The paper presented in this Mannheim Applied Seminar 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, the authors 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. The authors provide a theoretical explanation for this phenomenon, and they construct new estimates of the size and distribution of tax noncompliance in the United States. In their 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, the authors 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.
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27.04.2022 | 12:15 - 13:30 (CET)
ZEW – Leibniz-Zentrum für Europäische Wirtschaftsforschung
L 7, 1 68161 Mannheim