How Large are
the Classification Errors in the Social Security Disability
Award Process? Hugo Benitez-Silva (SUNY-Brook), Moshe
Buchinsky (UCLA), and John Rust (UMD)
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 study a subset of individuals from
the Health and Retirement Study (HRS) who applied for DI or
SSI benefits between 1992 and 1996. We compare the SSA's
ultimate award decision (i.e. after allowing for appeals) to
the applicant's self-reported disability status. We use
these data 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. Our
analysis also yields insights into the patterns of
self-selection induced by varying delays and award
probabilities at various levels of the application and
appeal process. We construct an optimal statistical
screening rule using a subset of objective health indicators
that the SSA uses in making award decisions that results in
significantly lower classification error rates than does
SSA's current award process.