A Quantitative Application of Enterprise and Social Embeddedness Theories to the Transnational Trafficking of Cocaine in Europe

ABSTRACT

Illegal enterprise and social embeddedness theories have highlighted the importance of market forces and social factors, respectively, for analyzing organized crime and organized criminal activities. This paper empirically demonstrates the joint explanatory power of these respective theories in the case of the transnational trafficking of cocaine. It does so by conceptualizing transnational cocaine trafficking as a network of relationships among countries; a network whose structure reflects the actions of manifold organized criminal groups. The analysis utilizes exponential random graph models to analyze quantitative data on cocaine trafficking which are ordinarily difficult to capture in empirical research. The analysis presented focuses on a set of 36 European countries. The results yield insights into the nature of the relationship among economic incentives, social ties, geographic features and corruption, and how, in turn, this relationship influences the structure of the transnational cocaine network and the modi operandi of cocaine traffickers.

Notes

¹ A conditional uniform graph test (or CUG test) was used to compare the density of the cocaine trafficking network against the density of a baseline model (Butts 2008). A low p-value (p < 0.001) suggests that the observed network is less centralized than would be anticipated from its size.

² Estimates of the effects of corruption are not statistically significant when alternative imputation methods for missing nodal attribute data are used. Appendix D provides details on missing data and alternative imputation methods. Appendix G reports the results of the robustness checks, where we used alternative methods to impute missing values. Results remain similar in both direction and magnitude in all the models; levels of significance are also similar, albeit with one exception. The estimates for the levels of corruption in importing countries lose statistical significance in the models based on networks where missing nodal attribute data were imputed using predictive mean matching and bootstrapping, and mean substitution.

³ Countries are labeled as European or not according to the division of macro geographic regions used by the United Nations (UNODC 2015a).

⁴ The index ranges from 0 for highly corrupt countries to 100 for very honest countries. We use the reciprocal of the index so that higher values indicate higher corruption levels.

Additional information

Notes on contributors

Alberto Aziani

Alberto Aziani is a research fellow at Università Cattolica del Sacro Cuore in Milan and researcher at Transcrime. His main research interests are illicit markets and organized crime. On these topics, he has conducted studies and developed projects for international organizations and funding institutions.

Giulia Berlusconi

Giulia Berlusconi is a lecturer in criminology at the University of Surrey. With a particular emphasis on fusing criminology scholarship with quantitative methodologies and, in particular, social network analysis, her research focuses primarily on co-offending and illicit markets.

Luca Giommoni

Luca Giommoni is a lecturer in criminology at the Cardiff School of Social Sciences, Cardiff University. His research interests are organized crime and how online technologies are changing illicit markets such as drug trafficking and human trafficking.