The How and Why of the New Public Corporation Tax Shelter Compliance Norm

Abstract:     
This paper examines the recent shift toward a stronger federal income tax compliance norm at public corporations, as evidenced by practitioner and government comments and survey results. The apparent demise of mass-marketed corporate tax shelters presents the clearest example of this new norm. The paper focuses on the organizational behavior of tax decisionmakers within public corporations as they respond to Sarbanes-Oxley, enforcement initiatives, and tax shelter regulation.

The paper identifies three elements that have contributed to the development of a stronger tax compliance norm. First, Sarbanes-Oxley has resulted in the expansion and increased transparency of public corporation tax decisionmaking groups. Organizational behavior insights suggest that this should produce more considered decisions. Second, civil and criminal enforcement has resulted in real concerns about personal and firm liability among organization leaders. This causes organization hierarchies to encourage and reward compliance. Third, the government has clearly identified objectionable tax shelter transactions and plainly labeled them unacceptable and even fraudulent. In the tax shelter area, this substantially reduces the ethical or legal uncertainty that otherwise presents an obstacle to the development of compliance norms in organizations.

The paper identifies elements of this story that do not fit neatly under the classic economic analysis of tax avoidance or evasion, including the importance of enforcement outside the tax context and the particular power of clear government rules. The conclusion also discusses what the government might do, in light of the organizational behavior influences identified by the paper, to preserve and expand the new public corporation tax compliance norm.

Number of Pages in PDF File: 58

Keywords: tax shelter, compliance, norm, Sarbanes-Oxley, organizational behavior

JEL Classification: H25, H26, K34

Accepted Paper Series