Date: Wednesday, March 8th, 2023
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Measuring and Mitigating Racial Disparities in Tax Audits
Abstract:
Government agencies around the world use data-driven algorithms to allocate enforcement resources. Even when such algorithms are formally neutral with respect to protected characteristics like race, there is widespread concern that they can disproportionately burden vulnerable groups. We study differences in Internal Revenue Service (IRS) audit rates between Black and non-Black taxpayers. Because neither we nor the IRS observe taxpayer race, we propose and employ 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. The main source of the disparity is differing audit rates by race among taxpayers claiming the Earned Income Tax Credit (EITC). Using counterfactual audit selection models for EITC claimants, we find that maximizing the detection of underreported taxes would not lead to Black taxpayers being audited at higher rates. In contrast, in these models, certain policies tend to increase the audit rate of Black taxpayers: (1) designing audit selection algorithms to minimize the “no-change rate”; (2) targeting erroneously claimed refundable credits rather than total under-reporting; and (3) limiting the share of more complex EITC returns that can be selected for audit. Our results highlight how seemingly technocratic choices about algorithmic design can embed important policy values and trade-offs.
Bio:
Hadi Elzayn is a researcher working at the intersection of computer science with economics, policy, and societal concerns, with a focus on both theoretical and applied questions around algorithmic fairness. He completed his Postdoctoral Fellowship at Stanford University’s RegLab, advised by Daniel Ho and Jacob Goldin; he earned his Ph.D. and M.A. in Applied Mathematics and Computational Sciences at the University of Pennsylvania, advised by Michael Kearns, and his Bachelors’ degrees in Mathematics and Economics from Columbia University. In addition to academic institutions, he has engaged in research collaborations with colleagues at the U.S. Department of Treasury and the Federal Reserve Bank of Philadelphia, and has prior work experience at firms including Facebook, Microsoft Research, and TGG Group. He is currently employed as a Research Scientist at Meta Platforms, where he works on fairness in online advertising for regulated verticals.