American Economic Journal Applied Economics· 2026Q1
Detection of Collusive Networks in Multistage Auctions
- 2citations
- Q1SCImago
- 2026year
Short summary
A new algorithm detects collusive firms in multistage auctions by identifying pairs that ignore the incentive to undercut each other's initial bids, a finding that predicts sanctions with 8.98x higher likelihood.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed an algorithm to detect collusive networks in multistage auctions.
- Identifies collusive firms by detecting pairs that ignore the incentive to undercut initial bids.
- Algorithm predicted sanctions by Ukraine's Antimonopoly Committee with 8.98x higher likelihood (SE 2.65x).
- Uncovered collusion involving 1,857 firms in 15.57% of auctions, increasing costs by 1.95%.
AI-generated from the title and abstract; the full text is not read.
Abstract
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%.
The authors' abstract, as published at the source. American Economic Journal Applied Economics, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences