How Large are the Classification Errors in the Social Security Disability Award Process?

Hugo Benitez-Silva, Moshe Buchinsky and John Rust ()
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Moshe Buchinsky: UCLA and NBER

No 05-02, Department of Economics Working Papers from Stony Brook University, Department of Economics

Abstract: This paper presents an .audit. of the multistage application and appeal process that the U.S. Social Security Administration (SSA) uses to determine eligibility for disability benefits from the Disability Insurance (DI) and Supplemental Security Income (SSI) programs. We use a subset of individuals from the Health and Retirement Study who applied for DI or SSI benefits between 1992 and 1996, to estimate classification error rates under the hypothesis that applicants' self-reported disability status and the SSA's ultimate award decision are noisy but unbiased indicators of a latent .true disability status. indicator. We find that approximately 20% of SSI/DI applicants who are ultimately awarded benefits are not disabled, and that 60% of applicants who were denied benefits are disabled. We also construct an optimal statistical screening rule that results in significantly lower classification error rates than does SSA's current award process.

Keywords: Social Security Disability Insurance; Supplemental Security Income; Health and Retirement Study; Classification Errors. (search for similar items in EconPapers)
JEL-codes: H5 (search for similar items in EconPapers)
Pages: 53 pages.
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://ms.cc.sunysb.edu/~hbenitezsilv/dice.pdf First version, 2005 (application/pdf)
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Working Paper: How Large are the Classification Errors in the Social Security Disability Award Process? (2004)
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