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The Economic Performance of RegionsMeasuring the Role of Clusters

Professor Michael E. Porter Institute for Strategy and CompetitivenessHarvard Business School

The Competitiveness Institute – Annual Meeting Gothenburg, Sweden

19 September 2003

This presentation draws on ideas from Professor Porter’s articles and books, in particular, The Competitive Advantage of Nations (The FreePress, 1990), “The Microeconomic Foundations of Economic Development,” in The Global Competitiveness Report 2002, (World EconomicForum, 2003), “Clusters and the New Competitive Agenda for Companies and Governments” in On Competition (Harvard Business School Press,1998), and the Clusters of Innovation Initiative (www.compete.org), a joint effort of the Council on Competitiveness, Monitor Group, and Professor Porter. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means - electronic,mechanical, photocopying, recording, or otherwise - without the permission of Michael E. Porter.

Additional information may be found at the website of the Institute for Strategy and Competitiveness, www.isc.hbs.edu



2 Copyright 2003 © Professor Michael E. Porter TCI Annual Conference – 09-19-03 CK

Research on Clusters and Competitiveness

• Understanding of the microeconomic foundations ofcompetitiveness and the role of clusters has made significant progressin the last decade

• Cluster-based approaches have become an accepted part of economicdevelopment

• In addition to further research on concepts or case studies, there is apressing need for empirical research in two areas:

– Creating more consistent cross-sectional and time series data

on clusters and competitiveness

– Developing a systematic body of knowledge about the appropriateprocesses of cluster development and the appropriate roles of government, the private sector, and other institutions




Agenda

• Conceptual Foundations

• Empirical Evidence

• Next Steps




Sources of Prosperity

ProductivityProductivity

Innovative CapacityInnovative CapacityInnovative Capacity

Competitiveness

ProsperityProsperityProsperity




Context for

FirmStrategy

and Rivalry

Context for

FirmStrategyand Rivalry

Related andSupportingIndustries

Related andSupportingIndustries

Factor(Input)

Conditions

Factor(Input)

Conditions

DemandConditions

DemandConditions

Productivity, Innovation, and the Business Environment

• Successful economic development is a process of successive economic upgrading, in which

the business environment in a nation evolves to support and encourage increasinglysophisticated ways of competing

Sophisticated and demanding localcustomer(s)

Local customer needs that anticipatethose elsewhere

Unusual local demand in specializedsegments that can be servednationally and globally

Presence of high quality,specialized inputs availableto firms –Human resources –Capital resources –Physical infrastructure –Administrative infrastructure –Information infrastructure

–Scientific and technologicalinfrastructure

–Natural resources

Access to capable, locally based suppliersand firms in related fields

Presence of clusters instead of isolatedindustries

A local context and rules thatencourage investment andsustained upgrading

–e.g., Intellectual propertyprotection

Meritocratic incentive systemacross institutions

Open and vigorous competitionamong locally based rivals




The California Wine Cluster

Educational, Research, & TradeOrganizations (e.g. Wine Institute,

UC Davis, Culinary Institutes)

Educational, Research, & TradeOrganizations (e.g. Wine Institute,

UC Davis, Culinary Institutes)

Growers/VineyardsGrowers/Vineyards

Sources: California Wine Institute, Internet search, California State Legislature. Based on researchby MBA 1997 students R. Alexander, R. Arney, N. Black, E. Frost, and A. Shivananda.

Wineries/ProcessingFacilities

Wineries/ProcessingFacilities

GrapestockGrapestock

Fertilizer, Pesticides,Herbicides

Fertilizer, Pesticides,Herbicides

Grape HarvestingEquipment

Grape HarvestingEquipment

Irrigation TechnologyIrrigation Technology

Winemaking EquipmentWinemaking Equipment

BarrelsBarrels

LabelsLabels

BottlesBottles

Caps and CorksCaps and Corks

Public Relations and

Advertising

Public Relations and

Advertising

Specialized Publications(e.g., Wine Spectator, Trade

Journal)

Specialized Publications(e.g., Wine Spectator, Trade

Journal)

Food Cluster Food Cluster

Tourism Cluster Tourism Cluster CaliforniaAgricultural Cluster

CaliforniaAgricultural Cluster

State Government Agencies(e.g., Select Committee on Wine

Production and Economy)



Clusters and Competitiveness

• Clusters Increase Productivity / Efficiency – Efficient access to specialized inputs, services, employees, information, institutions,and “public goods” (e.g. training programs)

– Ease of coordination and transactions across firms

– Rapid diffusion of best practices

– Ongoing, visible performance comparisons and strong incentives to improve vs.local rivals

• Clusters Stimulate and Enable Innovations

– Enhanced ability to perceive innovation opportunities

– Presence of multiple suppliers and institutions to assist in knowledge creation – Ease of experimentation given locally available resources

• Clusters Facilitate Commercialization

– Opportunities for new companies and new lines of established business are

more apparent

– Commercializing new products and starting new companies is easier because of available skills, suppliers, etc.

Clusters reflect the fundamental influence of externalities / linkagesacross firms and associated institutions in competition




Levels of Clusters

• There is often an array of clusters at different locations in a given field, eachwith different levels of specialization and sophistication

• Global innovation centers, such as Silicon Valley in semiconductors, are fewin number. If there are multiple innovation centers, they normally specialize indifferent market segments

• Other clusters focus on manufacturing, outsourced service functions, or playthe role of regional assembly or service centers

• Firms based in the most advanced clusters often seed or enhance clusters inother locations in order to reduce the risk of a single site, access lower costinputs, or better serve particular regional markets

• The challenge for an economy is to move from isolated firms to an array of clusters, and then to upgrade the breadth and sophistication of clusters tomore advanced activities




Leading Footwear Clusters

Vietnam/Indonesia

• OEM Production

• Focus on the low costsegment mainly for theEuropean market

China• OEM Production

• Focus on low costsegment mainly for theUS market

Portugal

• Production

• Focus on short-production runs in themedium price range

Romania

• Production subsidiariesof Italian companies

• Focus on lower tomedium price range

United States

• Design and marketing

• Focus on specific marketsegments like sport andrecreational shoes and boots

• Manufacturing only in selectedlines such as hand-sewncasual shoes and boots

Source: Research by HBS student teams in 2002

Italy

• Design, marketing,and production of premium shoes

• Export widely to theworld market




GeneralGeneral

• Chambers of Commerce

• Professional associations

• School networks

• University partner groups

• Religious networks

• Joint private/public advisorycouncils

• Competitiveness councils

• Chambers of Commerce

• Professional associations

• School networks

• University partner groups• Religious networks

• Joint private/public advisorycouncils

• Competitiveness councils

Cluster-specificCluster-specific

• Industry associations

• Specialized professionalassociations and societies

• Alumni groups of core cluster companies

• Incubators

• Industry associations

• Specialized professionalassociations and societies

• Alumni groups of core cluster companies

• Incubators

Institutions for Collaboration

• Institutions for collaboration (IFC) are formaland informal organizations that

- facilitate the exchange of informationand technology

- conduct joint activities

- foster coordination among firms

• IFCs can improve the business environmentby

- creating relationships and level of trustthat make them more effective

- defining of common standards

- conducting or facilitating the organizationof collective action in areas such as

procurement, information gathering, or international marketing

- defining and communicating commonbeliefs and attitudes

- providing mechanisms to develop acommon economic or cluster agenda

Source: Porter/Emmons, Institutions for Collaboration: Overview, HBS case 9-703-436, 2003



Institutions for CollaborationSelected Institutions for Collaboration, San Diego

Source: Clusters of Innovation project (www.compete.org)

GeneralGeneral

San Diego Chamber of Commerce

San Diego MIT Enterprise Forum

Corporate Director’s Forum

San Diego Dialogue

Service Corps of Retired Executives, San Diego

San Diego Regional Economic DevelopmentCorporation

Center for Applied Competitive Technologies

San Diego World Trade Center

UCSD Alumni

San Diego Regional Technology Alliance

San Diego Science and Technology Council

Office of Trade and Business Development

San Diego Chamber of Commerce

San Diego MIT Enterprise Forum

Corporate Director’s Forum

San Diego Dialogue

Service Corps of Retired Executives, San Diego

San Diego Regional Economic DevelopmentCorporation

Center for Applied Competitive Technologies

San Diego World Trade Center

UCSD Alumni

San Diego Regional Technology Alliance

San Diego Science and Technology Council

Office of Trade and Business Development

Cluster-SpecificCluster-Specific

Linkabit Alumni

Hybritech Alumni

Scripps Research Institute Alumni

BIOCOMM

UCSD Connect

Linkabit Alumni

Hybritech Alumni

Scripps Research Institute Alumni

BIOCOMM

UCSD Connect




Influences on CompetitivenessMultiple Geographic Levels

Broad Economic AreasBroad Economic Areas

Groups of NeighboringGroups of Neighboring

NationsNations

States, ProvincesStates, Provinces

Metropolitan Areas,Metropolitan Areas,

Rural AreasRural Areas

NationsNations

World EconomyWorld Economy




Shifting Responsibilities for Economic Development

Old ModelOld Model

• Government drives economic

development through policy

decisions and incentives

• Government drives economic

development through policy

decisions and incentives

New ModelNew Model

• Economic development is a

collaborative process involvinggovernment at multiple levels,

companies, teaching and

research institutions, and

institutions for collaboration

• Economic development is a

collaborative process involvinggovernment at multiple levels,

companies, teaching and

research institutions, and

institutions for collaboration




Agenda


• Empirical Evidence

• Next Steps




Measuring the Composition and Performance of RegionsSources of U.S. Data

• The U.S. provides the most accessible data on regional economies; we utilizetwo main datasets in our research

– County Business Patterns (CBP)

– Patent data from the U.S. Patent and Trademark Office (PTO) and CHIResearch

• The CBP data provides employment, wage, and establishment data for the1990 to 2001 period at the four-digit SIC level (five-digit NAICS level since1997)

– Building up from the county level, data can be aggregated to the level of metropolitan statistical areas (MSA), economic areas (EA), states, andother geographic units

– We use mainly data on the 172 EAs because they are meaningful, self-contained economic units

• The patent data, also for the 1990 to 2001 period, is matched to the CBP data

– Allocation of patent technology codes to SIC codes uses an algorithmdeveloped by Silverman (1999)

– Allocation to geographies is based on the location of the patentor




Economic Performance of RegionsEvidence from the United States

Source: Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003

• U.S. regions differ widely in terms of their economic performance

Wages

• Average wage levels range from $19,225 (McAllen, TX) to $49,351 (Bay Area, CA)

• Wage growth between 1990 and 2001 has averaged 3.6% annually, ranging from 1.9%(Wheeling, WV-OH) to 5.9% (Austin-San Marcos, TX)

• Wage growth during this period had a weak negative relationship to starting wage levels

Employment

• Average annual employment growth between 1990 and 2000 ranged from -.1% (Syracuse,NY) to 6.4% (Austin-San Marcos, TX)

• Employment growth in this period has had little relation to starting level wages but was

positively and significantly related to wage growth• Employment growth varies more strongly across regions than either wages or wage growth(Coefficients of variation: .47 vs. .17 and .16)

• Large regions with high absolute employment levels registered significantly higher wages butlower wage growth and employment growth

Patenting• Patenting intensity and growth in patenting intensity varies even more strongly across

regions than employment (Coefficient of variations are 1.3 and 1.2)

• Patenting intensity is positively and significantly related to wage levels. About 30% of theregional variation in wages is accounted for by variations in patenting intensity

• The breadth of innovators in a region, as measured by patentor concentration, is alsopositively related to wages




1.5%

2.5%

3.5%

4.5%

5.5%

6.5%

$10,000 $15,000 $20,000 $25,000 $30,000

Austin-San Marcos, TX

New York, NY

Bay Area, CA

Anchorage, AK

North Platte, NE-CO

McAllen, TX

Aberdeen, SD

Source: County Business Patterns; Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003

Economic Performance of RegionsWage Level and Growth, U.S. Economic Areas

Annual Wage GrowthRate, 1990-2001

Average Wage, 2001

y = 4E-05x + 2.8316

R2 = 0.0339P-value = .016Wheeling, WV-OH




-0.5%

0.5%

1.5%

2.5%

3.5%

4.5%

5.5%

6.5%

7.5%

0 1,000,000 2,000,000 3,000,000 4,000,000

y = -2E-07x + 2.2394

R2 = 0.0309P-value = .021

New York, NY(9,974,160, 0.61%)


Annual EmploymentGrowth Rate, 1990-

2001

Employment in Starting Period, 1990

Economic Performance of RegionsEmployment Level and Growth, U.S. Economic Areas

Los Angeles, CA(6,276,102, 0.75%)


Las Vegas, NV-AZ-UT




0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

-0.5% 0.5% 1.5% 2.5% 3.5% 4.5% 5.5% 6.5%


Annual Wage GrowthRate, 1990 – 2001

Annual Employment Growth Rate, 1990-2001

Economic Performance of RegionsEmployment and Wage Growth, 1990 – 2001, U.S. Economic Areas

Las Vegas, NV-AZ-UT


Bay Area, CA

y = 2E-07x + 3.4717R2 = 0.1439

P-value < .001

Wheeling, WV-OH




$15,000

$25,000

$35,000

$45,000

$55,000

0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000


Average Wage, 2001

Employment , 2001

Los Angeles, CA

New York, NYBay Area, CA

y = 3436.5Ln(x) - 15161R2 = 0.6908

Economic Performance of RegionsSize and Wage Level, U.S. Economic Areas

y = 0.0029x + 26325R2 = 0.5419

P-value < .001

Note: Since the employment size of the Los Angeles and New York EAs are substantially larger than the size of the rest of the regions, we alsoexamined the results after dropping these two observations. R2 rises and the coefficient of size remains positive but is somewhat higher for both the linear

and the non-linear specification of the regression

Boston, MA




0

50

100

150

200

250

300

Patents per 10,000Inhabitants, 2001

Economic Areas

Innovation Performance of RegionsPatenting Intensity, U.S. Economic Areas

Source: U.S. Patent and Trademark Office; CHI Research; Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003




$15,000

$25,000

$35,000

$45,000

$55,000

0 50 100 150 200 250 300

Average Wage, 2001

Patents per 100,000 Inhabitants, 2001

Rochester, NY

New York, NY

Bay Area, CA

y = 3729.6Ln(x) + 18486R2 = 0.4538

Boise City, ID-OH

y = 84.73x + 26495R2 = 0.2301

P-value < .001

Innovation Performance of RegionsPatenting Intensity and Wage Level, U.S. Economic Areas


San Diego, CA

Source: U.S. Patent and Trademark Office; CHI Research; County Business Patterns;Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003

Boston, MA




$15,000

$25,000

$35,000

$45,000

$55,000

0.0 0.2 0.4 0.6 0.8 1.0

Average Wage, 2001

Patent Herfindahl-Hirschman Index, 2001

y = -3752.8Ln(x) + 21269R2 = 0.3872

y = -12922x + 30961R2 = 0.1961

Source: U.S. Patent and Trademark Office; CHI Research; County Business Patterns;Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003

Innovation Performance of RegionsPatentor Concentration and Wage Level, U.S. Economic Areas

New York, NY

Bay Area, CA

Boston, MA

Anchorage, AK




Mapping Regional ClustersStatistical Definition of Clusters

• Many previous studies have used ad-hoc cluster definitions, in somecases supported by input-output data

• The Cluster Mapping Project set out to delineate the boundaries of

clusters statistically

– Cluster boundaries are based on the actual patterns of co-locationof industry employment across U.S. states

• The process of identifying cluster involves two steps: – Distinguishing local, traded, and natural-resource dependent

industries

– Grouping 590 traded industries into 41 traded clusters

• Findings and details of the methodology are available at the web site of the Institute for Strategy and Competitiveness www.isc.hbs.edu and in“Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003”.



Composition of Regional EconomiesUnited States, 2001

Traded ClustersTraded ClustersTraded Clusters Local ClustersLocal ClustersLocal Clusters Natural Resource-Driven Industries

Natural ResourceNatural Resource--

Driven IndustriesDriven Industries

31.6%

1.7%

$44,956

133.84.5%

144.1

21.7

590

31.6%31.6%

1.7%1.7%

$44,956$44,956

133.8133.84.5%4.5%

144.1144.1

21.721.7

590590

67.6%

2.8%

$28,288

84.23.7%

79.3

1.3

241

67.6%67.6%

2.8%2.8%

$28,288$28,288

84.284.23.7%3.7%

79.379.3

1.31.3

241241

0.8%

-1.0%

$33,245

99.02.0%

140.1

7.2

48

0.8%0.8%

--1.0%1.0%

$33,245$33,245

99.099.02.0%2.0%

140.1140.1

7.27.2

4848

Share of Employment

Employment Growth, 1990to 2001

Average Wage

Relative WageWage Growth

Relative Productivity

Patents per 10,000Employees

Number of SIC Industries

Note: 2001 data, except relative productivity which is 1997 data.Source: Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School




Broad Composition of Regional EconomiesSelected Findings

• 42% of employment in traded industries is goods producingindustries while 58% is in services

– The relative share of employment in service industries has increased inboth traded (+11.9%) and local industries (+0.5%) in the last decade

• Traded services industries have 20% higher average wages thantraded goods

Composition and wages

• The average level of local wages is strongly associated to the level oftraded wages in a region, with causality likely to run from traded tolocal wages

• The relative wage of regions is most strongly influenced by the relativewages for each broad industry group (80.6% of variation acrossregions) versus their relative shares (ratio of traded versus localemployment) (19.4%)





$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

$15,000 $25,000 $35,000 $45,000 $55,000 $65,000 $75,000


Average Local Wage,2001

Average Traded Wage, 2001

New York, NY

Bay Area, CA

y = 0.33x + 12069R2 = 0.6869

P-value < .001

Boston, MA

Broad Composition of Regional EconomiesLocal versus Traded Wages

Anchorage, AK

Reno, NV

Las Vegas, NV

Seattle, WA

Wheeling, WV-OH




Composition of Regional EconomiesEmployment in Traded Clusters

Note: Negative growth rates in italicsSource: Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School

Employment, 2001

5.6%

2.5%

2.5%

3.4%

3.3%3.1%

3.1%0.4%

0.2%1.6%

5.2%-0.1%

1.9%3.1%

-1.6% 1.7%

0.3%-5.1%

-1.7% -0.3%

-0.4% -0.4%

-3.3%

-0.4%

0.2%

2.5%

-1.2%

-6.3%

-0.3% 2.6%

3.6%

0.1%

0.8%0.6%

-1.6%

-0.4%

-9.3%

-1.0% -0.7%

-4.3% 0.8%

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Composition of Regional EconomiesWages in Traded Clusters

Source: Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School

Average Wage,2001

9.7%

7.8%

5.0%6.2%6.0%

3.8%

4.4%3.8%

4.8%5.4%

3.6%3.4%

5.2%3.3%

2.6%

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3.0%

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$20,000

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a n d F i

s h i n g P r o d

u c t s

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e

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a r

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a r e l

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i t a l i t y a

n d T o u

r i s m

Annual Wage Growth Rate, CAGR, 1990–2001




Plastics

Oil andGas

ChemicalProducts

Pharma-ceuticals

Power

Generation

AerospaceVehicles &Defense

Lightning &ElectricalEquipment

FinancialServices

Publishingand Printing

Entertainment

Hospitality

and Tourism

Transportationand Logistics

InformationTechnology

Communi-cations

Equipment

MedicalDevices

AnalyticalInstruments

Educationand

KnowledgeCreation

ApparelLeather

andSportingGoods

AgriculturalProducts

ProcessedFood

Furniture

BuildingFixtures,

Equipmentand

Services

Note: Clusters with overlapping borders or identical shadinghave at least 20% overlap (by number of industries) in both directions

Sporting,

Recreation and

Children’s

Goods

BusinessServices

DistributionServices

Fishing &Fishing

Products

Footwear

ForestProducts

HeavyConstruction

Services

Jewelry &PreciousMetals

ConstructionMaterials

PrefabricatedEnclosures

Textiles

Tobacco

HeavyMachinery

AerospaceEngines

Automotive

ProductionTechnology

Motor DrivenProducts

MetalManufacturing

Traded Cluster Overlap




Patents per 10,000Employees, 2001

Source: U.S. Patent and Trademark Office; CHI Research; Cluster Mapping Project, Harvard Business School.

Patenting Intensity by Traded Cluster

Average: 35.6

0

50

100

150

200

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t

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h i l d r

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Traded ClustersKey Observations

Cluster composition of regions

• The mix of clusters varies widely across U.S. regions with an average standarddeviation of employment rank for a given cluster of 6.8

• U.S. state economies were, on average, more concentrated in specific traded

clusters in 2000 than in 1990

Cluster mix and wage

• The average traded cluster wage in a region is most strongly influenced by therelative wages within given traded clusters (73.8% of variation across regions)versus the mix of clusters (26.2%)

Cluster specialization and economic performance

• The share of traded employment in a region in strong clusters (LQ ≥ .8) is

positively and significantly related to average wages and to patenting• Increasing concentration of employment in specific clusters is positively andsignificantly associated with wage growth


Specialization of Regional Economies




Specialization of Regional EconomiesSelect U.S. Geographic Areas

BostonAnalytical InstrumentsEducation and Knowledge CreationCommunications Equipment

BostonAnalytical InstrumentsEducation and Knowledge CreationCommunications Equipment

Los Angeles Area

ApparelBuilding Fixtures,Equipment andServices

Entertainment

Los Angeles Area

ApparelBuilding Fixtures,Equipment andServices

Entertainment

ChicagoCommunications EquipmentProcessed FoodHeavy Machinery

ChicagoCommunications EquipmentProcessed FoodHeavy Machinery

Denver, COLeather and Sporting GoodsOil and GasAerospace Vehicles and Defense

Denver, COLeather and Sporting GoodsOil and GasAerospace Vehicles and Defense

San DiegoLeather and Sporting GoodsPower GenerationEducation and KnowledgeCreation

San DiegoLeather and Sporting GoodsPower GenerationEducation and KnowledgeCreation

San Francisco-Oakland-San JoseBay AreaCommunications

EquipmentAgriculturalProductsInformationTechnology

San Francisco-Oakland-San JoseBay AreaCommunications

EquipmentAgriculturalProductsInformationTechnology

Seattle-Bellevue-Everett, WAAerospace Vehicles andDefenseFishing and FishingProducts

Analytical Instruments

Seattle-Bellevue-Everett, WAAerospace Vehicles andDefenseFishing and FishingProducts

Analytical Instruments

HoustonHeavy Construction ServicesOil and GasAerospace Vehicles and Defense

HoustonHeavy Construction ServicesOil and GasAerospace Vehicles and Defense

Pittsburgh, PAConstruction MaterialsMetal ManufacturingEducation and Knowledge

Creation

Pittsburgh, PAConstruction MaterialsMetal ManufacturingEducation and Knowledge

Creation

Atlanta, GADistributionTransportation and LogisticsBusiness Services

Atlanta, GADistributionTransportation and LogisticsBusiness Services

Raleigh-Durham, NCCommunications EquipmentInformation TechnologyEducation andKnowledge Creation

Raleigh-Durham, NCCommunications EquipmentInformation TechnologyEducation andKnowledge Creation

Wichita, KSAerospace Vehicles and

DefenseHeavy MachineryOil and Gas

Wichita, KSAerospace Vehicles and

DefenseHeavy Machinery

Oil and Gas

Note: Clusters listed are the three highest ranking clusters in terms of share of national employmentSource: Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School




0

1

2

3

4

-50 0 50 100

Specialization of Regional EconomiesAtlanta Metro Area

PercentageShare ofNational

ClusterEmployment

in 2000

Percentage Change, 1990–2000

= 0–19,999 = 20,000–49,999 = 50,000–99,999 = 100,000+

Power Generation(1.8, 320.1)

Oil and Gas

Agricultural Products

Leather Products

Heavy Construction Services

Heavy Machinery

Processed Food

Analytical InstrumentsProduction Technology

MetalManufacturing

Education andKnowledge Creation

DistributionServicesFinancial

Services

Transportation and Logistics(4.1, 74.7)

BusinessServices

Jewelry andPrecious Metals

PrefabricatedEnclosures

Furniture

Lighting andElectrical

Equipment

Apparel

Hospitalityand

Tourism

Pharmaceuticalsand Biotechnology

Atlanta’s AverageShare = 1.9%

Note: Uses narrow cluster definitions to avoid overlapSource: Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School

Motor DrivenProducts

Aerospace Vehiclesand Defense

Aerospace Engines(0.5, 601.7)

Textiles

Building Fixtures,

Equipmentand Services

Sporting Products

Automotive

IT

CommunicationsEquipment




-$30,000

-$20,000

-$10,000

$0

$10,000

$20,000

$30,000

-$25,000 -$20,000 -$15,000 -$10,000 -$5,000 $0 $5,000 $10,000 $15,000


Cluster Wage LevelEffect, 2001

Cluster Mix Effect, 2001

New York, NY

Bay Area, CA

Boston, MAAnchorage, AKLas Vegas, NV

Seattle, WA

Wheeling, WV-OH

Traded ClusterCluster Wage and Cluster Mix Effect


Median=$-3,419

Median=$-12,264

C




$15,000

$25,000

$35,000

$45,000

$55,000

50 100 150 200 250 300

Average RegionalWage, 2001

Share of Traded Employment in Strong Clusters (LQ ³ .8), 2001

y = 96.736x + 16218R2 = 0.377

New York, NY

Bay Area, CA

Boston, MA

Traded ClusterEmployment in Strong Clusters and Wage Levels


T d d Cl




2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10


Annual Regional WageGrowth Rate, 1990-2001

Change of Cluster Employment GINI, 1990-2001

y = 8.7905x + 3.6107R2 = 0.2626

P-value = .0001

Traded ClusterChange in Cluster Employment Concentration and Wage Growth

MANY

Economy

becoming less specialized

Economy

becoming more specialized

Explaining Average Regional Wages




Explaining Average Regional WagesMultiple Regression Model

Independent VariableIndependent Variable

• Total regional employment

• Patents per capita

• Patentor concentration

• Share of strong clusters inregional employment

• Cluster breadth

• Total regional employment

• Patents per capita

• Patentor concentration

• Share of strong clusters inregional employment

• Cluster breadth

EffectEffect

Positive, significant


Negative, significant





Negative, significant



Dependent variable: Regional Average Wage

Note: Regression uses 2001 data for 172 U.S. economic areasSource: Michael E. Porter, The Economic Performance of Regions”, Regional Studies , Vol. 37, 2003

Explained Variation (adjusted R2): 72.8%

Cluster Mapping Web Site




Cluster Mapping Web Site

• The web site of the Cluster Mapping Data project (access throughwww.isc.hbs.edu) provides

– Regional data (state, EA, MSA)

– Cluster and subcluster data – Time-series data

• Much of the data is available based on registration only. A subscription

site provides access to more detailed data and options for enhanceddata downloading

Agenda




Agenda


• Economic Performance of Regions

• Cluster Composition of Regions

• Next Steps

Future Empirical Research Agenda




Future Empirical Research AgendaCurrent Efforts

• Develop cluster data at the national level using international tradepatterns – Launch of new data website at www.isc.hbs.edu before the end of 2003

• Roll out the methodology to countries besides the United States – Canada, 2001 (www.competeprosper.ca)

– Sweden, 2003 (www.cluster-research.org)

• Analysis of rural regions and their relationships to nearby metropolitan

areas – On-going effort with the Economic Development Agency (EDA –

Department of Commerce) in the United States

• Collection of further data on the regional business environments to

understand the drivers of cluster composition and performance – Studies in selected U.S. regions in the Clusters of Innovation -project

– New initiative to collect cluster data via the Cluster Competitiveness Report offered by the “Fundacio Clusters I Competitivitat”(www.clustercompetitiveness.org)

Selected References on Clusters Competition Innovation




Selected References on Clusters, Competition, Innovation,and Regional Economies

Professor Michael E. Porter• “The Economic Performance of Regions”, Regional Studies, Vol. 37, 2003

• “UK Competitiveness: Moving to the Next Stage”, with Christian Ketels, DTI Economics Papers, No.3,London: 2003

• “The Competitive Advantage of Corporate Philanthropy,” with Mark Kramer, Harvard Business Review,December 2002

• “Building the Microeconomic Foundations of Prosperity: Findings from the MicroeconomicCompetitiveness Index” in The Global Competitiveness Report 2002-03, New York: Oxford UniversityPress, New York: Oxford University Press, 2002

• “Clusters of Innovation Initiative: Research Triangle Report,” (with the Council on Competitiveness,Monitor Group, and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2002

• “Clusters of Innovation Initiative: Pittsburgh Report,” (with the Council on Competitiveness, Monitor Group, and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2002

• “Clusters of Innovation Initiative: Atlanta Report,” (with the Council on Competitiveness, Monitor Group,and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2002

• “Clusters of Innovation Initiative: Wichita Report,” (with the Council on Competitiveness, Monitor Group,and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2002

• “Enhancing the Microeconomic Foundations of Prosperity: The Current Competitiveness Index” in The

Global Competitiveness Report 2001-02, New York: Oxford University Press, 2001

Selected References on Clusters, Competition, Innovation,




• “U.S. Competitiveness 2001,” with Debra van Opstal, Washington, DC: Council on Competitiveness, 2001

• “Innovation Lecture,” published by the Dutch Ministry of Economics, 2001

• “National Report: Clusters of Innovation Initiative,” (with the Council on Competitiveness, Monitor Group,and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2001

• “Clusters of Innovation Initiative: San Diego Report,” (with the Council on Competitiveness, Monitor Group,and ontheFRONTIER), Washington, DC: Council on Competitiveness, 2001

• “The Current Competitiveness Index: Measuring the Microeconomic Foundations of Prosperity” in TheGlobal Competitiveness Report 2000-01, New York: Oxford University Press, 2000

• “Location, Competition, and Economic Development: Local Clusters in a Global Economy,” (EconomicDevelopment Quarterly, February 2000, 15-34)

• “Locations, Clusters, and Company Strategy” in The Oxford Handbook of Economic Geography, (G. L.Clark, M.P. Feldman, and M.S. Gertler, eds.), New York: Oxford University Press, 2000

• “Attitudes, Values, Beliefs and the Microeconomics of Prosperity,” in Culture Matters: How Values ShapeHuman Progress, (L.E. Harrison, S.P. Huntington, eds.), New York: Basic Books, 2000

• “Clusters and the New Competitive Agenda for Companies and Governments” in On Competition, Boston:Harvard Business School Press, 1998

• The Competitive Advantage of Nations, New York: The Free Press, 1990

pand Regional Economies (continued)

Professor Michael E. Porter

Web resources




Web resources

• Institute for Strategy and Competitiveness www.isc.hbs.edu

– Cluster Mapping Project data.isc.hbs.edu/isc

– Cluster Profiles data.isc.hbs.edu/cp

• Clusters of Innovation Initiative

– Council on Competitiveness www.compete.org

– Monitor Company www.monitor.com

• Cluster Competitiveness Report

– “Fundacio Clusters I Competitivitat” www.clustercompetitiveness.org

Pres Mporter Economic Performance[1]

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