frameworks and applied research -...

1 Copyright 2012 © Michael E. Porter, Christian Ketels

Clusters and Competitiveness Frameworks and Applied Research

Richard Bryden

Director of Information Products

Institute for Strategy and Competitiveness

Harvard Business School

www.isc.hbs.edu

Workshop on Applied Research

Centro de Investigación e Inteligencia Económica, UPAEP

June 23, 2012

Content in this presentation is drawn from the work of Michael E. Porter, Christian Ketels, Mercedes Delgado, and Scott Stern.

I would like to acknowledge also the contribution of Sam Zyontz to this presentation.

http://www.isc.hbs.edu/

http://www.isc.hbs.edu/


Environmental

Quality and

Competitiveness

Creating

Shared Value

Antitrust and

Competition

Policy

Strategy for

Philanthropic

Organizations

Economic

Development

in Inner Cities

Strategy for

Cross-National

Regions

National

Competitiveness

Competitiveness

of States and

Sub-national

Regions

Clusters

and Cluster

Development

Innovation

and Innovative

Capacity

Competition

in Health Care

Corporate-

Level Strategy

Strategy and

the Internet

Competitive

Strategy

Internationalization

and Global Strategy

Competition and

Society

Institute for Strategy and Competitiveness Intellectual Agenda

Competition and

Firm Strategy

Competition and

Economic Development

Market

Competition


Research and

Publication

Information Course Platform

Institute for Strategy and Competitiveness Leverage Model

Institution Building


Research and

Publication

Information Course Platform

Institution Building

Competitiveness and Economic Development Main Activity Areas

• Microeconomics of

Competitiveness – ITESM-EGAP

– ITESM-Puebla

– Universidad Panamericana

– University of Sonora (UNISON)

– UPAEP

• Institutions for Competitiveness

• National Economic Strategy

• Cluster policy

• Export diversification

• Location and company performance

• MOC Network

• Competitiveness

Index

• Cluster Mapping

5 Copyright © 2012 Professor Michael E. Porter

What is Competitiveness?

• Nations and regions compete to offer a more productive environment for business

• Competitiveness is not a zero sum game

• Competitiveness depends on the long term productivity with which a nation or region

uses its human, capital, and natural resources

− Productivity sets sustainable wages, job growth, and standard of living

− It is not what industries a nation or region competes in that matters for prosperity, but

how productively it competes in those industries

− Productivity in a national or regional economy benefits from a combination of

domestic and foreign firms

A nation or region is competitive to the extent that firms operating there are able to

compete successfully in the global economy while supporting rising wages and

living standards for the average citizen


Macroeconomic Competitiveness

Microeconomic Competitiveness

Quality of the

National

Business

Environment

Sophistication

of Company

Operations and

Strategy

Macroeconomic

Policy

Social

Infrastructure

and Political

Institutions

State of Cluster

Development

Conceptual Framework for Competitiveness Key Building Blocks

Endowments

Size Natural

Resources

Geographic

Location


Components of Macroeconomic Competitiveness

•

• Fiscal policy – Government surplus/deficit

– Government debt

• Monetary policy – Inflation

Macroeconomic

Policies •

• Human development – Basic education

– Health

• Political institutions – Political freedom

– Voice and accountability

– Political stability

– Government effectiveness

– Decentralization of economic policymaking

• Rule of law – Security

– Civil rights

– Judicial independence

– Efficiency of legal framework

– Freedom from corruption

Social Infrastructure and

Political Institutions




Sophistication

of Company

Operations and

Strategy

Quality of the

National

Business

Environment

Social

Infrastructure

and Political

Institutions

Macroeconomic

Policies

State of Cluster

Development

Endowments

The external business

environment conditions

that enable company

productivity and

innovation

What Determines Competitiveness?




Sophistication

of Company

Operations and

Strategy

Quality of the

National

Business

Environment

Macroeconomic

Policies

Social

Infrastructure

and Political

Institutions

State of Cluster

Development

Endowments

A critical mass of firms

and institutions in each

field to harness efficiencies

and externalities across

related entities

Social

Infrastructure

and Political

Institutions

Macroeconomic

Policies





Sophistication

of Company

Operations and

Strategy

Quality of the

National

Business

Environment

Macroeconomic

Policies

Social

Infrastructure

and Political

Institutions

State of Cluster

Development

Endowments

Internal skills, capabilities,

and sophistication of

management practices of

companies

Social

Infrastructure

and Political

Institutions

Macroeconomic

Policies



• Macroeconomic competitiveness sets the potential for high productivity, but is not sufficient

• Productivity ultimately depends on improving the microeconomic capability of the economy and the

sophistication of local competition




Sophistication

of Company

Operations and

Strategy

Quality of the

Business

Environment

Social Infrastructure

and Political

Institutions

Macroeconomic

Policies

State of Cluster

Development

Endowments

12 Copyright 2012 © Christian Ketels

A New Definition of Competitiveness

“GDP relative to the available labor force

given the quality of a location to do business”

Linked to all ultimate drivers of

productivity, in particular those

amenable to policy action

Broad measure of productivity.

Productivity ultimately drives

prosperity, the key outcome policy

makers are concerned about

Captures both productivity of

employees and of labor

market institutions


Testing the Competitiveness Framework An Empirical Approach

• Data

– Broad set of data covering all dimensions of the framework

– Unit of observation is the average response per indicator, country, and year

– Data set is a panel across more than 130 countries and up to 8 years, using the

World Economic Forum’s Global Executive Survey and other sources

• Approach

– Step 1: Conduct separate, step-wise principal components analyses for MICRO,

SIPI, to derive their averages per country-year; simple average for MP

– Step 2: Comprehensive regression of MICRO, SIPI and MP on log GDP per capita

with endowment controls and year dummies.

Source: Delgado/Ketels/Porter/Stern, 2012

1 1 1

1 t

c,t MICRO c,t SIPI c,t MP c,t

END c,t t c,t

Ln Output per

Potential Worker MICRO SIPI MP

ENDOWMENTS year (1)

14 Copyright 2009 © Professor Michael E. Porter Competitiveness Master - 2009-04-20.ppt

Country Competitiveness Model Subindex Impact at Various Stages of Development

Subindex Low High Medium*

Linear Model (all

Economies)

MICRO 0.21 0.48 0.35 0.31

SIPI 0.49 0.36 0.42 0.41

MP 0.30 0.16 0.23 0.28

1 1 1 1

Stage of Development

Note: Medium Stage weights are the average of Low and High weights.


$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

-1.5% -0.5% 0.5% 1.5% 2.5% 3.5% 4.5%

Prosperity Performance in Mexican States

Real Growth Rate of GDP per capita, 2003-2010

Gro

ss

Do

mes

tic P

rod

uct

pe

r C

ap

ita ,

20

10

(in

co

nsta

nt

20

03

Me

xic

an

Pe

so

s)

Source: INEGI. Sistema de Cuentas Nacionales de México.

Mexico Real Growth

Rate of GDP per

Capita: 1.36%

Mexico GDP per Capita:

$77,212

Campeche (-4.9%, $333,700)

Baja California Sur

Distrito Federal

Nuevo León

Tabasco

Baja California

Querétaro Aguascalientes

Sonora

Zacatecas Nayarit

Veracruz Puebla

Coahuila

Chiapas

Tlaxcala

Quintana Roo

Tamaulipas

Chihuahua

Durango

Morelos

Colima

Jalisco

Sinaloa San Luis Potosí

Yucatán Guanajuato

México

Hidalgo

Michoacán

Oaxaca

Guerrero


The Changing Nature of International Competition

• Falling restraints to trade and investment

• Globalization of markets

• Globalization of value chains

• Shift from vertical integration to relying on outside suppliers, partners,

and institutions

• Increasing knowledge and skill intensity of competition

• Nations and regions compete on becoming the most productive

locations for business

• Many essential levers of competitiveness reside at the regional level

• Economic performance varies significantly across sub-national

regions (e.g., provinces, states, metropolitan areas)


Mexico’s Competitiveness Profile 2011

Macro (67)

Political Institutions

(76)

Rule of Law

(99)

Human Development (55)

Related and Supporting

Industries (36)

Demand Conditions

(56)

Context for Strategy and

Rivalry (62)

Factor Input Conditions

(59)

Micro (49)

Logistic. (53)

Admin (57) Skills (80) Capital (66)

Innov. (60)

GDP pc (49)

GCI (61)

Social Infra-

structure and Pol. Institutions (77)

Macroeconomic Policy (41)

National Business

Environment (50)

Company Operations

and Strategy

(49)

Sou

rce:

Un

pu

blis

hed

dat

a fr

om

th

e G

lob

al C

om

pet

itiv

enes

s R

epo

rt (

20

12

), a

uth

or’

s an

alys

is.

Significant

advantage

Moderate advantage

Neutral

Moderate disadvantage

Significant disadvantage

State of the Region-Report 2012

Comm. (71)


What is a Cluster?

A geographically proximate group of interconnected companies

and associated institutions in a particular field, linked by

commonalities and complementarities (external economies)

• An end product industry or industries

• Downstream or channel industries

• Specialized suppliers

• Providers of specialized services

• Related industries (those with important shared activities, labor,

technologies, channels, or common customers)

• Supporting Institutions: financial, training, trade associations,

standard setting, research


Massachusetts Medical Devices Cluster

A geographically proximate group of interconnected companies and associated

institutions in a particular field, linked by commonalities and complementarities

20 Copyright 2012 © Professor Michael E. Porter

The Evolution of Regional Economies San Diego

U.S. Military

Communications

Equipment

Sporting Goods

Analytical Instruments

Power Generation

Aerospace Vehicles

and Defense

Transportation

and Logistics

Information Technology

1910 1930 1950 1990 1970

Bioscience

Research

Centers

Climate and

Geography

Hospitality and Tourism

Medical Devices

Biotech / Pharmaceuticals

Education and

Knowledge Creation

21 Copyright © 2012 Professor Michael E. Porter

Clusters and Competitiveness

• Regions specialize in different sets of clusters

• Cluster strength directly impacts regional performance

• Each region needs its own distinctive competitiveness strategy

and action agenda

– Business environment improvement

– Cluster upgrading


• Assigning industries to clusters is challenging because there are numerous

types of externalities and they are hard to measure directly

• Some studies measure industry relatedness, but do not define clusters

– E.g., Ellison, Glaeser and Kerr (2010): input-output, skills and knowledge linkages

for manufacturing industries

• Very few studies define regional clusters:

– Feldman and Audretsch (1999) for science-based manufacturing clusters

– Feser and Bergman (2000) for input-output-based manufacturing clusters

– Porter (2003) for clusters of industries related by any type of externalities (in

both manufacturing and service)

A major constraint to the analysis of clusters has been the lack of a systematic approach to defining the

industries that should be included in each cluster and the absence of consistent empirical data on cluster

composition across a large sample of regional economies. Lack of large sample empirical data is

understandable, since knowledge spillovers and other positive externalities are difficult if not impossible to

measure directly.

We proceed indirectly, using the locational correlation of employment across traded industries to reveal

externalities and define cluster boundaries.

Defining clusters of related industries


• The 879 industries are grouped empirically into 3 types of industries (and

industries) with very different location drivers:

– Local clusters: utilities, retail clothing

– Natural Resource Dependent clusters: water supply, metal mining

– Traded clusters: footwear, biopharma, business services

Porter’s (2003) US Cluster Mapping Project

24 Copyright 2011 © Professor Michael E. Porter 20110226 – NGA v0225b

Traded Local Natural

Resource-Driven

27.4%

0.3%

$57,706

135.2%

3.7%

144.1

21.5

590

677

71.7%

1.5%

$36,911

86.5%

2.7%

79.3

0.3

241

352

0.9%

0.5%

$40,142

94.1%

2.4%

140.1

1.6

48

43

Share of Employment

Employment Growth Rate

Average Wage

Relative Wage

Wage Growth Rate

Relative Productivity

Patents per 10,000 Employees

Number of SIC Industries

Number of NAICS Industries

The Composition of Regional Economies United States

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard Bryden, Project Director.


• The 879 industries are grouped empirically into 3 types of clusters (and

industries) with very different location drivers:

– Local clusters: utilities, retail clothing

– Natural Resource Dependent clusters: water supply, metal mining

– Traded clusters: footwear, biopharma, business services

• The 592 traded industries are grouped into 41 traded clusters:

– Relatedness between a pair of industries is based on the employment

correlation of pairs of industries across regions. The locational correlation

captures any type of externalities (e.g. technology, skills, demand, or others)

– Industries are then grouped into clusters by maximizing within-cluster

relatedness

– Clusters often contain manufacturing and service industries and industries from

different parts of the SIC system

Porter’s (2003) US Cluster Mapping Project

26 Copyright 2008 © Michael E. Porter European Cluster Policy 01-22-08 CK

SUBCLUSTERS (16) SIC LABEL

Motor Vehicles 3711 Motor vehicles and car bodies Automotive Parts 2396 Automotive and apparel trimmings

3230 Products of purchased glass 3592 Carburetors, pistons, rings, valves 3714 Motor vehicle parts and accessories 3824 Fluid meters and counting devices

Automotive Components 3052 Rubber and plastics hose and belting 3061 Mechanical rubber goods

Forgings and Stampings 3322 Malleable iron foundries 3465 Automotive stampings

Flat Glass 3210 Flat glass Production Equipment 3544 Special dies, tools, jigs and fixtures

3549 Metalworking machinery, n.e.c. Small Vehicles and Trailers 3799 Transportation equipment, n.e.c. Marine, Tank & Stationary Engines 3519 Internal combustion engines, n.e.c. Related Parts 3364 Nonferrous die-casting, except aluminum

3452 Bolts, nuts, rivets, and washers 3493 Steel springs, except wire 3495 Wire springs 3562 Ball and roller bearings 3566 Speed changers, drives, and gears 3641 Electric lamps

Motors and Generators 3621 Motors and generators Related Vehicles 3795 Tanks and tank components Metal Processing 3316 Cold finishing of steel shapes

3398 Metal heat treating Machine Tools 3541 Machine tools, metal cutting types

3542 Machine tools, metal forming types 3545 Machine tool accessories

Related Process Machinery 3543 Industrial patterns 3548 Welding apparatus

Industrial Trucks and Tractors 3537 Industrial trucks and tractors Die-castings 3363 Aluminum die-castings

Automotive Cluster Broad Cluster Definition

NA

RR

OW

CL

US

TE

R

DE

FIN

ITIO

N


Furniture Building

Fixtures,

Equipment &

Services

Fishing &

Fishing

Products

Hospitality

& Tourism Agricultural

Products

Transportation

& Logistics

Competitiveness and the Composition of the Economy Linkages Across Traded Clusters

Plastics

Oil &

Gas

Chemical

Products

Biopharma-

ceuticals

Power

Generation

Aerospace

Vehicles &

Defense

Lightning &

Electrical

Equipment

Financial

Services

Publishing

& Printing

Entertainment

Information

Tech.

Communi-

cations

Equipment

Aerospace

Engines

Business

Services

Distribution

Services

Forest

Products

Heavy

Construction

Services

Construction

Materials

Prefabricated

Enclosures

Heavy

Machinery

Sporting

& Recreation

Goods

Automotive

Production

Technology Motor Driven

Products

Metal

Manufacturing

Jewelry &

Precious

Metals

Textiles

Footwear

Processed

Food

Tobacco

Medical

Devices

Analytical

Instruments Education &

Knowledge

Creation

Note: Clusters with overlapping borders or identical shading have at least 20% overlap

(by number of industries) in both directions.

Apparel

Leather &

Related

Products


Specialization of Regional Economies Leading Clusters in U.S. Economic Areas

Boston, MA-NH


Education and Knowledge Creation

Medical Devices

Financial Services

Los Angeles, CA

Entertainment

Apparel

Distribution Services


San Jose-San Francisco, CA

Business Services


Agricultural Products

Communications Equipment

Biopharmaceuticals

New York, NY-NJ-CT-PA

Financial Services

Biopharmaceuticals

Jewelry and Precious Metals

Publishing and Printing

Seattle, WA

Aerospace Vehicles and Defense


Entertainment

Fishing and Fishing Products

San Diego, CA

Medical Devices




Chicago, IL-IN-WI

Metal Manufacturing

Lighting and Electrical Equipment

Production Technology

Plastics

Denver, CO

Business Services

Medical Devices

Entertainment

Oil and Gas Products and

Services

Raleigh-Durham, NC


Biopharmaceuticals

Communications Equipment

Textiles

Atlanta, GA

Transportation and Logistics

Textiles

Motor Driven Products

Construction Materials

Dallas

Aerospace Vehicles and Defense


Services



Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard

Bryden, Project Director.

Houston, TX


Services

Chemical Products

Heavy Construction Services


Pittsburgh, PA


Metal Manufacturing

Chemical Products

Power Generation and

Transmission


Regions with high cluster specialization and high share of US employment (LQ>1.3 and top 10 employment)

Automotive Cluster Specialization by Economic Area

Detroit-Warren-Flint, MI

(LQ=6.51, Share=13.8%)

Adjacent EAs

tend to specialize in

the same cluster

Regions with high cluster specialization and moderate share (LQ>1.3 and cluster employment > 1000)

30 Copyright 2008 © Michael E. Porter European Cluster Policy 01-22-08 CK

Financial Services Clusters by Economic Areas, 1997

New York-Newark-Bridgeport

(LQ=2.24, SHARE=18.2%)

Minneapolis-St Paul-St-Cloud, MN-WI

Denver-Aurora-Boulder, CO

Chicago-Naperville-Michigan City, IL-IN-WI

Hartford, CT

(LQ=3.40, SHARE=3%)

Regions with high share of US financial services employment ( in top 10% of all regions; share>2.5%) &

high cluster specialization (LQ>1.01)

Regions with high cluster specialization (LQ>1.03 ; LQc,r >LQc 80-th Percentile)

Weak clusters with large employment size in high population areas


Clusters and Regional Prosperity: Leveraging the CMP data

• Using a mix of databases: – CMP, County Business Patterns (CBP) data, Census Bureau

Longitudinal Business Database (LBD), USPTO data

• Clusters, Jobs, Wages and Innovation – “Clusters, Convergence and Economic Performance,” Mercedes

Delgado, Michael E. Porter and Scott Stern, CES WP

• Clusters and New Business Creation – “Clusters and Entrepreneurship,” Mercedes Delgado, Michael E. Porter

and Scott Stern, JOEG 2010

• Evaluating U.S. Cluster Performance – Using the CMP data, we can examine the cluster composition of

regions: what are the strong clusters in a region? Which ones are

creating jobs/innovations?


Clusters and Region-industry Growth in

Employment, Patents, Establishments

ir2005

ir1990

1 2

3

y

y

0 ir,1990

outside i outside c

icr,1990 cr,1990

ln = α +δln( Industry Spec )+

β ln(Cluster Spec) +β ln(Related Clusters Spec )+

β ln(Cluster Spec in Neigh cr,1990 i r icr,tbors ) + α +α +ε .

• Dep. variable is the EA-industry (ir) growth rate in y ( employment/patents/…)

• E.g., Pharmaceutical preparations industry in Raleigh-Durham-Cary (NC) EA

• Two types of explanatory variables (based on y):

Convergence (δ ): Specialization of the EA in the industry

Agglomeration (β): Cluster environment for the focal EA-industry:

Specialization of the EA in the cluster (β1) and in related clusters (β2) and strength of

neighboring clusters (β3)

E.g., Strength of the biopharmaceutical and related clusters (Medical devices,

Analytical instruments) in the EA and strength of biopharma cluster in adjacent EAs

• Controls: Industry and EA FEs (αi , αr)


Clusters and Region-industry Growth in

Employment, Patents, Establishments

ir2005

ir1990

1 2

3

y

y

0 ir,1990

outside i outside c

icr,1990 cr,1990

ln = α +δln( Industry Spec )+

β ln(Cluster Spec) +β ln(Related Clusters Spec )+

β ln(Cluster Spec in Neigh cr,1990 i r icr,tbors ) + α +α +ε .

• For all measures of economic performance (employment, patents, establishments),

we find that

• Convergence (δ<0 )

• Cluster-driven agglomeration benefits (β> 0)

• Regional Industries in stronger clusters are associated with higher growth

• The positive impact of clusters on region-industry employment growth does not

come at the expense of innovation, investments or wages but enhances them


Clusters and Region-industry Wage Growth

ir2005

ir1990

1 2

3

Wage

Wage

0 i,r,1990

outside i outside c

c,r,1990 c,r,1990

ln = α +δln( Industry Wage )+

β ln(Cluster Wage )+β ln(Related Clusters Wage )+

β ln(Cluster Wage i c,r,1990 i r i,c,r,tn Neighbors ) + α +α +ε .

• Findings:

• Convergence (δ < 0)

• Cluster-driven wage growth (β> 0 ):

• Wages in the cluster (β1>0) and in neighboring clusters (β3>0)

• The “productivity” of the cluster influences the “productivity” growth of

the industries within the cluster


Clusters and Creation of New Regional Industries 1990-2005

• Sample: EA-industries non existing (zero employment) in the base year (1990)

• We examine the probability of the creation of a new EA-industry as of 2005

• Findings: β>0

• New regional industries emerge in regions with a stronger cluster environment

1 2

3

outside c

ir2005 0 c,r,1990 c,r,1990

c,r,1990 i r i,c,r,t

New EA - industry = α + β ln(Cluster Spec ) +β ln(Related Clusters Spec )+

β ln(Cluster Spec in Neighbors ) + α +α +ε .


Clusters and Regional Growth

• Findings (β> 0)

• The set of strong traded clusters in a region contribute to the employment

growth of other activities in that region

• Same findings for regional patent and wage growth

Outside strong clusters Outside

r,2005 strong clusters

0 r,1990 r,1990

r,1990

r,199

Employln ln(Employ ) Reg Cluster Strength

Employ

+ National Employ Growth

Strong clusters

0 05 Census Region r.


Clusters and Entrepreneurship, JOEG, 2010

• This paper focuses on early stage entrepreneurship, using two indicators

of start-up activity:

– count of new establishments by new firms in a EA-industry (i.e., start-up

establishments), and the

– employment in these new firms (i.e., start-up employment)

• We then compute the growth rate in start-up activity in regional industries

• We find that the strength of the cluster environment contributes to

– higher growth in new businesses formation in EA-industry

– higher growth in employment in new businesses in EA-industry

– higher survival rates of new business in EA-industry

y

y

0 0

0

irt0 ir,t icr,t i r icr,t

irt

ln = α +δln(y )+βln(Cluster Environment) +α + α +ε .


Clusters and Economic Outcomes: Entrepreneurship The Evidence

New Industries (+) New Business Formation (+)

Survial Rates

of New Businesses (+)

Job Growth

In New Businesses (+)

The stronger the cluster, the more

likely new industries within the

cluster are to emerge

The stronger the

cluster, the more

dynamic is the

process of new

business formation

The stronger

the cluster, the

higher the job

growth in new

businesses

The stronger the

cluster , the higher

the survial rate of

new businesses

Source: Porter, The Economic Performance of Regions, Regional Studies, 2003; Delgado/Porter/Stern, Clusters and Entrepreneurship, Journal of Economic

Geography, 2010; Delgado/bPorter/Stern, Clusters, Convergence, and Economic Performance, mimeo., 2010.

CLUSTER

39 2012 - State Competitiveness – Rich Bryden Copyright © 2012 Professor Michael E. Porter

State

State Traded Wage versus

National Average

Cluster Mix Effect

Relative Cluster

Wage Effect State

State Traded Wage versus

National Average

Cluster Mix Effect

Relative Cluster

Wage Effect

Connecticut +27,171 7,028 20,142 Oregon -10,359 -1,304 -9,056

New York +24,102 3,628 20,474 Missouri -10,427 -1,425 -9,002

Massachusetts +16,169 4,391 11,778 Alabama -10,934 -3,563 -7,371

New Jersey +13,535 3,761 9,774 Florida -11,007 -1,559 -9,448

California +9,573 349 9,224 Wisconsin -11,722 -3,516 -8,206

Maryland +6,651 2,496 4,155 Nebraska -11,777 241 -12,018

Washington +5,652 2,692 2,960 Utah -11,992 2,072 -14,064

Virginia +5,319 1,617 3,702 Tennessee -12,172 -3,156 -9,016

Illinois +2,658 16 2,642 Indiana -12,554 -4,840 -7,714

Colorado +1,662 2,416 -754 Vermont -13,368 -1,572 -11,796

Texas +352 2,494 -2,142 Oklahoma -13,572 497 -14,069

Delaware +164 11,060 -10,896 Nevada -14,277 -2,365 -11,911

Alaska -930 -2,417 1,487 North Dakota -14,394 1,004 -15,397

Pennsylvania -3,970 -995 -2,975 South Carolina -15,276 -5,067 -10,209

Louisiana -4,280 95 -4,375 Arkansas -15,378 -4,560 -10,818

Georgia -5,322 -1,102 -4,220 Hawaii -16,043 -12,555 -3,487

Minnesota -5,576 -425 -5,150 New Mexico -16,123 -288 -15,835

New Hampshire -6,387 374 -6,761 Kentucky -16,215 -5,024 -11,191

Arizona -7,021 1,149 -8,169 Maine -16,379 -968 -15,412

Kansas -7,705 2,241 -9,946 Iowa -16,606 -2,721 -13,885

Wyoming -8,057 1,040 -9,097 West Virginia -16,645 -3,894 -12,751

Michigan -8,176 -2,544 -5,633 Idaho -18,671 -787 -17,884

North Carolina -9,245 -4,330 -4,915 Mississippi -19,942 -5,291 -14,651

Ohio -9,284 -2,495 -6,788 Montana -20,073 -2,259 -17,815

Rhode Island -9,791 -2,290 -7,501 South Dakota -20,968 289 -21,257

Productivity Depends on How a State Competes,

Not What Industries It Competes In

On average, cluster strength is much more important (78.1%) than cluster mix

(21.9%) in driving regional performance in the U.S.

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard Bryden, Project Director. 2009 data.


Cluster Efforts Enhancing Competitiveness:

The Case for Action

• Agglomeration largely driven by business environment conditions and

‘automatic’ cluster effects in a market process

BUT

• Exploitation of localized spill-overs not automatic

• Exploration of opportunities for joint action not automatic

• Cluster efforts enable locations to benefit more from what they have


Cluster Efforts Enhancing Competiveness Creating Positive Feed-Back Loops

Clusters as Tool

Better Actions More Impact

Cluster initiatives provide a

platform to discuss

necessary improvements in

competitiveness at the level

where firms compete

The organization of

economic policies around

clusters leverages positive

spill-overs and mobilizes

private sector co-investment

42 Mexico Cluster Mapping – Rich Bryden Copyright © 2011 Professor Michael E. Porter

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

Aerospace Vehicles and DefenseTobacco

Oil and Gas Products and ServicesCommunications Equipment

Lighting and Electrical EquipmentJewelry and Precious Metals

Sporting, Recreational and Children's GoodsFishing and Fishing Products

FootwearProduction TechnologyAnalytical Instruments

Heavy MachineryMedical Devices

Motor Driven ProductsAgricultural Products

Leather and Related ProductsBiopharmaceuticals

Information TechnologyPlastics

Prefabricated EnclosuresMetal Manufacturing

Power Generation and TransmissionChemical Products

EntertainmentFinancial Services

Forest ProductsPublishing and Printing

Business ServicesFurniture

Distribution ServicesTransportation and LogisticsHeavy Construction Services

Construction MaterialsHospitality and Tourism

Building Fixtures, Equipment and ServicesTextiles

Education and Knowledge CreationProcessed Food

AutomotiveApparel

Puebla Employment in Traded Clusters

Employment 2008

Source: Mexico Censos 2009; Prof. Michael E. Porter, Cluster Mapping Project, Harvard Business School; Richard Bryden, Project Director. Contributions by Prof. Niels Ketelhohn.


Puebla Job Creation in Traded Clusters 2003 to 2008

Jo

b C

reati

on

, 2003 t

o 2

008

-20,000

-15,000

-10,000

-5,000

0

5,000

10,000

15,000A

uto

mo

tive

Bu

ild

ing

Fix

ture

s, E

qu

ipm

en

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es

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sp

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Net traded job creation,

2003 to 2008:

+38,254

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard Bryden, Project Director. Contributions by Prof. Niels Ketelhohn. * Percent change in national benchmark times starting regional employment. Overall traded job creation in the state, if it matched national benchmarks, would be +15,863

Indicates expected job creation

given national cluster growth.*


0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

-2.0% -1.0% 0.0% 1.0% 2.0% 3.0%

Change in Puebla’s share of National Employment, 2003 to 2008

Pu

eb

la’s

nati

on

al

em

plo

ym

en

t sh

are

, 2008

Employees 5,000 =

Traded Cluster Composition of the Puebla Economy

Overall change in the Puebla Share of

Mexican Traded Employment: +0.09%

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard Bryden, Project Director. Contributions by Prof. Niels Ketelhohn.

Puebla Overall Share of Mexican

Traded Employment: 4.20%

Added Jobs

Lost Jobs

Employment

2003-2008

Education and

Knowledge Creation

Textiles

Apparel

Information

Technology

Construction

Materials

Automotive

Processed

Food

Building Fixtures,

Equipment and Services


Heavy Machinery

Furniture Leather and

Related Products

Forest Products

Chemical

Products

Production Technology Metal Manufacturing


Furniture Building

Fixtures,

Equipment &

Services

Fishing &

Fishing

Products

Hospitality

& Tourism Agricultural

Products

Transportation

& Logistics

Puebla Cluster Portfolio, 2008

Plastics

Oil &

Gas

Chemical

Products

Biopharma-

ceuticals

Power

Generation &

Transmission

Aerospace

Vehicles &

Defense

Lighting &

Electrical

Equipment

Financial

Services

Publishing

& Printing

Entertainment

Information

Tech.

Communi

cations

Equipment

Aerospace

Engines

Business

Services

Distribution

Services

Forest

Products

Heavy

Construction

Services

Construction

Materials

Prefabricated

Enclosures

Heavy

Machinery

Sporting

& Recreation

Goods

Automotive

Production

Technology Motor Driven

Products

Metal

Manufacturing

Apparel

Leather &

Related

Products

Jewelry &

Precious

Metals

Textiles

Footwear

Processed

Food

Tobacco

Medical

Devices

Analytical

Instruments Education &

Knowledge

Creation

LQ > 3.0

LQ > 1.5

LQ > 1.0

LQ, or Location Quotient, measures the state’s share in cluster employment relative to its overall share of Mexican

employment. An LQ > 1 indicates an above average employment share in a cluster.


0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000

Aerospace Vehicles and DefenseTobacco

Jewelry and Precious MetalsConstruction Materials

Building Fixtures, Equipment and ServicesLeather and Related Products

Sporting, Recreational and Children's GoodsFootwear

Fishing and Fishing ProductsEntertainment

Oil and Gas Products and ServicesDistribution Services

Prefabricated EnclosuresHospitality and Tourism

Lighting and Electrical EquipmentProcessed Food

ApparelPublishing and Printing

FurnitureCommunications Equipment

Forest ProductsPlastics

Financial ServicesHeavy Construction Services

TextilesBusiness Services

BiopharmaceuticalsAnalytical Instruments

Metal ManufacturingTransportation and Logistics

Heavy MachineryEducation and Knowledge Creation

Information TechnologyMotor Driven ProductsAgricultural Products

Medical DevicesProduction Technology

Chemical ProductsAutomotive

Power Generation and Transmission

Puebla Wages in Traded Clusters vs. National Benchmarks

Wages, 2008

Puebla average traded

wage: 63,495 Pesos


Mexican average

traded wage: 86,006 Pesos

l Indicates average

national wage in

the traded cluster


$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

$350,000

$400,000

$450,000

$0 $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000

Mexico Value-Added and Wage Levels in Traded Clusters

Value-Added per Employee, 2008

Ave

rag

e W

ag

e p

er

Em

plo

ye

e,

20

08

Note: All values in current Mexican pesos


Power Generation and Transmission

Financial Services

Biopharmaceuticals

Oil and Gas Products and Services

Tobacco Chemical Products


$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000

Mexico Value-Added and Wage Levels in Traded Clusters (continued)

Value-Added per Employee, 2008

Ave

rag

e W

ag

e p

er

Em

plo

ye

e,

20

08

Note: All values in current Mexican pesos


Transportation and Logistics Automotive

Metal Manufacturing

Information Technology Aerospace Vehicles and Defense

Motor Driven Products Communications Equipment

Lighting and Electrical Equipment


Production Technology

Heavy Machinery Medical Devices


Business Services

Entertainment Plastics

Prefabricated Enclosures

Agricultural Products

Processed Food Forest Products

Heavy Construction Services

Publishing and Printing

Sporting, Recreational and Children's Goods


Fishing and Fishing Products

Footwear

Hospitality and Tourism Construction Materials

Textiles Apparel

Furniture Jewelry and Precious Metals

Leather and Related Products

Building Fixtures, Equipment and Services


Mexico Job Creation in Traded Clusters 2003 to 2008

Jo

b C

reati

on

, 2003 t

o 2

008

-150,000

-100,000

-50,000

0

50,000

100,000

150,000H

osp

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Ve

hic

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nd

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fen

se

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n T

ech

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re

To

ba

cco

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ort

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, R

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nd

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ild

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's G

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Pre

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Ap

pa

rel

Net traded job creation,

2003 to 2008:

+776,801


50 Copyright 2012 © Professor Michael E. Porter Mexico Cluster Mapping – Rich Bryden

0%

10%

20%

30%

40%

50%

60%

70%

-5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12%

Change in Mexico’s Share of NAFTA Employment, 2003 to 2008

Mexic

o e

mp

loym

en

t sh

are

in

NA

FTA

, 2008

Employees 100,000 =

Mexico Traded Cluster Specialization within NAFTA

Overall change in the Mexico Share of

NAFTA Traded Employment: +0.95%


Mexico Overall Share of NAFTA

Traded Employment: 16.3%

Added Jobs

Lost Jobs

Employment

2003-2008

Leather and

Related

Products

Footwear (67%, +20%)

Fishing and

Fishing Products

Apparel

Automotive

Textiles

Furniture

Power Generation

and Transmission

Oil and Gas

Products and Services

Prefabricated

Enclosures

Processed

Food

Communications

Equipment

Construction

Materials

Building Fixtures,

Equipment and Services


Biopharma

Information

Technology

Forest Products

Chemical

Products

Distribution

Services

Metal

Manufacturing

Production

Technology


Gracias

Please see: www.isc.hbs.edu/econ-clusters.htm

www.isc.hbs.edu/econ-natlcomp.htm

http://www.isc.hbs.edu/econ-clusters.htm



http://www.isc.hbs.edu/econ-natlcomp.htm



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