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.
For an overview of the changes in methodology between this release and the 2005 release see Estimation Procedure Changes. One change of note discussed there is that, starting in 2009, estimates of poverty for ages 5-17 for the District of Columbia (DC) are obtained from the county model, instead of the state model. For poverty of the other age groups and for median household income, estimates for DC are still obtained from the state model.
Several features of the state estimates should be noted.
A brief discussion of these features follows along with a presentation of the specific models used.
Bayesian Estimation Techniques
The models the SAIPE program uses to estimate income and poverty at the state level employ both direct survey-based estimates of income and poverty from the ACS and regression predictions of income and poverty based on administrative records and Census 2000 data. We combine the regression predictions with the direct sample estimates using Bayesian techniques. The Bayesian techniques weight the contribution of the two components (regression predictions and direct estimates) on the basis of their relative precision. This is done separately for each year.
The regression models used to develop the regression predictions are postulated for the true, unobserved poverty ratios and median income, but they are fitted to the direct ACS estimates allowing for the sampling errors in the data. If the variance of the error term in a given regression model (the model error variance) was known, then the Bayesian estimate for each state would be a weighted average (shrinkage estimate) of the state's regression prediction and direct ACS estimate. The two weights in this average add to 1.0, with the weight on the direct estimate computed as the model error variance divided by the total variance (model error variance plus sampling error variance). In this average, the larger the sampling variance of a direct sample estimate, the smaller its contribution to the shrinkage estimate, and the larger the contribution from the regression prediction. Since the model error variance is unknown, the Bayesian approach averages the shrinkage estimates computed over a plausible range of values of the model error variance, weighting the results for each of these values according to the posterior (conditional on the data) probability distribution of the model error variance developed from the Bayesian calculations. This is the Bayesian estimate used by SAIPE. It is generally very close to what one would get by estimating the model error variance by the mean of its posterior distribution and computing the corresponding shrinkage estimate. Technical details of the Bayesian approach are discussed in the paper "Accounting for Uncertainty About Variances In Small Area Estimation," (Bell, 1999) in the Published Papers section of this web site.
Note that for many states the sampling error variances of the direct ACS estimates are sufficiently low that in the Bayesian estimation the direct ACS estimate effectively gets most of the weight. Thus, for large states the Bayesian estimates are very close to the direct ACS estimates. For small states the Bayesian estimates potentially differ more from the direct ACS estimates (when the regression prediction for a state differs materially from its direct ACS estimate.)
Poverty Ratios and Numbers of People in Poverty
Deriving state-level estimates of the numbers of people in poverty of various ages involves two steps. The first step is to apply the models and Bayesian estimation techniques to the direct ACS state estimates of "poverty ratios." The second step is to multiply the resulting model-based poverty ratio estimates by corresponding demographic population estimates to convert the results to estimates of the numbers of people in poverty of various ages.
The poverty ratios used as the dependent variables in the regression models have the direct ACS-estimated number of people in poverty of the given age group in the numerator, and (approximately) the direct ACS-estimated poverty universe (those persons for whom the survey would determine if they are in poverty or not in poverty) of the given age group in the denominator. For ages 18-64 and 65 and over the denominator actually is the estimated "poverty universe" for the age groups, so these ratios are true "poverty rates." For ages 0-4 and 5-17 the denominators differ slightly from the estimated poverty universes, so the "poverty ratios" differ slightly from official poverty rates. For further discussion of this, see Denominators for Model-Based State and County Poverty Rates.)
For each age group the model-based poverty ratio estimates are multiplied by corresponding demographic state population estimates (adjusted to estimate the poverty universe for ages 18-64 and 65 and over, and the slightly different concept used for ages 0-4 and 5-17). These demographic estimates are generally very close to the denominators of the direct ACS estimated state poverty ratios, particularly for larger states and for the broader SAIPE age groups (i.e., 18-64 as opposed to 0-4). This is due to the use of substate demographic population estimates for detailed age groups as controls in the determination of the ACS final tabulation weights. Differences between the ACS and demographic state population estimates for the age groups used by SAIPE arise due to collapsing over these age groups that occurs in the application of the population controls for some areas. The ACS and demographic estimates of total population (all ages) will generally agree, however. For a discussion of the use of population controls in the ACS weighting, see Section 11.5 of the report on Design and Methodology: American Community Survey. (Note: Since the poverty universe is a subset of the total population, the demographic estimates of the poverty universe are slightly smaller than published Census Bureau population estimates which are for the total resident population.)
Controlling to the National Estimates
After converting the Bayesian estimates of poverty ratios to state estimates of numbers of people in poverty, we control these estimates to the direct national estimate of number people in poverty based on the ACS. We do not control estimates of state median household income to the national median because the estimation model does not produce the entire household income distribution, which would be required to do so.
Using Estimates from Census 2000 in the Models
The prior census results appear in some form in each of the models. In the models for the ACS poverty ratios of people age 65 and over, the 65 and over poverty ratio from Census 2000 is used as a predictor. For all the other models, the use of the prior census data is somewhat more complex.
For each of the poverty ratios for ages 0-4, 5-17, and 18-64, and for median household income, we first estimated a cross-sectional model for 1999, using the Census 2000 state estimates as the dependent variable and the 1999 values of the administrative data as predictor variables. The residuals from these cross-sectional regressions reflect the extent to which the model based only on the administrative data predictors either overestimates or underestimates poverty for each state, as measured by the census. We used the residuals from these cross-sectional regressions as predictors in the models for the ACS 2006 - 2009 data.
The Poverty Ratio Models
For concreteness we describe the poverty ratio models used for 2009. Completely analogous comments apply to the models used for 2006 – 2008, but with the variables shifted back appropriately in time (except for the Census 2000 estimates and Census 2000 residuals, which remain the same).
The dependent variable is the estimated state poverty ratio, with both the numerator and denominator estimated from the 2009 ACS.
The models of state poverty ratios employ an intercept term and the following predictor variables calculated for each state:
For further information on these variables, go to Information about Data Inputs.
Estimating the Total Number of People in Poverty.
We derive the estimate of the total number of people in poverty in a state by summing the separate model-based estimates of the number of people in poverty by age (not limited to related children). The age groups with separate models were 1) people under 5 years of age, 2) people age 5 to 17 years, 3) people age 18 to 64 years, and 4) people age 65 years and over.
The Model For Median Household Income
For the 2009 estimates the dependent variable is the direct state estimate of median household income from the 2009 ACS.
The 2009 regression model for state median household income employs an intercept term and the following predictor variables calculated for each state:
The analogous model is used for the 2006-2008 state median household income estimates, with the variables shifted back appropriately in time.