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Predicting local violence: Evidence from a panel survey in Liberia

Blair, RA; Blattman, C; Hartman, A; (2017) Predicting local violence: Evidence from a panel survey in Liberia. Journal of Peace Research , 54 (2) pp. 298-312. 10.1177/0022343316684009. Green open access

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Riots, murders, lynchings and other forms of local violence are costly to security forces and society at large. Identifying risk factors and forecasting where local violence is most likely to occur should help allocate scarce peacekeeping and policing resources. Most forecasting exercises of this kind rely on structural or event data, but these have many limitations in the poorest and most war-torn states, where the need for prediction is arguably most urgent. We adopt an alternative approach, applying machine learning techniques to original panel survey data from Liberia to predict collective, interpersonal and extrajudicial violence two years into the future. We first train our models to predict 2010 local violence using 2008 risk factors, then generate forecasts for 2012 before collecting new data. Our models achieve out-of-sample AUCs ranging from 0.65 to 0.74, depending on our specification of the dependent variable. The models also draw our attention to risk factors different from those typically emphasized in studies aimed at causal inference alone. For example, we find that while ethnic heterogeneity and polarization are reliable predictors of local violence, adverse economic shocks are not. Surprisingly, we also find that the risk of local violence is higher rather than lower in communities where minority and majority ethnic groups share power. These counterintuitive results illustrate the usefulness of prediction for generating new stylized facts for future research to explain. Ours is one of just two attempts to forecast local violence using survey data, and we conclude by discussing how our approach can be replicated and extended as similar datasets proliferate.

Type: Article
Title: Predicting local violence: Evidence from a panel survey in Liberia
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0022343316684009
Publisher version: http://doi.org/10.1177/0022343316684009
Language: English
Additional information: The final, definitive version of this paper will be published in Journal of Peace Research, published by SAGE Publishing, All rights reserved.
Keywords: Forecasting, violence, machine learning, surveys, Africa
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Political Science
URI: https://discovery.ucl.ac.uk/id/eprint/1537556
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