Vice versa: The decoupling of content and topic heterogeneity in collusion research
利用主题自然语言机器学习技术，系统回顾了约800篇关于合谋的经济学研究，发现研究主题趋向单一化，但整体内容却显著增长，导致内容与主题异质性脱钩。
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.