Most nonelderly Americans purchase health insurance through their employers, which sponsor a limited number of plans. Using a panel dataset representing over ten million insured lives, we estimate employees' preferences for different health plans and use the estimates to predict their choices if more plans were made available to them on the same terms, i.e., with equivalent subsidies and at large-group prices. Using conservative assumptions, we estimate a median welfare gain of 13 percent of premiums. A proper accounting of the costs and benefits of a transition from employer-sponsored to individually-purchased insurance should include this nontrivial gain. (JEL G22, I13, J32)

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

The Medical Expenditure Panel Survey (MEPS) is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers. Data is publicly-available for two of the four MEPS components: the Household Component and the Insurance Component. Access to Medical Provider Component and Nursing Home Component data requires an application to the Agency for Health Care Research and Quality (AHRQ).

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

A dataset of a longitudinal study of over 3,000 Mexican-Americans aged 65 or over living in five southwestern states. The objective is to describe the physical and mental health of the study group and link them to key social variables (e.g., social support, health behavior, acculturation, migration). To the extent possible, the study was modeled after the existing EPESE studies, especially the Duke EPESE, which included a large sample if African-Americans. Unlike the other EPESE studies that were restricted to small geographic areas, the Hispanic EPESE aimed at obtaining a representative sample of community-dwelling Mexican-American elderly residing in Texas, New Mexico, Arizona, Colorado, and California. Approximately 85% of Mexican-American elderly reside in these states and data were obtained that are generalizable to roughly 500,000 older people. The final sample of 3,050 subjects at baseline is comparable to those of the other EPESE studies. Data Availability: Waves I to IV are available through the National Archive of Computerized Data on Aging (NACDA), ICPSR. Also available through NACDA is the ����??Resource Book of the Hispanic Established Populations for the Epidemiologic Studies of the Elderly����?? which offers a thorough review of the data and its applications. All subjects aged 75 or older were interviewed for Wave V and 902 new subjects were added. Hemoglobin A1c test kits were provided to subjects who self-reported diabetes. Approximately 270 of the kits were returned for analyses. Wave V data are being validated and reviewed. A tentative timeline for the archiving of Wave V data is November 2006. Wave VI interviewing and data collection is scheduled to begin in Fall 2006. * Dates of Study: 1993-2006 * Study Features: Longitudinal, Minority oversamples, Anthropometric Measures * Sample Size: ** 1993-4: 3,050 (Wave I) ** 1995-6: 2,438 (Wave II) ** 1998-9: 1,980 (Wave III) ** 2000-1: 1,682 (Wave IV) ** 2004-5: 2,073 (Wave V) ** 2006-7: (Wave VI) Links: * ICPSR Wave 1: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/2851 * ICPSR Wave 2: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/3385 * ICPSR Wave 3: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4102 * ICPSR Wave 4: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4314 * ICPSR Wave 5: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25041 * ICPSR Wave 6: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/29654

This county geography dataset includes selected indicators (2011-2015 5-Year Averages) pertaining to population, age, race/ethnicity, language, housing, poverty/income, education, disability, health insurance, employment, and age*race*gender groups. This dataset is assembled annually from the U.S. Census American Community Survey American Factfinder website and is maintained by the Colorado Department of Public Health and Environment.

This dataset identifies selected economic characteristics by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The economic characteristics include employment status, commuting to work, occupation, class of worker, income and benefits, health insurance coverage, and percentage of families and people whose income in the past 12 months is below the poverty level.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: 2022 Wards (State Legislative Districts [Upper Chamber]). Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

This survey, conducted as an extension of the NATIONAL SURVEY OF PROBLEMS FACING ELDERLY AMERICANS LIVING ALONE, 1986 (ICPSR 9379) (NSPFEALA), was designed to investigate specific problems of the elderly in order to gain a better understanding of the economic, health, and social status of this group. The survey focused on many of the same issues investigated by the NSPFEALA to allow comparisons between Hispanic elderly and the elderly population as a whole. Respondents were given their choice of English or Spanish as the interview language. Elderly Hispanics were asked if they had serious problems with family relationships, loneliness, anxiety, care of a sick spouse or relative, paying for medical bills, having enough money to live on, or dependence on others. In the same vein, respondents were asked if they had disabilities that affected their daily activities such as bathing, dressing, walking, eating, and shopping, and who, if anyone, helped them to perform these functions. Respondents were also asked if they were generally satisfied with their lives and if they felt excited, restless, proud, pleased, bored, depressed, optimistic, or upset during the few weeks preceding the interview. In addition, the survey inquired about willingness to accept various changes in Social Security benefits and taxation and also queried respondents about their living arrangements (actual and preferred), social networks, general health, doctor visits and hospital stays during the last 12 months, coverage by and utilization of social programs and services, income and sources of income, fluency in English and Spanish, current and past employment, usual means of transportation, home ownership, ancestry, country of birth, year of immigration, religion, education, number of living children, age, sex, and marital status. Random-digit dialing. Sampling was restricted to telephone exchanges with 30 percent or more Hispanics. Sample sizes for the four target Hispanic subgroups were: Mexican-Americans -- 937, Puerto-Rican-Americans -- 368, Cuban-Americans -- 714, and other Hispanics -- 280. Persons of Hispanic origin or descent aged 65 years or older residing in households within the United States. Datasets: DS1: National Survey of Hispanic Elderly People, 1988

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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. Boundaries come from the US Census TIGER geodatabases. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

This special topic poll, conducted April 30 to May 6, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. This poll sought Americans' views on the most important problems facing the United States, their local communities and their own families. Respondents rated the public schools, crime, and drug problems at the national and local levels, their level of optimism about their own future and that of the country, and the reasons they felt that way. Respondents were asked whether they were better off financially than their parents were at their age, whether they expected their own children to be better off financially than they were, and whether the American Dream was still possible for most people. Respondents then compared their expectations about life to their actual experiences in areas such as job security, financial earnings, employment benefits, job opportunities, health care benefits, retirement savings, and leisure time. A series of questions asked whether the United States was in a long-term economic and moral decline, whether the country's main problems were caused more by a lack of economic opportunity or a lack of morality, and whether the United States was still the best country in the world. Additional topics covered immigration policy and the extent to which respondents trusted the federal, state, and local governments. Demographic variables included respondents' sex, age, race, education level, marital status, household income, political party affiliation, political philosophy, voter registration and participation history, labor union membership, the presence of children in the household, whether these children attended a public school, and the employment status of respondents and their spouses. telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis.The data collection was produced by Chilton Research Services of Radnor, PA. Original reports using these data may be found via the ABC News Polling Unit Website.According to the data collection instrument, code 3 in the variable Q909 (Education Level) included respondents who answered that they had attended a technical school.The original data file contained four records per case and was reformatted into a data file with one record per case. To protect respondent confidentiality, respondent names were removed from the data file.The CASEID variable was created for use with online analysis. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was at home at the time of interview. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Datasets: DS1: ABC News Listening to America Poll, May 1996

The Community Health Resources and Needs Assessment (CHRNA) project is a large-scale health needs assessment in diverse, low-income Asian American communities in New York City. The project uses a community-engaged and community venue-based approach to assess existing health issues, available resources, and best approaches to meet community health needs. Questions asked in the CHRNAs assess various determinants of health, including length of residence in the United States, English language proficiency, educational attainment, employment and income, perceived health, health insurance and access to care, nutrition and physical activity, mental health, screening for cancer and other chronic diseases, sleep deprivation, and connections to social and religious environments.

The first round of CHRNAs, conducted between 2004 and 2006, surveyed approximately 100 individuals were surveyed from each of the following Asian subgroups: Cambodians, Chinese, Filipinos, Japanese, Koreans, South Asians, Thai, and Vietnamese (n=1,201).

The Canadian Business Patterns contains data that reflects counts of business locations (as of December 2008) and business establishments (prior to December 2009) by: 9 employment size ranges, including "indeterminate" (as of December 1997); geography groupings: province/territory, census division, census subdivision (before December 2008), census metropolitan area and census agglomeration; and industry using the North American Industry Classification System (tables at the 2, 3, 4 and 6-digit level) as of December 1998. Before December 2004, these data were also presented using the Standard Industrial Classification (tables at the 1, 2, 3 and 4-digit level). The data published in the Canadian Business Patterns represents the current number of locations or establishments for a specific reference period which is taken from the Business Register Central Frame Data Base. It is not intended for use as a time series because changes that affect the continuity of the data might resu lt from changes in methodology. Some examples are: the change to another version of the Standard Geographical Classification (SGC) or the North American Industry Classification System (NAICS), the addition of the new territory of Nunavut and new rules to better identify inactive units.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsPolicy Development: Helps policymakers develop targeted interventions to address the needs of vulnerable populations.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability.Research: Provides a robust foundation for academic and applied research in socio-economic and demographic studies.Community Planning: Aids in the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities.Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2013-2017 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer