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Main Authors: Okamoto, Yuta, Ozaki, Yuuki
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.04265
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author Okamoto, Yuta
Ozaki, Yuuki
author_facet Okamoto, Yuta
Ozaki, Yuuki
contents We investigate how to learn treatment effects away from the cutoff 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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_04265
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
Okamoto, Yuta
Ozaki, Yuuki
Econometrics
We investigate how to learn treatment effects away from the cutoff 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.
title On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
topic Econometrics
url https://arxiv.org/abs/2412.04265