Nina Olson, Executive Director of the Center for Taxpayer Rights, has made available for download her article, “Racial Disparities in IRS Audits,” published in the Procedurally Taxing blog. The abstract is as follows:
Following up on Les’ post yesterday about the upcoming symposium on race and taxes, I want to draw our readers attention to an important study released yesterday, on the racial impact of Internal Revenue Service (IRS) audits, titled Measuring and Mitigating Racial Disparities in Tax Audits. Now, before I launch into what the study found, let me make my usual disclaimer: I am not an academic nor am I an economist; I have zero expertise in statistics or in computer modelling. But what I read in this incredibly coherent and well-written study is accessible to and understandable by someone such as myself (mathematical equations notwithstanding). Kudos to the authors! And everyone should read this.
The study estimated the audit rate for Black taxpayers is .81 to 1.24 percentage points higher than the audit rate of non-Black taxpayers, implying that Black taxpayers are audited between 2.9 to 4.7 times more than the rate of non-Black taxpayers. Further, the study projected that Black taxpayers claiming the Earned Income Tax Credit (EITC) on their returns are 2.9 to 4.4 times more likely to be audited than non-Black EITC claimants.
The IRS does not gather racial or ethnicity data of taxpayers, so the researchers had to impute race by various methods that have been used in other studies. The paper lays out the methodology and identifies the drawbacks to this. Then, by using various models, the researchers found that racial differences in income, family size, and household structure still don’t explain the large disparity in audit rates. Instead, it appears that audit selection algorithms based on the mere existence of underreported tax (i.e., a yes/no binary selection) or underreporting of refundable credits such as the EITC, the Additional Child Tax Credit, and the American Opportunity Tax Credit (AOTC) appear to contribute to the racial disparity in audit rates, whereas audit selection algorithms focusing on the predicted total amount of underreporting resulted in Blacks being audited less than non-Blacks. Thus, the study concludes that the objective of the predictive model underlying audit selection, along with operational considerations such as employee expertise, costs of audits, and congressional or other expectations, can be “critical drivers of disparity.”
Click here to read Nina Olson’s summary of “Racial Disparities in IRS Audits”
Posted by Melissa Zheng, Associate Editor, Wealth Strategies Journal.
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