Zemtsov et al. Determinants of Russian regional innovation output

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

Dependent variable

4

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

10

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: [email protected]

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