LDI Research Seminar Series

Brent Kreider, PhD
Associate Professor of Economics, Iowa State University

"Inferring Disability Status from Corrupt Data"

May 3, 2001, 12:00 p.m.
Colonial Penn Center Auditorium

Biosketch Abstract

Brent Kreider (PhD, University of Wisconsin-Madison) is an associate professor of economics at Iowa State University. He was previously an assistant professor of economics at the University of Virginia. His fields of specialization include public economics, health economics, applied econometrics, and labor economics. All of his empirical work includes a strong health component, with a current emphasis on developing methods for improving inferences about the effects of mental and physical health variables on work capacity and labor force participation outcomes. Much of this research is concerned with accurately predicting the impacts of proposed public policy changes on the behavior and welfare of the disabled. Although primarily an applied economist, he has also written several theoretical papers on optimal taxation and tax incidence.

We investigate what can be learned about the prevalence of work disability using self-reported assessments of work capacity. Although health status is widely recognized as a crucial determinant of labor supply behavior and participation in public transfer programs, there is a long-standing debate about the reliability of self-reported indicators. Anderson and Burkhauser (1985), in fact, labeled the appropriate use of health controls "the major unsettled issue in the empirical literature on the labor supply of older workers," and the debate has only grown stronger over time. Rather than focus on assumptions required to obtain point identification, we take a step back to evaluate what can be inferred about disability rates under a variety of assumptions that are weaker but arguably more credible than those imposed in the existing literature. Extending the work on corrupt samples developed by Horowitz and Manski (1995) and Dominitz and Sherman (1998), we develop a set of nonparametric bounds that, in the most basic setting, require only prior information restricting the fraction of persons who might misreport. These bounds inform the ongoing debate by effectively constraining the range of uncertainty regarding the effects of inaccurate reporting on inferences. Tighter bounds can be obtained with additional assumptions. The results clearly show that the strength of the conclusions one can draw depend directly on the strength of the assumptions one is willing to impose. If the true disability rate is nondecreasing with age, then our results provide evidence that conventional participation models which presume that self-reports are valid may be misspecified.

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