Tax Evasion at the Top of the Income Distribution: Theory and Evidence
This paper studies tax evasion at the top of the U.S. income distribution using IRS micro-data from (i) random audits, (ii) targeted enforcement activities, and (iii) operational audits. Drawing on this unique combination of data, we demonstrate empirically that random audits underestimate tax evasion at the top of the income distribution. Specifically, random audits do not capture most tax evasion through offshore accounts and pass-through businesses, both of which are quantitatively important at the top. We provide a theoretical explanation for this phenomenon, and we construct new estimates of the size and distribution of tax noncompliance in the United States. In our model, individuals can adopt a technology that would better conceal evasion at some fixed cost. Risk preferences and relatively high audit rates at the top drive the adoption of such sophisticated evasion technologies by high-income individuals. Consequently, random audits, which do not detect most sophisticated evasion, underestimate top tax evasion. After correcting for this bias, we find that unreported income as a fraction of true income rises from 7% in the bottom 50% to more than 20% in the top 1%, of which 6 percentage points correspond to undetected sophisticated evasion. Accounting for tax evasion increases the top 1% fiscal income share significantly.
Corresponding Author: Daniel Reck, firstname.lastname@example.org. We thank Gerald Auten, Brian Galle, Bhanu Gupta, Tom Hertz, Xavier Jaravel, Drew Johns, Barry Johnson, Camille Landais, Katie Lim, Emily Lin, Larry May, Alicia Miller, Erik Ogilvie, Annette Portz, Mary-Helen Risler, Peter Rose, Emmanuel Saez, Brenda Schafer, Clifford Scherwinski, Joel Slemrod, Matt Smith, Johannes Spinnewijn, David Splinter, Alex Turk, and Alex Yuskavage for helpful discussion, support, and comments on preliminary versions of this work. Jeanne Bomare and Baptiste Roux provided excellent research assistance. All remaining errors are our own. Financial support from the Washington Center for Equitable Growth, the Stone foundation, Arnold Ventures, and the Economic and Social Research Council is gratefully acknowledged. The views expressed here are those of the authors and do not necessarily reflect the official view of the Internal Revenue Service or the National Bureau of Economic Research. This project was conducted through the Joint Statistical Research Program of the Statistics of Income Division of the IRS. All data work for this project involving confidential taxpayer information was done at IRS facilities, on IRS computers, by IRS employees, and at no time was confidential taxpayer data ever outside of the IRS computing environment. Reck and Risch are IRS employees under an agreement made possible by the Intragovernmental Personnel Act of 1970 (5 U.S.C. 3371-3376).