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Screening For Bid Rigging—Does It Work?

Author

Listed:
  • David Imhof
  • Yavuz Karagök
  • Samuel Rutz

Abstract

This paper proposes a method to detect bid rigging by applying mutually reinforcing screens to a road construction procurement dataset from Switzerland in which no prior information about collusion was available. The screening method is particularly suited to address the problem of partial collusion, that is, collusion that does not involve all firms and/or all contracts in a specific dataset, implying that many of the classical markers discussed in the corresponding literature will fail to identify bid rigging. In addition to presenting new screens for collusion, it is shown how benchmarks and the combination of different screens may be used to identify subsets of suspicious contracts and firms. The discussed screening method succeeds in isolating a group of “suspicious” firms exhibiting the characteristics of a local bid-rigging cartel with cover bids and a—more or less pronounced—bid rotation scheme. Based on these findings, the Swiss Competition Commission (COMCO) opened an investigation and sanctioned the identified “suspicious” firms for bid rigging in 2016.

Suggested Citation

  • David Imhof & Yavuz Karagök & Samuel Rutz, 2018. "Screening For Bid Rigging—Does It Work?," Journal of Competition Law and Economics, Oxford University Press, vol. 14(2), pages 235-261.
    • Handle: RePEc:oup:jcomle:v:14:y:2018:i:2:p:235-261.
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    File URL: http://hdl.handle.net/10.1093/joclec/nhy006
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    Citations

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    Cited by:

    1. David P. Brown & Andrew Eckert, 2022. "Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?," Journal of Industrial Economics, Wiley Blackwell, vol. 70(1), pages 168-216, March.
    2. Cappelletti, Matilde & Giuffrida, Leonardo M., 2021. "Procuring survival," ZEW Discussion Papers 21-093, ZEW - Leibniz Centre for European Economic Research.
    3. Moohyung Cho & Tim Büthe, 2021. "From rule‐taker to rule‐promoting regulatory state: South Korea in the nearly‐global competition regime," Regulation & Governance, John Wiley & Sons, vol. 15(3), pages 513-543, July.
    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. Robert Clark & Decio Coviello & Adriano De Leverano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," Working Paper 1446, Economics Department, Queen's University.
    6. 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.
    7. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    8. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    9. Ricardo Carvalho Lima & Guilherme Mendes Resende, 2021. "Using the Moran’s I to detect bid rigging in Brazilian procurement auctions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 237-254, April.
    10. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    11. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    12. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    13. Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
    14. Matilde Cappelletti & Leonardo M. Giuffrida & Gabriele Rovigatti & Leonardo Maria Giuffrida, 2022. "Procuring Survival," CESifo Working Paper Series 10124, CESifo.
    15. Sylvain Chassang & Kei Kawai & Jun Nakabayashi & Juan Ortner, 2022. "Robust Screens for Noncompetitive Bidding in Procurement Auctions," Econometrica, Econometric Society, vol. 90(1), pages 315-346, January.
    16. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    17. 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.
    18. 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).
    19. 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.
    20. 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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