Stats SA quarterly financial statistics of municipalities (QFSM), is a quarterly survey that covers local, district and metropolitan municipalities in South Africa

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. In 2005, Stats SA undertook a major revision of the Labour Force Survey (LFS) which was conducted twice per year since 2000. This revision resulted in changes to the survey methodology, the survey questionnaire, the frequency of data collection and data releases, and the survey data capture and processing systems. The redesigned labour market survey, the QLFS, is now the principal vehicle for collecting labour market information on a quarterly basis. This report is the second annual report on the labour market in South Africa produced by Stats SA. The analysis is based on annual labour market data from 2004 to 2009. The report also includes a statistical appendix with historical data dating back to 2004 on an annual basis.

Objective The objective of this report is two-fold: first, to present annual labour market data backcast to 2004, and second, to analyse important aspects of the labour market in South Africa over the past five years.

Data sources Labour Force Survey – 2004 to 2007 (March and September each year) QLFS – 2008 to 2009 (Quarters 1 to 4)

Geographic coverage

the nine provinces of South Africa

Analysis unit

Individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Labour Force Survey and the Quarterly Labour Force Survey are based on a Master Sample and there have been three of them so far. The design of each is outlined below.

1999 Master Sample For the LFSs of February 2000 to March 2004, a rotating panel sample design was used to allow for measurement of change in people's employment situation over time. The same dwellings were visited on, at most, five different occasions. After this, new dwelling units were included for interviewing from the same PSU in the master sample. This means a rotation of 20% of dwelling units each time. The database of enumerator areas (EAs) established during the demarcation phase of Census '96 constituted the sampling frame for selecting EAs for the LFS. Small EAs consisting of fewer than 100 dwelling units were combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 dwelling units, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involved explicit stratification by province and within each province, by urban and non-urban areas (Census 1996 definitions). Independent samples of PSUs were drawn for each stratum within each province. The smaller provinces were given a disproportionately large number of PSUs compared to the bigger provinces. Simple random sampling was applied to select 10 dwelling units to visit in each PSU as ultimate sampling units. If more than one household was found in the same dwelling unit all such households were interviewed.

2004 Master Sample The 2004 Master Sample was used in the LFSs of September 2004 to September 2007. Enumeration Areas (EAs) that had a household count of less than twenty-five were omitted from the census frame that was used to draw the sample of PSUs for the Master Sample. Other omissions from the frame included all institution EAs except workers' hostels, convents and monasteries. EAs in the census database that were found to have less than sixty dwelling units during listing were pooled. This Master Sample was a multi-stage stratified sample. The overall sample size of PSUs was 3 000. The explicit strata were the 53 district councils. The 3 000 PSUs were allocated to these strata 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 counted 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 formed clusters. The size of the clusters differed depending on the specific survey requirements. The LFS used one of the clusters that contained ten dwelling units.

Current Master Sample The Quarterly Labour Force Survey (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 EAs that are drawn from across the country.

The sample is designed to be representative at provincial level and within provinces at metro/nonmetro 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 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 (i.e. the QLFS) is based on a stratified twostage 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 Each quarter, a 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. Thus, sampled dwellings 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 two 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).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire of QLFS is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

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. Since 2008, Stats SA have generated an annual report which is released under the name "Labour Market Dynamics in South Africa". This report is constructed using data from a version of the pooled data from all four quarters (all four QLFS datasets in the year) and is ascribed the same nomenclature. Includes a number of extra variables (including income information) that are not available in any of the QLFS data files. This makes it distinct from a datafile that simply appends all of the QLFS datafiles in a given year.

Abstract

The main aim of this report is to provide information on the characteristics of micro- and small businesses in South Africa and to gain an understanding of their operation and access to services as reported in the Survey of Employers and the Self-employed (SESE). The information presented in this report supplements those statistics collected by Stats SA on formal sector businesses registered for VAT. The main objectives of SESE are:

• To collect reliable data about people running businesses that are not registered for VAT. • To identify the non-income tax paying and income tax paying businesses within the non-VAT paying businesses. • To produce comprehensive statistical data on such small and micro-businesses, at the national and provincial level. • To determine the contribution of those businesses not registered for VAT and income tax towards the economic growth of the country.

Geographic coverage

National

Analysis unit

Units of analysis in the survey include: - Individuals - Enterprises

Universe

The lowest level of geographic aggregation covered by the SESE 2009 data is Province. Every person who has stayed in the households in selected dwelling units for at least four nights a week in the four weeks prior to the interview.

Kind of data

Sample survey data [ssd]

Sampling procedure

Currently, there is no sampling frame on which to base weights and raising factors for small unregistered businesses in South Africa. As a result, the research design used for SESE was based on a household based survey, consisting of two stages. The first stage involved identifying individuals who were running businesses through the Quarterly Labour Force Survey (QLFS). The second stage involved a follow-up, interviewing the owners of these businesses, to determine the nature of their business and their contribution to the economy. The focus of the survey was businesses who are not registered for Value Added Tax (VAT). These small and micro-businesses are generally excluded from the business frame which is used in surveys of the formal economy conducted by Statistics South Africa. Currently, there is no sampling frame on which to base weights and raising factors for small unregistered businesses in South Africa. As a result, the research design used for SESE was a household based survey, consisting of two stages. The first stage involved using the Quarterly Labour Force Survey (QLFS) enumeration to identify individuals who were running unregistered businesses. The second stage involved follow-up interviews with the owners of these businesses by QLFS enumerators. The QLFS data was collected in the middle two weeks of the month throughout the quarter. The SESE questionnaire was then administered to individuals in relevant households in the last week of the month, also throughout the quarter.

Sampling deviation

During the Quarterly Labour Force Survey (QLFS) of quarter three 2009, persons running businesses were screened and later interviewed for Survey of Employers and the Self-employed (SESE). The SESE interviews were not conducted at the same time with QLFS. This resulted in reduction of SESE persons as compared to the ones identified during QLFS screening. This was due to persons refusing to participate in SESE, persons not at home during SESE interviews, demolished structures, vacant dwellings, etc. If all qualifying SESE persons identified in QLFS quarter three responded positively during SESE interviews, there would be no adjustment of SESE weights. The final SESE weights would be the same as the QLFS calibrated weights. The SESE weight adjustment accounts for those persons who qualified for SESE, but refused to take part or were not available for interviews. Persons identified as ineligible for SESE were not accounted for when doing weights adjustment.

Mode of data collection

Face-to-face [f2f]

Research instrument

The files and the corresponding sections of the questionnaire are as follows: Business Data from question 4 to 17 Operations Data from question 18 to 39 General Data from question 40 to 47 Costs Data from question 48 to 54 Expenditure Data from question 55 to 56 Capital Data from question 57 to 61 Transport Data from question 62 to 63

Response rate

Response rates of SESE by province in 2009 Province Per cent Western Cape 79,5 Eastern Cape 96,4 Northern Cape 88,9 Free State 97,1 KwaZulu-Natal 93,9 North West 96,9 Gauteng 87,6 Mpumalanga 97,0 Limpopo 98,1 South Africa 93,9

In the third quarter of 2023, the Gauteng province in South Africa had the highest number of finance employees, with over 1.2 million. The Western Cape and KwaZulu-Natal also concentrated high numbers of employees in the sector, with 474,000 and 437,000, respectively. Since the same quarter in 2022, each region in the country has made considerable employment growth in the finance industry.

The Gross Domestic Product (GDP) in South Africa expanded 0.50 percent in the third quarter of 2025 over the previous quarter. This dataset provides - South Africa GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA) which collects information about the labour market activities of individuals aged 15 years or older who live in South Africa. Prior to the introduction of the QLFS in 2008, the Labour force Survey (LFS) was the major source of labour market information. The LFS was conducted in March and September each year over the period 2000–2007 and replaced the annual October Household Survey (OHS) as the principal vehicle for collecting labour market information.

This report is the seventh annual report produced by Stats SA on the labour market in South Africa. The report includes, for the third time, an analysis of labour market dynamics (discussed in Chapter 2). As in previous reports, annual historical data are included in a statistical appendix. Objective The objective of this report is to analyse the patterns and trends of annual labour market results over the period 2008 to 2014. Data sources Quarterly Labour Force Survey – 2008 to 2014 (Average of the results for Quarters 1 to 4 each year).

Geographic coverage

the nine provinces of South Africa

Analysis unit

Individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) is based on a master sample of which there have been three so far. The design of the current master sample follows.

Current master sample The Quarterly Labour Force Survey (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 EAs that are drawn from across the country.

The sample is designed to be representative at provincial level and within provinces at metro/nonmetro 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 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 (i.e. the QLFS) is based on a stratified twostage 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 Each quarter, a 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. Thus, sampled dwellings 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 two 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).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire of QLFS is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

In the fourth quarter of 2023, South Africa had over 2.4 million female employees within the community and social services industry. Following this, almost 1.56 million women worked in the trade sector, while roughly 1.3 million were employed by the finance industry.

MTN Group – one of Africa’s largest telecommunications providers – increased their overall number of subscribers to around *** million as of the first quarter of 2024. Nigeria, South Africa, Uganda, Ghana, and Ivory Coast are among MTN's major markets, accounting for more than ** million subscribers each. MTN Group revenue: voice still on top Outgoing voice calls were MTN Group’s largest revenue stream in 2022, generating approximately **** billion U.S. dollars for the company. Data was the close second most lucrative revenue stream, bringing in approximately **** billion U.S. dollars. The Nigerian market accounted for more revenue than any other country, with **** billion U.S. dollars coming from the region. Mobile Money: increasing financial inclusion to Africa While certainly not MTN Group’s largest revenue stream, the one billion U.S. dollars the company generated from fintech is indicative of the role mobile money and MTN's MoMo platform plays in the profit centers of modern telecommunications companies in Africa. Generally provided by a telecommunications company, mobile money enables those without a bank account to participate in the economy, allowing transactions to be made using almost any mobile phone.MTN Group’s offering – MoMo – grew by over six million active subscribers just in the fourth quarter of 2022. The total value of all mobile money transactions in Sub-Saharan Africa doubled from ***** billion U.S. dollars in 2019 to over *** billion by 2022, more than any other region in the world, showing the importance of mobile money to both customers and providers in the region.

New Zealand GDP: 2009-10p: Chain-Volume: sa: SI: Financial and Insurance Services data was reported at 4,060.000 NZD mn in Dec 2024. This records an increase from the previous number of 4,039.000 NZD mn for Sep 2024. New Zealand GDP: 2009-10p: Chain-Volume: sa: SI: Financial and Insurance Services data is updated quarterly, averaging 2,494.000 NZD mn from Jun 1987 (Median) to Dec 2024, with 151 observations. The data reached an all-time high of 4,060.000 NZD mn in Dec 2024 and a record low of 1,345.000 NZD mn in Jun 1990. New Zealand GDP: 2009-10p: Chain-Volume: sa: SI: Financial and Insurance Services data remains active status in CEIC and is reported by Stats NZ. The data is categorized under Global Database’s New Zealand – Table NZ.A014: SNA08: GDP by Industry: ANZSIC06: Chain-Volume: 2009-10 Price: Seasonally Adjusted.

Abstract

In October 2001, South Africans were enumerated to collect information on persons and households throughout the country, using a uniform methodology. Household data collected included data on each household and each person present in the household on Census night, as well as data on services available to the household. Data on household residents, and residents of hostels and the other types of collective living quarters was also captured, as well as data on individuals who spent census night in institutions and hotels.

Geographic coverage

The South African Census 2001 has national coverage.

Analysis unit

The units of analysis for the South Africa Census 2001 were households and individuals

Universe

The South African Census 2001 covered every person present in South Africa on Census Night, 9-10 October 2001 including all de jure household members and residents of institutions.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The data in the South African Census 2001 dataset is a 10% unit level sample drawn from Census 2001 as follows: 1) Households: • A 10% sample of households in housing units, and • A 10% sample of collective living quarters (both institutional and non-institutional) and the homeless.

2) Persons: • A sample consisting of all persons in the households and collective living quarters, and the homeless, drawn for the samples described above

3) Mortality: • A sample consisting of all mortality information for the households in housing units drawn in the 10% sample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered for the South African Census 2001, questionnaire A (for persons in households), questionnaire B (for persons in institutions) and questionnaire C (for institutions). The Household questionnaire covered household characteristics, such as dwellling type, home ownership, household assets, access to services and energy sources. A component of the questionnaire captures fertility data. Both the household and persons in institutions questionnaires collected data on individuals' characteristics, including age, population group, language, religion, citizenship, migration, mortality and disability, as well as means of travel. Economic characteristics of individuals included employment activities and data on unemployment.

Cleaning operations

The following publication can be consulted for a detailed account of the editing undertaken for the South African Census 2001: Computer editing specifications / Statistics South Africa. Pretoria: Statistics South Africa, 2003 369p. [Report No. 03-02-43 (2001)]. ISBN 0-621-34566-0.

Data appraisal

As part of the quality check for Census 2001, a Post-Enumeration Survey (PES) was conducted in November 2001, approximately one month after the census. Fieldworkers re-visited a scientifically selected sample of almost 1% of the census enumeration areas, to do an independent recount. The published census results are adjusted for undercount according to the findings of the PES. In addition to the check on coverage, the PES also involved an independent re-measurement of the basic characteristics of the population. Details on this process are available in the publication:

Statistics South Africa. 2004. Census 2001: post-enumeration survey: results and methodology. Report no. 03-02-17 (2001).