a. introduction to oecd cluster fg work & lessons from

A. Introduction to OECD Cluster FG work & Lessons

from analysing innovation clusters (Ed Bergman)

B. Cluster innovation policy lessons & implications for

innovation policy-makers (Pim den Hertog)

C. Cluster policy practice I: Denmark (Ninett Ribisch)

D. Cluster policy practice II: Netherlands (Victor Gilsing)

GENERAL DISCUSSION

IN PURSUIT OF INNOVATIVECLUSTERS. MAIN FINDINGS FROM

THE OECD CLUSTER FOCUS GROUP

PART A

Ed Bergman

NIS Conference on Network- and Cluster-oriented Policies, Vienna, 15-16 October 2001

I. OECD CLUSTER FOCUS GROUP

II. LESSONS FROM ANALYSING INNOVATION CLUSTERS

OUTLINE

I. OECD CLUSTER FG(1): OBJECTIVE

Explore (further) to what extent and in which respects various clusters - interpreted

as reduced form NIS - differ in their innovation performance and mechanisms of knowledge

transfer, as well as how to derive policy recommendations from the cluster approach

1. Methodologies for identifying clusters

2. Comparative case study work in selected cases (ICT, construction, agro-food)

3. Implications of adopting a cluster perspective

in innovation policy-making

I. OECD CLUSTER FG (2): 3 LINES OF WORK

I. OECD CLUSTER FG (3): 2 BOOKS

Phase 1: 1997-1999 (13 countries)

Phase 2: 1999-2001(11 countries)

I. OECD CLUSTER FG (4): DEFINITIONS

“Networks of production of strongly interdependent firms (including specialist suppliers) linked to each other in a value adding production chain. In some cases, clusters also encompass strategic alliances with universities, research institutes, knowledge-intensive business services, bridging institutions (brokers, consultants) and customers”(Roelandt & den Hertog, 1999)

“A group of business entreprises and non-business organisations for whom membership within the group is an important element of each member firm’s individual competitiveness. Binding the cluster together are buyer-supplier relationships, or common technologies, common buyers or distribution channels of common labour pools” (Bergman and Feser, 1999)

Cluster analysis at different levels of analysis

Level of analysis Cluster concept Focus of analysis National level (macro)

Industry group linkages in the economy as a whole

��Specialisation patterns of a national/regional economy

��Need for innovation and upgrading of products and processes in mega-clusters

Branch or industry level (meso)

Inter- and intra-industry-linkages in the different stages of the production chain of similar end product(s)

��SWOT and benchmark analysis of industries

��Exploring innovation needs

Firm level (micro)

Specialised suppliers around one or more core enterprises (inter-firm linkages)

��Strategic business development

��Chain analysis and chain management

��Development of collaborative innovation projects

Boekholt & Thuriaux, 1999

I. OECD CLUSTER FG (5): DEFINITIONS

II. LESSONS FROM ANALYSING CLUSTERS (1)

1. Every country/region has a unique “cluster blend”

2. Clusters are variation and selection environments that are inherently different

3. The idea of “ideal-type” innovation clusters is a fallacy:- history & country specificities- type of knowledge- stage of cluster development- networking practices


4. Industrial clusters may span geographical levels,crossing local and national boundries

5. Emerging technology-based clusters are more

likely to be part of wider international clusters

6. More mature clusters typically function at anational or regional scale where specific culturesand traditions or tastes produce product ‘niches’

7. Innovation in “low-tech” (actually, “stable-tech”)

clusters is more prevalent and complex than often depicted

8. This requires an open mind to rethink the

proper role of non-technological knowledge ininnovation


Connections between core literatures on clustering

Marshallian externalities,Industrial districts

Marshall [1890] 1961

Classical location theory

Weber (1929), Hoover (1937), Isard(1956), Richardson (1973) theories and

models of agglomeration economies

Strategicmanagement,

industrialorganization

Porter (1990, 1994, 1996),Doeringer and Terkla(1996), Enright (1990,

1996), Saxenian (1994),Scott (1986)

Neoclassical spatialeconomics

Lucas (1988), Krugman(1991a, 1991b, 1998), Rauch

(1993), Venables (1996)Fujita, Krugman and

Venables (1999)

Post-Fordism, flexiblespecialization

Piore and Sabel (1984),Scott and Storper (1987),

Schoenberger (1987) Scott(1988), Holmes (1986),

Gertler (1988, 1992, 1993),Sabel (1989), Storper (1989,

1992, 1995, 1997), Salaisand Storper (1992)

New industrialdistricts

Brusco (1982), Bellandi(1989, 1996), Goodman andBamford (1989), Becattini(1990) World Development

(1995), Harrison (1992),Bellini (1996), Pyke and

Sengenberger (1992)

Traditional economicgeography

New economic geography, regional science

EvolutionaryEconomics

Schumpeter (1912)

Nelson & Winter(1982)

NewInstitutionalEconomics

Coase (1937)

Williamson(1975, 1985)

Innovation Systems

Lundvall (1992)

Nelson (1993)

Carlsson (1995)

Edquist (1997)

OECD (1999, 2001)

Industrial Clustersare reducedNationalInnovationSystems

(Modified from Feser and Sweeney, 2001)

Modifications to both innovation and cluster theories have resulted

Main challenges for cluster analysts:

a. availability of (micro-level) innovation and production statistics at cluster level

b. identification of clusters in their infancyc. measure international clusters

d. make process of identification, selection and (effect of) clusters actions transparant and

verifiable


IN PURSUIT OF INNOVATIVECLUSTERS. MAIN FINDINGS FROM

THE OECD CLUSTER FOCUS GROUP

PART B

Pim den Hertog

NIS Conference on Network- and Cluster-oriented Policies, Vienna, 15-16 October 2001

III. WHY A CLUSTER APPROACH?

IV. CLUSTER INNOVATION POLICY LESSONS

V. IMPLICATIONS FOR INNOVATION POLICY-MAKERS

OUTLINE

III. WHY A CLUSTER APPROACH? (1): 4 BASIC ARGUMENTS

1. Clusters echoe/reflect the importance of interpendency & systemic nature of innovation

2. Clusters allow for identifying and addressing systemic imperfections & developing new

forms of governance

III. WHY A CLUSTER APPROACH? (2): 4 BASIC ARGUMENTS

3. Cluster approach is a way of customising innovation and other policies to the needs of

individual clusters

4. Clusters (analyses) are a tool for dialogue and learning

IV. CLUSTER INNOVATION POLICY LESSONS (1)

1. Mega-cluster analyses need to be combined

with action-oriented small-scale cluster approaches (see example Denmark)

2. Cluster studies as working tools to:- opening up a dialogue on innovation

- learn where policy can contribute- tool for proactively creating platforms &

programmes

3. Innovative clusters are shaped by all kind of

policies, including non-innovation policies

4. Some policies migh have perverse effects on innovation in clusters

5. Innovative clusters can be facilitated by varying customised mixes of innovation and

non-innovation policies


6. Clusters are useful frameworks to co-ordinate

policies and reduce complexity

7. The cluster approach does not offer standard policy recipes

8. Cluster policies are dependent on the stage in the cluster life cycle and should balance

creating and sustaining innovative clusters


9. A 1st risk for further development of the clusterapproach is “high-tech myopia”

10. A 2nd risk is adopting (counterproductive) standard policy models and standard tools


Policy rationales Cluster-oriented policy action

Tools

Lack of cluster identity and awareness

• Identification and public marketing of clusters

• Mapping exercises • External promotion of

regional clusters • External/internal

promotion of cluster member’s competencies

Government regulations hamper innovation or competitiveness

• Organise cluster specific fora to identify regulative bottlenecks and take actions to improve them

• Cluster platforms and focus groups

• Tax reform • Regulation reform

(environment, labour markets, financial markets)

Firms do not take up opportunities for collaboration with other firms

• Encourage and facilitate inter-firm networking

• Purchase innovative products through collaborative tender procedures

• Networking programmes • Brokerage training • Public procurement for

consortia

Firms, particularly SMEs, cannot access strategic knowledge

• Support cluster based retrieval and spread of information

• Organise dialogue on strategic cluster issues

• Set up cluster specific information and technology centres

• Platforms to explore market opportunities

• Foresight exercises Firms do not utilise the expertise of knowledge suppliers

• Collaborative R&D actions and cluster specific R&D facilities

• Set up cluster specific technology and research centres/initiatives

• Subsidise collaborative R&D and technology transfer

Lack of crucial elements in a cluster

• Attract or promote growth of firms in cluster

• Attract major R&D facilities

• Targeted inward investment

• Support start-up firms in particular cluster

IV. CLUSTER INNOVATION POLICY LESSONS (5):Rationales, policy actions & tools

(Boekholt & Thuriaux, 1999)

1. National advantage

2. Interfirm (SME) networking

3. Regional development

4 Industry-RTD clustering


IV. CLUSTER INNOVATION POLICY LESSONS (6):Policy models

Mega level Meso level Micro level 1. National advantage • Mapping

• Competitive markets • Regulations and

standardisation

• Foresights • Specialised RTD

facilities

• Collaborative RTD programmes

2. Inter-firm (SME) networking • Supply chain development

• Brokerage • Networking

programmes • Awareness raising

3. Regional development • Regional Competence Centre development

• Focused Inward investment

• Supply-chain associations

• Specialised technology transfer

• Marketing clusters

• Brokerage • Networking

programmes • Awareness raising

4. Industry-RTD clustering • Incentives RTD-industry collaboration (IPR, financial, etc.)

• Collaborative RTD centres programmes in specific areas

• Prioritisation of R&D expertise

• Technology circles • NTBF support • Procurement policy


IV. CLUSTER INNOVATION POLICY LESSONS (7):Policy models & analytical level

V. IMPLICATIONS FOR POLICY-MAKERS (1)

# change in day-to-day policy-practices

# experimentation and policy learning

# switching between analysis & pragmatic action

# co-ordination of various political jurisdictions

# increased need for accountability

# codifying good practices


i. close gap between analysis - daily policy practice

ii. work with the whole set of policy tools available

iii. understand which tool to use when

iv. handle complex cluster programmes

and projects

v. withstand the pressure to use cluster policies

for traditional industrial policy purposes

vi. act in various capacities (sparring partner, programmemanager, stimulating dialogue, etc.)

vii. switch between action and reflection

viii. switch between various clusters with different needs



Coordinating in a cluster framework the complex

set of policies that affect innovation in a cluster is a multidimensional balancing act between:

# analysis vs. highly practical policy actions# bottom-up initiatives - top-down steering

# established mature - newly emerging clusters# regional-national-international level

# various policy roles or capacities# customisation and accountability

Framework ConditionsFinancial environmentTaxation and incentivesPropensity to innovation andentrepreneurship Mobility ...

Education andResearch System

Professional educationand training

Higher educationand research

Public sectorresearch

CompanySystem

Large companies

Mature SMEs

New, Technology- Based Firms

IntermediariesResearch InstitutesBrokers

Consumers (final demand)Producers (intermediate demand)

Demand

Banking, venturecapital

IPR andinformationsystems

Innovation andbusiness supportsystem

Standards and normsInfrastructure

Source: Technopolis 2000

MEASURING THE KNOWLEDGE ECONOMY:Dutch Ministry of EA

CLUSTERBELEID ICT

TNOGTI’STTI’S

Framework ConditionsFinancial environmentTaxation and incentivesPropensity to innovation andentrepreneurship Mobility ...

Education andResearch SystemProfessional educationand training

Higher educationand research

Public sectorresearch

Company System

Large companies

Mature SMEs

New, Technology- Based Firms

IntermediariesResearchInstitutesBrokers

Consumers (final demand)Producers (intermediate demand)

Demand

Banking, venturecapital

IPR andinformationsystems

Innovation andbusiness supportsystem

Standards and normsInfrastructure

SYNTENSSENTERBRANCHESROM’S

PHILIPS

KOWBSO

BIE

N&B

KvK

TOP

ICES-KIS

R&D-SAMENWERKINGCIC-PROJECTEN

TwinningDreamstartBiopartnerRegeling

SCHOLINGSIMPULS

Source: Technopolis 2000/MIN OF EA-ROELANDT

MEASURING THE KNOWLEDGE ECONOMY :Portflio of innovation policy instruments

● To develop an analytical tool for assessing characteristics, functioning(esp. Innovation) and performance of various types of clusters(emerging/mature, technological/non-technological), including optionsfor improvement, and to test this tool in three clusters

● Three functions:

# analyse competitiveness, innovativeness and adaptation capability

of individual clusters

# support dialogue between cluster actors (by involving them)

# agendasetting ==> generate ideas for actions of firms, intermediaries (knowledge infrastructure a.o.) and government to improve functioning of clusters

MEASURING THE KNOWLEDGE ECONOMY:Goal and function of Dutch ClusterMonitor

A.1 Structure

A.2

Framework conditions NL

A.3

International context

B.1

Cluster- dynamics

B.2

Innovation style

B.3 Quality of demand

C.1 Economic performance

C.2

Innovation success

Basic characteristics Functioning Performance

Internal External

Internal External

C.3 Adaptation capability

MEASURING THE KNOWLEDGE ECONOMY:Relation model Dutch ClusterMonitor

Limited transparancy & limited v iew on economic

importance of cluster Lacking statistical index

( Structure )

Low lev el of industrial organisation

Links between regional MM clusters underdev eloped

Labour shortages ( Framework cond .)

New international entrants

Markets & networks serv ice f irms mainly locally oriented

(International context)

Co-operation between core and ‘ rim’ play ers

could be better (Cluster dynamics )

Perception required speed dif f ers

Balance f ormal / inf ormal Timely inv estments new knowl .

Interf ace knowl . inf rastruct . ( Innovation style )

‘ Educating’ clients Accomodating needs of

demanding customers ( Quality of demand )

Ambiguous image of Dutch MM cluster Export strategy is lacking ( Economic performance)

Production innov ation limited to f ew play ers

Serv ice f irms innov ate on the basis of existing tools

( Innovation success )

Internal External

Internal External

Quality of regular and knowledge management in

f ast-growing SMEs ( Adaptation capability )

Characteristics Functioning Performance

MEASURING THE KNOWLEDGE ECONOMY:Bottlenecks Dutch multimedia cluster

Invest in statistical index

Make economic importance cluster visible

(Structure)

Improve industrial organisation

Link regional spec. clusters Improve MM component in

education & training(Framework cond.)

Integrate foreign players in national clusterImprove export orientation

(International context)

(Cluster dynamics)

Match knowledgesupply/-demand

Improve (flex.) forms of knowledgetransfer between

firms & universities(Innovationstyle)

Help clients innovate with multimedia

Government as a lead user(Quality of demand)

Unambiguous imago & exportstrategy

for Dutch multimedia cluster(Economic performance)

(Innovation success)

InternExtern

InternExtern

Professionalize regular and knowledge manage-ment fast growing SMEs(Adaptation capability)

Characteristics Functioning Performance

MEASURING THE KNOWLEDGE ECONOMY:Points for improvement Dutch multimedia cluster

EXIT

EXIT

PHASE 1:INFORMATION PHASE 2:

INITIATIONPHASE 3:IMPLEMENTATIONTop-down Top-down

Bottom-up

Increas ing involvementGATE 1

GATE 2

Bottom-up

Bottom-up

Gilsing, 2001

MEASURING THE KNOWLEDGE ECONOMY:Objectivation in Dutch cluster policies

Figure 1. Different roles in different clusters

Projects1

Role of government Ant

heus

Twin

ning

ITS

Life

sci

ence

s

Wat

er c

lust

er

Mas

s in

divi

dual

isat

ion

EMV

T

Con

stru

ctio

n

PDI

ECP.

nl

Chairman

Catalyst/initiator

Process manager

Brokers

Connecting netw orks

Finance

Note: White = no role; grey = role. 1. Antheus is a regional cluster project at the micro level, aimed at increasing co-operation between a big aluminium plant and the smaller (aluminium-using) firms surrounding it. ITS stands for Intelligent Transport Systems. EMVT is the Dutch abbreviation for Electro Magnetic Power Technology. PDI stands for Product Data Interchange, a project mainly aimed at supporting this technology in the chemical cluster. The other projects are described in the main text.

Gilsing, 2001

MEASURING THE KNOWLEDGE ECONOMY:Various roles in various clusters

Figure 3. Danish clusters of competence: 29 examples

National Regional

Exis

ting

• Thermal technology

• Technical appliances for the disabled

• Pork

• Dairy products

• Water environment

• Fur

• Seed-growing

• Power electronics

• Hearing aids

• Wind technology

• Maritime industry

• Mobile/satellite communication in Northern Jutland

• Business Tourism in the Capital region

• Stainless steel in Eastern Jutland

• Horticulture at Funen

• Pharmaceuticals in the Oeresund region

• Textiles/clothing in Herning-Ikast

• Offshore industry in Esbjerg

• Furniture in Salling

• Transport in Eastern-Southern Jutland

Emer

ging

• Organic food

• Children’s play & learning

• Waste management

• Sensor technology

• Bio-informatics

• Movies/broadcasting in the Copenhagen region

• Oeresund Food Network

• PR/Communication in the Copenhagen region

• Pervasive Computing in Copenhagen and Aarhus

Holm Dalsgaard, 2001

MEASURING THE KNOWLEDGE ECONOMY:Selecting relevank clusters

1. Cluster-oriented vs. network-oriented policies

2. Creating new vs. sustaining established clusters

3. High tech myopia

4. Selectivity of cluster policies

5. Appropriate level of cluster-oriented policies

6. Use of ‘other policies’ in innovation policies

7. Real time policy learning

8. New breed of innovation policy-makes

a. introduction to oecd cluster fg work & lessons from

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