South Africa is expected to register the highest unemployment rate in Africa in 2024, with around ** percent of the country's labor force being unemployed. Djibouti and Eswatini followed, with unemployment reaching roughly ** percent and ** percent, respectively. On the other hand, the lowest unemployment rates in Africa were in Niger and Burundi. The continent’s average stood at roughly ***** percent in the same year. Large shares of youth among the unemployed Due to several educational, socio-demographic, and economic factors, the young population is more likely to face unemployment in most regions of the world. In 2024, the youth unemployment rate in Africa was projected at around ** percent. The situation was particularly critical in certain countries. In 2022, Djibouti recorded a youth unemployment rate of almost ** percent, the highest rate on the continent. South Africa followed, with around ** percent of the young labor force being unemployed. Wide disparities in female unemployment Women are another demographic group often facing high unemployment. In Africa, the female unemployment rate stood at roughly ***** percent in 2023, compared to *** percent among men. The average female unemployment on the continent was not particularly high. However, there were significant disparities among African countries. Djibouti and South Africa topped the ranking once again in 2022, with female unemployment rates of around ** percent and ** percent, respectively. In contrast, Niger, Burundi, and Chad were far below Africa’s average, as only roughly *** percent or lower of the women in the labor force were unemployed.

Historical chart and dataset showing South Africa unemployment rate by year from 1991 to 2024.

Youth Unemployment Rate in South Africa increased to 62.40 percent in the first quarter of 2025 from 59.60 percent in the fourth quarter of 2024. This dataset provides - South Africa Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

In 2024, the youth unemployment rate in South Africa increased by 1.2 percentage points (+2.01 percent) compared to 2023. In total, the youth unemployment rate amounted to 60.89 percent in 2024. This increase was preceded by a declining youth unemployment rate.The youth unemployment rate of a country or region refers to the share of the total workforce aged 15 to 24 that is currently without work, but actively searching for employment. It does not include economically inactive persons such as full-time students or the long-term unemployed.Find more statistics on other topics about South Africa with key insights such as labor participation rate among the total population aged between 15 and 64, labor force participation rate for males, and female labor force participation rate.

Historical chart and dataset showing South Africa youth unemployment rate by year from 1991 to 2024.

South Africa ZA: Unemployment Rate: % Change data was reported at -3.610 % in Mar 2018. This records a decrease from the previous number of 0.755 % for Dec 2017. South Africa ZA: Unemployment Rate: % Change data is updated quarterly, averaging 1.205 % from Mar 1995 (Median) to Mar 2018, with 81 observations. The data reached an all-time high of 15.748 % in Dec 2001 and a record low of -17.228 % in Dec 2006. South Africa ZA: Unemployment Rate: % Change 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: Labour Force, Employment and Unemployment: Quarterly.

Abstract

The 1991 South African population census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, family and employment details.

Geographic coverage

The South African Census 1991 covered the whole of South Africa. The "homelands" of Transkei, Bophuthatswana, Venda and Ciskei were enumerated separately and the dataset contains data files for Bophuthatswana, Venda and Ciskei. The dataset does not include a data file for the Transkei as this was never provided by Statistics South Africa.

Analysis unit

Households and individuals

Universe

The 1991 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 7 and 8 March 1991) were therefore 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 enumerated and included in the figures. The Diplomatic and Consular Corps of foreign countries were not included. Crews and passengers of ships were also not enumerated, except those who were present at the harbours of the RSA on census night. Similarly, residents of the RSA who were absent from the night were not enumerated. 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

Sampling deviation

As a result of the unplanned and unstructured nature of certain residential areas, as well as the inaccessibility of certain areas during the preparations for the enumeration of census, comprehensive door-to-door surveys were not possible. The Human Sciences Research Council had to enumerate these areas by means of sample surveys. 88 areas country-wide were enumerated on this basis.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1991 Population Census questionnaire covered particulars of households: dwelling type, ownership type, type of area (rural/urban) and particulars of individuals: relationship within household, sex, age, marital status, population group, birthplace, citizenship, duration of residency, religion, education level, language, literacy,employment status, occupation, economic sector and income.

Abstract

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

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

Geographic coverage

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

Analysis unit

Units of analysis in the survey include firms and individuals

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Mail Questionnaire [mail]

Research instrument

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

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

The variable

Response rate

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

Data appraisal

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

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

Finally, the variable

South Africa Unemployment data was reported at 7,990.947 Person th in Dec 2024. This records a decrease from the previous number of 8,010.520 Person th for Sep 2024. South Africa Unemployment data is updated quarterly, averaging 5,678.412 Person th from Mar 2008 (Median) to Dec 2024, with 68 observations. The data reached an all-time high of 8,383.824 Person th in Jun 2024 and a record low of 4,047.887 Person th in Dec 2008. South Africa Unemployment data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G024: Unemployment. [COVID-19-IMPACT]

South Africa ZA: Unemployment: National Estimate: Youth: % of Total Labour Force Aged 15-24 data was reported at 53.527 % in 2017. This records an increase from the previous number of 53.371 % for 2016. South Africa ZA: Unemployment: National Estimate: Youth: % of Total Labour Force Aged 15-24 data is updated yearly, averaging 53.052 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 61.437 % in 2003 and a record low of 45.607 % in 2008. South Africa ZA: Unemployment: National Estimate: Youth: % of Total Labour Force Aged 15-24 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: Employment and Unemployment. Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

ZA: Unemployment: National Estimate: Youth Female: % of Female Labour Force Aged 15-24 data was reported at 58.650 % in 2017. This records a decrease from the previous number of 59.310 % for 2016. ZA: Unemployment: National Estimate: Youth Female: % of Female Labour Force Aged 15-24 data is updated yearly, averaging 55.175 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 59.310 % in 2016 and a record low of 50.310 % in 2008. ZA: Unemployment: National Estimate: Youth Female: % of Female Labour Force Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

South Africa ZA: Unemployment: National Estimate: Female: % of Female Labour Force data was reported at 29.490 % in 2017. This records an increase from the previous number of 29.000 % for 2016. South Africa ZA: Unemployment: National Estimate: Female: % of Female Labour Force data is updated yearly, averaging 27.210 % from Dec 1998 (Median) to 2017, with 15 observations. The data reached an all-time high of 31.140 % in 2002 and a record low of 25.560 % in 2009. South Africa ZA: Unemployment: National Estimate: Female: % of Female Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

ZA: Part Time Employment: Female: % of Total Female Employment data was reported at 16.760 % in 2017. This records an increase from the previous number of 16.380 % for 2016. ZA: Part Time Employment: Female: % of Total Female Employment data is updated yearly, averaging 15.885 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 16.760 % in 2017 and a record low of 14.200 % in 2011. ZA: Part Time Employment: Female: % of Total Female Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Part time employment refers to regular employment in which working time is substantially less than normal. Definitions of part time employment differ by country.; ; International Labour Organization, Key Indicators of the Labour Market database.; Weighted average; Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.

South Africa ZA: Unemployment: National Estimate: Male: % of Male Labour Force data was reported at 25.540 % in 2017. This records an increase from the previous number of 24.550 % for 2016. South Africa ZA: Unemployment: National Estimate: Male: % of Male Labour Force data is updated yearly, averaging 22.840 % from Dec 1998 (Median) to 2017, with 15 observations. The data reached an all-time high of 25.540 % in 2017 and a record low of 19.710 % in 2008. South Africa ZA: Unemployment: National Estimate: Male: % of Male Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

South Africa ZA: Part Time Employment: % of Total Employment data was reported at 12.340 % in 2017. This records an increase from the previous number of 11.840 % for 2016. South Africa ZA: Part Time Employment: % of Total Employment data is updated yearly, averaging 11.585 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 12.340 % in 2017 and a record low of 10.360 % in 2011. South Africa ZA: Part Time Employment: % of Total Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Part time employment refers to regular employment in which working time is substantially less than normal. Definitions of part time employment differ by country.; ; International Labour Organization, Key Indicators of the Labour Market database.; Weighted average; Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.

South Africa Unemployment Rate: Male data was reported at 25.100 % in Dec 2018. This records a decrease from the previous number of 25.900 % for Sep 2018. South Africa Unemployment Rate: Male data is updated quarterly, averaging 23.300 % from Mar 2008 (Median) to Dec 2018, with 44 observations. The data reached an all-time high of 26.000 % in Sep 2017 and a record low of 18.800 % in Dec 2008. South Africa Unemployment Rate: Male data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G021: Unemployment Rate.

South Africa ZA: Unemployment Rate: % Change over Previous Period data was reported at 2.713 % in 2017. This records a decrease from the previous number of 5.424 % for 2016. South Africa ZA: Unemployment Rate: % Change over Previous Period data is updated yearly, averaging 2.713 % from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 365.948 % in 1998 and a record low of -11.198 % in 2006. South Africa ZA: Unemployment Rate: % Change over Previous Period 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: Labour Force, Employment and Unemployment: Annual.

South Africa ZA: Unemployment with Basic Education: % of Total Labour Force data was reported at 36.920 % in 2017. This records an increase from the previous number of 36.610 % for 2016. South Africa ZA: Unemployment with Basic Education: % of Total Labour Force data is updated yearly, averaging 34.400 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 36.920 % in 2017 and a record low of 32.240 % in 2009. South Africa ZA: Unemployment with Basic Education: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;