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transcript
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
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
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
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 Москва Moscow
R&D expenditures of the largest IT
multinationals compared to
Moscow city, Russia’s largest
patenting centre (billon USD)
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 (%)
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
iR
emplRnDemplRnDspillKnow
____
Neigh_innov is the sum of patents in neighboring regions
RnD_expenditure Neigh_innov Human_capital
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
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
Results
11
n
EAU
RndemplHighA
EAU
Innovln
1
exp_ln
1_ln
1ln
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
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
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
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