This article uses a recent first name list to develop an improvement to an existing Bayesian classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race/ethnicity. The new Bayesian Improved First Name Surname Geocoding (BIFSG) method is validated using a large sample of mortgage applicants who self-report their race/ethnicity. BIFSG outperforms BISG, in terms of accuracy and coverage, for all major racial/ethnic categories. Although the overall magnitude of improvement is somewhat small, the largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. When estimating the race/ethnicity effects on mortgage pricing and underwriting decisions with regression models, estimation biases from both BIFSG and BISG are very small, with BIFSG generally having smaller biases, and the maximum a posteriori classifier resulting in smaller biases than through use of estimated probabilities. Robustness checks using voter registration data confirm BIFSG's improved performance vis-a-vis BISG and illustrate BIFSG's applicability to areas other than mortgage lending. Finally, I demonstrate an application of the BIFSG to the imputation of missing race/ethnicity in the Home Mortgage Disclosure Act data, and in the process, offer novel evidence that the incidence of missing race/ethnicity information is correlated with race/ethnicity.

Release Date: 2022-11-10.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY22-308)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2021 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2021 ABS collection year produces statistics for the 2020 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.The data include U.S. firms with paid employees operating during the reference year 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. Employer 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. and state totals for all sectors. Employment reflects the number of paid employees during the pay period in the reference year that included March 12...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Sales and receipts of employer firms (reported in $1,000s of dollars). Number of employees (during the March 12 pay period). Annual payroll (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female. . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority. Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran. Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for businesses owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subgroup total because a Hispanic or Latino firm may be of any race, and because a firm could be tabulated in more than one racial group. For example, if a firm responded as both Chinese and Black majority owned, the firm would be included in the detailed Asian and Black estimates but would only be counted once toward the higher level all firms' estimates.. References such as "Hispanic- or Latino-owned" businesses refer only to businesses operating in the 50 states and the District of Columbia that self-identified 51 percent or more of their ownership in 2020 to be by individuals of Mexican, Puerto Rican, Cuban or other Hispanic or Latino origin. The ABS does not distinguish between U.S. residents and nonresidents. Companies owned by foreign governments or owned by other companies, foreign or domestic, are included in the category "Unclassifiable."...Industry and Geography Coverage:..The data are shown for the total for all sectors (00) and 2-digit NAICS code levels for:..United States. States and the District of Columbia. Metropolitan Statistical Areas...Data are also shown for the 3- and 4-digit NAICS code for:..United States. States and the District of Columbia...For more information about NAICS, see NAICS Codes & Understanding Industry Classification Systems. For information about geographies used by economic programs at the Census Bureau, see Economic Census: Economic Geographies...Footnotes:.Footnote 660 - Agriculture, forestry, fishing and hunting (Sector 11): Crop and Animal Production (NAICS 111 and 112) are out of scope..Footnote 661 - Transportation and warehousing...

Estimating differences between racial/ethnic groups often requires merging demographic variables from one dataset to variables of interest in another. A common method merges Home Mortgage Disclosure Act data to property databases. One alternative is to acquire this information from voter registration files; another is to predict race with a name-based algorithm. Compared to Census data, which method is more representative varies by location and group. We explore the practical implications of each method by using the matched samples in two empirical applications. Researchers can arrive at different conclusions about racial/ethnic disparities depending on the method selected.

Dataset contains the percent of denied mortgages based on the purpose of the application and disaggregated by race. Each cell represents the denial rate within that column's race/ethnicity category's total applications. Data pulled from the Consumer Financial Protection Bureau, collected by the Home Mortgage Disclosure Act, which requires many financial institutions to maintain, report, and publicly disclose information about mortgages.

Dataset contains home mortgage applications denied in LA City, LA County, and California, disaggregated by race. Data pulled from the Consumer Financial Protection Bureau, collected by the Home Mortgage Disclosure Act, which requires many financial institutions to maintain, report, and publicly disclose information about mortgages.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.CoverageThis dataset is focused on the data for Birmingham at 2021 constituency level. About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority.Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.Population valueThe value column represents All usual residents.

The Home Mortgage Disclosure Act (HMDA) database (Consumer Financial Protection Bureau, 2022) has compiled mortgage lending data since 1981, but the collection and dissemination methods have changed over time (Federal Financial Institutions Examination Council, 2018), creating barriers to conducting longitudinal analyses. This HMDA Longitudinal Dataset (HLD) organizes and standardizes information across different eras of HMDA data collection between 1981 and 2021, enabling such analysis. This collection contains two types of datasets: 1) HMDA aggregated data by census tract for each decade and 2) HMDA aggregated data by census tract for individual years. Items for analysis include borrower income values, mortgages by loan type (e.g., conventional, Federal Housing Administration (FHA), Veterans Affairs (VA), refinances), and mortgages by borrower race and gender.

This dataset provides Census 2021 estimates that classify usual residents in Birmingham by ethnic group, by religion, and by age.

Ethnic Group: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Religion: The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it. Age: A person's age on Census Day, 21 March 2021 in England and Wales.CoverageThis dataset is focused on the data for Birmingham at city level. About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority.Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.

This dataset shows the number of persons who have approached Birmingham City Council and presented as homeless or threatened with homelessness. Data is broken down by year and ethnicity.In England, local authorities have a statutory duty to prevent homelessness under the Homelessness Reduction Act 2017. This duty requires them to take reasonable steps to help individuals who are threatened with homelessness within 56 days to secure that accommodation does not cease to be available for their occupation. Small number suppression has been applied to those detailed ethnicities which are less than 10. All those individuals will be listed as a group called Data disclosure protection.

The housing register shows the waiting list for social housing broken down by ethnicity and year. To join a housing register you must have a housing need. This means that your current accommodation is not suitable for you or a family member of your household.The data shows how many joined the register each year via submission of an application. It does not portray those who are no longer active on the register.Small number suppression has been applied to those detailed ethnicities which are less than 10. All those individuals will be listed as a group called Data disclosure protection.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.CoverageThis dataset is focused on the data for Birmingham at Ward level. Also available at LSOA, MSOA and Constituency levels.About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:

Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority. Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.Population valueThe value column represents All usual residents.The percentage shown is the value as a percentage of All usual residents within the given geography.

Release Date: 2020-05-19.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-008)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2018 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2018 ABS collection year produces statistics for the 2017 reference year. The "Year" column in the table is the reference year. The ABS has a larger sample size during the benchmark year of 2017. Due to the larger size, more detailed data are shown for reference year 2017...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.Includes U.S. firms with paid employees, operating during the reference year 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. Employer 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. and state totals for all sectors. Employment reflects the number of paid employees during the pay period in the reference year that included March 12...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Sales and receipts of employer firms (reported in $1,000s of dollars). Number of employees (during the March 12 pay period). Annual payroll (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female. . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority. Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran. Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...Moreover, the 2017 reference year statistics include detailed race and ethnicity data tabulated for:.Hispanic subgroups. Mexican, Mexican American, Chicano. Puerto Rican. Cuban. Other Hispanic, Latino, or Spanish. . Asian subgroups. Asian Indian. Chinese. Filipino. Japanese. Korean. Vietnamese. Other Asian. . Native Hawaiian and Other Pacific Islander subgroups. Native Hawaiian. Guamanian or Chamorro. Samoan. Other Pacific Islander. ...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for businesses owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subgroup total because a Hispanic or Latino firm may be of any race, and because a firm could be tabulated in more than one racial group. For example, if a firm responded as both Chinese and Black majority owned, the firm would be included in the detailed Asian and Black estimates but would only be counted once toward the higher level all firms' estimates.. References such as "Mexican-owned," "Puerto Rican-owned," "Cuban-owned" or "other Hispanic- or Latino-owned" businesses refer only to businesses operating in the 50 states and the District of Columbia that self-identified 51 percent or more of their ownership in 2017 to be by individuals of Mexican, Puerto Rican, Cuban or other Hispanic or Latino origin. The ABS does not distinguish between U.S. residents and nonresidents. Companies owned by foreign governments or owned by other companies, foreign or domestic, are included in the category "Unclassifiable."...Industry and Geogr...

In 2021, the share of reported homicides committed by the police without information on the victim's ethnicity reached 100 percent in the state of Maranhão. This means that in no case of a civilian's death due to police intervention in this state was the ethnicity disclosed. On the other hand, the state of Pernambuco had no case in which the ethnicity was not identified. Furthermore, black was the ethnicity with the highest number of killings committed by the police.

Usually resident population by ethnic or cultural background by Local Electoral Areas. (Census 2022 Theme 2 Table 2 )Census 2022 table 2.2 is the population usually resident in Ireland by ethnic or cultural background. Details include population by ethnicity or cultural background. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. For the purposes of Local Authority elections, each county and city is divided into Local Electoral Areas (LEAs) which are constituted on the basis of Orders made under the Local Government Act, 1941. Statutory Instruments 610-638 of 2018 and 6-8, 27-28, 156-157 of 2019 state the current composition of LEAs.In general, LEAs are formed by aggregating Electoral Divisions. However, in a number of cases, Electoral Divisions are split between LEAs and in order to render them suitable for the production of statistics, the CSO has amended some LEA boundaries to ensure that statistical disclosure does not occur. As a result of these amendments, Census 2022 LEAs are comprised of whole Census 2022 Electoral Divisions.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. CSO Local Electoral Areas 2022

This Indicator measures the difference in denial rate of Home Mortgage Disclosure Act (HMDA) loans by race/ethnicity. The HMDA requires that any loan secured by a lien on a dwelling made for the purpose of purchasing a home is reportable on an annual basis to the Federal Financial Institutions Examination Council (FFIEC), which is the federal reporting agency of the Federal Reserve Board.

Dataset contains the reason for home mortgage applications denials in LA City disaggregated by race. Data pulled from the Consumer Financial Protection Bureau, collected by the Home Mortgage Disclosure Act, which requires many financial institutions to maintain, report, and publicly disclose information about mortgages.

This dataset shows the ethnicity of the lead applicant of the household currently in temporary accommodation to avoid homelessness according to the Homelessness Reduction Act 2017. The data includes both broad and detailed ethnicity.In England, local authorities have a statutory duty to prevent homelessness under the Homelessness Reduction Act 2017. This duty requires them to take reasonable steps to help individuals who are threatened with homelessness within 56 days to secure that accommodation does not cease to be available for their occupation. Small number suppression has been applied to those detailed ethnicities which are less than 10. All those individuals will be listed as a group called Data disclosure protection.

Abstract copyright UK Data Service and data collection copyright owner. The Privacy and Self-Disclosure Online Project studied the links between privacy and self-disclosure and contains three key studies: Study 1: automated interviews with 530 internet users questioned about their privacy concerns and protective behaviours. This is estimated to be the first study published that used an automated 'bot' (or robot) to interview people using an instant messaging client. Study 2: 562 people were asked to disclose personal information via a registration form at Time 1, and then completed privacy attitude and behaviour measures at Time 2. The dataset contains both parts, measuring their trust in the requester of the information and perceived privacy. Privacy was also experimentally manipulated at Time 1. Study 3: identity cards and privacy attitudes. 1,143 people were experimentally allocated to one of three possible implementations of ID cards in the UK, and their attitudes pre- and post-implementation measured. The dataset also contains their privacy attitudes. Further information about the project, including publications, may be found on the ESRC Privacy and Self-Disclosure Online Project grant award web page. Main Topics: The main topics included: privacy-related concerns and behaviours; privacy measures and self-disclosure using questions on sensitive topics such as income and ethnicity; attitudes towards ID cards. Convenience sample random sample of ICQ users (study 1) Self-completion Psychological measurements

Release Date: 2021-01-28.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-424)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2019 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2019 ABS collection year produces statistics for the 2018 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.This is the only table in the ABS series to provide information on select economic and demographic characteristics of business owners (CBO) for U.S. employer firms that reported the sex, ethnicity, race, and veteran status for up to four persons owning the largest percentage(s) of the business. The data include estimates for owners of U.S. respondent firms with paid employees operating during the reference year 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. Owners of employer firms with more than one domestic establishment are counted in each geographic area and industry in which the firm operates, but only once in the U.S. and state totals for all sectors. Firms are asked to report their employees as of the March 12 pay period...Data Items and Other Identifying Records:.Data include estimates on:.Number of owners of respondent employer firms. Percent of number of owners of respondent employer firms (%)...These data are aggregated at the owner level for up to four persons owning the largest percentages of the business by the following demographic classifications:.All owners of respondent firms. Sex. Female. Male. . . Ethnicity. Hispanic. Non-Hispanic. . . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Nonminority (Firms classified as non-Hispanic and White). . . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Nonveteran. . . ...Data Notes:.. Data are tabulated at the owner level.. Respondents are informed that Hispanic origins are not races and are instructed to answer both the Hispanic origin and race questions.. An owner can be tabulated in more than one racial group. This can result because:. The sole owner was reported to be of more than one race.. The majority owner was reported to be of more than one race.. A majority combination of owners was reported to be of more than one race.. . An owner cannot be tabulated with two mutually exclusive demographic classifications (e.g. both as a veteran and a nonveteran.). CBO data are not designed to produce estimates for all U.S. business owners as information was only collected for up to four owners per firm. Researchers analyzing data to create their own estimates are responsible for the validity of those estimates and should cite the Census Bureau as the source of the original data only....Owner Characteristics:.The ABS asked for information for up to four persons owning the largest percentage(s) of the business. Respondent firms include all firms that responded to the characteristics tabulated in this dataset and that reported sex, ethnicity, race, or veteran status for at least one business owner so that the classification of owners of respondent firms by sex, ethnicity, race, and veteran status could be determined. Furthermore, the ABS was designed to include select questions about owner characteristics from multiple reference periods and to incorporate new content each survey year based on topics of relevance. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a sex, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset...To see the specific survey questions for which estimates are provided in this table, visit the following:... Owner Characteristics collected on the 2019 Annual Business Survey...Industry and Geography Cover...