The impact of R&D and knowledge spillovers on the economic growth of Russian regions

Abstract

Russia presents an interesting case of a country which has strived to implement innovation policies since the transition period but so far has achieved mixed results. This study aims to analyze the impact of knowledge production and knowledge spillovers on regional growth in Russia within a framework of endogenous growth models. Applying GMM and spatial error panel modeling techniques to Rosstat data for 80 Russian regions from 2005 to 2013, the authors test the hypothesis about the relevance of R&D and expenditure on technological innovations (H1) and the hypothesis about the relevance of knowledge spillovers (H2) on the growth rates of GRP per capita. Hypothesis 1 was confirmed while Hypothesis 2 was rejected, indicating the absorptive capacities of innovatively lagging regions were not high enough for effective adaptation of new technologies from technologically innovative regions. Inclusion of indicators related to additional channels of knowledge diffusion in regressions resulted in the relevance of FDI and imports of goods and services for regional growth. Results of the study could form the basis for developing innovation policies for the Russian regions.

Re-balancing of intensive and extensive factors in the center-peripheral system under the influence of technological development

“…Crescenzi and Jaax (2017) examine the territorial dynamics of knowledge creation in Russia, finding local knowledge flows and injections of foreign knowledge. Knowledge spillover refers to the occurrence of the external effects of research activities (for example, in universities), which are used by other actors of regional innovation systems (Aldieri, Kotsemir, & Vinci, 2018;Kaneva & Untura, 2019).…”

Digital Effects and Inequality of People’s Quality of Life in the Globalization Era

“…[20] studied dependence of gross domestic product on different elements of investment potential. M. Kaneva and G. Untura [21] used dynamical panel regression and panel data model with spatial errors to study influence of Research and Development and knowledge dissemination on economic growth in Russian regions. In the given study the authors applied pulled regression and panel data model with fixed effects to evaluation of real gross regional product (GRP) per capita influence on the value of the Russian digital index in 2015-2018.…”

Regional Convergence in Russia: Estimating A Neoclassical Growth Model

“…Guriev and Vakulenko (2012) report 4.6% for 1995, Akhmedjonov et al, (2013 report 10% for 2000-2008, while Durand-Lasserve and Blöchliger (2018) report about 2.5% for 2005-2015. 6 Most of these studies abandoned the "initial conditions vs. reforms" perspective and considered a wide set of variables in their empirical growth equations including different measures of human capital (Akhmedjonov et al, 2013;Vakulenko, 2016), migration (Vakulenko, 2016), R&D and innovation (Kaneva and Untura, 2019), fiscal federal transfers and public spending (e.g., Di Bella et al, 2017;5 There are also a few studies that examine the issue of convergence in Russia at the city level. Ivanova (2018) establishes conditional sigma-and beta-convergence of real wages in spatially close cities in the period between 1996 and 2013.…”