By Martin Huber
We combine machine learning techniques with statistical screens computed from the distribution of bidsin tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct classification rate, however, differs across truly non-collusive and collusive processes, we also investigate tradeoffs in reducing false positive vs. false negative predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid-rigging cartels.
Featured News
CVS Health Explores Potential Breakup Amid Investor Pressure: Report
Oct 3, 2024 by
CPI
DirecTV Acquires Dish TV, Creating 20 Million-Subscriber Powerhouse
Oct 3, 2024 by
CPI
South Korea Fines Kakao Mobility $54.8 Million for Anti-Competitive Practices
Oct 3, 2024 by
CPI
Google Offers Settlement in India’s Antitrust Case Regarding Smart TVs
Oct 3, 2024 by
CPI
Attorney Challenges NCAA’s $2.78 Billion Settlement in Landmark Antitrust Cases
Oct 3, 2024 by
nhoch@pymnts.com
Antitrust Mix by CPI
Antitrust Chronicle® – Refusal to Deal
Sep 27, 2024 by
CPI
Antitrust’s Refusal-to-Deal Doctrine: The Emperor Has No Clothes
Sep 27, 2024 by
Erik Hovenkamp
Why All Antitrust Claims are Refusal to Deal Claims and What that Means for Policy
Sep 27, 2024 by
Ramsi Woodcock
The Aspen Misadventure
Sep 27, 2024 by
Roger Blair & Holly P. Stidham
Refusal to Deal in Antitrust Law: Evolving Jurisprudence and Business Justifications in the Align Technology Case
Sep 27, 2024 by
Timothy Hsieh