Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: An Unconditional MTE Approach

TL;DR

This paper develops a new approach to identify and estimate the effects of policies on binary treatments that are endogenous, using a novel class of marginal treatment effects and an estimator called UNIQUE, with an empirical application to college enrollment and wages.

Contribution

It introduces a new class of unconditional marginal treatment effects based on influence functions and proposes the UNIQUE estimator for policy effect estimation.

Findings

Unconditional policy effects can be expressed as weighted averages of new MTEs.

Conditions for point identification of policy effects are established.

Empirical analysis shows the impact of tuition subsidies on wage distribution quantiles.

Abstract

This paper studies the identification and estimation of policy effects when treatment status is binary and endogenous. We introduce a new class of marginal treatment effects (MTEs) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals who are indifferent about their treatment status. We provide conditions for point identification of the unconditional policy effects. When a quantile is the functional of interest, we introduce the UNconditional Instrumental Quantile Estimator (UNIQUE) and establish its consistency and asymptotic distribution. In the empirical application, we estimate the effect of changing college enrollment status, induced by higher tuition subsidy, on the quantiles of the wage distribution.