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Bayesian Hierarchical Modeling of U.S. County Poverty Rates

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Bayesian Hierarchical Modeling of U.S. County Poverty Rates

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Bayesian Hierarchical Modeling of U.S. County Poverty Rates
Download Bayesian Hierarchical Modeling of U.S. County Poverty Rates [PDF - <1.0 MB]

The U.S. Census Bureau Small Area Income and Poverty Estimates program produces biennual intercensal estimates of the poverty rates and counts of poor within counties for use in determining the allocation of federal funds to local jurisdictions. Numbers of poor are currently modeled through an empirical Bayes estimation method centered on a linear regression; the dependent variable is a log transformation of the three-year average of the March Current Population Survey (CPS) estimate of the number of poor for each county, and the independent variables are log transformations of administrative data.

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