Abstract
Spending on innovation increased annually in the 2000s in Russia’s regions, but innovation productivity varies greatly between regions. In the current climate of sanctions between Russia and Western countries and limitations on international technology transfer, there is a growing need to analyse the factors influencing regional innovation. Previous empirical studies using a knowledge production function approach have found that the main factor of the growth of regional innovation is increasing spending on research and development (R&D). Our econometric analyses show that the quality of human capital, a product of the number of economically active urban citizens with a higher education (the so-called creative class) has the greatest influence on the number of potentially commercializable patents. Other significant factors were buying equipment, which indicates a high rate of wear and tear of Russian machinery, and spending on basic research. The ‘centre-periphery’ structure of Russia’s innovation system favours the migration of highly qualified researchers to leading regions, which weakens the potential of the ‘donor regions’. However, at the same time, we see significantly fewer limitations on knowledge spillovers in the form of patents and - in this case - proximity to the ‘centres’ is a positive factor.
Highlights
The economic crisis in Russia and its aftermath led to a significant fall in the rate of growth
We find that the factor most positively and significantly associated with innovative production is human capital, which we operationalize as the number of economically active urban population with a higher education (HC_urb)
We argue that our indicator for human capital — the economically active urban population with higher education — is a substantial factor of innovation as it takes into account the significance of agglomeration
Summary
The economic crisis in Russia and its aftermath led to a significant fall in the rate of growth. Much literature in innovation studies emphasize the cumulative effect of R&D spending in previous periods of time Another determinant of the process of raising the technological level in a given economic sector is the positive externalities from knowledge in other sectors i.e. knowledge transfer. Many empirical studies have used a KPF approach The majority of these studies find that R&D spending, knowledge spillovers, the level of economic diversification, and human capital have significant effects on innovation output (Table 1). Brenner and Broekel suggest an alternative model that departs from the main assumptions of the KPF approach [Brenner, Broekel, 2009] Their critique of the knowledge production function is that innovation processes are clearly probabilistic in nature, in contrast to deterministic production processes. Key influence of R&D spending, positive influence of co-location of state and private research centres (knowledge spillovers)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
