Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/313789 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Economic Surveys [ISSN:] 1467-6419 [Volume:] 38 [Issue:] 5 [Publisher:] Wiley Periodicals, Inc. [Place:] Hoboken, NJ [Year:] 2023 [Pages:] 1686-1730
Publisher: 
Wiley Periodicals, Inc., Hoboken, NJ
Abstract: 
Collusive practices continue to be a significant threat to competition and consumer welfare. It should be of utmost importance for academic research to provide the theoretical and empirical foundations to antitrust authorities and enable them to develop proper tools to encounter new collusive practices. Utilizing topical natural language machine learning techniques allows me to analyze the evolution of economic research on collusion over the past two decades in a novel way. It enables me to review some 800 publications systematically. I extract the underlying topics from the papers and conduct a large set of uni‐ and multivariate time series and regression analyses on their individual prevalences. I detect a notable tendency towards monocultures in topics and an endogenous constriction of the topic variety. In contrast, the overall contents and issues addressed by these papers have grown remarkably. This caused a decoupling: Nowadays, more datasets and cartel cases are studied but with a smaller research scope.
Subjects: 
antitrust
collusion
neural networks
systematic review
topic modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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