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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.
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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.
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The Geographic Support System Initiative will integrate improved address coverage, spatial feature updates, and enhanced quality assessment and measurement.
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Metropolitan and micropolitan areas are geographic entities used by Federal statistical agencies in collecting, tabulating, and publishing Federal statistics.
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Definitions of geographic terms, why geographic areas are defined, and how the Census Bureau defines geographic areas.
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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.
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The Census Bureau's Director writes on how we measure America's people, places and economy.
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Profile America is a daily, 60-second feature that uses interesting vignettes for that day to highlight information collected by the Census Bureau.
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We estimate the number of people with health insurance coverage by state within demographic groups and income categories. The number insured in a group is the product of the number in the group and the proportion in that group who are insured. Correspondingly, our model has two main parts: one for estimating the numbers of people in state demographic and income groups, and one for estimating the proportions with health insurance in these groups. Each part is a hierarchical two-level regression model. We use Bayesian methods to estimate the model. We estimate the number without insurance as the difference between the number of people in a category and the number with insurance. The demographic groups and income categories are described in the Model Details section.
The dependent variables in the regression models are:
The CPS ASEC estimates of the number of people in a state demographic and income group, and of the proportion insured, are assumed to be unbiased. The other dependent variables are related to and indicative of these numbers or proportions but are not assumed to be unbiased estimates for them.
The universe for these health insurance estimates is the CPS poverty universe. Therefore, we use demographic estimates of the population adjusted to the CPS poverty universe.
For further information on the dependent variables and population estimates, see information about data inputs.
We control the estimates for states so the following conditions are met:
The CPS ASEC estimates for different states have different reliability because of the size of samples in each state. Our estimates consider this factor. Estimates from states with larger samples tend to be closer to the direct estimates.
We provide a confidence interval for each estimate that represents uncertainty from both sampling and modeling. These confidence intervals are Bayesian credible regions calculated using posterior standard deviations and a normal approximation.