RIEB Discussion Paper Series No.2026-20

Title

On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs

Abstract

This paper investigates how to learn treatment effects away from the cutoff point in multiple-cutoff regression discontinuity designs. Using a microeconomic model, we demonstrate that the parallel-trend type assumption proposed in the literature is justified when cutoff positions are assigned as if randomly and the running variable is non-manipulable (e.g., parental income). However, when the running variable is partially manipulable (e.g., test scores), extrapolations based on that assumption can be biased. As a complementary strategy, we propose a novel partial identification approach based on empirically motivated assumptions. We also develop a uniform inference procedure and provide two empirical illustrations.

Keywords

Decision model; External validity; Partial identification; Regression discontinuity designs

JEL Classification

C14, C21, D00, D84

Inquiries

Yuta OKAMOTO*
Graduate School of Economics, Hitotsubashi University, JAPAN
Junior Research Fellow, RIEB, Kobe University, JAPAN

Yuuki OZAKI
Attax Co., Ltd.

*This Discussion Paper won the Kanematsu Prize (FY 2025).