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Machine Learning with Screens for Detecting Bid-Rigging Cartels

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  • Huber, Martin
  • Imhof, David

Abstract

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.

Suggested Citation

  • Huber, Martin & Imhof, David, 2018. "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers 494, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  • Handle: RePEc:fri:fribow:fribow00494
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    References listed on IDEAS

    as
    1. A. Banerji & J.V. Meenakshi, 2004. "Buyer Collusion and Efficiency of Government Intervention in Wheat Markets in Northern India: An Asymmetric Structural Auctions Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 236-253.
    2. Feinstein, Jonathan S & Block, Michael K & Nold, Frederick C, 1985. "Asymmetric Information and Collusive Behavior in Auction Markets," American Economic Review, American Economic Association, vol. 75(3), pages 441-460, June.
    3. Patrick Bajari & Lixin Ye, 2003. "Deciding Between Competition and Collusion," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 971-989, November.
    4. Porter, Robert H & Zona, J Douglas, 1993. "Detection of Bid Rigging in Procurement Auctions," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 518-538, June.
    5. Imhof, David, 2017. "Econometric tests to detect bid-rigging cartels: does it work?," FSES Working Papers 483, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. Aryal, Gaurab & Gabrielli, Maria F., 2013. "Testing for collusion in asymmetric first-price auctions," International Journal of Industrial Organization, Elsevier, vol. 31(1), pages 26-35.
    7. Juan Jiménez & Jordi Perdiguero, 2012. "Does Rigidity of Prices Hide Collusion?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 41(3), pages 223-248, November.
    8. Baldwin, Laura H & Marshall, Robert C & Richard, Jean-Francois, 1997. "Bidder Collusion at Forest Service Timber Sales," Journal of Political Economy, University of Chicago Press, vol. 105(4), pages 657-699, August.
    9. Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
    10. Martin Pesendorfer, 2000. "A Study of Collusion in First-Price Auctions," Review of Economic Studies, Oxford University Press, vol. 67(3), pages 381-411.
    11. Imhof, David, 2017. "Simple Statistical Screens to Detect Bid Rigging," FSES Working Papers 484, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    12. Robert H. Porter & J. Douglas Zona, 1999. "Ohio School Milk Markets: An Analysis of Bidding," RAND Journal of Economics, The RAND Corporation, vol. 30(2), pages 263-288, Summer.
    13. Gaurab Aryal & Maria F. Gabrielli, 2011. "Testing for Collusion in Asymmetric First-Price Auctions," ANU Working Papers in Economics and Econometrics 2011-564, Australian National University, College of Business and Economics, School of Economics.
    14. Denisova-Schmidt, Elena & Huber, Martin & Leontyeva, Elvira & Solovyeva, Anna, 2017. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," FSES Working Papers 487, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
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    More about this item

    Keywords

    Bid rigging detection; screening methods; variance screen; cover bidding screen; structural and behavioural screens; machine learning; lasso; ensemble methods;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General

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