 The quality of the estimators of the ETIThomas Aronsson, Katharina Jenderny and Gauthier Lanot Journal of Public Economics, 2022, vol. 212, issue C Abstract: 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 estimators in the literature, the IV-regression estimator and the bunching estimator. Thereby, we aim to provide 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 principles are practically unbiased and more precise than the other estimators. Keywords: Elasticity of taxable income; Income tax; Indirect inference; IV estimation; Bunching; Monte Carlo simulations (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:212:y:2022:i:c:s0047272722000810 DOI: 10.1016/j.jpubeco.2022.104679 Access Statistics for this article Journal of Public Economics is currently edited by R. Boadway and J. Poterba More articles in Journal of Public Economics from Elsevier |
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