Risks From Introducing the Anticartel Government IT System
https://doi.org/10.18288/1994-5124-2025-2-56-81
Abstract
The paper provides an ex ante assessment of risks incurred by developing and implementing the Anticartel state information system (GIS), which will conduct automated screening of Russia’s state procurement in order to detect bid rigging (in other words, software analysis of procurement data using algorithms that identify collusion based on certain formal characteristics). The system will also apply artificial intelligence. Determining the risks of implementing this system is difficult because there is no publicly available information on the specific criteria that will be used in this screening process. This forces specialists to make do with partial data. This paper attempts to reconstruct the operating principles of the Anticartel GIS from external sources including public speeches by representatives of the Federal Antimonopoly Service of Russia together with the technical specifications provided to developers entering the competition to build and implement the system. Key questions prompted by examining Anticartel’s reconstructed features are put forward in order to facilitate analysis during an “expedited” regulatory impact assessment based on the assumption that the decision to employ such a digital screening system is irreversible. The questions and analysis will structure discussion of the risks, associated benefits, and costs of different options for implementation. The principal risks result from probable Type I errors in law enforcement and from the extent of their consequences, which will be amplified as the automated screening system rolls out to include the entire body of regulated procurement, as well as by the use of artificial intelligence. The main consequences of this type of error and how it would arise are explained in the article. In addition, certain risks from using digital tools for identifying collusion especially in commodity markets are identified.
Keywords
JEL: H57, L41
About the Authors
A. E. ShastitkoRussian Federation
Andrey E. Shastitko, Dr. Sci. (Econ.), Professor, Faculty Head, Department of Competition and Industrial Policy, Economics Faculty; Director of the Center for Research in Competition and Economic Regulation
1–46, Leninskie Gory, Moscow, 119234
82, Vernadskogo pr., Moscow, 119571
N. S. Pavlova
Russian Federation
Natalia S. Pavlova, Cand. Sci. (Econ.), Senior Researcher, Department of Competition and Industrial Policy, Economics Faculty; Senior Researcher, Center
for Research in Competition and Economic
1–46, Leninskie Gory, Moscow, 119234
82, Vernadskogo pr., Moscow, 119571
References
1. Avdasheva S. B., Kokorev R. A., Kryuchkova P. V., Plaksin S. M., Shastitko A. E. Ispol’zovanie otsenok reguliruyushchego vozdeystviya dlya sovershenstvovaniya korporativnogo zakonodatel’stva [Using Regulatory Impact Assessment For Improving Corporate Law]. Moscow, TEIS, 2006. (In Russ.)
2. Efimov K. D. Identifikatsiya karteley na elektronnykh auktsionakh goszakupok [Detecting Collusion in Procurement Auctions]. Cornell University Preprint, Computer Science Series “arxiv.org”, 2024. DOI: 10.48550/arXiv.2411.10811. (In Russ.)
3. Pavlova N. S., Plekhanova L. S. Kriterii effektivnosti gosudarstvennykh zakupok: neuchtennye effekty [Criteria for the Effectiveness of Public Procurement: Unrecorded Effects]. Zakon [Law], 2021, no. 8, pp. 41-51. (In Russ.)
4. Teslenko A. V. Bol’shoy tsifrovoy kot: promezhutochnyye itogi i perspektivy. V Mezhdunarodnaya nauchno-prakticheskaya konferentsiya «Antimonopolnaya politika: nauka, praktika, obrazovaniye» [The Big Digital Cat: Interim Results and Prospects. V Anniversary International Conference Antimonopoly Policy: Science, Practice, Education]. Moscow, Skolkovo Innovation Center, 2019. (In Russ.)
5. Shastitko A. E. Kartel’: organizatsiya, stimuly, politika protivodeystviya [Cartel: Organization, Incentives and Deterrence Policy]. Rossiyskiy zhurnal menedzhmenta [Russian Management Journal], 2013, vol. 11, no. 4, pp. 31-56. (In Russ.)
6. Shastitko A. E., Morozov A. N., Morosanova A. A. Otsenka effektov proektiruemykh institutsional’nykh izmeneniy: primer reformy tsifrovoy naruzhnoy reklamy [Expected Effects of Projected Institutional Changes in Digital Out-Of-Home Advertising]. Vestnik Sankt-Peterburgskogo universiteta. Ekonomika [St. Petersburg University Journal of Economic Studies], 2023, vol. 39, no. 3, pp. 328-351. DOI: 10.21638/spbu05.2023.303. (In Russ.)
7. Shastitko A. E. Novaya institutsional’naya ekonomicheskaya teoriya [New Institutional Economic Theory], 5th ed. Moscow, MAKS Press, 2024. (In Russ.)
8. Shastitko A. E., Shastitko A. A. Modelirovanie i empiricheskaya otsenka parallelizma v povedenii na torgakh [Modeling and Empirical Assessment of the Parallelism at an Auction]. Voprosy gosudarstvennogo i munitsipal’nogo upravleniya [Public Administration Issues], 2017, no. 4, pp. 7-28. (In Russ.)
9. Ezrachi A., Stucke M. Virtual’naya konkurentsiya: posuly i opasnosti algoritmicheskoy ekonomiki [Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy]. Moscow, Delo RANEPA, 2022. (In Russ.)
10. Avdasheva S., Golovanova S., Katsoulacos Y. The Impact of Performance Measurement on the Selection of Enforcement Targets by Competition Authorities: The Russian Experience in an International Context. Public Performance & Management Review, 2019, vol. 42, no. 2, pp. 329-356. DOI: 10.1080/15309576.2018.1441036.
11. Avdasheva S., Golovanova S., Korneeva D. Distorting Effects of Competition Authority’s Performance Measurement: The Case of Russia. International Journal of Public Sector Management, 2016, vol. 29, no. 3, pp. 288-306.
12. Becker G. Crime and Punishment: An Economic Approach. Journal of Political Economy, 1968, vol. 76, no. 2, pp. 169-217.
13. Easterbrook F. H. The Limits of Antitrust. Texas Law Review, 1984, vol. 63, no. 1, pp. 1-40.
14. Garoupa N., Rizzolli M. Wrongful Convictions Do Lower Deterrence. Journal of Institutional and Theoretical Economics, 2012, vol. 168, no. 2, pp. 224-231.
15. Harrington J. How Do Cartels Operate? Foundations and Trends in Microeconomics, 2006, vol. 2, no. 1, pp. 1-105.
16. Harrington J. How Do Hub-And-Spoke Cartels Operate? Lessons From Nine Case Studies. SSRN. 24 August 2018. https://ssrn.com/abstract=3238244 or http://dx.doi.org/10.2139/ssrn.3238244.
17. Harrington J., Wei Y. What Can the Duration of Discovered Cartels Tell Us About the Duration of All Cartels? The Economic Journal, 2017, vol. 127, no. 604, pp. 1977-2005. DOI: 10.1111/ecoj.12359.
18. Computational Competition Law and Economics: An Inception Report. Hellenic Competition Commission; BRICS Competition Law and Policy Centre, 2021. https://ild.hse.ru/data/2021/05/24/1438611719/Project%20Computational%20competition%20law%20and%20economics%20FINAL23.5.2021BIS2.pdf?ysclid=m65fhzjkc2463757711.
19. Huber M., Imhof D. Machine Learning With Screens for Detecting Bid-Rigging Cartels. International Journal of Industrial Organization, 2019, vol. 65, pp. 277-301. DOI: 10.1016/j.ijindorg.2019.04.002.
20. Katsoulacos Y., Avdasheva S., Golovanova S. Determinants of the (Slow) Development of Effect-Based Competition Enforcement: Testing the Impact of Judicial Review on the Choice of Legal Standards by Competition Authorities. Journal of Industry, Competition and Trade, 2021, vol. 21, no. 1, pp. 103-122.
21. Massarotto G., Ittoo A. Gleaning Insight From Antitrust Cases Using Machine Learning. Stanford Computational Antitrust, 2021, no. 1, pp. 16-37.
22. Data Screening Tools in Competition Investigations. OECD Competition Policy Roundtable Background Note. OECD, 2022. https://web-archive.oecd.org/2022-10-18/643539-datascreening-tools-in-competition-investigations-2022.pdf.
23. Rodríguez M., Rodríguez-Montequín V., Ballesteros-Pérez P., Love P., Signor R. Collusion Detection in Public Procurement Auctions With Machine Learning Algorithms. Automation in Construction, 2022, vol. 133, article 104047. DOI: 10.1016/j.autcon.2021.104047.
24. Silveira D., De Moraes L., Fiuza E., Cajueiro D. Who Are You? Cartel Detection Using Unlabeled Data. International Journal of Industrial Organization, 2023, vol. 88, article 102931. DOI: 10.1016/j.ijindorg.2023.102931.
Review
For citations:
Shastitko A.E., Pavlova N.S. Risks From Introducing the Anticartel Government IT System. Economic Policy. 2025;20(2):56-81. (In Russ.) https://doi.org/10.18288/1994-5124-2025-2-56-81