Cluster analysis of regional innovation activity in Russia in 2010-2015
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.
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World Bank. 2010. Innovation Policy: A Guide for Developing Countries. World Bank. https://openknowledge.worldbank.org/handle/10986/2460
George Deltas, Sotiris Karkalakos. (2013) Similarity of R&D Activities, Physical Proximity, and R&D Spillovers. Regional Science and Urban Economics, Volume 43, Issue 1, January 2013, pp. 124-131.
Almeida, B. Kogut (1999) Localization of Knowledge Spillovers and the Mobility of Engineers in Regional Networks. Management Science, 45 (1999), pp. 905-917
Bottazi, G. Peri (2003) Innovation and Spillovers in Regions: Evidence from European Patent Data. European Economic Review, 47 (2003), pp. 687-710
Cassandra C. Wang, Aiqi Wu (2016) Geographical FDI Knowledge Spillover and Innovation of Indigenous Firms in China. International Business Review 25 (2016), pp. 895–906
MiLin, Yum K. Kwan (2016) FDI Technology Spillovers, Geography, and Spatial Diffusion. International Review of Economics & Finance Volume 43, May 2016, pp. 257-274
Luciana Lazzeretti, Francesco Capone (2016) How Proximity Matters in Innovation Networks Dynamics along the Cluster Evolution. A Study of the High Technology Applied to Cultural Goods. Journal of Business Research 69 (2016), pp. 5855–5865
Doris Läpple et al. (2016) What Drives Innovation in the Agricultural Sector? A Spatial Analysis of Knowledge Spillovers. Land Use Policy 56 (2016), pp. 238–250
Dandan Li, Yehua Dennis Wei, Tao Wang (2015) Spatial and Temporal Evolution of Urban Innovation Network in China. Habitat International 49 (2015), pp. 484 – 496
Hägerstrand, Innovation Diffusion as a Spatial Process (Chicago: University of Chicago Press, 1967)
Roger Bivand (2015) Spatial Diffusion and Spatial Statistics: Revisiting Hägerstrand’s Study of Innovation Diffusion. Procedia Environmental Sciences 27 ( 2015 ), 106 – 111
Ermasova N.B. Factors Affecting Innovation Activities of Organizations// Ekonomika. Upravlenie. Pravo. — 2014. — № — P. 495-503.
Mariev O.S., Savin I.V. Factors of Innovative Activity of Russian Regions: Modelling and Empirical Analysis// Ekonomika regiona. — 2010. — №
Eriksson, T., Qin, Z., Wang, W. (2014). Firm-Level Innovation Activity, Employee Turnover and HRM Practices — Evidence from Chinese firms. China Economic Review, 30, 583-597.
García-Quevedo, J., Pellegrino, G., Vivarelli, M. (2014). R&D Drivers and Age: Are Young Firms Different? Research Policy, 43, 1544-1556.
Guariglia, A., Liu, P. (2014) To What Extent Do Financing Constraints Affect Chinese Firms’ Innovation Activities? International Review of Financial Analysis, 36, 223-240.
Zhang, H. (2015). How does Agglomeration Promote the Product Innovation of Chinese Firms? China Economic Review, 35, 105-120.
DOI: https://doi.org/10.15826/recon.2018.4.1.002
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