IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v10y2001i5p411-428.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

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, July.
    • Handle: RePEc:wly:hlthec:v:10:y:2001:i:5:p:411-428
    DOI: 10.1002/hec.626
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.626
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.626?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    2. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    3. 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.
    4. Hofer, T.P. & Katz, S.J., 1996. "Healthy behaviors among women in the United States and Ontario: The effect on use of preventive care," American Journal of Public Health, American Public Health Association, vol. 86(12), pages 1755-1759.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    5. Martin, Robert S., 2017. "Estimation of average marginal effects in multiplicative unobserved effects panel models," Economics Letters, Elsevier, vol. 160(C), pages 16-19.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Lyytikäinen, Teemu, 2009. "Three-rate property taxation and housing construction," Journal of Urban Economics, Elsevier, vol. 65(3), pages 305-313, May.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andr? Romeu-Santana & ?gel M. Vera-Hern?dez, "undated". "A Semi-Nonparametric Estimator For Counts With An Endogenous Dummy. Variable," UFAE and IAE Working Papers 452.00, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    2. Koen Jochmans & Vincenzo Verardi, 2022. "Instrumental‐variable estimation of exponential‐regression models with two‐way fixed effects with an application to gravity equations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1121-1137, September.
    3. Martin Schellhorn & Andreas E. Stuck & Christoph E. Minder & John C. Beck, 2000. "Health services utilization of elderly Swiss: evidence from panel data," Health Economics, John Wiley & Sons, Ltd., vol. 9(6), pages 533-545, September.
    4. Frank Windmeijer, 2006. "GMM for panel count data models," CeMMAP working papers CWP21/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. João Cotter Salvado, 2008. "The Determinants of Health Care Utilization in Portugal: An Approach with Count Data Models," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(III), pages 437-458, September.
    6. Andrea Ascani & Simona Iammarino, 2020. "Is green always attractive? The location choices of Chinese outward FDI," Discussion Paper series in Regional Science & Economic Geography 2020-08, Gran Sasso Science Institute, Social Sciences, revised Nov 2020.
    7. Sergi Jiménez‐Martín & José M. Labeaga & Maite Martínez‐Granado, 2002. "Latent class versus two‐part models in the demand for physician services across the European Union," Health Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 301-321, June.
    8. Óscar Lourenço & Carlota Quintal & Pedro Lopes Ferreira & Pedro Pita Barros, 2007. "A equidade na utilização de cuidados de saúde em Portugal: Uma avaliação baseada em modelos de contagem," Notas Económicas, Faculty of Economics, University of Coimbra, issue 25, pages 6-26, June.
    9. 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.
    10. Kajal Lahiri & Guibo Xing, 2002. "An Empirical Analysis of Medicare-eligible Veterans' Demand for Outpatient Health Care Services," Discussion Papers 02-01, University at Albany, SUNY, Department of Economics.
    11. Michael Pfaffermayr, 2020. "Trade creation and trade diversion of economic integration agreements revisited: a constrained panel pseudo-maximum likelihood approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 156(4), pages 985-1024, November.
    12. McLeod, Logan, 2011. "A nonparametric vs. latent class model of general practitioner utilization: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 30(6), pages 1261-1279.
    13. Ángel Marcos Vera‐Hernández, 1999. "Duplicate coverage and demand for health care. The case of Catalonia," Health Economics, John Wiley & Sons, Ltd., vol. 8(7), pages 579-598, November.
    14. Canan GÜNEŞ & Mustafa ÜNLÜ & Yasin BÜYÜKKÖR & Şenay ÜÇDOĞRUK BİRECİKLİ, 2016. "Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(30).
    15. Holl, Adelheid, 2004. "Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain," Regional Science and Urban Economics, Elsevier, vol. 34(3), pages 341-363, May.
    16. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    17. Branstetter, Lee & Chatterjee, Chirantan & Higgins, Matthew J., 2022. "Generic competition and the incentives for early-stage pharmaceutical innovation," Research Policy, Elsevier, vol. 51(10).
    18. Partha Deb & Chenghui Li & Pravin K. Trivedi & David M. Zimmer, 2006. "The effect of managed care on use of health care services: results from two contemporaneous household surveys," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 743-760, July.
    19. J. M. C. Santos Silva & Silvana Tenreyro, 2022. "The Log of Gravity at 15," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(3), pages 423-437, September.
    20. Xuepeng Liu, 2009. "GATT/WTO Promotes Trade Strongly: Sample Selection and Model Specification," Review of International Economics, Wiley Blackwell, vol. 17(3), pages 428-446, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:10:y:2001:i:5:p:411-428. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.