On Bunching and Identification of the Taxable Income Elasticity

Working Paper: NBER ID: w24136

Authors: Sren Blomquist; Whitney Newey; Anil Kumar; Cheyuan Liang

Abstract: The taxable income elasticity is a key parameter for predicting the effect of tax reform or designing an income tax. Bunching at kinks and notches in a single budget set have been used to estimate the taxable income elasticity. We show that when the heterogeneity distribution is unrestricted the amount of bunching at a kink or a notch is not informative about the size of the taxable income elasticity, and neither is the entire distribution of taxable income for a convex budget set. Kinks do provide information about the size of the elasticity when a priori restrictions are placed on the heterogeneity distribution. They can identify the elasticity when the heterogeneity distribution is specified across the kink and provide bounds under restrictions on the heterogeneity distribution. We also show that variation in budget sets can identify the taxable income elasticity when the distribution of preferences is unrestricted and stable across budget sets. For nonparametric utility with general heterogeneity we show that kinks only provide elasticity information about individuals at the kink and we give bounds analogous to those for isoelastic utility. Identification becomes more difficult with optimization errors We show in examples how results are affected by optimization errors.

Keywords: taxable income elasticity; bunching; notches; heterogeneity distribution

JEL Codes: C14; H20


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
unrestricted heterogeneity distribution (C46)amount of bunching at a kink or notch (C24)
kinks (L82)information about taxable income elasticity (H31)
variations in budget sets (D10)identification of taxable income elasticity (H30)
optimization errors (C61)complicate identification of taxable income elasticity (H31)

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