IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2105.00337.html
   My bibliography  Save this paper

Detecting bid-rigging coalitions in different countries and auction formats

Author

Listed:
  • David Imhof
  • Hannes Wallimann

Abstract

We propose an original application of screening methods using machine learning to detect collusive groups of firms in procurement auctions. As a methodical innovation, we calculate coalition-based screens by forming coalitions of bidders in tenders to flag bid-rigging cartels. Using Swiss, Japanese and Italian procurement data, we investigate the effectiveness of our method in different countries and auction settings, in our cases first-price sealed-bid and mean-price sealed-bid auctions. We correctly classify 90\% of the collusive and competitive coalitions when applying four machine learning algorithms: lasso, support vector machine, random forest, and super learner ensemble method. Finally, we find that coalition-based screens for the variance and the uniformity of bids are in all the cases the most important predictors according the random forest.

Suggested Citation

  • David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    • Handle: RePEc:arx:papers:2105.00337
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2105.00337
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kai Hüschelrath & Tobias Veith, 2014. "Cartel Detection in Procurement Markets," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 35(6), pages 404-422, September.
    2. 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).
    3. Patrick Bajari & Lixin Ye, 2003. "Deciding Between Competition and Collusion," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 971-989, November.
    4. 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.
    5. Porter, Robert H & Zona, J Douglas, 1993. "Detection of Bid Rigging in Procurement Auctions," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 518-538, June.
    6. Imhof, David, 2017. "Econometric tests to detect bid-rigging cartels: does it work?," FSES Working Papers 483, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Aryal, Gaurab & Gabrielli, Maria F., 2013. "Testing for collusion in asymmetric first-price auctions," International Journal of Industrial Organization, Elsevier, vol. 31(1), pages 26-35.
    8. Robert Clark & Decio Coviello & Jean-Fran�ois Gauthier & Art Shneyerov, 2018. "Bid Rigging and Entry Deterrence in Public Procurement: Evidence from an Investigation into Collusion and Corruption in Quebec," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 34(3), pages 301-363.
    9. Juan Jiménez & Jordi Perdiguero, 2012. "Does Rigidity of Prices Hide Collusion?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 41(3), pages 223-248, November.
    10. Clark, Robert & Coviello, Decio & Gauthier, Jean-Francois & Shneyerov, Art, 2018. "Evidence from an investigation into collusion and corruption in Quebec," Queen's Economics Department Working Papers 274727, Queen's University - Department of Economics.
    11. 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.
    12. Rieko Ishii, 2007. "Collusion in Repeated Procurement Auction: a Study of Paving Market in Japan," Discussion Papers in Economics and Business 07-16, Osaka University, Graduate School of Economics.
    13. Mats A. Bergman & Johan Lundberg & Sofia Lundberg & Johan Y. Stake, 2020. "Interactions Across Firms and Bid Rigging," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(1), pages 107-130, February.
    14. Sylvain Chassang & Kei Kawai & Jun Nakabayashi & Juan M. Ortner, 2019. "Data Driven Regulation: Theory and Application to Missing Bids," NBER Working Papers 25654, National Bureau of Economic Research, Inc.
    15. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    16. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    17. 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.
    18. 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.
    19. Timothy G. Conley & Francesco Decarolis, 2016. "Detecting Bidders Groups in Collusive Auctions," American Economic Journal: Microeconomics, American Economic Association, vol. 8(2), pages 1-38, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    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. 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.
    4. Lucas Gomes & Jannis Kueck & Mara Mattes & Martin Spindler & Alexey Zaytsev, 2024. "Collusion Detection with Graph Neural Networks," Papers 2410.07091, arXiv.org.
    5. 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).
    6. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    7. 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).
    8. 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.
    9. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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).
    3. 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).
    4. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    5. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    6. 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).
    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. 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.
    9. 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.
    10. 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.
    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. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    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. 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).
    15. 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.
    16. de Leverano, Adriano, 2019. "Collusion through market sharing agreements: Evidence from Quebec's road paving market," ZEW Discussion Papers 19-053, ZEW - Leibniz Centre for European Economic Research.
    17. 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.
    18. Imhof, David, 2017. "Simple Statistical Screens to Detect Bid Rigging," FSES Working Papers 484, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    19. Huang, Yangguang, 2019. "An empirical study of scoring auctions and quality manipulation corruption," European Economic Review, Elsevier, vol. 120(C).
    20. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2105.00337. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.