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A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels

Author

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  • Hannes Wallimann

    (Lucerne University of Applied Sciences and Arts
    University of Fribourg)

  • David Imhof

    (University of Fribourg
    Swiss Competition Commission
    UniDistance, Faculté d’économie)

  • Martin Huber

    (University of Fribourg)

Abstract

We propose a detection method for flagging bid-rigging cartels, particularly useful when cartels are incomplete. Our approach combines screens, i.e., statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging and it outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10315-w
    DOI: 10.1007/s10614-022-10315-w
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    References listed on IDEAS

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    Cited by:

    1. Frédéric Marty & Thierry Warin, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," CIRANO Working Papers 2023s-26, CIRANO.

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    More about this item

    Keywords

    Bid rigging detection; Screening methods; Descriptive statistics; Machine learning; Random forest; Lasso; Ensemble methods;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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