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Industry Clusters: Definition, Analysis, Action Joe Cortright

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Most jobs are here: schools, hospitals, grocery stores, restaurants ... Las Vegas, Orlando dominant centers of convention business ... – PowerPoint PPT presentation

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Title: Industry Clusters: Definition, Analysis, Action Joe Cortright


1
Industry ClustersDefinition, Analysis,
ActionJoe Cortright
  • October 2006

2
Roadmap
  • Definition What are Clusters? Why do firms
    cluster?
  • Analysis Finding clusters
  • Action Working with clusters

3
I. Why Cluster(s)?
  • What are they? How do they work?

4
What Kind of Economy?
  • While most jobs and businesses in every state
    area are the same
  • Restaurants, grocery stores, hospitals, beauty
    salons,
  • About a third differs Traded sector

5
Traded Sector Drives Growth
Most jobs are here schools, hospitals, grocery
stores, restaurants
Local
Suppliers
Sales to the rest of the world
Traded/Export Sector
But firms in this sector drive the economy
6
Strategies Differ
Promote Efficiency World Class Suppliers
Build Cluster Competitiveness
7
Defining Industry Clusters
  • Clusters are geographic concentrations of
    interconnected companies and institutions in a
    particular field, including
  • suppliers of specialized inputs, machinery,
    services
  • distribution channels and customers
  • manufacturers of complementary products
  • companies related by skills, technologies or
    common inputs
  • related institutions such as research
    organizations, universities, standard-setting
    organizations, training entities, and others

8
Specialization of Clusters
Source Council on Competitiveness
9
Different Places, Different Paths
  • Global Hubs - New York, Chicago
  • New Ideas Seattle (Microsoft, Amazon, Biotech,
    Starbucks)
  • High Tech Centers Austin, Boise
  • Entertainment Machine Las Vegas, Orlando
  • Education Centers - Providence, Philadelphia
  • Retirement Mecca Phoenix, S. Florida

10
High Tech Centers
Seattle
Portland
Minneapolis
Boston
Salt Lake City
Sacramento
Silicon Valley
Denver
Research Triangle Park
San Diego
Phoenix
Atlanta
Austin
11
High Tech is Specialized
Seattle - Software
Portland - Semiconductors - SME/EDA - Display -
Computers
Minneapolis - Computers - Medical Devices
Boston - Computers
Salt Lake City - Software - Medical Devices -
Storage Technology
Sacramento - Computers
Silicon Valley everything!
Denver - Telecommunications - Satellite - Storage
Research Triangle Park - Software
San Diego - Communications
Phoenix - Semiconductors
Atlanta - Database - Telecommunications
Austin - Semiconductors - Computers - SME
12
Porter Clusters
  • Starts from the business strategy standpoint
  • Says Economic success isnt random
  • Similar and related businesses draw advantages
    from proximity
  • Clustering holds for most traded goods autos,
    carpets, RVs, others

13
What makes Clusters Tick?
Source Michael Porter, Harvard Business School
14
Oregons Microbrew Cluster
Rivalry
Customers
Inputs
Suppliers
15
An Oregon Cluster
16
Micro-foundations of Clusters
  • Labor Market Pooling
  • Supplier Specialization
  • Knowledge Spillovers
  • Entrepreneurship
  • Path Dependence and Lock-In
  • Culture
  • Local Demand

17
Clusters Provide Advantages
Hard Economies
  • Supply chains
  • Labor pools
  • Specialized services
  • RD and technology
  • Capital

Reduced costs More options Quicker responses
Soft Economies
  • Association
  • Networking
  • Tacit learning
  • Knowledge leaks
  • Labor grapevines

Collective influence Innovation Imitation
18
Typologies of Clusters
  • Buyer-Supplier and Value Chain
  • Inter-Firm Relationships
  • Geographic Extent
  • Level of Activation/Awareness
  • Working, Latent, Potential
  • Cluster Life Cycle- Phase
  • Embryonic, Growing, Mature/Declining, Renewing
  • Other Issues

19
Less Known Origins of Clusters
20
II. Finding Clusters
  • Applying our definition to the real world
  • Quantiative and Qualitiative Techniques will be
    discussed later

21
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22
Cluster Methodology
Define region
Define geographic area for which cluster analysis
is done. I.e. Portland-Vancouver, OR-WA PMSA
Products
Industry cluster groups Oregon Employment
Department Industry Trade Associations Researche
rs at universities Local economic
developers Others
Identification of DataSources
Identify Key Partners
Use CEW data identify clusters using 3
criteriaLQ gt 1.25Average wages 10 above US
averageGrowth rate gt national growth rate
Quantitative Analysis
Identification of CandidateClusters
Industry
Qualitative Analysis
Competitiveness Analysis
Firm
Differentiation of - Existing Clusters- Emerging
Clusters- Target Industries
Conduct interviews or focus groups with industry
representatives. Collect data about the industry
sectorin general, cluster connections and
relationshipscluster drivers, support factors,
and challenges.
Collect additional information about particular
cluster such as Patents, key products, major
geographic concentrations, top 10 leading firms,
entrepreneurial activity, competitor regions for
specific cluster. Conduct Shift-Share analysis.
Ongoing
Identify Economic Development Policies Actions
Cluster-based Economic Development Strategy
Policies and actions should be identified in
collaboration with key partners. Should address
state, regional, and local scale.
Identify metrics and performance
indicators. Strategy development and assessment
is ongoing.
23
Cluster Mapping
  • A few places are well explored
  • Outlines are (mostly) clear
  • Much detail is still unknown

24
Representing Clusters- Ideal Type
25
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26
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27
Cluster Analysis
Define Cluster
Gather Data
Convene Firms
28
Micro foundations
  • Relatively little effort to characterize the
    different sources of cluster advantages across
    clusters, over time and among geographies
  • The Murder on the Orient Express problem All
    factors potentially contribute to clustering

29
Different Explanations for the Same Clusters
  • Silicon Valley Explanations
  • Subsidies from defense spending (Markusen)
  • Local higher education spillovers (Rogers and
    Larsen)
  • Unique business culture and relationships
    (Saxenian)
  • Extraordinary academic leader (Krugman)
  • Long history of radio television (Sturgeon)

30
Top Down v. Bottom Up Approaches
  • Characteristic Top Down Bottom-Up
  • Approach Quantitative Qualitative
  • Principal Data Secondary Data Primary Data
  • Methodology Statistical Modeling Case Studies
  • Industrial Proximity Classification
    System Descriptive
  • Scope Nationwide, Local,
  • Multi-Industry Single-Cluster
  • Dominant Logic Deductive Inductive
  • Measures Employment, Patents, Relationships,
  • Wages, Sales Institutions
  • Findings Broadly Applicable Narrowly Limited

31
Sectors are not Clusters
  • SECTORS
  • Most quantitative analysis relies on data
    organized according to the SIC or NAICS
    classification schemes to define industries
  • CLUSTERS
  • Qualitative analyses define clusters according to
    local relationships. Cluster theory maintains
    that clusters cut across sector lines many
    clusters are highly specialized

32
Limitations of SIC and NAICS
  • Industry classification systems represent a
    useful way of assessing industry connections, but
    like any taxonomy, they have important
    limitations.
  • Each firm or establishment is assigned to a
    single classification.
  • Clusters span multiple classifications.
    CTLego, BIC, and Schickare not classified as
    plastics companies.
  • Arbitrariness in classifications. NAICS 334
    SIC 35 (pt) SIC 36 SIC 38 pt)
  • Scale of some clusters is too small (snowmobiles,
    houseboats)
  • NAICS 55 headquarters industry 50,000
    establishments and 2.9 million employees
  • DB has twice as many manufacturing
    establishments as Census

33
The Limts to Classification
From Desrochers, 2002
34
III. Action
  • Working with clusters

35
Clusters as a Framework for Policy
  • An organizing principle for engaging a region in
    a discussion of its economic strengths and
    weaknesses
  • A flexible tool at the intersection of analysis
    and policy-making
  • Best efforts integrate quantitative and
    qualitative methods

36
Policy Measures and Micro-Foundations
  • Labor Market Pooling Labor market information,
    specialized training
  • Supplier Specialization Brokering, recruiting,
    entrepreneurship, credit
  • Knowledge-spillovers Networking, public
    sectorRD support
  • Entrepreneurship Assistance for start-ups,
    spin-offs
  • Lock-In Work to extend and refine (and
    re-combine) existing distinctive specializations
  • Culture Acknowledge and support cluster
    Organization
  • Local Demand Aggregate and strengthen local
    demand

37
What Can Go Wrong?
  • Wishful thinking (try to create)
  • Hype (Biotech)
  • Bandwagon (Biotech Conventions)
  • Lack of equity focus
  • Lack of organizational integration to serve
    clusters (fragmented)

38
Wishful Thinking
  • Generally not possible to create a cluster where
    none exists
  • Policy should focus on conditions for cluster
    growth, revival, and creation
  • Identifying emerging clusters should be a priority

39
Get Real
  • Assess your clusters competitive strength
  • Benchmark against leading clusters elsewhere
  • How is your cluster different or better?

40
Biotechnology Bandwagon
Boston
Source Brookings Institution, 2005
41
Convention Centers- Hype
  • Las Vegas, Orlando dominant centers of convention
    business
  • Both have more than 100,000 hotel rooms

Source Brookings Institution, 2005
42
Lack of Equity Focus
  • Clusters can reinforce regional disparities if
    youre not careful.
  • Cluster strategies can favor the strong and
    exclude the weak

43
Fragmented Cluster Approaches
  • Can be worse than no cluster approach at all!
  • If organizations dont work together
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