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Measuring the biases in self-reported disability status: evidence from aggregate data

Listed author(s):
  • Naoko Akashi-Ronquest
  • Paul Carrillo
  • Bruce Dembling
  • Steven Stern

Self-reported health status measures are generally used to analyse Social Security Disability Insurance's (SSDI) application and award decisions as well as the relationship between its generosity and labour force participation. Due to endogeneity and measurement error, the use of self-reported health and disability indicators as explanatory variables in economic models is problematic. We employ county-level aggregate data, instrumental variables and spatial econometric techniques to analyse the determinants of variation in SSDI rates and explicitly account for the endogeneity and measurement error of the self-reported disability measure. Two surprising results are found. First, it is shown that measurement error is the dominating source of the bias and that the main source of measurement error is sampling error. Second, results suggest that there may be synergies for applying for SSDI when the disabled population is larger.

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Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 18 (2011)
Issue (Month): 11 ()
Pages: 1053-1060

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Handle: RePEc:taf:apeclt:v:18:y:2011:i:11:p:1053-1060
DOI: 10.1080/13504851.2010.524603
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