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An Investigation of the Average Bid Mechanism for Procurement Auctions

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  • Wei-Shiun Chang

    (Institute for Entrepreneurial Studies and Innovation Management, Humboldt University of Berlin, 10099 Berlin, Germany)

  • Bo Chen

    (Department of Economics, Southern Methodist University, Dallas, Texas 75275)

  • Timothy C. Salmon

    (Department of Economics, Southern Methodist University, Dallas, Texas 75275)

Abstract

In a procurement context, it can be quite costly for a buyer when the winning seller underestimates the cost of a project and then defaults on the project midway through completion. The average bid auction is one mechanism intended to help address this problem. This format involves awarding the contract to the bidder who has bid closest to the average of the bids submitted. We compare the performance of this mechanism with the standard low price mechanism to determine how successful the average bid format is in preventing bidder losses as well as its impact on the price paid by the buyer. We find the average bid mechanism to be more successful than expected because, surprisingly, bidding behavior remains similar between the average bid and low price auctions. We provide an explanation for the bidding behavior in the average bid auction that is based on subjects having problems processing signals near the extremes of the distribution. This paper was accepted by Teck-Hua Ho, behavioral economics .

Suggested Citation

  • Wei-Shiun Chang & Bo Chen & Timothy C. Salmon, 2015. "An Investigation of the Average Bid Mechanism for Procurement Auctions," Management Science, INFORMS, vol. 61(6), pages 1237-1254, June.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:6:p:1237-1254
    DOI: 10.1287/mnsc.2013.1893
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    References listed on IDEAS

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    Cited by:

    1. Decio Coviello & Andrea Guglielmo & Giancarlo Spagnolo, 2015. "The Effect of Discretion on Procurement Performance," CEIS Research Paper 361, Tor Vergata University, CEIS, revised 17 Nov 2015.
    2. Stefano Galavotti & Luigi Moretti & Paola Valbonesi, 2018. "Sophisticated Bidders in Beauty-Contest Auctions," American Economic Journal: Microeconomics, American Economic Association, vol. 10(4), pages 1-26, November.
    3. Li Li & Jiayong Liu & Peng Jia & Rongfeng Zheng, 2020. "PSPAB: Privacy-preserving average procurement bidding system with double-spending checking," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    4. Nicola Branzoli & Francesco Decarolis, 2015. "Entry and Subcontracting in Public Procurement Auctions," Management Science, INFORMS, vol. 61(12), pages 2945-2962, December.
    5. Xu, Jiayan & Feng, Yinbo & He, Wen, 2017. "Procurement auctions with ex post cooperation between capacity constrained bidders," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1164-1174.
    6. Wei-Shiun Chang & Timothy C. Salmon & Krista J. Saral, 2016. "Procurement Auctions With Renegotiation And Wealth Constraints," Economic Inquiry, Western Economic Association International, vol. 54(3), pages 1684-1704, July.
    7. Sijing Deng & Jiayan Xu, 2020. "Ex ante and Ex post Subcontracting between Two Competing Bidders," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(01), pages 1-22, January.
    8. Feyza G. Sahinyazan & Marie‐Ève Rancourt & Vedat Verter, 2021. "Improving Transportation Procurement in the Humanitarian Sector: A Data‐driven Approach for Abnormally Low Bid Detection," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 1082-1109, April.
    9. Yigal Gerchak, 2016. "Procurement (Reverse) Auctions Where Service Providers have Official Ratings," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1-10, December.
    10. Decio Coviello & Andrea Guglielmo & Giancarlo Spagnolo, 2018. "The Effect of Discretion on Procurement Performance," Management Science, INFORMS, vol. 64(2), pages 715-738, February.
    11. Lagziel, David, 2019. "Credit auctions and bid caps," Games and Economic Behavior, Elsevier, vol. 113(C), pages 416-422.
    12. Xiaohu Qian & Shu-Cherng Fang & Min Huang & Qi An & Xingwei Wang, 2018. "Reverse auctions with regret-anticipated bidders," Annals of Operations Research, Springer, vol. 268(1), pages 293-313, September.
    13. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.

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