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Endogenous treatment effects for count data models with endogenous participation or sample selection

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  • Alfonso Miranda

    (Institute of Education, University of London)

  • Massimiliano Bratti

Abstract

We propose an estimator for models in which an endogenous dichotomous treatment affects a count outcome in the presence of either sample selection or endogenous participation using maximum simulated likelihood. We allow for the treatment to have an effect on both the participation or the sample selection rule and on the main outcome. Applications of this model are frequent in-but not limited to-health economics. We show an application of the model using data from Kenkel (Kenkel and Terza, 2001, Journal of Applied Econometrics 16: 165-184), who investigated the effect of physician advice on the amount of alcohol consumption. Our estimates suggest that in these data, a) neglecting treatment endogeneity leads to a wrongly signed effect of physician advice on drinking intensity, b) accounting for treatment endogeneity but neglecting endogenous participation leads to an upwardly biased estimate of the treatment effect, and c) advice affects only the drinking-intensive margin but not drinking prevalence.

Suggested Citation

  • Alfonso Miranda & Massimiliano Bratti, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," United Kingdom Stata Users' Group Meetings 2011 10, Stata Users Group.
  • Handle: RePEc:boc:usug11:10
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    File URL: http://repec.org/usug2011/UK11_Miranda.pdf
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    References listed on IDEAS

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    1. Donald S. Kenkel & Joseph V. Terza, 2001. "The effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 165-184.
    2. William Greene, 2009. "Models for count data with endogenous participation," Empirical Economics, Springer, vol. 36(1), pages 133-173, February.
    3. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1090-1109, September.
    4. Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
    5. Joseph V. Terza & Donald S. Kenkel & Tsui-Fang Lin & Shinichi Sakata, 2008. "Care-giver advice as a preventive measure for drinking during pregnancy: zeros, categorical outcome responses, and endogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 41-54.
    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 281-294, May-June.
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    Cited by:

    1. Berning, Joshua, 2015. "The role of physicians in promoting weight loss," Economics & Human Biology, Elsevier, vol. 17(C), pages 104-115.
    2. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1090-1109, September.
    3. Lyssenko, Nikita & Martinez-Espineira, Roberto, 2009. "`Been there done that': Disentangling option value effects from user heterogeneity when valuing natural resources with a use component," MPRA Paper 21976, University Library of Munich, Germany, revised 08 Apr 2010.
    4. Shaun A. Bond & Avis Devine, 2016. "Incentivizing Green Single-Family Construction: Identifying Effective Government Policies and Their Features," The Journal of Real Estate Finance and Economics, Springer, vol. 52(4), pages 383-407, May.
    5. repec:bpj:jecome:v:7:y:2018:i:1:p:19:n:1 is not listed on IDEAS
    6. Geraci, A. & Fabbri, D. & Monfardini, C., 2014. "Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work?," Health, Econometrics and Data Group (HEDG) Working Papers 14/03, HEDG, c/o Department of Economics, University of York.
    7. Fang, Kuangnan & Wang, Xiaoyan & Shia, Ben-Chang & Ma, Shuangge, 2016. "Identification of proportionality structure with two-part models using penalization," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 12-24.
    8. Giampiero Marra & Rosalba Radice & Silvia Missiroli, 2014. "Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models," Computational Statistics, Springer, vol. 29(3), pages 715-741, June.

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