arXiv 10 Oct 2025 · Econometrics
arXiv:2510.09257 · PDF · DOI · OpenAlex · Extracted main text
In the conventional regression-discontinuity (RD) design, the probability that units receive a treatment changes discontinuously as a function of one covariate exceeding a threshold or cutoff point. This paper studies an extended RD design where assignment rules simultaneously involve two or more continuous covariates. We show that assignment rules with more than one variable allow the estimation of a more comprehensive set of treatment effects, relaxing in a research-driven style the local and sometimes limiting nature of univariate RD designs. We then propose a flexible nonparametric approach to estimate the multidimensional discontinuity by univariate local linear regression and compare its performance to existing methods. We present an empirical application to a large-scale and countrywide financial aid program for low-income students in Colombia. The program uses a merit-based (academic achievement) and need-based (wealth index) assignment rule to select students for the program. We show that our estimation strategy fully exploits the multidimensional assignment rule and reveals heterogeneous effects along the treatment boundaries.
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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 | Londoño-Vélez, J., C. Rodríguez, and F. Sánchez (2020, May) (2020) Upstream and downstream impacts of college merit-based financial aid for low-income students: Ser pilo paga in colombia | 1.000 | 14 | 4 | 100% |
| 2 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014b, nov) (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs | 1.000 | 10 | 5 | 100% |
| 3 | Hahn, J., P. Todd, and W. V. der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 1.000 | 7 | 3 | 100% |
| 4 | Wong, V. C., P. M. Steiner, and T. D. Cook (2013) Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables | 1.000 | 5 | 3 | 100% |
| 5 | Zajonc, T (2012) Essays on Causal Inference for Public Policy | 0.987 | 26 | 6 | 96% |
| 6 | Papay, J. P., J. B. Willett, and R. J. Murnane (2011) Extending the regression-discontinuity approach to multiple assignment variables | 0.959 | 17 | 5 | 88% |
| 7 | Imbens, G. and S. Wager (2019, nov) (2019) Optimized regression discontinuity designs | 0.843 | 3 | 3 | 100% |
| 8 | DNP, CNC, and U. de los Andes (2016) Evaluación de impacto de corto plazo del programa Ser Pilo Paga | 0.811 | 4 | 2 | 100% |
| 9 | Choi, J.-y. and M.-j. Lee (2018) Regression discontinuity with multiple running variables allowing partial effects | 0.644 | 2 | 2 | 100% |
| 10 | Choi, J.-y. and M.-j. Lee (2018) Minimum distance estimator for sharp regression discontinuity with multiple running variables | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 45 scored citations.