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Regional Economics: Theory and Practice
 

Cluster identification, taking into account the comparative advantages of the region: the Southern Federal District case study

Vol. 13, Iss. 35, SEPTEMBER 2015

PDF  Article PDF Version

Received: 30 March 2015

Accepted: 14 May 2015

Available online: 9 October 2015

Subject Heading: REGION IN NATIONAL ECONOMY

JEL Classification: 

Pages: 2-16

Borovskaya M.A. Southern Federal University, Rostov-on-Don, Russian Federation
maborovskaya@sfedu.ru

Shevchenko I.K. Southern Federal University, Rostov-on-Don, Russian Federation
ikshevchenko@sfedu.ru

Razvadovskaya Yu.V. Southern Federal University, Rostov-on-Don, Russian Federation
yvrazvadovskaya@sfedu.ru

Fedotova A.Yu. Southern Federal University, Rostov-on-Don, Russian Federation
ayfedotova@sfedu.ru

Importance The article considers the issues of the formation of a network of innovative-technological clusters in the Russian economy which will contribute to the development of the economic potential of territorial branch complexes.
     Objectives The article deals with the problem of identification of cluster formations in the region's economy.
     Methods
Based on theoretical and empirical analyses, we substantiate the necessity to identify the specialization of the region in implementing the cluster approach to the development of territorial entities.
     Results The analysis revealed the industries on the basis of which the clusters can be created in the region. The paper confirms the hypothesis that the main factor of the cluster's competitiveness is its proximity with the specialization in the region. We found that the Southern Federal District has created favorable conditions for the development of the multipurpose cluster in industries of specialization of the region.
     Conclusions and Relevance We conclude that the comparative advantages of the territory in a particular industry allow building an effective system of specialization capable of ensuring the region's competitiveness based on new technologies and knowledge. The results obtained can be used for analysis of clustered entities in regions.

Keywords: localization, specialization, focus, cluster, management

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