The productivity puzzle: many researchers yet low patent activity in Russia’s regions
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Transcript of The productivity puzzle: many researchers yet low patent activity in Russia’s regions
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: [email protected]; [email protected]
Twitter: @ImogenWade; @newdevo