European Journal of Law and Economics

, Volume 46, Issue 1, pp 1–37 | Cite as

Resisting the extortion racket: an empirical analysis

  • Michele Battisti
  • Andrea Mario Lavezzi
  • Lucio Masserini
  • Monica Pratesi


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.


Organized crime Extortion Social mobilization Multilevel regression models 

JEL Classification

O17 K42 R11 C41 



We are grateful to Addiopizzo for providing data and for support to this research. We wish to thank in particular Caterina Alfano, Daniele Marannano, Giuseppe Pecoraro and Pico di Trapani. We also thank the Chamber of Commerce of Palermo, the Istat office of Palermo (in particular Dott. Li Vecchi), the Ufficio Toponomastica of Palermo City Council (in particular Dott. Salamone); Matteo Zucca and Marcella Milillo for help with the data; Luigi Balletta, seminar participants in Livorno (Structural Change, Dynamics and Economic Growth), Pisa, Petralia (VII Applied Economics Workshop), and Naples (Old and New Forms of Organised and Serious Crime.) and four anonymous referees for comments. Gabriele Mellia, Alice Rizzuti and Giorgio Tortorici provided excellent research assistance. Financial support from MIUR (PRIN 2009, “Structural Change and Growth”), and University of Palermo (FFR 2012), is gratefully acknowledged. Usual caveat applies.


  1. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.CrossRefGoogle Scholar
  2. Alexander, B. (1997). The rational racketeer: Pasta protection in depression era Chicago. The Journal of Law and Economics, 40, 175–202.CrossRefGoogle Scholar
  3. Asmundo, A., & Lisciandra, M. (2008). The cost of protection racket in sicily. Global Crime, 9, 221–240.CrossRefGoogle Scholar
  4. Balletta, L., & Lavezzi, A. M. (2016). Extortion, firm’s size and the sectoral allocation of capital (in press).Google Scholar
  5. Battisti, M., Fioroni, T., Lavezzi, A. M., Masserini, L., & Pratesi, M. (2017a). The costs and benefits of resisting the extortion racket (in press).Google Scholar
  6. Battisti, M., Kourtellos, A., Durlauf, S., & Lavezzi, A. M. (2017b). Social interactions and crime prevention (in press).Google Scholar
  7. Becker, G. (1957). The economics of discrimination. Chicago: University of Chicago Press.Google Scholar
  8. Becker, G. (1968). Crime and punishment: An economic approach. The Journal of Political Economy, 76, 169–217.CrossRefGoogle Scholar
  9. Becker, G., Murphy, K. M., & Tamura, R. (1990). Human capital, fertility and economic growth. Journal of Political Economy, 98, S12–S37.CrossRefGoogle Scholar
  10. Browne, W. J., & Draper, D. (2000). Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models. Computational Statistics, 15, 391–420.CrossRefGoogle Scholar
  11. Bueno de Mesquita, E., & Hafer, C. (2007). Public protection or private extortion? Economics and Politics, 20, 1–32.Google Scholar
  12. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Carlin, B. P., & Louis, T. A. (2000a). Bayes and empirical bayes methods for data analysis (2nd ed.). Boca Raton: Chapman and Hall-CRC.CrossRefGoogle Scholar
  14. Carlin, B. P., & Louis, T. A. (2000b). Empirical bayes: Past, present and future. Journal of America Statistical Association, 95, 1286–1289.CrossRefGoogle Scholar
  15. Clayton, D. G. (1996). Generalized linear mixed models. In W. R. Gilks, S. Richardson, & D. J. Spiegelhalter (Eds.), Markov chain Monte Carlo in practice. London: Chapman and Hall.Google Scholar
  16. Confesercenti. (2010). XI Rapporto di Sos Impresa Le mani della criminalita’ sulle imprese. Confesercenti.Google Scholar
  17. Congdon, P. (2006). Bayesian models for categorical data. New York: Wiley.Google Scholar
  18. Demidenko, E. (2004). Mixed models. Theory and applications. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  19. Dixit, A. (2016). Anti-corruption institutions: Some history and theory, mimeo. In International economic association institutions, governance and corruption, conference montevideo (URY).Google Scholar
  20. Draper, D. (2008). Bayesian multilevel analysis and MCMC. In J. de Leeuw (Ed.), Handbook of quantitative multilevel analysis. New York: Springer.Google Scholar
  21. Efron, B., & Morris, C. (1973). Stein’s estimation rule and its competitors—An empirical Bayes approach. Journal of the American Statistical Association, 68, 117–130.Google Scholar
  22. Efron, B., & Morris, C. (1975). Data analysis using Stein’s estimator and its generalizations. Journal of the American Statistical Association, 70, 311–319.CrossRefGoogle Scholar
  23. Fiasconaro, A. (2007). La citta’ avvolta dalla nube, La Sicilia. Retrieved from
  24. Fioroni, T., Lavezzi, A. M., & Trovato, G. (2017). Organized crime, corruption and poverty traps (in press).Google Scholar
  25. Forno, F., & Gunnarson, C. (2010). Everyday shopping to fight the Mafia in Italy. In M. Micheletti & A. McFarland (Eds.), Creative participation: Responsibility-taking in the political world. London: Paradigm Publisher.Google Scholar
  26. Gambetta, D. (1993). The sicilian Mafia: The business of private protection. Cambridge: Harvard University Press.Google Scholar
  27. Gambetta, D. (2009). Codes of the underworld. Princeton: Princeton University Press.Google Scholar
  28. Gambetta, D., & Reuter, P. (1995). Conspiracy among the many: The Mafia in legitimate industries. In G. Fiorentini & E. S. Peltzman (Eds.), The economics of organised crime. Cambridge: Cambridge University Press.Google Scholar
  29. Gelfand, A., & Smith, A. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398–409.CrossRefGoogle Scholar
  30. Goldstein, H. (2011). Multilevel statistical models. New York: Wiley.Google Scholar
  31. Gunnarson, C. (2014). Changing the game: Addiopizzo’s mobilisation against racketeering in Palermo. European Review of Organised Crime, 1(1), 39–77.Google Scholar
  32. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). New York: Wiley.CrossRefGoogle Scholar
  33. Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York: Routledge.Google Scholar
  34. Keogh, R., & Cox, R. J. (2014). Case-control studies. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. Konrad, K. A., & Skaperdas, S. (1998). Extortion. Economica, 65, 461–477.CrossRefGoogle Scholar
  36. La Spina, A. (2008). Recent anti-Mafia strategies: The Italian experience. In D. Siegel & H. Nelen (Eds.), Organized crime: Culture, markets and policies. New York: Springer.Google Scholar
  37. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.CrossRefGoogle Scholar
  38. Lavezzi, A. M. (2008). Economic structure and vulnerability to organised crime: Evidence from Sicily. Global Crime, 9, 198–220.CrossRefGoogle Scholar
  39. Lavezzi, A. M. (2014). Organised crime and the economy: A framework for policy prescriptions. Global Crime, 15(1–2), 164–190.CrossRefGoogle Scholar
  40. Maritz, J. S., & Lwin, T. (1989). Empirical bayes methods. London: Chapman and Hall.Google Scholar
  41. Meridionews (Redazione) (2015). Nuova intimidazione alla pasticceria Marsicano. ’Il rione mi boicotta e ora mi hanno levato le telecamere’. Meridionews. Retrieved from
  42. Mete, V. (2011). I lavori di ammodernamento dell’autostrada Salerno-Reggio Calabria. Il ruolo delle grandi imprese nazionali. In R. Sciarrone (Ed.), Alleanze nell’ombra. Rome: Donzelli.Google Scholar
  43. Morris, C. (1983). Parametric empirical bayes inference, theory and applications. Journal of the American Statistical Association, 78, 47–65.CrossRefGoogle Scholar
  44. Paoli, L. (2003). Mafia brotherhoods. Organized crime, Italian style. Oxford: Oxford University Press.Google Scholar
  45. Partridge, H. (2012). The determinants of and barriers to critical consumption: A study of Addiopizzo. Modern Italy, 17(3), 343–363.CrossRefGoogle Scholar
  46. Pedrini, M., & Ferri, L. M. (2014). Socio-demographical antecedents of responsible consumerism propensity. International Journal of Consumer Studies, 38(2), 127–138.CrossRefGoogle Scholar
  47. Pinheiro, I. C., & Bates, D. M. (1995). Approximations to the log-likelihood function in nonlinear mixed-effects models. Journal of Computational and Graphical Statistics, 4, 12–35.Google Scholar
  48. Pinotti, P. (2015). The economic costs of organized crime: Evidence from southern Italy. Economic Journal, 125, F203–F232.CrossRefGoogle Scholar
  49. Rabe-Hesketh, S., & Skrondal, A. (2006). Multilevel modelling of complex survey data. Journal of the Royal Statistical Society, Series A, 169, 805–827.CrossRefGoogle Scholar
  50. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modelling. Psychometrika, 69, 167–190.CrossRefGoogle Scholar
  51. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.). Thousand Oaks: Sage Publications.Google Scholar
  52. Schneider, J. C., & Schneider, P. T. (2003). Reversible destiny. Mafia, antimafia and the struggle for Palermo. Oakland, CA: University of California Press.Google Scholar
  53. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.CrossRefGoogle Scholar
  54. Skrondal, A., & Rabe-Hesketh, S. (2009). Prediction in multilevel generalised linear models. Journal of the Royal Statistical Society, Series A, 172(3), 659–687.CrossRefGoogle Scholar
  55. Snijders, T. A., & Bosker, R. (2011). Multilevel analysis. An introduction to basic and advanced multilevel modelling (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
  56. Struttura e dimensione delle imprese Archivio Statistico delle Imprese Attive (ASIA). Anno 2005, Rome.Google Scholar
  57. Tuerlinckx, F., Rijmen, F., Verbeke, G., & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. British Journal of Mathematical and Statistical Psychology, 59, 225–255.CrossRefGoogle Scholar
  58. Vaccaro, A., & Palazzo, G. (2015). Values against violence: Institutional change in societies dominated by organized crime. Academy of Management Journal, 58(4), 1075–1101.CrossRefGoogle Scholar
  59. Vaccaro, A. (2012). To pay or not to pay? Dynamic transparency and the fight against the mafia’s extortionists. Journal of Business Ethics, 106(1), 23–35.CrossRefGoogle Scholar
  60. Van Dijk, J. (2007). Mafia markers: Assessing organized crime and its impact upon societies. Trends in Organized Crime, 10, 39–56.CrossRefGoogle Scholar
  61. Varese, F. (2014). Protection and extortion. In L. Paoli (Ed.), Oxford handbook of organized crime (pp. 343–58). Oxford: Oxford University Press.Google Scholar
  62. Varese, F. (2009). The Camorra closely observed. Global Crime, 10, 262–266.CrossRefGoogle Scholar
  63. Vroom, V. H., & Pahl, B. (1971). Relationship between age and risk taking among managers. Journal of Applied Psychology, 55(5), 399.CrossRefGoogle Scholar
  64. Ziniti, A. (2015). Fuga dalla Vucciria. Chiude il ristorante Santandrea. La Repubblica. Retrieved from

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Michele Battisti
    • 1
  • Andrea Mario Lavezzi
    • 1
  • Lucio Masserini
    • 2
  • Monica Pratesi
    • 2
  1. 1.Department of LawUniversity of PalermoPalermoItaly
  2. 2.Department of Economics and ManagementUniversity of PisaPisaItaly

Personalised recommendations