Takuya Ishihara, Masayuki Sawada
arXiv 16 Sep 2020 · Econometrics
arXiv:2009.07551 · PDF · DOI · OpenAlex · Extracted main text
We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.
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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 | Hahn, J., P. Todd, and W. Van der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 1.000 | 6 | 5 | 100% |
| 2 | Lee, D. S. and T. Lemieux (2010) Regression Discontinuity Designs in Economics | 1.000 | 5 | 3 | 100% |
| 3 | McCrary, J (2008) Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test | 0.950 | 7 | 5 | 86% |
| 4 | Lee, D. S (2008) Randomized Experiments from Non-Random Selection in U.S | 0.874 | 5 | 2 | 100% |
| 5 | Gerard, F., M. Rokkanen, and C. Rothe (2020) Bounds on Treatment Effects in Regression Discontinuity Designs with a Manipulated Running Variable | 0.855 | 8 | 4 | 62% |
| 6 | Cattaneo, M. D., N. Idrobo, and R. Titiunik (2020) a): | 0.737 | 3 | 2 | 100% |
| 7 | Jepsen, C., P. Mueser, and K. Troske (2016) Labor Market Returns to the GED Using Regression Discontinuity Analysis | 0.737 | 3 | 2 | 100% |
| 8 | Angrist, J. D., V. Lavy, J. Leder-Luis, and A. Shany (2019) Maimonides’ Rule Redux | 0.693 | 5 | 1 | 100% |
| 9 | Arai, Y., Y.-C. Hsu, T. Kitagawa, I. Mourifié, and Y. Wan (2021) a): Testing Identifying Assumptions in Fuzzy Regression Discontinuity Designs | 0.644 | 2 | 2 | 100% |
| 10 | Cattaneo, M. D., N. Idrobo, and R. Titiunik (2024) A Practical Introduction to Regression Discontinuity Designs: Extensions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A unified test for regression discontinuity designs | 0.405 | 1 | 1 |
| 2 | Sensitivity Analysis for Linear Estimators | 0.405 | 1 | 1 |