Data and Code for: Detection of Collusive Networks in Multistage Auctions

We develop a method for detecting cartels in multistage auctions. Our approach
allows a firm to be collusive when facing members of its cartel yet competitive when
facing others. Intuitively, as initial bids are shaded, close initial bids not only imply
similar costs but also provide an incentive to undercut. We detect firm pairs that ignore
this incentive when facing each other. Our algorithm predicts Ukraine’s Antimonopoly
Committee’s sanctions: firm pairs classified as collusive are 8.98 times more likely
(standard error 2.65 times) to be sanctioned. It also uncovers additional collusion: 1,857
collusive firms participate in 15.57% of auctions, increasing costs by 1.95%. Cartels
typically comprise just two members, and members often share the same ZIP code.