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

The total population of South Africa amounted to approximately 63.20 million people in 2024. Following a continuous upward trend, the total population has risen by around 34.12 million people since 1980. Between 2024 and 2030, the total population will rise by around 5.88 million people, continuing its consistent upward trajectory.This indicator describes the total population in the country at hand. This total population of the country consists of all persons falling within the scope of the census.

Employment Rate in South Africa increased to 40.70 percent in the third quarter of 2025 from 40.20 percent in the second quarter of 2025. This dataset provides - South Africa Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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.

Recorded research data on rational recruitment decision-making within South African banks in a qualitative approach.Some interview recordings are stored in M4A files and some in MP4 files, Also included are transcripts for the interviews.

Youth Unemployment Rate in South Africa decreased to 58.50 percent in the third quarter of 2025 from 62.20 percent in the second quarter of 2025. This dataset provides - South Africa Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

South Africa Manufacturing Survey: Paper: RO: Average Labour Cost data was reported at 87.000 % in Dec 2018. This stayed constant from the previous number of 87.000 % for Sep 2018. South Africa Manufacturing Survey: Paper: RO: Average Labour Cost data is updated quarterly, averaging 48.000 % from Sep 1987 (Median) to Dec 2018, with 126 observations. The data reached an all-time high of 100.000 % in Mar 1992 and a record low of -58.000 % in Mar 1999. South Africa Manufacturing Survey: Paper: RO: Average Labour Cost data remains active status in CEIC and is reported by Bureau for Economic Research. The data is categorized under Global Database’s South Africa – Table ZA.S022: Business Survey: Manufacturing: Employment Weighted: by Industry.

South Africa RTS: SS: Employees: Female data was reported at 32,054.000 Person in 2015. This records an increase from the previous number of 29,137.000 Person for 2012. South Africa RTS: SS: Employees: Female data is updated yearly, averaging 29,969.000 Person from Jun 2005 (Median) to 2015, with 4 observations. The data reached an all-time high of 32,054.000 Person in 2015 and a record low of 21,053.000 Person in 2005. South Africa RTS: SS: Employees: Female 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.H015: Retail Trade Survey: Specialized Stores: Household Furniture, Appliances, Articles and Equipment.

The study examined how changes in years of education affect employment outcomes, labour market earnings, and their distribution. These issues were evaluated using the general household surveys for 2014–2018 and several econometric techniques. The findings indicate that education has a heterogeneous effect on employment across population groups and gender, with women having a higher probability of employment than men. Moreover, returns to education vary across the wage distribution, gender, education level, and exposure to the compulsory schooling law, with evidence suggesting that education may increase wage inequality. Furthermore, the analysis shows that starting school early is associated with lower educational attainment and reduced labour market earnings. However, among those who start school early, education increases average earnings.

The data set is qualitative drawing from selected key informants in the Department of Public Service (DPSA). Analyses was about the relationship between remuneration and employee perofmance.

The unemployment rate in South Africa stood at 33.17 percent in 2024. Between 1991 and 2024, the unemployment rate rose by 10.17 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend.

Data collected to determine the critical retention factors for women in mining in the SA coal mining industry. Raw data is on both excel and SPSS. Summary of responses, Consent form and the questionnaire used are also included.

South Africa RTS: SS: Expenditure: Employment data was reported at 6,583.000 ZAR mn in 2015. This records an increase from the previous number of 6,217.000 ZAR mn for 2012. South Africa RTS: SS: Expenditure: Employment data is updated yearly, averaging 5,987.000 ZAR mn from Jun 2005 (Median) to 2015, with 4 observations. The data reached an all-time high of 6,583.000 ZAR mn in 2015 and a record low of 3,476.000 ZAR mn in 2005. South Africa RTS: SS: Expenditure: Employment 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.H015: Retail Trade Survey: Specialized Stores: Household Furniture, Appliances, Articles and Equipment.

Dataset for a qualitative study on organisational culture and employee turnover intention in the South African banking sector

Topics covered in the questionnaire are: work and unemployment, respondent characteristics, household characteristics, personal and household income variables. The data set for dissemination contains 2885 cases and 262 variables.

Abstract

The Community Survey is a nationally representative, large-scale household survey which is designed to provide information on the extent of poor households in South Africa, their access to services, and levels of unemployment, at national, provincial and municipal levels. The main objectives of the survey are: 1. To fill data gaps between national population and housing censuses 2. To provide estimates at lower geographical levels than existing household surveys 3. To build capacities for the next census round 4. To provide inputs to the mid-year population projections.

Geographic coverage

The survey covered the whole of South Africa.

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The CS 2016 questionnaire consisted of six main sections, 11 sub-sections and a total of 225 questions. A first draft of the paper questionnaire was developed in February 2015 and various versions were reviewed and updated thereafter based on discussions with stakeholders. The target population of the survey was all persons in the sampled dwelling who were present on the reference night (i.e. the night between 6 and 7 March 2016). The final CAPI questionnaire was made up of three person rosters. One roster was utilised for the person information, one roster for emigration and one roster for mortality.

Data appraisal

The Community Survey 2016 data was released in 2017. There are four data files. These are files for households, persons, mortality, and emigration. The emigration file is currently not available. Statistics SA has not provided an explanation for the missing file. DataFirst is working to obtain this file, and will add the data file to the dataset we publish once we have it.

The Community Survey 2016 is also missing employment and income data. Data on employment type and employment status data was collected with questions 3.7.6 - 3.7.6.24 of the questionnaire. Income data was collected with questions 3.7.7. - 3.7.7.4. According to Statistics SA, the data from these questions was not released because changes in collection methodologies resulted in this data not being comparable with the employment and income data in the Quarterly Labour Force Survey.

Dataset for a quantitative research. Kram's mentorship theory was used to frame the philosophical grounding of this study. Kram's mentorship theory helped to broaden the understanding of the link between institutional mentorship and employability. The researcher utilised a quantitative survey research approach to explore existing research gaps by determining the effects of institutional mentorship in developing graduates holistically and whether institutional mentorship could positively impact the graduate unemployment crisis. The survey questions targeted alumni of a South African higher education institution. Due to ethical, confidentiality and privacy implications, their responses were anonymized. Probability sampling techniques have been used in this study. In that regard, the selection of respondents was made to allow each member of the targeted population an equal chance to be part of the sample. Therefore, the simple random sampling technique was utilised. Secondly, purposive sampling was utilised to target alumni of University X who had access to the online platform. Accordingly, University X alumni had an equal opportunity to participate. Data collected through the electronic survey were analyzed using Excel data analysis. The data analysis involved processing participants’ responses according to categories of individual and/ or groups of themes linked to the research problem, aim and questions. The editing process entailed verifying data captured by the software to ascertain accuracy and consistency between tables generated by the Excel data analysis.

South Africa is experiencing a rapidly growing diabetes epidemic that threatens its healthcare system. Research on the determinants of diabetes in South Africa receives considerable attention due to the lifestyle changes accompanying South Africa’s rapid urbanization since the fall of Apartheid. However, few studies have investigated how segments of the Black South African population, who continue to endure Apartheid’s institutional discriminatory legacy, experience this transition. This paper explores the association between individual and area-level socioeconomic status and diabetes prevalence, awareness, treatment, and control within a sample of Black South Africans aged 45 years or older in three municipalities in KwaZulu-Natal. Cross-sectional data were collected on 3,685 participants from February 2017 to February 2018. Individual-level socioeconomic status was assessed with employment status and educational attainment. Area-level deprivation was measured using the most recent South African Multidimensional Poverty Index scores. Covariates included age, sex, BMI, and hypertension diagnosis. The prevalence of diabetes was 23% (n = 830). Of those, 769 were aware of their diagnosis, 629 were receiving treatment, and 404 had their diabetes controlled. Compared to those with no formal education, Black South Africans with some high school education had increased diabetes prevalence, and those who had completed high school had lower prevalence of treatment receipt. Employment status was negatively associated with diabetes prevalence. Black South Africans living in more deprived wards had lower diabetes prevalence, and those residing in wards that became more deprived from 2001 to 2011 had a higher prevalence diabetes, as well as diabetic control. Results from this study can assist policymakers and practitioners in identifying modifiable risk factors for diabetes among Black South Africans to intervene on. Potential community-based interventions include those focused on patient empowerment and linkages to care. Such interventions should act in concert with policy changes, such as expanding the existing sugar-sweetened beverage tax.

Ethics reference: 2022_FBMSREC 046Abstract: Small, Medium, and Micro Enterprises (SMMEs) play a pivotal role in driving economic growth and fostering development globally, as well as in the specific context of South Africa. This importance is particularly evident in the civil, building, and mechanical engineering industries, where SMMEs contribute significantly to the country's Gross Domestic Product (GDP) and hold the potential to alleviate poverty, reduce unemployment, and promote inclusivity and fairness. This dataset explores the multifaceted significance of SMMEs in these industries.Globally, SMMEs are recognized as engines of economic growth due to their capacity to innovate, create jobs, and generate income. In South Africa, these enterprises have a profound impact on the economy, contributing poverty alleviation and income generation social inclusion. SMMEs involvement in the civil engineering, building construction, and mechanical engineering sectors are particularly crucial, as they drive infrastructure development, job creation, and skills enhancement as a future economic jack. While, the primary objective of the study is to investigate the challenges faced by SMMEs through the broader economic trends by unidentified household brand names which influence lack of capital cash-flow based on the marketing tools.The vital role of SMMEs in South Africa's GDP and poverty reduction is underscored by their potential to create employment opportunities, especially for marginalized communities, which finds expression in in the engineering and construction sector like many other economic sectors. These enterprises facilitate skills development and contribute to localized economic growth, thereby advancing inclusivity and social equity. However, SMMEs in the civil, building, and mechanical engineering industries confront an array of challenges, including limited access to financing, inadequate skills development, regulatory hurdles, and market access constraints. This empirical study evaluated the marketing tools that will influence capital cash-flow in SMMEs. The study employed comparative methodology comprised of quantitative closed-ended questionnaire and qualitative open-ended questionnaire. Which linked well with pragmatic paradigm. The study aimed at utilising 130 SMMEs participants from the engineering sector. Backed by this dataset, the study revealed that most private sectors are engaged in supporting the growth and mentoring of SMMEs. Additionally, the Free-State government provides financial support and facilitates marketing access to the SMMEs. The study recommends that the Free-State government should intensify programmes of skills knowledge development and marketing competition to maintain capital cash-flow sustainability in SMMEs.