Modeling economic security risks for Russian regions in the context of sanctions pressure

Modeling economic security risks for Russian regions in the context of sanctions pressure

Автор: Oleg A. Golovanov, Aleksandr N. Tyrsin, Elena V. Vasilyeva

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Рубрика: Public administration

Статья в выпуске: 5 т.16, 2023 года.

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The article investigates the problem of ensuring Russia's economic security in the conditions of increasing sanctions pressure. In order to assess and analyze emerging risks, we propose a multifactorial model that considers the economic security of Russian regions as a complex multidimensional system influenced by various interrelated risk factors. We use a list of indicators for monitoring and assessing Russia's economic security, approved by Presidential Decree 208, dated May 13, 2017. For the purpose of risk modeling, we establish two-level threshold values (“soft” and “hard”) of indicators based on expert assessment. The information base of the study includes data of the Federal State Statistics Service for Russia, as well as data in the context of constituent entities of the Ural Federal District by month for the period from January 2016 to March 2023. According to the calculation results, the aggravation of sanctions imposed by unfriendly countries has negatively affected the economic security of Russia as a whole and that of constituent entities of the Ural Federal District. Within the analyzed period, the risks created are significantly lower in comparison with the consequences of the COVID-19 pandemic, and they tend to decrease. Regional analysis shows that the most significant risk factor is the condition of agriculture, which has been significantly affected by the quarantine and sanctions restrictions imposed. Modeling economic security risks for Russian regions on the basis of the proposed approach in dynamics will help to promptly assess the current situation and put forward management recommendations in a timely manner, when economic security is compromised.

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Economic security, risk analysis, probability of an unfavorable outcome, crisis, country, region, sanctions, pandemic

Короткий адрес: https://sciup.org/147242066

IDR: 147242066   |   DOI: 10.15838/esc.2023.5.89.3

Список литературы Modeling economic security risks for Russian regions in the context of sanctions pressure

  • Anisimov A.L. (2022). Limitations on the effectiveness of institutionalization of economic development: On the example of the Economic Security Strategy of the Russian Federation for the period up to 2030. Finansovye rynki i banki, 3, 9–11 (in Russian).
  • Avdiysky V.I., Senchagov V.K. (2014). Methodology for determining the threshold values for main (priority) risk factors and threats to the economic security of business entities. Ekonomika. Nalogi. Pravo, 4, 73–78 (in Russian).
  • Aven T. (2019). The Science of Risk Analysis: Foundation and Practice. Routledge. DOI: 10.4324/9780429029189
  • Behrensdorf J., Broggi M., Beer M. (2019). Reliability analysis of networks interconnected with copulas. ASCEASME.Journal of Risk and Uncertainty in Engineering Systems, Part B Mechanical Engineering, 5, 041006-9. DOI 10.1115/1.4044043
  • Benzaghta M.A., Elwalda A., Mousa M.M. et al. (2021). SWOT analysis applications: An integrative literature review. Journal of Global Business Insights, 6(1), 54–72. DOI: 10.5038/2640-6489.6.1.1148
  • Bryant J., Zhang J.L. (2016). Bayesian forecasting of demographic rates for small areas: Emigration rates by age, sex, and region in New Zealand, 2014–2038. Statistica Sinica, 26, 1337–1363. DOI: 10.5705/ss.2014.200t
  • Cherubini U., Luciano E., Vecchiato W. (2004). Copula Methods in Finance. Chichester, UK: Wiley. Cox L.A. Jr. (2009). Risk Analysis of Complex and Uncertain Systems. Springer.
  • Devianto M.D., Fadhilla D.R. (2015). Time series modeling for risk of stock price with value at risk computation. Applied Mathematical Sciences, 9(56), 2779–2787. DOI: 10.12988/ams.2015.52144
  • Gimpelson V.E., Kapelyushnikov R.I. (2015). The Russian labour market model: Trial by recession. Zhurnal Novoi ekonomicheskoi assotsiatsii, 2(26), 249–253 (in Russian).
  • Ginevicius R., Gedvilaite D., Stasiukynas A., Suhajda K. (2022). Complex expert assessment of the state of business enterprises. Acta Polytechnica Hungarica, 19(2), 135–150. DOI: 10.12700/APH.19.2.2022.2.8
  • Glazyev S.Yu., Lokosov V.V. (2012). Assessment of threshold values of indicators of the state of Russian society and their use in the management of socio-economic development. Vestnik Rossiiskoi akademii nauk, 82(7), 587–614 (in Russian).
  • Golyashev A.V., Grigor’ev L.M., Lobanova A.A., Pavlyushina V.A. (2017). Features of recession recovering: Income and inflation. Prostranstvennaya ekonomika=Spatial Economics, 1, 99–124. DOI: 10.14530/se.2017.1.099-124 (in Russian).
  • Graziani R. (2020). Stochastic population forecasting: A Bayesian approach based on evaluation by experts. In: Mazzuco S., Keilman N. (Eds.). Developments in Demographic Forecasting. The Springer Series on Demographic Methods and Population Analysis, 49, 21–42. Cham: Springer. DOI: 10.1007/978-3-030-42472-5_2
  • Gurvich E.T., Prilepskiy I.V. (2016). The impact of financial sanctions on the Russian economy. Voprosy ekonomiki, 1, 5–35. DOI: 10.32609/0042-8736-2016-1-5-35 (in Russian).
  • Ilyenkova N.D. (2016). Stages of the risk and economic security analysis program. In: Analiz i sovremennye informatsionnye tekhnologii v obespechenii ekonomicheskoi bezopasnosti biznesa i gosudarstva: sbornik nauchnykh trudov i rezul’tatov sovmestnykh nauchno-issledovatel’skikh proektov [Analysis and Modern Information Technologies in Ensuring the Economic Security of Business and the State: Collection of Scientific Papers and Findings of Joint Research Projects]. Moscow: Auditor (in Russian).
  • Joe H. (2014). Dependence Modeling with Copulas. New York: Chapman and Hall/CRC.
  • Kabanova E.E. (2023). Prospects of the Russian agricultural complex in the conditions of sanctions. Ekonomicheskoe razvitie Rossii=Russian Economic Developments, 30(4), 44–52 (in Russian).
  • Karanina E.V., Maksimova N.A. (2022). Assessment of economic security risks of industrial enterprises by developing a multiple regression model. Problemy analiza riska=Issues of Risk Analysis, 19(2), 30–38. DOI: 10.32686/1812-5220-2022-19-2-30-38 (in Russian).
  • Kolpakov A.Yu., Safina E.V. (2020). Assessment of the costs of the oil-producing sector of Russia to reduce the risks of permafrost degradation under the influence of climate change. Nauchnye trudy: Institut narodnokhozyaistvennogo prognozirovaniya RAN, 18, 186–200. DOI: 10.47711/2076-318-2020-186-200 (in Russian).
  • Krivorotov V.V., Kalina A.V., Belik I.S. (2019). Threshold values of indicators for diagnostics of economic security the Russian Federation at the present stage. Vestnik UrFU. Seriya: Ekonomika i upravlenie=Bulletin of Ural Federal University. Series Economics and Management, 18(6), 892–910. DOI: 10.15826/vestnik.2019.18.6.043 (in Russian).
  • Kuklin A.A., Tyrsin A.N., Pecherkina M.S., Nikulina N.L. (2018). Risk diagnostics and management for welfare in regions (in the example of the Ural Federal District). Prostranstvennaya Ekonomika=Spatial Economics, 2, 36–51. DOI: 10.14530/se.2018.2.036-051 (in Russian).
  • Lavrikova Yu.G. (2017). Features of the processes of new industrialization in the Ural region. In: Neoindustrial’no orientirovannye preobrazovaniya v ekonomicheskom prostranstve Ural’skogo makroregiona [Neo-Industrially Oriented Transformations in the Economic Space of the Ural Macroregion]. Yekaterinburg: Ural’skii gosudarstvennyi ekonomicheskii universitet (in Russian).
  • Liu T., Yu Z. (2022). The analysis of financial market risk based on machine learning and particle swarm optimization algorithm. EURASIP Journal on Wireless Communications and Networking, 31. DOI: 10.1186/s13638-022-02117-3
  • Lobkova E.V. (2022). Application of the theory of fuzzy sets in the assessment of economic security risks in the context of the digital transformation of the regional economy. Ekonomicheskie nauki, 208, 111–118. DOI: 10.14451/1.208.111 (in Russian).
  • Lokosov V.V. (2021). Assessment of socio-economic risks by method of extremely critical (threshold) indicators. Narodonaselenie=Population, 24(3), 8–17. DOI: 10.19181/population.2021.24.3.1 (in Russian).
  • Lukashin Yu.P. (2003). Adaptivnye metody kratkosrochnogo prognozirovaniya vremennykh ryadov [Adaptive Methods of Short-Term Forecasting of Time Series]. Moscow: Finansy i statistika.
  • Mityakov S.N. (2019). Methods for assessing economic security risks. Ekonomicheskaya bezopasnost’=Economic Security, 2(1), 23–27. DOI: 10.18334/ecsec.2.1.100618 (in Russian).
  • Mityakov S.N., Mityakov E.S., Fedoseeva T.A. (2020). The system of indicators of economic security of a municipality as an integral element of a multi-level system of economic security. Mir novoi ekonomiki=The World of New Economy, 14(4), 67–80. DOI: 10.26794/2220-6469-2020-14-4-67-80 (in Russian).
  • Pavlov V.I. (2019). Problems and contradictions in the implementation of the Russian Federation’s economic security strategy for the period up to 2030. Ekonomicheskaya bezopasnost’=Economic Security, 2(1), 39–45. DOI: 10.18334/ecsec.2.1.100621 (in Russian).
  • Senchagov V.K., Mityakov S.N. (2011). Using the index method to assess the level of economic security. Vestnik Akademii ekonomicheskoi bezopasnosti MVD Rossii, 5, 41–50 (in Russian).
  • Serebrennikov S.S., Morgunov E.V., Mamaev S.M., Shervarli I.A. (2018). The strategy of economic safety of the Russian Federation for the period up to 2030. Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika=Tomsk State University Journal of Economics, 41, 20–28. DOI: 10.17223/19988648/41/1 (in Russian).
  • Simachev Yu.V., Fedyunina A.A., Ershova N.V., Misikhina S.G. (2021). Russian retail before, during and after the COVID-19 crisis. EKO=ECO, 5(563), 29–52 (in Russian).
  • Soboleva I.V., Sobolev E.N. (2021). Open and latent unemployment in the context of the pandemic. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 14(5), 186–201. DOI: 10.15838/esc.2021.5.77.11 (in Russian).
  • Solozhentsev E.D. (2006). Stsenarnoe logiko-veroyatnostnoe upravlenie riskom v biznese i tekhnike [Scenario Logic-Probabilistic Risk Management in Business and Technology]. Second edition. Saint Petersburg: Biznes-pressa.
  • Soshnikova L.A., Tamashevich V.N., Uebe G., Shefer M. (1999). Mnogomernyi statisticheskii analiz v ekonomike [Multidimensional Statistical Analysis in Economics]. Moscosw: YuNITI-DANA.
  • Tatarkin A.I., Kuklin A.A., Romanova O.A. et al. (1997). Ekonomicheskaya bezopasnost’ regiona: edinstvo teorii, metodologii issledovaniya i praktiki [Economic Security of the Region: Unity of Theory, Research Methodology and Practice]. Yekaterinburg: Izd-vo Ur. un-ta.
  • Tsukhlo S.V. (2019). Russian industry 2018: Stagnant but not in crisis. Ekonomicheskoe razvitie Rossii=Russian Economic Developments, 26(2), 45–48 (in Russian).
  • Tyrsin A.N., Surina A.A. (2017). Modeling of risk in multidimensional stochastic systems. Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel’naya tekhnika i informatika=Tomsk State University Journal of Control and Computer Science, 39, 65–72. DOI: 10.17223/19988605/39/9 (in Russian).
  • Vasiliev V.L., Ustyuzhina O.N., Sedov S.A. (2015). Risk and economic security: Relationship and methodology of the analysis. Kazanskii ekonomicheskii vestnik, 3(17), 90–94 (in Russian).
  • Vissarionov A.B., Gumerov R.R. (2017). Concerning the use of indicators’ marginal (threshold) values of the RussianFederation economic security. Upravlencheskie nauki=Management Sciences, 7(3), 12–20 (in Russian).
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