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ObjectivesWe test the effects of four policy scenarios on recruitment into organized crime. The policy scenarios target (i) organized crime leaders and (ii) facilitators for imprisonment, (iii) provide educational and welfare support to children and their mothers while separating them from organized-crime fathers, and (iv) increase educational and social support to at-risk schoolchildren.MethodsWe developed a novel agent-based model drawing on theories of peer effects (differential association, social learning), social embeddedness of organized crime, and the general theory of crime. Agents are simultaneously embedded in multiple social networks (household, kinship, school, work, friends, and co-offending) and possess heterogeneous individual attributes. Relational and individual attributes determine the probability of offending. Co-offending with organized crime members determines recruitment into the criminal group. All the main parameters are calibrated on data from Palermo or Sicily (Italy). We test the effect of the four policy scenarios against a baseline no-intervention scenario on the number of newly recruited and total organized crime members using Generalized Estimating Equations models.ResultsThe simulations generate realistic outcomes, with relatively stable organized crime membership and crime rates. All simulated policy interventions reduce the total number of members, whereas all but primary socialization reduce newly recruited members. The intensity of the effects, however, varies across dependent variables and models.ConclusionsAgent-based models effectively enable to develop theoretically driven and empirically calibrated simulations of organized crime. The simulations can fill the gaps in evaluation research in the field of organized crime and allow us to test different policies in different environmental contexts.

What drives the consumption of illicit cigarettes? While criminology has rarely addressed the divers of the illicit cigarette markets from a theoretical and empirical perspective, studies from other disciplines point to two classes of causes. Some studies stress the impact of cigarette prices and taxes on the market for illicit cigarette; others emphasize the importance of different non-price factors, including informal economy and corruption. This study tests the influence of both price and non-price factors on the illicit cigarette market. Multilevel growth curve analysis—three-level MLM for longitudinal measures—of the illicit cigarette market at the subnational level in the European Union. The analysis focuses on 247 regions in the EU between 2007 and 2013. This study shows that both price and non-price factors influence illicit cigarette consumption. Lower affordability of legal products, proximity to sources of cheap cigarettes, higher national income inequality, greater population density, and the levels of illicit cigarettes in neighboring regions are associated with higher illicit consumption. On the contrary, there is no empirical evidence of the role of two ‘usual suspects’: corruption and shadow economy. The paper also shows that the market for illegal cigarettes is shaped by both demand and supply factors. The geographic concentration of illicit consumption and smuggling calls for the creation of anti-illicit-trade units in most densely populated areas or custom task forces at the most sensitive borders. The disproportionate relevance of illicit flows from eastern non-EU countries suggests to increase the political pressure on these source countries. Finally, given the importance of the demand side in determining the size of the illicit market, price increases should be matched with consumer awareness campaigns. These campaigns should focus on the societal consequences of purchasing illicit cigarettes together with illustrating the harm of consuming tobacco products.

Despite growing evidence about heterogeneous pathways leading individuals into organized crime, there is limited knowledge about the differences in the criminal career between individuals who entered criminal organizations in their youth and those who joined at an older age. This study assesses the differences between early and late recruits in the Italian mafias through logistic regressions considering several criminal career parameters computed on the period prior to recruitment. Results show that recruitment in the mafias is far from a homogenous process. Early recruits report an early criminal onset, lower educational attainment, more serious offenses within a shorter time-span, and more frequent violent co-offending; late recruits show a later onset, more prolific and versatile—but less serious—offending.

This chapter provides the first analyses of the criminal career of the Italian mafias members. The analysis is based on the unique Proton Mafia Member dataset, provided by the Italian Ministry of Justice, with information on all individuals who received a final conviction for mafia offences since the 1980s. The PMM includes information on more than 11 thousand individuals and 182 thousand offences. The study explores the career of mafia members following a three-level approach, analyzing the macro, meso, and micro dimensions of the criminal careers of the mafiosi. At the macro level, Italian mafias’ member show different types of criminal trajectories, with a significant portion of the sample exhibiting a late onset and late persistence pattern. At the meso level, the four main types of mafias (the Sicilian Cosa Nostra, the Neapolitan Camorra, the Calabrian ‘Ndrangheta, and the Apulian mafias) report very similar traits although some distinctive patterns emerge. At the micro level, there are differences in the criminal career between early- and lately- recruited member, with the former showing higher frequency of violent, volume crime and the latter a more complex, white collar profile. A further exploration of the PMM data shows an escalation in both the number and the seriousness of crimes before joining the mafias, which subsequently stabilize afterwards.

This chapter presents a systematic review of the social, psychological, and economic factors relating to involvement and recruitment into organized crime groups (OCGs), including mafias, drug trafficking organizations (DTOs), gangs, and other criminal organizations. This review has three objectives: (i) identifying the most commonly reported factors leading to recruitment into OCGs, (ii) highlighting whether these factors are independent of one other or they are correlated, and (iii) assessing the validity and generalizability of research findings. The review searched all possible relevant studies indexed in selected databases and published in five languages (i.e. English, French, German, Italian, and Spanish), without limitations as to their year of publication or geographic origin. Starting from 48,731 potentially eligible records, integrated with experts’ suggestions, this systematic review includes 47 empirical studies employing quantitative, qualitative, or mixed-methods approaches. The findings show that social and economic factors are the most commonly reported factors relating to recruitment into OCGs, while psychological factors are marginal. Although factors are highly interrelated and shared across OCGs, their predominance varies across types of criminal organizations. For instance, individuals join mafias and gangs attracted by strong group identity, whereas individuals enter DTOs mainly for financial gain. In light of these findings, recommendations for future research and implications for prevention policies are discussed.

Brokerage is crucial for dark networks. In analyzing communications among criminals, which naturally induce bipartite networks, previous studies have focused on the classic Freeman's betweenness, conceived for one-mode matrices and possibly biasing the results. We explore different betweenness centrality including three inspired by the dual projection approach recently suggested by Everett and Borgatti 2013. We test these measures in identifying criminal leaders in a meeting participation network. Despite the expected high correlations among them, the measures yield different node rankings, capturing different characteristics of brokerage. Overall, the dual projection approaches show higher success than classic approaches in identifying the criminal leaders.

This study complements existing literature on the mobility of criminal groups (mainly based on country case studies) with the first systematic assessment of the worldwide activities of the four main types of Italian mafias (Cosa Nostra, Camorra, ’Ndrangheta and Apulian mafias) from 2000 to 2012. Drawing from publicly available reports, a specific multiple correspondence analysis identifies the most important associations among mafias, activities, and countries. The results show that the mafias concentrate in a few countries; drug trafficking is the most frequent activity, whereas money laundering appears less important than expected; a stable mafia presence is reported in a few developed countries (mainly Germany, Canada, Australia, and the United States). The mafias show significant differences: the ’Ndrangheta tends to establish structured groups abroad, whereas the other mafias mainly participate in illicit trades.

Mafia homicides are usually committed for retaliation, economic profit, or rivalry among groups. The variety of possible reasons suggests the inefficacy of a preventive approach. However, like most violent crimes, mafia homicides concentrate in space due to place-specific social and environmental features. Starting from the existing literature, this study applies the Risk Terrain Modeling approach to forecast the Camorra homicides in Naples, Italy. This approach is based on the identification and evaluation of the underlying risk factors able to affect the risk of a homicide. This information is then used to predict the most likely location of future events. The findings of this study demonstrate that past homicides, drug dealing, confiscated assets, and rivalries among groups make it possible to predict up to 85% of 2012 mafia homicides, identifying 11% of city areas at highest risk. By contrast, variables controlling for the socio-economic conditions of areas are not significantly related to the risk of homicide. Moreover, this study shows that, even in a restricted space, the same risk factors may combine in different ways, giving rise to areas of equal risk but requiring targeted remedies. These results provide an effective basis for short- and long-term targeted policing strategies against organized crime- and gang-related violence. A similar approach may also provide practitioners, policy makers, and local administrators in other countries with significant support in understanding and counteracting also other forms of violent behavior by gangs or organized crime groups.

Despite the heterogeneity of the illicit trade in cigarettes within countries, available studies mainly take national markets as their unit of analysis. The innovative contribution of this work is the focus on the phenomenon at the subnational level. Price and non-price factors are examined as determinants of the consumption of illicit cigarettes in 247 subnational areas of 28 European countries, exploiting a mixed linear model. This approach combines national and subnational data, thus accounting for the correlation among regions and explaining the important differences in the consumption of illicit cigarettes within a country. The size of the informal economy, the affordability of licit cigarettes, the rate of illicit cigarettes in the bordering regions, and the level of economic inequality emerge as the main etiological factors in the illicit cigarette trade in Europe.

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals’ co-participation in meetings. It draws from a case study on a large law enforcement operation (“Operazione Infinito”) tackling the ‘Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non-trivial way, with the internal organization of the ‘Ndrangheta into different “locali” (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the “locale” membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed.

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.

Through a novel data set comprising the criminal records of 11,138 convicted mafia offenders, we compute criminal career parameters and trajectories through group-based trajectory modeling. Mafia offenders report prolific and persistent careers (16.1 crimes over 16.5 years on average), with five distinct trajectories (low frequency, high frequency, early starter, moderate persistence, high persistence). While showing some similarities with general offenders, the trajectories of mafia offenders also exhibit significant differences, with several groups offending well into their middle and late adulthood, notwithstanding intense criminal justice sanctions. These patterns suggest that several mafia offenders are life-course persisters and career criminals and that the involvement in the mafias is a negative turning point extending the criminal careers beyond those observed in general offenders.