Offence versatility among co-offenders: A dynamic network analysis

Research examining co-offending has become increasingly popular over the last two decades. Despite this, there remains a dearth of research examining the dynamics of co-offending across time, largely due to limited access to longitudinal data. In the current paper we are interested in explaining crime versatility, and therefore we employ Relational Hyperevent Models (RHEM) to model the conditional probability that a given group of co-offenders engages in one set of crime categories rather than another. Thus, we are analyzing a two-mode network (actors by crime categories) and explain, conditional on a given group of co-offenders, their participation in the set of specific crime types involved in a particular crime event. With respect to co-offending, results reveal that, compared with solo offenders, groups of two or more co-offenders are more likely to engage in crime events involving more than just one crime category. Results suggest that in the context of co-offending both market and property crime show evidence of differential association and social learning. Naïve partners in co-offending partnerships learn the skills and knowledge needed to participate in co-offending involving market and property crime.