Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error

Brent Kreider ()

Staff General Research Papers Archive from Iowa State University, Department of Economics

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.

Keywords: partial identification; nonparametric bounds; contaminated sampling; classification error (search for similar items in EconPapers)
JEL-codes: C14 C21 I18 (search for similar items in EconPapers)
Date: 2006-04-15
New Economics Papers: this item is included in nep-ecm, nep-hea and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www2.econ.iastate.edu/papers/p3849-2006-04-15.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:isu:genres:12588

Access Statistics for this paper

More papers in Staff General Research Papers Archive from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().