[Title will be auto-generated] by Centro de Investigación e Inteligencia Económica (CIIE)

Clusters and Competitiveness Frameworks and Applied Research Workshop on Applied Research Centro de Investigaci贸n e Inteligencia Econ贸mica, UPAEP June 23, 2012

Richard Bryden Director of Information Products Institute for Strategy and Competitiveness Harvard Business School www.isc.hbs.edu

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. 1

Institute for Strategy and Competitiveness Intellectual Agenda Competition and Firm Strategy

Competition and Economic Development National Competitiveness

Competitive Strategy

Strategy and the Internet

Clusters and Cluster Development

CorporateLevel Strategy

Internationalization and Global Strategy

Competitiveness of States and Sub-national Regions

Market Competition

Competition in Health Care Antitrust and Competition Policy

Creating Shared Value

Strategy for Philanthropic Organizations

Competition and Society 2

Innovation and Innovative Capacity

Economic Development in Inner Cities

Strategy for Cross-National Regions

Institute for Strategy and Competitiveness Leverage Model

Research and Publication

Course Platform

Information

Institution Building

Competitiveness and Economic Development Main Activity Areas

• •

• • • • •

Institutions for Competitiveness National Economic Strategy Cluster policy Export diversification Location and company performance

Competitiveness Index Cluster Mapping

Research and Publication

Course Platform

Information

• Institution Building

•

Microeconomics of Competitiveness – – – – –

ITESM-EGAP ITESM-Puebla Universidad Panamericana University of Sonora (UNISON) UPAEP

MOC Network 4

What is Competitiveness? 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

•

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

•

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

•

Competitiveness is not a zero sum game

Conceptual Framework for Competitiveness Key Building Blocks Microeconomic Competitiveness Sophistication of Company Operations and Strategy

State of Cluster Development

Quality of the National Business Environment

Macroeconomic Competitiveness Social Infrastructure and Political Institutions

Macroeconomic Policy

Endowments

Natural Resources

Geographic Location

Size

Components of Macroeconomic Competitiveness

• •

Social Infrastructure and Political Institutions

•

Fiscal policy – –

Basic education Health

•

Political institutions – – – – –

•

Human development – –

Macroeconomic Policies

Monetary policy –

Political freedom Voice and accountability Political stability Government effectiveness Decentralization of economic policymaking

Government surplus/deficit Government debt

Inflation

Rule of law – – – – –

Security Civil rights Judicial independence Efficiency of legal framework Freedom from corruption

What Determines Competitiveness?

The external business environment conditions that enable company productivity and innovation

Microeconomic Competitiveness Quality of the National Business Environment

State of Cluster Development

Sophistication of Company Operations and Strategy

Macroeconomic Competitiveness Macroeconomic Policies

Social Infrastructure and Political Institutions

Endowments

What Determines Competitiveness?

Microeconomic Competitiveness Quality of the National Business Environment

State of Cluster Development

A critical mass of firms and institutions in each field to harness efficiencies and externalities across related entities

Sophistication of Company Operations and Strategy

Macroeconomic Competitiveness Social Infrastructure Macroeconomic and Political Policies Institutions

Social Macroeconomic Infrastructure Policies and Political Institutions

Endowments

What Determines Competitiveness?

Microeconomic Competitiveness Quality of the National Business Environment

State of Cluster Development

Sophistication of Company Operations and Strategy

Internal skills, capabilities, and sophistication of management practices of companies

Macroeconomic Competitiveness Social Infrastructure Macroeconomic and Political Policies Institutions

Social Macroeconomic Infrastructure Policies and Political Institutions

Endowments

What Determines Competitiveness? Microeconomic Competitiveness Quality of the Business Environment

State of Cluster Development

Sophistication of Company Operations and Strategy

Macroeconomic Competitiveness Social Infrastructure and Political Institutions

Macroeconomic Policies

Endowments

• 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

A New Definition of Competitiveness

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

“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

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.

Ln Output per Potential Workerc,t    MICROMICROc,t 1  SIPISIPIc,t 1  MPMPc,t 1 

ENDENDOWMENTSc,t 1   yeart   c,t t

Source: Delgado/Ketels/Porter/Stern, 2012

(1)

Country Competitiveness Model Subindex Impact at Various Stages of Development

Stage of Development *

Linear Model (all Economies)

Subindex

Low

High

Medium

MICRO

0.21

0.48

0.35

0.31

SIPI

0.49

0.36

0.42

0.41

0.30

0.16

0.23

0.28

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

Competitiveness Master - 2009-04-20.ppt

Prosperity Performance in Mexican States $180,000

Campeche

Mexico Real Growth Rate of GDP per Capita: 1.36%

(-4.9%, $333,700)

$160,000

Distrito Federal

Gross Domestic Product per Capita , 2010 (in constant 2003 Mexican Pesos)

Nuevo León

$140,000

$120,000 Tabasco Coahuila

$100,000

Baja California Sur Querétaro Aguascalientes Sonora

Quintana Roo Tamaulipas Chihuahua

$80,000 Baja California

Colima Jalisco

Durango Morelos

$60,000

Mexico GDP per Capita: $77,212

Guanajuato Yucatán Sinaloa San Luis Potosí México Nayarit Michoacán Veracruz Puebla

Zacatecas

Hidalgo Tlaxcala Chiapas

$40,000

Guerrero Oaxaca

$20,000

$0 -1.5%

-0.5%

0.5% 1.5% 2.5% Real Growth Rate of GDP per capita, 2003-2010 Source: INEGI. Sistema de Cuentas Nacionales de México. 15

3.5%

4.5%

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) 16

Mexico’s Competitiveness Profile 2011 GDP pc (49)

Source: Unpublished data from the Global Competitiveness Report (2012), author’s analysis.

Micro (49)

National Business Environment (50)

Macro (67)

GCI (61)

Company Operations

Social Infrastructure and Pol. Institutions (77)

and Strategy (49)

Related and Supporting Industries (36)

Political Institutions (76)

Demand Conditions (56)

Rule of Law (99)

Context for Strategy and Rivalry (62)

Human Development (55)

Macroeconomic Policy (41)

Significant advantage Moderate advantage Neutral

Factor Input Conditions (59)

Moderate disadvantage

Logistic. (53)

Innov. (60)

Comm. (71)

Admin (57)

Capital (66)

Skills (80)

Significant disadvantage 17

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 19

The Evolution of Regional Economies San Diego Hospitality and Tourism Sporting Goods

Climate and Geography

Transportation and Logistics Power Generation Communications Equipment

Aerospace Vehicles and Defense U.S. Military

Information Technology

Analytical Instruments Education and Knowledge Creation

Medical Devices Bioscience Research Centers

1910

1930

1950

Biotech / Pharmaceuticals

1970 20

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

Defining clusters of related industries • 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. 22

Porter’s (2003) US Cluster Mapping Project • 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

The Composition of Regional Economies United States

Natural Resource-Driven

Traded

Local

27.4% 0.3%

71.7% 1.5%

0.9% 0.5%

$57,706 135.2% 3.7%

$36,911 86.5% 2.7%

$40,142 94.1% 2.4%

144.1

79.3

140.1

Patents per 10,000 Employees

21.5

0.3

1.6

Number of SIC Industries Number of NAICS Industries

590 677

241 352

48 43

Share of Employment Employment Growth Rate

Average Wage Relative Wage Wage Growth Rate

Relative Productivity

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard Bryden, Project Director. 20110226 â&#x20AC;&#x201C; NGA v0225b

Porter’s (2003) US Cluster Mapping Project • 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

Automotive Cluster Broad Cluster Definition SUBCLUSTERS (16)

NARROW CLUSTER DEFINITION

Motor Vehicles Automotive Parts

Automotive Components Forgings and Stampings Flat Glass Production Equipment Small Vehicles and Trailers Marine, Tank & Stationary Engines Related Parts

Motors and Generators Related Vehicles Metal Processing Machine Tools Related Process Machinery Industrial Trucks and Tractors Die-castings European Cluster Policy 01-22-08 CK

SIC

LABEL

3711 2396 3230 3592 3714 3824 3052 3061 3322 3465 3210 3544 3549 3799 3519 3364 3452 3493 3495 3562 3566 3641 3621 3795 3316 3398 3541 3542 3545 3543 3548 3537 3363

Motor vehicles and car bodies Automotive and apparel trimmings Products of purchased glass Carburetors, pistons, rings, valves Motor vehicle parts and accessories Fluid meters and counting devices Rubber and plastics hose and belting Mechanical rubber goods Malleable iron foundries Automotive stampings Flat glass Special dies, tools, jigs and fixtures Metalworking machinery, n.e.c. Transportation equipment, n.e.c. Internal combustion engines, n.e.c. Nonferrous die-casting, except aluminum Bolts, nuts, rivets, and washers Steel springs, except wire Wire springs Ball and roller bearings Speed changers, drives, and gears Electric lamps Motors and generators Tanks and tank components Cold finishing of steel shapes Metal heat treating Machine tools, metal cutting types Machine tools, metal forming types Machine tool accessories Industrial patterns Welding apparatus Industrial trucks and tractors Aluminum die-castings 26

Competitiveness and the Composition of the Economy Linkages Across Traded Clusters Fishing & Fishing Products

Entertainment

Agricultural Products Processed Food

Jewelry & Precious Metals

Financial Services

Aerospace Vehicles & Information Defense Tech.

Building Fixtures, Equipment & Services

Lightning & Electrical Analytical Equipment Education & Instruments Power Knowledge Medical Generation Creation Devices Communications Publishing Equipment & Printing Biopharmaceuticals Chemical Products

Apparel

Motor Driven Products

Construction Materials Heavy Construction Services Forest Products

Heavy Machinery Production Technology

Tobacco

Oil & Gas Plastics

Footwear

Prefabricated Enclosures

Furniture

Transportation & Logistics

Distribution Services Business Services

Hospitality & Tourism

Textiles

Leather & Related Products

Note: Clusters with overlapping borders or identical shading have at least 20% overlap 27 (by number of industries) in both directions.

Specialization of Regional Economies Leading Clusters in U.S. Economic Areas Denver, CO Business Services Medical Devices Entertainment Oil and Gas Products and Services

Chicago, IL-IN-WI Metal Manufacturing Lighting and Electrical Equipment Production Technology Plastics

Pittsburgh, PA Education and Knowledge Creation Metal Manufacturing Chemical Products Power Generation and Transmission

Boston, MA-NH Analytical Instruments Education and Knowledge Creation Medical Devices Financial Services

Seattle, WA Aerospace Vehicles and Defense Information Technology Entertainment Fishing and Fishing Products New York, NY-NJ-CT-PA Financial Services Biopharmaceuticals Jewelry and Precious Metals Publishing and Printing

San Jose-San Francisco, CA Business Services Information Technology Agricultural Products Communications Equipment Biopharmaceuticals

Los Angeles, CA Entertainment Apparel Distribution Services Hospitality and Tourism

San Diego, CA Medical Devices Analytical Instruments Hospitality and Tourism Education and Knowledge Creation

Raleigh-Durham, NC Education and Knowledge Creation Biopharmaceuticals Communications Equipment Textiles

Dallas Aerospace Vehicles and Defense Oil and Gas Products and Services Information Technology Transportation and Logistics

Houston, TX Oil and Gas Products and Services Chemical Products Heavy Construction Services Transportation and Logistics

Atlanta, GA Transportation and Logistics Textiles Motor Driven Products Construction Materials

Source: Prof. Michael E. Porter, Cluster Mapping Project, Institute for Strategy and Competitiveness, Harvard Business School; Richard 28 Bryden, Project Director.

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 high share of US employment (LQ>1.3 and top 10 employment) Regions with high cluster specialization and moderate share (LQ>1.3 and cluster employment > 1000) 29

Financial Services Clusters by Economic Areas, 1997 Minneapolis-St Paul-St-Cloud, MN-WI Chicago-Naperville-Michigan City, IL-IN-WI

Hartford, CT (LQ=3.40, SHARE=3%)

New York-Newark-Bridgeport (LQ=2.24, SHARE=18.2%)

Denver-Aurora-Boulder, CO

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 ; LQ c,r >LQc 80-th Percentile) Weak clusters with large employment size in high population areas European Cluster Policy 01-22-08 CK

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 y  ln  ir 2005  = α0 + δln( Industry Spec ir,1990 ) +  yir1990  outside i c β1ln(Cluster Spec) icr,1990 + β 2ln(Related Clusters Spec outside cr,1990 ) + β3ln(Cluster Spec in Neighborscr,1990 ) + αi + αr + ε icr,t . •

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) 32

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  Wage ir 2005  ln   = α0 + δln( Industry Wagei,r,1990 ) + Wage ir1990   i outside c β1ln(Cluster Wageoutside )+ β ln(Related Clusters Wage c,r,1990 2 c,r,1990 ) + β3ln(Cluster Wage in Neighborsc,r,1990 ) + αi + αr + ε i,c,r,t . •

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

c New EA - industryir2005 = α0 + β1ln(Cluster Spec c,r,1990 ) + β2ln(Related Clusters Spec outside c,r,1990 ) +

β3ln(Cluster Spec in Neighborsc,r,1990 ) + αi + αr + ε i,c,r,t .

•

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

Clusters and Regional Growth  Employr,2005  ln   Employ r,1990  

Outside strong clusters

  0   ln(Employ

Outside strong clusters r,1990

)   Re g Cluster Strengthr,1990

Strong clusters + National Employ Growthr,199   Census Region   r . 0 05

• 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

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 y  ln  irt  = α0 + δln(yir,t0 ) + βln(Cluster Environment) icr,t0 + αi + αr + εicr,t .  yirt   0 • 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

Clusters and Economic Outcomes: Entrepreneurship The Evidence

CLUSTER

New Industries (+)

The stronger the cluster , the higher the survial rate of new businesses

Survial Rates of New Businesses (+)

New Business Formation (+)

The stronger the cluster, the more dynamic is the process of new business formation

The stronger the cluster, the more likely new industries within the cluster are to emerge The stronger the cluster, the higher the job growth in new businesses

Job Growth In 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. 38

Productivity Depends on How a State Competes, Not What Industries It Competes In State Connecticut New York Massachusetts New Jersey California Maryland Washington Virginia Illinois Colorado Texas Delaware Alaska Pennsylvania Louisiana Georgia Minnesota New Hampshire Arizona Kansas Wyoming Michigan North Carolina Ohio Rhode Island

State Traded Wage versus National Average +27,171 +24,102 +16,169 +13,535 +9,573 +6,651 +5,652 +5,319 +2,658 +1,662 +352 +164 -930 -3,970 -4,280 -5,322 -5,576 -6,387 -7,021 -7,705 -8,057 -8,176 -9,245 -9,284 -9,791

Cluster Mix Effect

Relative Cluster Wage Effect

7,028 3,628 4,391 3,761 349 2,496 2,692 1,617 16 2,416 2,494 11,060 -2,417 -995 95 -1,102 -425 374 1,149 2,241 1,040 -2,544 -4,330 -2,495 -2,290

20,142 20,474 11,778 9,774 9,224 4,155 2,960 3,702 2,642 -754 -2,142 -10,896 1,487 -2,975 -4,375 -4,220 -5,150 -6,761 -8,169 -9,946 -9,097 -5,633 -4,915 -6,788 -7,501

State Oregon Missouri Alabama Florida Wisconsin Nebraska Utah Tennessee Indiana Vermont Oklahoma Nevada North Dakota South Carolina Arkansas Hawaii New Mexico Kentucky Maine Iowa West Virginia Idaho Mississippi Montana South Dakota

State Traded Wage versus National Average -10,359 -10,427 -10,934 -11,007 -11,722 -11,777 -11,992 -12,172 -12,554 -13,368 -13,572 -14,277 -14,394 -15,276 -15,378 -16,043 -16,123 -16,215 -16,379 -16,606 -16,645 -18,671 -19,942 -20,073 -20,968

Cluster Mix Effect

Relative Cluster Wage Effect

-1,304 -1,425 -3,563 -1,559 -3,516 241 2,072 -3,156 -4,840 -1,572 497 -2,365 1,004 -5,067 -4,560 -12,555 -288 -5,024 -968 -2,721 -3,894 -787 -5,291 -2,259 289

-9,056 -9,002 -7,371 -9,448 -8,206 -12,018 -14,064 -9,016 -7,714 -11,796 -14,069 -11,911 -15,397 -10,209 -10,818 -3,487 -15,835 -11,191 -15,412 -13,885 -12,751 -17,884 -14,651 -17,815 -21,257

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. 39 2012 - State Competitiveness â&#x20AC;&#x201C; Rich Bryden

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

Puebla Employment in Traded Clusters Apparel Automotive Processed Food Education and Knowledge Creation Textiles Building Fixtures, Equipment and Services Hospitality and Tourism Construction Materials Heavy Construction Services Transportation and Logistics Distribution Services Furniture Business Services Publishing and Printing Forest Products Financial Services Entertainment Chemical Products Power Generation and Transmission Metal Manufacturing Prefabricated Enclosures Plastics Information Technology Biopharmaceuticals Leather and Related Products Agricultural Products Motor Driven Products Medical Devices Heavy Machinery Analytical Instruments Production Technology Footwear Fishing and Fishing Products Sporting, Recreational and Children's Goods Jewelry and Precious Metals Lighting and Electrical Equipment Communications Equipment Oil and Gas Products and Services Tobacco Aerospace Vehicles and Defense 0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

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. Mexico Cluster Mapping â&#x20AC;&#x201C; Rich Bryden 42 Copyright ÂŠ 2011 Professor Michael E. Porter

Mexico Cluster Mapping â&#x20AC;&#x201C; Rich Bryden

-20,000

Apparel

Education and Knowledge Creation

Distribution Services

Chemical Products

Textiles

Heavy Machinery

Oil and Gas Products and Services

Furniture

Fishing and Fishing Products

Motor Driven Products

Tobacco

5,000

Entertainment

10,000

Power Generation and Transmission

Communications Equipment

Production Technology

Lighting and Electrical Equipment

Jewelry and Precious Metals

Sporting, Recreational and Children's Goods

Analytical Instruments

Footwear

Metal Manufacturing

Plastics

Medical Devices

Biopharmaceuticals

Agricultural Products

Forest Products

Publishing and Printing

Leather and Related Products

Prefabricated Enclosures

Business Services

Financial Services

Information Technology

Heavy Construction Services

-15,000

Transportation and Logistics

Construction Materials

Processed Food

Hospitality and Tourism

Building Fixtures, Equipment and Services

Automotive

Job Creation, 2003 to 2008

Puebla Job Creation in Traded Clusters 2003 to 2008

15,000

Net traded job creation, 2003 to 2008: +38,254

-5,000

-10,000

Indicates expected job creation given national cluster growth.*

* 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 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. Copyright ÂŠ 2011 Professor Michael E. Porter

Traded Cluster Composition of the Puebla Economy 16.0%

Overall change in the Puebla Share of Mexican Traded Employment: +0.09%

14.0%

Construction Materials

Puebla’s national employment share, 2008

Textiles Apparel

12.0% Automotive

Employment 2003-2008

10.0%

Added Jobs

8.0%

Building Fixtures, Equipment and Services

Lost Jobs

Furniture

6.0% Education and Knowledge Creation

4.0%

Processed Food Forest Products

Puebla Overall Share of Mexican Traded Employment: 4.20%

Distribution Services Heavy Machinery

2.0%

Chemical Products

Information Technology

Metal Manufacturing

0.0% -2.0%

Leather and Related Products

-1.0%

Production Technology

0.0%

1.0%

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

2.0%

3.0% Employees 5,000 =

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. Mexico Cluster Mapping – Rich Bryden 44 Copyright © 2011 Professor Michael E. Porter

Puebla Cluster Portfolio, 2008 Fishing & Fishing Products

Entertainment

Processed Food

Transportation & Logistics Aerospace Vehicles & Information Defense Tech.

Distribution Services

Jewelry & Precious Metals

Financial Services

Business Services

Education & Knowledge Creation

Publishing & Printing

Analytical Instruments Medical Devices

Prefabricated Enclosures

Building Fixtures, Equipment & Services

Furniture Construction Materials Heavy Construction Services

Lighting & Electrical Equipment

Communi cations Equipment

Biopharmaceuticals Chemical Products

Apparel Leather & Related Products

Hospitality & Tourism

Agricultural Products

Textiles

Heavy Machinery Motor Driven Products

Production Technology

Tobacco

Oil & Gas

Plastics

LQ > 3.0 LQ > 1.5

Metal Automotive Aerospace Manufacturing Engines

Footwear 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. Mexico Cluster Mapping – Rich Bryden

Forest Products

Power Generation & Transmission

Puebla Wages in Traded Clusters vs. National Benchmarks Power Generation and Transmission Automotive Chemical Products Production Technology Medical Devices Agricultural Products Motor Driven Products Information Technology Education and Knowledge Creation Heavy Machinery Transportation and Logistics Metal Manufacturing Analytical Instruments Biopharmaceuticals Business Services Textiles Heavy Construction Services Financial Services Plastics Forest Products Communications Equipment Furniture Publishing and Printing Apparel Processed Food Lighting and Electrical Equipment Hospitality and Tourism Prefabricated Enclosures Distribution Services Oil and Gas Products and Services Entertainment Fishing and Fishing Products Footwear Sporting, Recreational and Children's Goods Leather and Related Products Building Fixtures, Equipment and Services Construction Materials Jewelry and Precious Metals Tobacco Aerospace Vehicles and Defense

Indicates average national wage in the traded cluster

Puebla average traded wage: 63,495 Pesos Mexican average traded wage: 86,006 Pesos

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

Wages, 2008 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. Mexico Cluster Mapping â&#x20AC;&#x201C; Rich Bryden

Mexico Value-Added and Wage Levels in Traded Clusters $450,000 Oil and Gas Products and Services

$400,000

Average Wage per Employee, 2008

$350,000

$300,000

$250,000 Power Generation and Transmission

$200,000 Financial Services Biopharmaceuticals

$150,000

Chemical Products

Tobacco

$100,000

$50,000

$0 $0

$2,000,000

$4,000,000 $6,000,000 $8,000,000 Value-Added per Employee, 2008

$10,000,000

$12,000,000

Note: All values in current Mexican pesos Source: Mexico Censos 2009; Prof. Michael E. Porter, Cluster Mapping Project, Harvard Business School; Richard Bryden, Project Director. Contributions by Prof. Niels Ketelhohn. Mexico Cluster Mapping â&#x20AC;&#x201C; Rich Bryden 47 Copyright ÂŠ 2011 Professor Michael E. Porter

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

Automotive

$120,000 Metal Manufacturing Aerospace Vehicles and Defense Analytical Instruments

$100,000

Motor Driven Products Communications Equipment Production Technology

Average Wage per Employee, 2008

Information Technology

Lighting and Electrical Equipment

Heavy Machinery Education and Knowledge Creation Medical Devices Prefabricated Enclosures Business Services Plastics Entertainment Agricultural Products

$80,000

Publishing and Printing Forest Products

$60,000

$40,000

Sporting, Recreational and Children's Goods

Processed Food Heavy Construction Services

Hospitality and Tourism Construction Materials Textiles Apparel Jewelry and Precious Metals Furniture Distribution Services Footwear Building Fixtures, Equipment and Services Leather and Related Products

$20,000 Fishing and Fishing Products

$0 $0

$100,000

$200,000

$300,000

$400,000

Value-Added per Employee, 2008

$500,000

$600,000

$700,000

Mexico Cluster Mapping â&#x20AC;&#x201C; Rich Bryden

-150,000

Apparel

Prefabricated Enclosures

Fishing and Fishing Products

Oil and Gas Products and Services

Jewelry and Precious Metals

Sporting, Recreational and Children's Goods

Tobacco

Furniture

Production Technology

Aerospace Vehicles and Defense

Motor Driven Products

Textiles

100,000

Lighting and Electrical Equipment

Heavy Machinery

Footwear

Distribution Services

Leather and Related Products

Forest Products

Chemical Products

Construction Materials

Entertainment

Agricultural Products

Metal Manufacturing

Biopharmaceuticals

Medical Devices

Communications Equipment

Plastics

Publishing and Printing

Power Generation and Transmission

Heavy Construction Services

Education and Knowledge Creation

Analytical Instruments

Transportation and Logistics

Building Fixtures, Equipment and Services

Financial Services

Information Technology

Processed Food

Automotive

Business Services

Hospitality and Tourism

Job Creation, 2003 to 2008

Mexico Job Creation in Traded Clusters 2003 to 2008

150,000

Net traded job creation, 2003 to 2008: +776,801

50,000

-50,000

-100,000

Mexico Traded Cluster Specialization within NAFTA 70%

Fishing and Fishing Products

Employment 2003-2008

Footwear

Added Jobs

Mexico employment share in NAFTA, 2008

60%

(67%, +20%)

Overall change in the Mexico Share of NAFTA Traded Employment: +0.95%

Lost Jobs

Apparel

Prefabricated Enclosures

50%

Power Generation and Transmission Processed Chemical Food Products

40%

Building Fixtures, Equipment and Services Construction Materials Biopharma

30%

Textiles Leather and Related Products

Furniture Analytical Instruments Automotive

Communications Equipment

20% Forest Products

Mexico Overall Share of NAFTA Traded Employment: 16.3%

Oil and Gas Products and Services

10%

Information Technology

Distribution Services

Production Technology

0% -5%

-4%

-3%

-2%

Metal Manufacturing

-1%

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

10%

11%

12%

Employees 100,000 =

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

Gracias

Please see:

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