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How communication makes the difference between a cartel and tacit collusion: A machine learning approach

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  • Andres, Maximilian
  • Bruttel, Lisa
  • Friedrichsen, Jana

Abstract

This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.

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  • Andres, Maximilian & Bruttel, Lisa & Friedrichsen, Jana, 2023. "How communication makes the difference between a cartel and tacit collusion: A machine learning approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 152, pages 1-1.
  • Handle: RePEc:zbw:espost:266561
    DOI: 10.1016/j.euroecorev.2022.104331
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    Citations

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    Cited by:

    1. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    2. Maximilian Andres, 2023. "Communication in the Infinitely Repeated Prisoner's Dilemma: Theory and Experiments," Papers 2304.12297, arXiv.org.
    3. Polemis, Michael, 2024. "Are Cartels Forever? Global Evidence Using Quantile Regression Analysis," MPRA Paper 120534, University Library of Munich, Germany.
    4. Zengqing Wu & Run Peng & Shuyuan Zheng & Qianying Liu & Xu Han & Brian Inhyuk Kwon & Makoto Onizuka & Shaojie Tang & Chuan Xiao, 2024. "Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents," Papers 2402.12327, arXiv.org, revised Jul 2024.
    5. Isogai, Shigeki & Shen, Chaohai, 2023. "Multiproduct firm’s reputation and leniency program in multimarket collusion," Economic Modelling, Elsevier, vol. 125(C).
    6. Lisa Bruttel & Gerald Eisenkopf & Juri Nithammer, 2024. "Pre-election communication in public good games with endogenous leaders," CEPA Discussion Papers 73, Center for Economic Policy Analysis.
    7. Maximilian Andres, 2024. "Equilibrium selection in infinitely repeated games with communication," CEPA Discussion Papers 75, Center for Economic Policy Analysis.
    8. Xu Han & Zengqing Wu & Chuan Xiao, 2023. ""Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion," Papers 2308.10974, arXiv.org, revised Jan 2024.
    9. Zengqing Wu & Run Peng & Xu Han & Shuyuan Zheng & Yixin Zhang & Chuan Xiao, 2023. "Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations," Papers 2311.06330, arXiv.org, revised Dec 2023.
    10. Lisa Bruttel & Maximilian Andres, 2024. "Communicating Cartel Intentions," CEPA Discussion Papers 77, Center for Economic Policy Analysis.

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    More about this item

    Keywords

    cartel; collusion; communication; machine learning; experiment;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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