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Tainted ties: the structure and dynamics of corruption networks extracted from deferred prosecution agreements

An exploratory study analysing the networks of corporate and public sector bribery.

Published onFeb 09, 2022
Tainted ties: the structure and dynamics of corruption networks extracted from deferred prosecution agreements
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Tainted ties: the structure and dynamics of corruption networks extracted from deferred prosecution agreements
Tainted ties: the structure and dynamics of corruption networks extracted from deferred prosecution agreements
Description

Corruption, bribery, and white-collar crime are inherently relational phenomena as actors involved in them exchange information, resources, and favours. These exchanges give rise to a network in which the actors are embedded. While this has been often emphasized in the literature, there is a lack of studies actually empirically examining the structural properties of corruption networks. We aim to fill this gap with this exploratory study analysing the networks of corporate and public sector bribery. We theoretically ground the network analysis in two compatible criminological frameworks: routine activity theory and analytical criminology. We extract information about relations and interactions among involved actors in three cases from Statements of Facts from Deferred Prosecution Agreements obtained from United Kingdom’s Serious Fraud Office. Our findings indicate that the bribery networks resemble core-periphery structure networks with ties predominantly concentrated between a few very central actors (core), who ad hoc engage with actors from the periphery. Moreover, we also see a strong tendency of actors to repeat interactions within certain dyads. In terms of temporal dynamics, we observe periods of relative inaction alternating with periods of frequent and repeated contacts triggered by the presence of contracts susceptible to corruption. We discuss these findings in terms of their policy implications for designing evidence-based intervention and prevention measures.

 

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