Identifying Marginal Treatment Effects in the Presence of Sample Selection

TL;DR

This paper develops methods to identify and bound the marginal treatment effect when sample selection bias is present, extending existing models with new assumptions and estimators, and demonstrates their application with empirical data.

Contribution

It introduces new bounds and estimators for the marginal treatment effect under sample selection, including monotonicity and stochastic dominance assumptions.

Findings

Derived sharp bounds on the MTE under various assumptions.

Extended Lee's trimming procedure to the MTE context.

Empirical illustration using real data demonstrates the approach's usefulness.

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, Munking and Trivedi (2006) to empirically illustrate the usefulness of our approach.