A Bayesian spatio-temporal model of variation in homicide rates for El Salvador - Carlos Carcach (PLoS ONE, 2025)
Most violence in El Salvador has been attributed to gangs. After being one of the most violent societies worldwide, in 2023, the country’s homicide rate reached a low 2.4 per 100,000 population. The decline is the outcome of a security plan by government that has attacked gangs directly under a state of exception. Nearly 80,000 gang members have been incarcerated. For the past 30 years or so, Salvadoran authorities and politicians alike, have participated in negotiations with gangs to reduce violence and gain electoral support in exchange for benefits for their members. This research studies violence as the outcome from the activities by gangs, politicians and governments, their interactions, and communities intervening in the realization of these interactions. As most data required for explaining these processes is either inexistent or difficult to access, a hierarchical Bayesian model was implemented for the spatio-temporal evolution of homicide with random effects that account for omitted variables at the level of local areas and time periods. The results support the view that unobserved covariates related to the district patterns of homicide have evolved over time. Two cycles appear in the evolution of homicide over the period under study, one from 2003 through to 2012, and another starting in 2013 and still going on at the time of writing. This finding reinforces the view that timing of government-gang negotiations drove the behavior of homicide rates in El Salvador during 2002–2021 together with the growth and expansion of gangs as seen from clustering of high-risk districts over time. The incarceration of scores of members and collaborators has both incapacitated gangs as key producers of violence, and deterred other forms of crimes. As a next step, the government should build collective efficacy, in particular among disadvantaged communities, to restrain the formation of gang-like groups in the time to come.