Machine learning with screens for detecting bid-rigging cartels
- Huber, Martin Departement of Economics, University of Fribourg, Switzerland
- Imhof, David Departement of Economics, University of Fribourg, Switzerland
-
2019
Published in:
- International Journal of Industrial Organization. - 2019, vol. 65, p. 277-301
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 84% of the total of bidding processes as collusive or non-collusive. We also discuss tradeoffs in reducing false positive vs. false negative predictions and find that false negative predictions increase much faster in reducing false positive predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid-rigging cartels.
- Faculty
- Faculté des sciences économiques et sociales et du management
- Department
- Département d'économie politique
- Language
-
- English
- Classification
- Economics
- License
- License undefined
- Identifiers
-
- RERO DOC 330183
- DOI 10.1016/j.ijindorg.2019.04.002
- Persistent URL
- https://folia.unifr.ch/unifr/documents/309311
Other files
Statistics
Document views: 115
File downloads:
- machinelearningwithscreensfordetectingbidriggingcartels.pdf: 264
- 2machinelearningwithscreensfordetectingbidriggingcartels_0.pdf: 256