Better Bunching, Nicer Notching

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, tax kink, earned income tax credit

JEL Classification: C14, H24, J20

Suggested Citation: Suggested Citation

Bertanha, Marinho and McCallum, Andrew H. and Seegert, Nathan, Better Bunching, Nicer Notching (August 14, 2020). Available at SSRN: https://ssrn.com/abstract=3144539 or http://dx.doi.org/10.2139/ssrn.3144539