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%

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

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in South Gorin. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In South Gorin, the median household income for the households where the householder is White decreased by $3,706(9.07%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $40,862 in 2011 and $37,156 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in South Gorin, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/south-gorin-mo-median-household-income-by-race-trends.jpeg" alt="South Gorin, MO median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in South Gorin.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Gorin median household income by race. You can refer the same here

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%

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)

Linked databases of research records of primary documents in named archive collections. Some 30 major collections have been worked on, producing a dataset of over 47,000 records of letters in family and related collections, with the dataset consisting of these 30 interrelated databases. A purpose-designed Virtual Research Environment (VRE) manages the epistolary data and provides tools to assist its analysis. Research questions include: In what ways was whiteness and its ‘others’ re/configured over time? How did people represent such things over time in their letter writing? What resistances and accommodations occurred in different areas of the country and from what people and networks? An important meta-concern is, how can long-term social change best be investigated and what are the problems and possibilities of letter writing in this. In addition to scholarly publications arising from the WWW research, the complete dataset with an extensive editorial apparatus is provided for secondary analysis purposes, published through HRI Online at the University of Sheffield, the U.K.'s leading publisher of primary research materials in the humanities and social sciences (see Related Resources).

Whites Writing Whiteness investigates how ideas about ‘race’ in South Africa changed from the 1770s to the 1970s and the role of whiteness in this. It is a qualitative longitudinal research project and its primary data is letter-writing within multi-generational family networks, located in South African archive collections. Such collections are the focus because a supremely serial form of data, consequently enabling detailed investigation of change as it unfolded over the long period the research interrogates. They represent different ethnic origins, language groups, economic circumstances and areas of the country and their contents are not seen in a referential way, as sources of true or distorted facts, but as inscribing a complex representational order.

Linked databases of research records of primary documents in named archive collections. Some 30 major collections have been worked on, producing a dataset of over 47,000 records of letters in family and related collections, with the dataset consisting of these 30 interrelated databases. A purpose-designed Virtual Research Environment (VRE) manages the epistolary data and provides tools to assist its analysis. Research questions include: In what ways was whiteness and its ‘others’ re/configured over time? How did people represent such things over time in their letter writing? What resistances and accommodations occurred in different areas of the country and from what people and networks? An important meta-concern is, how can long-term social change best be investigated and what are the problems and possibilities of letter writing in this. In addition to scholarly publications arising from the WWW research, the complete dataset with an extensive editorial apparatus is provided for secondary analysis purposes, published through HRI Online at the University of Sheffield, the U.K.'s leading publisher of primary research materials in the humanities and social sciences (see Related Resources).Whites Writing Whiteness investigates how ideas about ‘race’ in South Africa changed from the 1770s to the 1970s and the role of whiteness in this. It is a qualitative longitudinal research project and its primary data is letter-writing within multi-generational family networks, located in South African archive collections. Such collections are the focus because a supremely serial form of data, consequently enabling detailed investigation of change as it unfolded over the long period the research interrogates. They represent different ethnic origins, language groups, economic circumstances and areas of the country and their contents are not seen in a referential way, as sources of true or distorted facts, but as inscribing a complex representational order. The principal data collection method has been archival research. It has involved detailed work on over 30 major family and related collections, working on entire collections as well as in close detail on a sample of one in five documents across these collections.

Zambia and South African dataset (CODA)

These data comprise locations and individual metadata from 34 white sharks (Carcharodon carcharias) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority.

This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean".

The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows.

DeployID: This a factor variable identifying each individual shark. It has 34 levels.

SPOT: This is a numeric variable identifying the tag number unique to each shark.

Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time.

Type: This is a character variable identifying the type of location record.

Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS.

Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record.

Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record.

Area_tagged: This is a character variable that gives the area where the shark was tagged.

Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male.

TL: This is a numeric variable giving the total length of the shark in centimetres.

Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL).

White Shark microsatellite genotypesmicrosatellite genotypes of white shark individuals sampled in South Africa (SA) and the Northwest Atlantic (NWA)WS Data for Dryad.xlsx

This dataset is a collation of articles written by different authors on the history of South Africa during the apartheid regime (1948 to 1994). Apartheid in South Africa was the racial segregation under the all-white government of South Africa which dictated that non-white South Africans (a majority of the population) were required to live in separate areas from whites and use separate public facilities and contact between the two groups would be limited. The different racial group were physically separated according to their location, public facilities and social life.

This record provides an overview of the NESP Marine and Coastal Hub project "Updating knowledge of Australian white sharks". For specific data outputs from this project, please see child records associated with this metadata. The white shark is listed as vulnerable and migratory under Australia’s Environment Protection and Biodiversity Conservation Act 1999. The national White Shark Recovery Plan 2013 sets out research and management actions necessary to support the recovery of the white shark in Australian waters. Previous research funded by the National Environmental Science Program (NESP) provided updated estimates of white shark breeding population size and trend. However, the results were based on modest data sets and were limited by some critical knowledge gaps in relation to pupping and juvenile nursery areas, and uncertainty about how populations are connected between eastern and south-western Australia. Recent unpublished work has raised the prospect of a single Australian population. The White Shark Recovery Plan 2013 has identified a critical need for a quantitative assessment of population trends and evidence of any recovery of the white shark in Australian waters. This project will provide an update and reduce uncertainty regarding the status, trends, and population structure of white sharks in Australian waters. Specifically, it will focus efforts to identify critical habitats and biologically important areas for white sharks and improve the understanding of population status through advancing close-kin mark recapture research. Three project sub-components will involve: • Investigating the feasibility of filling knowledge gaps about juvenile and pupping areas and adult movements; • Investigating population structure to resolve mixing/connectivity questions; and • Updating population estimates based on significant new data. The project approach will comprise of: (1) A pilot study to assess the effectiveness of tagging adult females (>4.5 metres) and juveniles (>2 m) throughout the southern-western white shark range. Genetic samples will be gathered from around Australia and sought from South Africa and New Zealand to conduct a comprehensive update of white shark stock structure. (2) Using an expanded tissue sample set from New South Wales (~1000 samples) to update and refine estimates of adult population size and population trend for the eastern white shark population. Juvenile numbers will be estimated using data from the New South Wales shark management program. Additional samples from South Australia and Western Australia will be combined with previous samples in the southern-western population to refine estimates of population size. (3) Population estimates undertaken using close-kin mark-recapture, a technique that combines advanced genetics and statistical modelling to infer population demographics by identifying close-kin-pairs (parent-offspring or half-siblings) among a collection of sampled animals. Outputs • New genetic samples and sequencing data for white sharks [dataset] • Tracking data derived from 12 PAT tags [dataset] • Final technical report (including recommendations for systematic future research to assist in identifying additional critical habitat for the south-western white shark population) [written]

Tobacco use and oral sex (OS) are important risk factors for oral and oropharyngeal Human papillomavirus (HPV) infection. Little is known about the prevalence of OS practice in South Africa. This study aimed to determine the prevalence of OS practice and tobacco use in a South African patient population. This cross-sectional study used a structured questionnaire to collect socio-demographic characteristics, tobacco use, betel nut use and OS practice data from consenting adults (≥18 years; n = 850). Oral sex practices were recorded for patients 18–45 years-old (n = 514). Data analysis included chi-square and multiple logistic regression analyses. Of the study population, 55.2% (n = 468) were female, 88% (n = 748) self-identified as black Africans and 45.1% (n = 383) were unemployed. Furthermore, 19.7% (n = 167), 6.4% (n = 54) and 2.1% (n = 18) were current smokers, snuff users and betel nut users, respectively. Out of the 514 who answered the questionnaire in relation to OS, 22.8% (n = 115) reported to practice it. Oral sex practice in the age group 18–45 years was most common among the self-identified white participants (41.9%); and among tobacco users than among non-tobacco users (30.9% vs. 20.5%; p = 0.022). A multivariable-adjusted regression model showed that white South Africans were more likely to use tobacco than black Africans (OR = 5.25; 95% CI = 2.21–12.47). The practice of OS was more likely among those 18–35 years-old (OR = 1.67; 95% CI = 1.01–2.74), but had no significant association with tobacco use (OR = 1.06; 95% CI = 0.62–1.83). The observed age and ethnic differences in both risk behaviours suggest a need for targeted population intervention in order to reduce the risk for oral HPV infection.

Results from the Generalized Linear Mixed Effects Model (GLMM) (with year) showing the likelihood of white sharks being at the Island versus Inshore. (DOCX)