Attacking Networks of Tax Evasion: Theory and Evidence.

攻击逃税网络：理论和证据。

• 批准号: 2149432
• 负责人: Michael Best
• 金额: $ 36.26万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149432&HistoricalAwards=false
• 关键词: Attacking; Networks; Tax; Evasion; Theory

Ability to raise sufficient tax revenues efficiently and equitably to fund public services is one of the central challenges in economic development. This problem arises partly because of inability to create the capacity to enforce taxes effectively, thus leading to increased tax evasion. Reducing tax evasion requires a detailed understanding of the drivers of tax evasion and the optimal allocation of scarce tax enforcement resources. This research combines new theory and other innovative research methodologies to provide new insights into tax evasion by firms, the strength of enforcement spillovers through production networks, and how best to target enforcement activities. Working in partnership with tax authority, the project will help to increase the capacity to reduce tax evasion and to generate and use evidence on policies’ impacts. The results of this research project will inform policies to improve tax administration in the US to make it fair, efficient, reduce the deficit, and improve the allocation of resources. The lessons from this research can then serve as evidence to inform decisions in other countries. This project has two parts. In the first part extends the canonical model of tax evasion: firms make reports of their sales to, and purchases from, other firms in a production network, creating reporting spillovers. We derive the optimal targeting rules the government should use to minimize total tax evasion. The second part of the project leverages rich administrative tax return data on tax liabilities and production networks to perform two RCTs in collaboration with tax administrations. The first experiment will randomly deploy electronic invoicing and desk audits focused on discrepancies between trading partners' reports to learn the strength of their direct effects on targeted firms and their indirect effects on targeted firms' suppliers and clients. The project then takes these estimates and use them to calibrate the model and the second experiment tests the model’s optimal targeting rule against the status quo and random targeting. The results of this research project will inform policies to improve tax administration in the US to make it fair, efficient, reduce the deficit, and improve the allocation of resources. The lessons from this research can then serve as evidence to inform decisions in other countries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

提高足够的税收能力有效且平等地资助公共服务是经济发展的核心挑战之一。此问题的部分原因是由于无法创造有效执行税收的能力，从而导致税收进化增加。减少税收演变需要详细了解税收演变的驱动因素和最佳税务执行资源的最佳分配。这项研究结合了新的理论和其他创新研究方法，以提供有关公司税收进化的新见解，通过生产网络执法的实力以及如何最好地针对执法活动。该项目与税务机构合作，将有助于增加减少税收演变并生成政策影响的证据的能力。该研究项目的结果将告知政策，以改善美国的税收管理，使其公平，有效，降低防御并改善资源的分配。然后，这项研究的教训可以作为证据，以告知其他国家的决策。这个项目有两个部分。第一部分扩展了税收演变的规范模型：公司向生产网络中其他公司的销售报告并从其他公司那里购买，从而创建了报告。我们得出了政府应使用的最佳目标规则来最大程度地减少总税收进化。该项目的第二部分利用税收负债和生产网络的丰富行政纳税申报数据与税务管理部门合作执行两个RCT。第一个实验将随机部署电子发票和桌面审核，重点是交易合作伙伴的报告之间的差异，以了解其直接影响目标公司和对目标公司供应商和客户的间接影响。然后，该项目采用这些估计并使用它们来校准模型，第二个实验测试了模型对现状和随机靶向的最佳目标规则。该研究项目的结果将告知政策，以改善美国的税收管理，使其公平，有效，降低防御并改善资源的分配。然后，这项研究的教训可以作为证据，以为其他国家的决策提供信息。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。