A new report published this week found that the IRS audits Black taxpayers at a significantly higher rate than non-Black taxpayers.
The paper, published by Stanford’s Institute for Economic Policy Research, said that despite the IRS’s race-blind audit selection, Black taxpayers are audited 2.9 to 4.7 times more often than non-Black taxpayers.
The findings do not suggest bias from individual tax enforcement agents, who do not know the race of the people they are auditing. They also do not suggest any valid reason for the IRS to target Black Americans at such high rates; there is no evidence that group engages in more tax evasion than others.
Instead, the findings document discrimination in the computer algorithms the agency use to determine who is selected for an audit, according to the study by economists from Stanford University, the University of Michigan, the University of Chicago and the Treasury Department.
“Using counterfactual audit selection models, we find that maximizing the detection of underreported taxes would not lead to Black taxpayers being audited at higher rates,” the paper reads.
The study found that the largest disparity between the groups was among those claiming the earned income tax credit (EITC), which helps low- to moderate-income workers and families get a tax break, according to the IRS.
During an interview with CBS news, Stanford University law professor Daniel Ho, who co-wrote the study, said, “The IRS should drill down to understand and modify its existing audit selection methods to mitigate the disparity we’ve documented.”
“And we’ve shown they can do that without necessarily sacrificing tax revenue,” Ho added.
The IRS could instead program its algorithms to target audits toward more complicated returns with higher potential dollar value to the government if an audit found errors. In that case, the discrimination in the system would vanish, the authors concluded.
“Historically, there has been this idea that if federal agencies and other policymakers don’t have access to data on race and don’t explicitly take race into account when making policy decisions and allocating resources, the resulting outcome can’t be structurally biased,” Evelyn Smith, an author of the paper who is a University of Michigan economics graduate student and visiting fellow at Stanford’s RegLab, said to NPR.
