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

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  • Massimiliano Bratti

    (University of Milan)

  • Alfonso Miranda

    (Institute of Education, University of London)

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 (2001, Kenkel and Terza, 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 upward biased estimate of the treatment effect, and c) advice only affects the drinking-intensive margin but not drinking prevalence.

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Bibliographic Info

Paper provided by Stata Users Group in its series Mexican Stata Users' Group Meetings 2011 with number 05.

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Date of creation: 23 Jul 2011
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Handle: RePEc:boc:msug11:05

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Web page: http://www.stata.com/meeting/mexico11
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  1. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Mexican Stata Users' Group Meetings 2011 05, Stata Users Group.
  2. Frank Windmeijer & Joao Santos Silva, 1996. "Endogeneity in count data models; an application to demand for health care," IFS Working Papers W96/15, Institute for Fiscal Studies.
  3. William Greene, 2009. "Models for count data with endogenous participation," Empirical Economics, Springer, vol. 36(1), pages 133-173, February.
  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. 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.
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Cited by:
  1. 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, 09.
  2. A. Geraci & D. Fabbri & C. Monfardini, 2014. "Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work?," Working Papers wp921, Dipartimento Scienze Economiche, Universita' di Bologna.
  3. 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.
  4. 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.

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