Knowing the racial and ethnic composition of a community is often one of the first steps in understanding, serving, and advocating for various groups. This information can help enforce laws, policies, and regulations against discrimination based on race and ethnicity. These statistics can also help tailor services to accommodate cultural differences.This multi-scale map shows the most common race/ethnicity living within an area. Map opens at tract-level in Los Angeles, CA but has national coverage. Zoom out to see counties and states.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.

The National Survey on Drug Use and Health (NSDUH) series (formerly titled National Household Survey on Drug Abuse) primarily measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions included age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including pain relievers, tranquilizers, stimulants, and sedatives. The survey covered substance abuse treatment history and perceived need for treatment, and included questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders that allow diagnostic criteria to be applied. The survey included questions concerning treatment for both substance abuse and mental health-related disorders. Respondents were also asked about personal and family income sources and amounts, health care access and coverage, illegal activities and arrest record, problems resulting from the use of drugs, and needle-sharing. Questions introduced in previous administrations were retained in the 2014 survey, including questions asked only of respondents aged 12 to 17. These "youth experiences" items covered a variety of topics, such as neighborhood environment, illegal activities, drug use by friends, social support, extracurricular activities, exposure to substance abuse prevention and education programs, and perceived adult attitudes toward drug use and activities such as school work. Several measures focused on prevention-related themes in this section. Also retained were questions on mental health and access to care, perceived risk of using drugs, perceived availability of drugs, driving and personal behavior, and cigar smoking. Questions on the tobacco brand used most often were introduced with the 1999 survey. For the 2008 survey, adult mental health questions were added to measure symptoms of psychological distress in the worst period of distress that a person experienced in the past 30 days and suicidal ideation. In 2008, a split-sample design also was included to administer separate sets of questions (WHODAS vs. SDS) to assess impairment due to mental health problems. Beginning with the 2009 NSDUH, however, all of the adults in the sample received only the WHODAS questions. Background information includes gender, race, age, ethnicity, marital status, educational level, job status, veteran status, and current household composition.

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...

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

The Presbyterian Panel began in 1973 and is an ongoing panel study in which mailed and web-based questionnaires are used to survey representative samples of constituency groups of the Presbyterian Church (U.S.A.). These constituency groups include members, elders, pastors serving in a congregation and specialized clergy serving elsewhere. The August 2013 and February 2017 Panel surveys both dealt with race and ethnicity, and the inclusion of a number of identical questions in the two surveys allows for analysis of change over time. This dataset contains data from members and elders of the Presbyterian Church (U.S.A.).

About this dataset

Context

The dataset tabulates the population of Sacramento by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Sacramento across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.61% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Sacramento is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Sacramento total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sacramento Population by Race & Ethnicity. You can refer the same here

This data collection contains information gathered in the Survey of Income and Education (SIE) conducted in April-July 1976 by the Census Bureau for the United States Department of Health, Education, and Welfare (HEW). Although national estimates of the number of children in poverty were available each year from the Census Bureau's Current Population Survey (CPS), those estimates were not statistically reliable on a state-by-state basis. In enacting the Educational Amendments of 1974, Congress mandated that HEW conduct a survey to obtain reliable state-by-state data on the numbers of school-age children in local areas with family incomes below the federal poverty level. This was the statistic that determined the amount of grant a local educational agency was entitled to under Title 1, Elementary and Secondary Education Act of 1965. (Such funds were distributed by HEW's Office of Education.) The SIE was the survey created to fulfill that mandate. Its questions include those used in the Current Population Survey regarding current employment, past work experience, and income. Additional questions covering school enrollment, disability, health insurance, bilingualism, food stamp recipiency, assets, and housing costs enabled the study of the poverty concept and of program effectiveness in reaching target groups. Basic household information also was recorded, including tenure of unit (a determination of whether the occupants of the living quarters owned, rented, or occupied the unit without rent), type of unit, household language, and for each member of the household: age, sex, race, ethnicity, marital history, and education.

Release Date: 2023-10-26.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-FY23-0479)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2022 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 2022 ABS collection year produces statistics for the 2021 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 2021 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 warehousin...

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

Survey was conducted before and after the parliamentary election in Romania on 11 December 2016 as a case study on the effect of ethnic party campaigns in communities with different ethnic compositions: ethnic Romanians living in predominantly Hungarian counties, ethnic Romanians in the rest of the country where they are in the majority, and ethnic Hungarians especially from central and western Romania. Respondents were interviewed a first time five to six weeks before the election and another time four to five weeks after the election. The questionnaire consisted of questions on ethnic belonging, trust in individuals and institutions, perceptions of electoral integrity, opinions on parties, and items on life satisfaction and assessments of the current social and political conditions of Romania. While, due to questionnaire constraints, some questions were asked in only one of the interviews, others were recorded in both waves, allowing for analyses of changes over time.

The political mobilisation of ethnicity has led to tensions between ethnic groups in, for example, Belgium and Canada, and to violent conflict with disastrous consequences in such diverse cases as Cyprus, Rwanda, and Sri Lanka. Some observers point to the particularistic politics of ethnic parties as fomenting ethnic tensions and call for their regulation. Others argue that ethnic parties may be valuable vehicles in solving such tensions because they contribute to the integration of diverse ethnic groups. However, both views are so far based on assumptions rather than empirical evidence; to date, the effect of ethnic parties on ordinary people within society has not been examined directly. The project fills this gap, contributing to a better understanding of the links between ethnic parties and national unity within the population: Does the presence of ethnic parties affect the way people perceive the ethnic "other" or the nation? Is this effect positive, because ethnic parties as emancipatory vehicles increase the inclusion of ethnic minorities within the population? Or is it negative, because ethnic parties raise awareness of ethnic differences? To answer these questions, the project first conducts a global comparative analysis of 105 diverse countries, using a new multilevel dataset. It will then conduct in-depth studies of two countries to examine the nature of these links. Both the quantitative and in-depth analyses are needed to better understand whether there are general links between the presence of ethnic parties and diminished national unity throughout different contexts and to identify the nature of this link in important cases.

About this dataset

Context

The dataset tabulates the population of College Springs by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of College Springs across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 56.68% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the College Springs is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of College Springs total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Springs Population by Race & Ethnicity. You can refer the same here

In the context of COVID-19, RAND and the Robert Wood Johnson Foundation (RWJF) partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low to moderate-income backgrounds. The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations. Some questions used in the NSHA are fielded in this COVID-19 survey while others are newly used from other COVID-19 surveys or newly developed for this effort. The study is a longitudinal study, collecting data in four waves. The study also included 2 populations: A sample of populations at greater risk, and a general population sample. This study includes the results for Wave 4 for populations at greater risk. The questions in the surveys were largely similar across all four waves. Demographic info includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

About this dataset

Context

The dataset tabulates the population of Kennard by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Kennard across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 54.91% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Kennard is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Kennard total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Kennard Population by Race & Ethnicity. You can refer the same here

The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Data collected in this study measured the amount of time that people spent doing various activities in 2007, such as paid work, child care, religious activities, volunteering, and socializing. Respondents were interviewed only once about how they spent their time on the previous day, where they were, and whom they were with. The Eating and Health (EH) module includes questions related to eating, meal preparation, and health, all of which were asked after completion of the ATUS questions. Part 1, Respondent and Activity Summary File, contains demographic information about respondents and a summary of the total amount of time they spent doing each activity that day. Part 2, Roster File, contains information about household members and nonhousehold children under the age of 18. Part 3, Activity File, includes additional information on activities in which respondents participated, including the location of each activity and the total time spent on secondary child care. Part 4, Who File, includes data on who was present during each activity. Part 5, ATUS-CPS 2007 File, contains data on respondents and members of their household collected during their participation in the Current Population Survey (CPS). Parts 6-9 contain supplemental data files that can be used for further analysis of the data. Part 6, Case History File, contains information about the interview process. Part 7, Call History File, gives information about each call attempt. Part 8, Trips File, provides information about the number, duration, and purpose of overnight trips away from home for two or more nights in a row in a given reference month. Part 9, ATUS 2007 Replicate Weights File, contains base weights, replicate base weights, and replicate final weights for each case that was selected to be interviewed for the ATUS. Parts 10, 11, 12, and 13 correspond to the 2007 Eating and Health Module. Demographic variables include sex, age, race, ethnicity, education level, income, employment status, occupation, citizenship status, country of origin, and household composition.

The Los Angeles Metropolitan Area Surveys [LAMAS] 7, 1973 collection reflects data gathered in 1973 as part of the Los Angeles Metropolitan Area Studies (LAMAS). The LAMAS, beginning in the spring of 1970, are a shared-time omnibus survey of Los Angeles County community members, usually repeated twice annually. The LAMAS were conducted ten times between 1970 and 1976 in an effort to develop a set of standard community profile measures appropriate for use in the planning and evaluation of public policy. The LAMAS instruments, indexes, and scales were used to track the development and course of social indicators (including social, psychological, health, and economic variables) and the impact of public policy on the community. Questions in this survey cover respondents' attitudes toward the following topics: community and public services, local government politics, political efficacy, residential mobility, and integration of their neighborhood. In addition, participating researchers were given the option of submitting questions to be asked in addition to the core items. These additional question topics include: travel time to work, number of vehicles, means of transportation, and alcohol use, as well as drinking and driving. Demographic variables in this collection include sex, age, race, ethnicity, education, occupation, income, religion, marital status, birth place, and housing type.

About this dataset

Context

The dataset tabulates the population of Francesville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Francesville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.48% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Francesville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Francesville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Francesville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Blanca by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Blanca across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.68% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Blanca is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Blanca total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blanca Population by Race & Ethnicity. You can refer the same here

An emerging body of research has robustly found a link between immigration and preferences for redistribution. In particular, immigration has been proved to undermine native support for the welfare state (Alesina et al., 2022; Dahlberg et al., 2012; Eger, 2010; Ford, 2006; Stichnoth, 2012). This link can be interpreted within a rational resource-competition framework (since increasing the number of potential net recipients has welfare consequences). Yet different social identity theories (Hornsey, 2008; Tajfel & Turner, 1986; Turner et al., 1987) have also been invoked to stress that natives’ attitudes towards redistribution and the welfare state are grounded on ethnic/national identity as well as on related conceptions of cultural distance (Brandt et al. 2014; Chambers et al. 2013). Perceptions of cultural distance, in turn, make the overall effect of immigration on attitudes towards redistribution dependent on the characteristics of the immigrant pool —because some immigrant groups are perceived as more (or less) disserving than others (Verkuyten et al. 1996). In the European context, immigrants’ coming from Middle East and North African countries of majoritarian Muslim faith (MENAM) and their European-born descendants are known to be particularly at risk of discrimination and prejudice ( Strabac & Listhaug 2008; Strabac et al. 2014; Di Stasio et al. 2021; Polavieja et al. 2023). An important gap in the literature on redistribution and welfare nativism, however, concerns the potential role of immigrant characteristics other than cultural-religious or socioeconomic background, specifically, the role of phenotype (i.e. color or racial appearance). The importance of racial appearance as an additional source of prejudice and discrimination has been long neglected in the European context and, to our knowledge, to date no study on the impact of immigration on attitudes towards redistribution has tested whether immigrants’ physical (“racial”) appearance can influence European natives’ attitudes toward welfare deservedness. Recent field experimental research on racial discrimination in hiring has brought the question of racial discrimination to the fore by showing European employers are less likely to hire immigrant descendants with non-white phenotypes and, hence, that having “visible” phenotypes constitutes a serious barrier for the socio-economic integration of the second-generation in Europe (Polavieja et al. 2023). Building on this research, we propose an experiment to address the distinctive role of ethnicity (treatment 1) and phenotype (treatment 2) on native’s attitudes regarding welfare deservedness chauvinism (research question 1). Additionally, we test for two mediating mechanisms, welfare competition (research question 2) and disgust sensitivity (research question 3), which allows us to also contribute to the expanding literature on the neurocognitive basis of prejudice and the role of visceral emotions. To this end we draw on recent developments in cognitive psychology, political psychology and behavioral science. Our main research questions can thus be summarized as follows: Research Question 1: What is the distinctive role of immigrant-descendants’ ethnicity and phenotype as potentially different drivers of welfare chauvinism? Research Question 2: To what extent (rational) concerns about competition might help us explain welfare chauvinist responses amongst natives? Research Question 3: To what extent (irrational) disgust sensitivity can help us explain welfare chauvinism and, in particular, chauvinist responses triggered by phenotypic racism?

This study is part of the American National Election Study (ANES), a time-series collection of national surveys fielded continuously since 1948. The American National Election Studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. As with all Time Series studies conducted during years of presidential elections, respondents were interviewed during the two months preceding the November election (Pre-election interview), and then re-interviewed during the two months following the election (Post-election interview). Like its predecessors, the 2020 ANES was divided between questions necessary for tracking long-term trends and questions necessary to understand the particular political moment of 2020. The study maintains and extends the ANES time-series 'core' by collecting data on Americans' basic political beliefs, allegiances, and behaviors, which are so critical to a general understanding of politics that they are monitored at every election, no matter the nature of the specific campaign or the broader setting. This 2020 ANES study features a fresh cross-sectional sample, with respondents randomly assigned to one of three sequential mode groups: web only, mixed web (i.e., web and phone), and mixed video (i.e., video, web, and phone). The new content for the 2020 pre-election survey includes coronavirus pandemic, election integrity, corruption, impeachment, immigration and democratic norms. The pre-election survey also includes protests and unrest over policing and racism. The new content for the 2020 post-election survey includes voting experiences, anti-elitism, faith in experts or science, climate change, gun control, opioids, rural-urban identity, international trade, transgender military service, social media usage, misinformation, perceptions of foreign countries and group empathy. Phone and video interviews were conducted by trained interviewers using computer-assisted personal interviewing (CAPI) software on computers. Unlike in earlier years, the 2020 ANES did not use computer-assisted self interviewing (CASI) during any part of the interviewer-administered modes (video and phone). Rather, in interviewer-administered modes, all questions were read out loud to respondents, and respondents also provided their answers orally. Demographic variables include respondent age, education level, political affiliation, race/ethnicity, marital status, and family composition.