IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v104y2022i4p764-779.html

The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

Author

Listed:
  • Samuel Bazzi
  • Robert A. Blair
  • Christopher Blattman
  • Oeindrila Dube
  • Matthew Gudgeon
  • Richard Peck

Abstract

How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These "best-case" scenarios with annual data fall short of workable early-warning systems.

Suggested Citation

  • Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Peck, 2022. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 764-779, October.
    • Handle: RePEc:tpr:restat:v:104:y:2022:i:4:p:764-779
    DOI: 10.1162/rest_a_01016
    as

    Download full text from publisher

    File URL: https://doi.org/10.1162/rest_a_01016
    Download Restriction: Access to PDF is restricted to subscribers.
    File URL: https://libkey.io/10.1162/rest_a_01016?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions

    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:tpr:restat:v:104:y:2022:i:4:p:764-779. 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 MIT Press (email available below). General contact details of provider: https://direct.mit.edu/journals .

    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.