arXiv 6 Nov 2024 · Econometrics
arXiv:2411.03625 · PDF · DOI · OpenAlex · Extracted main text
This paper develops an econometric framework and tools for the identification and inference of a structural parameter in general bunching designs. We present point and partial identification results, which generalize previous approaches in the literature. The key assumption for point identification is the analyticity of the counterfactual density, which defines a broader class of distributions than many commonly used parametric families. In the partial identification approach, the analyticity condition is relaxed and various inequality restrictions can be incorporated. Both of our identification approaches allow for observed covariates in the model, which has previously been permitted only in limited ways. These covariates allow us to account for observable factors that influence decisions regarding the running variable. We provide a suite of counterfactual estimation and inference methods, termed the generalized polynomial strategy. Our method restores the merits of the original polynomial strategy proposed by Chetty et al. (2011) while addressing several weaknesses in the widespread practice. The efficacy of the proposed method is demonstrated compared to the polynomial estimator in a series of Monte Carlo studies within the augmented isoelastic model. We revisit the data used in Saez (2010) and find substantially different results relative to those from the polynomial strategy.
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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 | Emmanuel Saez (2010) Do taxpayers bunch at kink points? | 1.000 | 13 | 7 | 100% |
| 2 | Raj Chetty, John N. Friedman, Tore Olsen, and Luigi Pistaferri (2011) Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records | 0.971 | 12 | 7 | 92% |
| 3 | Leonard Goff (2024) Treatment Effects in Bunching Designs: The Impact of Mandatory Overtime Pay on Hours, 2024 | 0.928 | 4 | 3 | 100% |
| 4 | Sören Blomquist, Whitney K. Newey, Anil Kumar, and Che Yuan Liang (2021) On bunching and identification of the taxable income elasticity | 0.902 | 15 | 6 | 73% |
| 5 | Emmanuel Saez (2001) Using Elasticities to Derive Optimal Income Tax Rates | 0.843 | 4 | 3 | 75% |
| 6 | Marinho Bertanha, Andrew H. McCallum, and Nathan Seegert (2023) Better bunching, nicer notching | 0.763 | 18 | 5 | 44% |
| 7 | Stefan Pollinger (2024) Kinks know more: Policy evaluation beyond bunching with an application to solar subsidies | 0.644 | 2 | 2 | 100% |
| 8 | Michael Carlos Best, James S Cloyne, Ethan Ilzetzki, and Henrik J Kl… (2020) Estimating the Elasticity of Intertemporal Substitution Using Mortgage Notches | 0.511 | 2 | 1 | 100% |
| 9 | Richard Blundell, Dennis Kristensen, and Rosa Matzkin (2017) Individual counterfactuals with multidimensional unobserved heterogeneity | 0.511 | 2 | 1 | 100% |
| 10 | Henrik J. Kleven and Mazhar Waseem (2013) Using notches to uncover optimization frictions and structural elasticities: Theory and evidence from Pakistan | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 19 scored citations.