Abstract
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.
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Notes
- 1.
The research team considered as mafia offenses the crime of mafia association and other offenses aggravated by the mafia method (i.e.: Articles 416bis, 416ter, 418 of the Italian Criminal Code, and special laws 575/1965, Art. 7 special law 152/1991 and Art. 7 special law 203/1991).
- 2.
The intermittency parameter was discarded, as available data prevent identification of the prison times served by mafia members.
- 3.
The diversity index (DIi) for individual i is defined as \( D{I}_i=1-\sum \limits_{31}^{m=1}{p}_m^{(i)}\ast {p}_m^{(i)} \), where m = 1, 2…31 are the 31 crime categories identified in the dataset and \( {p}_m^{(i)} \)is the proportion of offenses committed by individual i in the crime category m. The diversity index can be interpreted as the probability that any two offenses drawn randomly from an individual’s set of offenses belong to two different crime categories (Piquero et al. 1999). When DIi=0, offender i is completely specialised on one type of crime. Conversely, a value of the index approaching 1 indicates that the offender engages in a diversity of crime categories. Given the number of crime categories identified in the dataset, the maximum value of the diversity index in this analysis is (31-1)/31 = 0.97.
- 4.
Some mafia members in the PMM still commit offences after age 60. However, considering the lack of information on the death date and the technical problem of high singularity of the matrix (the high occurrence of zero offences for the years from age 55 to age 75), the dataset was censored to age 60.
- 5.
Indeed, at a first stage the absolute variance of the entire matrix was too high for the correct computation of the models. Some individuals committed more than 100 offences in a single age/year, while zeros accounted for 75% of the cells of the matrix after the computation (98% before temporal censoring).
- 6.
A new variable (mafia association aggregated) was created by grouping the different values of mafia association by Italian region. This resulted in five categories for mafia association aggregated: “‘Ndrangheta” (grouping members of ‘Ndrangheta and Other Mafias Calabria), “Sicilian Mafia” (grouping members of Cosa Nostra, Stidda and Other Mafias Sicily), “Camorra” (consisting of Camorra members), “Apulian Mafia” (grouping members of Sacra Corona Unita and Other Mafias Apulia) and “Mafia Lucana” (consisting of members of Mafia Lucana). Results for “Mafia Lucana” are not presented in the following analysis as only 28 offenders in the dataset are members of this mafia organization, thus impeding to draw comparable conclusions on their criminal career’s patterns.
- 7.
For each Italian province where the number of mafia members born in that province was higher than 100, the relative frequencies of the mafia groups (values of mafia association aggregated) were calculated. If the relative frequency of a mafia group within a province was greater or equal than 85%, that value was imputed into a new variable named mafia association aggregated clean for all individuals born in that Italian province.
- 8.
The sample includes n = 129,571 offenses out of the total 182,867 offenses in the PMM dataset. This discrepancy is due to two reasons: firstly, offenses committed before 1982 or with missing year are excluded from these charts. Secondly, offenses with a missing value for the mafia association variable (after the data imputation) are excluded as well.
- 9.
The drop in the number of crimes committed by all mafia organizations in more recent years (approximately from 2007 onwards) might be influenced by the fact that the dataset contains information on mafia members’ final convictions. In Italy, proceedings for mafia-related offenses often last many years before reaching a final judgment. For example, the analysis of the data from the Casellario shows that on average Italian courts issue definitive judgments (i.e. irrevocable sentences) 6 years after the commission of the mafia association crime.
- 10.
The year of the first mafia association offence is employed as a proxy for the timing of entrance into the mafia organization. This methodological choice may present some flaws linked to the imprecision of the year of crime variable and to the possibility that mafia offenders committed some mafia association crimes at younger ages that might have gone undetected by law enforcement authorities. Nonetheless, some robustness checks relying on building time-buffers that “move” the year of recruitment to 1, 2 and 3 years before and after the year of the first mafia association crime showed that the choice of this last variable as year of entrance into the mafia organization is fairly robust (Savona et al. 2017a).
- 11.
Most results are robust to the change in the age limit defining “early” and “late” recruits (Savona et al. 2017a).
- 12.
Crimes have been grouped through a Principal Component Analysis that enabled to identify relevant correlations across the 31 crime categories and reduce them into six groupings (Savona et al. 2017a).
- 13.
“associative crimes” excludes the mafia association crimes, thus grouping together the crimes of “criminal association” and “drug trafficking criminal association”.
- 14.
These statistics refer to the mafia association variable before data imputation, see Section 6 for more details.
- 15.
These considerations arise from logistic regression models having as dependent variable the offender’s mafia association (Savona et al. 2017a, p. 227).
- 16.
The initial 11,144 mafia members were filtered based on the number of crimes they committed, maintaining in the sample only those members who committed at least four crimes. For each member was then computed the probability that crimes with no year were the first or last crimes of the member’s career. These two passages lowered the sample size from 11,144 to 5993.
- 17.
To test the robustness of the choice of the year of the first mafias offence as a proxy for the recruitment year, we calculated the distributions of the dimensions moving the recruitment moment backwards in time of 1, 3 and 5 years. The analyses focused on those members with information about all the six different dimensions, and on the four time-buffers defined for 0, 1, 3 and 5 years. The correlation was computed, for each dimension, before and after recruitment, and across the time-buffers. Results show strong correlations on average, pointing out the robustness of the choice. Further details about the robustness checks are in (Savona et al. 2017a).
- 18.
The offence of mafia association is excluded from the calculation of the seriousness, as it would have increased the average seriousness score for all mafia members.
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Savona, E.U., Calderoni, F., Campedelli, G.M., Comunale, T., Ferrarini, M., Meneghini, C. (2020). The Criminal Careers of Italian Mafia Members. In: Weisburd, D., Savona, E.U., Hasisi, B., Calderoni, F. (eds) Understanding Recruitment to Organized Crime and Terrorism. Springer, Cham. https://doi.org/10.1007/978-3-030-36639-1_10
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