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Simple Statistical Screens to Detect Bid Rigging

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  • Imhof, David

Abstract

The paper applies simple statistical screens to a bid-rigging cartel in Switzerland, and shows how well the screens detect it by capturing the impact of collusion on the discrete distribution of the bids. In case of bid rigging, the support for the distribution of the bids decreases involving a lower variance, illustrated by the coefficient of variance and the kurtosis statistic. Furthermore, when firms rig bids without side-payment, the difference between the first and the second lowest bids increases whereas the difference between the losing bids decreases, involving a negatively skewed distribution of the bids, highlighted by the relative distance and the skewness statistic. Finally, the collusive interaction screen shows that the behaviour of firms changed radically between the cartel and post-cartel periods. Therefore, the simple statistical screens proposed in this paper purpose to screen large dataset and to detect bidrigging cartels by using only information on bids.

Suggested Citation

  • 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.
  • Handle: RePEc:fri:fribow:fribow00484
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    File URL: http://doc.rero.ch/record/289133/files/WP_SES_484.pdf
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    References listed on IDEAS

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    Cited by:

    1. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    2. Shachat, Jason & Tan, Lijia, 2023. "How auctioneers set reserve prices in procurement auctions," European Journal of Operational Research, Elsevier, vol. 304(2), pages 709-728.
    3. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    4. 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.
    5. Lucas Gomes & Jannis Kueck & Mara Mattes & Martin Spindler & Alexey Zaytsev, 2024. "Collusion Detection with Graph Neural Networks," Papers 2410.07091, arXiv.org.
    6. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    7. Ricardo Carvalho Lima & Guilherme Mendes Resende, 2021. "Using the Moran’s I to detect bid rigging in Brazilian procurement auctions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 237-254, April.

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    More about this item

    Keywords

    bid rigging detection; screening methods; variance screen; cover bidding screen; structural and behavioural screens;
    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

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