Machine learning with screens for detecting bid-rigging cartels
BP2-STS
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
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Collections
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Faculty
- Faculté des sciences économiques et sociales et du management
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Language
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Classification
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Economics
- Other electronic version
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Faculté SES
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Series statement
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License
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License undefined
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Identifiers
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RERO DOC
308901
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RERO
R008781626
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Persistent URL
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https://folia.unifr.ch/unifr/documents/306680
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