Identifying Municipal Risk Factors for Leftist Guerrilla Violence in Colombia

Abstract

This study examines determinants of leftist violence at the municipal level in Colombia from 2000 through 2010. A multilevel GLMM model with a negative binomial distribution is used to take advantage of the information available at the municipal and department level. Surprisingly, inequality was not a significant covariate of violence, and agricultural GDP tended to reduce, instead of increase, guerrilla violence. The main risk factors identified include physical characteristics such as rugged topography and prior violence, but also factors that are candidates for policy action, such as unemployment, incorporation of the poor into public services, repression, and the energy and mining sector. These findings suggest interventions to decrease risks of guerrilla violence beyond merely strengthening the state. While repression tends to escalate violence, targeted policies to provide health benefits to those currently underserved, and securing mining and oil operations can effectively reduce the risk of violence.

Crossing thresholds: The use of spatial analysis to identify intensification of the Colombian conflict

“…Coca aerial eradication contributes to warlike clashes, geography, prior violence, and controls in all variables showed identical direction and significance as in Holmes et al (2018).…”

“…At the beginning of the 2000s, most of the HRV clustered activity took place in Chocó, Antioquia, Valle de Cauca, Cauca, La Guajira, and Bolívar. We replicated the Model 5 in Holmes et al (2018) which models warlike actions as the dependent variable. Even though socioeconomic factors explain some guerrilla behavior, geographic clusters of high incidences of HRVs also contribute to explaining guerrilla violence at the municipal level.…”

“…The dummy variables are now included in the generalized linear mixed model (GLMM) model of leftist guerrilla HRVs and warlike actions, the same specifications as in Holmes et al (2018) with the addition of the hot spots. The G * i is a z-score, so large positive z-scores represent municipalities with high values of HRVs and they are surrounded by other municipalities with high values of HRVs as well.…”

“…How the spatial dimension is conceptualized varies. We build on the analysis of Holmes et al (2018) on warlike actions, using the same multilevel model specification but with additional spatial clustering analysis. Braithwaite and Johnson (2012) use space-time modeling based on Knox contingency tables to summarize the number of events in time and space of the Iraqi insurgency.…”

“…For a full description of the data come see Holmes et al (2018). The population values are annual estimates from the 2005 census.…”