Point identification in the presence of measurement error in discrete variables: application - wages and disability

Eirini-Christina Saloniki and Amanda Gosling

Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York

Abstract: This paper addresses the problem of point identification in the presence of measurement error in discrete variables; in particular, it considers the case of having two “noisy†indicators of the same latent variable and without any prior information about the true value of the variable of interest. Based on the concept of the fourfold table and creating a nonlinear system of simultaneous equations from the observed proportions and predicted wages, we examine the need for different assumptions in order to obtain unique solutions for the system. We show that by imposing a simple restriction(s) for the joint misclassification probabilities, it is possible to measure the extent of the misclassification error in that specific variable. The proposed methodology is then used to identify whether people misreport their disability status using data from the British Household Panel Survey. Our results show that the probability of underreporting is greater than the probability of overreporting disability.

Keywords: measurement error; discrete; misclassification probabilities; identification; disability (search for similar items in EconPapers)
JEL-codes: C14 C35 J14 J31 (search for similar items in EconPapers)
Date: 2013-08
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Working Paper: Point identification in the presence of measurement error in discrete variables: application - wages and disability (2012)
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