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

  • 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)

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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File URL: http://hdl.handle.net/10.1002/hec.626
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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 10 (2001)
Issue (Month): 5 ()
Pages: 411-428

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Handle: RePEc:wly:hlthec:v:10:y:2001:i:5:p:411-428
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. Wooldridge, J.M., 1990. "Distribution-Free Estimation Of Some Nonlinear Panel Data Models," Working papers 564, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  3. 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 313-36, May-June.
  4. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  5. 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.
  6. 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 281-94, May-June.
  7. 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.
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