DP13829 The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia
|Author(s):||Samuel Bazzi, Robert Blair, Christopher Blattman, Oeindrila Dube, Matthew Gudgeon, Richard Peck|
|Publication Date:||June 2019|
|Keyword(s):||Civil War, Colombia, conflict, Forecasting, Indonesia, Machine Learning, prediction|
|JEL(s):||C52, C53, D74|
|Programme Areas:||Development Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=13829|
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 of finegrained 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.