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.
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.
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.
Editing: is a process that tries to ensure the accuracy, completeness, and consistency of the survey data. Efforts are made at all phases of collection, processing, and tabulation to minimize reporting, keying, and processing errors,
Although some edits are built into the Internet data collection instrument and the data entry programs, the majority of the edits are performed post collection. Edits consist primarily of four types: (1) consistency edits, (2) historical ratio edits of the current year's reported value to the prior year's value, (3) current year ratio edits, and (4) balance checks,
The consistency edits check the logical relationships of data items reported on the form. For example, if interest on debt is reported, then there must be debt.
The historical ratio edits compare data for the current year to data for the prior year or prior census year. If data fall outside of acceptable tolerance levels, the item is flagged for further review. For example, the reported property tax for the current year may be compared against the property tax last year, if the reporting unit was in last year's sample. If it was not in last year's sample, the current year value is compared to the prior census year value.
The current year ratio edits compare one data item on the form against a different data item. If data fall outside of acceptable tolerance levels, the item is flagged for further review. For example, airport expenditure to airport revenue is a current year ratio.
Balance checks are checks of linear relationships that exist in the data. Debt flow is an example of a balance check. The ending debt must equal the beginning debt plus the debt issued minus the debt retired.
After all data are edited and imputed, they are aggregated. A macro-edit, or aggregate-level, review is conducted with current year state aggregates compared to prior year and prior census aggregates. Macro-level ratio edits and tolerance levels were developed using the current year data.
For the ratio edits, consistency edits, balance checks, and macro edits, the edit results are reviewed by analysts and adjusted as needed. When the analyst is unable to resolve or accept the edit failure, contact is made with the respondent to verify or correct the reported data. The results of the action are tracked with a data edit flag.
Imputation: Not all respondents answer every item on the questionnaire. There are also questionnaires that are not returned despite efforts to gain a response. Imputation is the process of filling in missing or invalid data with reasonable values in order to have a complete data set for analytical purposes. For census years, the complete data set is also needed for sample design purposes.
For nonresponding general purpose governments, imputations for missing units are based on recently reported historical data from either a prior year annual survey or the most recent census, adjusted by a growth rate. If no historical data are available, data from a randomly selected similar unit are adjusted by the ratio of the populations of the nonresponding and randomly selected donor governments.
The imputations for nonresponding special districts are done similarly. If prior year reported data are available, the prior year data for the nonrespondent are adjusted by a growth rate that is determined from reporting units that are similar to the nonrespondent. Special districts are similar if they are of the same function code and similar geography, e.g., police protection in a state or water transport in a region. For nonresponding special districts with no recently reported data available, data are used from a randomly selected donor that is similar to the nonrespondent. In cases where secondary data sources exist, the data from those sources are used.
For individual questionnaire items that are not reported by general purpose governments or dependent and independent school districts, either data from another source, pro-rating of totals, or prior year data are used to give a complete dataset.
Note: Between years 2002 through 2006, individual government imputed data were not released to the public. For 2007 through 2011, individual unit data are available upon request. The data carry imputation and edit flags to help the users determine the usability of the data for their purposes.
Estimation: After the data were edited and imputed, the estimates were calculated using a regression estimator for most variables. For capital outlay and debt variables, a Horvitz-Thompson estimator was used. Downloadable files of the final estimates are available on the website.
Variance: Data that are derived from the annual sample survey are subject to sampling error. The statistics in this report that are based wholly or partly on data from the sample are apt to differ from the results of a census covering all governments. Estimates based on a sample survey are subject to sampling variability. The particular sample used is one of a large number of all possible samples of the same size that could have been selected using the same sample design. Each of the possible samples would yield somewhat different results.
The standard error is a measure of the variation among the estimates from all possible samples and thus is a measure of the precision with which an estimate from a particular sample approximates the average results of all possible samples. A bootstrap variance estimator is used to estimate the variance for the 2011 Annual Survey of Local Government Finances. Each viewable table contains a column that gives users the coefficients of variation (or relative standard error) that have been computed for these estimates. The coefficient of variation is the estimated standard error expressed as a percent of the estimated total or proportion.
State government financial statistics result from a complete canvass of all state government agencies. Consequently, there is no associated measure of sampling error, such as the coefficient of variation. However, these statistics are subject to non-sampling error. Such error includes inaccuracies in classification, coverage, and processing.
Although efforts were made at all phases of collection, processing, and tabulation to minimize errors, the data were still subject to errors from imputing for missing data, errors from miscoding, and errors in coverage. Every effort was made to keep such errors to a minimum through examining, editing, and tabulating the data.
The CVs (coefficient of variation) presented in tables can be used to derive the standard error of the estimate. The standard error can then be used to derive interval estimates with prescribed levels of confidence that the interval includes the average results of all samples:
b. intervals defined by 1.6 standard errors above and below the sample estimate will contain the true value about 90 percent of the time;
c. intervals defined by two standard errors above and below the sample estimate will contain the true value about 95 percent of the time.
The user can calculate the standard error by multiplying the CV presented in the tables by the corresponding estimate. The CVs presented in the tables are in percentage form and must be divided by 100 before being multiplied by the estimate. This standard error estimate can then be used to get a 90 percent interval estimate by multiplying it by 1.6 and adding the result to the estimated total to get the upper bound and subtracting it from the estimated total to get the lower bound.