In our companion Domestic Political Violence Model blog, we published yesterday the list of countries predicted to have increases in political violence for 2011 to 2015. The map below shows the countries with expected increase in political violence grouped by Very High Risk, High Risk, and Medium Risk. Our forecast is based on four different models. In the Very High Risk category, all four models predicted an increase. In the High Risk category, three models predicted an increase. In the Medium Risk category, half of the models predict an increase in violence. The countries in each category are sorted based on the size of the mean residual, so the states with the most pent-up demand for violence are listed at the top. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So United Kingdom and Israel are not expected to have the same level of violence but are expected to have the same magnitude increase in political violence.
United Kingdom, Israel, Sri Lanka, Iran, Colombia, Zimbabwe, South Africa, Haiti, Egypt, Philippines, Guinea-Bissau, Venezuela, Chile, Syria, Chad, Belarus, Guinea, Kyrgyzstan, Greece make up the very high risk list. Israel, Sri Lanka, Iran, Colombia, South Africa, Egypt, Chile, Syria, Chad, Belarus, and Kyrgyzstan are returning countries from our 2010-2014 forecast. Of our 2010-2014 forecast, Syria, Egypt, and Libya saw the most violent protest in the Arab Spring of 2011. United Kingdom, Zimbabwe, Haiti, Philippines, Guinea-Bissau, Venezuela, Guinea, and Greece are the new additions to our very high risk list. United Kingdom tops the list as the pent-up demand for increased violence was certainly evident in the London Riots over the Summer of 2011. Greece saw substantial increase in political violence due to the measures introduced by the Greek government to address the debt crisis.
It is worth noting that our 2011-2015 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2010. Similarly, our 2010-2014 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2009. We publish our forecast based on our acquisition date of the event dataset. As the event dataset is available on a real-time basis – albeit at a higher cost, we can publish our forecast in real-time if needed.
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. In our model, the magnitude of future political violence directed towards the state is heightened by coercion, often thought of as violations of physical integrity rights, and by coordination, or the tools by which groups can associate and organize against the state. Conversely, the magnitude of political violence is lessened by capacity, defined as the ability of the state to project itself throughout its territory.
For the event dataset, we use the Integrated Data for Event Analysis (IDEA) framework. IDEA event dataset is based on the Reuters Global News Service, and organized in a “who” did “what” to “whom” manner for each particular event. This framework allows researchers to isolate events of interest for their particular project. Using this framework allows us to capture and isolate domestic anti-government violence. For the dependent variable, our model uses the Goldstein scores that captures the overall level and intensity of domestic antigovernment violence within a state in a given year.
