IDEAS home Printed from https://ideas.repec.org/p/tse/wpaper/31570.html
   My bibliography  Save this paper

Can Machine Learning Help Predict the Outcome of Asylum Adjudications?

Author

Listed:
  • Chen, Daniel L.
  • Eagel, Jess

Abstract

In this study, we analyzed 492,903 asylum hearings from 336 different hearing locations, rendered by 441 unique judges over a thirty-two year period from 1981-2013. We define the problem of asylum adjudication prediction as a binary classification task, and using the random forest method developed by Breiman [2], we predict twenty-seven years of refugee decisions. Using only data available up to the decision date, our model correctly classifies 82 percent of all refugee cases by 2013. Our empirical analysis suggests that decision makers exhibit a fair degree of autocorrelation in their rulings, and extraneous factors such as, news and the local weather may be impacting the fate of an asylum seeker. Surprisingly, granting asylum is predominantly driven by trend features and judicial characteristics- features that may seem unfair- and roughly one third-driven by case information, news events, and court information.

Suggested Citation

  • Chen, Daniel L. & Eagel, Jess, 2017. "Can Machine Learning Help Predict the Outcome of Asylum Adjudications?," TSE Working Papers 17-782, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:31570
    as

    Download full text from publisher

    File URL: http://nber.org/~dlchen/papers/Can_Machine_Learning_Help_Predict_the_Outcome_of_Asylum_Adjudications.pdf
    File Function: Full text
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Daniel L. & Dunn, Matt & Sagun, Levent & Sirin, Hale, 2017. "Early Predictability of Asylum Court Decisions," TSE Working Papers 17-781, Toulouse School of Economics (TSE).

    More about this item

    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:tse:wpaper:31570. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/tsetofr.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.