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.

中文翻译：

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衡量和减少税务审计中的种族差异


世界各地的税务机关都依靠审计来发现少报的纳税义务，并验证纳税人是否有资格获得他们所申请的福利。我们研究了黑人和非黑人纳税人之间美国国税局审计率的差异。由于我们和国税局都没有观察纳税人的种族，因此我们提出并使用一种新的部分识别策略来估计这些差异。尽管审计选择不分种族，但我们发现黑人纳税人的审计率是非黑人纳税人的 2.9 至 4.7 倍。造成这种差异的一个重要驱动因素是申请收入所得税抵免 （EITC） 的纳税人不同种族的审计率不同。使用反事实审计选择模型来探索出现差异的原因，我们发现最大限度地检测少报的税款不会导致黑人 EITC 申请人以更高的税率进行审计。相反，EITC申请人之间的审计差异主要源于一项政策决定，即优先检测可退还抵免的超额申报，而不是其他形式的不合规行为。修改审计选择算法以针对低报税款总额，同时保持经审计的 EITC 申请人数量固定，将减少经审计的黑人纳税人的比例，并导致更多审计侧重于准确报告业务收入和扣除额，减少关注申报受抚养人资格的审计，提高每次审计成本， 以及更多检测到的违规行为。