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.
Collection of audio features and sound bites.
The Census Bureau packages data and information into easy-to-understand visuals.
Browse Census Bureau images.
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.
How we provide the best mix of timeliness, relevancy, quality, and cost for the data we collect.
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 Census Bureau.
Explore Census programs targeted for particular needs.
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.
Listen to audio files on fun facts, historical figures, and celebrations of the month.
Find media toolkits, advisories, and all the latest Census news.
See what's coming up in releases and reports.
The following items represent changes in the estimation procedures used for school district, county, and state poverty estimates for 2006-2009 relative to the estimation procedures used for 2005. These changes reduce the comparability of the estimates between years and should be considered when making such comparisons. See General Cautions about Comparisons of Estimates.
Changes to County-Level Entities
The definition and inventory of county-level entities change on an irregular basis, based on the decisions of state and local governments. SAIPE provides estimates for all county-level entities (excluding Kalawao, Hawaii) that have been in effect for a full calendar year of American Community Survey sample. For the 2006-2009 period, two sets of substantial changes occurred, both affecting county-level entities in the state of Alaska. For details of these changes, refer to Substantial Changes to Counties and County-Level Equivalents.
ACS Group Quarters
The 2006-2009 American Community Survey (ACS) estimates, which are used for the latest SAIPE state and county modeling, had one change relative to the 2005 ACS estimates used for 2005 SAIPE modeling.
In 2005 and prior years, poverty status in the ACS was determined only for individuals living in households. Residents of group quarters, such as nursing homes, dormitories, shelters and other joint living quarters were not surveyed. In the 2006-2009 ACS, some group quarter residents are included in the poverty universe, which is the term used for individuals for whom poverty status is determined. The group quarters residents included in the poverty universe are non-institutional group quarters residents, apart from college dormitories and military housing. These quarters include shelters, halfway houses, emergency housing facilities, and other structures not classified elsewhere.
Residents of these non-institutional group quarters in general have higher poverty rates than residents of households. Thus, when comparing estimates from 2006-2009 to those from earlier years, this specific caution should be considered. For more information, see the Detailed Information on 2006 ACS Data Comparisons.
Special Processing for Areas Affected by Hurricanes Katrina and Rita
The Federal Emergency Management Agency (FEMA) declared 117 counties to be disaster areas in Alabama, Louisiana, Mississippi, and Texas as a result of hurricanes Katrina and Rita in August and September 2005, respectively. The Population Estimates Program (PEP) of the U.S. Census Bureau used supplemental data, such as the U.S. Postal Services' National Change of Address Form, to track the movements of people displaced by Hurricanes Katrina and Rita. More details can be obtained from the Population Estimates Program methodology.
The SAIPE program used these PEP population estimates for the Hurricane-affected areas in the production of its 2006-2009 income and poverty estimates. Where the relation of other inputs in the SAIPE models to population estimates diverged from historical norms for the 2006-2009 models, we adjusted these inputs back near their historical relationships. Adjustments of this nature were required at the county level for decennial census poverty, transfer income, personal income growth and Supplemental Nutrition Assistance Program (SNAP) benefits in 2006, and for decennial census poverty, personal income growth, and tax exemptions in 2007, and for decennial census poverty in 2008-2009.
Note as of Oct. 1, 2008, the federal Food Stamp Program is now named Supplemental Nutrition Assistance Program (SNAP).
Further information about Hurricanes Katrina and Rita is available at:
Estimates of 5-17 Poverty for the District of Columbia (DC)
Starting in 2009, estimates of 5-17 poverty for DC are obtained from the county model for this age group, rather than the state model. Examination of our input data (regression variables) revealed that DC is much more like other concentrated urban counties in regard to these data, whereas it is quite different in this respect from the 50 states. While this fact does not invalidate use of state model predictions for DC, it means that the county model assumptions may be more likely to hold for DC than the state model assumptions. In addition, examination of model fitting results from 2005-2009 suggested the county model may fit better for DC than the state model, though this is difficult to assess from such a limited set of observations. However, in 2006 the state model produced a large (standardized) residual for DC, and in 2009 a slightly large residual. The county model did not produce a large (standardized) residual for DC for any year.
For poverty of the other age groups and for median household income, estimates for DC are still obtained from the state model. In general, standardized residuals have not been as large for the other age groups.
Note that, in earlier years when Current Population Survey direct estimates were used in the models rather than ACS direct estimates, it would not have been sensible to obtain model-based estimates for DC from the county model since this model used 3-year averages of CPS data whereas the state model used single-year estimates. Hence, the county model-based and state model-based estimates for DC were not exactly comparable (the state estimates, based on the single-year data, were more recent), and the raking of county to state estimates adjusted for the timing differences for each state and for DC.
There were no changes to the methods used to estimate state SAIPE poverty levels for 2006-2009 compared to the 2005 methods. Regarding the input data used, a special adjustment was made for SNAP benefits in the state of Vermont to account for a known small historical error by Vermont Agency for Human Services in extracting Vermont SNAP cases. Additionally, the demographic estimates of the poverty universe used to transform poverty levels into reported rates now include residents of non-institutional group quarters, not elsewhere classified. This is consistent with the ACS group quarters change cited above.
There were no changes to the methods used to estimate county SAIPE poverty levels for 2006, 2007 or 2008 compared to the 2005 methods. Regarding the input data used, the demographic estimates of the poverty universe used to transform poverty levels into reported rates now include residents of non-institutional group quarters, not elsewhere classified. This is consistent with the ACS group quarters change cited above.
As described in County-Level Estimation Details, when estimating the coefficients in the county-level model, we take logarithms of the dependent and all independent variables. The shrinkage estimate, combining the resulting fitted value and ACS estimate, is also created in the log-scale. When transforming to the original levels for publication, adjustments are required to maintain the optimal statistical properties of the estimates. For SAIPE 2009 estimates, modifications were made to these adjustments. These modifications had negligible impact on the final estimated levels (less than 0.1%), but did change the estimated measures of uncertainty, that is, the standard errors used to construct confidence intervals. This modification to the standard errors reduces the year-to-year changes in the confidence interval that can occur from SAIPE’s use of survey estimates.
School District Level
The SAIPE school district estimates incorporate the latest available boundary updates produced by the School District Review Program (SDRP), which are updated every two years. Thus for 2009 estimates, boundaries were utilized from the 2009-2010 School District Review Program, completed in 2009 and can be obtained by downloading the SAIPE 2009 school district estimates database, available by state or complete national format. Previous estimates utilized previous versions of the SDRP boundaries. Previous estimates utilized previous versions of the SDRP boundaries.
Finally, regarding the methodology for school districts, there were no production changes for 2006-2009 estimates relative to 2005.