Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288471 
Year of Publication: 
2020
Citation: 
[Journal:] International Tax and Public Finance [ISSN:] 1573-6970 [Volume:] 27 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2020 [Pages:] 951-979
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Bunching
Elasticity of taxable income
Optimal bunching window
JEL: 
H21
H24
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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