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

Author

Listed:
  • Yuta Okamoto

    (Graduate School of Economics, Hitotsubashi University, JAPAN and Junior Research Fellow, Research Institute for Economics and Business Administration, Kobe University, JAPAN)

  • Yuuki Ozaki

    (Attax Co., Ltd., JAPAN)

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.

Suggested Citation

  • Yuta Okamoto & Yuuki Ozaki, 2026. "On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs," Discussion Paper Series DP2026-20, Research Institute for Economics & Business Administration, Kobe University.
    • Handle: RePEc:kob:dpaper:dp2026-20
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    File URL: https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2026-20.pdf
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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D00 - Microeconomics - - General - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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