IDEAS home Printed from https://ideas.repec.org/a/oup/jcomle/v14y2018i2p235-261..html
   My bibliography  Save this article

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.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/joclec/nhy006
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Matilde Cappelletti & Leonardo M. Giuffrida & Gabriele Rovigatti, 2022. "Procuring Survival," CESifo Working Paper Series 10124, CESifo.
    5. 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.
    6. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    7. Clark, Robert & Coviello, Decio & de Leverano, Adriano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," ZEW Discussion Papers 20-052, ZEW - Leibniz Centre for European Economic Research.
    8. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    15. Cappelletti, Matilde & Giuffrida, Leonardo M., 2021. "Procuring survival," ZEW Discussion Papers 21-093, ZEW - Leibniz Centre for European Economic Research.
    16. 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).
    17. 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.
    18. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    19. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    20. Huber, Martin & Imhof, David, 2023. "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    21. 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).
    22. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jcomle:v:14:y:2018:i:2:p:235-261.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/jcle .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.