Health, Mental Health and Labor Productivity: The Role of Self-Reporting Bias
This paper relates physical and mental health status to labor force participation and compares these relationships among self-report and proxy respondents. Previous research has conjectured that self-reports of health status may lead to an upward bias in the estimated effect of health on labor productivity because subjects who are out of the labor force may be more likely to understate their health status so as to justify their lack of employment. Also, we integrate mental health into our study by using two alternative approaches-logistic regression analysis and propensity scoring methods. We find that among the cohort of self-reporters, physical health has a substantially stronger impact on labor productivity than mental health; precisely the opposite patterns were obtained when physical and mental health status were reported by proxy respondents. These results suggest the self-reports may lead to a bias in estimating labor productivity costs of physical versus mental health on labor force participation by overestimating the importance of good physical health and underestimating the role of good mental health. This in turn suggests that the benefits of more generous mental health insurance benefits may have been underappreciated in the medical policy debates.
|Date of creation:||Jun 2004|
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- Sickles, Robin C. & Taubman, Paul, 1993. "Mortality and morbidity among adults and the elderly," Handbook of Population and Family Economics,in: M. R. Rosenzweig & Stark, O. (ed.), Handbook of Population and Family Economics, edition 1, volume 1, chapter 11, pages 559-643 Elsevier.
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"An Analysis of the Health and Retirement Status of the Elderly,"
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- John Bound, 1991. "Self-Reported Versus Objective Measures of Health in Retirement Models," Journal of Human Resources, University of Wisconsin Press, vol. 26(1), pages 106-138.
- Guido W. Imbens, 1999. "The Role of the Propensity Score in Estimating Dose-Response Functions," NBER Technical Working Papers 0237, National Bureau of Economic Research, Inc.
- Hernan M. A & Brumback B. & Robins J. M, 2001. "Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 440-448, June. Full references (including those not matched with items on IDEAS)
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