A data-driven procedure to determine the bunching window: an application to the Netherlands

Nicole Bosch, Vincent Dekker and Kristina Strohmaier ()
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Nicole Bosch: CPB Netherlands Bureau for Economic Policy Analysis
Vincent Dekker: University of Hohenheim
Kristina Strohmaier: University of Tübingen

International Tax and Public Finance, 2020, vol. 27, issue 4, No 7, 979 pages

Abstract: Abstract We extend the bunching approach introduced by Saez (Am Econ J Econ Policy 2:180–212, 2010) by proposing an intuitive, data-driven procedure to determine the bunching window. By choosing the bunching window ad hoc, researchers throw away informative data points for estimating the counterfactual income distribution in the absence of the kink. Assuming a descending bunching mass to both sides of the threshold, the proposed algorithm produces a distribution of lower and upper bounds for the bunching window. In each iteration, the bunching window is defined as all contiguous bin midpoints around the threshold that lie outside of the confidence band resulting from running a local regression through all data points outside of the excluded region. Monte Carlo simulations provide evidence that our data-driven procedure outperforms larger bunching windows in terms of bias and efficiency. In our application for the Netherlands, we find clear evidence of bunching behaviour at all three thresholds of the Dutch tax schedule with a precisely estimated elasticity of 0.023 at the upper threshold, which is driven by self-employed, women and joint tax filers.

Keywords: Bunching; Elasticity of taxable income; Optimal bunching window (search for similar items in EconPapers)
JEL-codes: H21 H24 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10797-020-09590-w

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