WASHINGTON — Black taxpayers are at least three times as likely to be audited by the Internal Revenue Service as other taxpayers, even after accounting for the differences in the types of returns each group is most likely to file, a team of economists has concluded in one of the most detailed studies yet on race and the nation’s tax system.
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 I.R.S. 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 uses 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.
Some of that discrimination appears to be rooted in decisions that I.R.S. officials made over the last decade as they sought to maintain tax enforcement in the face of budget cuts, by relying on automated systems to select returns for audit.
Those decisions have produced an approach that disproportionately flags tax returns with potential errors in the claiming of certain tax credits, like the earned-income tax credit, which supplements low-income workers’ incomes in an effort to alleviate poverty. Those tax returns are more often selected for audits, regardless of how much in owed taxes the agency might recover.
The result is audit rates of Black Americans that are between three and five times the rate of other taxpayers, even when comparing that group to other taxpayers who also claim the E.I.T.C.
The I.R.S. does not detail how it selects returns for audit. But the researchers were able to isolate several apparent explanations for why Black taxpayers are targeted so much more frequently. One is complexity: It is much harder for the agency to audit returns that include business income, because that process requires expertise from individual auditors. Such returns appear to be audited less often than returns from otherwise similar taxpayers who do not report income from a business.
Black taxpayers are far less likely than others to report business income. And Black taxpayers appear to disproportionately file returns with the sort of potential errors that are easy for I.R.S. systems to identify, like underreporting certain income or claiming tax credits that the taxpayer does not qualify for, the authors find.
In effect, the researchers suggest that the I.R.S. has focused on audits that are easier to conduct and as a result, finds itself disproportionately auditing a historically disadvantaged group rather than other taxpayers, including high net-worth individuals.
“What the I.R.S. chooses to focus on when it conducts audits can either undercut or complement our progressive tax system,” said Daniel Ho, an author of the study who is the faculty director of Stanford’s Regulation, Evaluation and Governance Lab, known as RegLab, where the study originated.
The I.R.S. 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,” said Evelyn Smith, an author of the paper who is a University of Michigan economics graduate student and visiting fellow at Stanford’s RegLab.
One lesson from the study, she said, “is that absolutely is not true.”
On his first day in office, President Biden signed a series of executive orders seeking to advance racial equity in the federal government and the nation. One of them included a directive to the White House budget office to “study methods for assessing whether agency policies and actions create or exacerbate barriers to full and equal participation by all eligible individuals.”
That order inspired researchers at the RegLab, which uses machine learning and other advanced techniques to help governments improve policies. It eventually yielded the study, which the authors will present publicly on Tuesday. It was conducted by Stanford researchers including Ms. Smith, Mr. Ho and Hadi Elzayn, along with Thomas Hertz and Robin Fisher of the Treasury Department’s Office of Tax Analysis, Arun Ramesh of the University of Chicago and Jacob Goldin of Chicago and Treasury.
The group wanted to use machine learning to improve the federal auditing process, and they wanted to know if that process was infused with racial bias. But they couldn’t easily observe it, because the I.R.S. does not ask taxpayers to declare their race on tax forms, or otherwise track race in any way.
Instead, the researchers built a way to essentially fill in the blanks on taxpayer race, through a partnership with Treasury that gave them access to 148 million tax returns and 780,000 audits, primarily from 2014, but ranging from 2010 to 2018.
They used taxpayer names — first and last — and the census demographics of their neighborhoods to effectively guess the race of any given filer. Then they examined those results in a small sample of returns from taxpayers who had reported their race elsewhere, on state election forms, in order to be confident that their estimates were correct.
The eventual findings were stark and surprising, the authors said. They saw an immediate correlation between the racial composition of neighborhoods and the audit rates in those areas — vivid signs of significantly higher audit rates for Black taxpayers.
Black Americans are disproportionately concentrated in low-wage jobs. They are more likely than whites to claim the E.I.T.C. The authors wondered if that prevalence in claiming the credit might explain why Black taxpayers face more audits, because IRS data show the agency audits people who claim the E.I.T.C. at higher rates than other taxpayers.
But as the research progressed, the authors found the share of Black Americans claiming the E.I.T.C. only explained a small part of the audit differences. Instead, more than three-quarters of the disparity stems from how much more often Black taxpayers who claim the credit are audited, compared to E.I.T.C. claimants who are not Black.
Treasury officials are aware of the findings. The department started an advisory committee last fall to help it focus on disparities faced by Americans of color. This month, researchers from the department published an analysis of racial disparities in the tax code. It found a wide range of tax advantages that largely help higher-income Americans, like the mortgage interest deduction and preferential tax rates for investment income, disproportionately benefit white taxpayers.
Department officials are in the process of increasing tax enforcement on high earners and corporations that do not pay what they owe, using money from a sprawling climate, health and tax bill Mr. Biden signed into law last summer.
Asked about the study this week, a Treasury spokeswoman pointed to a letter that the deputy Treasury secretary, Wally Adeyamo, wrote last fall to the I.R.S. commissioner on those enforcement efforts, which in effect prioritized cracking down on groups of high-income taxpayers.
“Historic challenges and underfunding have led to audit rates for those at the top of the distribution decreasing more than the correspondence audits of those at the bottom in the last decade, which should change,” Mr. Adeyamo wrote.
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