Screen for collusive behavior: A machine learning approach

Melissa Bantle

No 01-2024, Hohenheim Discussion Papers in Business, Economics and Social Sciences from University of Hohenheim, Faculty of Business, Economics and Social Sciences

Abstract: The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior in the price setting of the filling stations. Therefore, the algorithm identifies anomalies in the data-generating process. The results show that various anomalies can be detected with this method. These anomalies in the price setting behavior are then discussed with respect to their implications for the competitiveness of the market.

Keywords: Machine Learning; Cartel Screens; Fuel Retail Market (search for similar items in EconPapers)
JEL-codes: C53 K21 L44 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-ene, nep-ind, nep-law and nep-reg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hohdps:285380

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