8 datasets found
  1. w

    Dataset of books about Black people-South Africa-Politics and government

    • workwithdata.com
    Updated Apr 17, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Work With Data (2025). Dataset of books about Black people-South Africa-Politics and government [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=j0-book_subject&fop0=%3D&fval0=Black+people-South+Africa-Politics+and+government&j=1&j0=book_subjects
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    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

    This dataset is about books. It has 26 rows and is filtered where the book subjects is Black people-South Africa-Politics and government. It features 9 columns including author, publication date, language, and book publisher.

  2. N

    South Gorin, MO median household income breakdown by race betwen 2011 and...

    • neilsberg.com
    csv, json
    Updated Jan 3, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2024). South Gorin, MO median household income breakdown by race betwen 2011 and 2021 [Dataset]. https://www.neilsberg.com/research/datasets/ce86f079-8924-11ee-9302-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 3, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Missouri, South Gorin
    Variables measured
    Median Household Income Trends for Asian Population, Median Household Income Trends for Black Population, Median Household Income Trends for White Population, Median Household Income Trends for Some other race Population, Median Household Income Trends for Two or more races Population, Median Household Income Trends for American Indian and Alaska Native Population, Median Household Income Trends for Native Hawaiian and Other Pacific Islander Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To portray the median household income within each racial category idetified by the US Census Bureau, we conducted an initial analysis and categorization of the data from 2011 to 2021. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). It is important to note that the median household income estimates exclusively represent the identified racial categories and do not incorporate any ethnicity classifications. Households are categorized, and median incomes are reported based on the self-identified race of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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

  3. w

    Dataset of books about Black people-South Africa-Economic conditions

    • workwithdata.com
    Updated Apr 17, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Work With Data (2025). Dataset of books about Black people-South Africa-Economic conditions [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=j0-book_subject&fop0=%3D&fval0=Black+people-South+Africa-Economic+conditions&j=1&j0=book_subjects
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    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

    This dataset is about books. It has 7 rows and is filtered where the book subjects is Black people-South Africa-Economic conditions. It features 9 columns including author, publication date, language, and book publisher.

  4. T

    South Africa Youth Unemployment Rate

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +15more
    csv, excel, json, xml
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TRADING ECONOMICS, South Africa Youth Unemployment Rate [Dataset]. https://tradingeconomics.com/south-africa/youth-unemployment-rate
    Explore at:
    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 - Mar 31, 2025
    Area covered
    South Africa
    Description

    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.

  5. d

    Loudoun County 2020 Census Population Patterns by Race and Hispanic or...

    • catalog.data.gov
    • data.virginia.gov
    • +1more
    Updated Jan 31, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Loudoun County GIS (2025). Loudoun County 2020 Census Population Patterns by Race and Hispanic or Latino Ethnicity [Dataset]. https://catalog.data.gov/dataset/loudoun-county-2020-census-population-patterns-by-race-and-hispanic-or-latino-ethnicity
    Explore at:
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Loudoun County GIS
    Area covered
    Loudoun County
    Description

    Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

  6. f

    Healthrise diabetes dataset noPII.

    • plos.figshare.com
    bin
    Updated Dec 11, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sanele Listen Mandlenkosi Madela; Nigel Walsh Harriman; Ronel Sewpaul; Anthony David Mbewu; David R Williams; Sibusiso Sifunda; Thabang Manyaapelo; Anam Nyembezi; Sasiragha Priscilla Reddy (2023). Healthrise diabetes dataset noPII. [Dataset]. http://doi.org/10.1371/journal.pone.0293250.s001
    Explore at:
    binAvailable download formats
    Dataset updated
    Dec 11, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sanele Listen Mandlenkosi Madela; Nigel Walsh Harriman; Ronel Sewpaul; Anthony David Mbewu; David R Williams; Sibusiso Sifunda; Thabang Manyaapelo; Anam Nyembezi; Sasiragha Priscilla Reddy
    License

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

    Description

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

  7. c

    The formal processes of inheritance in Johannesburg, South Africa 1900-2019

    • datacatalogue.cessda.eu
    Updated May 27, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bolt, M (2025). The formal processes of inheritance in Johannesburg, South Africa 1900-2019 [Dataset]. http://doi.org/10.5255/UKDA-SN-853806
    Explore at:
    Dataset updated
    May 27, 2025
    Dataset provided by
    University of Birmingham
    Authors
    Bolt, M
    Time period covered
    Jan 1, 2016 - Mar 11, 2019
    Area covered
    South Africa
    Variables measured
    Individual, Organization, Family, Household, Event/process, Text unit
    Measurement technique
    I conducted extensive ethnographic research within Johannesburg's inheritance system. This involved shadowing officials across different parts of the administration of deceased estates, and related expert practitioners. It involved sitting in on legal advice consultations held within on the site of government administration and in the offices of legal NGO ProBono.Org. It involved attending court hearings, in Johannesburg's the High Court and Magistrate's Court. It meant interviewing state and civil-society employees, as well people encountering the system as members of the public, both via processes of snowball sampling emerging out of research in the deceased estates process. Finally, with the benefit of ethnographic insight into the composition of deceased estates files, I conducted archival research to capture a large sample of files over Johannesburg's history and aggregate metrics such as regarding wealth accumulation, wealth transfer and kinship.
    Description

    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...

  8. Extreme poverty as share of global population in Africa 2025, by country

    • statista.com
    Updated Feb 3, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Extreme poverty as share of global population in Africa 2025, by country [Dataset]. https://www.statista.com/statistics/1228553/extreme-poverty-as-share-of-global-population-in-africa-by-country/
    Explore at:
    Dataset updated
    Feb 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Africa
    Description

    In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

  9. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Work With Data (2025). Dataset of books about Black people-South Africa-Politics and government [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=j0-book_subject&fop0=%3D&fval0=Black+people-South+Africa-Politics+and+government&j=1&j0=book_subjects

Dataset of books about Black people-South Africa-Politics and government

Explore at:
Dataset updated
Apr 17, 2025
Dataset authored and provided by
Work With Data
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

This dataset is about books. It has 26 rows and is filtered where the book subjects is Black people-South Africa-Politics and government. It features 9 columns including author, publication date, language, and book publisher.

Search
Clear search
Close search
Google apps
Main menu