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Cluster analysis of regional innovation activity in Russia in 2010-2015

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  • Pushkarev, A. A.

Abstract

In this article, the indicators of innovation activity in Russian regions are discussed and the regions are divided into five groups, according to their performance in these indicators. Our cluster analysis is based on the recent research and includes several groups of indicators such as innovation activity of enterprises, training of highly qualified personnel, research and development, state support for innovation, and application of innovative technologies. We used the data provided by Rosstat (Federal State Statistics Service) for 83 Russian regions in the period between 2010 and 2015. In terms of their innovation activity, Russian regions can be divided into five groups, two of which are Moscow and St.Petersburg, the two biggest Russian cities that play a special role in Russian economy. Overall, the level of innovation activity in Russia can be assessed as lower middle, although in the given period some regions managed to improve their performance in this sphere. The average level of innovation activity varies considerably across regions, which means that the state innovation policy should be more diversified. Moscow, St.Petersburg, Nizhny Novgorod and Sverdlovsk regions have demonstrated consistent high-level performance and can thus be regarded as prospective centres of innovation. These centres can positively influence the neighbouring areas through the knowledge and technology spillover effect. Although no definitive conclusion can be drawn about the connection between the regions' geographical location and their innovation activity, there is evidence that the most active Russian regions tend to concentrate in the European part of the country. Our findings can be used as guidelines for devising and modifying federal and regional innovation policies.

Suggested Citation

  • Pushkarev, A. A., 2018. "Cluster analysis of regional innovation activity in Russia in 2010-2015," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 4(1), pages 10-17.
    • Handle: RePEc:aiy:journl:v:4:y:2018:i:1:p:10-17
    DOI: 10.15826/recon.2018.4.1.002
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    References listed on IDEAS

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