Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Jun 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

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.

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.

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.

In 2024, unemployment rate in Southern Africa was estimated at 28.64 percent. The region recorded the highest rate on the African continent, with South Africa having the highest unemployment levels among African countries. Moreover, Northern Africa registered an unemployment rate of 11.15 percent in 2024, while Eastern Africa had the lowest unemployment levels at 4.74 percent. Overall, the continent's average rate was seven percent in the same year.

In 2024, the unemployment rate in Southern Africa was ***** percent. The rate is projected to increase to ***** percent by 2025, which would still represent one of the highest unemployment rates registered from 2010 onwards. The Southern African economy was negatively impacted by the COVID-19 pandemic in 2020, representing the worst affected region in Africa.

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.

The unemployment rate in Africa was expected to reach seven percent in 2024. In the period under review, unemployment in the continent peaked at 7.2 percent in 2021. Unemployment levels varied significantly across African countries. South Africa was estimated to register the highest rate in 2024 at around 30 percent.

Description: This is aggregated data of individuals or households. The data originates from the South African censuses of 1991, 1996, 2001 and 2011, as well as the community survey of 2007. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status. The data set consists of 156 variables and 257 cases. It contains the same socio-economic variables for different time periods, namely 1991, 1996, 2001, 2007 and 2011. Combined ranking - municipalities were ranked for each year, i.e. 1991, 1996, 2001, 2007 and 2011, in terms of unemployment rate and assigned a rank value. The combined unemployment ranking is calculated by adding up the ranking per individual year. Population density - this was calculated by dividing the total population of a municipality in 1991 by the area and the answer is expressed as number of people per square kilometer. Urban - the number of urban people in an area in a specific year. Rural - the number of rural people in an area in a specific year. Per capita income - the per capita income in a specific area and year. The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census. Abstract: Global unemployment has risen in the past few years and spatial data is required to address the problem effectively. South African unemployment literature focused mostly on a national level of spatial analysis. Some literature refers to spatial aspects that affect unemployment trends, but does not assign a location, e.g. a suburb or municipality. The research was conducted to obtain an understanding of geographical unemployment changes in South Africa over time. The data sets from the South African censuses of 1991, 1996, 2001 and 2011, as well as the community survey of 2007 were compared by spatial extent and associated attributes. The representation of change over time was explored and aggregation to a common boundary, such as municipalities was suggested to overcome modifiable areal unit problems. Census data is spatially more detailed than labour force survey data, and census data from pre-1991 might not reflect the post-apartheid labour trends effectively. To determine which unemployment data set is useful for a spatial understanding of unemployment in South Africa, the attributes of various datasets were compared, the completeness of the spatial data, as well as the geographic scale of presentation. South African census data represents employment statistics at the most detailed spatial level. Census data is collected every five to ten years. Initial data capture for censuses was usually at Enumerator Area (EA) level. Prior to 1991 the spatial data (EA and census district boundaries) were represented on hard copy maps only and no digital spatial data were captured. In the 1991 census, unemployment statistics were not directly calculated at EA level. To generate these statistics the number of employed people was subtracted from the economically active population. In the 1996 census, the number of unemployed, employed and economically active people per small area layer (SAL) was provided by Stats SA. The data were re-aggregated by the Human Sciences Research Council (HSRC), which could then be compared with EA data from other years. The 2001 census attribute data was not released at an EA level, and this consequently made comparisons with the previous two censuses very difficult. However, the spatial boundaries for the EAs were made available, and statistical modelling techniques were used by the HSRC to compute unemployment statistics for these boundaries. CS 2007 released statistics only at a municipality level. The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census. Municipalities were ranked for each year in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities. This resulted in a new data set of aggregated data of individuals or households. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status. All people in South Africa on the date of the census in 1991, 1996, 2001 and 2011, as well as the households at the time when the 2007 Community Survey (CS) was conducted. The South African Census 1996 covered every person present in South Africa on Census Night, 9-10 October 1996 (except foreign diplomats and their families). The South African Census 2001 and 2011 covered every person present in South Africa on Census Night, 9-10 October 2001 or 9-10 October 2011 respectively, including all de jure household members and residents of institutions. The South African Census 1991 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). The South African Community Survey 2007 covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results. Sampling is not applicable since the data used here refers to aggregated data of the universe.

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

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.

In 2024, around **** percent of the African youth, those aged between 15 and 24 years old, were expected to be unemployed. According to data from the International Labor Organization, this figure has remained stable since 2021. The rate of unemployment among youths in the continent has fluctuated in the period under review, overall slightly dropping in comparison to the share in 2012, the lowest in the period reviewed.

Abstract

The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities.

Geographic coverage

The scope of the General Household Survey 2004 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2004 are individuals and households.

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the GHS 2004 a multi-stage stratified sample was drawn, using probability proportional to size principles.

The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of Census 1996. As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample was allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated.

The master sample is divided into five independent clusters. In order to avoid respondent fatigue (the LFS is a rotating panel survey which is conducted twice yearly), the GHS sample uses a different cluster from the Labour Force Survey clusters.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2004 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

83,9% of the expected 31 400 interviews were successfully completed. It was not possible to complete interviews in 9,7% of the sampled dwelling units. An additional 6,3% of all interviews were not conducted for various reasons such as the sampled dwelling units had become vacant or had changed status (e.g. they were used as shops/small businesses at the time of the enumeration but were originally listed as dwelling units).

Sampling error estimates

Estimation and use of standard error The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.

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.

Urban food security has long been viewed as secondary to rural food security in Africa, and with the migration of large numbers of individuals from rural to urban settings, it has become crucial to place more focus on urban food security. More so, in Southern African peri-urban areas, where high unemployment rates amongst the youth exist. Often, the interventions toward reducing food insecurity in urban settings are taken from those previously designed for application in the rural context. In this study, we aimed to measure the status of food security and identify the factors driving and constraining household food security amongst peri-urban households in Tembisa, South Africa, with the purpose of gaining an in depth understanding of the drivers of urban food insecurity within peri-urban communities. In order to accomplish this, FANTA's Household Food Insecurity Access Scale (HFIAS), which measures levels of food security and the Household Dietary Diversity Scale (HDDS), which measures the level of nutritional intake of households was applied. Food prices of the formal and informal markets were monitored over a period of 6 months. A significant decline in household food access over a 4-year period (2013–2016) was observed in addition to low-quality diets. The most commonly used coping methods during periods of low income included borrowing either money or food from friends and neighbors, this was done in conjunction with various other coping strategies. Much of the declining food access was attributed to the inflation of food prices, the lack of employment, lack of formal employment and a high number of household members to breadwinner ratios. High reliance solely on financial capital remains a limitation to the livelihood of urban households. Informal markets are an imperative driver of food security in these peri-urban communities and provide improved food price stability, temporal, and geographical food access through less volatile food pricing, compared to formal markets. Furthermore, government initiatives such as social grants and school feeding schemes have proven to be critical in reducing the vulnerability to food insecurity of most households.

Discover the potential job losses in South Africa due to Trump's tariffs, impacting agriculture and automotive sectors.

As of the second quarter of 2024, nearly 3.83 million people in South Africa worked within the community and social services industry. The sector concentrated the highest number of employees, followed by the trade industry, which employed about 3.36 million people. A struggling labor market The South African labor market faces severe challenges and obstacles. In 2023, the country had the highest unemployment rate in Africa, with almost 30 percent of the labor force being jobless. In addition, only 40 percent of the population was employed in 2021. Indeed, South Africans were the most concerned globally about finding jobs and being unemployed. According to a survey, 64 percent of South African respondents reported being worried about unemployment as of September 2023. A highly unequal country South Africa is the most income-unequal country in the world, as it registered a Gini score of 63 in 2021. The major reasons for this inequality originate from the country’s infamous Apartheid regime and the failure of the job market to provide enough opportunities for its people. For example, the unemployment rate among Black South Africans was close to 37 percent, compared to eight percent for white South Africans. Furthermore, unemployment in the country was more widespread among individuals with a lower level of education. Specifically, in 2023, over 50 percent of the jobless South Africans had an education level lower than matric (grade 12).

In 2022, the unemployment rate in Southern Africa was 32.2 percent, the highest rate on the African continent. Female unemployment in the Southern region reached around 34 percent, compared to a rate of 31 percent among men. Northern Africa recorded the second highest unemployment level, with a wide gender gap: The unemployment rate stood at 23 percent among women and nine percent among men. In the same year, the average rate in Africa was eight percent.

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tharisa (THS) Stock: A Spark in the South African Mining Sector?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data