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The effects of self-assessed health: Dealing with and understanding misclassification bias


  • Cheny, L.;
  • Clarke, P.M.;
  • Petrie, D.J.;
  • Staub, K.E.;


Categories of self-assessed health (SAH) are often used as a measure of health status. However,the difficulties with measuring overall health mean that the same individual may select into different SAH categories even though their underlying health has not changed. Thus,their observed SAH may involve misclassification, and the chance of misclassification may differ across individuals. As shown in this paper,if neglected, misclassification can lead to substantial biases in not only the estimation of the effects of SAH on outcomes, but also on the effects of other variables of interest,such as education and income. This paper studies nonlinear regression models where SAH is a key explanatory variable, but where two potentially misclassified measures of SAH are available.In contrast to linear regression models, the standard approach of using one SAH measure as an instrumental variable for the other cannot produce consistent estimates. However, we show that the coefficients can be identified from the joint distribution of the outcome and the two misclassified measures without imposing additional structure on the misclassification, and we propose simple likelihood-based approaches to estimate all parameters consistently via a convenient EM algorithm. The estimator is applied to data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, where we exploit the natural experiment that in some waves individuals were asked the same question about their health status twice, and almost 30% of respondents change their SAH response. We use the estimator to (i) obtain the first reliable estimates of the relationship between SAH and long-term mortality and morbidity, and to (ii) document how demographic and socio-economic determinants shape patterns of misclassification of SAH.

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  • Cheny, L.; & Clarke, P.M.; & Petrie, D.J.; & Staub, K.E.;, 2018. "The effects of self-assessed health: Dealing with and understanding misclassification bias," Health, Econometrics and Data Group (HEDG) Working Papers 18/26, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:18/26

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    References listed on IDEAS

    1. Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2014. "Misreported schooling, multiple measures and returns to educational qualifications," Journal of Econometrics, Elsevier, vol. 181(2), pages 136-150.
    2. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    3. Denise Doiron & Denzil G. Fiebig & Meliyanni Johar & Agne Suziedelyte, 2015. "Does self-assessed health measure health?," Applied Economics, Taylor & Francis Journals, vol. 47(2), pages 180-194, January.
    4. Anirban Basu & Norma B. Coe & Cole G. Chapman, 2018. "2SLS versus 2SRI: Appropriate methods for rare outcomes and/or rare exposures," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 937-955, June.
    5. Au, Nicole & Johnston, David W., 2014. "Self-assessed health: What does it mean and what does it hide?," Social Science & Medicine, Elsevier, vol. 121(C), pages 21-28.
    6. Amanda Gosling & Eirini‚ÄźChristina Saloniki, 2014. "Correction Of Misclassification Error In Disability Rates," Health Economics, John Wiley & Sons, Ltd., vol. 23(9), pages 1084-1097, September.
    7. McCallum, J. & Shadbolt, B. & Wang, D., 1994. "Self-rated health and survival: A 7-year follow-up study of Australian elderly," American Journal of Public Health, American Public Health Association, vol. 84(7), pages 1100-1105.
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    More about this item


    misreporting; measurement error; multinomial regressor; discrete and limited dependent variables; subjective health; mortality; chronic conditions;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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