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.
The Census Bureau’s Small Area Income and Poverty Estimates Program (SAIPE) produces model-based poverty estimates at the county and state level. SAIPE uses Fay-Herriot (1979) models with dependent variables obtained from direct survey poverty estimates (currently obtained from ACS, but prior to 2005 obtained from CPS), and regression predictor variables derived from tabulations of IRS tax data, SNAP (Supplemental Nutrition Assistance Program, formerly food stamps) program data, and previous census estimates (since 2000, these have been the Census 2000 long form estimates). Although the latter have consistently been important predictors in the state and county models, as time advances and the Census 2000 poverty estimates become further removed from the production year, questions arise about their continued value in the model, and particularly about whether they might be somehow replaced in the model by ACS estimates for previous years. At the county level this would suggest consideration be given to replacing Census 2000 estimates with ACS 5-year estimates formed from data for the 5 years preceding the production year (because the only estimates published for all counties are 5-year estimates.) At the state level, the Census 2000 estimates could be replaced by single-year ACS estimates for the year immediately preceding the production year.
In using previous census poverty estimates to define regression variables, SAIPE has ignored the fact that these are survey estimates obtained from the long form and so contain sampling error. At the state level, the sampling errors of the Census 2000 long form estimates used by SAIPE are essentially negligible and can be ignored. This is less true at the county level, however, particularly for small counties. Furthermore, in considering the replacement in the model of previous census estimates with previous ACS estimates, this issue becomes more pressing, as the ACS sampling variances are higher. We illustrate this point in the report. When a predictor variable, such as Census 2000 long form data or previous ACS data, contains nonnegligible sampling error, a bivariate Fay-Herriot model with that predictor as a second dependent variable, can account for that uncertainty. We take that approach in this study, using bivariate models in which “current year” ACS estimates define one of the dependent variables, and either Census 2000 estimates or previous ACS estimates define the second dependent variable. We then compare prediction error variances (posterior variances) from these models to assess which predictor variable—Census 2000 estimates or previous ACS estimates—yields the lowest prediction error variances for the current year. We do this for models at the state and county level for which ACS single-year estimates for 2009 provide the current year estimates. We also obtain county model results with ACS single-year estimates for 2010 providing the current year estimates.
There are two general conclusions from our study. One is that the differences in prediction error variances depending on which data define the second dependent variable are generally not large. The second conclusion is that, among the three candidates, prediction error variances from using ACS multi-year estimates from previous years as the second dependent variable were in some cases lower, and were generally not higher, than those from the other two candidate models. This suggests that replacing the univariate Fay-Herriot models that use Census 2000 estimates to define regression predictor variables with bivariate Fay-Herriot models that use previous 5-year ACS estimates as the second dependent variable may yield improvements as we move further away from Census 2000, and this change is unlikely to do worse.
Elizabeth T. Huang and William R. Bell. (2012). An Empirical Study on Using Previous American Community Survey Data Versus Census 2000 Data in SAIPE Models for Poverty Estimates. Center for Statistical Research & Methodology Research Report Series (Statistics #2012-04). U.S. Census Bureau. Available online at <https://www.census.gov/srd/papers/pdf/rrs2012-04.pdf>.
This symbol indicates a link to a non-government web site. Our linking to these sites does not constitute an endorsement of any products, services or the information found on them. Once you link to another site you are subject to the policies of the new site.