IDEAS home Printed from https://ideas.repec.org/a/eee/eecrev/v175y2025ics0014292125000650.html
   My bibliography  Save this article

Leaving the past behind: Effects of clean slate regulation on employment and earnings

Author

Listed:
  • Dasgupta, Kabir
  • Ghimire, Keshar
  • Plum, Alexander

Abstract

We investigate the labor market implications of New Zealand’s clean slate initiative. The clean slate regulation allows automatic concealment of criminal records of previously convicted individuals who remain free of convictions for at least seven years (rehabilitation period) since their last sentence. We use detailed administrative data on criminal court charges to identify our sample of previously convicted individuals who are expected to have their criminal records automatically concealed upon completing their rehabilitation period. By linking our sample to high-frequency tax records including information on employment and earnings, we apply a difference-in-differences framework as well as models developed for staggered assignment of a treatment to study the causal mechanisms. Our analysis reveals that the clean slate reform did not affect eligible individuals’ employment propensity, but led to a modest but precisely estimated two-percent increase in monthly earnings of employed individuals.

Suggested Citation

  • Dasgupta, Kabir & Ghimire, Keshar & Plum, Alexander, 2025. "Leaving the past behind: Effects of clean slate regulation on employment and earnings," European Economic Review, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:eecrev:v:175:y:2025:i:c:s0014292125000650
    DOI: 10.1016/j.euroecorev.2025.105015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0014292125000650
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.euroecorev.2025.105015?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

    Clean slate; Conviction; Employment; Earnings; Difference-in-Differences; Staggered treatment;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law

    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:eecrev:v:175:y:2025:i:c:s0014292125000650. 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/eer .

    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.