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

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  • Massimiliano Bratti
  • Alfonso Miranda

Abstract

In this paper, 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 the participation or the sample selection rule and on the main outcome. Applications of this model are frequent in–but no limited to–health economics. We show an application of the model using data from Kenkel and Terza (2001), who investigate the effect of physician advice on the amount of alcohol consumption. Our estimates suggest that in these data (i) neglecting treatment endogeneity leads to a wrongly signed effect of physician advice on drinking intensity, (ii) accounting for treatment endogeneity but neglecting endogenous participation leads to an upward biased estimate of the treatment effect and (iii) advice affects only the drinking intensive margin but not drinking prevalence. Copyright © 2011 John Wiley & Sons, Ltd.

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  • 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.
    • Handle: RePEc:wly:hlthec:v:20:y:2011:i:9:p:1090-1109
    DOI: 10.1002/hec.1764
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    15. 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.
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