The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

Example of assigning ethnic class using Ethnicity Estimator.

This layer shows Race and Ethnicity. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of population that are Hispanic or Latino (of any race). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B02001, B03001, DP05Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined differences in youth's mental health and substance abuse needs in seven different racial/ethnic groups of justice-involved youth. Using de-identified data from the Survey of Youth in Residential Placement (SYRP), it was assessed whether differences in mental health and substance abuse needs and services existed in a racially/ethnically diverse sample of youth in custody. Data came from a nationally representative sample of 7,073 youth in residential placements across 36 states, representing five program types. An examination of the extent to which there were racial/ethnic disparities in the delivery of services in relation to need was also conducted. This examination included assessing the differences in substance-related problems, availability of substance services, and receipt of substance-specific counseling. One SAS data file (syrp2017.sas7bdat) is included as part of this collection and has 138 variables for 7073 cases, with demographic variables on youth age, sex, race and ethnicity. Also included as part of the data collection are two SAS Program (syntax) files for use in secondary analysis of youth mental health and substance use.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population by race/ethnicity and change data by county in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameTotPop_e# Total population, 2017TotPop_m# Total population, 2017 (MOE)Hisp_e# Hispanic or Latino (of any race), 2017Hisp_m# Hispanic or Latino (of any race), 2017 (MOE)pHisp_e% Hispanic or Latino (of any race), 2017pHisp_m% Hispanic or Latino (of any race), 2017 (MOE)Not_Hisp_e# Not Hispanic or Latino, 2017Not_Hisp_m# Not Hispanic or Latino, 2017 (MOE)pNot_Hisp_e% Not Hispanic or Latino, 2017pNot_Hisp_m% Not Hispanic or Latino, 2017 (MOE)NHWhite_e# Not Hispanic, White alone, 2017NHWhite_m# Not Hispanic, White alone, 2017 (MOE)pNHWhite_e% Not Hispanic, White alone, 2017pNHWhite_m% Not Hispanic, White alone, 2017 (MOE)NHBlack_e# Not Hispanic, Black or African American alone, 2017NHBlack_m# Not Hispanic, Black or African American alone, 2017 (MOE)pNHBlack_e% Not Hispanic, Black or African American alone, 2017pNHBlack_m% Not Hispanic, Black or African American alone, 2017 (MOE)NH_AmInd_e# Not Hispanic, American Indian and Alaska Native alone, 2017NH_AmInd_m# Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)pNH_AmInd_e% Not Hispanic, American Indian and Alaska Native alone, 2017pNH_AmInd_m% Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)NH_Asian_e# Not Hispanic, Asian alone, 2017NH_Asian_m# Not Hispanic, Asian alone, 2017 (MOE)pNH_Asian_e% Not Hispanic, Asian alone, 2017pNH_Asian_m% Not Hispanic, Asian alone, 2017 (MOE)NH_PacIsl_e# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017NH_PacIsl_m# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)pNH_PacIsl_e% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017pNH_PacIsl_m% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)NH_OthRace_e# Not Hispanic, some other race alone, 2017NH_OthRace_m# Not Hispanic, some other race alone, 2017 (MOE)pNH_OthRace_e% Not Hispanic, some other race alone, 2017pNH_OthRace_m% Not Hispanic, some other race alone, 2017 (MOE)NH_TwoRace_e# Not Hispanic, two or more races, 2017NH_TwoRace_m# Not Hispanic, two or more races, 2017 (MOE)pNH_TwoRace_e% Not Hispanic, two or more races, 2017pNH_TwoRace_m% Not Hispanic, two or more races, 2017 (MOE)NH_AsianPI_e# Non-Hispanic Asian or Pacific Islander, 2017NH_AsianPI_m# Non-Hispanic Asian or Pacific Islander, 2017 (MOE)pNH_AsianPI_e% Non-Hispanic Asian or Pacific Islander, 2017pNH_AsianPI_m% Non-Hispanic Asian or Pacific Islander, 2017 (MOE)NH_Other_e# Non-Hispanic other (Native American, other one race, two or more races), 2017NH_Other_m# Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)pNH_Other_e% Non-Hispanic other (Native American, other one race, two or more races), 2017pNH_Other_m% Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

Despite comprising of a smaller share of the U.S. population than African Americans or Hispanics, the most represented non-white U.S. CEOs were of an Asian background. They made up 55 percent of CEO positions at Fortune 500 and S&P 500 companies in 2024. By comparison, 11 percent of CEOs at the time were African American. The rise of environmental, social, and corporate governance (ESG) Investments in ESG have risen dramatically over last few years. In November 2023 there were approximately 480 billion U.S. dollars in ESG ETF assets worldwide, compared to 16 billion U.S. dollars in 2015. ESG measures were put in place to encourage companies to act responsibly, with the leading reason for ESG investing stated to be brand and reputation according to managers and asset owners. Gender diversity With the general acceptance of ESG in larger companies, there has still been a significant employment gap of women working in senior positions. For example, the share of women working as a partner or principal at EY, one of the largest accounting firms in the world, was just only 28 percent in 2023.

This dataset includes all personal names listed in the Wikipedia category “American people by ethnic or national origin” and all subcategories fitting the pattern “American People of [ ] descent”, in total more than 25,000 individuals. Each individual is represented by a row, with columns indicating binary membership (0/1) in each ethnic/national category.

Ethnicity inference is an essential tool for identifying disparities in public health and social sciences. Existing datasets linking personal names to ethnic or national origin often neglect to recognize multi-ethnic or multi-national identities. Furthermore, existing datasets use coarse classification schemes (e.g. classifying both Indian and Japanese people as “Asian”) that may not be suitable for many research questions. This dataset remedies these problems by including both very fine-grain ethnic/national categories (e.g. Afghan-Jewish) and more broad ones (e.g. European). Users can chose the categories that are relevant to their research. Since many Americans on Wikipedia are associated with multiple overlapping or distinct ethnicities/nationalities, these multi-ethnic associations are also reflected in the data.

Data were obtained from the Wikipedia API and reviewed manually to remove stage names, pen names, mononyms, first initials (when full names are available on Wikipedia), nicknames, honorific titles, and pages that correspond to a group or event rather than an individual.

This dataset was designed for use in training classification algorithms, but may also be independently interesting inasmuch as it is a representative sample of Americans who are famous enough to have their own Wikipedia page, along with detailed information on their ethnic/national origins.

DISCLAIMER: Due to the incomplete nature of Wikipedia, data may not properly reflect all ethnic national associations for any given individual. For example, there is no guarantee that a given Cuban Jewish person will be listed in both the “American People of Cuban descent” and the “American People of Jewish descent” categories.

CalEnviroScreen scores represent a combined measure of pollution and the potential vulnerability of a population to the effects of pollution. Like the previous versions, CalEnviroScreen 4.0 does not include indicators of race/ethnicity or age. However, the distribution of the CalEnviroScreen 4.0 cumulative impact scores by race or ethnicity is important. This information can be used to better understand issues related to environmental justice and racial equity in California. CalEPAs racial equity team has released a StoryMap using CalEnviroScreen 3.0 data that examines the connection between racist land use practices of the 1930s and the persistence of environmental injustice. The CalEPA StoryMap, along with this analysis, are examples of information that can be used to better understand issues related to environmental justice and racial equity in California.

To assess differences in psychological outcomes as well as risk and protective factors for these outcomes among several USA ethnic groups and identify correlates of these psychological outcomes among adults with diabetes in the second Diabetes Attitudes, Wishes and Needs (DAWN2) study. The core USA DAWN2 sample was supplemented by independent samples of specific ethnic minority groups, yielding a total of 447 White non-Hispanics, 241 African Americans, 194 Hispanics, and 173 Chinese Americans (n = 1055). Multivariate analysis examined ethnic differences in psychological outcomes and risk/protective factors (disease, demographic and socioeconomic factors, health status and healthcare access/utilization, subjective burden of diabetes and social support/burden). Separate analyses were performed on each group to determine whether risk/protective factors differed across ethnic groups. Psychological outcomes include well-being, quality of life, impact of diabetes on life domains, diabetes distress, and diabetes empowerment. NCT01507116. Ethnic minorities tended to have better psychological outcomes than White non-Hispanics, although their diabetes distress was higher. Levels of most risk and protective factors differed significantly across ethnic groups; adjustment for these factors reduced ethnic group differences in psychological outcomes. Health status and modifiable diabetes-specific risk/protective factors (healthcare access/utilization, subjective diabetes burden, social support/burden) had strong associations with psychological outcomes, especially diabetes distress and empowerment. Numerous interactions between ethnicity and other correlates of psychological outcomes suggest that ethnic groups are differentially sensitive to various risk/protective factors. Potential limitations are the sample sizes and representativeness. Ethnic groups differ in their psychological outcomes. The risk/protective factors for psychological outcomes differ across ethnic groups and different ethnic groups are more/less sensitive to their influence. These findings can aid the development of strategies to overcome the most prominent and influential psychosocial barriers to optimal diabetes care within each ethnic group.

Characteristics of the sample by race/ethnicity groups and physical activity levels.

Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.

This statistic shows the shares of food-secure and food-insecure individuals in the United States in 2023 by race and ethnicity. At that time, **** percent of people living in the United States lived in food-secure households. However, figures revealed disparities by race and ethnicity. For example, during that year, the shares of Black non-Hispanic and Hispanic individuals living in low food security households were ***** times higher than those of White non-Hispanics. Furthermore, ***** percent of Black non-Hispanic individuals lived in homes with very low food security, the highest share across all races and ethnicities.

Summary File 4 is repeated or iterated for the total population and 335 additional population groups: 132 race groups,78 American Indian and Alaska Native tribe categories, 39 Hispanic or Latino groups, and 86 ancestry groups.Tables for any population group excluded from SF 2 because the group's total population in a specific geographic area did not meet the SF 2 threshold of 100 people are excluded from SF 4. Tables in SF 4 shown for any of the above population groups will only be shown if there are at least 50 unweighted sample cases in a specific geographic area. The same 50 unweighted sample cases also applied to ancestry iterations. In an iterated file such as SF 4, the universes households, families, and occupied housing units are classified by the race or ethnic group of the householder. The universe subfamilies is classified by the race or ethnic group of the reference person for the subfamily. In a husband/wife subfamily, the reference person is the husband; in a parent/child subfamily, the reference person is always the parent. The universes population in households, population in families, and population in subfamilies are classified by the race or ethnic group of the inidviduals within the household, family, or subfamily without regard to the race or ethnicity of the householder. Notes follow selected tables to make the classification of the universe clear. In any population table where there is no note, the universe classification is always based on the race or ethnicity of the person. In all housing tables, the universe classification is based on the race or ethnicity of the householder.

Dataset population: Households

Multiple ethnic group households

Multiple ethnic groups classifies households by the diversity in ethnic group of household members in different relationships.

For example, different ethnic groups between generations or within partnerships.

For Northern Ireland only, 'Same ethnic group' means within the same ethnic group as defined by the 12-way ethnic classification (White, Chinese, Irish Traveller, Indian, Pakistani, Bangladeshi, Other Asian, Black Caribbean, Black African, Black Other, Mixed, Other).

'Different ethnic groups within partnerships' includes all households where there are different ethnic groups within partnerships whether or not there are also different ethnic groups between generations.

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.

We provide datasets that that estimate the racial distributions associated with first, middle, and last names in the United States. The datasets cover five racial categories: White, Black, Hispanic, Asian, and Other. The provided data are computed from the voter files of six Southern states -- Alabama, Florida, Georgia, Louisiana, North Carolina, and South Carolina -- that collect race and ethnicity data upon registration. We include seven voter files per state, sourced between 2018 and 2021 from L2, Inc. Together, these states have approximately 36MM individuals who provide self-reported race and ethnicity. The last name datasets includes 338K surnames, while the middle name dictionaries contains 126K middle names and the first name datasets includes 136K first names. For each type of name, we provide a dataset of P(race | name) probabilities and P(name | race) probabilities. We include only names that appear at least 25 times across the 42 (= 7 voter files * 6 states) voter files in our dataset. These data are closely related to the the dataset: "Name Dictionaries for "wru" R Package", https://doi.org/10.7910/DVN/7TRYAC. These are the probabilities used in the latest iteration of the "WRU" package (Khanna et al., 2022) to make probabilistic predictions about the race of individuals, given their names and geolocations.