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Knowledge spillover effects: empirical evidence from Russian regions

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Abstract

This study contributes to existing literature on the relationship between productivity and innovation through the knowledge spillover effects. To this end, we consider both a theoretical model and an empirical analysis in Russia. The investigation is based upon a dataset composed of 85 Russian regions for the period 2010–2014. In particular, the effect of R&D Spillovers are analysed through the use of spatial econometric techniques. In so doing, we have allowed the productivity of each region to be affected by the productivity of nearby regions. Results show that R&D significantly affects Russian regions productivity and that productivity spillover across regions matter.

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Notes

  1. See more on these initiatives as well as on cluster policy on the portal of Russian Cluster Obsevatory (launched by Institute for Statistical Studies and Economics of Knowledge (ISSEK))—the leading Russia scientific, methodological, analytical and consulting centre specializing in research in the field of cluster policy. http://cluster.hse.ru/info/.

  2. E.g. easily downloadable files with aggregated statistical data on R&D and Innovation activity in Russia are available for free here (in Russian Language interface): http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/science_and_innovations/science/#.

  3. As mentioned on “HSE data books” portal https://www.hse.ru/en/primarydata/ “These data books present the results of statistical innovation surveys in the Russian Federation. They contain internationally compatible indicators characterizing the level of innovative activity in industry and services. These publication covers statistical data reflecting innovation expenditure and output, co-operational linkages, and factors hampering innovation. Specific chapters are devoted to ecological innovation and innovative activities in the regions of the Russian Federation. International comparisons with a wide range of innovation indicators are provided as well. These data book include information of the Federal Service for State Statistics, Organisation for Economic Co-operation and Development, European Commission, Eurostat, national statistical agencies, and results of own methodological and analytical studies of the HSE Institute for Statistical Studies and Economics of Knowledge”. Latest issue of “Indicators of Innovation in Russian Federation (2015)” in English can be downloaded here https://www.hse.ru/en/primarydata/innov2015. The latest Russian issue of “Indicators of Innovation in Russian Federation (2017)” can be downloaded here: https://issek.hse.ru/news/204006500.html.

  4. From a statistical perspective, Moran’s I is a measure of spatial autocorrelation which is characterized by a correlation among locations in space. Spatial correlation is more relevant than one-dimensional autocorrelation since it is multidimensional. In particular, Moran’s I is defined as: \(I = \frac{z'Wz}{z'z}\), where z is an N—vector of standardized regions, W is an N × N row-standardized spatial weight matrix and N is the number of observations.

  5. We use ‘xsmle’ STATA command (2017) for the estimation procedure.

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Acknowledgements

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of the subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program. The authors are grateful to Editor and two anonymous reviewers whose comments greatly improved the quality of the paper. The results, conclusions, views or opinions expressed in this paper are only attributable to the authors.

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Authors and Affiliations

  1. Department of Business and Economic Studies, University of Naples Parthenope, Naples, Italy

    Luigi Aldieri

  2. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, Myasnitskaya Street 9-11, Moscow, Russian Federation, 101000

    Maxim Nikolaevich Kotsemir

  3. Department of Economic and Statistic Sciences, University of Salerno, Fisciano, Italy

    Concetto Paolo Vinci

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  1. Luigi Aldieri
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  2. Maxim Nikolaevich Kotsemir
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  3. Concetto Paolo Vinci
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Correspondence to Luigi Aldieri.

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Aldieri, L., Kotsemir, M.N. & Vinci, C.P. Knowledge spillover effects: empirical evidence from Russian regions. Qual Quant 52, 2111–2132 (2018). https://doi.org/10.1007/s11135-017-0624-2

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