Title

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

Campus Units

Economics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

6-2007

Journal or Book Title

Journal of the American Statistical Association

Volume

102

Issue

478

First Page or Article ID Number

432

Last Page

441

DOI

10.1198/016214506000000997

Abstract

Measurement error in health and disability status has been widely accepted as a central problem in 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. We also find that under relatively weak nonparametric assumptions, nonworkers appear to systematically overreport disability.

Comments

This is a workiing paper of an article from Journal of the American Statistical Association 102, no. 478 (2007): 432-441. doi: 10.1198/016214506000000997.

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