Carolina Caetano, Gregorio Caetano, Juan Carlos Escanciano
arXiv 1 Jul 2020 · Econometrics · publishedJournal of Applied Econometrics (2023) · 6 citations (OpenAlex)
arXiv:2007.00185 · PDF · DOI · OpenAlex · Extracted main text
We study identification and estimation in the Regression Discontinuity Design (RDD) with a multivalued treatment variable. We also allow for the inclusion of covariates. We show that without additional information, treatment effects are not identified. We give necessary and sufficient conditions that lead to identification of LATEs as well as of weighted averages of the conditional LATEs. We show that if the first stage discontinuities of the multiple treatments conditional on covariates are linearly independent, then it is possible to identify multivariate weighted averages of the treatment effects with convenient identifiable weights. If, moreover, treatment effects do not vary with some covariates or a flexible parametric structure can be assumed, it is possible to identify (in fact, over-identify) all the treatment effects. The over-identification can be used to test these assumptions. We propose a simple estimator, which can be programmed in packaged software as a Two-Stage Least Squares regression, and packaged standard errors and tests can also be used. Finally, we implement our approach to identify the effects of different types of insurance coverage on health care utilization, as in Card, Dobkin and Maestas (2008).
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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 | Card, Dobkin and Maestas (2008) The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare,\ | 0.928 | 4 | 4 | 100% |
| 2 | Imbens and Lemieux (2008) Regression Discontinuity Designs: A Guide to Practice, \ | 0.737 | 3 | 2 | 100% |
| 3 | Calonico, Cattaneo, Farrell and Titiunik (2019) Regression-Discontinuity Designs using Covariates | 0.644 | 2 | 2 | 100% |
| 4 | Lee and Lemieux (2010) Regression Discontinuity Designs in Economics,\ | 0.644 | 2 | 2 | 100% |
| 5 | Hahn, Todd and van der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,\ | 0.511 | 2 | 2 | 50% |
| 6 | Cattaneo and Escanciano (2017) Regression Discontinuity Designs: Theory and Applications | 0.511 | 2 | 1 | 100% |
| 7 | Agarwal, Chomsisengphet, Mahoney and Stroebe (2018) Do Banks Pass through Credit Expansions to Consumers Who want to Borrow?\ | 0.405 | 1 | 1 | 100% |
| 8 | Angrist and Lavy (1999) Using Maimonides' rule to estimate the effect of class size on scholastic achievement,\ | 0.405 | 1 | 1 | 100% |
| 9 | Brollo, Nannicini, Perotti and Tabellini (2013) The Political Resource Curse, \ | 0.405 | 1 | 1 | 100% |
| 10 | Buser (2015) The Effect of Income on Religiousness,\ | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Regression Discontinuity Designs | 0.405 | 1 | 1 |