IDEAS home Printed from https://ideas.repec.org/p/fri/fribow/fribow00483.html
   My bibliography  Save this paper

Econometric tests to detect bid-rigging cartels: does it work?

Author

Listed:
  • Imhof, David

Abstract

This paper tests how well the method proposed by Bajari and Ye (2003) performs to detect bidrigging cartels. In the case investigated in this paper, the bid-rigging cartel rigged all contracts during the collusive period, and all firms participated to the bid-rigging cartel. The two econometric tests constructed by Bajari and Ye (2003) produce a high number of false negative results: the tests do not reject the null hypothesis of competition, although they should have rejected it. A robustness analysis replicates the econometric tests on two different sub-samples, composed solely by cover bids. On the first sub-sample, both tests produce again a high number of false negative results. However, on the second sub-sample, one test performs better to detect the bidrigging cartel. The paper interprets these results, discusses alternative methods, and concludes with recommendations for competition agencies.

Suggested Citation

  • 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.
    • Handle: RePEc:fri:fribow:fribow00483
    as

    Download full text from publisher

    File URL: http://doc.rero.ch/record/288965/files/WP_SES_483.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Banerji & J.V. Meenakshi, 2004. "Buyer Collusion and Efficiency of Government Intervention in Wheat Markets in Northern India: An Asymmetric Structural Auctions Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 236-253.
    2. McAfee, R Preston & McMillan, John, 1992. "Bidding Rings," American Economic Review, American Economic Association, vol. 82(3), pages 579-599, June.
      • McAfee, R. Preston & McMillan, John., 1990. "Bidding Rings," Working Papers 726, California Institute of Technology, Division of the Humanities and Social Sciences.
    3. David Gilo & Yossi Moshe & Yossi Spiegel, 2006. "Partial cross ownership and tacit collusion," RAND Journal of Economics, RAND Corporation, vol. 37(1), pages 81-99, March.
    4. Feinstein, Jonathan S & Block, Michael K & Nold, Frederick C, 1985. "Asymmetric Information and Collusive Behavior in Auction Markets," American Economic Review, American Economic Association, vol. 75(3), pages 441-460, June.
    5. Bernard Lebrun, 1996. "Existence of an equilibrium in first price auctions (*)," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 7(3), pages 421-443.
    6. 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.
    7. 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.
    8. Harrington, Joseph Jr. & Chen, Joe, 2006. "Cartel pricing dynamics with cost variability and endogenous buyer detection," International Journal of Industrial Organization, Elsevier, vol. 24(6), pages 1185-1212, November.
    9. 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.
    10. Bernard Lebrun, 2002. "Continuity of the first price auction Nash equilibrium correspondence," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 20(3), pages 435-453.
    11. Baldwin, Laura H & Marshall, Robert C & Richard, Jean-Francois, 1997. "Bidder Collusion at Forest Service Timber Sales," Journal of Political Economy, University of Chicago Press, vol. 105(4), pages 657-699, August.
    12. Robert H. Porter & J. Douglas Zona, 1999. "Ohio School Milk Markets: An Analysis of Bidding," RAND Journal of Economics, The RAND Corporation, vol. 30(2), pages 263-288, Summer.
    13. Martin Pesendorfer, 2000. "A Study of Collusion in First-Price Auctions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(3), pages 381-411.
    14. David Gilo & Yossi Moshe & Yossi Spiegel, 2006. "Partial Cross Ownership and Tacit Collusion," RAND Journal of Economics, The RAND Corporation, vol. 37(1), pages 81-99, Spring.
    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. 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).
    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. 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.
    4. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    5. 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).
    6. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(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. 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.
    2. 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.
    3. 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.
    4. David Imhof & Yavuz Karagök & SAMUEL RUTZ, 2017. "Screening for Bid-rigging. Does it Work?," Working Papers 2017-09, CRESE.
    5. Patrick Bajari & Garrett Summers, "undated". "Detecting Collusion in Procurement Auctions: A Selective Survey of Recent Research," Working Papers 01014, Stanford University, Department of Economics.
    6. Ilya Morozov & Elena Podkolzina, 2013. "Collusion detection in procurement auctions," HSE Working papers WP BRP 25/EC/2013, National Research University Higher School of Economics.
    7. Wang, Hong, 2017. "Information acquisition versus information manipulation in multi-period procurement markets," Information Economics and Policy, Elsevier, vol. 40(C), pages 48-59.
    8. 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).
    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).
    10. Huang, Yangguang, 2019. "An empirical study of scoring auctions and quality manipulation corruption," European Economic Review, Elsevier, vol. 120(C).
    11. 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.
    12. Loyola, Gino, 2008. "On bidding markets: the role of competition," UC3M Working papers. Economics we083318, Universidad Carlos III de Madrid. Departamento de Economía.
    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. 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.
    15. 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.
    16. Sumit Joshi & Poorvi Vora, 2013. "Weak and strong multimarket bidding rings," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 53(3), pages 657-696, August.
    17. Tostao, Emilio & Chung, Chanjin & Brorsen, B. Wade, 2006. "Integrating the Structural Auction Approach and Traditional Measures of Market Power," 2006 Annual meeting, July 23-26, Long Beach, CA 21244, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Imhof, David & Karagök, Yavuz & Rutz, Samuel, 2016. "Screening for bid-rigging - does it work?," FSES Working Papers 468, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    19. Joseph E. Harrington, Jr, 2005. "Detecting Cartels," Economics Working Paper Archive 526, The Johns Hopkins University,Department of Economics.
    20. Lebrun, Bernard, 2006. "Uniqueness of the equilibrium in first-price auctions," Games and Economic Behavior, Elsevier, vol. 55(1), pages 131-151, April.

    More about this item

    Keywords

    Bid rigging; Detection methods; Screens; Conditional independence test; Exchangeability test;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • 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
    • L40 - Industrial Organization - - Antitrust Issues and Policies - - - General

    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:fri:fribow:fribow00483. 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: Mustapha Obbad (email available below). General contact details of provider: https://edirc.repec.org/data/wsffrch.html .

    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.