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.

The minimum hourly wage in South Africa reached ***** South African rand (**** U.S. dollars) in 2024. Compared to the previous year, this was an increase from ***** South African rand (**** U.S. dollars) per hour. This represented an increment of *** percent, which was the highest in the period reviewed. South Africa's Minister of Employment and Labor, Thusla Nxesi, announced the increase in February with effect from March 1, 2024.

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.

Minimum Wages in South Africa increased to 28.79 ZAR/Hour in 2025 from 27.58 ZAR/Hour in 2024. This dataset provides - South Africa Minimum Wages- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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

The survey had 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.

South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 23.500 % in 2014. This stayed constant from the previous number of 23.500 % for 2010. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 23.500 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 25.500 % in 2000 and a record low of 20.300 % in 2005. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % 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.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

As of 2024, an individual living in South Africa with less than 1,109 South African rand (roughly 62.14 U.S. dollars) per month was considered poor. Furthermore, individuals having 796 South African rand (approximately 44.60 U.S. dollars) a month available for food were living below the poverty line according to South African national standards. Absolute poverty National poverty lines are affected by changes in the patterns of household consumers and fluctuations in prices of services and goods. They are calculated based on the consumer price indices (CPI) of both food and non-food items separately. The national poverty line is not the only applicable threshold. For instance,13.2 million people in South Africa were living under 2.15 U.S. dollars, which is the international absolute poverty threshold defined by the World Bank. Most unequal in the globe A prominent aspect of South Africa’s poverty is related to extreme income inequality. The country has the highest income Gini index globally at 63 percent as of 2023. One of the crucial obstacles to combating poverty and inequality in the country is linked to job availability. In fact, youth unemployment was as high as 49.14 percent in 2023.

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services

Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample. Wave 2 of NIDS re-interviewed respondents interviewed in Wave 1, gathering information on developments in their lives since they were interviewed in 2008. Wave 3 of the survey took place between April and December 2012 and re-interviewed respondents from Waves 1 and 2.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the survey are individuals and households. The NIDS questionnaires attempted to gather information on all members of the household; including those that were resident and those that were non-resident at the time of the interview. Those that were resident provided the base sample of individuals who will remain in the NIDS sample over time. Information about non-resident members is essential in understanding the household and family support systems that individuals have around them at the time of the interview.

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services.

Dates: 2008 – ongoing. First 5 “waves” implemented by SALDRU.

Funding: The Presidency (2008 – 2013); The Department of Planning, Monitoring and Evaluation (2014 – Present).

SALDRU people: Murray Leibbrandt, Ingrid Woolard, Cecil Mlatsheni and Reza C. Daniels.

Coverage: Nationally representative of the South African population.

Initial Sample size (2008): Approximately 28 000 individuals.

Data: The survey’s questionnaires, technical documents and reports for Wave 1, Wave 2, Wave 3, Wave 4 and Wave 5 are available for download from DataFirst’s Open Data Portal. NIDS produces public release data, which is also available for download from DataFirst’s Open Data Portal and secure data, which can only be accessed through DataFirst’s Secure Research Data Centre.

Included sections: Household Living Standards; Household Composition and Structure; Mortality; Household Food and Non-food Spending and Consumption; Household Durable Goods, Household Net Assets; Agriculture; Demographics; Birth Histories and Children; Parents and Family Support; Labour Market Participation and Economic Activity; Income and Expenditure; Grants; Contributions Given and Received; Education; Health; Emotional Health; Household Decision-making; Wellbeing and Social Cohesion; Anthropometric Measurements; Personal Ownership and Debt.

Geographic coverage

The NIDS data is nationally representative. The survey began in 2008 with a nationally representative sample of over 28,000 individuals in 7,300 households across the country. The survey is repeated every two years with these same household members, who are called Continuing Sample Members (CSMs). The survey is designed to follow people who are CSMs, wherever they may be in SA at the time of interview. The NIDS data is therefore, by design, not representative provincially or at a lower level of geography (e.g. District Council).

Analysis unit

Households and individuals

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

NIDS is a national panel (longitudinal) survey which began with a sample of 28 000 South Africans. NIDS' cycles of data collection, referred to as "waves" were undertaken. In Wave 1 (2008), 400 Enumerator Areas, comprising of 7296 households were selected for inclusion in the NIDS sample. 300 fieldworkers spread out across all nine provinces of the country in search of the 28 226 people that formed part of these selected households; successfully interviewing 26 776 of these individuals during Wave 1.

In subsequent waves, the original sample members are tracked and re-interviewed. Anyone that they live with at the time is also interviewed. In Wave 2 (2010-2011) 28 537 individuals were interviewed; in Wave 3 (2012) 32 582 were interviewed; and in Wave 4 (2014-2015) 37 368 were interviewed. Data collection for Wave 5 took place in 2017 and included a sample "top-up" to increase the number of white, Indian and high income respondents who had experienced low baseline response rates in Wave 1 and higher attrition rates between Waves 1-4. During Wave 5, 39,434 individuals were successfully interviewed, of which, 2016 were from the "top-up" sample. The data for Wave 5 was released at the end of August 2018.

More information on NIDS sampling refer to NIDS Technical Paper Number 1 http://www.nids.uct.ac.za/publications/technical-papers/108-nids-technical-paper-no1/file

Mode of data collection

Face-to-face [f2f]

Abstract

A comprehensive survey was conducted by Central Statistical Service (later Statistics South Africa) in October 1995 in order to determine the income and expenditure of households in South Africa. This survey shows the earnings and spendings of South African households and the pattern of household consumption. The survey covered the metropolitan, urban and rural areas of South Africa. The main purpose of the survey was to determine the average expenditure patterns of households in the different areas concerned. 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. The 1995 IES differed from previous household surveys of its kind in South Africa, since it was a countrywide survey covering metro, urban and rural areas, rather than a more limited sub-set of households in 12 major metro/urban areas of the country covered by the 1990 IES.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The 1995 IES differed from previous household surveys of its kind in South Africa, since it was a countrywide survey covering metro, urban and rural areas, rather than a more limited sub-set of households in 12 major metro/urban areas of the country previously referred to. By extending the sample to include the whole country, a clearer indication of the life circumstances of all South Africans in all parts of the country could be inferred.

Kind of data

Sample survey data

Sampling procedure

Two surveys, namely the CSS’s annual October household survey (OHS) and the IES were run concurrently during October 1995. Information for the IES was obtained, as far as possible, from the same 30 000 households that were visited for the 1995 OHS. Altogether, 3 000 enumerator areas (EAs) were drawn for the sample, and ten households were visited in each EA. The sample was stratified by race, province, urban and non-urban area. The 1991 population census was used as a frame for drawing the sample, including estimates of the size of the population in the formerly independent TBVC (Transkei-Bophuthatswana-Venda-Ciskei) states. More details on the sampling frame and sampling procedure are given in the report on the 1995 OHS, Living in South Africa (CSS, 1996).

Mode of data collection

Face-to-face [f2f]

The information collected in household surveys, such as this one, is used to describe and understand the living conditions and experiences of South Africans. Often, however, different surveys use different sample areas and interview different households, making it difficult to know whether the living standards or circumstances of particular households have improved. The aim of this survey is to determine whether or not there have been any changes in the socio-economic conditions of those households interviewed in 1993. This information will be used to understand the dynamics of household behaviour over time.

As of 2024, around **** million people in South Africa are living in extreme poverty, with the poverty threshold at **** U.S. dollars daily. This means that ******* more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over **** million South Africans will live on a maximum of **** U.S. dollars per day. Who is considered poor domestically? Poverty is measured using several matrices. For example, local authorities tend to rely on the national poverty line, assessed based on consumer price indices (CPI) of a basket of goods of food and non-food components. In 2023, the domestic poverty line in South Africa stood at ***** South African rand per month (around ***** U.S. dollars per month). According to a survey, social inequality and poverty worried a significant share of the South African respondents. As of September 2024, some ** percent of the respondents reported that they were worried about the state of poverty and unequal income distribution in the country.   Eastern Cape residents received more grants South Africa’s labor market has struggled to absorb the country’s population. In 2023, almost a third of the economically active population was unemployed. Local authorities employ relief assistance and social grants in an attempt to reduce poverty and assist poor individuals. In 2023, almost ** percent of South African households received state support, with the majority share benefiting in the Eastern Cape.

Seychelles recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

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.

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

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

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.

Data appraisal

All continous household income and expenditure data collected during the Income and Expenditure Survey 2010-2011 are contained in the Total IES data file. The household data file contains only categorical variables. For example, expenditure data on electricity collected with the questions in sub-section 5.7 of the questionnaire will be found in the "Total_IES" data file under the COICOP codes 04511010, 04511110, 04404500. This is explained under "Data Organisation" on page 6 of the metadata record for the IES 2010 2011, which documents how the data files are organised and the variables in each data file.

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 (%)

In the second quarter of 2024, the unemployment rate among Black South Africans was 36.9 percent, marking a year-on-year change of 0.8 percent compared to the second quarter of 2023. On the other hand, the unemployment rate among white South Africans was 7.9 percent in the second quarter of 2024, with a 0.5 percent year-on-year change. Unemployment prevalent among youth and women The unemployment rate is the share of the labor force population that is unemployed, while the labor force includes individuals who are employed as well as those who are unemployed but looking for work. South Africa is struggling to absorb its youth into the job market. For instance, the unemployment rate among young South Africans aged 15-24 years reached a staggering 60.7 percent in the second quarter of 2023. Furthermore, women had higher unemployment rates than men. Since the start of 2016, the unemployment rate of women has been consistently more than that of men, reaching close to 36 percent compared to 30 percent, respectively. A new minimum wage and most paying jobs      In South Africa, a new minimum hourly wage went into effect on March 1, 2022. The minimum salary reached 23.19 South African rand per hour (1.44 U.S. dollars per hour), up from 21.69 South African rand per hour (1.35 U.S. dollars per hour) in 2021. In addition, the preponderance of employed South Africans worked between 40 and 45 hours weekly in 2021. Individuals holding Executive Management and Change Management jobs were the highest paid in the country, with salaries averaging 74,000 U.S. dollars per year.

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 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 [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

The master sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types area: urban, tribal and farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4) and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

There are a number of aspects in which the 2013 version of the master sample differs from the 2007 version. In particular, the number of primary sample units increased. Mining strata were also introduced which serves to improve the efficiency of estimates relating to employment in mining. The number of geo-types was reduced from 4 to 3 while the new master sample allows for the publication of estimates of the labour market at metro level. The master sample was also adjusted Given the change in the provincial distribution of the South African population between 2001 and 2011. There was also an 8% increase in the sample size of the master sample of PSUs to improve the precision of the QLFS estimates. The sample size increased most notable in Gauteng, the Eastern Cape and KwaZulu-Natal. For more details on the differences between the two master samples please consult the section 8 (technical notes) of the QLFS 2015 Q3 release document (P0211).

From the master sample frame, the QLFS takes draws employing 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. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population.

For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings are expected to 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, two quarters and a new household moves in, 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 (or unoccupied).

Mode of data collection

Face-to-face [f2f]

Abstract

The 1993 Project for Statistics on Living Standards and Development was an integrated household survey similar in design to a World Bank Living Standards Measurement Survey. The survey collected data on the socio-economic condition of households. Households in Kwazulu-Natal province were re-surveyed from March to June 1998 for the Kwazulu-Natal Income Dynamics Study. Combining these two survey datasets has yielded a panel (or longitudinal) dataset in which the same individuals and households have been interviewed at two points in time, 1993 and 1998. These are the first two waves of the KIDS panel study.

The institutions collaborating in the KIDS study include the University of KwaZulu-Natal (UKZN), the University of Wisconsin-Madison and the International Food Policy Research Institute (IFPRI).

Geographic coverage

The survey covered households in the KwaZulu-Natal Province, on the east coast of South Africa.

Analysis unit

Households and individuals

Universe

The Kwazulu Natal Income Dynamics Study 1993-1998 covered all household members.

Kind of data

Sample survey data

Sampling procedure

The 1993 sample was selected using a two-stage self-weighting design. In the first stage, clusters were chosen with probability proportional to size from census enumerator subdistricts (ESD) or approximate equivalents where an ESD was not available. In the second stage, all households in each chosen cluster were enumerated and a random sample of them selected. (See PSLSD, 1994, for further details.) In 1993, the KwaZulu-Natal portion of the PSLSD sample was designed to be representative at the provincial level, conditional on the accuracy of the 1991 census and other information used for the sampling frame, and contained households of all races. Due to the geographic concentration of African and Indian households, KIDS-unlike the PSLSD-limits its scope to African and Indian households. In the KwaZulu-Natal province, Africans represent 85 percent of the population and Indians represent 12 percent. Compared with their representation nationally, White and Coloured people are underrepresented in KwaZulu-Natal. Effectively, the numbers of White and Coloureds in the KwaZulu-Natal sample are too small, and too geographically concentrated in a few clusters, to permit meaningful inference. The KIDS study has thus been limited to the first two population groups.

PSLSD was a survey of households. However, households are a complicated object to define, particularly in longitudinal studies. To transform KIDS from a single-round household survey into a longitudinal household panel study required a redefinition of the sampling unit. In 1998, a decision was made to follow the core household members with the intention of capturing the major decision makers within the household. A household member is a core person if he/she satisfied any of the following criteria (the self-declared head of household from the 1993 survey):

• Spouse/partner of the self-declared head of household (from the 1993 survey)
• Lives in a three generation household and all of the following are true:
• Child of the self-declared household head, son/daughter-in-law of the household head, or niece/nephew of self-declared head
• At least 30 years old
• Has at least one child living in household
• Spouse/partner of person satisfying criteria

Thus all heads of households and spouses of heads are automatically classified as core and in some three-generation households, adult children are also included in this cateogry. In this way, we can see the 1993 survey as the baseline information for a random sample of dynasties. The efforts of the 1998 and 2004 surveyors to find the location of the 1993 core members can then be seen as a way to keep track of the 1993 dynasties.

Mode of data collection

Face-to-face [f2f]

Research instrument

KIDS re-interviews the KwaZulu-Natal (KZN) sample of the 1993 nationwide survey known as the Project for Statistics on Living Standards and Development (PSLSD.) The original project was financed by the World Bank and had the characteristics of the Living Standard Measurement Surveys. Reflecting their origin, all three waves of fieldwork for KIDS-1993, 1998, and 2004-collected information on household composition, expenditure on food and on other durable and non-durable goods, education, health, agricultural production, employment, and additional sources of labor and non-labor income. To ensure comparability, the 1998 and 2004 questionnaires largely followed the 1993 version of the questionnaire, however, a few modules have been added and removed. For example, the 1998 survey added sections on assets to marriage, economic shocks, and social capital and trust.