Graph and download economic data for Income Before Taxes: Unemployment and Workers' Compensation, Veterans' Benefits, and Regular Contributions by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUOTHREGINLB0902M) from 2013 to 2023 about veterans, contributions, compensation, asian, benefits, workers, tax, white, income, unemployment, and USA.

As of the first quarter of 2025, the unemployment rate for people of white ethnicity in the United Kingdom was 3.7 percent, the lowest of the provided ethnic groups in this quarter. By contrast, the unemployment rate for people in the Pakistani ethnic group was 13.1 percent.

We examine differential impacts of COVID-19 on minority unemployment in the U.S. beyond the initial recession. The Black-White unemployment gap did not immediately rise as much as for other groups, but subsequently increased and was stubbornly higher through 2021 Q3. The Latinx-White gap generally decreased after peaking in May 2020 to pre-pandemic levels by 2021 Q4, but experienced a unique “hiccup” in early 2021. The Asian-White gap peaked later (in June 2020) and was high in Q3 of 2020 (the “stall”), and remained positive through 2021 Q4. Decomposition methods are used to examine factors that contribute to these patterns.

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

In 2022, the highest and lowest rates of economic inactivity were in the combined Pakistani and Bangladeshi (33%) and white 'other’ (15%) ethnic groups.

Women and Unemployment was a study designed to study the effects of unemployment on women. The researcher examined the psychological and physical consequences of job loss for women. Specifically, the study focused on what working means to women, the effects of women's unemployment on family life and children, the effects of job loss on women's health, the meaning of work in women's lives, how the women lost their jobs, the support they had around their job loss, and, if applicable, how they found new employment. In presenting and exploring the varied and unique consequences of unemployment for women, the study also aimed to dispel the belief that job loss has identical effects for men and women. The sample consisted of 124 women from a large northeastern metropolitan area who had experienced involuntary unemployment for at least six weeks within the past two years. The sample was cross-sectional in terms of age, ethnicity, socio-economic status, educational level and occupation. The sample of women completing the initial survey questionnaires was 77.6% Caucasian, 13.8% African-American, 2.6% Latina, and 0.9% Asian-American. The majority of the women were mothers. An initial survey questionnaire was given to assess background information, previous work history and cursory unemployment experience. Personal interviews were conducted with a subsample of 30 women selected to represent the larger population of unemployed women in order to obtain detailed information regarding the unemployment experience. The interview consisted of sections on background information, work history, the job loss, health and social support, the job search, and the woman's future outlook. Interviewed participants also completed a booklet with the CES-D scale to measure depression and the Anxiety Scale of the MAACL as a measure of anxiety. The Murray Archive holds additional analogue materials for this study (paper questionnaires for all subjects as well as interview schedules, respondent booklets, transcripts and audiotapes of interviews). If you would like to access this material, please apply to use the data.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Race/Ethnicity, Educational Attainment, Unemployment, Health Insurance, Disability and Vehicle Access.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Race/Ethnicity

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WPop: Total population of those who identify as white alone (B01001A).

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PWPop: Percentage of total population that identifies as white alone (B01001A).

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BPop: Total population of those who identify as black or African American alone (B01001B).

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PWPop: Percentage of total population that identifies as black or African American alone (B01001B).

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AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).

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PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).

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APop: Total population of those who identify as Asian alone (B01001D).

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PAPop: Percentage of total population that identifies as Asian alone (B01001D).

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PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).

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PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).

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OPop: Total population of those who identify as Some Other Race alone (B01001F).

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POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).

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MPop: Total population of those who identify as Two or More Races (B01001G).

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PMPop: Percentage of total population that identifies as Two or More Races (B01001G).

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WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).

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PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).

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LPop: Total population of those who identify as Hispanic or Latino (B01001I).

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PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).

Educational Attainment

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EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).

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PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).

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EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

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PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

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EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

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PEdSC1824: Percentage of population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

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EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

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PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

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EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).

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PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).

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Ed912nD: Total population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

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PEd912nD: Percentage of population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

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EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).

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PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).

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EdSC: Total population ages 25 and over with some college education but no degree (S1501).

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PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).

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EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).

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PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).

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EdB: Total population ages 25 and over with bachelor’s degree (S1501).

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PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).

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EdG: Total population ages 25 and over with a graduate or professional degree (S1501).

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PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).

Unemployment, Health Insurance, Disability

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UnempR: Unemployment rate among the population ages 16 and over (S2301).

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UnIn: Total non-institutionalized population without health insurance (B27001).

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PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).

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Disab: Total non-institutionalized population with a disability (S1810).

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PDisab: Percentage of non-institutionalized populations with a disability (B27001).

Vehicle Access

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OwnNV: Total number of owner-occupied households without a vehicle (B25044).

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POwnNV: Percentage of owner-occupied households without a vehicle (B25044).

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OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).

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POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).

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RentNV: Total number of renter-occupied households without a vehicle (B25044).

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PRentNV: Percentage of renter-occupied households without a vehicle (B25044).

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RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).

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POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

Labour force characteristic estimates by visible minority group, region, age group, and gender.

This collection consists of data that tracked how ten city governments in the United States responded to morality issues in the last decade of the 20th century. The ten cities varied in their geographic properties and were characterized by their locations, e.g., South City, Metro City, and Coast City. Morality issues were defined as issues concerning actions or behaviors that were regulated by a deeply held belief and/or a religious value. The issues falling within this categorization were gay rights, abortion rights, abortion clinic protests, needle exchange programs for drug users, hate speech, hate groups, gambling policies and regulations, animal rights, and regulations pertaining to the sex industry, which included pornography, prostitution, and adult entertainment. Incidents or events in the ten cities related to these moral issues were identified. The data were generated by scanning local newspapers to isolate and gather relevant information about the selected cities, interviewing political elites (e.g., mayor, city manager, and council person), and reviewing public government records for the selected cities. Part 1, Ten City Data, contains data on 451 incidents related to morality issues in the ten cities. Part 2, Subset of Ten City Data With City-Specific Variables, is a subset of the cases included in Part 1 and also includes a broader array of city-specific contextual variables. The variables shared by Part 1 and Part 2 are whether a city had a mayor or a city manager, whether city council elections were at-large or by district, the percentage or share of the city council elected by a particular district, the strength and prevalence of the city's homosexual community, the percentage of residents in the county who attended religious services, the percentage of residents in the county who identified themselves as Catholic or as religious fundamentalists, and whether activists involved with this issue were more likely to be from the left or right, politically. Additional shared variables are city population in 1990 and 1998 (in thousands), the percentage of population change between 1980-1990 and 1990-1998, the metro area population in 1990 (in thousands), the percentage of population change in the metro area from 1980-1990 and from 1990-1996, the percentage of female, Asian, White, Black, and Hispanic residents, the median household income, the percentage of married residents, the percentage of female-headed households, the 1997 unemployment rate, the percentage of same gender partnerships, the total number of churches, the number of churches per capita, the percentage of households with children under the age of 19, the percentage of the population aged 18-34, the percentage of residents that were college educated, income per capita, the percentage of foreign-born residents, the percentage of residents living in poverty, and the acceptability and prevalence of the city's "unconventional" or "counter" culture. The variables contained only in Part 2, Subset of Ten City Data With City-Specific Variables, are the type of community education present, the type of social culture in the community, the percentage of the work force employed in education or technology related jobs, the percentage of women in the work force, and the total number of churches in the county.

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.

Abstract copyright UK Data Service and data collection copyright owner. This survey aims to examine the public's attitude to race relations in Britain Main Topics: Attitudinal/Behavioural Questions Whether ever lived elsewhere in Great Britain, whether lived abroad for more than a year, main problems facing community, problems most in need of improvement (e.g. vandalism, unemployment, traffic, racial prejudice), satisfaction with facilities in area (e.g. standard of education, community - police relations, housing conditions), whether had contact with someone of a different race or nationality from various walks of life (e.g. employer, friend, neighbour, nurse), distance from nearest Irish/Asian/ English/Polish/West Indian family, proportion of coloured to white people in area. Racial mix at place of employment, whether socialises with coloured people during lunch/tea-breaks/outside work. Cultural differences and discrimination: agreement/disagreement with several statements concerning cultural differences and discrimination (e.g., white people are frightened of anything foreign), whether foreigners should adopt English customs. Whether any racial unrest has occurred in the areas and which group of people was mainly to blame. Whether race relations are improving in local area/whole country, whether believes coloured people have better or poorer jobs/education/housing, countries from which would prefer neighbours to come/not to come, opinion of segregation, whether believes people of different nationalities are discriminated against (e.g. by, the Health Service, Publicans, the Police, Social Security), which group tends to be favoured (white or coloured). Employment: agreement/disagreement with several statements, concerning the employment situation for white/coloured people, how difficult it would be to get another job, whether ever been unemployed for more than one month. Education: whether has children at school, whether schools in general are meeting the needs of children, main problems facing schools in the area (e.g., staff shortages, over-crowding of immigrants), whether ever met child's teachers, whether school has PTA and whether attends meetings, whether regularly looks at child's homework, whether child has opportunity to take part in any activities outside school hours, if so - what activities, whether any member of family belongs to Boy Scouts, Girl Guides/Brownies, local youth club. Agreement/disagreement with several statements concerning the education of coloured children (e.g. 'coloured children have not brought down the standards of education'). Number of years schooling in Britain and how long ago, examinations taken. Whether discrimination or other factors are to blame for the fact that coloured people have worse jobs, lower educational standards and poorer housing than white people. Immigration: number of immigrants in previous years (including Europeans), number of coloured immigrants and from which countries. Ideas for improving race relations, knowledge of specialist organisations, whether ever visited local Community Relations Council/Office and why. Whether such organisations exist to help white people/coloured people (both groups, whether race relations will improve or deteriorate over next few years. Background Variables Occupation of respondent and head of household, number of electors and adults in household, sex, social class, family composition, number of children and age cohort, nationality/race, birth place, length of residence in U.K./neighbourhood, accommodation tenure, age cohort, age finished full-time education, employment status, trade union membership (self and family), society affiliation (self and family), newspaper readership, number of hours per week spent watching television. Quota sample Face-to-face interview

Dataset population: Persons aged 16 and over

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Ethnic group

Ethnic group classifies people according to their own perceived ethnic group and cultural background.

This topic contains ethnic group write-in responses without reference to the five broad ethnic group categories, e.g. all Irish people, irrespective of whether they are White, Mixed/multiple ethnic groups, Asian/Asian British, Black/African/Caribbean/Black British or Other ethnic group, are in the "Irish" response category. This topic was created as part of the commissioned table processing.

This statistic shows the share of ethnic groups in Australia in the total population. 33 percent of the total population of Australia are english.

Australia’s population

Australia’s ethnic diversity can be attributed to their history and location. The country’s colonization from Europeans is a significant reason for the majority of its population being Caucasian. Additionally, being that Australia is one of the most developed countries closest to Eastern Asia; its Asian population comes as no surprise.

Australia is one of the world’s most developed countries, often earning recognition as one of the world’s economical leaders. With a more recent economic boom, Australia has become an attractive country for students and workers alike, who seek an opportunity to improve their lifestyle. Over the past decade, Australia’s population has slowly increased and is expected to continue to do so over the next several years. A beautiful landscape, many work opportunities and a high quality of life helped play a role in the country’s development. In 2011, Australia was considered to have one of the highest life expectancies in the world, with the average Australian living to approximately 82 years of age.

From an employment standpoint, Australia has maintained a rather low employment rate compared to many other developed countries. After experiencing a significant jump in unemployment in 2009, primarily due to the world economic crisis, Australia has been able to remain stable and slightly increase employment year-over-year.