14 datasets found
  1. m

    Milwaukee County Workforce Demographics 07/12/2023

    • data.county.milwaukee.gov
    Updated Aug 1, 2023
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    Milwaukee County Workforce Demographics 07/12/2023 [Dataset]. https://data.county.milwaukee.gov/items/2e8299ee7dc3486581b5a1ad6d56680c
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    Dataset updated
    Aug 1, 2023
    Dataset authored and provided by
    Milwaukee County GIS & Land Information
    Area covered
    Milwaukee County
    Description

    Data updated quarterly.

    Data Attributes and Definitions - 
    -  Department: The department the employee works in.
    -  Department ID: The numeric identifier for the department (typically 4
    

    digits). - Job: The name for the job assigned to the employee. - Category: Grouping of employees in similar jobs/leadership roles. - Sub Category: Secondary grouping of employees within a category. - Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).

    -  Gender: Designates the employee's
    

    gender (self-identified). - Age: The chronological number (age) assigned to the employee based on date of birth. - Age Group: Grouping of employees having approximately the same age or age range. - Original Hire Date: Date upon which the employee was originally hired.

    -  Last Hire Date: Date upon which an
    

    employee was hired; may be a rehire date. - Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.) - Data As of: The date to which the given data applies to.

  2. U.S. median household income 2023, by race and ethnicity

    • statista.com
    Updated Sep 16, 2024
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    Statista (2024). U.S. median household income 2023, by race and ethnicity [Dataset]. https://www.statista.com/statistics/233324/median-household-income-in-the-united-states-by-race-or-ethnic-group/
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    Dataset updated
    Sep 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, the gross median household income for Asian households in the United States stood at 112,800 U.S. dollars. Median household income in the United States, of all racial and ethnic groups, came out to 80,610 U.S. dollars in 2023. Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, Black, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. with 8.6 percent unemployed, according to the Bureau of Labor Statistics in 2021. Hispanic individuals (of any race) were most likely to go without health insurance as of 2021, with 22.8 percent uninsured.

  3. Foster care in the U.S. - number of children 2021, by race/ethnicity

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). Foster care in the U.S. - number of children 2021, by race/ethnicity [Dataset]. https://www.statista.com/statistics/255404/number-of-children-in-foster-care-in-the-united-states-by-race-ethnicity/
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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 30, 2021
    Area covered
    United States
    Description

    In 2021, there were 168,063 white children in foster care in the United States. This is compared to 86,645 Black or African American children and 85,215 Hispanic children who were in foster care.

    Foster care in the United States

    Foster care is where minors are taken care of in different institutions, such as a group home or private home of a caregiver certified by the state (called a foster parent). The procedure for becoming a foster parent in the United States varies from state to state. It is up to the state to determine the process; however it is overseen by the Department of Child Protective Services. It is sometimes seen as a precursor to adoption, which is different from fostering a child. There are many barriers to fostering and adopting children, such as high costs and long wait times, which can discourage people from doing it.

    Who are foster children?

    The number of children in foster care in the United States has decreased slightly since 2011. When looked at by age, most of the children in foster care in 2020 were one year old, and slightly more male children were in foster care than female children. Most of the children in foster care were placed into non-relative foster family homes, and in most cases, the primary goal of foster care is to reunify children with their parents or primary caregivers.

  4. m

    Milwaukee County Workforce Demographics 04/10/2024

    • data.county.milwaukee.gov
    Updated Apr 26, 2024
    + more versions
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    Milwaukee County Workforce Demographics 04/10/2024 [Dataset]. https://data.county.milwaukee.gov/datasets/93b1081bc09241e797eb6899fd7f7246
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    Dataset updated
    Apr 26, 2024
    Dataset authored and provided by
    Milwaukee County GIS & Land Information
    Area covered
    Milwaukee County
    Description

    Data updated quarterly.Data Attributes and Definitions -- Department: The department the employee works in.- Department ID: The numeric identifier for the department (typically 4 digits).- Job: The name for the job assigned to the employee.- Category: Grouping of employees in similar jobs/leadership roles.- Sub Category: Secondary grouping of employees within a category.- Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).- Gender: Designates the employee's gender (self-identified).- Age: The chronological number (age) assigned to the employee based on date of birth.- Age Group: Grouping of employees having approximately the same age or age range.- Original Hire Date: Date upon which the employee was originally hired.- Last Hire Date: Date upon which an employee was hired; may be a rehire date.- Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.)- Data As of: The date to which the given data applies to.

  5. m

    Milwaukee County Workforce Demographics 04/12/2023

    • data.county.milwaukee.gov
    Updated Jun 15, 2023
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    Milwaukee County GIS & Land Information (2023). Milwaukee County Workforce Demographics 04/12/2023 [Dataset]. https://data.county.milwaukee.gov/datasets/milwaukee-county-workforce-demographics-04-12-2023/about
    Explore at:
    Dataset updated
    Jun 15, 2023
    Dataset authored and provided by
    Milwaukee County GIS & Land Information
    Area covered
    Milwaukee County
    Description

    Data updated quarterly.Data Attributes and Definitions -- Department: The department the employee works in.- Department ID: The numeric identifier for the department (typically 4 digits).- Job: The name for the job assigned to the employee.- Category: Grouping of employees in similar jobs/leadership roles.- Sub Category: Secondary grouping of employees within a category.- Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).- Gender: Designates the employee's gender (self-identified).- Age: The chronological number (age) assigned to the employee based on date of birth.- Age Group: Grouping of employees having approximately the same age or age range.- Original Hire Date: Date upon which the employee was originally hired.- Last Hire Date: Date upon which an employee was hired; may be a rehire date.- Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.)- Data As of: The date to which the given data applies to.

  6. Breakdown of population in Malaysia 2019-2024, by ethnicity

    • statista.com
    Updated Aug 22, 2024
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    Statista (2024). Breakdown of population in Malaysia 2019-2024, by ethnicity [Dataset]. https://www.statista.com/statistics/1017372/malaysia-breakdown-of-population-by-ethnicity/
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    Dataset updated
    Aug 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Malaysia
    Description

    As of July 2024, 70.4 percent of the Malaysian population were classified as Bumiputera, 22.4 percent were classified as ethnic Chinese, and 6.5 percent as ethnic Indians. Those who do not fall under these three main ethnic groups are classified as ‘Other’. Malaysia is a multi-ethnic and multi-religious society with three main ethnicities and language groups. Who are Malaysia’s Bumiputera? Bumiputera, meaning sons of the soil, is a term used to categorize the Malays, as well as the indigenous peoples of Peninsular Malaysia, also known as orang asli, and the indigenous peoples of Sabah and Sarawak. As of July 2023, the Bumiputera share of the population in Sabah was 89 percent, while that in Sarawak was 76.1 percent. Thus, the incorporation of the states of Sabah and Sarawak during the formation of Malaysia ensured that the ethnic Malays were able to maintain a majority share of the Malaysian population. Bumiputera privileges and ethnic-based politics The rights and privileges of the Malays and the natives of Sabah and Sarawak are enshrined in Article 153 of Malaysia’s constitution. This translated, in practice, to a policy of affirmative action to improve the economic situation of this particular group, through the New Economic Policy introduced in 1971. 50 years on, it is questionable whether the policy has achieved its aim. Bumiputeras still lag behind the other ethnic two major groups in terms of monthly household income. However, re-thinking this policy will certainly be met by opposition from those who have benefitted from it.

  7. 2014 Economic Surveys: SE1400CSCB21 | Statistics for U.S. Employer Firms...

    • data.census.gov
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    ECN, 2014 Economic Surveys: SE1400CSCB21 | Statistics for U.S. Employer Firms That Totally or Partly Paid Employee Benefits by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014 (ECNSVY Annual Survey of Entrepreneurs Annual Survey of Entrepreneurs Characteristics of Businesses) [Dataset]. https://data.census.gov/table/ASECB2014.SE1400CSCB21?q=Ben%20Hugo%20MD
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ECN
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2014
    Area covered
    United States
    Description

    Release Date: 2016-09-23..Table Name. . Statistics for U.S. Employer Firms That Totally or Partly Paid Employee Benefits by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014. ..Release Schedule. . This file was released in September 2016.. ..Key Table Information. . These data are related to all other 2014 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2014 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2014 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The top fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms That Totally or Partly Paid Employee Benefits by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Employee benefits that were totally or partly paid by the business in 2014. . All firms. Health insurance. Contributions to retirement plans, including 401(k), Keogh, etc.. Profit sharing and/or stock options. Paid holidays, vacation, and/or sick leave. Tuition assistance and/or reimbursement. None of the above. Total reporting. Item not reported. . . . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (ASECB). Years in business (YIBSZFI). Employee benefits that were totally or partly paid by the business in 2014 (BENEFITS). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1400CSCB21 table at: https://www2.census.gov/programs-surveys/ase/data/2014/SE1400CSCB21.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at https://www.census.gov/programs-surveys/ase.html.. Email general, nonsecure, and unencrypted...

  8. Total population of South Africa 2022, by ethnic groups

    • statista.com
    Updated Jun 30, 2024
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    Total population of South Africa 2022, by ethnic groups [Dataset]. https://www.statista.com/statistics/1116076/total-population-of-south-africa-by-population-group/
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    Dataset updated
    Jun 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    South Africa
    Description

    As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

    Increase in number of households

    The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

    Main sources of income

    The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

  9. Z

    Dataset for meta-analysis "The motherhood penalty's size and factors"

    • data.niaid.nih.gov
    • zenodo.org
    Updated Sep 16, 2024
    + more versions
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    Kalabikhina, Irina (2024). Dataset for meta-analysis "The motherhood penalty's size and factors" [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_13710304
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    Dataset updated
    Sep 16, 2024
    Dataset provided by
    Kalabikhina, Irina
    Zhuravleva, Sofiia
    Kuznetsova, Polina
    License

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

    Description

    PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

    Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

    The data is presented in WORD format.

    Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

    The data is presented in EXCEL format.

    Description of table headers:

    estimate_number - Number of the estimate

    paper_number - Number of the paper

    paper_name - Paper (year and first author)

    paper_excluded - Paper was excluded from the final sample

    survey - Data source

    table_in_paper - Number of the table with the regression results in the paper

    coeff - Regression coefficient for parenthood variable (estimate)

    se - SE of the estimate

    t - t-value of the estimate

    ols - Estimate is obtained using the OLS method

    fixed_effects - Estimate is obtained using the fixed effects method

    panel - Model considers panel data (for several years)

    quintile - Estimate is obtained using the quintile regression method

    other - Estimate is obtained using other methods

    selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

    hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

    annual_earnings - Annual earnings are considered in the model

    monthly_wage - Monthly wage is considered in the model

    daily_wage - Daily wage is considered in the model

    hourly_wage - Hourly wage is considered in the model

    min_age_kid - Child's age (minimum)

    max_age_kid - Child's age (maximum)

    motherhood - Model uses a dummy variable of the presence of children

    num_kids - Model uses a variable of the number of children

    kid1 - Model uses a variable of the presence of one child

    kid2p - Model uses a variable of the presence of two or more children

    kid2 - Model uses a variable of the presence of two children

    kid3p - Model uses a variable of the presence of three or more children

    kid3 - Model uses a variable of the presence of three children

    kid4p - Model uses a variable of the presence of three or more children

    race/nationality - Model includes a race/ethnicity variable

    age - Model includes the age variable

    marstat - Model includes the marital status variable

    oth_char_hh - Model includes any other variables of other household characteristics

    settl_type - Model includes a variable of the type of settlement (urban, rural)

    region - Model includes a variable of the region of the country

    education - Model includes information on the level of education

    experience - Model includes a variable of work experience

    pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

    tenure - Model includes a variable of the duration of employment at the current job

    interruptions - Model includes a variable of employment interruptions (related to motherhood)

    occupation - Model includes an occupation variable

    industry - Model includes a variable of the industry of employment

    union - Model includes a variable of trade union membership

    friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

    hours - Model includes a variable of the number of hours worked

    sector - Model includes a variable of the type of employer ownership (public or private)

    informal - Model includes a variable of informal employment

    size_ent - Model includes a variable of the employer size

    min_age_woman - Woman's age (minimum)

    max_age_woman - Woman's age (maximum)

    mean_age_woman - Woman's age (mean)

    restricted - Sample is limited

    private - Model considers only private sector employees

    state - Model considers only public sector employees

    full_time - Model considers only full-time workers

    part_time - Model considers only part-time workers

    better_educated - Model considers only women with a high level of education

    lower_educated - Model considers only women with a low level of education

    married - Model includes only married women

    single - Model includes only single women

    natives - Model includes only native women (born in the country)

    immigrants - Model includes only immigrant women (born abroad)

    race - Model includes only women of a particular race

    min_year - Time period (minimum year)

    max_year - Time period (maximum year)

    journal - Type of publication

    usa - Sample includes women from the USA

    western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

    north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

    south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

    east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

    china - Sample includes women from China

    Russia - Sample includes women from Russia

    others - Sample includes women from other countries

    country - Country name

  10. s

    Household income

    • ethnicity-facts-figures.service.gov.uk
    csv
    Updated Sep 5, 2022
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    Race Disparity Unit (2022). Household income [Dataset]. https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/pay-and-income/household-income/latest
    Explore at:
    csv(261 KB)Available download formats
    Dataset updated
    Sep 5, 2022
    Dataset authored and provided by
    Race Disparity Unit
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    In the 3 years to March 2021, black households were most likely out of all ethnic groups to have a weekly income of under £600.

  11. Transportation Dataset

    • kaggle.com
    Updated Oct 2, 2023
    + more versions
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    Amit Zala (2023). Transportation Dataset [Dataset]. https://www.kaggle.com/datasets/amitzala/transportation-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 2, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Amit Zala
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

    SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

    ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
    Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

  12. Number of lynchings in the U.S. by state and race 1882-1968

    • statista.com
    Updated Aug 12, 2024
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    Statista (2024). Number of lynchings in the U.S. by state and race 1882-1968 [Dataset]. https://www.statista.com/statistics/1175147/lynching-by-race-state-and-race/
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    Dataset updated
    Aug 12, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Lynching in the United States is estimated to have claimed over 4.7 thousand lives between 1882 and 1968, and just under 3.5 thousand of these victims were black. Today, lynching is more commonly associated with racial oppression, particularly in the south, however, in early years, victims were more commonly white (specifically Mexican), and lynchings were more frequent in western territories and along the southern border. It was only after Reconstruction's end where the lynching of black people became more prevalent, and was arguably the most violent tool of oppression used by white supremacists. Nationwide, the share of the population who was black fluctuated between 10 and 13 percent in the years shown here, however the share of lynching victims who were black was almost 73 percent. North-south divide Of the 4.7 thousand victims of lynching between 1882 and 1968, over 3.5 thousand of these were killed in former-Confederate states. Of the fourteen states where the highest number of lynching victims were killed, eleven were former-Confederate states, and all saw the deaths of at least one hundred people due to lynching. Mississippi was the state where most people were lynched in these years, with an estimated 581 victims, 93 percent of whom were black. Georgia saw the second most lynchings, with 531 in total, and the share of black victims was also 93 percent. Compared to the nationwide average of 73 percent, the share of black victims in former-Confederate states was 86 percent. Texas was the only former-Confederate state where this share (71 percent) was below the national average, due to the large number of Mexicans who were lynched there. Outside of the south Of the non-Confederate state with the highest number of lynching victims, most either bordered the former-Confederate states, or were to the west. Generally speaking, the share of white victims in these states was often higher than in the south, meaning that the majority took place in the earlier years represented here; something often attributed to the lack of an established judiciary system in rural regions, and the demand for a speedy resolution. However, there are many reports of black people being lynched in the former border states in the early-20th century, as they made their way northward during the Great Migration. Between 1882 and 1968, lynchings were rare in the Northeast, although Connecticut, Massachusetts, New Hampshire and Rhode Island were the only states** without any recorded lynchings in these years.

  13. Foreign population Spain 2023, by nationality

    • statista.com
    Updated Jan 22, 2025
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    Statista (2025). Foreign population Spain 2023, by nationality [Dataset]. https://www.statista.com/statistics/445784/foreign-population-in-spain-by-nationality/
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    Dataset updated
    Jan 22, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2023
    Area covered
    Spain
    Description

    As recorded by the source, Moroccans ranked as the foreign nationality with more residents in Spain in 2023, closely followed by Romanians. After years of losing its foreign population, Spain’s immigration figures started to pick up in 2015, with the number of people that moved to the Mediterranean country surpassing the number of foreigners that decided to leave.

    A matter of balance The net migration rate of Spain changed its course mainly due to the great inflow of foreigners that move to reside in the Mediterranean country. Spain’s immigration flow slowed down after the 2008 financial crisis, albeit the number of foreigners that opted to change their residence saw a significant growth in the last years. In 2022, Colombians ranked first as the foreign nationality that most relocated to Spain, distantly followed by Moroccans and Ukranians.

    Spain does not have the highest number of immigrants in Europe In recent years, the European Union confronted a rising number of refugees arriving from the Middle East. Migration figures show that Germany accommodated approximately 15 million foreign-born citizens, ranking it as the country that most hosted immigrants in Europe in 2022. By comparison, Spain’s foreign population stood slightly over seven million, positioning the Western Mediterranean country third on the European list of foreign-born population. Unfortunately, thousands of persons have died ore gone missing trying to reach Spanish territory, as more and more irregular migrants opt to use dangerous maritime routes to arrive at Southern Europe from Africa's coasts.

  14. Wealth of billionaires around the world by region 2023

    • statista.com
    • flwrdeptvarieties.store
    Updated Mar 21, 2025
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    Einar H. Dyvik (2025). Wealth of billionaires around the world by region 2023 [Dataset]. https://www.statista.com/topics/2229/billionaires-around-the-world/
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    Dataset updated
    Mar 21, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Einar H. Dyvik
    Area covered
    World
    Description

    In 2023, the highest total wealth owned by the world's billionaires was found in North America, reaching five trillion U.S. dollars. This comes as no surprise as North America also is the world region with the highest number of billionaires. Europe was the region where the second largest amount of wealth was found in 2023.

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Milwaukee County Workforce Demographics 07/12/2023 [Dataset]. https://data.county.milwaukee.gov/items/2e8299ee7dc3486581b5a1ad6d56680c

Milwaukee County Workforce Demographics 07/12/2023

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Dataset updated
Aug 1, 2023
Dataset authored and provided by
Milwaukee County GIS & Land Information
Area covered
Milwaukee County
Description

Data updated quarterly.

Data Attributes and Definitions - 
-  Department: The department the employee works in.
-  Department ID: The numeric identifier for the department (typically 4

digits). - Job: The name for the job assigned to the employee. - Category: Grouping of employees in similar jobs/leadership roles. - Sub Category: Secondary grouping of employees within a category. - Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).

-  Gender: Designates the employee's

gender (self-identified). - Age: The chronological number (age) assigned to the employee based on date of birth. - Age Group: Grouping of employees having approximately the same age or age range. - Original Hire Date: Date upon which the employee was originally hired.

-  Last Hire Date: Date upon which an

employee was hired; may be a rehire date. - Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.) - Data As of: The date to which the given data applies to.

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