Shunsuke Imai, Koshi Nishida, Yuta Okamoto
arXiv 2 Oct 2026 · Econometrics
arXiv:2610.02938 · PDF · Extracted main text
Many policies assign treatment according to whether a score crosses a cutoff. Regression discontinuity (RD) designs identify the effect of treatment assignment, but the threshold policy itself may also reshape individuals' incentives, inducing behavioral responses that affect outcomes even holding treatment status fixed---a channel that conventional RD designs cannot capture. We develop a framework that exploits randomized variation in policy thresholds, together with rank-invariance-type restrictions, to identify the treatment-assignment and incentive-response effects. We illustrate the empirical relevance of this distinction using data from a merit-based scholarship experiment in Malawi. In this illustration, the conventional RD estimate is positive, while the incentive-response effect is negative and more than twice as large in magnitude. These findings suggest that threshold policies may generate unintended adverse behavioral responses, potentially reflecting discouragement induced by demanding thresholds, and caution against relying solely on conventional RD estimates when evaluating threshold policies.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | James Berry and Hyuncheol Bryant Kim and Hyuk Harry Son (2022) When student incentives do not work: Evidence from a field experiment in Malawi | 1.000 | 5 | 3 | 100% |
| 2 | Leuven, Edwin and Oosterbeek, Hessel and van der Klaauw, Bas (2010) The Effect of Financial Rewards on Students' Achievement: Evidence from a Randomized Experiment | 0.843 | 3 | 3 | 100% |
| 3 | Moyu Liao (2025) Treatment Effects in the Regression Discontinuity Model with Counterfactual Cutoff and Distorted Running Variables | 0.693 | 6 | 1 | 100% |
| 4 | Imbens, Guido and Kalyanaraman, Karthik (2012) Optimal Bandwidth Choice for the Regression Discontinuity Estimator | 0.644 | 3 | 2 | 67% |
| 5 | David S. Lee (2008) Randomized experiments from non-random selection in U.S. House elections | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, Victor and Hansen, Christian (2005) An IV Model of Quantile Treatment Effects | 0.511 | 2 | 1 | 100% |
| 7 | Londoño-Vélez, Juliana and Rodríguez, Catherine and Sánchez, Fabio (2020) Upstream and Downstream Impacts of College Merit-Based Financial Aid for Low-Income Students: Ser Pilo Paga in Colombia | 0.511 | 2 | 1 | 100% |
| 8 | Londoño-Vélez, Juliana and Álvarez, Luis Esteban and Rodríguez, Cath… (2025) Equity and Efficiency in Financial Aid Targeting | 0.511 | 2 | 1 | 100% |
| 9 | Angrist, Joshua and Lang, Daniel and Oreopoulos, Philip (2009) Incentives and Services for College Achievement: Evidence from a Randomized Trial | 0.405 | 1 | 1 | 100% |
| 10 | Marinho Bertanha (2020) Regression Discontinuity Design with Many Thresholds | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.