Attacking Networks of Tax Evasion: Theory and Evidence.

能否有效和公平地筹集足够的税收，为公共服务提供资金，是经济发展面临的主要挑战之一。出现这一问题的部分原因是无法建立有效执行税收的能力，从而导致逃税增加。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，为公司逃税、通过生产网络的执法溢出的强度以及如何最好地针对执法活动提供了新的见解。该项目与税务当局合作，将有助于提高减少逃税的能力，并生成和使用有关政策影响的证据。这一研究项目的结果将为改善美国税收管理的政策提供参考，使其公平、高效、减少赤字，并改善资源配置。这项研究的经验可以作为证据，为其他国家的决策提供参考。这个项目包括两个部分。在第一部分中，扩展了逃税的规范模型：公司对生产网络中的其他公司的销售和采购进行报告，从而产生报告溢出。我们得出了政府应使用的最优目标规则，以将总逃税降至最低。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单数据，与税务机关合作进行了两个区域技术审查。第一个实验将随机部署电子发票和案头审计，重点关注贸易伙伴报告之间的差异，以了解它们对目标公司的直接影响的力度，以及它们对目标公司的供应商和客户的间接影响的力度。然后，该项目采用这些估计值并使用它们来校准模型，第二个实验针对现状和随机目标测试模型的最优目标规则。这一研究项目的结果将为改善美国税收管理的政策提供参考，使其公平、高效、减少赤字，并改善资源配置。这项研究的经验教训可以作为其他国家决策的依据。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.