arXiv 8 Jun 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2406.05548 · PDF · DOI · OpenAlex · Extracted main text
In studies of educational production functions or intergenerational mobility, it is common to transform the key variables into percentile ranks. Yet, it remains unclear what the regression coefficient estimates with ranks of the outcome or the treatment. In this paper, we derive effective causal estimands for a broad class of commonly-used regression methods, including the ordinary least squares (OLS), two-stage least squares (2SLS), difference-in-differences (DiD), and regression discontinuity designs (RDD). Specifically, we introduce a novel primitive causal estimand, the Rank Average Treatment Effect (rank-ATE), and prove that it serves as the building block of the effective estimands of all the aforementioned econometrics methods. For 2SLS, DiD, and RDD, we show that direct applications to outcome ranks identify parameters that are difficult to interpret. To address this issue, we develop alternative methods to identify more interpretable causal parameters.
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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 | Jonathan Roth and Pedro HC Sant'Anna (2023) When is parallel trends sensitive to functional form? | 0.928 | 4 | 3 | 100% |
| 2 | Denis Chetverikov and Daniel Wilhelm (2023) Inference for rank-rank regressions | 0.874 | 8 | 2 | 100% |
| 3 | Guido W Imbens and Thomas Lemieux (2008) Regression discontinuity designs: A guide to practice | 0.874 | 6 | 2 | 100% |
| 4 | Yanqin Fan and Sang Soo Park (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.843 | 3 | 3 | 100% |
| 5 | Wenlong Ji, Lihua Lei, and Asher Spector (2023) Model-agnostic covariate-assisted inference on partially identified causal effects self | 0.843 | 3 | 3 | 100% |
| 6 | Susan Athey and Guido W Imbens (2006) Identification and inference in nonlinear difference-in-differences models | 0.811 | 4 | 2 | 100% |
| 7 | Guido W Imbens and Joshua D Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 3 | 67% |
| 8 | Joshua Angrist (1995) Estimating the labor market impact of voluntary military service using social security data on military applicants, 1995 | 0.644 | 2 | 2 | 100% |
| 9 | Kirill Borusyak and Peter Hull (2024) Negative weights are no concern in design-based specifications | 0.644 | 2 | 2 | 100% |
| 10 | Jiafeng Chen and Jonathan Roth (2023) Logs with zeros? some problems and solutions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Conditional Rank-Rank Regression$^*$ | 0.405 | 1 | 1 |