TY  - RPRT
        AU  - Bartalotti, Otávio
AU  - Kédagni, Désiré
AU  - Possebom, Vítor Augusto
        TI  - Identifying Marginal Treatment Effects in the Presence of Sample Selection
        PY  - 2021/May/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 14428
        UR  - https://www.iza.org/publications/dp14428
        AB  - This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Our analysis extends to discrete instruments. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee's (2009) trimming procedure to the MTE context. We propose estimators for the bounds derived and use data made available by Deb, Munkin, and Trivedi (2006) to empirically illustrate the usefulness of our approach. 
        KW  - sample selection
KW  - instrumental variable
KW  - marginal treatment eect
KW  - partial identication
KW  - principal stratication
KW  - program evaluation
KW  - mixture models
        ER  -