Bid-rigging networks and state-corporate crime in the construction industry

Reeves-Latour, Maxime; Morselli, Carlo

doi:10.1016/j.socnet.2016.10.003

Cited by 65 publications

(51 citation statements)

References 17 publications

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“…Many of these efforts have been ultra-creative. Morselli and his collaborators (Morselli & Ouelett, 2018;Reeves-Latour & Morselli, 2017), for instance, have built extensive databases using public procurement records to pinpoint likely collusion among construction companies across space and time. Cross-disciplinary scholars, such as those trained in business, have used machine learning techniques to review financial statistics reported by companies to differentiate firms that likely engage in accounting fraud from those that do not (Jofre & Gerlach, 2018).…”

Section: Resultsmentioning

confidence: 99%

Reimagining Sutherland 80 years after white‐collar crime*

Simpson

2019

Criminology

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Eighty years ago, Edwin H. Sutherland conceptualized and defined white‐collar crime. In this article, I engage retrospectively with Sutherland's ideas and work to emphasize important aspects that continue to guide research today; to note where he was prescient as well as shortsighted. I center this discussion around “corporate crime” or crimes by business. Four main themes are discussed: 1) law and official responses to corporate offending—the data problem, 2) corporate crime and the life cycle of organizations, 3) psychological and trait‐based explanations, and 4) consequences of definitional ambiguity.

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Section: Resultsmentioning

confidence: 99%

Reimagining Sutherland 80 years after white‐collar crime*

Simpson

2019

Criminology

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“…However, a particular corruption process typically involves a rather small number of people that interact at much finer scales, and indeed much less is known about how these relationships form and evolve over time. Notable exceptions include the work of Baker and Faulkner [26] that investigated the social organization of people involved in price-fixing conspiracies, and the work of Reeves-Latour and Morselli [27] that examines the evolution of a bid-rigging network. Such questions are best addressed in the context of network science [28][29][30][31][32][33][34] and complex systems science [35] -two theoretical frameworks that are continuously proving very useful to study various social and economic phenomena and human behaviour [36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

The dynamical structure of political corruption networks

Ribeiro

Alves

Martins

et al. 2018

Journal of Complex Networks

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Corruptive behaviour in politics limits economic growth, embezzles public funds, and promotes socio-economic inequality in modern democracies. We analyse well-documented political corruption scandals in Brazil over the past 27 years, focusing on the dynamical structure of networks where two individuals are connected if they were involved in the same scandal. Our research reveals that corruption runs in small groups that rarely comprise more than eight people, in networks that have hubs and a modular structure that encompasses more than one corruption scandal. We observe abrupt changes in the size of the largest connected component and in the degree distribution, which are due to the coalescence of different modules when new scandals come to light or when governments change. We show further that the dynamical structure of political corruption networks can be used for successfully predicting partners in future scandals. We discuss the important role of network science in detecting and mitigating political corruption.

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“…Previous work on cartels has considered co-bidding networks of firms [54,48,38], but using network topology to detect groups of firms within markets is a relatively new idea. For example Conley and Decarolis [16] use an agglomerative clustering method to group firms based on their bidding behavior.…”

Section: Introductionmentioning

confidence: 99%

A network approach to cartel detection in public auction markets

Wachs

Kertész²

2019

Sci Rep

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Competing firms can increase profits by setting prices collectively, imposing significant costs on consumers. Such groups of firms are known as cartels and because this behavior is illegal, their operations are secretive and difficult to detect. Cartels feel a significant internal obstacle: members feel short-run incentives to cheat. Here we present a network-based framework to detect potential cartels in bidding markets based on the idea that the chance a group of firms can overcome this obstacle and sustain cooperation depends on the patterns of its interactions. We create a network of firms based on their co-bidding behavior, detect interacting groups, and measure their cohesion and exclusivity, two group-level features of their collective behavior. Applied to a market for school milk, our method detects a known cartel and calculates that it has high cohesion and exclusivity. In a comprehensive set of nearly 150,000 public contracts awarded by the Republic of Georgia from 2011 to 2016, detected groups with high cohesion and exclusivity are significantly more likely to display traditional markers of cartel behavior. We replicate this relationship between group topology and the emergence of cooperation in a simulation model. Our method presents a scalable, unsupervised method to find groups of firms in bidding markets ideally positioned to form lasting cartels.

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Bid-rigging networks and state-corporate crime in the construction industry

Cited by 65 publications

References 17 publications

Reimagining Sutherland 80 years after white‐collar crime*

Reimagining Sutherland 80 years after white‐collar crime*

The dynamical structure of political corruption networks

A network approach to cartel detection in public auction markets

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