How Communication Makes the Difference between a Cartel and Tacit Collusion: A Machine Learning Approach

Maximilian Andres, Lisa Bruttel and Jana Friedrichsen

No 10024, CESifo Working Paper Series from CESifo

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.

Keywords: cartel; collusion; communication; machine learning; experiment (search for similar items in EconPapers)
JEL-codes: C92 D43 L41 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-cta, nep-exp, nep-law and nep-reg
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Related works:
Journal Article: How communication makes the difference between a cartel and tacit collusion: A machine learning approach (2023)
Journal Article: How communication makes the difference between a cartel and tacit collusion: A machine learning approach (2023)
Working Paper: How Communication Makes the Difference between a Cartel and Tacit Collusion: A Machine Learning Approach (2022)
Working Paper: How communication makes the difference between a cartel and tacit collusion: a machine learning approach (2022)
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