to pool EAs was that they should contain a minimum of one hundred households. However, during listing it was discovered that there were discrepancies between the information on the database and what was on the ground.

Therefore, in the second stage of pooling, EAs that were found to have less than sixty dwelling units during listing were pooled. The Master Sample is a multi-stage stratified sample. The overall sample size of PSUs was 3000. The explicit strata were the 53 district councils/metros (DCs). The 3000 PSUs were allocated to these DCs using the power allocation method. The PSUs were then sampled using probability proportional to size principles. The measure of size used was the number of households in a PSU as calculated in the census. The sampled PSUs were listed with the dwelling unit as the listing unit. From these listings systematic samples of dwelling units per PSU were drawn. These samples of dwelling units form clusters. The size of the clusters differs depending on the specific survey requirements. The LFS uses one of the clusters that contain ten dwelling units.

Mode of data collection

Face-to-face [f2f]

South Africa Manufacturing Survey: Kwazulu Natal: Factory Workers data was reported at 0.000 % in Dec 2018. This records a decrease from the previous number of 19.000 % for Sep 2018. South Africa Manufacturing Survey: Kwazulu Natal: Factory Workers data is updated quarterly, averaging -24.000 % from Jun 1995 (Median) to Dec 2018, with 95 observations. The data reached an all-time high of 48.000 % in Jun 2011 and a record low of -92.000 % in Jun 1999. South Africa Manufacturing Survey: Kwazulu Natal: Factory Workers data remains active status in CEIC and is reported by Bureau for Economic Research. The data is categorized under Global Database’s South Africa – Table ZA.S023: Business Survey: Manufacturing: Employment Weighted: by Region.

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.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. 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

Sampling procedure

The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33 000 dwelling units.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

Mode of data collection

Face-to-Face and Computer Assisted Personal and Telephone Interview

The majority of the male employees (around 5 million people) in South Africa worked from 40 to 45 hours per week. Furthermore, more than 3.1 million worked over 45 hours weekly, while 405,000 worked from 15-29 hours per week. On the other hand, the number of individuals working less than 15 hours was only 226,000. Both men and women followed the same trend of working hours.

Time series data for the statistic Contributing family workers, male (% of male employment) (modeled ILO estimate) and country South Africa. Indicator Definition:Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.The indicator "Contributing family workers, male (% of male employment) (modeled ILO estimate)" stands at 0.4949 as of 12/31/2023, the lowest value since 12/31/2020. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -11.08 percent compared to the value the year prior.The 1 year change in percent is -11.08.The 3 year change in percent is -18.67.The 5 year change in percent is 63.26.The 10 year change in percent is 36.43.The Serie's long term average value is 0.838. It's latest available value, on 12/31/2023, is 40.97 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is +71.63%.The Serie's change in percent from it's maximum value, on 12/31/1993, to it's latest available value, on 12/31/2023, is -70.17%.

Wage and salaried workers, male (% of male employment) (modeled ILO estimate) in South Africa was reported at 81.51 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Wage and salary workers; male (% of males employed) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

As of the second quarter of 2024, nearly 3.83 million people in South Africa worked within the community and social services industry. The sector concentrated the highest number of employees, followed by the trade industry, which employed about 3.36 million people. A struggling labor market The South African labor market faces severe challenges and obstacles. In 2023, the country had the highest unemployment rate in Africa, with almost 30 percent of the labor force being jobless. In addition, only 40 percent of the population was employed in 2021. Indeed, South Africans were the most concerned globally about finding jobs and being unemployed. According to a survey, 64 percent of South African respondents reported being worried about unemployment as of September 2023. A highly unequal country South Africa is the most income-unequal country in the world, as it registered a Gini score of 63 in 2021. The major reasons for this inequality originate from the country’s infamous Apartheid regime and the failure of the job market to provide enough opportunities for its people. For example, the unemployment rate among Black South Africans was close to 37 percent, compared to eight percent for white South Africans. Furthermore, unemployment in the country was more widespread among individuals with a lower level of education. Specifically, in 2023, over 50 percent of the jobless South Africans had an education level lower than matric (grade 12).

This line chart displays self-employed workers (% of total employment) by date using the aggregation average in South Africa. The data is about countries per year.

In the fourth quarter of 2023, the trade industry in South Africa had the highest number of men employees, around 1.8 million. Another 1.66 million were working within the finance sector, while 1.47 million were employed by the community and social services industry.

Wage and salaried workers, total (% of total employment) (modeled ILO estimate) in South Africa was reported at 83.69 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Wage and salaried workers; total (% of total employed) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

As of the fourth quarter of 2023, the majority of the female employees (around 4.2 million) in South Africa worked from 40 to 45 hours per week. Around 1.6 million women worked over 45 hours weekly, while 708,000 women worked from 15-29 hours per week. Both women and men followed the same trend of working hours.

South Africa ZA: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment data was reported at 82.704 % in 2017. This records a decrease from the previous number of 82.712 % for 2016. South Africa ZA: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment data is updated yearly, averaging 82.653 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 84.146 % in 2008 and a record low of 79.848 % in 1991. South Africa ZA: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Wage and salaried workers (employees) are those workers who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Graph and download economic data for Purchasing Power Parity Converted GDP Laspeyres per person counted in total employment for South Africa (RGDPTEZAA629NUPN) from 1960 to 2010 about South Africa, PPP, workers, employment, and GDP.

Abstract

The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa. It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).

All editions of the LFS have been updated (some more than once) since their release. These version changes are detailed in a document available from DataFirst (in the "external documents" section titled "LFS 2000-2008 Collated Version Notes on the South African LFS").

Geographic coverage

National Coverage

Analysis unit

Households (dwellings) and individuals

Universe

The LFS 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 LFS is a twice-yearly rotating panel household survey. A rotating panel sample involves visiting the same dwelling units on a number of occasions (in this instance, five at most), and replacing a proportion of these dwelling units each round. New dwelling units are added to the sample to replace those that are taken out. The pilot round of LFS fieldwork took place in February 2000, based on a probability sample of 10 000 dwelling units. This survey took place six months later, using a larger probability sample of 30,000 dwelling units. Among the 10,000 households visited in February, approximately 40% were re-visited in September 2000. The fieldworkers had some difficulty in identifying certain dwelling units in the sample, particularly in those areas where there are no addresses.

The Master Sample is based on the 1996 Population Census of enumeration areas (EA) and the estimated number of dwelling units from the 1996 Population Census. All 3000 PSUs included in the Master Sample were used in the Labour Force Survey. A PSU is either one EA or several EAs when the number of dwelling units in the base or originally selected EA was found to have less than 100 dwelling units. Each EA had to have approximately 150 dwelling units but it was discovered that many contained less. Thus, in some cases, it has been found necessary to add EAs to the original (census) EA to ensure that the minimum requirement of 100 dwellings, in the first stage of forming the PSUs, was met. The size of the PSUs in the Master Sample varied from 100 to 2445 dwelling units. Special dwellings such as prisons, hospitals, boarding houses, hotels, guest houses (whether catering or self-catering), schools and churches were excluded from the sample.

Explicit stratification of the PSUs was done by province and area type (urban/rural). Within each explicit stratum, the PSUs were implicitly stratified by District Council, Magisterial District and, within the magisterial district, by average household income (for formal urban areas and hostels) or EA. The allocated number of EAs was systematically selected with "probability proportional to size" in each stratum. Once the PSUs included in the sample were known, their boundaries had to be identified on the ground. After boundary identification, the next stage was to list accurately all the dwelling units in the PSUs.

The second stage of the sample selection was to draw from the dwelling units listing whereby a systematic sample of 10 dwelling units was drawn from each PSU. As a result, approximately 30,000 households (units) were interviewed. However, if there was growth of more than 20% in a PSU, then the sample size was increased systematically according to the proportion of growth in the PSU.

A new master sample was drawn in September 2004, benchmarked to Census 2001, for the LFS for the proceeding five years.

Mode of data collection

Face-to-face [f2f]