1. 1 Anna Bykova National Research University “Higher School of Economics” 5 th April, 2012 Moscow IDENTIFICATION OF REGIONAL INNOVATION CLUSTERS: EMPIRICAL EVIDENCE IN RUSSIA

2. 2 The brief story about the research…

3. Outline of the Presentation Motivation for the Research & Research Questions Theoretical Background & Research Design Data & Methodology Results & Discussions 3

4. Motivation for the Research: Theoretical Problems and Practical Implications 4 Glokalization: General consensus that the most competitive companies generate innovation in cooperation within localized regions (Porter, 2002, 2011; Elllison et al., 2010) Many gaps in theory and empirical results of innovation cluster’s concept (Chesbrough, 2006; Immarino, McCann, 2006; Audretsch, Feldman, 1996; Maggioni et al., 2006 and others) Regional Innovation Scoreboard 2006: strong positive and significance relation between success clusters’ presence and regional innovation development The particular significance of research in this area in Russia stems from the fact: the high degree of uneven socio-economic development of regions a life in “isolation” from each other do not allow to raise their competitiveness These perspectives become even worse after the crisis Companies’ location should be considered not only as a factor of their effectiveness, but also as factor of their survival

5. Research Questions 5 How to identify regional clusters? Where the cluster conditions are more favorable? What kind of factors influence on their development? Create clusters or Support clusters? The Research Problem: Analysis of external factors promoting the concentration and relationships between companies The study of cluster appearing and developing terms The Research Problem: Analysis of external factors promoting the concentration and relationships between companies The study of cluster appearing and developing terms

6. Research Design 6 Regional Innovation cluster Innovation cluster in the region: Spatial proximity organizations that promote innovations in strong cooperation (Aydalot, Keeble, 1998) The research is based on… Agglomeration theory results Arthurs et al. (2007) “Indicators to Support Innovation Clustering Policy” OECD (2001) “Innovative Clusters: Drivers of National Innovation Systems” The main principle of the scorecard Regional level of aggregation Input and output indicators Factors direct and direct link with cluster development

7. Research Design: The scorecard 7 Input Environment: regional attractiveness for company’s location Capital expenses Regulation rules, laws R&D expenditures Partners for cooperation Migration situation Drivers: resources for cluster’s generation Human Capital Innovation Infrastructure Level of company's innovativeness Process Cluster’s “life” Critical mass of cooperation's in different sectors Sustainability of relationships Innovation activity results Output How clusters influence on regional competitiveness? Technological development Global orientation Level of innovation products Attractiveness for firm creation

8. The data 18 regions of Ural and Volga Districts Federal Statistical Agency and its Regional Departments Open source qualitative and quantitative data 8

9. The sample description 9 IndictorsMinimumMaximumMeanStd. Deviation Output Indicators Innovation products in regional GDP, % 0.226.56.17.1 Input Indicators Business R&D in regional GDP, %0.0010.0500.0110.013 Personal with tertiary education, %10.025.020.03.0 % of Innovative activity3.329.310.86.2 Large to SMEs companies ratio, %1.95.63.81.1 % of sustainable relations20604010 Patents on 1 innovative firm, %1.89.35.72.3

10. Methodology of Cluster Identification Cluster Analysis: Hierarchical agglomerative algorithm average Communication between groups The Euclidean square distance metric “Stars” clusters regions: Groups with different cluster generation and development terms Statistical significance of group differences: The median test for independent samples 10

11. Main results: Cluster mapping (Regions with “Stars” clusters) 11 Udmurtia republic Orenburg obl. Mari El rep. Penza obl. Chuvashiaya rep. Kirov obl. Ulyanovsk obl. Mordovia rep. Saratov obl. Kurgan obl. Bashkortostan rep. Chelyabinsk obl. Nigh. Novgorod obl. Permskiy krai Tatarstan rep. Samara obl. Sverdlovsk obl. Tyumen obl.

12. Main results: Cluster mapping (Regions with “Stars” clusters) 12 Regions’ groups 1234 Bashkortostan, Tatarstan, Perm krai, Nig. Novgorod obl., Chelyabinsk obl., Samara obl., Mari El rep., Mordovia rep., Udmurtia rep., Chuvashia rep., Kirov obl., Orenburg obl., Penza obl., Saratov obl., Ulyanovsk obl., Kurgan obl. Sverdlovsk obl. Tyumen obl. Stars The differences between groups are statically significant

13. Conclusion & Discussion 13 The 3 rd stars-group of regions has a greater chance on cluster success In the 3 rd stars-group we can identify the innovation proto- clusters in different industries As an additional argument (first step in analysis of clusters) for further investigation of clusters on lower level of aggregation An argument for interaction and innovation infrastructure development at working out industrial and innovation region policies

14. 14 Thanks for your attention! Questions and Comments