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

Akashi-Ronquest, Naoko, Carrillo, Paul, Dembling, Bruce, & Stern, Steven (2011) Measuring the biases in self-reported disability status : Evidence from aggregate data. Applied Economics Letters, 18(11), pp. 1053-1060.

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Abstract

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. © 2011 Taylor & Francis.

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ID Code: 73257
Item Type: Journal Article
Refereed: Yes
Additional URLs:
DOI: 10.1080/13504851.2010.524603
ISSN: 1466-4291
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 Routledge
Deposited On: 02 Jul 2014 05:17
Last Modified: 02 Jul 2014 23:16

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