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Endogenous Health Groups and Heterogeneous Dynamics of the Elderly

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  • Amengual, D.; Bueren, J.; Crego, J.A.;

Abstract

Health dynamics and its associated medical and care costs have been identified by the macro literature as a major concern of the elderly. Due to its multidimensionality, however, a dicult task faced by researchers is to summarize health parsimoniously into a single state variable. We propose a panel Markov switching model to identify patterns of health heterogeneity where individuals can move across health groups as they age. To estimate the model, we use Markov chain Monte Carlo techniques to exploit information from both the crosssectional and time series dimensions. We identify health groups for individuals in the Health and Retirement Survey for the US. Results show that there exists four clearly diVerentiated groups depending on individualÂ’s physical and mental disabilities. Furthermore, we show that health groups outperform other measures of health commonly used in the literature at explaining the variance in the use of nursing homes, home health care, out of pocket medical expenses and predicted mortality.

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  • Amengual, D.; Bueren, J.; Crego, J.A.;, 2017. "Endogenous Health Groups and Heterogeneous Dynamics of the Elderly," Health, Econometrics and Data Group (HEDG) Working Papers 17/18, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:17/18
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    1. Mariacristina De Nardi & Giulio Fella & Gonzalo Paz-Pardo, 2020. "Nonlinear Household Earnings Dynamics, Self-Insurance, and Welfare," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 890-926.
    2. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2015. "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?," NBER Working Papers 20913, National Bureau of Economic Research, Inc.
    3. Mariacristina De Nardi & Eric French & John B. Jones, 2010. "Why Do the Elderly Save? The Role of Medical Expenses," Journal of Political Economy, University of Chicago Press, vol. 118(1), pages 39-75, February.
    4. Bound, John & Stinebrickner, Todd & Waidmann, Timothy, 2010. "Health, economic resources and the work decisions of older men," Journal of Econometrics, Elsevier, vol. 156(1), pages 106-129, May.
    5. Crossley, Thomas F. & Kennedy, Steven, 2002. "The reliability of self-assessed health status," Journal of Health Economics, Elsevier, vol. 21(4), pages 643-658, July.
    6. Eric French & John Bailey Jones, 2011. "The Effects of Health Insurance and Self‐Insurance on Retirement Behavior," Econometrica, Econometric Society, vol. 79(3), pages 693-732, May.
    7. Karen A. Kopecky & Tatyana Koreshkova, 2014. "The Impact of Medical and Nursing Home Expenses on Savings," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(3), pages 29-72, July.
    8. Currie, Janet & Madrian, Brigitte C., 1999. "Health, health insurance and the labor market," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 50, pages 3309-3416, Elsevier.
    9. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "The dynamics of health in the British Household Panel Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 473-503.
    10. Mariacristina De Nardi & Eric French & John Bailey Jones, 2016. "Medicaid Insurance in Old Age," American Economic Review, American Economic Association, vol. 106(11), pages 3480-3520, November.
    11. Josep Pijoan-Mas & José-Víctor Ríos-Rull, 2014. "Heterogeneity in Expected Longevities," Demography, Springer;Population Association of America (PAA), vol. 51(6), pages 2075-2102, December.
    12. van der Klaauw, Wilbert & Wolpin, Kenneth I., 2008. "Social security and the retirement and savings behavior of low-income households," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 21-42, July.
    13. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    14. John Ameriks & Joseph Briggs & Andrew Caplin & Matthew D. Shapiro & Christopher Tonetti, 2020. "Long-Term-Care Utility and Late-in-Life Saving," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2375-2451.
    15. Loretti I. Dobrescu, 2015. "To Love or to Pay: Savings and Health Care in Older Age," Journal of Human Resources, University of Wisconsin Press, vol. 50(1), pages 254-299.
    16. Daniel Barczyk & Matthias Kredler, 2018. "Evaluating Long-Term-Care Policy Options, Taking the Family Seriously," Review of Economic Studies, Oxford University Press, vol. 85(2), pages 766-809.
    17. repec:eee:labchp:v:3:y:1999:i:pc:p:3309-3416 is not listed on IDEAS
    18. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    19. R. Anton Braun & Karen A. Kopecky & Tatyana Koreshkova, 2017. "Old, Frail, and Uninsured: Accounting for Puzzles in the U.S. Long-Term Care Insurance Market," FRB Atlanta Working Paper 2017-3, Federal Reserve Bank of Atlanta, revised 01 Jul 2017.
    20. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    21. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2015. "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Dynamics?," Staff Reports 710, Federal Reserve Bank of New York.
    22. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
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