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A maximum likelihood bunching estimator of the elasticity of taxable income

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  • Thomas Aronsson
  • Katharina Jenderny
  • Gauthier Lanot

Abstract

This paper develops a maximum likelihood (ML) bunching estimator of the elasticity of taxable income (ETI). Our structural approach provides a natural framework to simultaneously account for unobserved preference heterogeneity and optimization errors and for measuring their relative importance. We characterize the conditions under which the parameters of the model are identified and show that the ML estimator performs well in terms of bias and precision. The paper also contains an empirical application using Swedish data, showing that both the ETI and the standard deviation of the optimization friction are precisely estimated, albeit relatively small.

Suggested Citation

  • Thomas Aronsson & Katharina Jenderny & Gauthier Lanot, 2024. "A maximum likelihood bunching estimator of the elasticity of taxable income," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 200-216, January.
    • Handle: RePEc:wly:japmet:v:39:y:2024:i:1:p:200-216
    DOI: 10.1002/jae.3015
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    References listed on IDEAS

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    Cited by:

    1. Lv, Xiaofeng & Yang, Yifan & Lu, Yue & Hong, Liming, 2025. "Deviation signs-based bunching methods," Economic Modelling, Elsevier, vol. 143(C).

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