In collaboration with our academic partners Prof. Cingranelli at the Political Science Department, SUNY Binghamton University and Profs. Sam Bell and Amanda Murdie at the Department of Political Science, Kansas State University, we developed a Domestic Political Violence Model that forecasts political violence levels five years into the future. The model enables policymakers, particularly in the COCOMs, to proactively plan for instances of increased domestic political violence, with implications for resource allocation and intelligence asset assignment. Our model uses the IDEA dataset for political event coding, plus numerous indicators from the CIRI Human Rights Dataset, Polity IV Dataset, World Bank, OECD, Correlates of War project, and Fearon and Laitin datasets. Here is our model’s forecast for 2010 – 2014 as a ranked list:
- Sri Lanka
- Czech Republic
- South Africa
- Democratic Republic of the Congo
- Kyrgyz Republic
Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So Iran and Sri Lanka are not expected to have the same level of violence but are expected to have the same magnitude increase in violence.
There some unexpected countries on our list like Czech Republic and Italy. Time will tell the accuracy of our model’s predictions although recent political violence in Ecuador is an early indicator of the model’s effective performance. The model uses nuanced measures of repression and captures variables that can be manipulated by policy makers. Our project page has further details on the model.