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Analysis of collusive bidding behaviour

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  • 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
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    Citations

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    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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).

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