Modeling cluster development using programming methods: case of Russian Arctic regions

Tatiana Kudryavtseva,Mohammed Ali Berawi,Angi Skhvediani

doi:10.9770/jesi.2020.8.1(10)

Abstract

The aim of this research is to show how the process of data analysis can be automated through development of an information system. The information system can be used for the identification of economic clusters and analysis of the regional potential for economic growth. The authors used data on the Russian Arctic regions with extreme social, geographical, and economic conditions collected from 2009 to 2016 as an example. The authors have designed a database using MS Access software. The authors used the methodology of the European cluster observatory and the approach suggested by M. Porter to identify economic clusters. This methodology was complemented by introduction parameters, which mirror the strength and employment dynamic of the clusters. Based on the employment data of 83 Russian regions during the period of 2009–2016 the authors have calculated cluster localization parameters for nine Russian regions, which are partly or fully located in the Arctic zone. The authors suggest that the cluster structure in this area is weak and most of the significant clusters are declining. The only significant cluster, which is growing in all regions, is the «Oil and Gas» cluster. In conclusion, the authors state that the obtained results are vital for policy makers and can be used for elaborating the regional economic development strategy in order to support regional diversification and specialization, which are closely related to positive spillovers.

Highlights

Creating conditions for the economic development of regions is one of the most important tasks for regional governments, who nowadays, in large part, are supported by informational systems (Morrissey, 2016; Rytova & Gutman, 2019)
General information In accordance with the methodology for cluster identification discussed in Section 2.1 and the database design presented in section 2.2, we have received analytical results for all 83 Russian regions for the 2009–2016 period
We present the architecture of the database for automated identification of clusters in the Russian regions

Summary

Introduction

Creating conditions for the economic development of regions is one of the most important tasks for regional governments, who nowadays, in large part, are supported by informational systems (Morrissey, 2016; Rytova & Gutman, 2019) During this process, a regional government should take into account social, economic, and geographical factors, which can affect the development of each concrete territory (Andreyeva et al, 2018; Dvas et al, 2018; Baltgailis, 2019; Petrenko et al, 2019). Analysts should process multidimensional data which reflect the current situation Based on such analyses, they should receive specific results, which can be used for determining potential directions for development of the region (Degtereva et al, 2018; Kichigin, 2017; Kozlov et al, 2017; Thill, 2019). It is essential to develop informational systems to support and enhance the processes of policy making and, positively affect regional economic development (Chun et al, 2010; Höchtl et al, 2016; Velasquez & Hester, 2013; Prodani et al, 2019)

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Conclusion