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Assessment of innovation potential for Russian regions

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  • Stepan Zemtsov

Abstract

Innovation development is declared as one of the key objectives of social and economic policy in Russia. The purpose of the work was to identify regions with the highest innovation capacity and developed regional innovation system, where support of innovation activities would be the most effective. The hypothesis was that innovation capacity can be expressed as a probability function, which dependent on density and concentration of innovators and intensity of their interaction. Taking in account the hypothesis, gravity model of patent activity per capita was used to estimate creative potential of Russian regions. Patent activity in Russia declined significantly from 60000 granted patents in 1989 to 22500 in 2012. The largest cities and closed science cities are still the sources of new technologies, but activity in the Moscow core decreased from 230 to 30 patents / 100 thousand residents in 1999. Patents are not innovations in the full sense of the term, because they may not be implemented. The official Russian statistics is not perfect because of optionality of statistical forms filling. Most of approaches for capacity assessment in Russia based on index compilation and have several disadvantages: correlation between indicators, non-normal distribution of indicators, etc. Considering the disadvantages the author collected a database of 38 indicators of innovation sphere, and conducted normal distribution, correlation and factor analyses. The indicators of the first factor are: estimation of economic-geographical position; percentage of residents in cities with population more than 200 thousand people (%); percentage of people with a higher education (%), number of university students per 10 thousand people; percentage of employees in R & D sector in total employment (%); number of registered patents per 1000 employees; percentage of organizations with a website (%). Six groups of regions were identified: ?innovation core' (index = 1 ? 0.7); ?highly developed' (0.7 ? 0.6); ?regions with a strong science sector' (0.6 ? 0.5); ?regions of basic sectors of the economy' (0.5 ? 0.4); ?regions with limited potential' (0.4 ? 0.3); and ?peripheral regions' (less than 0.3). To prove the correctness of the chosen indicators probit-regression between the index and international PCT-applications was made. The regression results are compared with the results for other existing indexes. The probability of new technology generation in Moscow among all regions close to 1, and it is close to zero in Chukotka. The work has confirmed the hypothesis of high concentration of potential in major agglomerations and research centres.

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  • Stepan Zemtsov, 2014. "Assessment of innovation potential for Russian regions," ERSA conference papers ersa14p138, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa14p138
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    Cited by:

    1. B. S. Zhikharevich & O. V. Rusetskaya, 2018. "Fluctuations in the Relative Dynamics of Development of Russia’s Regional Centers in 2003–2013," Regional Research of Russia, Springer, vol. 8(1), pages 34-45, January.
    2. Vera Barinova & Denis Burkov & Stepan Zemtsov & Vladimir Eremkin, 2016. "Uncovering Regional Clustering of high technology SMEs: Russian Case," Working Papers 147, Gaidar Institute for Economic Policy, revised 2016.

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    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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