Unemployment Rate in South Africa increased to 32.90 percent in the first quarter of 2025 from 31.90 percent in the fourth quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of unemployed persons in South Africa increased to 8228 Thousand in the first quarter of 2025 from 7991 Thousand in the fourth quarter of 2024. This dataset provides - South Africa Unemployed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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.

South Africa unemployment rate for 2024 was 33.17%, a 1.07% increase from 2023.

<ul style='margin-top:20px;'>

<li>South Africa unemployment rate for 2023 was <strong>32.10%</strong>, a <strong>1.17% decline</strong> from 2022.</li>
<li>South Africa unemployment rate for 2022 was <strong>33.27%</strong>, a <strong>0.74% decline</strong> from 2021.</li>
<li>South Africa unemployment rate for 2021 was <strong>34.01%</strong>, a <strong>4.79% increase</strong> from 2020.</li>
</ul>Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

South Africa is expected to register the highest unemployment rate in Africa in 2024, with around 30 percent of the country's labor force being unemployed. Djibouti and Eswatini followed, with unemployment reaching roughly 28 percent and 25 percent, respectively. On the other hand, the lowest unemployment rates in Africa were in Niger and Burundi. The continent’s average stood at roughly seven 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 11 percent. The situation was particularly critical in certain countries. In 2022, Djibouti recorded a youth unemployment rate of almost 80 percent, the highest rate on the continent. South Africa followed, with around 52 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 eight percent in 2023, compared to 6.6 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 38 percent and 31 percent, respectively. In contrast, Niger, Burundi, and Chad were far below Africa’s average, as only roughly one percent or lower of the women in the labor force were unemployed.

Employment Rate in South Africa decreased to 40.30 percent in the first quarter of 2025 from 41.10 percent in the fourth quarter of 2024. This dataset provides - South Africa Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). The survey collects data on the labour market activities of individuals aged 15 years and above who live in South Africa. The objective of the QLFS is to collect quarterly information about persons in the labour market, i.e., those who are employed; those who are unemployed and those who are not economically active. The Quarterly Labour Force Survey (QLFS) frame has been developed as a general-purpose household survey frame that can be used by all other household surveys, irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30,000 dwellings per quarter. The sample is based on information collected during the 2001 Population Census conducted by Stats SA. The sample is designed to be representative at provincial level and within provinces at metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms, and tribal. This implies, for example, that within a metropolitan area the sample is representative of the different geography types that may exist within that metro.

Abstract

This data, Networks and Employment Transitions Study (NETS), is a construction of a panel data set based on the KwaZulu Income Dynamics Study (KIDS) and a sub-sample of the KIDS households. The resulting data set thus contains a partial third wave of the original KwaZulu Natal households of the Project for Statistics on Living Standards and Development (PSLSD) and is meant to represent the baseline sample of the Networks and Employment Transitions Study (NETS). NETS is essentially a study of labour market dynamics that takes advantage of the panel structure of the KIDS data. NETS is primarily about transitions out of unemployment into employment. Thus the population of individuals we were interested in following over time were those who classified themselves as unemployed in the first wave of KIDS. The final sample frame for this study was comprised of individuals who were unemployed in 1993, but who were observed as either unemployed or employed in 1998.

Geographic coverage

Province of KwaZulu-Natal

Analysis unit

Households and individuals

Universe

The survey covered all unemployed individuals who are aged 19-64 in 1993

Kind of data

Sample survey data

Sampling procedure

The sampling algorithm behind the NETS data was designed to ensure sucient variation in employment status. Given that the KIDS panel was not meant to reveal anything about the racial dimensions of living standards, all Indian households were deleted from this sample which led to a target sample of 677 individuals. Clusters (or magisterial districts) known to have been fabricated by fieldworkers in the first wave of the panel (cluster numbers 217 and 218) were then deleted (see Carter et al (2003) for more details). Owing to the wide geographic dispersion of the areas contained in the data and the cost implications of this, all clusters with fewer than 5 households were deleted from the frame. Thus clusters 74, 76, 79, 200, 202, 208, 210, 212, 215, 219, 226, 230, 231, and 239 were not sampled, in addition to all clusters where no individuals experienced a transition out of unemployment and into employment. This left a total of 358 households located in 45 separate clusters spread throughout the province of KwaZulu-Natal covering 1749 individuals aged 19-64 in 2002. This target sample contained the majority of the original 677 individuals, save for those no longer in the sample owing to deletion of the clusters mentioned above. These individuals were denoted as "core" members of the target sample. Demographic details of these individuals and other spatial data such as hand-drawn maps and aerial photographs were used to locate the geographical position of the 358 households that these individuals were observed as residents of in 1998. The tracking process began by plotting the rough geographic location of each sample cluster of households. In addition to those household members denoted as core, the questionnaire also allowed for new economically active individuals joining the household to be captured. The names of those individuals identied as core persons were pre-listed on each household questionnaire. Since individuals aged 15-60 were interviewed in 1998, the target sample thus ultimately comprised the 19-64 age cohort (though allowing for new household members effectively increased the range of ages beyond the 64 year cut-off). In addition, a further 206 new household members were interviewed, increasing the potential sample to 1955. As far as was practical, individual members of each household were interviewed directly. This meant that in certain cases, more than one visit to the household was required in order to complete the interview. Individuals identied as core respondents who were no longer resident within the household had to be tracked and interviewed with a separate survey instrument. The tracking rule we applied was as follows: if the person had moved to within a five-kilometre radius of the original household, then a face-to-face interview was completed, if the individual could be located. If the individual had moved further away, they were contacted telephonically, if this was possible.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire covers information on the structure and workings of the social network groups, and the benefits associated with membership, as well as a general discussion about the community

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

As of the fourth quarter of 2023, the unemployment rate in South Africa stood at 32.1 percent. The majority of unemployed individuals had an education level below matric (grade 12), while those that had finished their matric year represented around 34 percent. Graduates had the lowest share of unemployment at approximately 10 percent.

In 2023, the youth unemployment rate in South Africa remained nearly unchanged at around 49.14 percent. Still, 2023 marked the second consecutive decline of the youth unemployment rate. The youth unemployment rate refers to the share of the economically active population aged 15 to 24 currently without work but in search of employment. The youth unemployment rate does not include economically inactive persons such as the long-term unemployed or full-time students.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.

The unemployment rate in South Africa declined to 27.99 percent in 2023. Nevertheless, the last two years recorded a significantly higher unemployment rate than the preceding years.The unemployment rate refers to the share of the workforce that is currently not working but is actively searching for work. It does not include the economically inactive population, such as the long-term unemployed, those aged under 15 years, or retired persons.Find more statistics on other topics about South Africa with key insights such as gross tertiary enrollment ratio, youth literacy rate (people aged 15-24), and Gender Parity Index (GPI) in youth literacy.

In 2023, the labor force participation rate for males in South Africa remained nearly unchanged at around 64.27 percent. However, 2023 marked the third consecutive increase of the rate. Male labor force participation is the share of men over 15 years who are economically active. For example, all men providing labor in a specific period for the production of goods and services.Find more statistics on other topics about South Africa with key insights such as youth unemployment rate, labor participation rate among the total population aged between 15 and 64, and female labor force participation rate.

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