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Detecting Indicators of Horizontal Collusion in Public Procurement with Machine Learning Methods

https://doi.org/10.33293/1609-1442-2020-1(88)-109-127

Abstract

Improvement of procurement procedures and their digitization help prevent and identify cartels, but at the same time lead to the emergence of new anticompetitive schemes. In this paper we focus on electronic auctions, which have become the main method of public procurement in Russia in recent years. As e-auctions provide access to many big government orders; the incentives for bidders to join anti-competitive agreements are increased. Therefore, the development of methods to detect bid rigging at electronic auctions is of high practical importance. The aim of this work was to develop a method for detecting signs of horizontal collusion at an auction. We use machine learning methods to train classifiers that predict the presence or absence of cartel in electronic auctions, depending on the distribution of bidders, the time of submission of applications, the duration of the auction and the number of participants. Variables for the model were selected on the basis of distribution plots built for sample of cartels and random sample. The study is based on data from public procurement Web portal and the information about bid rigging from cases of the Federal Antimonopoly Service. The results showed that the Random forest model most accurately predicts the detection of the cartels on electronic auctions. The accuracy of the prediction is 84%, and the recall and precision of the model are 83 and 87%, respectively. The most significant variables for the classification are the level of price reduction, the difference in the time of application filing of participants and the value of the maximum starting price of contract.

About the Authors

Glafira O. Molchanova
Russian Presidential Academy of National Economy and Public Administration, Moscow
Russian Federation
Junior researcher at the laboratory for data analysis and industry dynamics, Institute for Industrial Economics


Alexey I. Rey
Russian Presidential Academy of National Economy and Public Administration, Moscow
Russian Federation
Cand.Econ.Sci., Head of laboratory for data analysis and industry dynamics, Institute for Industrial Economics


Dmitry Yu. Shagarov
Russian Presidential Academy of National Economy and Public Administration, Moscow
Russian Federation
Junior researcher at the laboratory for data analysis and industry dynamics, Institute for Industrial Economics


References

1. Jakobsson M. (2007). Bid rigging in Swedish procurement auctions. Working Paper. Mimeo: Uppsala University. Department of Economics (Sweden).

2. Abrantes-Metz R.M., Froeb L.M., Geweke J.F., Taylor C.T. (2006). A variance screen for collusion. International Journal of Industrial Organization, no. 24, pp. 467–486.

3. Levenstein M.C., Suslow V.Y., Oswald L.J. (2004). Contemporary international cartels and developing countries: Economic effects and implications for competition policy. Antitrust Law Journal, no. 71, pp. 801–852.

4. Abrantes-Metz R.M., Kraten M., Metz A.D., Seow G. (2012). Libor manipulation. Journal of Banking and Finance, no. 36, pp. 136–150.

5. Morozov I., Podkolzina E. (2013). Collusion detection in procurement auctions. SSRN Electronic Journal. 10.2139/ssrn.2221809.

6. Andreyanov P., Davidson A., Korovkin V. (2016). Corruption vs Collusion: Evidence from Russian Procurement Auctions. Technical Report. mimeo: UCLA.

7. OECD (2016). Fighting bid rigging in public procurement: Report on implementing the OECD Recommendation. URL: http://www.oecd.org/daf/competition/Fighting-bid-rigging-in-public-procurement-report‑2016.pdf.

8. Aryal G., Gabrielli M.F. (2013). Testing for collusion in asymmetric first-price auctions. International Journal of Industrial Organization, vol. 31, pp. 26–35.

9. Pesendorfer M. (2000). A study of collusion in first-price auction. The Review of Economic Studies, no. 67, pp. 381–411.

10. Bajari P., Ye L. (2003). Deciding between competition and collusion. The Review of Economics and Statistics, no. 85, pp. 971–989.

11. Porter R.H., Zona J.D. (1993). Detection of bid rigging in procurement auctions. The Journal of Political Economy, no. 101, pp. 518–538.

12. Balsevich A., Podkolzina E.A. (2014). Indicators of corruption in public procurement: The example of Russian regions. Higher School of Economics. Research Paper No. WP BRP 76/EC/2014. URL: Available at SSRN: https://ssrn.com/abstract=2530518 or http://dx.doi.org/10.2139/ssrn.2530518.

13. Porter R.H., Zona J.D. (1999). Ohio school milk markets: An analysis of bidding. RAND Journal of Economics, no. 30, pp. 263–288.

14. Bolotova Y., Connor J., Miller D. (2008). The Impact of collusion on price behavior: Empirical results from two recent cases. International Journal of Industrial Organization, no. 26, pp. 1290–1307.

15. Symeonidis G. (2003). In which industries is collusion more likely? Evidence from the UK. Journal of Industrial Economics, no. 51, pp. 45–74.

16. Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, vol. 16, pp. 321–357.

17. Chotibhongs R., Arditi D. (2012). Analysis of collusive bidding behavior. Construction Management and Economics, no. 30, pp. 221–231.

18. Connor J.M. (2001). Global price fixing: Our customers are the enemy. Boston: Kluwer Academic Publishers.

19. Grout P., Sonderegger S. (2007). Structural approaches to cartel detection. In: E. C-D, Atanasiu I. (eds.). European Competition Law Annual: 2006. Enforcement of Prohibition of Cartels. Hart Publishing, pp. 83–103.

20. Harrington J.E. (2008). Detecting cartels. In: Handbook of Antitrust Economics, Buccirossi P. (ed.). Cambridge: The MIT Press.

21. Huber M., Imhof D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, vol. 65, pp. 277–301.

22. Imhof D., Karagoek Y., Rutz S. (2018). Screening for bid rigging, does it work? Journal of Competition Law and Economics, no. 14 (2), pp. 235–261.

23. Ivanov D.I., Nesterov A.S. (2019). Identifying bid leakage in procurement auctions: Machine learning approach. arXiv:1903.00261 [econGN].

24. Jakobsson M. (2007). Bid rigging in Swedish procurement auctions. Working Paper. Mimeo: Uppsala University. Department of Economics (Sweden).

25. Levenstein M.C., Suslow V.Y., Oswald L.J. (2004). Contemporary international cartels and developing countries: Economic effects and implications for competition policy. Antitrust Law Journal, no. 71, pp. 801–852.

26. Morozov I., Podkolzina E. (2013). Collusion detection in procurement auctions. SSRN Electronic Journal. 10.2139/ssrn.2221809.

27. OECD (2016). Fighting bid rigging in public procurement: Report on implementing the OECD Recommendation. URL: http://www.oecd.org/daf/competition/Fighting-bid-rigging-in-public-procurement-report‑2016.pdf.

28. Pesendorfer M. (2000). A study of collusion in first-price auction. The Review of Economic Studies, no. 67, pp. 381–411.

29. Porter R.H., Zona J.D. (1993). Detection of bid rigging in procurement auctions. The Journal of Political Economy, no. 101, pp. 518–538.

30. Porter R.H., Zona J.D. (1999). Ohio school milk markets: An analysis of bidding. RAND Journal of Economics, no. 30, pp. 263–288.

31. Symeonidis G. (2003). In which industries is collusion more likely? Evidence from the UK. Journal of Industrial Economics, no. 51, pp. 45–74.


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Molchanova G.O., Rey A.I., Shagarov D.Yu. Detecting Indicators of Horizontal Collusion in Public Procurement with Machine Learning Methods. Economics of Contemporary Russia. 2020;(1):109-127. (In Russ.) https://doi.org/10.33293/1609-1442-2020-1(88)-109-127

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