  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.
