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

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

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%.

Suggested Citation

  • Battaglini, Marco & Guiso, Luigi & Lacava, Chiara & Miller , Douglas L. & Patacchini, Eleonora, 2023. "Refining Public Policies with Machine Learning: The Case of Tax Auditing," CEPR Discussion Papers 17796, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17796
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    References listed on IDEAS

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    1. Marco Battaglini & Luigi Guiso & Chiara Lacava & Eleonora Patacchini, 2019. "Tax Professionals: Tax-Evasion Facilitators or Information Hubs?," NBER Working Papers 25745, National Bureau of Economic Research, Inc.
    2. Monica P Bhatt & Sara B Heller & Max Kapustin & Marianne Bertrand & Christopher Blattman, 2024. "Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 1-56.
    3. Christopher R. Knittel & Samuel Stolper, 2021. "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 440-444, May.
    4. William C Boning & Nathaniel Hendren & Ben Sprung-Keyser & Ellen Stuart, 2025. "A Welfare Analysis of Tax Audits Across the Income Distribution," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(1), pages 63-112.
    5. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    6. Jongbin Jung & Connor Concannon & Ravi Shroff & Sharad Goel & Daniel G. Goldstein, 2020. "Simple rules to guide expert classifications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 771-800, June.
    7. M. Hino & E. Benami & N. Brooks, 2018. "Machine learning for environmental monitoring," Nature Sustainability, Nature, vol. 1(10), pages 583-588, October.
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    Cited by:

    1. Lange Thomas & Melsom Anne May, 2024. "Tax Compliance among Managers: Evidence from Randomized Audits," Nordic Tax Journal, Sciendo, vol. 2024(1), pages 1-29.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance

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