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雇主学习溢出效应下的最优所得税

Optimal Income Taxation with Spillovers from Employer Learning
American Economic Journal: Economic Policy · 2023 · Ashley Craig

中文摘要

我研究了当雇主无法完全观测人力资本投资时的最优所得税。在模型中,关于工人生产率的贝叶斯推断压缩了工资分布,从而降低了人力资本投资的私人回报。由此产生了一种外部性:在信息相同的情况下,如果工人的总体生产率更高,雇主对每名工人的生产率判断就会更为乐观。这种外部性的重要性取决于雇主判断的准确程度以及人力资本投资的响应程度。就美国而言,将这一外部性纳入考量会降低大多数工人的最优边际税率;在收入5万至10万美元的区间内,最大降幅为9至13个百分点。

Abstract

I study optimal income taxation when human capital investment is imperfectly observable by employers. In the model, Bayesian inference about worker productivity compresses the wage distribution, lowering the private return to human capital investment. An externality arises: given the same information, employers are more optimistic about each individual if workers are generally more productive. The significance of this externality hinges on the accuracy of employers’ beliefs and the responsiveness of human capital. For the United States, taking it into account lowers optimal marginal tax rates for most workers, reducing them by a maximum of 9–13 percentage points between $50,000 and $100,000. (JEL D83, H21, H24, J24, J31, M51, M52)
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