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Detecting Algorithmic Collusion: Insights from Moment Screening Methods

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  • Yalçıner YALÇIN
  • Selcen ÖZTÃœRK

Abstract

The development of global, automated, and dynamic manufacturing processes is having a growing impact on industries. Virtual machines commonly function behind the scenes, supporting a variety of operations. Algorithms are the essential intelligence of these virtual machines, greatly increasing efficiency and effectiveness within marketplaces. Algorithms have the ability to promote competition and increase efficiency, eventually improving market competitiveness. However, algorithmic collusion can be maintained using “dynamic pricing†techniques, which are typically associated with automated pricing. Algorithmic collusion leads to increases in prices and/or decreases in the quality of products and services. The main objective and the function of competition authorities is to fight against those formations. In this regard, cartel screening is an important first step toward detecting collusive activity. In this paper, we used several moment screens to capture the effects of algorithmic pricing. Our findings suggest that algorithmic pricing exhibits non-collusive behavior within the particular industry and time frame examined in our analysis.

Suggested Citation

  • Yalçıner YALÇIN & Selcen ÖZTÃœRK, 2024. "Detecting Algorithmic Collusion: Insights from Moment Screening Methods," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3.
    • Handle: RePEc:fis:journl:240306
    DOI: 10.25295/fsecon.1477143
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    References listed on IDEAS

    as
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    5. Wallimann, Hannes & Sticher, Silvio, 2023. "On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement," Transport Policy, Elsevier, vol. 143(C), pages 121-131.
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    7. Juan Jiménez & Jordi Perdiguero, 2012. "Does Rigidity of Prices Hide Collusion?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 41(3), pages 223-248, November.
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    10. 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.
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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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