Determinants of fast-growing SMEs' concentration in the Russian regions

SMEs play an important role in the development of regional innovative systems because of their potential to accept new technologies and show fast growing rates. There is an interdependence between emergence of fast growing SMEs ('gazelles') and innovative development of regions. High level of regional innovative development creates a fertile environment for increasing the number of fast growing companies, while we assume that large number of 'gazelles' creates a favourable environment for the dissemination of innovations in regions via spillover effect (NESTA Business growth and innovation, 2009). Fast-growing companies may contribute more than 50% to GDP growth (Europe INNOVA Gazelles Innovation Panel, 2008). There are several works, that explain growth of firms as a stochastic phenomenon (Gibrat, 1929), or as a combination of endogenous (Penrose, 1955) and exogenous factors (Delmar, Davidsson, Gartner, 2003). In our work we assume that regional innovation performance (as a share of RnD personnel in employment, share RnD expenditures in gross regional product, etc.) may be a significant factor because of knowledge spillover effects (Audretsch, Feldman, 2004), affecting more competitive firms. There were no works on Russian regional data that could prove it. The article analyses a variety of endogenous (intra-firm) and exogenous (regional) factors, which determine the share of fast-growing firms in Russian regions. The analysed firms were fast-growing manufacturing SMEs during post-crisis period (2009-2012), the main focus was on the determinants of the companies' share in total number of manufacturing firms in a region. The dataset was collected from SPARK (Professional market and company analysis system), and consists of information about income, owners, location, industry and several financial indicators. Regional factors, according to Russian Federal State Statistical service, include research and development indicators (such as RnD expenditures, RnD employees, etc.), urbanization rate, human capital, investment climate, etc. There are 419 manufacturing fast-growing companies (?gazelles?) from 9220 companies in database, which is approximately 5%. Econometric analyses demonstrates a strong correlation between the share of high-growth companies in regions and indicators of regional innovation performance: number of researchers per 10 000 people, the number of PCT applications per economically active population, the share of employees with higher education in the total number of population in economically active age, and the number of applications for inventions submitted to the Russian Patent Bureau by national applicants per the economically active population. Determined factors could be used for elaborating recommendations for implementation of industrial policy in Russia.