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Partially Identifying True Rates of Health Insurance Coverage Under Contaminated Sampling

Brent Kreider, Iowa State University

Abstract: This paper derives simple closed-form identification regions for the U.S. nonelderly
population's prevalence of health insurance coverage in the presence of household reporting errors.
The methods extend Horowitz and Manski's (1995) nonparametric analysis of contaminated samples
for the case that the outcome is binary. In this case, draws from the alternative distribution (i.e., not
the distribution of interest) might naturally be defined as response errors. The derived identification
regions can dramatically reduce the degree of uncertainty about the outcome distribution compared
with the contaminated sampling bounds. These regions are estimated using data from the Medical
Expenditure Panel Survey (MEPS) combined with health insurance validation data available for a
nonrandom portion of the sample.