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Machine Learning with Screens for Detecting Bid-Rigging Cartels

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  • Huber, Martin
  • Imhof, David

Abstract

We combine machine learning techniques with statistical screens computed from the distribution of bids in 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.

Suggested Citation

  • Huber, Martin & Imhof, David, 2018. "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers 494, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    • Handle: RePEc:fri:fribow:fribow00494
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    References listed on IDEAS

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    18. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
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    Citations

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    Cited by:

    1. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    2. Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Post-Print hal-03829877, HAL.
    3. Martinez-Carrasco, José & ConceiçaÞo, Otavio & Dezolt, Ana Lúcia, 2023. "More Information, Lower Price? Access Market-based Reference Prices and Gains in Public Procurement Efficiency," IDB Publications (Working Papers) 12754, Inter-American Development Bank.
    4. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    5. Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Papers 2210.14518, arXiv.org.
    7. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    8. Frédéric Marty, 2022. "From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements," GREDEG Working Papers 2022-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    9. Chengyan Gu, 2023. "Market segmentation and dynamic price discrimination in the U.S. airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 338-361, October.
    10. 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).
    11. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    12. Dmitry I. Ivanov & Alexander S. Nesterov, 2019. "Stealed-bid Auctions: Detecting Bid Leakage via Semi-Supervised Learning," Papers 1903.00261, arXiv.org, revised Nov 2020.
    13. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    14. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    15. 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).
    16. 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.
    17. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).

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    More about this item

    Keywords

    Bid rigging detection; screening methods; variance screen; cover bidding screen; structural and behavioural screens; machine learning; lasso; ensemble methods;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General

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