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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.

The growing research on co-offending over the life course is based on relatively small, general offending samples, followed for a short period, and from a limited set of countries. This study expands current knowledge by examining the relation of co-offending with age, criminal experience, crime type, and crime seriousness in a large sample of Italian serious organized crime offenders with criminal careers spanning between age 14 and late adulthood. The sample includes 160,262 offenses by 10,530 Italian mafia offenders. After preliminary descriptive statistics, random-effects logistic regressions estimate how age, criminal experience, crime type, and seriousness are independently related to the co-offending probability. Co-offending decreases only moderately with both the age and criminal experience of organized crime offenders, while more prolific offenders exhibit a stable co-offending prevalence as their experience increases. Co-offending varies significantly by crime type, and it is more frequent for more serious offenses. In line with previous research, co-offending has a relevant functional component even for organized crime offenders: results suggest it is instrumental to perpetrating specific offense types and more serious offenses. However, results also show that specific social and criminal contexts promote co-offending among older and more experienced offenders.

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 paper provides a narrative synthesis of the results of a systematic review of the social, psychological and economic factors leading to recruitment into organised crime. This is based on the analysis of evidence emerging from 47 qualitative, quantitative and mixed-method studies published in or before 2017. While the selected studies varied markedly in method and quality, several factors emerged as particularly important in understanding recruitment into organised criminal groups. These included the role of social relations (family, kinship, friendship and work-relations), criminal background and criminal skills.

Criminal organizations exploit their presence on territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, development and analysis of an agent-based model (ABM) that simulates a neighborhood of Palermo (Sicily) with the aim to understand the pathways that lead individuals to recruitment into organized crime groups (OCGs). Using empirical data on social, economic and criminal conditions of the area under analysis, we use a multi-layer network approach to simulate this scenario. As the final goal, we test different policies to counter recruitment into OCGs. These scenarios are based on two different dimensions of prevention and intervention: (i) primary and secondary socialization and (ii) law enforcement targeting strategies., 15 pages, 2 figures. Paper accepted and in press for the Proceedings of the 2019 Social Simulation Conference (Mainz, Germany)

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.

While indicators assessing the quality of life often comprise measures of crime or fear of crime, these components usually refer to property or violent crimes. More complex crimes, which may significantly impact on the social, economic, and political conditions of local communities, are often overlooked, mostly due to problems in adequately measuring the levels of e.g. organised crime and corruption. Indeed, despite the growing scholarly attention, measurements of organised crime are rare and frequently affected by important methodological limitations. This study addresses this issue by proposing the Mafia Presence Index (MPI), a composite indicator measuring the presence of the mafias in Italy. The MPI aggregates variables measuring different dimensions of mafia presence, namely the presence and activities of mafia groups, mafia violence, and infiltration in politics and the economy. Furthermore, the analysis explores the validity and robustness of the MPI by considering possible alternative variables and by assessing the impact of different calculation strategies. Results show that the MPI is a parsimonious and consistent measure of mafia presence, relying on a core set of five variables directly related to mafia presence. The index is also robust to different calculation methods and is negatively associated with the most popular indexes measuring the quality of life in Italy.

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.

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted from the judicial documents of operation “Montagna”, conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration.

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.

This interview with Ernesto Savona was conducted in 2014. It discusses Savona’s background, influences, career and current thinking and activities. After graduating in 1965 in Palermo, he has worked at Rome “La Sapienza”, Trento and Cattolica del Sacro Cuore of Milan universities. His expertise in the study of organized crime led to him being made consultant to the United Nations, the Council of Europe, the European Union and several national governments. From 1990 to 1994 he was visiting fellow and project director at the National Institute of Justice, the Research Center of the U.S. Department of Justice, Washington D.C., where he managed two projects on organized crime and international money laundering. For the term 2003–2004, he has been president of the European Society of Criminology. From 2003 on he has been editor-in-chief of the European Journal on Criminal Policy and Research. For the term 2010/2011 he has been chair of the Global Agenda Council on Organized Crime of the World Economic Forum. In 1994 he set up Transcrime as a research group on transnational crime located in the Catholic University of Milan and the University of Trento. Transcrime analyses organized crime, illegal markets and money laundering, evaluates crime prevention policies, identifies criminogenic opportunities in legislation and develops risk analyses and criminal risk models for both governments and private sector actors such as banks, professionals and business interests.

This article focuses on four different criminal markets: counterfeiting, illegal firearms trafficking, gambling and waste management. Despite recurrent allegations that these markets have a high mafia presence, there is a lack of reliable estimates of their sizes and the revenues that they generate. Figures in reports and media vary significantly, and the methods used to obtain them are often obscure. This study develops four different estimation methodologies with which to estimate the four criminal markets in Italy at the national and regional levels. Considering that these are the first attempts to estimate these markets, the aim of the article is to stimulate debate on how to improve measurement of the crime proceeds from these criminal markets.

In recent years, official reports, the academic literature and the media have dedicated increased attention to foreign criminal groups in Italy. The actual nature of foreign criminal organisations, and particularly their comparability to Italian mafias, is still under researched. This study explores the application of the offence of mafia-type association (Article 416-bis of the Criminal Code) to foreign criminal organisations. It analyses the theoretical applicability of the offence as well as statistics and jurisprudence of relevant cases. Results show that the offence of mafia-type association is theoretically applicable to foreign criminal groups. However, the number of cases is very low. This is due to two concurring factors: foreign criminal groups do not always have the same characteristics of domestic mafias (stability, continuity, use of the mafia method); the evidentiary difficulties have brought the prosecution and the courts to apply other offences, carrying high penalties but without the need to prove the mafia method.

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.

This article presents the Mafia Index (MI), an index measuring the presence of mafias at the provincial level. In the abundant literature on Italian mafias, relatively few studies have attempted to measure the presence of mafias across the country. A review of previous attempts points out the limitations and methodological shortcomings of existing measurements. The study provides an operational definition of ‘mafia’ and selects the most appropriate indicators and variables according to multiple criteria. The MI combines data on mafia-type associations, mafia murders, city councils dissolved for infiltration by organised crime, and assets confiscated from organised crime and covers the period between 1983 and 2009. The MI highlights not only the strong concentration of the mafias in their original territories but also their significant presence in the central and northern provinces. This confirms that mafias should not be regarded as typically Southern Italian phenomena, but rather as a national problem.

"This article analyzes the European legal framework on cybercrime. Initially, it argues the challenges of cybercrime to traditional criminal justice systems. Subsequently, it focuses on the criminal law framework on cybercrime with a mainly European perspective. The European legal framework provides a three-path solution: the reduction of frictions among national legislations, the introduction of new investigative powers and the facilitation of international cooperation. The article presents and discusses each solution. Further, it argues that the effective implementation of the main legal instruments does not seem to depend on the legal enforceability of these international measures. Contrarily, other, non legal, factors such as national security, politics, the economy and the public opinion appear to stimulate the spontaneous implementation of the European legal framework. In this context, the added value of the EU action is rather low, although the Treaty of Lisbon and the Stockholm Programme may improve this situation in the long term." [auhtor's abstract]

This chapter argues that the definition of organised crime by the European Union Framework Decision on the Fight against Organised Crime (henceforth FD) is uncertain and vague, and that this makes the FD a poor instrument with little added value for the approximation of criminal legislation against organised crime. This criticism of the FD is based on both legal and criminological arguments, since the FD appears to be flawed from both perspectives. In the chapter, the first section provides brief background information on organised crime, harmonisation and approximation of criminal law in the EU. The second section then analyses the definition of "criminal organisation" as provided by the FD and the model offences criminalizing participation in a criminal organisation. Subsequently, the third section summarises the FD's problems relative to its added value in the approximation of criminal legislation against organised crime and suggests some possible improvements. Keywords:criminal legislation; European Union (EU); framework decision (FD); harmonisation; organised crime

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.