Better Bunching, Nicer Notching

55 Pages Posted: 23 Mar 2018 Last revised: 20 Aug 2020

See all articles by Marinho Bertanha

Marinho Bertanha

University of Notre Dame - Department of Economics

Andrew H. McCallum

Federal Reserve Board

Nathan Seegert

University of Utah - Department of Finance

Date Written: August 14, 2020

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, tax kink, earned income tax credit

JEL Classification: C14, H24, J20

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

Marinho Bertanha (Contact Author)

University of Notre Dame - Department of Economics ( email )

Notre Dame, IN 46556
United States

Andrew H. McCallum

Federal Reserve Board ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

HOME PAGE: http://www.andrewhmccallum.com

Nathan Seegert

University of Utah - Department of Finance ( email )

David Eccles School of Business
Salt Lake City, UT 84112
United States

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