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Public procurement cartels:A large-sample testing of screens using machine learning
Title / Series / Name
International Journal of Industrial Organization
Publication Volume
104
Publication Issue
Pages
Editors
Keywords
Bid-rigging
Cartel screening
Europe
Machine learning
Public procurement
Industrial Relations
Aerospace Engineering
Economics and Econometrics
Strategy and Management
Industrial and Manufacturing Engineering
Cartel screening
Europe
Machine learning
Public procurement
Industrial Relations
Aerospace Engineering
Economics and Econometrics
Strategy and Management
Industrial and Manufacturing Engineering
URI
https://hdl.handle.net/20.500.14018/28704
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2026-01
Language
ISBN
Identifiers
10.1016/j.ijindorg.2025.103228