Research report

Machine learning with screens for detecting bid-rigging cartels

    01.04.2018

28

English We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct classification rate, however, differs across truly non-collusive and collusive processes, we also investigate tradeoffs in reducing false positive vs. false negative predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid- rigging cartels
Collections
Faculty
Faculté des sciences économiques et sociales
Language
  • English
Classification
Economics
Other electronic version

Faculté SES

Series statement
  • Working Papers SES ; 494
License
License undefined
Identifiers
  • RERO DOC 308901
  • RERO R008781626
Persistent URL
https://folia.unifr.ch/unifr/documents/306680
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