8 datasets found
  1. S

    South Africa Population: 15 to 64 Years: White

    • ceicdata.com
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    CEICdata.com, South Africa Population: 15 to 64 Years: White [Dataset]. https://www.ceicdata.com/en/south-africa/population/population-15-to-64-years-white
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    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 1, 2015 - Mar 1, 2018
    Area covered
    South Africa
    Variables measured
    Population
    Description

    South Africa Population: 15 to 64 Years: White data was reported at 2,978.591 Person th in Sep 2018. This records a decrease from the previous number of 2,987.055 Person th for Jun 2018. South Africa Population: 15 to 64 Years: White data is updated quarterly, averaging 3,143.298 Person th from Mar 2008 (Median) to Sep 2018, with 43 observations. The data reached an all-time high of 3,277.317 Person th in Mar 2008 and a record low of 2,978.591 Person th in Sep 2018. South Africa Population: 15 to 64 Years: White 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.G001: Population.

  2. t

    Race by Percentages in South Africa

    • theafricangourmet.com
    csv
    Updated Nov 19, 2016
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    Chic African Culture (2016). Race by Percentages in South Africa [Dataset]. https://www.theafricangourmet.com/2017/01/you-cant-hide-your-lying-eyes.html
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    csvAvailable download formats
    Dataset updated
    Nov 19, 2016
    Authors
    Chic African Culture
    Variables measured
    Race
    Description

    This dataset provides the racial demographics of South Africa based on percentages as reported in the 2018 South African Census.

  3. Population Census 1985 - South Africa

    • catalog.ihsn.org
    • dev.ihsn.org
    • +2more
    Updated Mar 29, 2019
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    Statistics South Africa (2019). Population Census 1985 - South Africa [Dataset]. https://catalog.ihsn.org/catalog/2865
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    Statistics South Africahttp://www.statssa.gov.za/
    Time period covered
    1985
    Area covered
    South Africa
    Description

    Geographic coverage

    The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

    The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

    DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

    Analysis unit

    The units of analysis under observation in the South African census 1985 are households and individuals

    Universe

    The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

    Data appraisal

    UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
    Whites 7.6%
    Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

  4. Population Census 1980 - South Africa

    • catalog.ihsn.org
    • dev.ihsn.org
    • +2more
    Updated Mar 29, 2019
    + more versions
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    Statistics South Africa (2019). Population Census 1980 - South Africa [Dataset]. https://catalog.ihsn.org/catalog/2868
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    Statistics South Africahttp://www.statssa.gov.za/
    Time period covered
    1980
    Area covered
    South Africa
    Description

    Abstract

    The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect, process and disseminate detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

    Geographic coverage

    The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the following so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the areas of the Transkei and Bophuthatswana. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

    Analysis unit

    The units of analysis of the 1980 census includes households, individuals and institutions

    Universe

    The 1980 South African census covered all household members (usual residents).

    The 1980 South African 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 6 and 7 May 1980) were 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 not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. 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 [cen]

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

    The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons are employed as domestics by you? (Include garden workers) (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

  5. South African Census 1985 - South Africa

    • datafirst.uct.ac.za
    Updated Mar 29, 2020
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    Statistics South Africa (2020). South African Census 1985 - South Africa [Dataset]. http://www.datafirst.uct.ac.za/Dataportal/index.php/catalog/146
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    Dataset updated
    Mar 29, 2020
    Dataset authored and provided by
    Statistics South Africahttp://www.statssa.gov.za/
    Time period covered
    1985
    Area covered
    South Africa
    Description

    Abstract

    The population census conducted in South Africa in 1985 covered the whole of South Africa, but excluded the "Homelands" of Transkei, Bophutatswana, Ciskei, and Venda. This dataset is the full census, as opposed to the 10% sample datasets provided by Statistics South Africa from 1996 onwards.

    Geographic coverage

    The 1985 census covered the so-called white areas of South Africa - the provinces of the Cape, the Orange Free State, Transvaal, and Natal - and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

    The 1985 Census dataset has 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

    DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

    Analysis unit

    The units of analysis under observation in the South African census 1985 are households and individuals

    Universe

    All persons who were present on Republic of South African territory during census night were enumerated. 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 but not included in the final data. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. 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 [cen]

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

    Data appraisal

    UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
    Whites 7.6%
    Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

  6. T

    South Africa Youth Unemployment Rate

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, South Africa Youth Unemployment Rate [Dataset]. https://tradingeconomics.com/south-africa/youth-unemployment-rate
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    csv, xml, json, excelAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 31, 2013 - Jun 30, 2025
    Area covered
    South Africa
    Description

    Youth Unemployment Rate in South Africa decreased to 62.20 percent in the second quarter of 2025 from 62.40 percent in the first quarter of 2025. This dataset provides - South Africa Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  7. s

    Data from: Survey of truth and reconciliation in South Africa, 2000-2001

    • scholardata.sun.ac.za
    • icpsr.umich.edu
    Updated May 8, 2024
    + more versions
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    Gibson, James L. (2024). Survey of truth and reconciliation in South Africa, 2000-2001 [Dataset]. http://doi.org/10.25413/sun.24412063.v2
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    Dataset updated
    May 8, 2024
    Dataset provided by
    SUNScholarData
    Authors
    Gibson, James L.
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    The purpose of this study was to explore the relationship between truth acceptance and reconciliation among South Africans during and since the political transition from Apartheid to democracy. The study investigated the extent to which South Africans participated in the truth as promulgated by the Truth and Reconciliation Commission and the degree to which they were "reconciled." The Truth and Reconciliation Commission (TRC) was based on the Promotion of National Unity and Reconciliation Act of 1995. The TRC investigated past gross human rights violations and granted amnesty to individuals in exchange for full and public disclosure of information related to these crimes. The hypothesis that truth acceptance leads to reconciliation was tested in this research. Data were collected through a rigorous and systematic survey of South Africans. Nearly all relevant segments of the South African population were included in the sample, as well as representative subsamples of at least 250 respondents of most major racial/ethnic/linguistic groups. Questions about the TRC investigated respondent awareness, knowledge, and approval of the activities of the TRC. Respondents were asked for their opinions on the effectiveness of the TRC in its efforts to provide a true and unbiased account of South Africa's history and in awarding compensation to those who suffered abuses under the Apartheid regime. Other questions about the TRC asked respondents how important it was to find out the truth about the past and achieve racial reconciliation. Demographic variables include age, marital status, education level, and employment status.Response Rates: A total of 3,727 interviews were completed. In the primary sample, 3,139 interviews were completed. The boost sample included 588 completed interviews. The overall response rate for the survey was approximately 87 percent.(1) This study was conducted in collaboration with Amanda Gouws (Stellenbosch University, South Africa), Charles Villa-Vicencio (Institute for Justice and Reconciliation, Cape Town, South Africa), and Helen Macdonald (Institute for Justice and Reconciliation, Cape Town, South Africa).(2) Two weight variables are included in the dataset. One weight variable (NATWT) should be used when analysis is not conducted by race, and the other (RACEWT) should be used when conducting analyses comparing respondent race. (3) Users must cite the original NSF grant number in all materials produced from this project.South African population, aged 18 and over.The area probability sample included a primary sample of South Africans of all races and a boost sample of white South Africans. Representative subsamples of at least 250 respondents of most major racial, ethnic, and linguistic groups were also included.

  8. f

    Prevalence and unmet need for diabetes care across the care continuum in a...

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    • +1more
    tiff
    Updated May 30, 2023
    + more versions
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    Andrew Stokes; Kaitlyn M. Berry; Zandile Mchiza; Whadi-ah Parker; Demetre Labadarios; Lumbwe Chola; Charles Hongoro; Khangelani Zuma; Alana T. Brennan; Peter C. Rockers; Sydney Rosen (2023). Prevalence and unmet need for diabetes care across the care continuum in a national sample of South African adults: Evidence from the SANHANES-1, 2011-2012 [Dataset]. http://doi.org/10.1371/journal.pone.0184264
    Explore at:
    tiffAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Andrew Stokes; Kaitlyn M. Berry; Zandile Mchiza; Whadi-ah Parker; Demetre Labadarios; Lumbwe Chola; Charles Hongoro; Khangelani Zuma; Alana T. Brennan; Peter C. Rockers; Sydney Rosen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    South Africa faces an epidemic of chronic non-communicable diseases (NCDs), yet national surveillance is limited due to the lack of recent data. We used data from the first comprehensive national survey on NCDs—the South African National Health and Nutrition Examination Survey (SANHANES-1 (2011–2012))—to evaluate the prevalence of and health system response to diabetes through a diabetes care cascade. We defined diabetes as a Hemoglobin A1c equal to or above 6.5% or currently on treatment for diabetes. We constructed a diabetes care cascade by categorizing the population with diabetes into those who were unscreened, screened but undiagnosed, diagnosed but untreated, treated but uncontrolled, and treated and controlled. We then used multivariable logistic regression models to explore factors associated with diagnosed and undiagnosed diabetes. The age-standardized prevalence of diabetes in South Africans aged 15+ was 10.1%. Prevalence rates were higher among the non-white population and among women. Among individuals with diabetes, a total of 45.4% were unscreened, 14.7% were screened but undiagnosed, 2.3% were diagnosed but untreated, 18.1% were treated but uncontrolled, and 19.4% were treated and controlled, suggesting that 80.6% of the diabetic population had unmet need for care. The diabetes care cascade revealed significant losses from lack of screening, between screening and diagnosis, and between treatment and control. These results point to significant unmet need for diabetes care in South Africa. Additionally, this analysis provides a benchmark for evaluating efforts to manage the rising burden of diabetes in South Africa.

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CEICdata.com, South Africa Population: 15 to 64 Years: White [Dataset]. https://www.ceicdata.com/en/south-africa/population/population-15-to-64-years-white

South Africa Population: 15 to 64 Years: White

Explore at:
Dataset provided by
CEICdata.com
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Jun 1, 2015 - Mar 1, 2018
Area covered
South Africa
Variables measured
Population
Description

South Africa Population: 15 to 64 Years: White data was reported at 2,978.591 Person th in Sep 2018. This records a decrease from the previous number of 2,987.055 Person th for Jun 2018. South Africa Population: 15 to 64 Years: White data is updated quarterly, averaging 3,143.298 Person th from Mar 2008 (Median) to Sep 2018, with 43 observations. The data reached an all-time high of 3,277.317 Person th in Mar 2008 and a record low of 2,978.591 Person th in Sep 2018. South Africa Population: 15 to 64 Years: White 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.G001: Population.

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