A spatial one-sided error model to identify where unarrested criminals live

Abstract

The place of residence of unarrested criminals is mostly unknown. Existing research has not yet exploited that arrested criminals are a lower bound for criminals to enhance law enforcement and design structural policies. Based upon the stochastic frontier analysis, we propose a model to identify neighborhoods where unarrested criminals are likelier to live. We illustrate our approach empirically by considering Medellín, Colombia, a natural experimental field to analyze crime. We identify that unarrested murderers and drug dealers often reside in overlapping or neighboring areas with shared risk factors, reflecting the city’s history of drug-related violence. In addition, we find that employment policies targeting the young and unemployed living in the central-east and the north can mitigate homicides and motorcycle thefts. These findings illustrate how our proposal can be implemented to strengthen state capacities and design targeted, place-based policies for preventing and mitigating crime.