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

Abstract

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.

From text to ties: Extraction of corruption network data from deferred prosecution agreements

“…Situated in the context of previous research, this data can thus contribute to the research on corruption networks and the way they operate (Lauchs et al, 2011;Diviák et al, 2019;Luna-Pla and Nicolás-Carlock, 2020;Wachs et al, 2020). Exploratory probe into three of the network extracted here reveals remarkable structural similarities in the centralization of their structures despite the differences in the sizes of the networks or their time span (Diviák and Lord, 2022). In terms of methodological research, our data collection framework may be further extended to other sources of textual data and it may also be automated using available methods for automated text analysis (natural language processing methods).…”

A large-scale empirical investigation of specialization in criminal career

“…In other words, our data does not contain information about undetected or unsolved cases of criminality. This is a common limitation in data-driven studies of criminal behavior, which by nature are limited to prosecuted or highly visible activities 69 . At the same time, events in our data are when individuals are charged with crimes - not all events are correctly assigned to the guilty individual.…”

Corruption risks in a mature democracy : Mechanisms of social advantage and danger zones for corruption

“…To begin with, there is a rich tradition of case studies on corruption, which are common within fields such as sociology, criminology, organisational research, and anthropology, and these provide pivotal building blocks for the formation of a theoretical base to better understand corruption (e.g., Diviák and Lord 2022;Slingerland 2018;Brooks 2016). While case studies are extremely important for understanding the mechanisms behind corruption, one limitation is that they are restricted to forms of corruption that have already been exposed and where detailed information is already available.…”

“…Corruption in real-world settings is rarely observed directly, and traditional corruption measures are typically operationalised at the macro rather than the micro level. That said, promising examples of recent corruption research which have succeeded in drawing such micro-macro links are found in the fields of network science, complexity science, and computational social science (e.g., Diviák and Lord 2022;Villamil et al 2022;Granados and Nicolás-Carlock 2021). The prerequisite and key to this development is the availability of large-scale data with a high level of granularity based on observations at the micro level.…”