Organised Crimes and Repeat Victimisation: Modelling victimisation patterns of extortion against Mexican businesses


Research consistently shows that crime concentrates on a few repeatedly victimised places and targets. However, such research has been mainly concerned with ‘traditional’ crimes against individuals and households and has paid little attention to the victimisation patterns of organised crimes. As such, it is not clear whether organised crimes exhibit concentration patterns consistent with repeat victimisation, nor whether the mechanisms that explain concentration in ‘traditional’ crimes also explain the concentration of organised crimes. This thesis is concerned with the victimisation patterns of extortion against businesses, a quintessential organised crime activity. While the extortion phenomenon is often understood as an institutionalised practice of extra-legal territorial control exerted by organised criminal groups, this thesis approaches extortion predominantly from a situational perspective, focusing on the incidents that constitute the extortion phenomenon. Ultimately, the goal is to identify the incident-, victim-, and area-level characteristics that affect the patterns of extortion victimisation. To achieve this, it relies on secondary analysis of Mexico’s National Commercial Victimisation Survey 2014, one of the largest business victimisation surveys in the world. The thesis uses quantitative modelling to answer four research questions: is extortion concentrated beyond what is expected by chance? What could explain repeat extortion victimisation patterns? What are the predictors of compliance with extortion demands? And, do patterns and mechanisms of repeat victimisation vary according to the type of extortion suffered? The findings suggest that incident-, and victim-level measures are more relevant than area-level characteristics to understand extortion victimisation patterns. Furthermore, the findings suggest that event dependence is a stronger predictor of extortion concentration than risk heterogeneity. Lastly, the thesis discusses the implications of the findings for academia, crime statistics, and crime prevention policy.

UCL (University College London)