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RESEARCH PRODUCT
Resisting the extortion racket: an empirical analysis
Monica PratesiLucio MasseriniAndrea Mario LavezziMichele Battistisubject
Economics and EconometricEconomics and EconometricsEngineeringOperations researchmedia_common.quotation_subjectPopulation01 natural sciencesHuman capitalOrganized crime010104 statistics & probabilityMultilevel regression modelsExtortion; Multilevel regression models; Organized crime; Social mobilization; Business and International Management; Economics and Econometrics; LawRacketeering0502 economics and businessmedia_common.cataloged_instanceOrganised crime050207 economics0101 mathematicsBusiness and International ManagementSettore SECS-P/01 - Economia PoliticaeducationEmpirical evidencemedia_commoneducation.field_of_studyDiscrete choiceExtortionbusiness.industry05 social sciencesMultilevel regression modelExtortionSocial mobilizationDemographic economicsPsychological resiliencebusinessLawdescription
While the contributions on the organized crime and Mafia environments are many, there is a lack of empirical evidence on the firm’s decision to resist to extortion. Our case study is based on Addiopizzo, an NGO that, from 2004, invites firms to refuse requests from the local Mafia and to join a public list of “non-payers”. The research is based on a dataset obtained linking the current administrative archives maintained by the chambers of commerce and the list updated by the NGO. The objective of this paper is twofold: first, to gather sound data on the characteristics of the Addiopizzo joiners; second to model the probability to join Addiopizzo by a two-level logistic regression model. We find that the resilience behavior is likely to be the result of both individual (firm) and environmental factors. In particular, we find that firm’s total assets, firm’s age and being in the construction sector are negatively correlated with the probability of joining AP, while a higher level of human capital embodied in the firm and a higher number of employees are positively correlated. Among the district-level variables, we find that the share of district’s population is negatively correlated with the probability to join, while a higher level of socio-economic development, including education levels, are positively correlated.
year | journal | country | edition | language |
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2018-06-14 | European Journal of Law and Economics |