By Vartuhi Tonoyan, Robert Strohmeyer & Michael Woywode

1. Does A Cluster‘sLocation Matter for Companies‘ Survivaland Growth? Conclusionsfromthe German Biotechnology Industry By Vartuhi Tonoyan, Robert Strohmeyer & Michael Woywode Presentation for the Howe School of Technology Alliance Meeting, November 13

2. Who We Are… Dr. Vartuhi Tonoyan, Assistant Professor • Study of Economics in Armenia and Germany (graduation with distinction) • Dissertation in Management / Entrepreneurship at the University of Mannheim, Germany (graduation with summa cum laude) Topic: “Corruption, Entrepreneurship and Institutional Environment: A Cross-National Comparison of Emerging and Mature Market Economies” • Head of Entrepreneurship Research Division at the Mannheim’s Institute for SME Research, Germany • 1-Year of Post-doc study of Entrepreneurship at the Graduate School of Business of the Stanford University • Joined the Stevens Institute of Technology as Assistant Professor in September of 2011

3. Who We Are… Robert Strohmeyer, Diploma of Sociology • Study of Social Sciences and Statistics at the University of Mannheim (magna cum laude) • PhD-Candidate at the University of Mannheim, Germany • Visiting Research Scholar at the Stevens Institute of Technology Dr. Michael Woywode, Full Professor • Head of Institute for Small Business Research at the University of Mannheim • Research Associate at the ZEW Mannheim

4. Research Areas and Selected Projects (1) • High-Technology Entrepreneurshipand Innovation • Emergenceand Evolution of New High-Tech Industries: • Biotechnology Industry in Germany andthe US • AppIndustry in the US • Strategic AlliancesandCollobarations in High-Tech Industries • AllocationofControlRights in Bio-PharmaceuticalAlliances • Evaluation ofPubliclyFunded R&D Projects • Impact of Public R&D Funding on Private R&D Investments • Project Management • Biotechnology Entrepreneurs‘ DecisiontoPersistwithUnder-Performing R&D Projects

5. Research Areas and Selected Projects (2) II. Corruptionand Development Economics • Impact ofCorruption on Firm Innovativeness in Emerging andMature Market Economies III. Entry and Growth DeterminantsofEntrepreneurial Companies; EntrepreneurialDecision-Making • Gender gap in entrepreneurship • Gender-specificdifferences in firm performance (such asemploymentgrowthandinnovation)

6. Empirical Evidence on Cluster Effects • Lack of studies regarding the impact of cluster embeddedness on firm performance (notable exceptions are Baptista & Swann (1998) for the UK, Dahl & Pedersen (2004) for Denmark, Geenhuizen & Reyes-Gonzalez (2007) for the Netherlands, Whittington et al. (2009) for the US, and Wennberg & Lindquist, 2010, Powell et al. 2011) • Geenhuizen & Reyes-Gonzalez (2007, p. 1683): There is a shortage of studies in which the theoretical and policy claims of the cluster advantages in biotechnology are critically evaluated on basis of a systematic and longitudinal analysis • Questions to be answered: Does it pay to locate in a cluster? If so, what cluster(s) to choose? What differentiates good clusters from bad ones? • Answers to these questions should be relevant both for high-tech entrepreneurs and policy-makers !

7. Definitions of Clusters • No ubiquitous definition of clusters Porter (1998, p. 199) “Cluster is a geographic concentration “of interconnected companies, specialized suppliers and service providers, firms in related industries, and associated institutions (e.g. universities, standards agencies, and trade associations) in particular fields that compete but also cooperate” • Other definitions do not assume vertical or horizontal relationships between companies and/or co-operations between high-tech companies and associated institutions • Still others argue that networking and cooperation should be an important prerequisite for clusters (i.e. geographical concentration of companies is not a sufficient criteria) • Common to all definitions is the geographical concentration of firms in specific regions • No agreement regarding the level of geographical concentration or quorum of organizations or maximum of possible area of expansion of a cluster

8. Mechanisms of Cluster Effects on Firm Performance (1) • Transportation costs (industries locate close to resources in order to minimize transportation costs) • Availability of specialized labor force • Intra-industrial specializations (within core industry) • Inter-industrial specializations (specialized suppliers, investors, end product purchaser)

9. Mechanisms of Cluster Effects on Firm Performance (2) • Transaction cost perspective: Reduces costs for finding business counterparts, defining contract conditions, and monitoring agreements • Knowledge spillover (transfer of explicit and implicit knowledge) • Increases the legitimation of companies

10. Mechanisms of Cluster Effects on Firm Performance (3) • Criticism: • The benefits of co-operation can also be reached via social networking which does not require regional proximity (such as strategic alliances) (Bathelt et al., 2004) • Very strong clusters might produce adverse effects due to congestion and hyper-competition among firms and personnel (vgl. Folta et al., 2006; Sorenson & Audia, 2000) • Lock-in effects: isolation/ freezing of structures withing a cluster

11. Research Questions • Does the cluster embeddedness result in a higher firm performance? • Is there a cluster heterogeneity? What differentiates successful clusters from less successful ones?

12. Multilevel-Analysis with Panel Data Level 1: Firm-Level Characteristics • Firm size and age, business model, venture capital funding and public R&D subsidies, cooperations (with biotech firms, Big Pharma industry, and also research institutes) Level 2: Cluster-Level Characteristics • Cluster size and cluster age, the level of venture capital funding and public R&D subsidies, cooperation density, international collaborations

13. Hypotheses (1) Cluster Perspective • Cluster Size • Cluster effectsareexpected after only a minimumsizeof a clusterhasbeenreached • Cluster’s Composition and Degree of Maturation • Biotech firms which operate in clusters with a higher percentage of companies focusing on product development and sales will be more successful • Biotech firms which operate in more mature clusters, i.e. clusters with a higher percentage of established companies, will be more successful • Cluster’s Degree of Internationalization • Biotech firms which operate in clusters with a higher degree of internationalization will be more successful

14. Hypotheses (2) Cluster Perspective (cont.) • Network Composition and Existence of Big Players on Cluster Level: • Biotechnology firms which operate in clusters with a more balanced network composition in terms of cooperation with other biotech companies, research institutes and industrial companies will be more successful • Biotechnology firmswhich operate in clusters with a higher number pharmaceutical companies will be more successful • Cluster’s Level of Funding (Venture Capital Money and Public Subsidies) and Science Dominance • Biotechnology firmswhich operate in clusters with a more balanced composition in terms of venture capital funding and the science dominance will be more successful

15. Data, Variables and Methods Data • Panel data (1998-2008): N= 1064 biotechnology firms in Germany; Sources: BioCom and Creditreform data Methods • Panel-Econometrics (Wooldrige, 2000) • Multilevel-Analysis (Raudenbush & Bryk, 1992) Dependent and Independent Variables • The dependent variable measures bio-technology firm performance (employment growth) • Independent variables at two levels: • Level 1 predictors: firm characteristics (such as age, business models, funding) • Level 2 predictors: cluster characteristics (cluster size, the level of venture capital money and public R&D subsidies at the cluster level, cooperation at the cluster level)

16. Multilevel Modeling Source: Raudenbush & Bryk (2002), p. 128

17. Descriptive Statistics (1) Figure 1: Number of Dedicated Biotechnology Enterprises in Germany (1998-2008) Source: ifm/ZEW Bio-Tech Entrepreneurship Panel 2000-2009 (based on Biocom Data)

18. Descriptive Statistics (2) Figure 2: Number of Employees in Dedicated Biotechnology Enterprises in Germany (1998-2008) Source: ifm/ZEW Bio-Tech Entrepreneurship Panel 2000-2009 (based on Biocom Data)

19. Descriptive Statistics (3) Figure 3: Share of Enterprises in Biotech Industry By Colors / Fields of Activity Source: ifm/ZEW Bio-Tech Entrepreneurship Panel 2000-2009 (based on Biocom Data)

20. Results of Panel Estimation (1)

21. Results of Panel Estimation (2) Source: ifm/ZEW Bio-Tech Entrepreneurship Panel 2000-2009 (based on Biocom Data) M1 is firm level model, M2 = M1 plus cooperation variables, M3 controls for cluster level characteristics

22. Summary of Results (1) Firm-Level Results: • Firm age and business models are not related with employment growth • Venture capital is a strong positive predictor of employment growth • Public R&D subsidies have a positive effect on firm growth • Collaborations with big pharmaceutical industry companies have no effects on employment growth • In contrast, collaborations with a foreign research institute or university strongly positively influence employment growth

23. Summary of Results (2) Cluster-Level Results • Cluster size matters: the bigger a cluster in terms of the number of enterprises, the higher the probability of firm growth • But, the degree of cluster’s maturation is not significant in models • Strong VC investments effect on the cluster level: the higher the percentage of firms in a cluster which have received venture capital, the higher the probability of employment growth of firms in this cluster (independent of whether the firm itself has received VC-funding or not) • A high number of collaborations at the cluster level is not related with employment growth

24. Limitations • Survivor bias : we have analyzed only those firms in the panel which survived over 2004-2008 • Next step: we will implement a selection equation • Reference category: non-clustered firms (a very low N, ca. 30 biotechnology companies)

25. Implications for Future Research • Firm innovativeness (patents) / growth in sales as dependent variables • Panel for 1998-2008 (to analyze exogenous changes in the environment of biotech companies, such as the impact of the financial crisis in 2001 in Germany and/or the world financial crisis in 2008 on firm performance) • Analysis of the institutional environment of clusters and thus rules which regulate the attraction and retention of the venture capital investments , and/or the labor market mobility and flexibility • A US-German comparison of the effects of cluster embeddedness on firm growth • Comparison of performance of firms involved in clusters with the performance of their non-clustered counterparts involved in strategic alliances

26. Thank you for your attention ! Presentation by Vartuhi Tonoyan Howe School of Technology Management Stevens Institute of Technology vtonoian@stevens.edu

27. Backup • Variables Description: • Business model: R&D only: no services, no production, no sales and distribution • Business Model: R&D & Services: hybrid model, doing both R&D and biotech services on technology platforms • Venture Capital: received VC in year X (dummy) • Public Subsidies: received Public subsidies in year X (dummy) • Coop: Industry / Big Pharma : dummy • Coop: Biotech: dummy • Coop: Foreign Reseach Institut /University: dummy • Cluster Level Variables • Cluster size : number of BT firms in cluster X • Cluster Mature (mean age) : mean of age in cluster • Cluster VC (% of Enterp.): %-share of enterprises in cluster X which received VC • Cluster Subsidies (% of Enterp.): %-share of enterprises in cluster X received public subsidies • Cluster: Cooperation Density: mean of number of co-operations in cluster X

28. Cluster Identification • Usage of MPCluster plugin for MS MapPoint • Comparison with Zip-Code method • K-Means algorithm • Parameters: • max radius: 59 Miles (90 Km), • min # of firms: 10 • Key Take-Away: • 91.67% of the identified clusters are identical with officially existing clusters