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Measuring Organised Crime Presence at the Municipal Level

Author

Listed:
  • Marco Dugato

    (Università Cattolica del Sacro Cuore – Transcrime)

  • Francesco Calderoni

    (Università Cattolica del Sacro Cuore – Transcrime)

  • Gian Maria Campedelli

    (Università Cattolica del Sacro Cuore – Transcrime)

Abstract

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.

Suggested Citation

  • Marco Dugato & Francesco Calderoni & Gian Maria Campedelli, 2020. "Measuring Organised Crime Presence at the Municipal Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(1), pages 237-261, January.
    • Handle: RePEc:spr:soinre:v:147:y:2020:i:1:d:10.1007_s11205-019-02151-7
    DOI: 10.1007/s11205-019-02151-7
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    3. Carmine Gambardella & Rosaria Parente & Alessandro Ciambrone & Marialaura Casbarra, 2021. "A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing," Data, MDPI, vol. 6(10), pages 1-13, October.

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