Eric Auerbach, Yong Cai, Ahnaf Rafi
arXiv 9 Apr 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2404.06471 · PDF · DOI · OpenAlex · Extracted main text
This paper studies regression discontinuity designs (RDD) when linear-in-means spillovers occur between units that are close in their running variable. We show that the RDD estimand depends on the ratio of two terms: (1) the radius over which spillovers occur and (2) the choice of bandwidth used for the local linear regression. RDD estimates direct treatment effect when radius is of larger order than the bandwidth and total treatment effect when radius is of smaller order than the bandwidth. When the two are of similar order, the RDD estimand need not have a causal interpretation. To recover direct and spillover effects in the intermediate regime, we propose to incorporate estimated spillover terms into local linear regression. Our estimator is consistent and asymptotically normal and we provide bias-aware confidence intervals for direct treatment effects and spillovers. In the setting of Gonzalez (2021), we detect endogenous spillovers in voter fraud during the 2009 Afghan Presidential election. We also clarify when the donut-hole design addresses spillovers in RDD.
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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 | Gonzalez (2021) Cell phone access and election fraud: evidence from a spatial regression discontinuity design in Afghanistan | 1.000 | 12 | 4 | 100% |
| 2 | Hahn, Todd and Van der Klaauw (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 1.000 | 6 | 3 | 100% |
| 3 | Dal Torrione, Arduini and Forastiere (2024) Regression Discontinuity Designs Under Interference | 1.000 | 6 | 3 | 100% |
| 4 | Armstrong and Kolesár (2020) Simple and honest confidence intervals in nonparametric regression | 0.855 | 8 | 4 | 62% |
| 5 | Bramoullé, Djebbari and Fortin (2009) Identification of peer effects through social networks | 0.843 | 3 | 3 | 100% |
| 6 | Hudgens and Halloran (2008) Toward causal inference with interference | 0.843 | 3 | 3 | 100% |
| 7 | Aronow, Basta and Halloran (2017) The regression discontinuity design under interference: a local randomization-based approach | 0.811 | 4 | 2 | 100% |
| 8 | Calonico, Cattaneo and Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.754 | 7 | 4 | 43% |
| 9 | Wand and Jones (1995) | 0.737 | 5 | 3 | 40% |
| 10 | Armstrong and Kolesár (2018) Optimal inference in a class of regression models | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Linear estimation of global average treatment effects | 0.405 | 1 | 1 |
| 2 | Regression Discontinuity Aggregation, with an Application to the Union Effects on Inequality | 0.405 | 1 | 1 |
| 3 | Causal Identification under Interference: The Role of Treatment Assignment Independence | 0.405 | 1 | 1 |