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The Impact of Health Changes on Labor Supply: Evidence from Merged Data on Individual Objective Medical Diagnosis Codes and Early Retirement Behavior

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  • Bent Jesper Christensen

    (School of Economics, Aarhus University and CREATES)

  • Malene Kallestrup Lamb

    (School of Economics, Aarhus University)

Abstract

People quit the labor force for many different reasons, voluntarily or not, through various arrangements such as unemployment benefits, disability benefits or specially designed early retirement schemes. This paper complements the existing literature by considering a large, register-based sample including objective medical diagnosis codes. We estimate detailed hazard models of duration until retirement, controlling for unobserved heterogeneity and nonparametric baseline hazards, as well as observed heterogeneity through time-varying explanatory variables. These include diagnosis codes, along with a host of demographic, labor market and financial regressors. The panel structure of the data allows following individuals year by year from the age of 50 and precisely measure changes in objectively measured health and other regressors, as well as labor market status. We consider 12 broad, mutually exclusive and exhaustive categories of health diagnoses defined by aggregation across ICD codes. The use of objective medical diagnosis codes should eliminate the justification bias due to self-reports of health, and the large sample size obtained by using register rather than survey data should mitigate the e¤ect of any remaining mismeasurement of true work incapacity. Together, these improvements should help distinguish empirically important effects of health and economic variables on retirement. We distinguish a number of alternative exit routes, in particular, disability, early retirement, unemployment, and others (including out of the labor force and welfare). We estimate both single risk models, lumping all retirement states, and competing risk specifications, including all separate exit routes. Throughout, females are included in the estimations, and we present separate results by gender. We find sizeable differences in retirement behavior across marital status, gender, labor market attachment, occupation, income, and in particular health. We find that the disability retirement exit route that requires specific medical criteria to be met is different from the early retirement route. The latter shares similarities with private pension schemes in a number of countries, including the U.S., where benefits are tied to previous wages, and employers also contribute to this retirement scheme. These differences are pronounced within labor market attachment, income, and in particular health. Furthermore, unemployment followed by early retirement is different from unemployment followed by other programs regarding marital status, gender, income, and health. These comparisons hinge on the competing risk framework. Finally, even when using objective medical diagnosis measures we still find significant effects from health on retirement. Thus, not all health impact on retirement reported in earlier literature was due to justification bias.

Suggested Citation

  • Bent Jesper Christensen & Malene Kallestrup Lamb, 2010. "The Impact of Health Changes on Labor Supply: Evidence from Merged Data on Individual Objective Medical Diagnosis Codes and Early Retirement Behavior," CREATES Research Papers 2010-62, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2010-62
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    Cited by:

    1. Botosaru, Irene, 2011. "A Duration Model with Dynamic Unobserved Heterogeneity," TSE Working Papers 11-262, Toulouse School of Economics (TSE), revised Nov 2013.
    2. Pinna Pintor, Matteo & Fumagalli, Elena & Suhrcke, Marc, 2024. "The impact of health on labour market outcomes: A rapid systematic review," Health Policy, Elsevier, vol. 143(C).
    3. Malene Kallestrup-Lamb, 2011. "The Role of the Spouse in Early Retirement Decisions for Older Workers," CREATES Research Papers 2011-38, Department of Economics and Business Economics, Aarhus University.
    4. Ilmakunnas, Pekka & Ilmakunnas, Seija, 2018. "Health and retirement age: Comparison of expectations and actual retirement," MPRA Paper 102618, University Library of Munich, Germany.
    5. Martinson Ankrah Twumasi & Yuansheng Jiang & Bismark Addai & Zhao Ding & Abbas Ali Chandio & Prince Fosu & Dennis Asante & Anthony Siaw & Frank Osei Danquah & Bright Asiamah Korankye & Gideon Ntim-Amo, 2021. "The Impact of Cooperative Membership on Fish Farm Households’ Income: The Case of Ghana," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    6. Martinson Ankrah Twumasi & Dennis Asante & Jesse Nuamah Brako & Zhao Ding & Yuansheng Jiang, 2023. "The Relationship between Chronic Non-Communicable Diseases of Fish Farm Household Members and Production Efficiency: The Case of Ghana," IJERPH, MDPI, vol. 20(5), pages 1-13, February.
    7. Apergis, Nicholas & Mustafa, Ghulam & Dastidar, Sayantan Ghosh, 2021. "An analysis of the impact of unconventional oil and gas activities on public health: New evidence across Oklahoma counties," Energy Economics, Elsevier, vol. 97(C).
    8. Gerke, Oke & Lauridsen, Jørgen T., 2013. "Determinants of early retirement in Denmark. An empirical investigation using SHARE data," Discussion Papers on Economics 4/2013, University of Southern Denmark, Department of Economics.
    9. Xin Deng & Miao Zeng & Dingde Xu & Feng Wei & Yanbin Qi, 2019. "Household Health and Cropland Abandonment in Rural China: Theoretical Mechanism and Empirical Evidence," IJERPH, MDPI, vol. 16(19), pages 1-13, September.
    10. Danny Wende & Alexander Karmann & Ines Weinhold, 2024. "Deprivation as a fundamental cause of morbidity and reduced life expectancy: an observational study using German statutory health insurance data," International Journal of Health Economics and Management, Springer, vol. 24(2), pages 257-277, June.
    11. O'Halloran, Jamie & Oxholm, Anne Sophie & Pedersen, Line Bjørnskov & Gyrd-Hansen, Dorte, 2021. "Time to retire? A register-based study of GPs’ practice style prior to retirement," Social Science & Medicine, Elsevier, vol. 281(C).
    12. Bérengère Davin & Xavier Joutard & Alain Paraponaris, 2019. ""If You Were Me": Proxy Respondents' Biases in Population Health Surveys," Working Papers halshs-02036434, HAL.
    13. Boman, Anders, 2015. "Spending time together? Effects on the retirement decision from partner’s labour market status," Working Papers in Economics 618, University of Gothenburg, Department of Economics.
    14. Shuolin Shi & Ralf A. Wilke, 2022. "Variable selection with group structure: exiting employment at retirement age—a competing risks quantile regression analysis," Empirical Economics, Springer, vol. 62(1), pages 119-155, January.
    15. Martinson Ankrah Twumasi & Hongyun Zheng & Love Offeibea Asiedu-Ayeh & Anthony Siaw & Yuansheng Jiang, 2023. "Access to Financial Services and Its Impact on Household Income: Evidence from Rural Ghana," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(4), pages 869-890, August.

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    More about this item

    Keywords

    Competing risks; Duration analysis; Grouped data; Justification bias; Objective medical diagnosis codes; Retirement; Unobserved heterogeneity.;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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