Graph and download economic data for Unemployment Rate - Asian (LNU04032183) from Jan 2000 to Sep 2025 about asian, 16 years +, household survey, unemployment, rate, and USA.

View monthly updates and historical trends for US Unemployment Rate: Asian. from United States. Source: Bureau of Labor Statistics. Track economic data wi…

In 2023, about 344,000 Asian Americans were unemployed and looking for a job. This is an increase from the previous year, when about 306,000 Asian Americans were unemployed looking for work nationwide.

View monthly updates and historical trends for US Unemployment Rate: Single Asian Men. from United States. Source: Bureau of Labor Statistics. Track econo…

According to projections for 2019 to 2026, the unemployment rate will remain relatively stable in most world regions. In 2025 and 2026, it is estimated to be highest in Central and Western Asia followed by Africa and Latin America and the Caribbean. On the other hand, it was estimated to be lowest in Southeast Asia and Arab States (GCC).

View quarterly updates and historical trends for US Unemployment Rate: Single Asian Women. from United States. Source: Bureau of Labor Statistics. Track e…

In 2023, about 23.51 percent of unemployed Black or African American individuals had been jobless for at least 27 weeks. This was slightly higher amongst Asian individuals, at 23.55 percent. See the monthly unemployment rate in the U.S. here.

Graph and download economic data for Income Before Taxes: Unemployment and Workers' Compensation, Veterans Benefits by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUOTHBNFTSLB0902M) from 1984 to 2012 about veterans, asian, white, compensation, benefits, workers, tax, income, unemployment, and USA.

By 2060, the share of White residents in the United States is projected to decrease to below 45 percent. On the other hand, the share of ethnically Hispanic Americans will increase to some 27 percent by then. African Americans will remain the largest racial minority, at just under 15 percent. Racial and social gaps in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. Still, economic and social disparities remain persistent. Black families, for example, are significantly poorer than the general population by measure of median annual household income. Similarly, unemployment rates are higher among minorities, except for Asian Americans, who have the lowest rate of unemployment. Demographic shifts and population growth As the racial makeup of the United States evolves, the overall population continues to expand. Projections indicate that the U.S. population will reach 341.7 million by 2025. This growth occurs despite declining fertility rates, and has largely been driven by international migration to the country.

View monthly updates and historical trends for US Unemployment Rate: Asian Age 75 and Over. from United States. Source: Bureau of Labor Statistics. Track …

In 2024, about 62.7 percent of the Asian community was employed. The highest employment rate was found among Mexican-Americans, at 64.1 percent, and the lowest employment rate was found among Puerto Ricans, at 55.2 percent. In total, around 60 percent of all working-age Americans were employed at this time.

This scatter chart displays GDP (current US$) against unemployment (% of total labor force) in Southern Asia. The data is about countries.

Graph and download economic data for Income Before Taxes: Unemployment and Workers' Compensation, Veterans Benefits by Race: Asian (CXUOTHBNFTSLB0904M) from 2003 to 2012 about veterans, asian, compensation, benefits, workers, tax, income, unemployment, and USA.

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· WPop: Total population of those who identify as white alone (B01001A).· PWPop: Percentage of total population that identifies as white alone (B01001A).· BPop: Total population of those who identify as black or African American alone (B01001B).· PWPop: Percentage of total population that identifies as black or African American alone (B01001B).· AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).· PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).· APop: Total population of those who identify as Asian alone (B01001D).· PAPop: Percentage of total population that identifies as Asian alone (B01001D).· PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).· PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).· OPop: Total population of those who identify as Some Other Race alone (B01001F).· POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).· MPop: Total population of those who identify as Two or More Races (B01001G).· PMPop: Percentage of total population that identifies as Two or More Races (B01001G).· WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).· PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).· LPop: Total population of those who identify as Hispanic or Latino (B01001I).· PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).Educational Attainment· EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).· PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).· EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).· PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).· EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).· 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).· EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).· PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).· EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).· PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).· 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).· 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).· EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).· PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).· EdSC: Total population ages 25 and over with some college education but no degree (S1501).· PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).· EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).· PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).· EdB: Total population ages 25 and over with bachelor’s degree (S1501).· PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).· EdG: Total population ages 25 and over with a graduate or professional degree (S1501).· PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).Unemployment, Health Insurance, Disability· UnempR: Unemployment rate among the population ages 16 and over (S2301).· UnIn: Total non-institutionalized population without health insurance (B27001).· PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).· Disab: Total non-institutionalized population with a disability (S1810).· PDisab: Percentage of non-institutionalized populations with a disability (B27001).Vehicle Access· OwnNV: Total number of owner-occupied households without a vehicle (B25044).· POwnNV: Percentage of owner-occupied households without a vehicle (B25044).· OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).· POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).· RentNV: Total number of renter-occupied households without a vehicle (B25044).· PRentNV: Percentage of renter-occupied households without a vehicle (B25044).· RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).· POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

From 2000 until 2019, youth unemployment fluctuated between 13 and 15.5 percent, before it rose above 17 percent in 2020 during the Covid-19 pandemic. Youth unemployment Just like the general unemployment rate, youth unemployment is recorded and monitored to gauge the job market situation in a country and worldwide. Youth unemployment includes unemployed individuals aged 15 to 24, typically referring to those who have either just finished school or graduated and are looking for jobs. In order to be registered as unemployed, a person must be able to work, unemployed, and looking for a job. Usually youth unemployment is higher than adult unemployment, as many graduates do not find employment right after they have graduated. Regional breakdown The world region with the highest youth unemployment rate has been the Arab World for the past two decades, while East Asia and the Pacific has generally had the lowest rate. Apart from the sharp rise in 2020, the most notable increase came in 2009 as a result of the Great Recession; while this increase can be observed on a global scale, its impact on youth unemployment was more severely felt in more advanced economies in Europe and North America.

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.

This scatter chart displays GDP (current US$) against unemployment (% of total labor force) in Central Asia. The data is filtered where the date is 2023. The data is about countries per year.

The global unemployment insurance market is experiencing robust growth, driven by rising unemployment rates globally and increasing government initiatives to provide social safety nets. While precise figures for market size and CAGR are not provided, we can infer a significant market based on the listed companies, many of which are major players in the global insurance sector. Considering the involvement of global insurance giants like Allianz, Generali, and AIA, coupled with the presence of significant regional players across Europe, Asia, and North America, a conservative estimate for the 2025 market size would be $500 billion USD. Assuming a moderate growth trajectory reflective of increasing economic volatility and government spending on social programs, a Compound Annual Growth Rate (CAGR) of 5-7% over the forecast period (2025-2033) is plausible. This translates to a projected market value exceeding $800 billion USD by 2033. Market drivers include government regulations mandating unemployment insurance, increasing job insecurity across various industries, and rising awareness of the social and economic benefits of these programs. Trends indicate a shift towards digital platforms for claims processing and benefit disbursement, alongside a growing focus on personalized risk assessments and tailored unemployment insurance products. However, market restraints include economic downturns affecting government budgets and the challenges of effectively managing fraudulent claims. The market is highly segmented by region, with developed economies in North America and Europe holding significant shares. However, rapidly developing economies in Asia and Latin America are showcasing significant growth potential due to expanding workforces and increasing urbanization. The competitive landscape is characterized by both large multinational insurers and smaller, regional players. Strategic partnerships, mergers, and acquisitions are common strategies employed by companies seeking to expand their market share and product offerings within this dynamic sector. Overall, the future of the unemployment insurance market presents substantial opportunities for growth and innovation, particularly as governments and insurers continue to adapt to evolving economic conditions and technological advancements.

Urban Institute racial and economic inclusion indexes for 2016; extracted from source: https://apps.urban.org/features/inclusion/?topic=map.

The racial inclusion index is made up of five measures: racial segregation (white/person of color dissimilarity index), homeownership gap, educational attainment gap, poverty rate gap, and share of people of color. All racial gap measures calculate the disparity between white non-Hispanic residents and residents of color. For this analysis, we define people of color as any person identifying in US Census Bureau records as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, other race, two or more races, or Hispanic or Latino white. We recognize the issues that arise with placing all these groups under one umbrella—both in defining identity in comparison with whiteness and in papering over differences in how different groups experience state-sanctioned, institutionalized, systemic, and individual forms of racism. This broad racial disparity measure allows us to compare cities with differing demographic patterns while limiting the size of sampling error for groups within cities that have small populations.

The economic inclusion index is made up of four measures: income segregation (rank-order information theory index), rent burden, share of 16- to 19-year-olds who are not in school and have not graduated, and working poor. The overall inclusion index is the composite of the racial and economic inclusion indices. The economic health index is made up of four indicators: percentage change in employed people period over period, median family income, unemployment rate, and housing vacancy rate.