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Errors-in-Variables Model for County-level Poverty Estimation
Errors-in-Variables Model for County-level Poverty Estimation
Abstract
The Small Area Income and Poverty Estimates (SAIPE) program at the U.S. Census Bureau has a model for poverty which relates direct estimates from the Annual Social and Economic Supplement (ASEC) of the Current Population Survey to various Administrative Records (AR) and the last decennial census through a regression model with random effects.
In this paper a Hierarchical Bayes (HB) model is described. Various data are modeled as functions of poverty. Further, the variances of the data sources, conditioned on poverty, are modeled explicitly. This avoids the well-known problems associated with regressions when the predictors are measured with error. Further, the model is easily extended to handle poverty-related data from other surveys or AR, such as the American Community Survey or data from the National School Lunch Program (NSLP).
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Page Last Revised - October 8, 2021
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