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Crime Patterns between Tradition and Change: A Territorial Analysis of the Italian Provinces


Violent criminality associated with Mafia-type organizations has been crucial for the image of Italy’s crime. This paper purpose is to verify whether the contemporary situation still matches that image and what has been the impact on crime of more recent factors, above all foreign immigration. Indeed, Italy has been characterized by a recent but tumultuous migratory flow, which has possibly posed difficulties for immigrants’ integration. The territorial analysis, carried out on 103 Italian provinces, shows two discrete scenarios. When all the provinces are considered, serious offences such as intentional homicides, extortions, criminal conspiracies and robberies are still associated with a traditional scenario of socio-economic backwardness and poor social capital. However, when only the Central and Northern provinces are selected, a new, non-traditional form of criminality, associated with “modern” features, such as the presence of immigrants and their problems of integration, clearly emerges.

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  1. 1.

    A census category: “professionals” are people in white-collar professional occupations; “entrepreneurs”, people who organize and manage a company.

  2. 2.

    The diffusion of Mafia-type criminal organizations was measured by the rate of people charged with membership of such organizations.

  3. 3.

    These magazines are Panorama, L’Espresso and Il Mondo.

  4. 4.

    The selection of these groups of immigrants was based on the number of people charged with criminal offences (abs. val.). The countries these groups have come from are Morocco, Albania, Romania, Senegal, former Yugoslavia and Tunisia.

  5. 5.

    All the regression models used “robust” error estimations to control for errors heteroscedasticity, via the Huber-White sandwich estimator of variance.

  6. 6.

    For the Moran’s I—the measure of spatial autocorrelation developed by P. A. P. Moran—a proximity weights matrix, based on the inverse distance between the provinces centroids, was used. Software: Stata modules Spatwmat, Spatgsa by M. Pisati and Spwmatrix, Splagvar by P. W. Jeanty.

  7. 7.

    For the hot spot analysis, a binary weights matrix, based on the provinces queen contiguity, was used. Software: Stata modules Spmat, by D. M. Drukker and Spmap, by M. Pisati.

  8. 8.

    The spatial regression models used a row-standardized weights matrix, based on the inverse distance between the provinces centroids. Software: Stata modules Spatwmat, Spatreg by M. Pisati, and Spmlreg by P. W. Jeanty. By using spatial data, one violates the ordinary least squares regression assumption that regressors are linearly independent. Therefore, we used maximum likelihood models, which leave out of consideration such assumption.

  9. 9.

    The Limited Information Maximum Likelihood model estimation is robust even with relatively weak instruments and small samples and therefore is better than the 2SLS and GMM estimations.

  10. 10.

    And, controlling for remittances 1995–2005, the instrument doesn't affect Y (exclusion restriction).


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Correspondence to Luigi M. Solivetti.



See Table 6.

Table 6 Dependent and independent variables used in the analysis: descriptive statistics

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Solivetti, L.M. Crime Patterns between Tradition and Change: A Territorial Analysis of the Italian Provinces. Soc Indic Res 128, 531–558 (2016).

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  • Immigration
  • Homicides
  • Criminal organizations
  • Social capital
  • Spatial analysis