Does regional innovation performance affect fast-growing

firms in Russia?

Stepan ZEMTSOV, The Russian Presidential Academy of National Economy and Public Administration, Russia

Alla SOROKINA, Gaidar Institute for Economic Policy, Russia Vera BARINOVA, Gaidar Institute for Economic Policy, Russia

31/10/2014 Sheffield, UK

Introduction

• Fast-growing companies (‘gazelles’) may contribute more than 50% to GDP growth (Europe INNOVA Gazelles Innovation Panel, 2008)

• Gazelle is company with 20% real (inflation-adjusted) sales growth (geometric average) in the last five years (2008-2012)

• Growth of firms can be explained as a stochastic phenomenon (Gibrat, 1929), or as a combination of endogenous (Penrose, 1955) and exogenous factors (Delmar, Davidsson, Gartner, 2003)

• Growth of ‘gazelles’ has been explained by their endogenous innovation performance (implementation of product, process, marketing and organizational innovations)

• The main object of this article is endogenous (intra-firm) and exogenous (regional) factors, which determine the share and the growth rates of fast-growing firms in Russian regions

Two stages of research 1. Identification of endogenous and regional factors,

which determine fast-growing high-tech companies’ sales growth, during post-crisis period (2009-2012). Database was collected via national survey of high-tech firms (“Rating-TechUp” http://www.ratingtechup.ru/en/rate/) in 2013 year. The database contains information about RnD in firms that was very useful for our purposes

2. Identification of the determinants of fast-growing manufacturing SMEs concentration on regional level, during post-crisis period (2009-2012). The database was collected from SPARK (Professional market and company analysis system) and the Russian official statistics (“Russian regions”)

Sales growth of high-tech firms. Hypothesis

• It is not common to rely on inner innovation activity (training costs, RnD expenditures, etc.) for Russian companies. But it should be the case for high-tech companies!

• Some factors of sales growth of high-tech firms can be identified despite the Gibrat’s law

• Sales growth of high-tech companies is higher in Moscow and Saint Petersburg, which has the highest innovation capacity

Model 1. Sales growth Evans D. The relationship between firm growth, size, and age: Estimates for 100 manufacturing

industries. // The journal of industrial economics. 1987, p. 567-581. Gibrat R. Les Inegalite Economiques, Paris, Librairie du Recueil Sirey. 1931. Delmar F., Davidsson P., Gartner W.B. Arriving at the High-Growth

Firm. //Journal of Business Venturing. 2003, №18(2), p. 189–216.

Sales_growth - geometric mean of sales growth rate from 2010 to 2012

Age - the age of the company, years

Sales_2009 – sales in 2009, mln . rubles

New_product_p_sale - share of new products in sales, %

Education_cost_p_empl - the cost of training per employee per year, rub

Additional_factors - additional factors (equipment age, RnD expenditures, etc.)

Regional_factors - regional characteristics (Moscow – regions; share of processing industry in GRP)

Industry_factors – industrial specialization (ICT – others; high tech – middle tech)

factorsIndustryfactorsgionalfactorsAdditional

emplptEducationsalepproductNew

SalesAgegrowthSalesLn

__Re_

)__cos_ln()___ln(

)2009_ln()ln()_(

43

210

Model results OLS. 84 firms. Dependent variable: Sales_growth

Model 2.1 Model 2.2

const 8,09*** 9,7***

Sales_2009 -0,23*** -0,21***

RnD_p_sale_2010 0,08* 0,08*

Education_cost_p_empl 0,05*** 0,06***

Equip_age -0,4*** -0,37**

Process_ind_p_GRP -0,64**

Capital -0,36*

R2 0,44 0,47

Adjusted R2 0,41 0,43 Significance (p-value): *** - 0.005; ** - 0.05; * - 0.1

Results

• Firms’ size (Sales_2009) is a factor, which decreases the rate of growth (significant in both model specification)

• Not age itself negativity affects firms’ growth but its connection with the age of equipment (Equip_age)

• Training costs (Education_cost_p_empl) were significant positive factor, and in the year of implementation.

• It is important to invest in R & D (Rnd_p_sale_2010), while these costs are paying off after two years

• There is a negative correlation between firm growth and localization effects, measured as the share of processing industries in the region (variable Process_ind_p_GRP) It may indicate saturation of the regional markets

• Firm registration in Moscow or in Saint Peetrsburg (Capital) have a negative impact on sales growth, which may be also because of the high saturation of the capital markets

Concentration of manufacturing gazelles. Hypothesis

• 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 for gazelles concentration, because they are trying to use knowledge spillover effects

• Market potential and geographical position are also an important factors

Model 2. Gazelles’ concentration in regions

Gazelle – a proportion of gazelles of processing industry in a region among all processing industry firms Xi – a number of variables, which characterize: • human Capital (HC) • dynamic and structural characteristics of the economy

(Economics) • the institutional environment of the region (Institutions)

economic-geographical features (Geography) • innovative activity (Innovation) • agglomeration effects (Agglomeration) • localization effects (Localization)

)ln()ln( 00 ii XGazelle

)

(

iii

iiiii

onLocalizatiionAgglomeratInnovation

GeographynsInstitutioEconomicsHCfX

Market (regional economic) potential (GRP_potential) and economic-

geographical position (EGP)

• GRPj – GRP value (calculated by the index of the physical volume, taking into account inter-regional price index) in the region j, for which the potential is determined

• GRPi – GRP in the i-th region of Russia • Distanceji – distance from the regional center of the region j to the

regional center of region i by automobile road in kilometers

EGP: arithmetic average of binary ranks (0 or 1) of seven variables: capital status, agglomeration (more than 1 million inhabitants), seaside location (but non-freezing sea), the neighborhood with the Moscow region, proximity to agglomeration (over 1 million inhabitants), the cross-border provision and comfort of living, market and natural resource potential

jiijj ceDisGRPGRPpotentialGRP tan/_

Model results

OLS. 74 regions. Dependent variable: Gazelle

const -23,64*** 2,4

GRP_potential 0,46***

EGP 0,58** 0,89***

Process_ind 0,53*** 0,4***

Educ_years 9,04***

PCT_appl 0,06*

Urbanization -0,45 -0,3

RnD_empl 0,45***

Import_per_GRP 0,05

R2 0,74 0,64

Adjusted R2 0,72 0,61 Significance (p-value): *** - 0.005; ** - 0.05; * - 0.1

Results of the model 2.1

• Fast-growing manufacturing firms operating in regions with significant market potential or near it (GRP_potential). Major markets serve as a reliable source of growth in the post-crisis period. In regions that have market potential more on 1%, the share of gazelles is 0.46% higher

• EGP (EGP) of a region is an important factor. It is useful to locate near the largest metropolitan area, in seaside region or in a border region, which can be explained by foreign trade activity of gazelles

• Human potential of the region (average years of schooling of employees - Educ_years), despite the decline in the quality of education in Russia, still has an impact on the growth of manufacturing firms. A strategy of rapid growth at a significant period of time is almost impossible without technical training and relevant knowledge

Results of the model 2.2

• Fast-growing companies may take an advantage of the high innovation potential of a region. The concentration of gazelles was above 0.45% higher (excluding GRP_potential) in a region, where the proportion of people employed in R & D (RnD_empl) was more on 1%, This dependence is much lower for PCT-applications per capita (PCT_appl)- the concentration was 0.04% higher

• Localization effects (Process_ind - the share of employment in manufacturing) prevail over agglomeration effects (Urbanization - the proportion of urban population) in Russia. Manufacturing gazelles prefer to localize in the regions with a high share of manufacturing in GRP than in the highly urbanized regions

Conclusion and further research • No Gibrat’s law for fast-growing high-tech firms in Russia • The larger the company is, the lower its sales growth will be • The higher R & D per employee expenditure and training

cost are, the higher sales growth will be. Modernization of equipment is also an important factor

• It was found that the key factors of concentration of fast-growing manufacturing companies in the region are: human capital, the market potential of the region and its neighbors, favorable geographical position, as well as high research activity (share of employment in R & D and patenting activity)

• Russian high-tech companies are using inner and regional innovation factors to increase its sales growth

• It will be an important to use multi-level modeling to compare an importance of firm and regional innovation performance in sales growth

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