Arbeitspapier
A data-driven procedure to determine the bunching window: An application to the Netherlands
This paper presents new empirical evidence on taxpayers' responsiveness to taxation by estimating the compensated elasticity of taxable income with respect to the net-of-tax rate in the Netherlands. Applying the bunching approach introduced by Saez (2010), we find small, but clear evidence of bunching behaviour at the thresholds of the Dutch tax schedule with a precise estimated elasticity of 0.023 at the upper threshold. In line with the literature, we find much larger estimates for women and self-employed individuals, but we can also identify significant bunching behaviour for wage employed individuals which we can attribute to tax deductions for couples. We add to the bunching literature by proposing to rely on the information criteria to determine the counterfactual model, as well as developing an intuitive, data-driven procedure to determine the bunching window.
- Language
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Englisch
- Bibliographic citation
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Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 05-2016
- Classification
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Wirtschaft
Taxation and Subsidies: Efficiency; Optimal Taxation
Personal Income and Other Nonbusiness Taxes and Subsidies; includes inheritance and gift taxes
- Subject
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Elasticity of Taxable Income
Optimal Bunching Window
- Event
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Geistige Schöpfung
- (who)
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Dekker, Vincent
Strohmaier, Kristina
Bosch, Nicole
- Event
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Veröffentlichung
- (who)
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Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
- (where)
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Stuttgart
- (when)
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2016
- Handle
- URN
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urn:nbn:de:bsz:100-opus-12276
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Dekker, Vincent
- Strohmaier, Kristina
- Bosch, Nicole
- Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
Time of origin
- 2016