Measuring and Mitigating Racial Disparities in Tax Audits

Abstract

Abstract Tax authorities around the world rely on audits to detect underreported tax liabilities and to verify that taxpayers qualify for the benefits they claim. We study differences in Internal Revenue Service audit rates between Black and non-Black taxpayers. Because neither we nor the IRS observe taxpayer race, we propose and use a novel partial identification strategy to estimate these differences. Despite race-blind audit selection, we find that Black taxpayers are audited at 2.9 to 4.7 times the rate of non-Black taxpayers. An important driver of the disparity is differing audit rates by race among taxpayers claiming the Earned Income Tax Credit (EITC). Using counterfactual audit selection models to explore why the disparity arises, we find that maximizing the detection of underreported taxes would not lead to Black EITC claimants being audited at higher rates. Rather, the audit disparity among EITC claimants stems largely from a policy decision to prioritize detecting overclaims of refundable credits over other forms of noncompliance. Modifying the audit selection algorithm to target total underreported taxes while holding fixed the number of audited EITC claimants would reduce the share of audited taxpayers who are Black and would lead to more audits focused on accurate reporting of business income and deductions, fewer audits focused on the eligibility of claimed dependents, higher per audit costs, and more detected noncompliance.