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Learning to tax: Interjurisdictional tax competition under incomplete information

机译:学习税收:不完全信息下的跨辖区税收竞争

摘要

We present a multi-period model in which countries set source-based taxes with- out having precise information how their and their neighbours' tax rates affect the tax base. Countries can learn from past experience and from observing their neighbours' outcomes and/or tax policy choices. We consider the sequence of Markov perfect equilibria and show that the beliefs become more precise over time and, eventually, correct. The precision of beliefs in a given period increases in the number of observed countries. In equilibrium, tax rates are inefficiently low if the value of learning is positive and the pace of learning increases in the level of tax rates (because higher tax rates trigger larger tax base effects which helps learning); in the presence of fiscal externalities, tax rates are too homogeneous (because variance in tax policies enhances learning). If, due to fiscal externalities, the value of learning is negative, the opposite may be true. From the vieoint of empirical measurement, the model generates time patterns that look as if countries react to each other even if there are no .scal externalities. We conclude that the existing evidence may therefore be inconclusive with regard to the existence of tax competition.
机译:我们提出了一个多时期的模型,在该模型中,国家/地区设定了来源税,而没有准确的信息说明其本国及其邻国的税率如何影响税基。各国可以从过去的经验中学习,也可以通过观察邻居的成果和/或税收政策选择来学习。我们考虑了马尔可夫完美平衡的顺序,并证明了随着时间的推移,信念变得更加精确,最终是正确的。在一定时期内,信念的准确性会增加被观察国家的数量。在均衡中,如果学习的价值是正值,并且学习的步伐加快了税率的水平,那么税率就会低效率低(因为较高的税率会触发更大的税基效应,这有助于学习);在存在财政外部性的情况下,税率过于单一(因为税收政策的差异会促进学习)。如果由于财政外部性,学习的价值为负,则相反。从经验测量的角度来看,该模型生成的时间模式看起来似乎在国家之间相互反应,即使没有规模外部性。我们得出的结论是,现有证据可能就税收竞争的存在尚无定论。

著录项

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 入库时间 2022-08-20 21:03:33

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