The average monthly salary for South Africans who were employed in the formal non-agricultural sector was just over 26,800 South African rands (comparable to roughly 1,500 U.S. dollars) in November 2023, which represented a yearly increase of tw0 percent. During the period under review, the overall growth trend was positive, with the earnings increasing by 24.4 percent from 21,500 South African rands (approximately 1,180 U.S. dollars) in November 2018.    Minimum wage and highest-paid professions    Starting in March 2023, the minimum hourly wage in the country increased to 25.42 South African rands (comparable to 1.40 U.S. dollars), which represented an increase of 9.6 percent from 23.19 South African rands (1.27 U.S. dollars) per hour in the preceding year. On the other hand, professionals in executive and change management positions were paid the highest salaries in South Africa, with an average of 74,000 U.S. dollars yearly. Individuals with jobs in retail, trade, and craft followed, receiving an average of 66,000 U.S. dollars per annum.       Highest unemployment among Black South Africans In 2022, the unemployment rate in South Africa was nearly 30 percent following an increasing trend since 2008. The rate was highest among Black South Africans reaching as high as 36.8 percent in the second quarter of 2023. Moreover, Colored South Africans followed with around 22 percent, while white South Africans had a much lower unemployment rate of over 7 percent.

Key information about South Africa Monthly Earnings

• South Africa Monthly Earnings stood at 1,589 USD in May 2025, compared with the previous figure of 1,533 USD in Feb 2025
• South Africa Monthly Earnings data is updated quarterly, available from Nov 2004 to May 2025, with an average number of 1,430 USD
• The data reached the an all-time high of 1,877 USD in Aug 2011 and a record low of 968 USD in Feb 2009

*This indicator will be no longer available from March 2026, 4th onwards. CEIC has a plan to remove the non-standard frequency indicator after the standard frequency indicator has been added and be available on this table. CEIC converts Monthly Earnings into USD. Statistics South Africa provides Nominal Average Monthly Earnings in local currency based on 2016 Business Sampling Frame. Federal Reserve Board average market exchange rate is used for currency conversions. Monthly Earnings exclude Agriculture sector. Monthly Earnings prior to Q2 2015 are based on 2013 Business Sampling Frame and prior to Q2 2013 are based on 2009 Business Sampling Frame. Monthly Earnings are in quarterly frequency, ending in February, May, August, November of each year.


Further information about South Africa Monthly Earnings

• In the latest reports, South Africa Population reached 63 million people in Jun 2025
• Unemployment Rate of South Africa dropped to 32 % in Sep 2025
• The country's Labour Force Participation Rate dropped to 60 % in Sep 2025

As of 2022, around ** percent of registered voters were considering emigrating from South Africa, and were in the monthly income bracket of between ****** and ****** South African rand (around *** and ***** U.S. dollars). A share of ** percent who were earning between ***** and ***** South African rand (around *** and *** U.S. dollars) per month, stated that they were not considering emigrating.

Wages in South Africa increased to 29290 ZAR/Month in the second quarter of 2025 from 28289 ZAR/Month in the first quarter of 2025. This dataset provides - South Africa Total Quarterly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of 2024, Morocco had the highest average monthly salaries in Africa. Employees in the country earned around ***** U.S. dollars per month. South Africa and Tunisia followed, with average monthly salaries amounting to ***** U.S. dollars and ***** U.S. dollars, respectively.

Abstract

South Africa's first Living Conditions Survey (LCS) was conducted by Statistics South Africa over a period of one year between 13 October 2014 and 25 October 2015. The main aim of this survey is to provide data that will contribute to a better understanding of living conditions and poverty in South Africa for monitoring levels of poverty over time. Data was collected from 27 527 households across the country. The survey used a combination of the diary and recall methods. Households were asked to record their daily acquisitions in diaries provided by Statistics SA for a period of a month. The survey also employed a household questionnaire to collect data on household expenditure, subjective poverty, and income.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The sample for the survey included all domestic households, holiday homes and all households in workers' residences, such as mining hostels and dormitories for workers, but excludes institutions such as hospitals, prisons, old-age homes, student hostels, and dormitories for scholars, boarding houses, hotels, lodges and guesthouses.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Survey 2014-2015 sample was based on the LCS 2008-2009 master sample of 3 080 PSUs. However, there were 40 PSUs with no DU sample, thus the sample of 30 818 DUs was selected from only 3 040 PSUs. Amongst the PSUs with no DU sample, 25 PSUs were non-respondent because 19 PSUs were not captured on the dwelling frame, and 6 PSUs had an insufficient DU count. The remaining 15 PSUs were vacant and therefore out-of-scope. Among the PSUs with a DU sample, 2 974 PSUs were respondent, 50 PSUs were non-respondent and 16 PSUs were out-of-scope. The scope of the Master Sample (MS) is national coverage of all households in South Africa. It was designed to cover all households living in private dwelling units and workers living in workers' quarters in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Living Conditions Survey 2014-2015 used three data collection instruments, namely a household questionnaire, a weekly diary, and the summary questionnaire. The household questionnaire was a booklet of questions administered to respondents during the course of the survey month. The weekly diary was a booklet that was left with the responding household to track all acquisitions made by the household during the survey month. The household (after being trained by the Interviewer) was responsible for recording all their daily acquisitions, as well as information about where they purchased the item and the purpose of the item. A household completed a different diary for each of the four weeks of the survey month. Interviewers then assigned codes for the classification of individual consumption according to purpose (COICOP) to items recorded in the weekly diary, using a code list provided to them.

Data appraisal

Anthropometric data collected during the survey are not included in the dataset.

As of 2022, Seychelles was the African country with the highest estimated minimum gross monthly wage, standing at ****** U.S. dollars. It was followed by Morocco at ****** U.S. dollars and South Africa ****** U.S. dollars. Among the selected nations, only **** countries had a minimum wage above *** U.S. dollars on the continent. Minimum wage adjustments Legislations regarding minimum wages vary significantly across countries. The minimum remuneration of employees is usually proportionate to a specific area's cost of living. Determining a minimum wage aims to increase employees' living conditions while reducing poverty and inequality. Due to rising prices and inflation, governments occasionally adjust the minimum salary. In Africa, Sierra Leone experienced the highest increase in the minimum wage in recent years, with a growth of almost ** percent between 2010 and 2019. However, governments can also lower minimum wages. Liberia and Burundi reduced the lowest possible remuneration by around ** percent and ***** percent, respectively, between 2010 and 2019. Widespread informal employment Despite legislation in force, minimum wages are not always guaranteed. In fact, several forms of employment allow employers to avoid paying minimum wages. In addition, undeclared work remains a common practice in many countries worldwide. The situation is particularly critical in some African countries. According to estimates, over ** percent of the working population in Niger, The Democratic Republic of Congo, Benin, and Madagascar engaged in informal employment between 2019 and 2023. In Egypt and South Africa, the share stood at ** percent and ** percent, respectively. Seychelles had the lowest rate on the continent at around ** percent.

Wages in Manufacturing in South Africa increased to 24258 ZAR/Month in the second quarter of 2025 from 23921 ZAR/Month in the first quarter of 2025. This dataset provides - South Africa Total Quarterly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

BASE YEAR	2024
HISTORICAL DATA	2019 - 2023
REGIONS COVERED	North America, Europe, APAC, South America, MEA
REPORT COVERAGE	Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
MARKET SIZE 2024	7.42(USD Billion)
MARKET SIZE 2025	7.77(USD Billion)
MARKET SIZE 2035	12.3(USD Billion)
SEGMENTS COVERED	Policy Type, Coverage Duration, Target Customer, Distribution Channel, Regional
COUNTRIES COVERED	US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA
KEY MARKET DYNAMICS	Rising freelance workforce, Increased awareness of income protection, Economic uncertainty driving demand, Technological advancements in insurance, Regulatory changes impacting offerings
MARKET FORECAST UNITS	USD Billion
KEY COMPANIES PROFILED	Lincoln Financial Group, Northwestern Mutual, MassMutual, State Farm, Zurich Insurance Group, AIG, Pacific Life, MetLife, Prudential Financial, Chubb, New York Life Insurance, Allianz, Transamerica, Guardian Life, AXA
MARKET FORECAST PERIOD	2025 - 2035
KEY MARKET OPPORTUNITIES	Increased gig economy participation, Rising unemployment rate concerns, Growing awareness of income protection, Digital insurance platforms expansion, Customizable policy offerings
COMPOUND ANNUAL GROWTH RATE (CAGR)	4.7% (2025 - 2035)

Abstract

The Income and Expenditure Survey is conducted every five years in South Africa.The main purpose of the survey is to determine the average expenditure patterns of households in different areas of the country. This survey forms the basis for the determination of the "basket" of consumer goods and services used for the calculation of the Consumer Price Index.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey are households

Universe

The survey covered private dwellings, workers' hostels, residential hotels, and nurses' and doctors' quarters, but excluded hospitals and clinics, hotels and guest houses, prisons, schools and student hostels and old-age homes.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the IES 2010/2011 was obtained from Statistics South Africa’s Master Sample (MS) based on the 2001 Population Census enumeration areas (EAs). The scope of the Master Sample (MS) is national coverage of all households in South Africa and the target population consists of all qualifying persons and households in the country. In summary, it has been designed to cover all households living in private dwelling units and workers living in workers’ quarters in the country. The IES 2010/2011 sample is based on an extended sample of 3 254 PSUs, which consists of the 3 080 PSUs in the Master Sample and a supplement of 174 urban PSUs selected from the PSU frame. The IES sample file contained 31 419 sampled dwelling units (DUs). The 31 419 sampled DUs consist of 31 007 DUs sampled from the 3 080 design PSUs in the Master Sample and 412 DUs from the supplemented 174 urban PSUs. In the case of multiple households at a sampled DU, all households in the DU were included.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were four modules in the household questionnaire with eighteen subsections. The first module collected general household data and data on household members. Modules 2 to 4 collected data on consumption expenditure, household finances and income. The diary was a booklet in which the respondent recorded weekly expenditure data. A household completed a different diary for each week of the survey period.

Response rate

From the 31 419 dwelling units sampled across South Africa, 33 420 households were identified. Out of these, there was a sample realisation of 27 665 (82,8%) households, with the remaining 5 755 (17,2%) households being classified as out of scope.

South Africa ZA: Wages Index data was reported at 275.340 2010=100 in 2018. This records an increase from the previous number of 261.578 2010=100 for 2017. South Africa ZA: Wages Index data is updated yearly, averaging 127.770 2010=100 from Dec 2009 (Median) to 2018, with 10 observations. The data reached an all-time high of 275.340 2010=100 in 2018 and a record low of 87.920 2010=100 in 2009. South Africa ZA: Wages Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s South Africa – Table ZA.IMF.IFS: Wages, Labour Cost and Employment Index: Annual.

Abstract

The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

Geographic coverage

The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the "independent states" of Bophuthatswana, Transkei, and Venda. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

Households and individuals

Universe

The 1980 South African census covered all household members (usual residents).

The 1980 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980) were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census enumeration data

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons employed as domestics (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

Unemployment Rate in South Africa decreased to 31.90 percent in the third quarter of 2025 from 33.20 percent in the second quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The 2005-2006 IES was designed to collect information on items and services acquired by South African households, various sources of income acquired by participating households (monetary or in-kind) and details as to how they spent this income (on expenditure, remittances, etc.). This was accomplished by collecting details of all expenditure by a participating household and all acquisitions of goods and services for the household’s own consumption within a given reference period. Studies of this nature play an important role in evaluating changes in consumption patterns, levels of income and income distribution. The results of the survey serve as an input into identifying the goods and services that should be included in the Consumer Price Index (CPI) basket of goods and services.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Consumption expenditure items/services

Universe

The IES 2005/2006 included all domestic households, holiday homes and all households in workers’ residences such as mining hostels and dormitories for workers. It did not include institutions such as hospitals, prisons, old-age homes, student hostels and dormitories for scholars. Also excluded were boarding houses, hotels, lodges and guest houses.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design • A newly designed Master Sample, consisting of 3 000 Primary Sampling Units (PSUs), based on the 2001 Population Census Enumeration Areas, was used as the sampling frame. The Master Sample is used for all household surveys conducted by Statistics South Africa (Stats SA). • The 3 000 primary sampling units (PSUs) from the Master Sample were representatively divided into four quarterly allocations of 750 each. • Within each quarterly allocation, a random sample of 250 PSUs was selected every month. • Eight dwelling units were systematically selected from each of the sampled PSUs for fieldwork. In total, 24 000 dwelling units were covered during the twelve months of data collection for the IES 2005/2006. This process ensured that the sample was evenly spread over the twelve months, while it remained nationally representative in each quarter.

Mode of data collection

Face-to-face [f2f]

Response rate

From the 24 000 dwelling units sampled across South Africa, 25 192 households were identified. Out of these, there was a sample realisation of 22 617 households, with the remaining 2 575 households were classified as out of scope due to a number of reasons, such as listing error, vacant dwelling, unoccupied dwelling, etc.

Monthly and long-term South Africa Wages data: historical series and analyst forecasts curated by FocusEconomics.

As of January 2025, the average income per unit sold of hotel accommodations in South Africa reached roughly ***** South African rand (around ** U.S. dollars). As of January 2023, the income per sold unit night has been steady and above 1,000 South African rand.

This dataset provides values for MINIMUM WAGES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Abstract

The Survey of Employers and the Self-employed (SESE) was conducted by Statistics South Africa (Stats SA) in September 2005.

• to collect data on micro- and small businesses in South Africa, their operation and access to services • to collect income tax information on non-VAT paying businesses. • to determine the contribution of these businesses to the economic growth of the country

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the survey include individuals and enterprises

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire covered background data on the small business (from questions 4 to 17 of the questionnaire) and its business operations, including the premises, the establishment of the business and start-up finance (question 18 to 39). Data is also collected on production and the workforce (question 40 to 54), expenditure (question 55 to 56), business capital (question 57 to 61) and transport (question 62 to 63).

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the extent of poor households in South Africa, and their access to services, and levels of unemployment, at national, provincial and municipal levels.

The main objectives of the survey were: 1. To fill data gaps from the absence of a national population census in 2006 2. To provide estimates at lower geographical levels than existing household surveys 3. To build capacities for conducting Census 2011 4. To provide inputs to the mid-year population projections.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tw0 was the selection of dwelling units. Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas). On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).
The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof. The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units. The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Data appraisal

The Community Survey results were released on 24 October 2007. After the evaluation of the data by the Stats Council, the Community Survey was found to be comparable in many aspects with other Stats SA surveys, censuses and other external sources. However, there are some areas of concern where Statistics South Africa is urging users to be more cautious when using the Community Survey data.

The main concerns are:

·The institutional population is merely an approximation to 2001 numbers and it is not new data. ·The measure of unemployment in the Community Survey is higher and less reliable due to the differences in questions asked relative to the normal Labour Force Surveys. ·The income includes unreasonably high income for children due to presumably misinterpretation of the question, e.g. listing parent's income for the child. ·The distribution of households by province has very little congruence with the General Household Survey or Census 2001. ·The interpretation of grants or those receiving grants need to be done with caution. ·Since the Community Survey is based on random sample and not a Census, any interpretation should be understood to have some random fluctuation in data, particularly concerning the small population for some cells. The user should understand that the figures are within a certain interval of confidence.

Users should be aware of these statements as part of the cautionary notes:

·The household estimates at municipal level differ slightly from the national and provincial estimates in terms of the household variables profile; ·The Community Survey has considered as an add-on an approximation of population in areas not covered by the survey, such as institutions and recreational areas. This approximation of people could not provide the number of those households (i.e. institutions). Thus, there is no household record for those people approximated as living out of CS scope; ·Any cross-tabulation giving small numbers at municipal level should be interpreted with caution such as taking small value in given table's cell as likely over or under estimation of the true population; ·No reliance should be placed on numbers for variables broken down at municipal level (i.e. age, population group etc.). However, the aggregated total number per municipality provides more reliable estimates;

·Usually a zero total figure (excluding those in institutions) reflects the fact that no sample was realised and in such cases this is likely to be a significant underestimate of the true population. ·As an extension from the above statement, in a number of instances the number realised in the sample, though not zero, was very small (maybe as low as a single individual) and in some cases had to