IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30777.html
   My bibliography  Save this paper

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

Author

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

Abstract

We study the extent to which 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, audits and their outcome, we develop a new approach to address the so called selective labels problem - the fact that a ML algorithm must necessarily be trained on endogenously selected data. We document the existence of substantial margins for raising revenue from audits by improving the selection of taxpayers to audit with ML. Replacing the 10% least productive audits with an equal number of taxpayers selected by our trained algorithm raises detected tax evasion by as much as 38%, and evasion that is actually payed back by 29%.

Suggested Citation

  • Marco Battaglini & Luigi Guiso & Chiara Lacava & Douglas L. Miller & Eleonora Patacchini, 2022. "Refining Public Policies with Machine Learning: The Case of Tax Auditing," NBER Working Papers 30777, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:30777
    Note: PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30777.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • H2 - Public Economics - - Taxation, Subsidies, and Revenue
    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:30777. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.