January 2021

Better Bunching, Nicer Notching

Marinho Bertanha, Andrew H. McCallum, and Nathan Seegert

Abstract:

We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in meaningfully different estimates. We provide the Stata package bunching to implement our procedures.

Keywords: partial identification, censored regression, bunching, notching

DOI: https://doi.org/10.17016/FEDS.2021.002

PDF: Full Paper

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Last Update: January 12, 2021