Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329636 
Year of Publication: 
2025
Series/Report no.: 
DICE Discussion Paper No. 430
Publisher: 
Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf
Abstract: 
Competition authorities increasingly rely on economic screening tools to identify markets where firms deviate from competitive norms. Traditional screening methods assume that collusion occurs through secret agreements. However, recent research highlights that firms can use public announcements to coordinate decisions, reducing competition while avoiding detection. We propose a novel approach to screening for collusion in public corporate statements. Using natural language processing, we analyze more than 300,000 earnings call transcripts issued worldwide between 2004 and 2022. By identifying expressions commonly associated with collusion, our method provides competition authorities with a tool to detect potentially anticompetitive behavior in public communications. Our approach can extend beyond earnings calls to other sources, such as news articles, trade press, and industry reports. Our method informed the European Commission's 2024 unannounced inspections in the car tire sector, prompted by concerns over price coordination through public communication.
Subjects: 
Communication
Collusion
NLP
Screening
Text Analysis
JEL: 
C23
D22
L1
L4
L64
ISBN: 
978-3-86304-429-9
Document Type: 
Working Paper

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