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The model for the proportion with health insurance is a two-level hierarchical model. The data sources are:
At the first level, the CPS ASEC direct estimate and the number in Medicaid or CHIP are modeled, conditional on the proportions insured. They are each modeled as a regression model with the proportion insured as the independent variable. The errors are normally distributed and independent between age, sex, race and IPR categories, as well as between the direct estimate and Medicaid/CHIP. The proportions are latent variables - unknown quantities - that are to be estimated by the model.
The CPS ASEC estimated proportions with health insurance are modeled such that the expected values of the CPS ASEC proportions are the unknown proportions of people with health insurance in each of the corresponding categories and the variances are the sampling variances.
The Medicaid and CHIP participation data is broken down into eight age by sex categories for each state. Each age by sex category is modeled as a linear regression where the independent variables are the numbers of people with insurance coverage and IPR ≤ 200% for a subset of the demographic categories contained within the age by sex category. The model variance is modeled as proportional to the number of people in the state and Medicaid age by sex groups.
The proportion of people with health insurance in state by demographic group and IPR category is modeled as a logistic regression with normal errors. The independent variables for the model are:
The model variance is modeled as constant.