Refining public policies with machine learning: The case of tax auditing

Marco Battaglini, Luigi Guiso, Chiara Lacava, Douglas L. Miller and Eleonora Patacchini

Journal of Econometrics, 2025, vol. 249, issue PC

Abstract: We study how machine learning techniques can be used to improve tax auditing efficiency using administrative data without the need of randomized audits. Using Italy’s population data on sole proprietorship tax returns and audits, our new approach addresses the challenge that predictions must be trained on human-selected data. There are substantial margins for raising revenue from audits by improving the selection of taxpayers to audit with machine learning. Replacing the 10% least promising audits with an equal number selected by our algorithm raises detected tax evasion by as much as 39%, and evasion that is actually paid back by 29%.

Keywords: Tax enforcement; Tax evasion; Policy prediction problems (search for similar items in EconPapers)
JEL-codes: C55 H26 (search for similar items in EconPapers)
Date: 2025
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Related works:
Working Paper: Refining Public Policies with Machine Learning: The Case of Tax Auditing (2023)
Working Paper: Refining Public Policies with Machine Learning: The Case of Tax Auditing (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:249:y:2025:i:pc:s0304407624001921

DOI: 10.1016/j.jeconom.2024.105847

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