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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.

Suggested Citation

  • 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.
  • Handle: RePEc:boc:msug11:05
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    Cited by:

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    4. Quinones, Esteban J. & Barham, Bradford L., 2018. "Endogenous Selection, Migration and Occupation Outcomes for Rural Southern Mexicans," Staff Paper Series 587, University of Wisconsin, Agricultural and Applied Economics.
    5. 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.
    6. 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.
    7. Bérengère Davin & Xavier Joutard & Alain Paraponaris, 2019. ""If You Were Me": Proxy Respondents' Biases in Population Health Surveys," Working Papers halshs-02036434, HAL.
    8. Karol Wyszynski & Giampiero Marra, 2018. "Sample selection models for count data in R," Computational Statistics, Springer, vol. 33(3), pages 1385-1412, September.
    9. Robert J. Batt & Christian Terwiesch, 2017. "Early Task Initiation and Other Load-Adaptive Mechanisms in the Emergency Department," Management Science, INFORMS, vol. 63(11), pages 3531-3551, November.
    10. 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.
    11. Wanglin Ma & Xiaobing Wang, 2020. "Internet Use, Sustainable Agricultural Practices and Rural Incomes: Evidence from China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1087-1112, October.
    12. Mgendi, By George & Mao, Shiping & Qiao, Fangbin, 2022. "Does agricultural training and demonstration matter in technology adoption? The empirical evidence from small rice farmers in Tanzania," Technology in Society, Elsevier, vol. 70(C).
    13. Bago, Jean-Louis & Ouédraogo, Moussa & Akakpo, Koffi & Lompo, Miaba Louise & Souratié, Wamadini M. & Ouédraogo, Ernest, 2019. "Early Childhood Education and Children Development : Evidence from Ghana," MPRA Paper 95868, University Library of Munich, Germany.
    14. Sara Amoroso & Simone Vannuccini, 2019. "Teaming up with Large R&D Investors: Good or Bad for Knowledge Production and Diffusion?," SPRU Working Paper Series 2019-20, SPRU - Science Policy Research Unit, University of Sussex Business School.
    15. Geraci Andrea & Fabbri Daniele & Monfardini Chiara, 2018. "Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-19, January.
    16. 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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