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dc.contributor.authorAntonova, I. S.en
dc.contributor.authorPchelintsev, E. A.en
dc.date.accessioned2024-04-05T16:36:19Z-
dc.date.available2024-04-05T16:36:19Z-
dc.date.issued2023-
dc.identifier.citationAntonova, IS & Pchelintsev, EA 2023, 'Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns', Mathematics, Том. 11, № 17, 3704. https://doi.org/10.3390/math11173704harvard_pure
dc.identifier.citationAntonova, I. S., & Pchelintsev, E. A. (2023). Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns. Mathematics, 11(17), [3704]. https://doi.org/10.3390/math11173704apa_pure
dc.identifier.issn2227-7390-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Gold3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176413988&doi=10.3390%2fmath11173704&partnerID=40&md5=e0a1b16fe9da62f06f2fbae94ad86eb21
dc.identifier.otherhttps://www.mdpi.com/2227-7390/11/17/3704/pdf?version=1693277870pdf
dc.identifier.urihttp://elar.urfu.ru/handle/10995/130946-
dc.description.abstractCreative industry is considered the driver of modern urban development. It raises the new wave of issues of re-industrialization policy in single-industry towns. Nevertheless, the algorithms of current mathematical modeling in regional economies are not complex enough, leaving out spatial errors and variety in models used. We present eight steps of econometric analysis, considering local-level data. For the research, a balanced data panel was formed for 38 single-industry towns in Siberia and the Urals in Russia, for the period of 2013–2017. For mathematical modeling of the process of concentration of creative industries in single-industry towns, first, we present the specific indices of concentration, variety, and spatial disparities. Then, we test the full list of possible models for the variables. Then, we evaluate the regional offset. We describe the sufficient F-test, Hausman test and Breusch–Pagan Lagrange multiplier tests, choosing the most appropriate model. Finally, we evaluate the spatial autorepression of residuals. This algorithm allows us prove the data period and identify the tendency of spatial heterogeneity growth. We assume it to be the growing spillover effect in creative industries. At the same time, despite the positive trend of decreasing concentration of creative industries in single-industry towns, mono-industry continues to have a meaningful impact on their development, which forms the basis of path dependence. In this regard, the main actor of development in towns is city-forming enterprise, through the tools of corporate social responsibility. In view of the latter, it is proposed to develop tools for corporate creative responsibility in single-industry organizations within cities and regions. Finally, the general concern about the growth of spatial differentiation at the level of cities and regions is not yet significant. © 2023 by the authors.en
dc.description.sponsorshipRussian Science Foundation, RSF: 22-18-00679en
dc.description.sponsorshipThe research was supported by RSF (project No. 22-18-00679).en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.relationinfo:eu-repo/grantAgreement/RSF//22-18-00679en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.rightscc-byother
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/unpaywall
dc.sourceMathematics2
dc.sourceMathematicsen
dc.subjectCONCENTRATION AND DIVERSIFICATIONen
dc.subjectCREATIVE INDUSTRYen
dc.subjectECONOMETRIC MODELINGen
dc.subjectHYPOTHESIS TESTINGen
dc.subjectIT-INDUSTRYen
dc.subjectMONO-INDUSTRYen
dc.subjectPANEL DATAen
dc.subjectREGRESSION MODELSen
dc.subjectSINGLE-INDUSTRY TOWN (MONOTOWNS)en
dc.subjectSTATISTICAL ANALYSISen
dc.titleEconometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Townsen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.type|info:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/math11173704-
dc.identifier.scopus85176413988-
local.contributor.employeeAntonova, I.S., School of Engineering Entrepreneurship, Business School, Tomsk Polytechnic University, 30, Lenina Avenue, Tomsk, 634050, Russian Federation, Graduate School of Economics and Management, Ural Federal University, 19, Mira Street, Ekaterinburg, 620002, Russian Federationen
local.contributor.employeePchelintsev, E.A., International Laboratory of Random Process Statistics and Quantitative Financial Analysis, Tomsk State University, 36, Lenina Avenue, Tomsk, 634050, Russian Federationen
local.issue17-
local.volume11-
dc.identifier.wos001062410800001-
local.contributor.departmentSchool of Engineering Entrepreneurship, Business School, Tomsk Polytechnic University, 30, Lenina Avenue, Tomsk, 634050, Russian Federationen
local.contributor.departmentGraduate School of Economics and Management, Ural Federal University, 19, Mira Street, Ekaterinburg, 620002, Russian Federationen
local.contributor.departmentInternational Laboratory of Random Process Statistics and Quantitative Financial Analysis, Tomsk State University, 36, Lenina Avenue, Tomsk, 634050, Russian Federationen
local.identifier.pure45146352-
local.description.order3704-
local.identifier.eid2-s2.0-85176413988-
local.fund.rsf22-18-00679-
local.identifier.wosWOS:001062410800001-
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