Alternative parametric bunching estimators of the ETI

Thomas Aronsson (), Katharina Jenderny and Gauthier Lanot
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Thomas Aronsson: Department of Economics, Umeå University, Postal: Department of Economics, Umeå University, S 901 87 Umeå, Sweden, http://www.econ.umu.se

No 956, Umeå Economic Studies from Umeå University, Department of Economics

Abstract: We propose a maximum likelihood (ML) based method to improve the bunching approach of measuring the elasticity of taxable income (ETI), and derive the estimator for several model settings that are prevalent in the literature, such as perfect bunching, bunching with optimization frictions, notches, and heterogeneity in the ETI. We show that the ML estimator is more precise and likely less biased than ad-hoc bunching estimators that are typically used in the literature. In the case of optimization frictions in the form of random shocks to earnings, the ML estimation requires a prior of the average size of such shocks. The results obtained in the presence of a notch can differ substantially from those obtained using ad-hoc approaches. If there is heterogeneity in the ETI, the elasticity of the individuals who bunch exceeds the average elasticity in the population.

Keywords: Bunching Estimators; Elasticity of Taxable Income; Income Tax (search for similar items in EconPapers)
JEL-codes: C51 H24 H31 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2018-06-19
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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