Public procurement cartels: A large-sample testing of screens using machine learning

Mihály Fazekas, Bence Tóth, Johannes Wachs and Aly Abdou

International Journal of Industrial Organization, 2026, vol. 104, issue C

Abstract: Due to the high budgetary costs of public procurement cartels, it is crucial to measure and understand them. The literature developed screens that work well for selected cartel types and with high quality data, but it didn’t produce generalisable knowledge supporting policy and law enforcement on typically available datasets. We simultaneously measure multiple cartel behaviours on publicly available data of 73 cartels from 7 European countries covering 2004–2021. We apply machine learning methods, using diverse cartel screens characterising pricing and bidding behaviours in a predictive model. Combining many indicators in a random forest algorithm achieves 70–84 % prediction accuracy, distinguishing behavioural traces of confirmed cartels from non-cartels across different cartel types and countries (accuracy is 97 % when trained and tested on a single cartel case, typical of the literature). Most screens contribute to prediction in line with theory. These results could improve cartel detection and investigations and support pro-competition policies.

Keywords: Cartel screening; Bid-rigging; Public procurement; Europe; Machine learning (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 D22 D40 K42 L41 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:indorg:v:104:y:2026:i:c:s0167718725000943

DOI: 10.1016/j.ijindorg.2025.103228

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