1. Local Cluster Concepts, Facts and Theory  The Topic  Concepts  Theory  Facts Thomas Brenner DIMETIC October 2007

2. Why Is This Topic Relevant ?  Scientific perspective:  Spatial distribution of industries  Success of certain regions  Policy perspective:  Why are there differences between regions ?  How can unsuccessful regions be made successful ?  How can clusters be established ?  How can they be made more successful ?

3. How Is This Topic Treated in the Literature ?  Case studies:  Study of emergence  Examination of success  Theoretical approach:  Mathematical: New Economic Geography  Conceptual: Various concepts  Spatial econometrics:  Spatial distribution of industries  Identification of clusters

4. New Economic Geography  Mathematical  Two- or few-country models of specialisation  Stability of local clusters  Simulations  Local externalities: centripedal and centrifugal forces  Explanation for existence of agglomerations  Spatial distribution

5. Conceptual Works  Discussion of one or a few important factors  Industrial districts  Innovative milieux  Local clusters  Various definitions  Dominant definition: Porter  Learning regions, regional innovation systems etc.

6. Discussion of Important Factors  Common labour market, service firms and cooperation (Marshall)  Flexible specialisation (Piore & Sabel)  Social networks (Becattini)  Diamond model (Porter)  Innovative milieux (Camagni)  Learning regions (Lundvall)  Spin-offs (Klepper)

7. Industrial Districts  Marshallian:  Common labour market  Service firms  Spillover / Interaction  Italian:  Plus: Social network  Characteristics  Concentration of small firms  Strong interaction  Manufacturing industry

8. Innovative Milieux  Concentration of innovative actors  This creates sometimes  Synergies through interaction  Innovative atmosphere

9. Porter’s Diamond Model Chanc e polic y Local factors and resources Firm strategy and competition Related and service industries Demand

10. Definition: What Are Clusters ?  Scientific:  Many different concepts and definitions  Common: Agglomeration of firms of one or a few industries in a region  Different additional conditions, industry and region requirements  Policy:  Some regionally interacting firms  Sometimes higher requirements

11. Factors: What Causes/Characterises Clusters ?  Supplier-buyer linkages (ID,IM,LC)  Cooperation (ID,IM) and competition (LC)  Social contacts (ID)  Innovations (IM)  Local research (LC)  Local policy (LC)  Spillover/synergies (IM)  Labour market (ID)  Demand (LC)

12. Emergence vs. Success  Success  How do firms benefit in clusters ?  What are the mechanisms behind local externalities ?  Does not explain existence !  Emergence  Why do local clusters exist ?  How do they emerge ?  What are the mechanisms behind the self- reinforcement of clusters ?

13. positive feedback-loops: example: Theory: Self-augmenting Processes Number of firms Founding situation Mechanisms (examples): Accumulation of human capital Cooperation among firms Choice of co-location Spillover Interaction with public research Spinoffs Interaction with local policy

14. Theoretical Model (Schematic) External conditions and fixed local factors Local firm population Flexible local conditions Endogenous dynamics

15. Theoretical Model (Mathematical) e fl

16. Stable States I Weak local self-augmenting processes: e f

17. Stable States II Weak local self-augmenting processes: e f Stable states Unstable states

18. Two stable states: Prediction: Static situation Number of firms Regions with cluster Regions without cluster Critical mass Number of regions

19. Prediction: Distribution Should hold for Number of firms Number of employees Number of patents Number of regions Number critical mass

20. Example: Printing Number of employees Log (frequency)

21. Example: Clothing Number of employees Log (frequency)

22. Example: Glas Number of employees Log (frequency)

23. Emergence of Clusters  What are the relevant dynamics and mechanisms ?  What are the relevant local factors ?  How do we study these processes ?

24. How do local clusters emerge? 1st stage: Emergence of the market

25. How do local clusters emerge? 1st stage: Emergence of the market 2nd stage: Emergence of cluster

26. How do local clusters emerge? 1st stage: Emergence of the market 2nd stage: Emergence of cluster 3rd stage: Stability

27. How do local clusters emerge? 1st stage: Emergence of the market 2nd stage: Emergence of cluster 3rd stage: Stability 4th stage: Disappearance

28. Necessary conditions I I. Sufficiently strong local self-augmenting processes

29. Necessary conditions II 1st stage: Emergence of the market: II. Sufficiently supportive local conditions Market and local attractiveness

30. Necessary conditions III 2nd stage: Emergence of cluster: III. Sufficiently fast development Market and local attractiveness

31. Necessary conditions Substitutes:  Different mechanisms creating self-augmenting dynamics  Different aspects of local conditions  Different aspects of local developments Complements:  The self-augmenting dynamics have to be strong enough and  The local conditions have to be sufficiently good and  The local developments have to be sufficiently fast

32. Analysis of Factors and Dynamics  Case studies  What were the initial conditions ?  How did the region develop ?  Meta-study:  Van der Linde 2003 (Internet, Diamond model)  Own study: 159 cases  Analysis of the literature (222 works)

33. Meta-study  Factors, dynamics and events analysed  12 self-augmenting processes  17 local factors  6 events  Characterisation:  I: is argued to be important and present  U: is argued to be unimportant or ot present  N: is not mentioned

34. Self-augmenting Processes ProcessIUN F-HC1161033 COOP872250 COLOC83373 INTRA-SPILL811464 F-EDU/RES661974 SPIN60495 F-POL4910100 INTER-SPILL461112 F-OPIN449106 F-VC356118

35. Local Factors FactorIUN LABOUR1051044 NET783744 UNI/RES702267 TRAD661083 IND61296 LOC-POL561885 INFRA521097 CULT521493 GEOGR512106 DEMAND492090

36. Events DynamicsIUN LEAD-FIRM62493 SPEC-POL531096 HIST520107 PROM227130 INNO154140

37. Comparison: Time MechanismPost 1950 vs. Ante 1950 Post 1970 vs. Ante 1950 F-EDU/RES> F-VC> LOC-POL> GEOGR< TECH-PARK>> IND< SPEC-POL>> CHANCE<

38. Comparison: Industry MechanismHigh tech vs. Low tech Know. vs. Non- know. SPIN, SUPSTART, F-EDU/RES>> F-HC, F-VC> COOP<< NAT-POL, UNI/RES, LABOUR, TECH-PARK, CAPITAL, LIFE >> LOC-POL, TYPE> GEOGR, WAGE<< SPEC-POL, LEAD-FIRM>> HIST<

39. Comparison: Country MechanismDeveloped vs. developing Anglo- saxion Asia F-EDU/RES, SPIN>< F-POL, F-OPIN< COOP> WAGE< LIFE>< CULT, UNI/RES< TYPE, INFRA> LEAD-FIRM><<

40. Measuring Regional Innovation Systems  What is a RIS?  Innovativeness of regions  Measurement approaches  Local factors Thomas Brenner DIMETIC October 2007

41. What is a RIS ? Two definitions in the literature:  Innovative regions  Research infrastructure  Innovative firms and industries  Similar to national innovation systems  All elements and interactions  That contribute to innovation generation  In a region

42. What is a RIS ? Innovative regions  How are they defined?  Case studies  Innovation output, but no threshold  Innovation inputs, such as R&D expenditures or public research  What does it mean?

43. What is a RIS ? System perspective  Number of interacting elements  With interactions like  Joint research  Cooperation (more general)  Flow of knowledge  Flow of people  Flow of money

44. Innovativeness of Regions Regional Innovation Scoreboard  Ranking of EU regions according to a number of variables  Human resources in science and technology  Participation in life-long learning  Public R&D expenditures  Business R&D expenditures  Employment in medium/high-tech manufacturing  Employment in high-tech services  European patent applications

45. Innovativeness of Regions From a presentation by Hugo Hollanders (MERIT)

46. Innovativeness of Regions  What is innovativeness of regions?  Total innovation output?  Innovation output per inhabitant?  Innovation output per R&D employee?  What does it mean?

47. Innovativeness of Regions

48. Measurement Approaches Usual approaches:  Innovation scoreboard  Input and output variables  Total innovation output  Innovation output per inhabitant  Innovation output per R&D employee  Knowledge production function and production frontier  Non-parametric frontier approach

49. Local Factors  What are the local factors that play a role for the innovation output of regions:  Regression  Nations  Regions  Firms  Questionnaire  How to frame the questions ?  Case studies  How to generalise ?

50. Local Factors  What are the local factors that play a role for the innovation output of regions  What are the implications for policy?  What are the implications for firms? FEPRHKNWOICUPOLMKP

51. Identification of Local Cluster and Policy Issues  Identification of Local Clusters  Problems and Local Resources  Policy activities  Policy recommendations Thomas Brenner DIMETIC October 2007

52. Identification of Local Clustering  Which Industries show clustering ?  Ignored in the literature  Some discussion about low-tech industries  Where are local clusters ?  Italian approach  Identification according to specialisation ratio  Own approach: Distribution estimation

53. Italian Approach  Definition of labour market areas  Specialisation (share of industry’s workers)  Additional conditions  Dominance of manufacturing  Dominance of small and medium size firms  Results  Identification of industrial districts  Repeated

54. Specialisation ratio  Regions according to statistical office  Specialisation (share of industry’s workers) higher than 3  Sometimes additional conditions  Certain number of related industries  Results  Identification of local clusters

55. Distribution Estimation Exponential function: P(f) = a 1 f Boltzmann-like distribution: P(f) = f a 2 f Cluster part: P(f) = (f-a 4 ) a 5 (f-a4) if f>a 4

56. Industries with Local Clusters  Manufacturing industries:  Clustering can be proved for around 50%  For the number of workers and  For the number of firm sites  Service industries:  Clustering can be proved for around 30% for each number  Approach does not apply for around 20%

57. Industries with Local Clusters sub-industriesindustries with clusters food93 tabacco1- textile76 clothing32 leather33 wood54 paper5- printing32 petroleum processing31 chemicals71 plastics21 glas, ceramics & stones84

58. Industries with Local Clusters sub-industriesindustries with clusters metal production55 metal products74 machinery72 office machines1- electronics62 telecommunication32 instruments53 cars3- other transportation53 furniture, toys,...64 recycling22

60. Problems with Identification Local rescourcesFrequency Number of employees Frequency

61. Problems with Identification Number of employees Local rescources Frequency

62. Problems with Identification  What are the reasons for industrial agglomerations?  Local externalities  Local resources  History  Approaches  Ellison/Glaeser: Dartboard approach  Bottazzi et. al.: Model of local externalities  Own approach: Model of local externalities plus local resources

63. Location Model Entry: Exit:

64. Example: Automobiles Number of firms Local resources

65. Problems with Location Model  Not all factors can be empirically measured  What are the original factors ?  What factors develop because of the agglomeration of the industry ?

66. Policy approaches  Cluster organisation  Network financing and promotion  Monetary support for cluster activities  Technology parks  Improvement of local conditions

67. What is supported  Coordination  Networking  Joint innovation and research  Subsidising specific locations  Public research  Education

68. Basic questions  Should policy be involved ?  When should activities been taken ?  Where should activities been taken ?  What should be done ?

69. First Stage: Where Do Clusters Emerge? 1st and 2nd stage: Emergence of clusters: Sufficiently supportive local conditions Market and local attractiveness

70. Second Stage: Will Potential be Used ? 2st stage: improve local dynamics Market and local attractiveness

71. Third Stage: Stable Situation 3rd stage: Emergence unlikely Market and local attractiveness

72. Which is the Right Region ? Should locations be supported by policy? Number of regions Activity critical mass NO YES NO

73. Contact: brenner@econ.mpg.de

74. Measurement Approaches

77. Knowledge Production Function  Cobb-Douglas function  Inputs are  R&D expenditures  Universities  Local factors (such as population density, GDP, education, …)  Linear function

78. Measurement Approaches

79. Non-parametric frontier approach output input x x x x x x x x x x x x x x x x x x

83. Measurement Approaches

84. Private R&D  Mechanisms  Firms’ R&D employees produce most innovations  Empirical evidence  High correlation  R&D expenditure is even often used to measure innovativeness  Implications  Innovativeness depends on innovative firms

85. Public Research  Mechanisms  Source of ideas and knowledge  Cooperation partner  Source of human capital  Empirical evidence  Econometric evidence, regionally bound  Source of ideas: average importance  Cooperation: 13%  Implications  Firm location  Set-up of research institutes

86. Education  Mechanisms  Availability of highly qualified labour  Empirical evidence  Evidence for university graduates  Importance for large firms  Implications  Firm location  Set-up of universities

87. Education Small firms Medium firms Large firms With R&D Without R&D Qualified labour2,222,523,263,052,18 Ideas for new prod.&proc.2,532,42,042,192,5 Information2,322,242,332,292,31 Direct support2,082,282,672,372,25 New instruments and techniques 2,222,202,422,392,17 Results from basic research2,192,041,962,181,98 Consulting1,861,962,332,022,04 Relevance of Universities:

88. Networks and Cooperation  Mechanisms  Most innovations are produced jointly  13% are done allone  Empirical evidence  Cooperation is frequent  Effect is not proved  Implications  ??

89. Networks and Cooperation PartnerPercentage Material and component supplier55,2% Machinery supplier54% Customer43,7% Associations35,1% Other firm sites of the same company27,9% Universities25,9% Central lab of the company24,2% Public research institutes21,5% Consulting firms19,3% Competitors8,5%

90. Capital market  Mechanisms  Financial resources for innovations  Empirical evidence  High percentage for start-ups and first innovation project, than decreasing  Local effect ?  Implications  Start-up process important

91. Labour market laws  Mechanisms  Flexibility important for innovation projects  Innovative employees need liberty and belongingness  Empirical evidence  Innovative firms use more frequent less flexible rule  Implications  Innovative firms have specific requirements

92. Patent laws  Mechanisms  Innovation protection motivates innovation  Protection hinders further development  Empirical evidence  30% of innovations are patent protected  Case studies  Patent changes  Implications  Dependent on technological advancement

93. Culture  Mechanisms  Innovation attitude  Cooperation attitude  Empirical evidence  Different strengths in innovation processes  Different institutions and laws  Different interaction cultures  Implications  Firm organisation and policy has to be adapted

94. Industrial structure  Mechanisms  Ideas and cooperation  Empirical evidence  Case studies with path-dependence  Spillovers  Implications  Firm location  Regional policy