Gauthier Lanot

Thematic seminars
big data and econometrics seminar

Gauthier Lanot

Umeå universitet
Estimating the ETI

IBD Salle 03

Îlot Bernard du Bois - Salle 03

5-9 boulevard Maurice Bourdet
13001 Marseille

Tuesday, November 26 2019| 2:00pm to 3:15pm

Ewen Gallic: ewen.gallic[at]
Pierre Michel: pierre.michel[at]


Measuring the elasticity of taxable income (ETI) is central for tax policy design. Yet, there are few arguments which support or infirm that current methods yield measurements of the ETI that can be trusted. Our first purpose is to use simulation methods to assess the bias and precision of the prevalent methods used in the literature (IV estimation and bunching methods). Thereby, we aim at (i) explaining the huge differences in empirical results, and (ii) providing arguments in favor of or against using these methods. Our second purpose is to suggest indirect inference estimation to improve the quality of the measurement.
We find that the IV regression estimators may suffer from considerable bias and be quite imprecise (even in large samples),
 whereas the bunching estimators perform better in our controlled environment. We also show that using more of the information available in the data, estimators based on indirect inference principles produce more precise estimates of the ETI than any of the most commonly used methods.
[if time allows,  I will discuss  maximum likelihood (ML) based method to improve the bunching approach of measuring the elasticity of taxable income (ETI), and derive the estimator for several model settings that are prevalent in the literature, such as perfect bunching, bunching with optimization frictions, notches, and heterogeneity in the ETI. We show that the ML estimator is more precise and likely less biased than ad-hoc bunching estimators that are typically used in the literature. In the case of optimization frictions in the form of random shocks to earnings, the ML estimation requires a prior of the average size of such shocks. The results obtained in the presence of a notch can differ substantially from those obtained using ad-hoc approaches. If there is heterogeneity in the ETI, the elasticity of the individuals who bunch exceeds the average elasticity in the population.]

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