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Who are you? Cartel detection using unlabeled data

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  • Silveira, Douglas
  • de Moraes, Lucas B.
  • Fiuza, Eduardo P.S.
  • Cajueiro, Daniel O.

Abstract

We propose a data-driven machine learning approach to flag bid-rigging cartels in the Brazilian road maintenance sector. First, we apply a clustering algorithm to group the tenders based on their attributes. Second, we use the labels created by the clustering algorithm as a target variable to predict them using a classifier. We rank the screens according to their relevance to decrease the number of false positive (detecting cartel when it does not exist) and false negative (not detecting cartel when it does exist) predictions. Our results shed light on the need to use a range of screens to recognize the vast profile of strategies practiced by bid-rigging cartels, such as misleading competitive dynamics, bid combination, and cover bidding behavior. Our method can improve cartels’ deterrence in different economic sectors, especially when labeled data are not available. In a controlled environment with a simulated labeled dataset, the overall average accuracy of the algorithm is 99.33%. In a real-world cartel case with a labeled dataset, the overall average accuracy is 80.25%. When applied to the road maintenance unlabeled dataset, our model identified a group containing 273 (31% of the total) suspicious tenders. We conclude by offering a policy prescription discussion for antitrust authorities.

Suggested Citation

  • Silveira, Douglas & de Moraes, Lucas B. & Fiuza, Eduardo P.S. & Cajueiro, Daniel O., 2023. "Who are you? Cartel detection using unlabeled data," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:indorg:v:88:y:2023:i:c:s0167718723000139
    DOI: 10.1016/j.ijindorg.2023.102931
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    Cited by:

    1. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    2. Wallimann, Hannes & Sticher, Silvio, 2023. "On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement," Transport Policy, Elsevier, vol. 143(C), pages 121-131.
    3. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-7, University of Alberta, Department of Economics.
    4. Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
    5. Bovin, Andreas & Bos, Iwan, 2023. "Market Shares as Collusive Marker: Evidence from the European Truck Industry," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2024. "Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets," Working Papers 2024-2, University of Alberta, Department of Economics.
    7. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).

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