Users can access data pertaining to individuals with disabilities. Topics include but are not limited to: people with disabilities’ access to employment, technology, healthcare, and community based services. Background The Disability Statistics Center is based at the Institute for Health and Aging at the University of California, San Francisco (UCSF). The Disability Statistics Center generates reports ranging from employment opportunities, Medicaid home and community-based services, mobility device use, computer and internet use, wheelchair use, vocational rehabilitation, education, medical expenditures, and functional limitations among people with disabilities. User functiona lity Data is presented in report or abstract form and can be downloaded in PDF or HTML formats by clicking on the publications link. All reports and abstracts use United States data. Additional data sources are listed under “Finding Disability Data” and include data from the United States as well as international data. Data Notes The data sources are clearly referenced for each article. The most recent publications are from 2003. There is no indication on the site when the data will be updated.

Key Table Information.Table Title.Disability Characteristics.Table ID.ACSST1Y2024.S1810.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of hous...

2013-2023 Virginia Disability Characteristics by Census Tract. Contains estimates and margins of error.

Special data considerations: Large negative values do exist (more detail below) and should be addressed prior to graphing or aggregating the data. A null value in the estimate means there is no data available for the requested geography.

A value of -888,888,888 indicates that the estimate or margin of error is not applicable or not available.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table S1810 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

This report provides county-level estimates of the number of Veterans who received VA Disability Compensation benefits during fiscal year 2020. It includes the Veterans’ total service-connected disability (SCD) rating, age group, and sex. Blank values represent small cell counts that have been suppressed to protect the identity of Veterans. In the "Total: Disability Compensation Recipients" column, each blank cell represents less than 10 Veterans. Some categories may not sum to the total due to missing information (e.g., age, sex, etc.). Source: Department of Veterans Affairs, Office of Enterprise Integration, United States Veterans Eligibility Trends & Statistics (USVETS) 2020 and Veterans Benefits Administration VETSNET FY 2020 compensation data. Prepared by National Center for Veterans Analysis & Statistics, www.va.gov/vetdata.

Much is known about the effects of candidate characteristics, such as gender, age, and ethnicity, on voter perceptions and support. Meanwhile, very little research on citizens' attitudes towards and stereotypes about disabled candidates and politicians has been conducted. This dataset provides the opportunity to explore how citizens perceive disabled candidates. The data comes from a survey conducted in Britain and the US in 2020 and 2021 among representative samples of the populations of approx. 3,000 respondents in each country. The surveys include two survey experiments with conjoint designs, where respondents are presented with vignettes describing two fictional candidates competing for election. A range of candidate attributes are randomly varied, including whether candidates are blind, Deaf, wheelchair users, or not described as disabled. The design allows identifying the effects of disability and other candidate attributes on respondents' perceptions of the candidates' traits, issue priorities, issue competence, and representative links. The surveys also include a range of measures of respondents' socio-demographic characteristics, in particular their experience with disability, and political attitudes.This project asks how voters perceive disabled election candidates and whether these perceptions influence their vote choice. While almost one in five people in the UK have a disability, the numbers are much lower among politicians, with currently only five MPs known to be disabled. This under-representation might hamper the representation of the interests of disabled citizens and dampen their political engagement. It also indicates unequal access to political office. While the underlying reasons are manifold, potential prejudices among voters not only pose an electoral hurdle but might also prevent disabled candidates from running and parties from nominating them. Understanding how voters perceive and evaluate disabled candidates is thus essential to addressing the barriers to elected office that disabled people face. This project collects novel survey data from the UK and other countries in order to investigate how citizens perceive disabled candidates in terms of their character traits, issue priorities and competence, and representative links. It uses survey experiments with to test how citizens react to disabled candidates while minimising social desirability bias and demand effects. The data come from a survey conducted in the UK (Wave 1: 20 May - 6 July 2020; Wave 2: 8 January - 5 February 2021) and the US (8 January - 7 February 2021). It was conducted online through the platform Qualtrics, who also provided the samples from opt-in online panels. The quota samples (UK: N=2,998; US: N=3,011) are representative of the populations along age groups, gender, and region. The survey included two near-identical survey experiments with conjoint designs. The experiment presented respondents with vignettes describing fictional election (House of Commons/Representatives) candidates, including a set of socio-demographic and political attributes which were randomly varied, and asked respondents to evaluate them. The effects of candidate characteristics on respondent evaluations can be analysed. The survey includes a range of socio-demographic and attitudinal variables, focusing particularly on disability.

Context

There are more than 26.8 million people or 2.2% of the population currently who have disabilities in India (Census 2011) which itself is said to be a very conservative estimate. There is a lot of stigma associated with the disabled community and a very high inequality in terms of social as well as monetary status between the disabled community and the entire population.

Content

The data in the csv file gives us the statewise values of the following:

1.State 2.number_disabled : It gives the total number of people in the region that are disabled. 3.total_population: It gives the total number of people in the region. 4.percent_disabled: It gives the total percentage of the people disabled in the given region. 5.literacy_rate_disabled : It represents the literacy rate of the disabled community in the region. 6.literacy_rate_general : It shows the total literacy rate of the population in the state. 7.workforce_rate_disabled : It tells us the total percent of all the disabled people that are part of the workforce in the given region.(inclusive all ages). 8.workforce_rate_general : It shows the total percent of all the people that are part of the workforce in the given region(inclusive of all ages).

This study focuses on employers' hiring practices and general attitudes regarding handicapped people. Respondents were asked to compare the handicapped with non-handicapped in ability and job performance. Questions were asked on companies' policies and programs to educate employees about the handicapped, number of handicapped people hired, and reasons for not hiring handicapped people

VBA EDUCATION PROGRAM BENEFITS to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA’s determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

VBA HOUSING BENEFITS PROGRAM to provide veterans who are eligible for a Specially Adapted Housing grant with loan directly from the VA in certain circumstances. Permanently and totally disabled Veterans who served on active duty on or after September 16, 1940 and are eligible for a Specially Adapted Housing grant. VA may make loans up to $33,000 to eligible applicants if (a) the veteran is eligible for a VA Specially Adapted Housing grant, and (b) a loan is necessary to supplement the grant, and (c) home loans from a private lender are not available in the area where the property involved is located.

VBA BENEFIT PROGRAM to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA's determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

This is survey focuses on public attitudes toward disabled persons and their knowledge of the Americans with Disabilities Act of 1990. Variables include discrimination, job opportunities, equal pay, access in public places, and government assistance.

Household Pulse Survey (HPS): HPS is a rapid-response survey of adults ages ≥18 years led by the U.S. Census Bureau, in partnership with seven other federal statistical agencies, to measure household experiences during the COVID-19 pandemic. Detailed information on probability sampling using the U.S. Census Bureau’s Master Address File, questionnaires, response rates, and bias assessment is available on the Census Bureau website (https://www.census.gov/data/experimental-data-products/household-pulse-survey.html).

Data from adults age ≥18 years are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). Data are weighted to represent total persons age 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Data from adults age ≥18 years are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). For more information on this survey, see https://www.census.gov/programs-surveys/household-pulse-survey.html.

Data are weighted to represent total persons age 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Responses in the Household Pulse Survey (https://www.census.gov/programs-surveys/household-pulse-survey.html) are self-reported. Estimates of vaccination coverage may differ from vaccine administration data reported at COVID-19 Vaccinations in the United States (https://covid.cdc.gov/covid-data-tracker/#vaccinations).

This survey focuses on the employment, lifestyles, political and religious participation and educational levels of the disabled. Variables include type and severity of disability, life satisfaction, social impact, employment, health insurance, health care, technology and computers, and importance of spirituality to those with disabilities.

This dataset explores the days of work lost per worker due to illness or disability by province in Canada for 2003-2007. Note: Includes full-time paid workers only. Source: Statistics Canada, CANSIM, table 279-0029. Last Modified: 2008-05-22.

This survey focuses on how fully disabled Americans participate in the political life of their community. Variables include satisfaction with government, past voting behavior, participation in campaigns, disability issues, identity with other disabled people, access to voting booth or machine, use of the absentee ballot, alienation and trust in government.

Parking Permits for People with Disabilities (PPPD- State) are issued to people who are eligible to obtain a New York State parking permit, and the person has been certified by a physician as having a disability that severely affects the person’s ability to walk for long distances.

Total, disability-free, and disabled life expectancy by gender and living arrangement for non-Hispanic whites at age 50: Population-based estimates.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

United States US: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data was reported at 9.790 % in 2015. This records an increase from the previous number of 9.720 % for 2014. United States US: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data is updated yearly, averaging 9.720 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 9.930 % in 2010 and a record low of 9.380 % in 2012. United States US: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.; ; National statistical offices or national database and publications compiled by United Nations Statistics Division; ;