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Proportional treatment effects for count response panel data: effects of binary exercise on health care demand

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  • Myoung‐Jae Lee
  • Satoru Kobayashi

Abstract

We define conditional and marginal treatment effects appropriate for count data, and then conduct an empirical analysis for the effects of exercise on health care demand using panel data from the Health Retirement Study. The response variables are office visits to doctors and hospitalization days, and the treatments of interest are light and vigorous exercises. We found that short‐run light exercise increases health care demand by 3–5%, whereas long‐run light exercise decreases it by 3–6%. We also found that short‐run vigorous exercise decreases health care demand by 1–2%, whereas long‐run vigorous exercise decreases it by 1–3%. However, many of these numbers are not statistically significantly different from zero. These findings suggest that it will be difficult to reduce health care cost much by encouraging people to do more exercise—at least in the short‐run. Copyright © 2001 John Wiley & Sons, Ltd.

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  • Myoung‐Jae Lee & Satoru Kobayashi, 2001. "Proportional treatment effects for count response panel data: effects of binary exercise on health care demand," Health Economics, John Wiley & Sons, Ltd., vol. 10(5), pages 411-428, July.
    • Handle: RePEc:wly:hlthec:v:10:y:2001:i:5:p:411-428
    DOI: 10.1002/hec.626
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    References listed on IDEAS

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    Cited by:

    1. Fali Huang & Myoung-Jae Lee, 2010. "Dynamic treatment effect analysis of TV effects on child cognitive development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 392-419.
    2. Mullahy, John, 2018. "Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects," Journal of Health Economics, Elsevier, vol. 61(C), pages 151-162.
    3. John Mullahy, 2020. "Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials," NBER Working Papers 27895, National Bureau of Economic Research, Inc.
    4. Javier Gardeazabal & Todd Sandler, 2015. "INTERPOL's Surveillance Network in Curbing Transnational Terrorism," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(4), pages 761-780, September.
    5. Majo, M.C., 2010. "A microeconometric analysis of health care utilization in Europe," Other publications TiSEM 1cf5fd2f-8146-4ef8-8eb5-e, Tilburg University, School of Economics and Management.
    6. Myoung-Jae Lee, 2004. "Selection correction and sensitivity analysis for ordered treatment effect on count response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 323-337.
    7. Martin, Robert S., 2017. "Estimation of average marginal effects in multiplicative unobserved effects panel models," Economics Letters, Elsevier, vol. 160(C), pages 16-19.
    8. Majo, M.C. & van Soest, A.H.O., 2011. "The Fixed-Effects Zero-Inflated Poisson Model with an Application to Health Care Utilization," Other publications TiSEM 68cf0f9b-fc68-4017-97a9-a, Tilburg University, School of Economics and Management.
    9. Myoung-jae Lee & Sanghyeok Lee, 2021. "Difference in Differences and Ratio in Ratios for Limited Dependent Variables," Papers 2111.12948, arXiv.org, revised Aug 2023.
    10. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    11. Myoung‐jae Lee & Sang‐jun Lee, 2005. "Analysis of job‐training effects on Korean women," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 549-562, May.
    12. Lyytikäinen, Teemu, 2009. "Three-rate property taxation and housing construction," Journal of Urban Economics, Elsevier, vol. 65(3), pages 305-313, May.
    13. Yuriy Pylypchuk & Julie Hudson, 2009. "Immigrants and the use of preventive care in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 18(7), pages 783-806, July.
    14. Govert E. Bijwaard & Andrew M. Jones, 2019. "An IPW estimator for mediation effects in hazard models: with an application to schooling, cognitive ability and mortality," Empirical Economics, Springer, vol. 57(1), pages 129-175, July.
    15. John Mullahy, 2021. "Discovering treatment effectiveness via median treatment effects—Applications to COVID‐19 clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1050-1069, May.

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