Refining Public Policies with Machine Learning: The Case of Tax Auditing

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

No 17796, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We study how ML 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 ML. Replacing the 10% least promising audits with an equal number selected by our algorithm raises detected tax evasion by as much as 38%, and evasion that is actually paid back by 29%.

JEL-codes: C55 H26 (search for similar items in EconPapers)
Date: 2023-01
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Working Paper: Refining Public Policies with Machine Learning: The Case of Tax Auditing (2022)
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