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

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  • Saloniki, E-C.
  • Gosling, A.

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.

Suggested Citation

  • Saloniki, E-C. & Gosling, A., 2013. "Point identification in the presence of measurement error in discrete variables: application - wages and disability," Health, Econometrics and Data Group (HEDG) Working Papers 13/16, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:13/16
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    References listed on IDEAS

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    1. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
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    More about this item

    Keywords

    measurement error; discrete; misclassification probabilities; identification; disability;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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