Network Models of Regional Innovation Clusters and their Impact on Economic Growth
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The document discusses the concept of regional innovation clusters (RIC) and their role in driving economic growth, emphasizing the importance of active networks that extend beyond geographical boundaries. It highlights limitations in current cluster analyses and proposes a combination of network and cluster analysis to identify emerging technologies and streamline economic development strategies. The research offers a framework for enhancing innovation-driven growth by utilizing data sets and social network analysis to connect firms and institutions more effectively.
Network Models of Regional Innovation Clusters and their Impact on Economic Growth
- 1. Network Models of Regional Innovation Clusters and their Influence on Economic Growth Evolving the Regional Innovation Cluster Paradigm for an Innovation Driven Economy C. Scott Dempwolf, PhD Research Assistant Professor & Director U.S. Economic Development Administration September 4, 2014 UMD – Morgan State Center for Economic Development
- 2. Regional Innovation Clusters (RIC) “A geographically-bounded, active network of similar, synergistic or complementary organizations in a sector or industry that leverages the region’s unique competitive strengths to create jobs and broaden prosperity.” (EDA, 2011) This research… • Validates current cluster theory and policies • Exposes their limitation • Offers useful extensions We have an opportunity to take Regional Innovation Clusters to the next level – to respond to innovation-driven growth Limits of Cluster Analysis 1. Active networks are not geographically bounded 2. presence of networks is assumed but not measured 3. NAICS-based clusters not sensitive to emergence 4. based on employment data; does not connect economic growth to innovation 5. Backward-looking
- 3. Innovation Driven Growth a simple stylized model Basic Research Development Invention Product Improvement Production Research Parks Incubators Production Employment Business Attraction Business Expansion Business Retention Innovation based Production TBED Response years
- 4. Innovation Driven Growth how do we measure it now? Basic Research Development Invention Product Improvement Production Research Parks Incubators Production Employment First Employment Data Available Clusters defined by established Industries, not emerging technologies Business Attraction Business Expansion Business Retention ~ 5 years +/- Innovation based Production Regional Cluster Analysis years Bottom Line Industry clusters -by whatever name- reflect the state of innovation about five years ago
- 5. Innovation Driven Growth gaining early actionable intelligence Basic Research Development Invention Product Improvement Production Production Employment First Employment Data Available This approach can shorten the lag between real-time innovation and actionable economic development intelligence by several years while also revealing rich talent pools, emerging technology trends, and specific E.D. targets Clusters defined by established Industries, not emerging technologies Research Parks Patents SBIR Awards NIH Awards NSF Awards NASA Awards ~ 5 years +/- Innovation based Production A New Approach Available Data Sets Innovation Network Analysis years ~ 4 years +/- State Investment Data Incubators Business Attraction Business Expansion Business Retention
- 6. Networks & Network Models Georgia Innovation Network 2008 – 2010 Locations of selected actors Networks made up of nodes (vertices) and links (ties, edges) Nodes are actors, agents or objects People, Organizations, Agencies, Documents, Places * Links are the relationships that connect the nodes Regional innovation clusters are geographically concentrated but also have important ties to distant actors
- 7. Analyzing Regional Innovation Networks Extract relationships from patent and research grant data - about 7M records Use social network analysis (SNA) to analyze and visualize network structure Theoretical grounding in sociology and science of complexity Behavior of the core network guides behavior of whole network Clustering based on intensity of relationships This reveals emerging technologies - what people and firms are working on – and specialized talent pools Battelle Innovation Network 2005 – 2010 Created with NodeXL
- 8. 1. Innovation is more global and more interconnected than previously thought 2. Network structure influences manufacturing employment growth within about 3 years of patent application (more for med & pharma) 3. Economic development strategies that enhance innovation networks may be a cost-effective alternative to current capital intensive strategies. 4. Innovation networks are (or could be) drivers of economic development in tier 2 manufacturing regions. PA Innovation Clusters Westinghouse Westinghouse cluster, Pittsburgh PA Dissertation Conclusions Network graphics created with NodeXL Allegheny County Westmoreland County Core 2nd tier 3rd tier
- 9. Regional Innovation Clusters are Complex, Emergent Systems Networks are Ideal for Modeling Complex Systems: • involve many interconnected or interacting parts • exhibit emergence - behaviors that cannot be understood or predicted by looking at the components of the system alone • Emergence is characteristic of self-organizing networks • The behavior of the whole network is driven by the behavior of the core • Thus we can focus on the core and filter out the noise Pennsylvania Innovation Networks 1990 - 2007 in the periphery
- 10. Applications 1. Illinois Battery Cluster (2014) Identifying emerging opportunities Combining cluster and network analysis to develop targeted strategies 2. Great Lakes Patent network (2011) Finding current opportunity for growth in large active clusters Identifying talent pools 3. Georgia Tech Research Network (2013) Visualizing & managing the research portfolio Identifying University collaborations 4. Maryland Innovation Network (2011) Biotech & Pharma – differentiating comingled clusters Zooming in to look at Baltimore’s Clusters 5. Startups, Venture Capital & Accelerators (2014) The CrunchBase network for Maryland The CrunchBase network for Illinois 6. New Jersey Solar –PV Research & Manufacturing network (2012) Visualizing the university – industry gap Developing a targeted strategy
- 11. Illinois Battery Cluster The Illinois Battery Cluster illustrates how network analysis can augment industry cluster analysis by identifying emerging technologies and opportunities for innovation – led growth. Using network and cluster analysis together economic developers can rapidly develop detailed strategies, identifying the specific firms, institutions and agencies involved and how they need to connect to achieve economic growth.
- 12. Illinois Battery Cluster Cluster Analysis • Battery manufacturing split between two clusters • Communications (335912: 1 est; empl N/A for 2012) • Lighting (335911: 11 est; empl N/A for 2012) • NAICS 33591 Battery manufacturing 2013 (BLS) – 2013 Location Quotient = .34 – 18 establishments – 565 employees Communications Cluster Communications Equipment Components sub-cluster Lighting Cluster Storage Batteries sub-cluster Conclusion: Limited opportunity
- 13. Illinois Battery Cluster Network Analysis • 2012 - $120M JCESR created at Argonne • ‘5-5-5’ goal significant industry growth ~ 2017 • Network identifies specific firms + real & potential research ties in specific technologies Conclusion: The combination of limited production capacity (from cluster analysis), strong research capacity & research investment suggest specific economic development strategies to capture future job growth. • Build industry partnerships around existing firms & supply chains to facilitate growth • Target specific firms for attraction to grow cluster rapidly
- 14. Great Lakes Regional Innovation & Manufacturing Clusters (core)
- 15. Great Lakes Innovation Clusters Impact on Planning Practice
- 16. Potential University Applications Well suited for integrating and managing research across multiple institutions via open networks rather than institutional structure Visualizing a Research Portfolio Offers both a big picture and details of technology commercialization areas and opportunities Georgia Tech Innovation Network 2008 - 2010 (2 steps) Created with NodeXL
- 17. Maryland Innovation Clusters 2008 - 2010 This analysis showed that the clustering algorithm is sensitive enough to distinguish between pharma and biotech. Maryland Innovation Network 2008 – 2010 Created with NodeXL Baltimore Innovation Network 2008 – 2010 Created with NodeXL
- 18. CrunchBase startup networks 2005-2014 Illinois Startup Network Although similar in size the Illinois network exhibits more robust structure Little discernable structure; clustering appears weak Maryland Startup Network Some structure and beginnings of clusters apparent
- 19. Maryland Startup Network (CrunchBase 2005 - 2014) When clustered, spatial agglomeration is the main organizing factor both locally and for distant capital sources; DBED & TEDCO feature prominently, followed by a few investment firms.
- 20. Maryland Startup Network (CrunchBase 2005 - 2014) Removing New York, Boston and San Francisco nodes diminishes spatial influence as an organizing influence, allowing technology clusters to emerge. (between-cluster ties hidden in this graph)
- 21. Illinois Startup Network (CrunchBase 2005 - 2014) Spatial agglomeration is an important factor in Chicago and North Shore clusters; Excelerate Labs & HealthBox are prominent accelerators locally; Strong ‘portfolio’ organization in remaining clusters
- 22. New Jersey Solar PV Cluster 2008 - 2010 Fruchterman-Reingold layout In NodeXL This analysis revealed significant gaps between solar PV research & development and solar PV component manufacturing. Grid layout in NodeXL Production Core Research Core
- 23. Next Steps Academic Research • Publications • Presentations at SSTI, TCI Global • Complete County-level application – St. Mary’s County, MD CEDS • Seek NSF SciSIP funding for additional network research; validation & calibration of the economic model • Pending proposal with NIST to evaluate their impact on innovation and commercialization (alternative metrics to patent counts) • Collaboration with UMD HCIL on improvements to visualizations and NodeXL software Commercialization • Launch startup company (fall 2014) • Engage ten pilot communities / regions over the next two years – Mix of different sizes, scales, level of organization, density – Focus primarily on manufacturing regions – Some with cluster strategies, some without • Pilot studies may include – A network report (limited version of Illinois Roadmap) – Traditional cluster analysis using the Harvard tool for regions that don’t have it – An interactive network model – On-site training & Technical Assistance • Evaluation of performance across all pilot regions