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

1. Does regional innovation
performance affect fast-growing
firms in Russia?
Stepan ZEMTSOV, The Russian Presidential Academy of National
Economyand Public Administration,Russia
Alla SOROKINA, Gaidar Institute for Economic Policy, Russia
Vera BARINOVA, Gaidar Institute for Economic Policy, Russia
31/10/2014
Sheffield, UK

2. 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

3. 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”)

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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



11. 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/_ 

12. 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

13. 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

14. 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

20. 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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