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

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

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%

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%

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.

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 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 so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the "independent states" of Bophuthatswana, Transkei, and Venda. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

Households and individuals

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

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 employed as domestics (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)

In 1913, South Africa’s Land Act set aside 87% of the country’s land for exclusive use and ownership by white people, helping to divide the nation into a relatively prosperous white heartland and a cluster of increasingly impoverished black reserves on the periphery and within cities (Walker, 2017). More than a century later, South Africa is still struggling to redress this historical injustice and the inequality it continues to foster. In December 2017, the ruling African National Congress (ANC) resolved to move ahead with land expropriation without compensation to speed up its land-reform program, although a multitude of policy details remain hotly contested (Grootes, 2018). The National Assembly has adopted a motion by the opposition Economic Freedom Fighters (EFF) endorsing this approach despite warnings of economic meltdown from the opposition Democratic Alliance (DA) (Goba, 2018). While the public debate rages on, Parliament’s Joint Constitutional Review Committee has been reviewing comments and submissions on the issue and is expected to announce its recommendation in November regarding a possible constitutional amendment (Parliamentary Monitoring Group, 2018). How do average citizens view land reform? Findings from the 2018 Afrobarometer survey show that South Africans want the government to prioritize redistributing land taken during forced removals of Black South Africans half a century ago, followed by agricultural land and vacant land in cities. But the survey also shows majority support for the government’s “willing seller-willing buyer” policy and for the right of farmers to retain ownership of land tenanted by laborers.

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.

HLA Class II Haplotype Frequency Distributions (for 99% haplotypes per population) and HLA Class II Simulated Populations (Genotype level information for sample sizes of 1000, 5000, 10000 simulated individuals) for 4 broad and 21 detailed US population groups.

Broad population groups: African Americans (AFA), Asian and Pacific Islanders (API), Caucasians (CAU), Hispanics (HIS).

Detailed population groups: African American (AAFA), African (AFB), South Asian Indian (AINDI), American Indian - South or Central American (AISC), Alaska native of Aleut (ALANAM), North American Indian (AMIND), Caribbean Black (CARB), Caribbean Hispanic (CARHIS), Caribbean Indian (CARIBI), European Caucasian (EURCAU), Filipino (FILII), Hawaiian or other Pacific Islander (HAWI), Japanese (JAPI), Korean (KORI), Middle Eastern or North Coast of Africa (MENAFC), Mexican or Chicano (MSWHIS), Chinese (NCHI), Hispanic - South or Central American (SCAHIS), Black - South or Central American (SCAMB), Southeast Asian (SCSEAI), Vietnamese (VIET).

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.

This dataset results from an anthropological project investigating how will-making and the formal processes of inheritance shape the passing on of property and the making of socio-economic class in Johannesburg, South Africa. While the number of people making wills is rising, and will-making is a key focus of attempts to shape citizens as legally aware individual decision-makers, most people die intestate. Appeals to state processes have popular appeal as ways to seek official protection, despite popular awareness of limited state capacity. Family dynamics are often better enforced than the law. In post-apartheid South Africa, ending segregation meant including everyone in the same legal code, but this often enshrined the norms of the white elite. Intestate succession is seen as profound injustice because it prioritises nuclear family over kin group, and asset over patrimony, even as custom norms are often used to justify male control and marginalise widows. This is made more complicated by patchy regulation and enforcement. People’s unequal abilities to navigate the system, and even manipulate it, become central determinants of who benefits and whose version of kinship is counted. I conducted extensive ethnographic research within this system, shadowing officials and other expert practitioners; sitting in on legal advice consultations; attending court hearings; interviewing state and civil-society employees, as well people encountering the system as members of the public. This was complemented by archival research to enable the analysis of information in deceased estates files across time. The dataset consists of 1) anonymized example case studies from key Johannesburg Institutions – the Master’s Office (where deceased estates are processed), the High Court, the Magistrate’s Court, legal clinics – and from interviews with practitioners and members of the public; 2) an Excel database aggregating information about inheritance from around 500 deceased estates files over Johannesburg's history, along with an illustrative example of a deceased estates file and a document showing and explaining features of the original MS Access database.

Since the end of apartheid, South Africa's black middle class has grown exponentially, as a new stratum of black citizens has moved into government and corporate employment. As more South Africans accumulate substantial property, its disbursement becomes a new terrain on which battles of kinship obligation are fought. This project approaches class reproduction through an ethnographic focus on wills and testaments: the processes through which they are made, and the disputes surrounding their execution. The result is an innovative lens that attends to the role of experts and bureaucrats in shaping the dynamics of class. It extends my interest in class reproduction, explored in my forthcoming book (CUP 2015) based on fieldwork in South Africa since 2006. In South Africa, as the post-apartheid black middle class ages and considers family futures, the project is especially timely. The project addresses key anthropological concerns. It combines political-economic (inequality) and cultural (lifestyle) perspectives on the middle class, and these with scholarship on state institutions. And it extends existing work on class and status reproduction by transcending generations. How do black middle-class South Africans pass on the property that shapes their kin's status and life chances? As will-making is promoted ever more widely, how do institutions that facilitate it inflect experiences of kinship and property before death forces the issue? How does this compare with the established white middle class? Within families, how are competing definitions of ownership, rights and entitlements judged? When expressions of future plans also become expressions of state regulation, how does this affect access to family property (e.g. township houses)? Who is included and excluded, in the bottleneck of bureaucracy and legal process? How and why are particular possessions valued? How are people's roles and entitlements constituted in the process? Amidst increasing inequality and precarity, how is will-making talked about? How have concerns about property and inheritance been reflected in the media? Given the South African black middle class's diverse history, how does will-making today compare with the past? Examining class reproduction over time means combining ethnographic and historical methods. For the former, I will begin with long-term observation in the Johannesburg High Court where disputes around wills are heard, and trace cases out from the formal probate process to fieldwork with individuals and families. Meanwhile, I will work with will consultants and lawyers, and interview judges, members of financial organisations, and the experts responsible for designing their online will templates. The former Dean of Law at Wits University (where I am a Research Associate) has expressed...