Zemtsov S. Innovation potential of regions in Nothern Eurasia

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Zemtsov S. Innovation potential of regions in Nothern Eurasia

  1. 1. INNOVATION POTENTIAL OF REGIONS IN NOTHERN EURASIA Stepan Zemtsov, Vyacheslav Baburin Lomonosov Moscow State University Faculty of geography Department of economic and social geography of Russia
  2. 2. Hypothesis, purpose, structure • Object – innovation potential of Russian regions as a set of conditions, which positively affect an ability to generate and diffuse new technologies • Hypothesis – Innovation potential is concentrated in the largest agglomerations, and Russian Northern regions have lower innovation potential than other regions (opposite to Pilyasov’s idea of creativity index) • Purpose – to identify regions with the highest potential, where publiс support of innovation activities would be the most effective Structure 1. Assessment of innovation potential 2. Assessment of innovativeness (ability to be first in absorption of innovation) 3. Assessment of regional innovation clusters potential
  3. 3. Figure 1. Russian Northern Territory. Regions on the scheme from the west to the east: 1 – Murmansk oblast, 2 – Karelia Republic, 3 – Arkhangelsk oblast, 4 – Nenetsky autonomous district, 5 – Komy Republic, 6 – Perm oblast, 7 – Khanty-Mansiyskiy autonomous district, 8 – Yamalo-Nenetskiy autonomous district, 9 – Tyumen, 10 – Tomsk oblast, 11 – Altay Republic, 12 – Krasnoyarsk kray, 13 – Tyva Republic, 14 – Irkutsk oblast, 15 – Buryatia Republic, 16 – Yakutia Republic, 17 – Zabaykalskiy kray, 18 – Amurskaya oblast, 19 – Khabarovsk oblast, 20 – Primorskiy kray, 21 – Magadan oblast, 22 – Sakhalin oblast, 23 – Chukotka autonomous district, 24 – Kamchatka kray.
  4. 4. Structure I. Theoretical background II. Generation of innovation (assessment of innovation potential) III. Diffusion of innovation (assessment of innovativeness) IV. Regional innovation clusters (assessment of potential and territorial priorities) V. Conclusion
  5. 5. I.2. Cartogram of concentration in innovation activity
  6. 6. I.3. Assessment of innovation potential. Criticism of other approaches • Too many indicators (more than 15: HSE, NAIRIT, Bortnik, etc.) – hard to compose, hard to understand the purpose and results, results can be averaged • Some indicators are not correlated (Pilyasov, Zubarevich, HSE, etc.) - results can be averaged • Some indicators are highly correlated (Pilyasov, NAIRIT) – results have a bias • No diffusion stage • No inner structure of regions • Doubtful results of leadership: Chechnya, Mordovya, Magadan, etc. 6
  7. 7. I.4. Methodology 1. 38 indicators of innovation potential and activity, based on expert interviews, existing literature and indices 2. Indicators were divided using conceptual model: Conditions of social economic space (SESP: economic geographical position) Development factors of territorial socio-economic system (SES), according to spheres of social life (economic, political, social, cultural) Instruments of regional innovation system (RIS), according to stages of innovation cycle (education – science – applied science –production – consumption) 3. Factor, correlation and normal distribution analysis to select proxy indicators
  8. 8. Socio-economic space Territorial socioeconomic system Regional innovation system 1.1. Economic-geographical position (capital, agglomeration, coastal area) 1.2. Population density 1.3. Percentage of urban citizens (urbanization ) 1.4. Percentage of population in cities with more than 200 th. people Technological sphere 2.1. Percentage of ICT expenditure in GDP 2.2. Computers per capita 2.3. Computers with Internet per capita 2.4. Percentage of organizations with web-site 2.5. Percentage of organizations with special programs Economic sphere 3. GDP per capita Social sphere 4.1. Percentage of people with high education 4.2. Migration per capita 4.3. Percentage of foreign migrants Cultural sphere 5.1. Percentage of households, where members are of different ethnic group Informational sphere 6.1. Percentage of Internet users Education 7.1. Number of university students per capita Science 8.1. Number of scientists per capita 8.2. Number of registered patents per 1000 employees Transfer (R’n’D) 9.1. Percentage of employees in R & D sector in total employment 9.2. Percentage of R’n’D expenditure in GDP 9.3. Percentage of R’n’D organizations Production 10.1. Percentage of technological innovations expenditure in GDP 10.2. Number of new technologies per 1000 employees 10.3. Percentage of innovation active organizations 10.4. Innovative production percentage in total production Consumption
  9. 9. I.6. Index of innovation potential The first factor (Index of absorption): urbanization (%), computers with Internet access per 100 employees, GDP per capita, percentage of multinational families (%), percentage of Internet-users (%), and mobile phones per capita. The second factor (Index of innovation potential): SESP: • economic-geographical position (points) TSES: • percentage of residents in cities with population more than 200 thousand people (%) • percentage of people with a higher education in the population (%) RIS: • number of university students per 10 thousand people • percentage of employees in R & D sector in total employment (%) • number of registered patents per 1000 employees • percentage of organizations with a website (%)
  10. 10. I.7. Integral innovation potential 1.‘The highest’ 2.’High’ 3.‘High-middle’ 4.‘Low-middle’ 5.‘Low’ 6.‘Periphery’
  11. 11. I.8. Assessment of development potential of regional innovation systems 1.‘The highest’ 2.’High’’ 3.‘Middle’ 4.‘Low’ 5.‘Periphery’
  12. 12. II.1. Spatial diffusion of innovation Mobile phones usage, or subscriptions (active SIM cards per 100 people) per capita
  13. 13. II.2. Clusters by diffusion of innovation. Cluster analysis and comparison with Roger’s model
  14. 14. II.3. Bass model (Bass, 1969) Population of Nmax individuals can be divided on two categories: • innovators (those with a constant propensity to purchase, a) • imitators (those whose propensity to purchase is influenced by the amount of previous adopters, b) The model can be rewritten from original differential form in terms of its discrete analogue (quadratic equation – parabola) N(t+1) – N(t) = a*Nmax + (b*Nmax – a) *N(t) – b*N(t)2 = A1 + A2*N(t) + A3* N(t)2 + e(t) where a = A1/Nmax, b = – A3* Nmax, Nmax = (–A2±√(A22 –4*A1*A3))/2*A3)
  15. 15. II.4. Index of innovativeness (a) 1.‘Innovators’ 2.’Early adopters’ 3.‘Early majority’ 4.‘Late majority’ 5.‘Early laggards’ 6.‘Late Laggards’
  16. 16. II.5. Priorities for regional innovation policy
  17. 17. III.1. Regional innovation clusters in ‘Environmental management’ 130 organizations: two universities – forecasting centres and 12 universities – members of the network, interacting with outside universities (12 organizations), research organizations (42) and entities (62). The market is about 6,2 trillion rubles (140 billion euro) from 2012 to 2020. The index of competence ( I KMP ) I KMP = ( I C + ( I NT × I VTZ )) where I C – subindex of the number of university competencies, I NT – subindex of new technologies, IVTZ – subindex of transfer centres The index of interaction( IVZ ) I VZ = I SV × I TR × I SR , where I SV – subindex of the number of associated organization (or interactions), I TR – Shannon index of the share of connections between different cities, I SR – Shannon index of the share of organizations of different stages of the innovation cycle.
  18. 18. III.2. Regional and interregional innovation clusters in ‘Environmental management’
  19. 19. Conclusion • Innovation potential of Russian regions can be described by core-periphery model: the largest cities are the centres for generation and diffusion of innovation on the northern and southern peripheries. The largest innovation centres in Northern Territories are Tomsk, Perm’ and Khabarovsk • After the collapse of the Soviet Union the innovation space was divided into a number of isolated and poorly connected centres, concentration increased, and "lifeless" periphery was formed. These negative processes have not been overcome, despite the economic achievements of the 2000s. • Less than 10 per cent of patents (2010) was generated in the Northern regions • Most of the Northern regions have the low rate of diffusion, except coastal and borderlands (Murmansk oblast, Khabarovsk, Primorsky and Kamchatka kray). At the initial phase, most regions have similar level of saturation (parameter a), but further absorption stops due to the low population density. • More than 30% of ‘Environmental management’ organizations were located in the northern regions. Interregional clusters (Tyumen, Perm’ and Siberian (Tomsk)) were identified. The regions are actively included in network with universities and science centres, serving as the ‘field’ for experiments and main consumers of new technologies.
  20. 20. Thank you for your attention! Спасибо за внимание!

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