RIEB Discussion Paper Series No.2024-22

Title

Instrumented Difference-in-Differences with Heterogeneous Treatment Effects

Abstract

Many studies exploit variation in the timing of policy adoption across units as an instrument for treatment, and use instrumental variable techniques. This paper formalizes the underlying identification strategy as an instrumented difference-in-differences (DIDIV). In a simple setting with two periods and two groups, our DID-IV design mainly consists of a monotonicity assumption, and parallel trends assumptions in the treatment and the outcome. In this design, a Wald-DID estimand, which scales the DID estimand of the outcome by the DID estimand of the treatment, captures the local average treatment effect on the treated (LATET). In contrast to Fuzzy DID design considered in de Chaisemartin and D'Haultfoeuille (2018), our DID-IV design does not ex-ante require strong restrictions on the treatment adoption behavior across units, and our target parameter, the LATET, is policy-relevant if the instrument is based on the policy change of interest to the researcher. We extend the canonical DID-IV design to multiple period settings with the staggered adoption of the instrument across units, which we call a staggered DID-IV design. We propose an estimation method in staggered DID-IV design that is robust to treatment effect heterogeneity. We illustrate our findings in the setting of Oreopoulos (2006), estimating returns to schooling in the United Kingdom. In this application, the two-way fixed effects instrumental variable regression, which is the conventional approach to implement a staggered DID-IV design, yields a negative estimate, whereas our estimation method indicates the substantial gain from schooling.

Keywords

Difference-in-differences; Instrumental variable; Local average treatment effect; Returns to education

JEL Classification

C21, C23, C26

Inquiries

Sho MIYAJI*
Graduate School of Economics, The University of Tokyo
Junior Research Fellow, RIEB, Kobe University

*This Discussion Paper won the Kanematsu Prize (FY 2023).