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.

能否有效和公平地筹集足够的税收，为公共服务提供资金，是经济发展的核心挑战之一。 出现这一问题的部分原因是无法建立有效征税的能力，从而导致逃税现象增加。 减少逃税需要详细了解逃税的驱动因素，并优化配置稀缺的税收执法资源。这项研究结合了新的理论和其他创新的研究方法，为企业逃税，通过生产网络的执法溢出效应的强度以及如何最好地针对执法活动提供了新的见解。该项目将与税务当局合作，帮助提高减少逃税以及生成和使用政策影响证据的能力。该研究项目的结果将为改善美国税收管理的政策提供信息，使其公平，高效，减少赤字，改善资源配置。这项研究的经验教训可以作为证据，为其他国家的决策提供信息。 这个项目有两个部分。 在第一部分中，扩展了逃税的规范模型：公司报告他们的销售，并从其他公司在生产网络中购买，创造报告溢出。我们得出的最佳目标规则，政府应该使用，以尽量减少总逃税。该项目的第二部分利用关于纳税义务和生产网络的丰富的行政纳税申报数据，与税务部门合作进行两项RCT。 第一个实验将随机部署电子发票和桌面审计，重点是贸易伙伴报告之间的差异，以了解其对目标公司的直接影响以及对目标公司的供应商和客户的间接影响。然后，该项目采取这些估计，并使用它们来校准模型，第二个实验测试模型的最佳目标规则对现状和随机目标。该研究项目的结果将为改善美国税收管理的政策提供信息，使其公平，高效，减少赤字，改善资源配置。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。