Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236459 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14428
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
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.
Subjects: 
sample selection
instrumental variable
marginal treatment eect
partial identication
principal stratication
program evaluation
mixture models
JEL: 
C14
C31
C35
Document Type: 
Working Paper

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