Inferring disability status from corrupt data

Brent Kreider () and John V. Pepper ()

Journal of Applied Econometrics, 2008, vol. 23, issue 3, pages 329-349

Abstract: In light of widespread concerns about the reliability of self-reported disability, we investigate what can be learned about the prevalence of work disability under various assumptions on the reporting error process. Developing a nonparametric bounding framework, we provide tight inferences under our strongest assumptions but then find that identification deteriorates rapidly as the assumptions are relaxed. For example, we find that inferences are highly sensitive to how one models potential inconsistencies between subjective self-assessments of work limitation and more objective measures of functional limitation. These two indicators appear to measure markedly different aspects of health status. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2008
View list of references View citations in EconPapers

Downloads: (external link)
http://hdl.handle.net/10.1002/jae.979 Link to full text; subscription required (text/html)
http://qed.econ.queensu.ca:80/jae/2008-v23.3/ Supporting data files and programs (text/html)

Related works:
Working Paper: Inferring Disability Status from Corrupt Data (2003)
Working Paper: Inferring Disability Status from Corrupt Data (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:jae:japmet:v:23:y:2008:i:3:p:329-349

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Series data maintained by Christopher F. Baum ().