Alla SOROKINA, Gaidar Institute for Economic Policy, Russia

Imogen WADE, School of Slavonic and East European Studies, University College London, United Kingdom (*Corresponding and presenting author)

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

Sheffield, UK.

31.10.2014

CONTENTS

Introduction

Research question

Conceptual framework

Methodology & data

Hypothesis

Results

Conclusion

Introduction Relatively high absolute number of R&D researchers per

million residents (EBRD, 2012)

19th position globally for R&D researchers per mln population (UNESCO, 2014)

BUT…

Russia 23rd – 25th position for international (PCT) patent resident applications per million population between 2000-2012 (WIPO, 2014)

Regional concentration of patenting within Russia ~ handful of ‘patenting’ regions

Introduction ctd.) Relatively less attention paid to regional innovation in post-

Soviet space

Regions of Russia spending increasingly more on innovation support mechanisms

Presence of necessary infrastructure for innovation support in leading regions

Yet we still see large variation between regions in terms of results of innovation e.g. patents

Research Question

What factors determine patenting activity at a regional level in Russia?

Conceptual Framework

Knowledge production function model (Jaaf, 1989) and (Romer, 1990)

Concentration of innovation and interactions knowledge spillovers at regional level

Innovation as function of number of R&D employees (human capital) and R&D expenses

Significant at sector, regional, and local levels (Feldman and Florida, 1994)

Methodology

To verify results from data on Russian patent applications (as a dependent variable), we supplemented with data on regions’ PCT-applications (international patents)

Panel regressions with fixed effects for patent activity for whole time period + time lags

Methodology Full model:

)ln()ln()_ln(

)exp_ln()_ln()_ln()_ln(

654

3210

iii

iiii

RXemplRnD

RnDstockPatCapHumanactPat

tionSpecializaionAgglomeratspilloversKnowledgedemandPatInfraX i __

investForeignEGPdensPopRi __

Data sources Primarily use official state data from Russia:

Variable Abbreviation

Meaning Source Time period

Patent activity Pat_PCT International patent applications PCT per 10 mln population

OECD patent database

1998-2011

Patent activity Pat_rus Russian patent applications per million people

Rospatent 1998-2012

Human capital High_educ Share of employees with higher education, %

Rosstat, ‘Regions of Russia’

1998-2012

Human capital Educ_years Average no. of years of education of employees, %

Rosstat, ‘Regions of Russia’

2000-2012

Data sources ctd.) Variable Abbreviation Meaning Source

Time period

Patent stock

Pat_stock_1 No. of registered patents per capita since 1998

Rospatent

1998-2012

Pat_stock_2 No. of used patents per capita since 1994

Rospatent

1998-2012

R&D expenditures

RnD_exp R&D expenses per capita, roubles per person

RnD_basic Basic R&D expenses per capita, roubles per person

RnD_applied Applied R&D expenses per capita, roubles per person

RnD_dev Development R&D expenses per capita, roubles per person

Main hypothesis

Patent activity in Russia is hard to model econometrically because of strong dependence on local authorities’ actions and local elites’ interests

Main results - correlations

Model 1 (dependent variable = number of Russian patent applications per million people) highly correlated with:

R&D spending on product development per capita (0.72) and no. of R&D employees (0.71)

…and weakly correlated with: patent potential (0.24)

Russian patent applications by region

Main results – innovation potential

Innovation potential of regions

Pi – number of granted patents per 100,000 residents of town i

D – distance from town j, the potential of which we measure by distance from town i, km (by tarmac road).

measures potential knowledge spillovers between regions (the closer to innovation active centres, higher chance of interactions)

jiij DPV /D

Distribution of PCT patents across Russian regions per 10 million population (2005-11)

Patents used in industry (patent stock) per capita

Russian patent applications in the ‘least patenting regions’, 1998-2012

Results – Russian patents

Results of model 1

Panel regression with fixed effects. 984 observations. Dependent variable: Pat_rus

Variables Coefficients (standard error)

Constant 3,19***

(0,19)

2,96***

(0,32)

2,52***

(0,34) 1,04 (0,85)

7,76***

(1,6)

5,86***

(1,61)

High_edu

c

0,27***

(0,09)

0,27***

(0,08)

0,18***

(0,07)

0,18***

(0,07)

0,26***

(0,08)

0,18***

(0,06)

Pat_stock2 0,1***

(0,03)

0,1***

(0,03) 0,02 (0,04) 0,02 (0,04)

0,07*

(0,03) 0,01 (0,04)

RnD_exp 0,05***

(0,02)

0,05***

(0,02) 0,01 (0,02) 0,01 (0,02)

0,04**

(0,02) 0 (0,02)

PhD

0,04 (0,05) 0,05 (0,05) 0,05 (0,05) 0,04 (0,05) 0,04 (0,05)

Pat_poten

tial

0,56***

(0,11)

0,56***

(0,11)

0,5***

(0,11)

Urban

0,35*

(0,18)

Pop_dens

-1,54***

(0,53)

-1,06**

(0,52)

R2 0,86 0,86 0,87 0,87 0,86 0,87

Adjusted

R2

0,85 0,85 0,86 0,86 0,85 0,86

Significance (p-value) at the following levels: *** - 0,005; ** - 0,05; * - 0,1

Results – PCT (international) patents

Results of model 2

Panel regression with fixed effects. 984 observations. Dependent variable: Pat_PCT

variables Coefficients (standard error)

const -27,42*** (3,53) -12,53** (5,81) -12,94** (5,84) -13,06** (5,78)

Educ_years 11,8*** (1,37) 5,26**

(2,42) 5,57** (2,44)

5,37**

(2,44)

Pat_stock1

0,27*** (0,07) 0,17*

(0,1)

0,07

(0,1)

RnD_appl

0,08*

(0,04)

0,08*

(0,04)

Pat_potential

0,55**

(0,23)

R2 0,69 0,7 0,7 0,71

Adjusted R2 0,66 0,67 0,67 0,67

Significance (p-value) at the following levels: *** - 0,005; ** - 0,05; * - 0,1

Conclusions

Knowledge production function model is quite useful to analyse patent activity in regions of Russia (high R-squared values – 87% for first model and 67% for our second model)

However, different factors are significant for Russian and international patent applications.

Conclusions

Russian patent applications most influenced by: The share of employees with higher education, the

accumulated number of used patents, R&D expenditures Population density appears to have a negative influence

(increase in density associated with a 1-1.5 fall in patents) ~ explained by Russia’s regional dynamics or political factors?

When we include patenting potential in regression, it is one of the most significant factors, along with share of employees with HE + population density indicates a concentration of patenting activity in several clusters of regions.

No agglomeration or localisation effects found

Conclusions Average years of education (human capital) +

expenditures on applied research + patenting potential are most significant explanatory variables for the number of international patent applications (although patenting potential only significant at 5% confidence level)

Human capital + R&D spending + a region’s patenting potential = most important factors affecting patenting at regional level of Russia

Political factors also play a role – needs further study

Thank you!

Questions / comments welcome

Email: imogen.wade.10@ucl.ac.uk; Zemtsov@ranepa.ru

Twitter: @ImogenWade; @newdevo