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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

    (Department of Economics, Sungkyunkwan University, Chongro-gu, Seoul, South Korea)

  • Satoru Kobayashi

    (Asset Management Division I, The Mitsubishi Trust and Banking Corporation, Minato-ku, Tokyo, Japan)

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.

Suggested Citation

  • 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.
  • 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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    1. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
    2. repec:aph:ajpbhl:10.2105/ajph.86.12.1755_4 is not listed on IDEAS
    3. Windmeijer, F A G & Silva, J M C Santos, 1997. "Endogeneity in Count Data Models: An Application to Demand for Health Care," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 281-294, May-June.
    4. Myoung-jae Lee, 2000. "Median treatment effect in randomized trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 595-604.
    5. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    6. 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.
    7. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    8. Winkelmann, Rainer & Zimmermann, Klaus F, 1995. " Recent Developments in Count Data Modelling: Theory and Application," Journal of Economic Surveys, Wiley Blackwell, vol. 9(1), pages 1-24, March.
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    Citations

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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. Lyytikäinen, Teemu, 2009. "Three-rate property taxation and housing construction," Journal of Urban Economics, Elsevier, vol. 65(3), pages 305-313, May.
    3. Sang-jun Lee & Myoung-jae Lee, 2005. "Analysis of job-training effects on Korean women," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 549-562.
    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. 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.
    8. Majo, M.C. & van Soest, A.H.O., 2011. "The Fixed-Effects Zero-Inflated Poisson Model with an Application to Health Care Utilization," Discussion Paper 2011-083, Tilburg University, Center for Economic Research.
    9. 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.
    10. repec:eee:ecolet:v:160:y:2017:i:c:p:16-19 is not listed on IDEAS

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