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 have found that the key factor of the growth of regional innovation is greater spending on research and development (R&D), thus confirming the main assumptions of a knowledge production function model. Our research show that the quality of human capital, as measured by 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 in regions of Russia. Other significant factors were spending on acquisition of equipment, which indicates a high rate of wear and tear of Russian machinery, and spending on basic research, which creates the foundation for developing new technologies. Russia’s innovation system has a ‘centre-periphery’ structure that favours the migration of highly qualified researchers to leading regions; proximity to such regions negatively influences innovation levels of the sending 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.

1. DETERMINANTS OF REGIONAL
INNOVATION OUTPUT IN RUSSIA: ARE
PEOPLE OR CAPITAL MORE
IMPORTANT?
Authors:
S. Zemtsov (RANEPA, IEP), A.Muradov (MIPT)
I. Wade (HSE), V. Barinova (RANEPA, IEP)
Speaker:
Stepan Zemtsov,
PhD, senior researcher
Laboratory for corporate strategies and firm behavior
studies, RANEPA
Innovation Economics Department, Gaidar Institute for
Economic Policy, IEP
HSE (Moscow)
16.06.2016
9th MEIDE Conference
Model-based Evidence on Innovation and Development

2. Aims and methods
2
• Economic crisis in Russia
• Borrowing new technologies is limited because of current climate of sanctions
• Necessary infrastructure was mostly created
• Internal factors, determining innovation, become more relevant and necessary
• The aim was to determine regional factors of innovation output
• Our method was based on the knowledge production function and its
modifications [Griliches, 1979; Romer, 1990; Brenner, Broekel, 2009]
tititi
tititi
KSpillAgglom
CapHumanyRndInnov
,,4,3
,2,1,
)ln()ln(
)_ln()_ln()ln(




i — region of Russia in time t
Innova – indicator of innovation output
Rnd_any — all types of R&D expenditures
Hum_Cap — indicators of human capital
KSpill — measures of potential knowledge spillovers
Agglom — indicators of potential agglomeration effects

3. Dependent variable
3
• Innovation output was often related to patents [Griliches, 1979,
2007]
• There is very low quality of Russian patents – high volatility by
years, small number of patents or extreme growth in some regions
Innov is the number of potentially commercialized patents
Pat_rus is the number of submitted patent applications registered by
agencies of the Federal Service for Intellectual Property (Rospatent)
Pat_PCT — the number of submitted PCT patent applications
0.08 and 0.5 are shares of commercialized patents in previous
periods (8% and 50%)
PCTPatrusPatInnov _5.0_08.0 

4. Dependent variable
4

5. Independent variable
RnD expenditures
5
7,12
8,16
9,01 8,71 9,04
9,81 10,41
11,38
3,90
5,55
7,10
6,18 6,34
7,86 7,55 8,09
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
2007 2008 2009 2010 2011 2012 2013 2014
Apple IBM Intel Microsoft МоскваMoscow
R&D expenditures of the largest IT
multinationals compared to
Moscow city, Russia’s largest
patenting centre (billon USD)

6. Independent variable
Human capital
6
emplHighUrbanActEconurbHC ___ 
Human capital –
economically active
city citizens with
higher education
(creative class)
Econ _ Act —
economically active
population (thousand
people)
Urban — the
proportion of urban
population (%)
High _ empl — the
proportion of
employees with a
higher education (%)

7. Independent variable
Knowledge spillovers
7
Know_spill – number of potential interregional interactions of researches
RnD_empli — number of R&D staff of region I
RnD_emplj — number of employees in regions j, located at a distance of Rij
α – the coefficient of resistance from the environment



j ij
ji
i
R
emplRnDemplRnD
spillKnow 
___
_
Neigh_innov is the sum of patents in neighboring regions
RnD_expenditure Neigh_innov Human_capital

8. Results
8
Fixed effects model. Dependent variable: number of potentially commercializable patents
1 2 3 4 5 6
Constant
0.23
(0.26)
0.17
(0.24)
0.31
(0.24)
0.60**
(0.24)
0.05
(0.24)
0.34
(0.24)
Number of economically active
urban residents with a higher
education (HC_ urb)
0.56***
(0.05)
0.53***
(0.05)
0.49***
(0.05)
0.39***
(0.06)
0.34***
(0.06)
0.29***
(0.06)
Real domestic spending on
purchase of equipment
0.06***
(0.01)
-
0.05***
(0.01)
0.05***
(0.01)
0.04***
(0.01)
0.05***
(0.01)
Real domestic spending on basic
research
-
0.05***
(0.01)
0.05***
(0.01)
0.04***
(0.01)
0.03***
(0.01)
0.04***
(0.01)
Real domestic spending on
applied research
-
0.03***
(0.01)
0.02**
(0.01)
0.02**
(0.01)
0.02*
(0.01)
0.01
(0.01)
Potential for interactions between
researchers
- - -
-0.36***
(0.08)
-
-0.27***
(0.07)
Sum of patents in neighbouring
regions
- - - -
0.32***
(0.05)
0.27***
(0.05)
LSDV R2 0.95 0.95 0.95 0.95 0.95 0.95
P-value: *** - 0,01; ** - 0,05; * - 0,1

9. Results
9
Fixed effects model. Dependent variable: number of potentially commercializable patents per
economically active urban resident
Regression equalization 1 2 3
Constant
1.86**
(0.16)
1.77***
(0.16)
1.79**
(0.16)
Share of employed with higher education
0.51***
(0.06)
0.48***
(0.06)
0.45***
(0.06)
Real domestic spending on acquisition of
equipment per economically active urban citizen
0.06***
(0.01)
-
0.05***
(0.01)
Real domestic spending on basic research per
economically active urban resident
-
0.05***
(0.01)
0.05***
(0.01)
Real domestic spending on applied research per
economically active urban resident
-
0.03***
(0.01)
0.03**
(0.01)
LSDV R2 0.84 0.85 0.85
Akaike's Information Criterion (AIC) 459.21 451.06 433.10
P-value: *** - 0,01; ** - 0,05; * - 0,1

10. Results
10

11. Results
11
    
























n
EAU
Rnd
emplHighA
EAU
Innov
ln
1
exp_
ln
1
_ln
1
ln
High_ empl — the proportion of employees with a higher education
Rnd_infra — spending on R&D
n — the growth rate of the economically active urban population (EAU) in the region
α and β — the elasticity of innovation output by human and physical capital respectively

12. Conclusions
12
• Economically active urban population with higher education
— is a substantial factor of innovation output that also takes into
account the significance of agglomeration effects
• 1% increase in the quantity and quality of human capital leads
to an average rise of innovation output of 0.5%
• 1% increase in all kinds of RnD expenditures leads to an
average rise of innovation output of only 0.15%
• From the start of the 2000s decade, we see that human capital
has played an increasingly important role in innovation in
Russia
• There is a presence of a strong centre-periphery structure of
the Russian national innovation system
• 1% increase in average patenting level in neighbouring regions
leads to an average rise of innovation output of 0.3%
• The main contribution of the research is the finding that human
capital is key for innovation at a regional level

13. Conclusions
Regional policy advice
13
The main recommendations are:
• to develop higher education in major conurbations
• to support innovative projects and to place
innovation infrastructure in the largest metropolitan
areas of the country
• to create jobs for employees with higher qualification
• to increase investment in technological equipment in
RnD organizations
• to create centers of technology transfer
• do not try to maintain the high-tech industries in
remote areas with weak innovation potential, since it
is inefficient

14. Thank you for attention
Stepan Zemtsov,
PhD/senior researcher
E-mail: zemtsov@ranepa.ru
URL: http://www.ranepa.ru/prepodavateli/sotrudnik/?742
Laboratory for corporate strategies and firm behavior studies
Russian Presidential Academy of National Economy and Public Administration,
RANEPA
Innovation Economics Department
Gaidar Institute for Economic Policy, IEP
For citation:
Zemtsov S., Muradov A., Wade I., Barinova V. (2016) Determinants of regional
innovation output in Russia: are people or capital more important? Foresight
and STI Governance, vol. 10, no 2, pp. 29–42