The quality of the estimators of the ETI

• 成果类型: Article
• 署名作者: Aronsson, Thomas; Jenderny, Katharina; Lanot, Gauthier
• 署名单位: Umea University
• 刊物名称: JOURNAL OF PUBLIC ECONOMICS
• ISSN/ISSBN: 0047-2727
• DOI: 10.1016/j.jpubeco.2022.104679
• 发表日期: 2022
• 关键词: Elasticity of taxable income; income tax; indirect inference; IV estimation; Bunching; Monte Carlo simulations

摘要

The elasticity of taxable income (ETI) is a central statistic for tax policy design. One purpose of the present paper is to use Monte Carlo simulation techniques to assess the bias and precision of the prevalent esti-mators in the literature, the IV-regression estimator and the bunching estimator. Thereby, we aim to pro-vide arguments in favor of, or against, using these methods. Another is to suggest indirect inference estimation to improve the quality of the measurement of the ETI. While IV-regression estimators perform well in terms of bias under certain conditions, they are more variable than bunching estimators. We also find that bunching estimators can be biased downward. The estimators based on indirect inference prin-ciples are practically unbiased and more precise than the other estimators.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).