2003
DOI: 10.2139/ssrn.337224
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Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors

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 facto… Show more

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Cited by 114 publications
(212 citation statements)
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References 44 publications
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“…In this environment, insurance reporting errors are allowed to be arbitrarily related to true insurance status and healthcare use. These results extend parts of the analyses of Horowitz and Manski (1998) and Kreider and Pepper (2007). Second, we formally assess how statistical identification of a treatment effect decays with the degree of uncertainty about the status quo.…”
Section: Introductionsupporting
confidence: 60%
“…In this environment, insurance reporting errors are allowed to be arbitrarily related to true insurance status and healthcare use. These results extend parts of the analyses of Horowitz and Manski (1998) and Kreider and Pepper (2007). Second, we formally assess how statistical identification of a treatment effect decays with the degree of uncertainty about the status quo.…”
Section: Introductionsupporting
confidence: 60%
“…All bounds are consistent under the maintained assumptions, but as is noted by Manski and Pepper (2000) the MIV bounds may have nite-sample biases due to the fact that the bounds are obtained by taking maxima and minima over collections of nonparametric regression estimates. 5 Kreider and Pepper (2007) propose a bias-correction method that uses the bootstrap distribution to estimate the nite-sample bias. Supposeθ is the initial estimate of an MIV upper or lower bound and θ k is the estimate of the k th bootstrap replication, the bias is then estimated asbias = 1…”
Section: Miv-boundsmentioning
confidence: 99%
“…Here again, the evidence is mixed. For example, Benitez-Silva et al [20] and Stern [21] find little evidence of bias in reported disability status while Kreider [22] and Kreider and Pepper [23] do find evidence of justification bias.…”
Section: International Evidence On Health and Retirementmentioning
confidence: 99%