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

Samuel Bazzi, Robert Blair, Chris Blattman, Oeindrila Dube, Matthew Gudgeon and Richard Peck

No bkrn8, SocArXiv from Center for Open Science

Abstract: Policymakers can take actions to prevent local conflict before it begins, if such violence can be accurately predicted. We examine the two countries with the richest available sub-national data: Colombia and Indonesia. We assemble two decades one fine- grained violence data by type, alongside hundreds of annual risk factors. We predict violence one year ahead with a range of machine learning techniques. Models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best. Rich socio-economic data also substitute well for these histories. Even with such unusually rich data, however, the models poorly predict new outbreaks or escalations of violence. \Best case" scenarios with panel data fall short of workable early-warning systems.

Date: 2019-06-11
New Economics Papers: this item is included in nep-big, nep-law and nep-sea
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Citations: View citations in EconPapers (12)

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https://osf.io/download/5cffcd9c745b280019346d42/

Related works:
Journal Article: The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia (2022)
Working Paper: The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia (2019)
Working Paper: The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia (2019)
Working Paper: The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:bkrn8

DOI: 10.31219/osf.io/bkrn8

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