Introducing a new way to navigate by topics. Access the latest news, data, publications and more around topics of interest.
Our population statistics cover age, sex, race, Hispanic origin, migration, ancestry, language use, veterans, as well as population estimates and projections.
This section provides information on a range of educational topics, from educational attainment and school enrollment to school districts, costs and financing.
We measure the state of the nations workforce, including employment and unemployment levels, weeks and hours worked, occupations, and commuting.
Our statistics highlight trends in household and family composition, describe characteristics of the residents of housing units, and show how they are related.
Health statistics on insurance coverage, disability, fertility and other health issues are increasingly important in measuring the nation's overall well-being.
We measure the housing and construction industry, track homeownership rates, and produce statistics on the physical and financial characteristics of our homes.
The U.S. Census Bureau is the official source for U.S. export and import statistics and regulations governing the reporting of exports from the U.S.
The U.S. Census Bureau provides data for the Federal, state and local governments as well as voting, redistricting, apportionment and congressional affairs.
Search an alphabetical index of keywords and phrases to access Census Bureau statistics, publications, products, services, data, and data tools.
Geography provides the framework for Census Bureau survey design, sample selection, data collection, tabulation, and dissemination.
Geography is central to the work of the Bureau, providing the framework for survey design, sample selection, data collection, tabulation, and dissemination.
Find resources on how to use geographic data and products with statistical data, educational blog postings, and presentations.
The Geographic Support System Initiative will integrate improved address coverage, spatial feature updates, and enhanced quality assessment and measurement.
Work with interactive mapping tools from across the Census Bureau.
Find geographic data and products such as Shapefiles, KMLs, TIGERweb, boundary files, geographic relationship files, and reference and thematic maps.
Metropolitan and micropolitan areas are geographic entities used by Federal statistical agencies in collecting, tabulating, and publishing Federal statistics.
Find information about specific partnership programs and learn more about our partnerships with other organizations.
Definitions of geographic terms, why geographic areas are defined, and how the Census Bureau defines geographic areas.
We conduct research on geographic topics such as how to define geographic areas and how geography changes over time.
Visit our library of Census Bureau multimedia files. Collection formats include audio, video, mobile apps, images, and publications.
Official audio files from the Census Bureau, including "Profile America," a daily series of bite-sized statistics, placing current data in a historical context.
Infographics include information on the Census Bureau's history of data collection, our nation's veterans and the American Community Survey.
Read briefs and reports from Census Bureau experts.
Watch Census Bureau vignettes, testimonials, and video files.
Read research analyses from Census Bureau experts.
Access data through products and tools including data visualizations, mobile apps, interactive web apps and other software.
Developer portal to access services and documentation for the Census Bureau's APIs.
Explore Census Bureau data on your mobile device with interactive tools.
Find a multitude of DVDs, CDs and publications in print by topic.
These external sites provide more data.
Download extraction tools to help you get the in-depth data you need.
Learn more about our data from this collection of e-tutorials, presentations, webinars and other training materials. Sign up for training sessions.
Explore Census data with interactive visualizations covering a broad range of topics.
Learn how we serve the public as the most reliable source of data about the nation's people and economy.
Information about the U.S. Census Bureau.
Information about what we do at the U.S. Census Bureau.
Our researchers explore innovative ways to conduct surveys, increase respondent participation, reduce costs, and improve accuracy.
Our surveys provide periodic and comprehensive statistics about the nation, critical for government programs, policies, and decisionmaking.
Learn about other opportunities to collaborate with us.
Explore the rich historical background of an organization with roots almost as old as the nation.
Explore prospective positions available at the U.S. Census Bureau.
Information about the current field vacancies available at the U.S. Census Bureau Regional Offices.
Discover the latest in Census Bureau data releases, reports, and events.
The Census Bureau's Director writes on how we measure America's people, places and economy.
Find interesting and quirky statistics regarding national celebrations and major events.
Profile America is a daily, 60-second feature that uses interesting vignettes for that day to highlight information collected by the Census Bureau.
Find media toolkits, advisories, and all the latest Census news.
See what's coming up in releases and reports.
Starting in 2008, SAHIE began utilizing the American Community Survey (ACS) as the base. For years prior to 2008, the SAHIE estimates utilized the Annual Social and Economic Supplement to the Current Population Survey (CPS ASEC). Other input data sources remain the same, as described further on this page.
The definitions of health insurance coverage differ between the two surveys. Insured was defined from the CPS ASEC as being covered SOME TIME during the past calendar year. The ACS health insurance question asks "Is this person CURRENTLY covered by [specifically stated] health insurance or health coverage plans?"
Due to these definitional differences, comparisons between 2008-2011 SAHIE estimates and earlier years are not recommended. Guidance on comparisons within SAHIE datasets is available.The remainder of this page provides a summary of the demographic and income model methodology used for the SAHIE estimates. Additional methodological detail is available at the below individual links. Technical papers that describe previous versions of the model are available on the Publications page.
The SAHIE program produces model-based estimates of health insurance coverage within counties and states. Estimates are provided by sex (female, male, both), race/ethnicity (all races, non-Hispanic White, non-Hispanic Black, Hispanic), age (0-18, under 65, 18-64, 40-64, 50-64), and income groups. For 2008 and 2009, county-level estimates for the age 50-64 group are not available.
For estimation, SAHIE uses statistical models that combine survey data from the American Community Survey (ACS) with administrative records data and Census data. The models are “area level” models because we use survey estimates and administrative data at certain levels of aggregation, rather than individual survey and administrative records. Our modeling approach is similar to that of common models developed for small area estimation, but with some additional complexities.
The published estimates are based on aggregates of modeled demographic groups. For states, we model at a base level defined by the full cross-classification of: five age groups, four race/ethnicity groups, both sexes and five income groups. For counties, we model at a base level defined by the same age, sex and income groups, but no race/ethnicity breakdown.
We use estimates from the Census Bureau’s Population Estimates Program for the population in groups defined for states by age by race/ethnicity by sex, and for counties by age by sex. We treat these populations as known. Within each of these groups, the number with health insurance coverage in any of the income categories is given by that population multiplied by two unknown proportions to be estimated: the proportion in the income category and the proportion insured within that income category. The models have two largely distinct parts - an “income part” and an “insurance part” - that correspond to these proportions. We use survey estimates of the proportions in the income groups and of the proportion insured within those groups. We assume these survey estimates are unbiased. We also assume functional forms for the variances of the survey estimates that involve parameters that are estimated.
We treat supplemental variables that predict one or both of the unknown income and insurance proportions in two ways: some of these variables are fixed predictors in regression components of the model; others are random, modeled in ways similar to the survey data.
There is a regression component in both the income and insurance parts of the model. In each case, a transformation of the proportion is predicted by a linear combination of fixed predictors. Some of these predictors are categorical variables that define demographic groups; others are continuous. The continuous fixed predictors include variables regarding employment from the County Business Patterns data file, educational employment, and demographic population.
The supplemental variables we treat as random include data from the decennial census, aggregated federal income tax data, and participation in the Supplemental Nutrition Assistance Program and Medicaid/Children’s Health Insurance Program. For 2010, the decennial census data was replaced with lagged ACS five-year estimates. We model these in ways similar to survey estimates, in that their distributions depend on the proportions being estimated. But they are not unbiased estimators of these proportions or related totals. Instead, we assume that their expectations are parametric functions of either the total or the number insured in an income group. We typically assume they are normally distributed with variances that depend on unknown parameters.
We formulate the model in a Bayesian framework and report the posterior means as the point estimates. We use the posterior means and variances together with a normal approximation to calculate symmetric 90-percent confidence intervals, and report their half-widths as the margins of error.
We control the estimates to be consistent with specified national totals. As a result, when the estimates are summed over the states, they match specified national ACS survey estimates. We match the national estimates for both the number insured and the number uninsured for the following groups:
Our margin of error estimates take into account that these controls are not without error.
We also control the estimates from the SAHIE county models to the state small area estimates of the number insured and uninsured by demographic group. As a result, there is arithmetic consistency across the geographic levels for many of the demographic groups.