Potential and Problems of Regional Cluster Policies: Evidence from Germany

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Institute of Geography

Matthias Kiese ••••Urban and Regional Economics

ARL International Summer School 2015 “Winners and Losers: Why are the Effects of Regional Policy so Different?”

Prague, August 25^th, 2015

Potential and Problems of Regional Cluster Policies: Evidence from Germany

2 Institute of Geography

1) What are Cluster Policies?

2) Impacts of Regional Cluster Policies in Germany 3) Problems: Stylised Facts

4) Implications and Research Needs

Agenda

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ARL International Summer School 2015 “Winners and Losers: Why are the Effects of Regional Policy so Different?” 3 Prague, August 25^th, 2015

• interconnected companies,

• specialized suppliers,

• service providers,

• firms in related industries,

• and associated institutions (e.g. universities, standards agencies, chambers of commerce, trade associations…)

in particular fields that compete but also cooperate

(cf. Porter 2008: p. 215 f.)

. Clusters

= geographic concentrations of

What is a Cluster?

Customers

Manufacturer

Intermediate goods

Suppliers Vertical Dimension

(Value Chain) Lateral/diagonal Dimension

• Business Services: KIBS, Financial Services (Banks, VC…)

• Research & Education ⇒ specialized labor

• Chambers, associations

• Network organizations

• Specialized infrastructure Horizontal Dimension

Compe- titors

Territorial boundary External Dimension

Kiese 2012, p. 39

Institutional Dimension

• Values, Norms and Rules

The Cluster as a Localized Value System

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Porter 2006, p. 6

The Massachusetts Life Sciences Cluster

• All efforts of government to develop and support clusters (in a particular region)

(Hospers/Beugelsdijk 2002, p. 382)

• Industrial, structural, technology or innovation policy promoting regional specialisation

• Public efforts to develop concentrations of industry or network structures into clusters, or to promote existing clusters

(cf. Bruch- Krumbein/Hochmuth 2000, p. 69 f.)

What is (Regional) Cluster Policy?

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• Identification of clusters with their specific profiles, strengths and weaknesses

• Identification and mobilisation of cluster members

• Development of common visions, strategies and projects

Elements of Cluster Policy

Maier et al. 2012, pp.163 f. (translated and amended)

• Improvement of the general business environment, e.g. taxation, regulations constraining innovation and firm growth

• Formation of networks between firms, including research, education and other supporting organisations

• Provision and sharing of information on market and technology trends

• Promotion of entrepreneurship to reach a critical mass of firms for localisation economies to kick in

• Attraction of mobile firms to fill gaps in regional value chains or to increase the agglomeration of firms

• Building and upgrading cluster-specific infrastructure, e.g. research and training centres

• Locational marketing to build cluster/place brand recognition

• European Union

• Identification and cross-border networking of clusters

• Knowledge exchange and dissemination of best practice among policymakers and practitioners (cluster managers)

• Funding for clusters through structural funds

• Federal Government: Cluster contests BioRegio (1996-2004), InnoRegio (1999-2006), Spitzencluster-Wettbewerb (2008-2017,

cf. EFI 2015, p. 39

)

• Federal States (Länder)

(cf. Buhl/Meier zu Köcker 2008)

, e.g. North Rhine-Westphalia:

• 16 NRW-Clusters + open RegioCluster contest

• Cluster contests for disbursement of structural funds

• Regions and municipalities

• Out of 144 cities > 50.000 inhabitants, 63 % claimed to have a coherent strategy for the development of cluster, networks, fields of technology or competence

(Hollbach-Grömig/Floeting 2008)

• Case studies: see below

Cluster Policy as Multi-level Governance

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Hannover Region:

hannoverimpuls Wolfsburg AG Braunschweig Region

Regensburg Nuremberg Region Central Franconia dortmund-project

Wuppertal-Solingen-Remscheid kompetenzhoch3

Cartography: Stephan Pohl

• State-level policies

• NRW ~ mature industries facing structural change

• Bavaria ~ late

industrialisation, hightech industries

• Niedersachsen ~ „normal“

region

Case Studies of Regional Cluster Policies

• Seven regional (sub-state) case studies

• 2006-2007: 110 semi-structured interviews with 134 practitioners, consultants and independent observers

Kiese 2012

State/Region/City Programme/Organisation (Start year in brackets)

NRW Network projects since 1993, Fields of competence (2000-2005), cluster policy (*03/2007)

Lower Saxony Regional growth concepts (since 2004)

Bavaria Antecedents: Offensive Zukunft Bayern (1994-1999), High-Tech-Offensive (2000-2005) Allianz Bayern Innovativ (Cluster-Offensive + Regional management, *02/2006) Dortmund Incubator and technology park since 1985, industry targeting in local economic

development since 1997, dortmund-project (*7/2000) Wuppertal-Solingen-

Remscheid

kompetenzhoch³(*2001)

Wolfsburg AutoVision (concept, 1998) ⇒Wolfsburg AG (cluster management organisation, *1999) Hannover Hannover-Projekt (concept, 2002) ⇒hannoverimpuls GmbH (organisation, 2003) Braunschweig Projekt REGION BRAUNSCHWEIG GMBH (*02/2005)

Central Franconia Nuremberg programme (1994) ⇒Regional Vision (1998/2005): five fields of competence with decentral network initiatives

Regensburg BioRegio/BioPark (1996/1998), strategic partnerships for sensor technology (06/2003) and ICT security (03/2006)

Case Studies: Overview

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1) What are Cluster Policies?

2) Impacts of Regional Cluster Policies in Germany 3) Problems: Stylised Facts

4) Implications and Research Needs

Agenda

Source: Wolfsburg AG 2005, p. 16 (own translation)

Case Study Wolfsburg: AutoVision Cluster Strategy

Realisation

Concept implementation

Building linkages between clusters Realisation of further

modules CMO with units

• ICT incubator

• Leisure

• Supplier attraction

• Personnel service agency Cluster initiation

• Mobility and leisure

Years AutoVison GmbH

• Venture

• Service

• People

Strengthening of clusters

• Mobility and leisure Cluster initiation

• IT and health business

Mobile Life Campus Cluster Hub

• Mobility

• IT

• Leisure

• Health business

Implement and build-up Build-up and establish Build-up, establish and network

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• + 43,255 jobs with compulsory social security contributions (June 1997: 73,363 ⇒ June 2014: 116,618 = +59%)

• +16,000 permanent jobs in clusters (“key growth areas”)

• Unemployment rate of 4.8% ( ∅ 2014; 1998: 19.3%)

Wolfsburg AG: Balance Sheet

Wolfsburg AG 2015, p. 39

• 586 companies founded in or attracted to WOB in key growth areas (June 2015)

• Including 183 supplier companies relocated to WOB

• 100-200 million € drain on consumer spending in 1997 reversed to a 70 million € surplus in 2008

• Benchmarked as a German region with “very high prospects” in the Prognos city rankings published in 2004, 2007, 2010 and as a German region with “the best prospects” in 2013

The Case of hannoverimpuls

• Customer Relations Management since 2004: Annual survey of employment figures for all firms and start-ups using the organisation’s services

• Figures grossed up for non-response, multiplier effects and general growth of focus industries (based on McKinsey & Co. formula)

• 2013: 3,379 jobs created (+47% vis-a-vis target of 2,300)

• 2003-2013: 33,701 jobs created (+10.2% vis-a-vis target of 30,570)

Kiese 2008b, p. 224-227; Data: hannoverimpuls 2008, p. 36; LHH 2014, pp. 108 & 112; *) from 2009 only start-ups

2003-

2008 2009 2010 2011 2012 2013 2009-2013

Firms* 2,970 564 619 657 624 475 2,741

Employment 12,558 1.814 2,682 4,246 3,735 3,379 15,856

• „jobs originally crated through hannoverimpuls“

(hannoverimpuls 2007)

• „a performance record that is unique in its clarity and significance”

(LHH 2007, p. 5)

• „Game of questions and answers with politicians“; “absolutely voodoo”

(interviewed practitioners)

Regional Cluster Policy: What has been achieved?

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• Professionalization, focus and strategic orientation of local and regional economic development efforts

• E.g. Dortmund

• Innovation centre ⇒ ten specialised incubators

• Start-up contests

What has really been achieved?

• Process benefits:

Improving a region’s organising capacity

• Indicator: Capacity to attract public funding from higher levels (e.g.

Dortmund, Central

Franconia)

Source: Adapted from van den Berg et al. 1997, p. 260

Source: Own Calculations based on MWEIMH-NRW 2015

Capacity to Attract: ERDF-RCE in NRW, 2007-2013

Dortmund is among

the top recipients!

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Phoenix West (dortmund-project 2007, p. 26)

• New 115 hectare technology park for micro and nano technologies, manufacturing process technology and IT on former steel mill site

• MST.factory as specialised business incubator with cleanroom facilities, cost 50 million €

Phoenix West: Urban Renewal through Cluster Policy

MST.factory on PHOENIX West (Kiese et al.

2007)

Mio. € Source of Funding Utilisation Source

80.0 ERDF Objective 2 2000-2006 funding period

Phoenix-West 1

19.0 of which: infrastructure 1

36.6 of which: MST.factory 2

19.6 National regional policy (federal &

state government, allocated 2009)

Improvement of location, perception and environment

3

Sources: 1) StadtbezirksMarketing Dortmund-Hörde 2006, 2) BMWi 2008, p. 24; 3) MWME-NRW 2009

1) What are Cluster Policies?

2) Impacts of Regional Cluster Policies in Germany 3) Problems: Stylised Facts

4) Implications and Research Needs

Agenda

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1. Technocratic understanding of clusters in policy and practice 2. For simplicity‘s sake, clusters are understood as networks 3. Spatial mismatch between cluster and policy ⇒ over-/

underbounding

4. Temporal mismatch (short-termism vs. cluster development) 5. Herd behaviour (ICT, bio, nano…)

6. From horizontal demonstration effects to top-down diffusion 7. Inflationary use of cluster term ⇒ meaning, credibility ⇓ ⇓ ⇓ ⇓ 8. Lack of explicit theoretical foundation/reference

9. Sloppy identification of cluster potential 10. Declining cluster focus over time

Stylized Facts on Regional Cluster Policies in Germany

Cf. Kiese 2012, pp. 323-331

1. Technocratic understanding of clusters

• „Let‘s form a cluster!“

• Dominance of bureaucratic rationality

(cf. Kiese 2008a)

Understanding of Clusters and Matching Problems

2. For simplicity‘s sake, clusters are understood as networks

• Focus on co-operation, neglect of competition

• Geography of clusters ignored in state-level policies attempting at networking across administrative territory

Cf. Kiese 2012, pp. 323-326

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Cluster Networks

• Spatially bounded • No spatial dimension

• Focuses co-operation and competition

• More than co-operative than competitive

• Element of networks (external dimension)

• Part of clusters, but usually extending beyond clusters’ spatial confines

• Policy: Concept • Policy: Instrument/Tool

...but commonly equated in policy and practice!

e.g. Bavaria: Clusters as „organised state-wide networks of firms and research organisations”

(see Stoiber 2006, S. 10)

Clusters and Networks: Conceptual Differences

Cf. Kiese 2012, p. 39 f.

1. Technocratic understanding of clusters

• „Let‘s form a cluster!“

• Dominance of bureaucratic rationality

(cf. Kiese 2008a)

Understanding of Clusters and Matching Problems

2. For simplicity‘s sake, clusters are understood as networks

• Focus on co-operation, neglect of competition

• Geography of clusters ignored in state-level policies attempting at networking across administrative territory

Cf. Kiese 2012, pp. 323-326

3. Spatial mismatch between cluster and policy

• Densities and scales of interaction vs. parochial thinking

• Danger of over- or underbounding

• Neglect of external cluster dimension

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Own visualisation inspired by Bennett/McCoshan 1993, p. 222

Administrative Region (AR)

•

• • • •

• Manufacturing firms

Knowledge-intensive business services Universities, research organisations

Specialised infrastructure, supporting organisations

•

• • •

•

• • • •

•

• • •

•

• • •

• •

•

• • • • • •

•

• • •

• • •••• • • • •

• • •

•

Overbounding

• • •

• Manufacturing firms

Knowledge-intensive business services Universities, research organisations

Specialised infrastructure, supporting organisations

• • •

•

• • •

• •

•

• • • •

•

• • •

•

• AR1 AR2

Underbounding

Own inspiration inspired by Bennett/McCoshan 1993, p. 222

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1. Technocratic understanding of clusters

• „Let‘s form a cluster!“

• Dominance of bureaucratic rationality

(cf. Kiese 2008a)

Understanding of Clusters and Matching Problems

2. For simplicity‘s sake, clusters are understood as networks

• Focus on co-operation, neglect of competition

• Geography of clusters ignored in state-level policies attempting at networking across administrative territory

Cf. Kiese 2012, pp. 323-326

3. Spatial mismatch between cluster and policy

• Densities and scales of interaction vs. parochial thinking

• Danger of over- or underbounding

• Neglect of external cluster dimension

4. Temporal mismatch

• Cluster development requires long gestation periods

• Electoral cycles demand short-term and visible effects

5. Herd behaviour

• Megatrends and key technologies (biotech, nanotech, ICT, health- related technologies...)

• Demonstration effects (“me too”)

⇒ Convergence of cluster policy portfolios

Inflation of Cluster Policy

Cf. Kiese 2012, pp. 326-330

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Convergence of Cluster Portfolios: Herd Behaviour?

NRW L Saxony Bavaria Dortmund Berg Wolfsburg Hannover Brauns. Nuremberg Regensburg Total

Number of clusters 16 none¹ 19 3 5 4 5 6 5 ⇒ 7 3

ICT 8

A utomotive, transport

technology, telematics 7

Health services, life sciences,

biotech, medtech 7

Mechanical eng, process technology (mechatronics,

automation) 5

Logistics 4

Environmental technology 4

Energy 3

Media, event &

communication 3

Nano & micro technology 3

New materials 3

Chemical industry 2

Food processing 2

Financial Services 2

Tourism, leisure &

entertainment 2

Sensors 2

'Innovative Services' 1

Electronics & electrical

engineering 1

Forestry & wood processing 1

Cultural economy 1

Plastic products 1

A ircraft & spacecraft,

satellite navigation 1

Metal processing 1

Optical technologies 1

Product design &

development 1

1) in Regional Growth Concepts; but focus in technology policy and state initiatives hightech cluster, science-based manufacturing-based generic technologies service-based

Cf. Kiese 2012, pp. 232, 313

5. Herd behaviour

• Megatrends and key technologies (biotech, nanotech, ICT, health- related technologies...)

• Demonstration effects (“me too”)

⇒ Convergence of cluster policy portfolios

Inflation of Cluster Policy

Cf. Kiese 2012, pp. 326-330

6. Top-Down Diffusion

• Originally bottom-up and horizontal diffusion

• Increasingly vertical diffusion EU ⇒ (federal ⇒ ) states ⇒ regions/

municipalities

• Professionalisation of economic development practice and specialisation of consultants as transfer agents

(cf. Stone 2004, Kiese 2010)

7. Inflation

• „Clusters“ as meaningless labels?!

• Danger of arbitrariness

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8. Lack of theory

• Porter’s definition only reference to academic literature

• Incremental, cumulative, path-dependent learning by doing ⇒ coagulated experience and implicit theories

(Hofmann 1993)

Danger of Conceptual Dilution

9. Insufficient identification of cluster potential

• Methodological toolbox used only scantily

• Political decisions shaped by interest groups, proportional representation and concerns for spatial equity

⇒ Inflation, danger of promoting „wishful thinking“ clusters

(Enright 2003, p. 104)

Cf. Kiese 2012, pp. 323-331

Methods for Cluster Identification

Data: Hollbach-Grömig/Floeting 2008, p. 11 (n=94)

Research probably skewed through methodology (postal survey)!

Own experience External expertise Growth forecasts Workshops with stakeholders Network analysis Measures of concentration Value chain analysis Contests Others

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Methods for Cluster Identification: Case Studies

Translated from Kiese 2008a, p. 138

8. Lack of theory

• Porter’s definition only reference to academic literature

• Incremental, cumulative, path-dependent learning by doing ⇒ coagulated experience and implicit theories

(Hofmann 1993)

Danger of Conceptual Dilution

9. Insufficient identification of cluster potential

• Methodological toolbox used only scantily

• Political decisions shaped by interest groups, proportional representation and concerns for spatial equity

⇒ Inflation, danger of promoting „wishful thinking“ clusters

(Enright 2003, p. 104)

Cf. Kiese 2012, pp. 323-331

10.Declining cluster orientation during implementation

• Promotion of too many “clusters” without sufficient potential to reach critical mass

• Generic project easier to realise (“early wins”)

• Bureaucratic rationality: CMO fighting for budgets and survival

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1) What are Cluster Policies?

2) Impacts of Regional Cluster Policies in Germany 3) Problems: Stylised Facts

4) Implications and Research Needs

Agenda

Challenges for Regional Cluster Policies

Problem Solution

Understanding of

clusters moderating and facilitating role of policy appreciate organic forces of cluster evolution Matching problems

(space & time) overcome parochial thinking to form regional alliances that match the spatial extent of clusters

leadership and long-term strategy

Inflation identify and promote original regional assets rather than just follow fads; marry trends and tradition

(cf. Hospers 2004)

use participatory methods (e.g., foresight,

cf. Koschatzky 2005

) to discover regional trajectories

Conceptual dilution, declining cluster orientation

careful and objective analysis of cluster potential

exploit toolbox for cluster identification more fully, including open cluster contests as discovery device

continuous monitoring, independent evaluation

continuous review and adaptation of concept/strategy

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• Comparative cluster policy research

• Structural & institutional variety ⇒ ⇒ ⇒ ⇒ design, implementation and outcomes of cluster policy

• Interregional

(e.g., Kiese 2012 for Germany)

and international

(e.g., Sternberg et al. 2010 for the U.S.

vs. Germany)

Research Needs

• Different perspectives proved usefulness

• Institutional (varieties of capitalism

[cf. Sternberg et al. 2010]

, regional & multilevel governance)

• Policy diffusion/transfer and learning

(cf. Kiese 2010)

• Public choice

(cf. Kiese 2008, Kiese/Wrobel 2011)

• Evolutionary perspective: Cluster policy learning across time and space

(cf. Kiese 2010)

• Independent evaluation of cluster policies

• Conceptual and methodological challenges

• Practice of commissioned evaluation governed by political-administrative system

(cf. Kiese 2009, 2014)

Silicon Valley Silicon X Y Valley

Thank you very much for your attention!

Cluster Policies: Beware of Context!

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