Conservatism, mobility, isolation: Approach to the study of agents’ behavior in the public procurement market

 
PIIS042473880026993-1-1
DOI10.31857/S042473880026993-1
Publication type Article
Status Published
Authors
Affiliation: Federal State Budgetary Educational Institution of Higher Education
Address: Russian Federation,
Affiliation: Federal State Budgetary Educational Institution of Higher Education "State University of Management"
Address: Russia
Affiliation: Federal State Budgetary Educational Institution of Higher Education "State University of Management"
Address: Russia
Journal nameEkonomika i matematicheskie metody
EditionVolume 59 no. 4
Pages45-57
Abstract

The purpose of the article is to substantiate the application of the authors’ approach and methodology based on a combination of machine learning technologies and the construction of directed graphs with their subsequent clustering for a systematic study of the quantitative and qualitative characteristics of the public procurement market and the behavior of agents in this market. As a result of the study, regional and sectoral factors influencing the relationship between agents of the public procurement market were identified. Such factors were not previously identified, and were determined only thanks to the combination of machine learning technologies and the theory of networks and graphs proposed by the authors. Another result of the study-the models of relationships in public procurement market are systematized in the authors’ interpretation, integrating the macroeconomic situation in the market and the marketing strategies of market players. Such stable patterns of behavior of agents of the public procurement market as "isolation", "conservatism" and "mobility" were identified, and it was substantiated that the isolated or conservative behavior of market players increases the likelihood of corrupt conspiracies. All of the above was not systematically studied before. So, it has scientific novelty and high practical significance. The research contributed to the increment of scientific knowledge in application of the theory of networks and graphs, in problems of state regulation of the economy, counteraction to the monopolization of markets and encouraging competition. The practical results of the work are related to the generation of recommendations to the Russian authorities, regulators of the public procurement market and bidders on the choice of effective market behavior strategies.

 

Keywordsstate and municipal procurement, behavior strategies of public procurement participants, fragmentation of the public procurement market, isolation, conservatism and mobility in the public procurement market, theory of networks, graphs for public procurement, machine learning.
AcknowledgmentThe team of authors would like to thank the Bauman Moscow State Technical University, the State University of Management and JSC "Unified Electronic Trading Platform" for supporting the research activities of the employees. The study was supported by the Russian Science Foundation grant No. 23-28-01644.
Received04.08.2023
Publication date28.12.2023
Number of characters26591
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1. Agalarov Z.S. (2022). Conceptual approach to mathematical modeling of the results of production diversification as a direction of long-term strategic development. Microeconomics, 2, 49–57. DOI: 10.33917/mic-2.103.2022.49-57 (in Russian).

2. Aleinikova M.Yu., Golovanov D.A. (2022). Methods for improving the system of external control over the conclusion and execution of state contracts in the Russian Federation. Management Accounting, 6 (3), 658–666. Available at: https://elibrary.ru/htkzwp, https://uprav-uchet.ru/index.php/journal/article/view/2218/1567 (in Russian).

3. Anchishkina O.V. (2011). The scope of public procurement as an object of economic analysis. STAP: Economic theory, analysis, practice, 1, 73–86. Available at: https://elibrary.ru/nyezmv, https://cyberleninka.ru/article/n/sfera-gosudarstvennyh-zakupok-kak-obekt-ekonomicheskogo-analiza (in Russian).

4. Filippov D.V., Andronova E.S., Rey A.I. (2022). Influence of affiliation of public procurement counterparties on the risk of an FAS complaint. Russian Economic Bulletin, 5, 1, 230–238. Available at: https://elibrary.ru/rvgbpo (in Russian).

5. Folliot Lalliot L., Yukins C.R. (2020). COVID-19: Lessons learned in public procurement. Time for a New Normal, 3, 46–58. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3685860

6. Ghorbani M., Brooks B.R., Klauda J.B. (2021). Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2. Biophysical Journal, 120 (14), 2902–2913. DOI: 10.1016/j.bpj.2021.02.047

7. Gmurman V.E. (2020). Probability theory and mathematical statistics: A textbook for universi-ties. 12th ed. Moscow: Yurait. 479 с. ISBN: 978-5-534-00211-9. Available at: https://urait.ru/bcode/449646 (in Russian).

8. Grubenmann T., Lehmann J. (2021). Geolog: Scalable logic programming on spatial data. In: A. Formisano, Y.A. Liu et al. (eds.). Electronic proceedings in theoretical computer science. International conference on logic programming (technical communications), 345, 191–204. DOI: 10.4204/eptcs.345.34

9. Gürpinar T. (2020). Blockchain technology in procurement-a systematic literature mapping. Kon-ferenzband zum Scientific Track der Blockchain Autumn School 2020, 7–13. DOI: 10.48446/opus-11859

10. Hosseini M.R., Martek I., Banihashemi S. et al. (2020). Distinguishing characteristics of corrup-tion risks in Iranian construction projects: A weighted correlation network analysis. Sci. Eng. Ethics., 26, 205–231. DOI: 10.1007/s11948-019-00089-0

11. Ivanov A.E., Gilenko E.V., Golubeva A.A., Bezhenar O.N. (2020). Vertical and horizontal coor-dination in the public procurement system of the Russian Federation. Organizational and managerial mechanisms of anti-corruption activities: Russian and foreign experience, 102–107. Moscow: RuSCIence, Plekhanov Russian University of Economics. Available at: https://elibrary.ru/aneprc (in Russian).]

12. Izmalkov S.B., Sonin K.I. (2017). Fundamentals of contract theory (Nobel Prize in economics 2016 — Oliver Harty Bengt Holmström). Voprosy Ekonomiki, 1, 5–21. DOI: 10.32609/0042-8736-2017-1-5-21 (in Russian).

13. Jiwei Z., Bing W., Liang L., Jiangrui W. (2020). Bidder network community division and collu-sion suspicion analysis in Chinese construction project. Advances in Civil Engineering, ID 6612848, 14. DOI: 10.1155/2020/6612848

14. Keynes J. (2013). General theory of employment, interest and money. Series: General manager's library. Timeless classics. Vol. IV (LII)). Moscow: Businesscom. 408 p. ISBN: 978-5-91663-155-5 (in Russian).

15. Kormen T., Leizerson Ch., Rivest R., Shtain K. (2019). Introduction to algorithms. Textbook. Transl. from English. Moscow, Saint Petersburg: Dialektika. 1323 p. Available at: https://search.rsl.ru/ru/record/01009814867 (in Russian). Originally published in 2009 by The MIT Press.

16. Lyra M.S., Curado A., Damásio B. et al. (2021). Characterization of the firm-firm public pro-curement co-bidding network from the State of Ceará (Brazil) municipalities. Applied Net-work Science, 6, 77. DOI: 10.1007/s41109-021-00418-y

17. Molchanova G.O., Rei A.I., Shagarov D.Yu. (2020). Detection of signs of horizontal collusion in public procurement using machine learning methods. Economics of Contemporary Russia, 1 (88), 109–127. Available at: https://elibrary.ru/lfraes DOI: 10.33293/1609­1442­2020­1(88)­109­127 (in Russian).

18. Myasoedov A.I. (2020). Risks of growth of protectionism in the global economy. Research result. Business and Service Technologies, 2, 65–77. Available at: https://cyberleninka.ru/article/n/riski-rosta-protektsionizma-v-globalnoy-ekonomike (in Russian).

19. Orekhova S.V., Zarutskaya V.S., Kislitsyn E.V. (2021). An empirical study of network interac-tion in the market. The Manager, 12, 1, 32–46. DOI: 10.29141/2218­5003­2021­12­1­3 (in Russian).

20. Pamučar D., Bozanic D., Puška A., Marinković D. (2022). Application of neuro-fuzzy system for predicting the success of a company in public procurement. Decision Making: Applications in Management and Engineering, 5 (1), 135–153. DOI: 10.31181/dmame0304042022p

21. Reeves-Latour M., Morselli C. (2017). Bid-rigging networks and state-corporate crime in the construction industry. Social Networks, 51, 158-170. DOI: 10.1016/j.socnet.2016.10.003

22. Rei A.I., Andronova E.S., Shatilov A.A., Gordeev D.I., Shagarov D.Yu., Filippov D.V., Davletov A.A. (2020). Algorithms and methods for the risk management system in public procurement. Available at: https://ssrn.com/abstract=3792741 DOI: 10.2139/ssrn.3792741 (in Russian).

23. Rodionova Yu. (2020). Conflict resolution in Russian public procurement: Understanding supplier strategies in a state dominated system. Higher School of Economics Research Paper no. WP BRP 28/PSP/2020. DOI: 10.2139/ssrn.3661223

24. Schlicht E. (2012). Isolation and aggregation in economics. Springer Science & Business Media. Available at: https://epub.ub.uni-muenchen.de/3/1/schlicht_isolation.pdf

25. Shul'dyakova V.V. (2011). Neoclassical and institutional approaches to the essence of monopsony. Vestnik Saratov State Socio-Economic University, 1, 43–46. Available at: https://elibrary.ru/ohwnjj, https://cyberleninka.ru/article/n/neoklassicheskiy-i-institutsionalnyy-podhody-k-suschnosti-monopsonii (in Russian).

26. Sozaeva D.A., Gonchar K.V. (2022). Competitive strategies of public procurement bidders. Jour-nal of Modern Competition, 16, 3, 91–104. Available at: https://elibrary.ru/zhaphh DOI: 10.37791/2687-0649-2022-16-3-91-104 (in Russian).

27. Swords M. (2019). Finding patterns in procurements and tenders using a graph database. Availa-ble at: http://kth.diva-portal.org/smash/get/diva2:1415996/FULLTEXT01.pdf

28. Velasco R.B., Carpanese I., Interian R., Paulo Neto O.C.G., Ribeiro C.C. (2021). A decision support system for fraud detection in public procurement. International Transactions in Op-erational Research, 28, 27–47. DOI: 10.1111/itor.12811

29. Wachs J., Fazekas M., Kertész J. (2021). Corruption risk in contracting markets: A network science perspective. International Journal of Data Science and Analytics, 12, 45–60. DOI: 10.1007/s41060-019-00204-1

30. Wachs J., Kertész J. (2019). A network approach to cartel detection in public auction markets. Scientific Reports, 9, 10818. DOI: 10.1038/s41598-019-47198-1

31. Wachs J., Yasseri T., Lengyel B., Kertész J. (2019). Social capital predicts corruption risk in towns. Royal Society Open Science, 6182103182103. DOI: 10.1098/rsos.182103

32. Wang K.W., Yu W. (2011). Model for analysis of heterogeneity in product acquisition procure-ment. Journal of the Chinese Institute of Engineers, 34, 7, 877–887. DOI: 10.1080/02533839.2011.591917

33. Yakovlev A.A., Demidova O.A., Podkolzina E.A. (2015). An empirical analysis of the public procurement system in Russia. Moscow: Higher School of Economics. 357 p. Available at: https://elibrary.ru/uoyqcd (in Russian).

34. Zhemkova A.M. (2020). Analysis of the effectiveness of public procurement procedures based on game-theoretic models. Available at: https://ssrn.com/abstract=3710564 DOI: 10.2139/ssrn.3710564 (in Russian).

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