IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v30y2012i3p221-231.html
   My bibliography  Save this article

Analysis of collusive bidding behaviour

Author

Listed:
  • Ranon Chotibhongs
  • David Arditi

Abstract

Researchers have attempted to develop methods that detect collusive bidding. But no method can detect collusion with certainty unless it is based on legal evidence. A method is proposed to detect collusive bidding behaviour that improves the performance of previous methods. It analyses the historical bidding data provided by a construction owner in a two-step approach which is mainly based on a multiple regression model. The first step involves identifying the potential cartel bidders using the residual test and the cost structure stability test developed in earlier work. The second step is the focus of this paper and involves comparing the behaviour of the potential cartel bidders and non-cartel bidders by analysing bid distributions, their cost dispersion, and the differences in their cost structures. After conducting the second step of the study, it was found that the suspected cartel bidders identified in Step 1 behaved in ways to confirm collusion. Also, in an unrelated search, it was found that two of the six potential cartel bidders who were identified in this study had been audited by the public agency for bid fraud, and that another potential cartel bidder had been found guilty by the courts and forbidden from doing business with the public agency.

Suggested Citation

  • Ranon Chotibhongs & David Arditi, 2012. "Analysis of collusive bidding behaviour," Construction Management and Economics, Taylor & Francis Journals, vol. 30(3), pages 221-231, January.
    • Handle: RePEc:taf:conmgt:v:30:y:2012:i:3:p:221-231
    DOI: 10.1080/01446193.2012.661443
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01446193.2012.661443
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01446193.2012.661443?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Pablo Ballesteros-P�rez & Martin Skitmore & Eugenio Pellicer & M. Carmen Gonz�lez-Cruz, 2015. "Scoring rules and abnormally low bids criteria in construction tenders: a taxonomic review," Construction Management and Economics, Taylor & Francis Journals, vol. 33(4), pages 259-278, April.
    2. Xun Liu & Sen Lin & Lixing Liu & Fei Qian & Kun Zhang, 2020. "Exploring the Factors Triggering Occupational Ethics Risk of Technology Transaction in Chinese Construction Industry," IJERPH, MDPI, vol. 17(4), pages 1-18, February.
    3. Xiaowei Wang & Kunhui Ye & Taozhi Zhuang & Rui Liu, 2022. "The Influence of Collusive Information Dissemination on Bidder’s Collusive Willingness in Urban Construction Projects," Land, MDPI, vol. 11(5), pages 1-14, April.
    4. 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.
    5. 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).
    6. Xiaowei Wang & Wuyan Long & Meiyue Sang & Yang Yang, 2022. "Towards Sustainable Urbanization: Exploring the Influence Paths of the Urban Environment on Bidders’ Collusive Willingness," Land, MDPI, vol. 11(2), pages 1-14, February.
    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. 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).

    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:taf:conmgt:v:30:y:2012:i:3:p:221-231. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RCME20 .

    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.