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The role of self-reporting bias in health, mental health and labor force participation: a descriptive analysis

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  • Justin Leroux
  • John Rizzo
  • Robin Sickles

Abstract

Previous research on male subjects has conjectured that subjective self-reports of health status may lead to an upward bias in the estimated effect of health on labor force participation 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. In the descriptive analysis conducted in this article, we compare the effects of mental and physical health status on labor force participation, employing propensity score methods to investigate whether these effects differ for self- and proxy respondents. The authors initially find some evidence that seems to suggest systematic differences between proxy and self-reporters in the effects of health on labor force participation, raising the possibility that self-reporters may be biased in their health assessments. After we control for objective baseline indices of mental and physical health status, however, differences between subjective health assessments and labor force participation become smaller and statistically insignificant. These results suggest that self-reports do not lead to overestimates of the importance of good physical or mental health on labor force participation, after one controls for objective health conditions in the models. Although we conclude that propensity score matching is a useful way to align observations with covariates in estimating the effects of health on labor force participation, we find that the appropriate specification of matching variables—and in particular, the inclusion of objective health measures—is critical for understanding whether self-reporting bias matters in this context. Copyright Springer-Verlag 2012

Suggested Citation

  • Justin Leroux & John Rizzo & Robin Sickles, 2012. "The role of self-reporting bias in health, mental health and labor force participation: a descriptive analysis," Empirical Economics, Springer, vol. 43(2), pages 525-536, October.
  • Handle: RePEc:spr:empeco:v:43:y:2012:i:2:p:525-536
    DOI: 10.1007/s00181-010-0434-z
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    References listed on IDEAS

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    1. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
    2. 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.
    3. Markus Frölich, 2007. "Propensity score matching without conditional independence assumption--with an application to the gender wage gap in the United Kingdom," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 359-407, July.
    4. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    5. 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.
    6. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    7. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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    Cited by:

    1. Maciej Lis & Iga Magda, 2014. "Dynamika płac w cyklu życia a indywidualny stan zdrowia," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 121-142.
    2. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    3. Ding Liu & Daniel L. Millimet, 2021. "Bounding the joint distribution of disability and employment with misclassification," Health Economics, John Wiley & Sons, Ltd., vol. 30(7), pages 1628-1647, July.
    4. Aisa, Rosa & Larramona, Gemma & Pueyo, Fernando, 2015. "Active aging, preventive health and dependency: Heterogeneous workers, differential behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 1-9.

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    More about this item

    Keywords

    Health economics; Propensity scoring; Logistic regression; Self-reported health; Labor force participation; Mental health; C10; C14; I18; J24;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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