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An assessment of regional innovation system efficiency in Russia: the application of the DEA approach

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Abstract

The main aim of this study is to compare Russian regions according to their ability to create new technologies efficiently and to identify factors that determine these differences over a long period of time. We apply data envelopment analysis (DEA) to assess the relationship between the results of patenting and resources of a regional innovation system (RIS). Unlike previous studies, we apply the DEA method over a long period, comparing regions to one another and over time. In general, RIS efficiency in Russia increased during the period, especially in the least developed territories. There was significant regional differentiation. The most efficient RIS were formed in the largest agglomerations with leading universities and research centers: the cities Moscow and Saint Petersburg and the Novosibirsk, Voronezh, and Tomsk regions. Econometric calculations show that RIS efficiency was higher in technologically more developed regions with the oldest universities and larger patent stock. Time is a crucial factor for knowledge accumulation and creating links between innovative agents within RIS. Entrepreneurial activity was also a significant factor because it helps to convert ideas and research into inventions and new technologies and it enhances the interaction between innovative agents. It is advantageous to be located near major innovation centres because of more intensive interregional knowledge spillovers. Public support of more efficient regions can lead to a more productive regional innovation policy.

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Notes

  1. The use of normalized indicators may lead to a misinterpretation of the real relationships within the RIS and some relative variables cannot be higher than 100%, however, it is possible in the DEA model if it is a “desired” output. Therefore, it is more appropriate to use the absolute (or per capita) numbers.

  2. The closer are agents, the higher is the probability of their interaction. In this case, geographical proximity is an indicator of technological, institutional, and social proximity (Boschma 2005).

  3. NTD intellectual property newsletter. URL: https://docplayer.net/27207042-Ntd-intellectual-property.html.

  4. Russian regions. Socio-economic indictors. URL: https://www.fedstat.ru/indicator/39279.

  5. OECD. Database. URL: http://stats.oecd.org/Index.aspx?DatasetCode=PATS_REGION.

  6. The indicator (HC) takes into account the most likely generators of innovation—people who have sufficient knowledge, qualification, and infrastructure to carry out research on a permanent basis. We do not use the number of researchers (Crescenzi and Jaax 2017) because many urban residents with higher education (not only researchers) tend to produce new technologies, so it is more valid for our purposes (Zemtsov et al. 2016).

  7. We collected data from the official websites of the Russian universities.

  8. According to data of the Russian statistical service. Russian regions. Socio-economic indictors. URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156.

  9. We measured the distance by the length of railway tracks between the regional capital cities. Where there was no railway line, we used the length of highways, and occasionally we used the length of rivers.

  10. We calculated the indicator according to data of the Russian statistical service. Russian regions. Socioeconomic indictors. URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156.

  11. We calculated the indicator using data from RUSLANA. URL: https://ruslana.bvdep.com/version-2017106/home.serv?product=Ruslana.

  12. We calculated the indicator according to data of the Russian statistical service. Russian regions. Socioeconomic indictors. URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156.

  13. According to data of the Russian statistical service. Russian regions. Socioeconomic indicators of cities. URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138631758656.

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Acknowledgements

The research leading to these results was supported by the Ministry of Science and Higher Education of the Russian Federation (Project ID: RFMEFI60217X0021). The authors are grateful to Vera Barinova from the Gaidar Institute for Economic Policy for valuable comments.

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Authors and Affiliations

  1. Institute of Applied Economic Research (IAER RANEPA), Russian Presidential Academy of National Economy and Public Administration, Prospekt Vernadskogo, 82/1, Moscow, Russian Federation, 119571

    Stepan Zemtsov

  2. Institute for Statistical Studies and Economics of Knowledge (ISSEK NRU HSE), National Research University Higher School of Economics, Myasnitskaya street, 20, Moscow, Russian Federation, 101000

    Maxim Kotsemir

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Appendix

Appendix

See Tables 3, 4, and 5.

Table 3 Description of some studies on innovation system efficiency with DEA method
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Table 4 Average RIS efficiency scores in the studied Russian region in 1998–2012. (Color figure online)
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Table 5 Data source and time horizon for explanatory variables used in RIS efficiency model
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Zemtsov, S., Kotsemir, M. An assessment of regional innovation system efficiency in Russia: the application of the DEA approach. Scientometrics 120, 375–404 (2019). https://doi.org/10.1007/s11192-019-03130-y

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