IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v249y2025ipcs0304407624001921.html
   My bibliography  Save this article

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

Author

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

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%.

Suggested Citation

  • Battaglini, Marco & Guiso, Luigi & Lacava, Chiara & Miller, Douglas L. & Patacchini, Eleonora, 2025. "Refining public policies with machine learning: The case of tax auditing," Journal of Econometrics, Elsevier, vol. 249(PC).
    • Handle: RePEc:eee:econom:v:249:y:2025:i:pc:s0304407624001921
    DOI: 10.1016/j.jeconom.2024.105847
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407624001921
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2024.105847?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Tax enforcement; Tax evasion; Policy prediction problems;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:249:y:2025:i:pc:s0304407624001921. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.