Abstract
Research QuestionIs there a ‘power few’ individuals in Denmark who, through consistent co-offending, produce the highest frequency of crimes and the most harm to society amongst all co-offenders?DataWe analysed official statistics from the Police Crime Case Management System in Denmark on all 437,717 charges for violations of the Danish Criminal Code, the Illegal Substances Act and the Weapons Act, in which co-offender relationships were identified from 2007 to 2017, equal to 28% of the national total of all 1,554,943 such charges filed against both solo offenders and co-offenders in that time period.MethodsWe cross-referenced charging records with crime harm values taken from the Danish Crime Harm Index to measure the severity of all offence types charged. A social network analysis (SNA) algorithm was applied to the data to test for centrality and identify key co-offenders.FindingsWhile 7.5% of the co-offending population accounted for 50% of crime volume, only 3.6% of the co-offenders accounted for 50% of total crime harm. The latter made up just 1.2% of the overall offender population in Denmark, but contributed 24% of overall harm. Social network analysis of how central that power few was in relation to other co-offenders suggests an even smaller cohort of co-offenders—the ‘power few of the power few’—who are disproportionality more connected to other co-offenders.ConclusionsThe ‘power few’ phenomenon exists in co-offender networks, with a pronounced concentration of harm caused by a small number of co-offenders. The evidence suggests that targeting co-offenders based on social network analysis can enhance the harm potentially reduced by both investigations and crime prevention strategies.
Highlights
Evidence-based targeting aims to find the most efficient and cost-effective allocation of police resources
The evidence points to a clear “Pareto curve” phenomenon: are most criminal events attributable to a small group of people, places or times (Sherman 2007); so, too, is most crime harm concentrated in a small percentage of places (Weinborn et al 2017), victims (Dudfield et al 2017) and victim-offenders (Sandall et al 2018)
Englefield and Ariel (2017) used social network analysis (SNA) to explore the interconnectivity of groups of co-offenders, showing that some crime categories are more likely to see offenders engage in the recruitment of younger and less-experienced offenders—narcotics distribution and auto theft, for example
Summary
Evidence-based targeting aims to find the most efficient and cost-effective allocation of police resources. The Danish Police force identifies offenders who will be subjected to prioritised targeting based on a variety of factors, including the number of prior offences, but especially crimes indicating their role in a criminal network (gangs, organised crime groups, etc.). Englefield and Ariel (2017) used SNA to explore the interconnectivity of groups of co-offenders, showing that some crime categories are more likely to see offenders engage in the recruitment of younger and less-experienced offenders—narcotics distribution and auto theft, for example To this extent, SNA—as a conceptual as well as a technical approach—can be applied in the search for the most harmful power few offending networks in Denmark.
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