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Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors

Journal of the American Statistical Association (JASA), June 2007, 432-41

Brent Kreider and John Pepper

Measurement error in health and disability status has been widely accepted as a central problem for social
science research.  Long-standing debates about the prevalence of disability, the role of health in labor market
outcomes, and the influence of federal disability policy on declining employment rates have all emphasized
issues regarding the reliability of self-reported disability.  In addition to random error, inaccuracy in survey
datasets may be produced by a host of economic, social, and psychological factors that can lead respondents
to misreport work capacity.  We develop a nonparametric foundation for assessing how assumptions on the
reporting error process affect inferences on the employment gap between the disabled and nondisabled.
Rather than imposing the strong assumptions required to obtain point identification, we derive sets of bounds
that formalize the identifying power of primitive nonparametric assumptions that appear to share broad
consensus in the literature.  Within this framework, we introduce a finite-sample correction for the analog
estimator of the monotone instrumental variable (MIV) bound.  Our empirical results suggest that conclusions
derived from conventional latent variable reporting error models may be driven largely by ad hoc distributional
and functional form restrictions. Under relatively weak nonparametric assumptions, nonworkers appear to
systematically overreport disability.