Employing Local Peacekeeping Data to Forecast Changes in Violence

Abstract

One way of improving forecasts is through better data. We explore how much we can improve predictions of conflict violence by introducing data reflecting third-party efforts to manage violence. By leveraging new sub-national data on all UN peacekeeping deployments in Africa, 1994–2020, from the Geocoded Peacekeeping (Geo-PKO) dataset, we predict changes in violence at the local level. The advantage of data on peacekeeping deployments is that these vary over time and space, as opposed to many structural variables commonly used. We present two peacekeeping models that contain several local peacekeeping features, each with a separate set of additional variables that form the respective benchmark. The mean errors of our predictions only improve marginally. However, comparing observed and predicted changes in violence, the peacekeeping features improve our ability to identify the correct sign of the change. These results are particularly strong when we limit the sample to countries that have seen peacekeeping deployments. For an ambitious forecasting project, like ViEWS, it may thus be highly relevant to incorporate fine-grained and frequently updated data on peacekeeping troops.