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.

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.

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

Table from the American Community Survey (ACS) S1810 disability characteristics by age. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): S1810Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

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.

This layer shows disability status by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of elderly (65+) with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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 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.

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 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.

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.

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:YouthOlder AdultsFemaleRacial MinorityEthnic MinorityForeign-BornDisabledLimited English ProficiencyLow-IncomeCensus tables used to gather data from the 2018-2022 American Community Survey 5-Year EstimatesUsing U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group.Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates.For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipdSource of tract boundaries: 2020 US Census Bureau, TIGER/Line ShapefilesNote: Tracts with null values should be symbolized as "Insufficient or No Data".Data Dictionary for Attributes:(Source = DVRPC indicates a calculated field)FieldAliasDescriptionSourceyearIPD analysis yearDVRPCgeoid2011-digit tract GEOIDCensus tract identifierACS 5-yearstatefp2-digit state GEOIDFIPS Code for StateACS 5-yearcountyfp3-digit county GEOIDFIPS Code for CountyACS 5-yeartractceTract numberTract NumberACS 5-yearnameTract numberCensus tract identifier with decimal placesACS 5-yearnamelsadTract nameCensus tract name with decimal placesACS 5-yeard_classDisabled percentile classClassification of tract's disabled percentage as: well below average, below average, average, above average, or well above averagecalculatedd_estDisabled count estimateEstimated count of disabled populationACS 5-yeard_est_moeDisabled count margin of errorMargin of error for estimated count of disabled populationACS 5-yeard_pctDisabled percent estimateEstimated percentage of disabled populationACS 5-yeard_pct_moeDisabled percent margin of errorMargin of error for percentage of disabled populationACS 5-yeard_pctileDisabled percentileTract's regional percentile for percentage disabledcalculatedd_scoreDisabled percentile scoreCorresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4calculatedem_classEthnic minority percentile classClassification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above averagecalculatedem_estEthnic minority count estimateEstimated count of Hispanic/Latino populationACS 5-yearem_est_moeEthnic minority count margin of errorMargin of error for estimated count of Hispanic/Latino populationACS 5-yearem_pctEthnic minority percent estimateEstimated percentage of Hispanic/Latino populationcalculatedem_pct_moeEthnic minority percent margin of errorMargin of error for percentage of Hispanic/Latino populationcalculatedem_pctileEthnic minority percentileTract's regional percentile for percentage Hispanic/Latinocalculatedem_scoreEthnic minority percentile scoreCorresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4calculatedf_classFemale percentile classClassification of tract's female percentage as: well below average, below average, average, above average, or well above averagecalculatedf_estFemale count estimateEstimated count of female populationACS 5-yearf_est_moeFemale count margin of errorMargin of error for estimated count of female populationACS 5-yearf_pctFemale percent estimateEstimated percentage of female populationACS 5-yearf_pct_moeFemale percent margin of errorMargin of error for percentage of female populationACS 5-yearf_pctileFemale percentileTract's regional percentile for percentage femalecalculatedf_scoreFemale percentile scoreCorresponding numeric score for tract's female classification: 0, 1, 2, 3, 4calculatedfb_classForeign-born percentile classClassification of tract's foreign born percentage as: well below average, below average, average, above average, or well above averagecalculatedfb_estForeign-born count estimateEstimated count of foreign born populationACS 5-yearfb_est_moeForeign-born count margin of errorMargin of error for estimated count of foreign born populationACS 5-yearfb_pctForeign-born percent estimateEstimated percentage of foreign born populationcalculatedfb_pct_moeForeign-born percent margin of errorMargin of error for percentage of foreign born populationcalculatedfb_pctileForeign-born percentileTract's regional percentile for percentage foreign borncalculatedfb_scoreForeign-born percentile scoreCorresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4calculatedle_classLimited English proficiency percentile classClassification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above averagecalculatedle_estLimited English proficiency count estimateEstimated count of limited english proficiency populationACS 5-yearle_est_moeLimited English proficiency count margin of errorMargin of error for estimated count of limited english proficiency populationACS 5-yearle_pctLimited English proficiency percent estimateEstimated percentage of limited english proficiency populationACS 5-yearle_pct_moeLimited English proficiency percent margin of errorMargin of error for percentage of limited english proficiency populationACS 5-yearle_pctileLimited English proficiency percentileTract's regional percentile for percentage limited english proficiencycalculatedle_scoreLimited English proficiency percentile scoreCorresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4calculatedli_classLow-income percentile classClassification of tract's low income percentage as: well below average, below average, average, above average, or well above averagecalculatedli_estLow-income count estimateEstimated count of low income (below 200% of poverty level) populationACS 5-yearli_est_moeLow-income count margin of errorMargin of error for estimated count of low income populationACS 5-yearli_pctLow-income percent estimateEstimated percentage of low income (below 200% of poverty level) populationcalculatedli_pct_moeLow-income percent margin of errorMargin of error for percentage of low income populationcalculatedli_pctileLow-income percentileTract's regional percentile for percentage low incomecalculatedli_scoreLow-income percentile scoreCorresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4calculatedoa_classOlder adult percentile classClassification of tract's older adult percentage as: well below average, below average, average, above average, or well above averagecalculatedoa_estOlder adult count estimateEstimated count of older adult population (65 years or older)ACS 5-yearoa_est_moeOlder adult count margin of errorMargin of error for estimated count of older adult populationACS 5-yearoa_pctOlder adult percent estimateEstimated percentage of older adult population (65 years or older)ACS 5-yearoa_pct_moeOlder adult percent margin of errorMargin of error for percentage of older adult populationACS 5-yearoa_pctileOlder adult percentileTract's regional percentile for percentage older adultcalculatedoa_scoreOlder adult percentile scoreCorresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4calculatedrm_classRacial minority percentile classClassification of tract's non-white percentage as: well below average, below average, average, above average, or well above averagecalculatedrm_estRacial minority count estimateEstimated count of non-white populationACS 5-yearrm_est_moeRacial minority count margin of errorMargin of error for estimated count of non-white populationACS 5-yearrm_pctRacial minority percent estimateEstimated percentage of non-white populationcalculatedrm_pct_moeRacial minority percent margin of errorMargin of error for percentage of non-white populationcalculatedrm_pctileRacial minority percentileTract's regional percentile for percentage non-whitecalculatedrm_scoreRacial minority percentile scoreCorresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4calculatedtot_ppTotal population estimateEstimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born)ACS 5-yeartot_pp_moeTotal population margin of errorMargin of error for estimated total population of tractACS 5-yeary_classYouth percentile classClassification of tract's youth percentage as: well below average, below average, average, above average, or well above averagecalculatedy_estYouth count estimateEstimated count of youth population (under 18 years)ACS 5-yeary_est_moeYouth count margin of errorMargin of error for estimated count of youth populationACS 5-yeary_pctYouth population percentage estimateEstimated percentage of youth population (under 18 years)calculatedy_pct_moeYouth population percentage margin of

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; ;

2024 Updates DVRPC performed an analysis to create the CHSTP Priority Score, a layer that helps users visualize where there is a potentially high need to improve transit service for vulnerable populations to reach essential services in the Greater Philadelphia region. As a metropolitan planning organization, Delaware Valley Regional Planning Commission (DVRPC) is responsible for updating the region's Coordinated Human Services Transportation Plan (CHSTP). The CHSTP update engaged a variety of stakeholders to identify unmet needs and service gaps, recommend innovative transportation access solutions, and empower communities to climb "ladders of opportunity" toward greater social and economic mobility. As part of the CHSTP update, DVRPC created the CHSTP Priority Score Map Toolkit. This interactive web-based tool demonstrates disparities in access to essential services like hospitals, health clinics, recreational spaces, senior centers, and more in the Greater Philadelphia region. Users can view layers representing different datasets including the locations of essential services; bus routes, transit stops, and rail lines; transit walksheds; distributions of vulnerable populations like seniors, households in poverty, and people with disabilities; and areas where transit access is low. https://github.com/dvrpc/gis-chstp includes all code for the analysis.

Below are several of the analyses included in this dataset. Vulnerable Populations answers the question, “Who lives here?” and highlights populations in need. Essential Services answers the question, “Where do people need to go?” and highlights areas with more services in the region. Population-Services Mismatch answers the question, “Where is there a gap between areas of need and essential services?” This layer highlights areas where there are higher numbers of vulnerable populations but fewer essential services and vice versa. Transit Accessibility answers the question, “How is transit service distributed?” and highlights areas in the region with lower transit accessibility. Priority Score answers the question, “Where can transit service be improved to help vulnerable populations access essential services?” This layer, the result of our analysis, highlights areas with higher numbers of vulnerable populations or essential services, but lower transit accessibility and vice versa. Name Field Source Additional Info Notes VULNERABLE POPULATIONS Total Number of Households hh ACS B11001_001E American Community Survey 5-Year Data (2018-2022) Total Number of People pop ACS B01003_001E American Community Survey 5-Year Data (2018-2022) Households with 1 or More People with Disability hh1_dis ACS B22010, Estimate; Household received Food Stamps/SNAP in the past 12 months American Community Survey 5-Year Data (2018-2022) Number of Households Below Poverty Line hh_pov ACS B17017 Estimate; Income in the past 12 months below poverty level: American Community Survey 5-Year Data (2018-2022) People 65 or Older _65older ACS B01001, summarized by sex and age groups American Community Survey 5-Year Data (2018-2022) Vulnerable Population Rank vul_pop_rank DVRPC calculated ESSENTIAL SERVICES Activity Centers for Seniors or Disabled ss_cnt Overture Map Overture Map (2024) Food Stores food_cnt Overture Map Overture Map (2024) Health Care Facilities hc_cnt Overture Map Overture Map (2024) Number of Educational Institutions school_cnt NCES https://nces.ed.gov/collegenavigator/ ; https://nces.ed.gov/surveys/pss/privateschoolsearch/ ; https://nces.ed.gov/ccd/schoolsearch/ Parks/Open Space Present os_check DVRPC DVRPC Parks/Open Space (2016) Trails trail_cnt DVRPC DVRPC Circuit Trails (2020) Essential Services Total es_sum DVRPC calculated Jobs sum_jobs Census LODES Census LODES Essential Services Rank es_rank DVRPC calculated Access Gap access_gap_rank DVRPC calculate the difference of vulnerable population rank and essential service rank for access gap TRANSIT ACCESSIBILITY Transit Accessibilty Zones t_45min_zone_cnt ; t_zone_quantile DVRPC DVRPC Travel Models (2023), How many areas a person could access in a 45 minute transit trip Essential Services in 45 minute TAZ zones t_es_cnt; t_job_cnt; t_45min_es_job_avg Overture Maps, DVRPC travel model jobs in block group and other essential services grouped into separate bins then averaged Daily Departures (by TAZ) total_departures, depart_quantile GTFS - SEPTA, NJTRANSIT, PATCO Frequency of service Walkability Rank walkshed_quantile DVRPC pedestrian network, GTFS - SEPTA, NJTRANSIT, PATCO Walkability of the block group to transit stations/stops Transit Accessibilty Rank transit_access_rank DVRPC Priority Score chstp_score DVRPC calculated

The 1991 New [Social Security] Beneficiary Followup (NBF) is the second wave of the Social Security Administration's NEW [SOCIAL SECURITY] BENEFICIARY SURVEY, 1988: UNITED STATES (ICPSR 8510). Together, the two surveys are referred to as the New Beneficiary Data System (NBDS). The NBDS contains information on the changing circumstances of aged and disabled Title II beneficiaries. This wave includes information from administrative records as well as data from followup interviews with survivors from the original survey. The NBS was conducted in late 1982 with a sample representing nearly 2 million persons who had begun receiving Social Security benefits during a 12-month period in 1980-1981. Personal interviews were completed with three types of beneficiaries: 9,103 retired workers, 5,172 disabled workers, and 2,417 wife or widow beneficiaries. In addition, interviews were obtained from 1,444 aged persons who were entitled to Medicare benefits but were not receiving Social Security payments because of high earnings. The NBS interviews covered a wide range of topics, including demographic characteristics of the respondent, spouse, and any other persons in the household, as well as marital and childbearing history, employment history, current income and assets, and health. Selected data were also gathered from spouses and added from administrative records. The NBF followup interviews were conducted throughout 1991 with surviving original sample persons from the NBS and surviving spouses of NBS decedents. The NBF updated information on economic circumstances obtained in the NBS, and added or expanded sections dealing with health, family contacts, and post-retirement employment. The interviews also probed major changes in living circumstances that might cause changes in economic status (for example, death of a spouse, episodes of hospitalization, and changes of residence). In addition, disabled workers were asked about their efforts to return to work, experiences with rehabilitation services, and knowledge of Social Security work incentive provisions. Since the 1982 survey, selected information on the NBS respondents has been compiled periodically from Social Security, Supplemental Security Income (SSI), and Medicare records. These administrative data, which can be linked to the survey data, make it possible to analyze changes in NBS respondents' covered earnings, cash benefits, participation in the SSI program, and health expenses. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06457.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

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

The SCD Veterans are broken out by SCD ratings (0-20 percent; 30-40 percent; 50-60 percent and 70-100 percent) for FY 1986 to FY 2020. Source: Department of Veterans Affairs, Veterans Benefits Administration; 1985-1998: COIN CP-127 Reports; 1999-2019: Annual Benefits Reports Prepared by the National Center for Veterans Analysis and Statistics, Office of Enterprise Integration, Department of Veterans Affairs, May 2021

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.