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dc.contributor.authorRazumovskaia, E.en
dc.contributor.authorYuzvovich, L.en
dc.contributor.authorKniazeva, E.en
dc.contributor.authorKlimenko, M.en
dc.contributor.authorShelyakin, V.en
dc.date.accessioned2021-08-31T15:09:02Z-
dc.date.available2021-08-31T15:09:02Z-
dc.date.issued2020-
dc.identifier.citationThe effectiveness of Russian government policy to support smes in the COVID-19 pandemic / E. Razumovskaia, L. Yuzvovich, E. Kniazeva, et al. — DOI 10.3390/joitmc6040160 // Journal of Open Innovation: Technology, Market, and Complexity. — 2020. — Vol. 6. — Iss. 4. — P. 1-20. — 160.en
dc.identifier.issn21998531-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Gold, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85096293015&doi=10.3390%2fjoitmc6040160&partnerID=40&md5=ad9afd413c1ec57f109d36fd8637c3a1
dc.identifier.otherhttps://www.mdpi.com/2199-8531/6/4/160/pdfm
dc.identifier.urihttp://elar.urfu.ru/handle/10995/103333-
dc.description.abstractThis study was aimed at developing a cognitive—econometric model for assessing the effectiveness of the current governmental policies to support enterprises in Russia in the context of pandemic propagation. Using the Granger test and correlation analysis, we formed a system of key indicators that characterizes the economic development of SMEs (small and medium-sized enterprises) in Russia. Based on the revealed causal relationships and correlation coefficients, a model describing the impact of public policy support instruments on SME economic development was built using cognitive modeling. By means of the additive convolution method, the correlation coefficient between the Russia Small Business Index (RSBI) and the COVID-19 prevalence rate was used to predict the 2020 year-end RSBI value. Regarding the RSBI index forecast, the effectiveness of instruments of the state support for SMEs was evaluated. It was determined how much these indicators of the anti-crisis package of measures should change to increase SMEs’ business activities. The developed cognitive model can be utilized by private and governmental institutions to continuously monitor the effectiveness of public policies that support SMEs. It can also be used as a preventive indicator to evaluate the impact of the anti-crisis measures during pandemics and in the case of other exogenous risks threatening SMEs. The originality of the research results was determined by the econometric methods applied to empirically assess the effectiveness and degree of impact of governmental measures on the operation of SMEs under conditions of uncertainty. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherMDPI AGen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceJ. Open Innov.: Technol. Mark. Complex.2
dc.sourceJournal of Open Innovation: Technology, Market, and Complexityen
dc.subjectANTI-CRISIS POLICY MEASURESen
dc.subjectCOVID-19en
dc.subjectDEVELOPMENTen
dc.subjectEFFECTIVENESSen
dc.subjectENTREPRENEURSHIPen
dc.subjectGOVERNMENT SUPPORTen
dc.subjectRUSSIAen
dc.subjectSMESen
dc.titleThe effectiveness of Russian government policy to support smes in the COVID-19 pandemicen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/joitmc6040160-
dc.identifier.scopus85096293015-
local.contributor.employeeRazumovskaia, E., Department of Finance, Money Circulation, and Credit, Ural Federal University Named after the First President of Russia B.N., Yeltsin, 19 Mira St., Yekaterinburg, 620002, Russian Federation, Department of Finance, Money Circulation, and Credit, Ural State University of Economics, 8 Marta St., Yekaterinburg, 620144, Russian Federation
local.contributor.employeeYuzvovich, L., Department of Finance, Money Circulation, and Credit, Ural State University of Economics, 8 Marta St., Yekaterinburg, 620144, Russian Federation
local.contributor.employeeKniazeva, E., Department of Finance, Money Circulation, and Credit, Ural State University of Economics, 8 Marta St., Yekaterinburg, 620144, Russian Federation
local.contributor.employeeKlimenko, M., The Sverdlovsk Region Legislative Assembly, 10 Borisa Yeltsina St., Yekaterinburg, 620031, Russian Federation
local.contributor.employeeShelyakin, V., Territorial Fund of Compulsory Medical Insurance of Sverdlovsk Region, 54 Moskovskaya St., Yekaterinburg, 620102, Russian Federation
local.description.firstpage1-
local.description.lastpage20-
local.issue4-
local.volume6-
local.contributor.departmentDepartment of Finance, Money Circulation, and Credit, Ural Federal University Named after the First President of Russia B.N., Yeltsin, 19 Mira St., Yekaterinburg, 620002, Russian Federation
local.contributor.departmentDepartment of Finance, Money Circulation, and Credit, Ural State University of Economics, 8 Marta St., Yekaterinburg, 620144, Russian Federation
local.contributor.departmentThe Sverdlovsk Region Legislative Assembly, 10 Borisa Yeltsina St., Yekaterinburg, 620031, Russian Federation
local.contributor.departmentTerritorial Fund of Compulsory Medical Insurance of Sverdlovsk Region, 54 Moskovskaya St., Yekaterinburg, 620102, Russian Federation
local.identifier.pure20118372-
local.identifier.puref5f2d3ef-9b66-430b-b202-02c26845d9a2uuid
local.description.order160-
local.identifier.eid2-s2.0-85096293015-
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