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Bid Roundness Under Collusion in Japanese Procurement Auctions

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  • Rieko Ishii

Abstract

We analyze the “roundness level” of bids—defined as the number of zeros at the end of the bid—in public procurement auctions for construction works in Okinawa Prefecture, Japan, where a bid-rigging case was filed. We hypothesize that bid rigging increases the roundness of bids through the bid coordination process. Specifically, winners choose round numbers to avoid any miscommunication when they announce their planned bids to other ring members, and losers prefer round numbers when they arbitrarily bid above the winning bid. We find that (1) there is a positive relationship between the roundness of a bid and its relative value as a fraction of the reserve price, (2) the roundness of bids is higher when collusion is active than when it is inactive, (3) among the ring bids, the roundness of the lowest bids is even higher than that of the other bids, and (4) bids by non-ring members are also round when collusion is active. Copyright Springer Science+Business Media New York 2014

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  • Rieko Ishii, 2014. "Bid Roundness Under Collusion in Japanese Procurement Auctions," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(3), pages 241-254, May.
    • Handle: RePEc:kap:revind:v:44:y:2014:i:3:p:241-254
    DOI: 10.1007/s11151-013-9408-6
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    2. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    3. 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).
    4. Aljoscha Janssen, 2022. "Price dynamics of Swedish pharmaceuticals," Quantitative Marketing and Economics (QME), Springer, vol. 20(4), pages 313-351, December.
    5. Fabian Ocker & Karl‐Martin Ehrhart & Marion Ott, 2018. "Bidding strategies in Austrian and German balancing power auctions," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    6. 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.
    7. 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).
    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. Xiaoli Wang & Yun Liu & Yanbing Ju, 2018. "Sustainable Public Procurement Policies on Promoting Scientific and Technological Innovation in China: Comparisons with the U.S., the UK, Japan, Germany, France, and South Korea," Sustainability, MDPI, vol. 10(7), pages 1-27, June.
    10. Chaudhry, Sajid M. & Bajoori, Elnaz & Nandeibam, Shasi, 2019. "Clustered pricing in the corporate loan market: Theory and empirical evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 275-296.
    11. Eremina, Anastasia & Zoroastrova, Irina & Maksimov, Andrey, 2018. "Empirical analysis of municipal peculiarities influence on price outcomes of public purchases," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 84-101.
    12. Lucas Gomes & Jannis Kueck & Mara Mattes & Martin Spindler & Alexey Zaytsev, 2024. "Collusion Detection with Graph Neural Networks," Papers 2410.07091, arXiv.org.
    13. 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.

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    Keywords

    Auction; Bid rigging; Cartel;
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