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U OF CINCINNATI

Date: May 19, 2009

I, Guler Irem Yelkenci , hereby submit this original as part of the requirements for the degree of:

Masters in Community Planning

It is entitled: An Assessment of Knowledge Foundations:

The Case of Istanbul

Guler Irem Yelkenci Student Signature:

This work and its defense approved by: Rainer Vom Hofe, PhD. Committee Chair: Menelaos Triantafillou, ASLA, AICP

Approval of the electronic document:

I have reviewed the Thesis/Dissertation in its final electronic format and certify that it is an accurate copy of the document reviewed and approved by the committee.

Committee Chair signature: Rainer Vom Hofe, PhD.

An Assessment of Knowledge City Foundations: The Case of Istanbul

A Thesis submitted to the

Division of and Advanced Studies of the University of Cincinnati

in partial fulfillment of the requirements for the degree of

Master of Community Planning

in Department of School of Planning of the College of Design, Art, Architecture, and Planning

19 May 2009

by

Guler Irem Yelkenci

B.A., City and Regional Planning, Istanbul Technical University, Turkey 2006

Thesis Committee: Rainer Vom Hofe, PhD. Menelaos Triantafillou, ASLA, AICP

ABSTRACT

The concept of Knowledge has gained significance in a world characterized by escalating use of , and increased importance of human . What began as an international discourse, has recently become a policy concern of local and regional governments. Within the practice of Urban Planning, the phenomenon is examined under the concept of “Knowledge City”. This study attempts to answer the question of where Istanbul’s strengths and weaknesses are in its transition to a knowledge city.

As Knowledge Economy gains momentum around the world, there arises a need to analyze, quantify, and qualify the foundations at the city level. Because we are at the early stages of evolution of the knowledge , there is neither a coherent framework nor a unified methodology for the design and implementation of successful knowledge cities. Common features of successful knowledge cities are under investigation in the research community. This study first investigates the recognized measurement methods at the international level, and then utilizes an emerging framework to assess Istanbul’s potential as a knowledge city.

The recent studies from the European Institute of Comparative Urban Research identifies the knowledge foundational areas for a city as: knowledge base, industrial structure, quality of life, diversity, accessibility, urban scale, and social equity. The institute also offers comparable measures for each foundational area. In accordance with the framework measures, this thesis reveals Istanbul’s strengths and weakness within each foundation for becoming a knowledge city.

ACKNOWLEDGEMENTS

I would like to direct my sincere appreciation to all my committee members for their guidance and encouragement. Their patience and support throughout the whole process of this study was priceless and considerable. I would like to thank my committee chair, Dr. Rainer Vom Hofe, for introducing me to the field of , and giving me both professional and non-professional advice in the times that I most needed it. His enthusiasm to teach has always been a big inspiration for me. My committee member, Prof. Menelaos Triantafillou for giving extremely valuable insights, and providing me with the motivation I needed, and special thanks to my advisor Dr. David Edelman for simply being a great person. Thanks to you, I will continue my life as a more knowledgeable and successful individual.

I would like to mention the name of the most valuable person of all the faculty members and students, Connie Dean, and thank her for all the work she has done for all of us. I also would like to acknowledge my dear friend Carl Victor Pierson for being such a big support and showing me the greatest patience in the world.

Finally, I cannot thank enough my precious family who always helped, encouraged, and supported me in every possible way. TABLE OF CONTENTS

ABBREVIATIONS 2 LIST OF FIGURES 3 LIST OF TABLES 4

INTRODUCTION 5

DEFINITION AND IMPORTANCE OF KNOWLEDGE ECONOMY 6 KNOWLEDGE ECONOMY IN EUROPE 8

LITERATURE REVIEW 14

FEATURES OF THE KNOWLEDGE ECONOMY 15 1. 17 2. TECHNOLOGICAL CHANGE 18 3. 19 4. PRESENCE IN ALL SECTORS 20 5. 21 6. NETWORKS 22 MEASURING KNOWLEDGE ECONOMY 23 ASSESSMENT AT INTERNATIONAL LEVEL 24 THE KNOWLEDGE CITY CONCEPT AND ITS ASSESSMENT 30 THE KNOWLEDGE CITY CONCEPT 32 ERGAZAKIS’ FRAMEWORK 37 EURICUR FRAMEWORK 43 KEY FINDINGS OF THE LITERATURE REVIEW 45

METHODOLOGY 47

RESEARCH FRAMEWORK AND THE SEVEN FOUNDATIONS 48

ANALYSIS: ASSESSING ISTANBUL’S KNOWLEDGE FOUNDATIONS 57

KNOWLEDGE ECONOMY FOUNDATIONS IN ISTANBUL 57 1. KNOWLEDGE BASE 57 2. INDUSTRIAL STRUCTURE 63 3. QUALITY OF LIFE/AMENITIES 66 4. ACCESSIBILITY 68 5. DIVERSITY 70 6. URBAN SCALE 71 7. SOCIAL EQUITY 72 LIMITATIONS AND FINDINGS 74

CONCLUSION 78

APPENDIX 81

REFERENCES 83

1 Abbreviations

CIP Competitiveness and Framework Programme/EU CORDIS Community Research & Development /EU ESRC Economic and Social Research Council EU European Union EURICUR European Institute for Comparative Urban Research FP7 Seventh Framework Program/EU GDP ICT Information and Communication IMM Istanbul Metropolitan Municipality IT Information Technologies KAM Knowledge Assessment Methodology KBE Knowledge-based Economy KC Knowledge City KE Knowledge Economy OECD Organisation for Economic Co-operation and Development PwC PriceWaterhouse Coopers R&D Research and Development SPO State Planning Organisation/Turkey STI , Technology and Innovation UN United Nations WB WEF World Economic Forum

2 List of Figures

Cumulative Projected GDP Growth by 2020. 13 Chain-Link Model of Innovation in the . 17 Ergazakis’ Knowledge City Concept. 37 Developmental and Operational Functions of a KC. 38 The EURICUR’s Approach to Knowledge City. 44 The Foundations and Activities of a Knowledge City. 48 Total Number of Students in 2000-2007. 59 The Trend in Graduate and PhD Students 2000-2007. 59 The Number of Students in Universities and Further Establishments per 1000 Resident , 2004. 61 Students in Tertiary Education per 100 Resident Population Aged 20-34, 2004. 61 IMM Public Satisfaction Survey. 67 Neighborhood Satisfaction in Istanbul. 68 Number of Passengers Carried Through the International Flights. 69 Percentage of Foreign Population, 2000. 71 Population of Cities, 2001. 72 Number of Hospital Beds per 1000 Residents. 73 Rates, 2001. 74

3 List of Tables

Ranking and Scores of Potential EU Member Countries. 11 Variables Used in the “Standard” 14-Variable Scoreboards. 26 OECD Science, Technology and Industry Scoreboard 2007 Indices. 29 The Seven Foundations and Their Comparison Measures. 54 The Seven Foundations and Their Indicators. 55 The Share of Medical Students 2000-2007. 62 Istanbul’s Share of the National GDP. 64 Istanbul’s Share of GDP by Industry Sectors. 64 Shares in Istanbul by Industry Sectors. 65 Summary of the Findings. 75

4 INTRODUCTION

“ Economic success is increasingly based on upon the effective utilization of

intangible assets such as knowledge, skills and innovative potential as the key

for . The term “knowledge economy” is used to

describe this emerging economic structure” (ESRC1 Knowledge Economy, 2005).

In the 1980’s, advancements in neoclassical growth models resulted in the creation of the

‘New Growth Theory’. New Growth Theory emphasized the importance of new

technologies and human capital in the process (Romer, 1990). Conceptually

rooted in New Growth Theory, knowledge economy (KE) or the “new economy”

highlights the use of knowledge in driving nations’ success in the new economic era.

Today, almost all developed are moving towards a knowledge-based economy

(Organisation for Economic Co-Operation & Development [OECD2], 1996). Many

nations, regions and cities are challenging each other on a global scale. Some are doing

significantly better in knowledge activities by attracting a more talented and

growing in knowledge based industries. This study aims to assess the city of Istanbul’s

readiness in becoming competitive in the new economy.

1 The Economic and Social Research Council (ESRC), an independent in the United Kingdom (UK), established in 1965, funds research and training in social and economic issues. It receives most of its funding through the Department of Innovation, Universities, and Skills of UK Government, and it funds over 2,500 researchers in academic institutions and policy research institutes throughout the UK.

2 OECD was established in 1961 and aims to support sustainable in more than 100 countries. OECD provides a setting where governments compare policy experiences, seek answers to common problems, identify good practice, and coordinate domestic and international policies. The organization is also a reliable data source for national and regional KE measures.

5 This beginning chapter introduces the KE concept and underlines its recognition

worldwide with an emphasis on its significance in Europe and Turkey. The literature

review presents the key features of KE along with its recognized measurement methods

at the international levels, and investigates Knowledge City (KC) concept and two

assessment methods at the city level. The methodology section discusses the chosen

EURICUR3 framework more in depth, which is then applied to Istanbul’s case in our

analysis, and finally the conclusion section portrays Istanbul’s strengths and weaknesses

for becoming a KC.

Definition and Importance of Knowledge Economy

Currently, significant effort is spent on defining what the KE is and how its effectiveness

is measured. These efforts are critical in forming measures to be used for strategic development at various geographical scales (i.e., international, regional, national,

metropolitan, city or firm level). Two internationally recognized , the

Organization for Co-operation and Development (OECD) and the World Bank, (WB)

provide KE definitions and valuable strategic guidance.

The OECD defines the KE as “an expression coined to describe trends in advanced

economies towards greater dependence on knowledge, information and high skill levels,

3 The European Research Institute for Comparative Studies (EURICUR) Framework was developed by Leo Van Den Berg, Peter M.J. Pol, Willem Van Winden and Paulus Woets. These scholars analyzed several European cities including Amsterdam, Dortmund, Eindhoven, Helsinki, Manchester, , Rotterdam, Zaragoza, Enschede, Aachen, and Leuven which have evolved into high-grade local knowledge economies. In order to understand the Framework, we investigated two studies carefully: The book European cities in the knowledge economy, 2005, and the article European cities in the knowledge economy: Towards a typology, 2007. We observed that the Research framework has changed slightly in the 2007 publishing. This study uses the most recent version of the framework, namely the latter.

6 and the increasing need for ready access to all of these for the and public sectors” (Knowledge-Based Economy, 2005). OECD identifies knowledge as the central element for economic development. Although the pace may vary, all OECD countries are now moving towards a knowledge-based economy, (KBE) meaning that they are strongly dependent on the production, and use of knowledge more than ever before.

Knowledge-intensive sectors such as higher education, communication and information technologies are observed to be growing extensively in these countries, some of which it generates 50 percent of gross domestic product (OECD, 1996).

The WB is another international organization that supports knowledge activities. Their projects concentrate on establishing an environment suitable for , jobs and sustainable growth through four pillars that the KE is built upon:

1. “Education & Training: An educated and skilled population is needed to create,

share and use knowledge.”

2. “Information Infrastructure: A dynamic information infrastructure is required to

facilitate the effective communication, dissemination and processing of

information.”

3. “Economic Incentive & Institutional Regime: A regulatory and economic

environment that enables the free flow of knowledge, supports investment in

information and communications technology (ICT), and encourages

.”

4. “Innovation Systems: A network of research centers, universities, think tanks,

private enterprises and community groups is necessary to tap into the growing

7 compilation of global knowledge, assimilate and adapt it to local needs, and create

new knowledge” (World Bank, 2008).

Both the OECD and the WB concede knowledge as the main catalyst for economic growth; they both concentrate their efforts in developing measures and providing strategic guidance worldwide. WB’s primary however, is associated with developing underdeveloped economies. Thus for guidance, this study will primarily rely on OECD, whose focus is to compare policy experiences of developed economies in the subject matter.

Knowledge Economy in Europe

Great importance is given to KE’s in Europe as well. In 2000, the European Union (EU) set the to address the issue of global competitiveness in KE. The strategy aims to close the gap between Europe and United States in competitiveness (EUROPA,

2000). It endeavors to make the EU "the most competitive and dynamic knowledge-based economy in the world" (EurActiv, 2007). The Lisbon Strategy rests on the following three pillars: economic, social, and environmental. These pillars and their importance for

KE are explained as follows;

• “An economic pillar prepares the ground for the transition to a competitive,

dynamic, knowledge-based economy. Emphasis is placed on the need to adapt

constantly to changes in the information and to boost research and

development.

• The social pillar is designed to modernize the European social model by investing

8 in human and combating social exclusion. The member states are

expected to invest in education and training, and to conduct an active policy for

employment, making it easier to move to a knowledge economy.

• The environmental pillar, which was added at the Göteborg European Council

meeting in June 2001, draws attention to the that economic growth must be

decoupled from the use of natural resources” (EUROPA, 2000).

The EU’s core emphasis on the Lisbon Agenda4 is a “knowledge triangle”: research,

education and innovation. There are numerous programs, initiatives, and support

measures that EU supports under the Lisbon Agenda (European Commission: Cordis,

n.d.). The Seventh Framework Program (FP7), bundles all research-related EU initiatives

together under a common roof. FP7 works in accordance with a series of different

programs and funds such as the Competitiveness and Innovation Framework Program

(CIP), Education and Training programs, and Structural and Cohesion Funds for regional and competitiveness (European Commission, n.d). With its extensive coverage, comprehensive content and ambitious goals the Lisbon Agenda is the leading regional strategy in Europe.

4 The Lisbon Strategy is known as the Development Plan of European Union on innovation.

9 Knowledge Economy in Turkey and Istanbul

Today, Turkey as an emerging liberalizing economy attracts attention worldwide.

Country’s growth and increasing global competitiveness in the new economy are

recognized both by the OECD and EU countries (OECD, 2005). Especially after 1999,

when Turkey was announced as an accession country in EU, the country put a clear

emphasis on stimulating its transition to the new economy by aligning its efforts with the

Lisbon Agenda (Bilen, 2005).

In 2006, the State Planning Organization of Turkey (SPO) has established a new five- year development plan that focuses on becoming a . The action plan

associated with this national strategy recognized many priorities including integration of

ICT in , technical modernization of public services, global competitiveness in

ICT sector, improved efficiency and access to IT infrastructure and services, and the

encouragement of R&D (State Planning Organization [SPO], 2006). Even though Turkey

is not a EU member yet, its progress has been evaluated based on its accordance to EU’s

innovation policy; namely the Lisbon Strategy.

The World Economic Forum’s5 (WEF) Review of Lisbon Strategy 2006, Measuring

Europe’s Progress, is one of the few studies that tracked EU counties’ progress in KE

along with candidate countries. WEF evaluated the progress in target areas of the Lisbon

Strategy; , innovation and R&D, , network industries,

5 The WEF research, conducted in 2006, included six potential accession and official candidate countries: Bulgaria, Croatia, Macedonia, Romania, Serbia & Montenegro and Turkey. In 2007, Bulgaria and Romania became EU members.

10 financial services, enterprise, social inclusion, and (World

Economic Forum, 2006). Based on WEF’s findings Turkey performed better than some of the recent members of the union. It placed 2nd in the overall ranking among six accession and candidate countries (Table 1).

Sub-Indexes

Final

Country Rank Score Society Information and Innovation R&D Liberalization Industries Network Financial Services Enterprises Social Inclusion Sustainable Development Croatia 1 3.93 3.69 3.32 4.07 4.65 4.53 3.81 3.40 3.96 Turkey 2 3.92 3.22 3.27 4.46 4.12 4.91 4.21 3.52 3.66 Romania 3 3.59 3.21 3.17 3.89 3.51 4.19 3.81 3.62 3.33 Bulgaria 4 3.31 3.09 2.92 3.49 3.86 3.88 3.43 2.87 3.00 Macedonia, FYR 5 3.28 2.51 2.79 3.56 3.71 3.98 3.51 3.17 3.04 SERBIA & 6 2.80 2.80 2.94 3.50 3.39 3.77 3.32 2.80 2.59 Montenegro EU25 Average 4.84 4.58 4.24 4.92 5.36 5.60 4.59 4.40 5.05 Table 1 Ranking and Scores of Potential EU Member Countries. Source: WEF, Review of Lisbon Strategy; Measuring Europe’s Progress on Reform, 2006.

Turkey appears to be the leader in target areas of liberalization, financial services and the enterprise environment in which it performed not far beyond the EU averages. Based on the WEF indexes; Turkey is one percent (0.46 points) less liberalized than EU’s average,

12 percent (0.69 points) less successful in financial services and only eight percent (0.38 points) less competitive in terms of its enterprises.

Due to the wide regional disparities, Turkey’s progress can better be observed in its metropolitan cities. A significant portion of this progress can be assigned to Turkey’s

11 economic center, Istanbul, which has gone through significant changes over the past century. This mega-city has seen its population increased more than tenfold since 1950.

Over time, by producing almost one-third of the national , it has established itself as the country’s industrial, financial and logistics center (OECD, 2008). Aligning with the new economic conditions of our era and Turkey’s accession to EU, Istanbul is going through a serious transition to be more competitive in the global arena.

Istanbul is already recognized as a highly innovative and attractive city worldwide.

OECD (2008) acknowledges Istanbul among the fastest growing OECD metro-regions, mentioning that its economy is changing from one driven by labor-intensive activities to one based on knowledge industries. Similarly, the business magazine, Fast

(Park, 2007), includes Istanbul among the 30 fastest growing cities that attract the best and brightest knowledge workers. The Pricewaterhouse Coopers (PwC) also emphasizes

Istanbul’s emerging situation in the new economy (Hawksworth, Hoehn, & Gyles, 2007).

The UK Economic Outlook (Hawksworth, H. & G, 2007) illustrates GDP projections on how far fast-growing cities in emerging economies will challenge the dominance of current leading global cities like New York, Tokyo, Paris and London by 2020

(Hawksworth, H. & G., 2007). The study states that Istanbul is projected to achieve an important economic growth relative to fellow mega-cities. In the ranking for cumulative projected GDP growth, Istanbul ranks 3rd after Shanghai and Mumbai (Figure 1) and is

estimated to become one of the top 30 largest economies by 2020.

12 Percent of Cumulative Real GDP Growth: 2006-2020

180

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140

120

100

80

60

40

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i l s a ai u ro o i h b ulo i res LA r g City a cow i York aka n Pa s Pa s a umb an w Tokyo O h M Istan Mo os A London chicag e ao n S S de J N Mexico Philidelphia io Bue R Globaly Competitive Cities Figure 1 Cumulative Projected GDP Growth by 2020. Source: UK Economic Outlook March 2007; Which are the Largest City Economies in the World and How Might this Change by 2020?, (Hawksworth, H. & G., 2007).

As mentioned, Istanbul is on its way to becoming globally competitive in the following

decade. Therefore, it is worthwhile to investigate Istanbul’s potential in the new economy

and its readiness to establish knowledge activities. This study aims to accomplish this

task by revealing city’s strengths and weaknesses in knowledge foundations using the

EURICUR research framework, which is designed for KE assessment of cities of Europe.

The next chapter presents the key features of KE’s along with their measurement

methods at the international level, and investigates the concept of the Knowledge City

(KC).

13 LITERATURE REVIEW

The common definitions and the characteristics of Knowledge Economy (KE) have been reviewed in chapter one with the understanding that KE is a dynamic subject. Chapter one also tackled with the importance of KE in the European Union (EU) along with efforts from both Turkey and Istanbul to become a knowledge-based economy.

Throughout chapter two, the KE phenomenon is revealed by taking a closer look at its key features, and measurement techniques at the international scale, along with its assessment in the urban environment.

This Literature review aims to increase our understanding on the KE concept. The review is organized into three sections:

1. Features of a Knowledge Economy

2. Measuring Knowledge Economy

3. Knowledge City Concept and its Assessment.

The first section, Features of the Knowledge Economy, provides information on KE characteristics. The second section, Measuring Knowledge Economy, pays attention to the relationship between the key factors of the KE and the currently used indicators. It focuses on measurement methods applicable at the international level, which in turn leads to identify relevant measures for the regional/city level. The third section is where the practice of urban planning is introduced to the KE. Within this section, a new concept, the “Knowledge City,” is presented, the requirements of the KE in an urban scale are

14 discussed, and the two existing frameworks of assessment at regional/city level are reviewed.

Features of the Knowledge Economy

Although there is no consensus on a finite list of KE characteristics, it is generally accepted that knowledge itself is an economic “good” in the new economic environment.

This modifies older economic models, in which tangible, visible, assets were the most crucial factors. Compared to older economies, the “New Economy” is deemed to generate more economic from intangible assets that are mainly associated with knowledge.

The introduction of knowledge to the new economy is considered to generate change in organizational processes. Those changes are easily observable in customer and supplier relations, R&D, attitudes, brand names, and in the use of software (Sveiby, 1997), which has direct results on .

The OECD (1996) recognizes knowledge as the “driver of productivity and economic growth” towards which many developed economies are moving. As it is claimed in the

Science, Technology, and Industry Outlooks, the ability to create, distribute and exploit knowledge is increasingly central to competitive advantage, wealth creation and better standards of living (Malhotra, 2003). Consequently, KE is considered a means of enhancing growth and productivity.

The OECD (1996) divides the knowledge activities into three categories: knowledge production, knowledge transmission, and . It is reasonable to assume

15 that the knowledge activities start with the knowledge production through research and development (R&D), and then continue with the knowledge transmission through education and developing . They are then followed by the knowledge transfer, which includes invention, innovation, adoption and diffusion of practices and technologies. Although the order of presentation of the key activities appears to follow a general linear model, is not meant to. Each activity can, and does, occur independently, as well as in conjunction with other knowledge activities (Gault, 2005).

The KE requires considerable communication among different entities, such as firms, , academic institutions and consumers. It also requires considerable feedback between science, engineering, product development, , and marketing

(OECD, 1996). Kline and Rosenberg (1986) describe the model of innovation in the KE as a “chain-link model,” as illustrated in the figure 2. The figure compares the conventional linear innovation model to the chain-link model of the new economy. Kline and Rosenberg’s chain-link model suggest a more complicated and integrated approach to the innovation process than the conventional approach. In the model, all the different actions pass through knowledge, allowing continuous value added knowledge by providing constant feedback. Therefore the activities and processes of KE are neither mutually exclusive nor independent from one another.

16

Figure 2 Chain-Link Model of Innovation in the New Economy. Source: Klein, S.J. and N. Rosenberg (1986), “An Overview of Innovation”, in R. Landau and N. Rosenberg (eds.), The Positive Sum Strategy: Harnessing Technology for Economic Growth, National Academy Press, Washington, DC.

The followings are some of the KE features that are frequently mentioned in the

literature:

1. Research and Development

Research and Development (R&D) is the most recognized engine for knowledge creation,

but its impact in KE is maximized when R&D is integrated in the business processes.

Today, R&D activities can be found in a wide range of environments varying from colleges to businesses, and recently a mixture of both. Especially in developed economies

17 the collaboration between business and non-business R&D entities is rising significantly

(OECD, 1996).

Regardless of a public or private funding source, R&D is one of the most commonly used

indicators for the KE (Brinkley, 2006). Today, almost all KE assessment methods use

in R&D as an indicator in a variety of ways. Some (EU Innovation

Scoreboard) consider national expenditures in R&D as a comparable measure across

countries, while some (OECD, The STI Scoreboard) look at R&D expenditures in

specific industries to identify how knowledge intensive they are.

2. Technological change

Technological change or the use of technology is commonly agreed to be the key driver

of the KE (Arundel, 2005). Because the rapid diffusion of information and

communication technologies (ICT), such as the Internet, knowledge transfer over long

distances at a very little cost and time are capable. Technology is crucial to knowledge

creation and dissemination (OECD, 1996).

As information and knowledge become more accessible and transferable through new

technologies, information also gets more and more adaptable to business processes

through the use of new technological tools. This results in the need for a highly skilled workforce that has the capability of reaching, selecting, and integrating information and tools to existing business processes.

18 3. Globalization

Globalization is another widely accepted driver for the KE. Today’s nations, regions and cities are competing with each other on a global scale. There are two areas in which the impacts of globalization on the new economy can easily be observed. It can be observed by noting the changes in business processes and markets due the introduction of new technologies, and it can be observed from the amount of for the high skilled workforce as a result of improvements in transportation and national immigration policies.

Following the improvements in ICT, knowledge became highly accessible to a wide range of people around the globe. The flow of knowledge became more instant, which in the larger picture, resulted in the disappearance of borders between local markets. More specifically in today’s economic environment, many businesses tend to deliver their and services regardless of physical location. With the introduction of new technologies and new ways of business collaboration, the conventional supply chain model that is so highly tied to spatial location is increasingly losing its importance in today’s economy (Leadbeatter, 2008).

Impacts of globalization are also important for business workforce needs. Florida (2007) and Arundel (2005) attract attention to the effects of globalization on workforce movements. According to Florida (2007), the US has a in KE mostly because it has welcoming foreign policies for immigrants and low entry barriers.

Arundel agrees with Florida, suggesting that the global component of KE is crucial to

19 sustain a competitive advantage for an economy, especially in human resources (Arundel,

2005). According to the author, the most common policy solution in Europe is to tap into

the global market by trying to attract highly skilled immigrants. However, he claims that

this solution appears increasingly problematic, since it is frequently overlooked that the

major factor that drives knowledge workers to move abroad is the lack of opportunities at

their home countries or regions. Therefore, he suggests that the global component of KE

in workforce must be understood as a relative force between countries or regions.

4. Presence in all industry sectors

One of the common misinterpretations of KE is to treat it as a separate industry. Due to its close relation with technology and R&D, most of the time it is associated with certain industry sectors such as ICT or general R&D oriented industries like pharmaceuticals or micro-engineering technologies. However, as previously mentioned KE activities are observed throughout all production process. Therefore, it does not depend on a specific industry but it impacts all industries with varying degrees.

Brinkley (2006) asserts that the KE exists in all sectors, and the OECD approaches the

issue in the same way. According to the OECD, knowledge intensive activities can be

concentrated in several sectors, such as ICT manufacturing; knowledge based “market

services,” including post and telecommunications, , insurance, and business

services. These knowledge intensive activities can also be found in “non-market services”

such as education and health.

20 The R&D expenditures, intensive use of Internet and ICT are among the indicators used

to identify the knowledge intensity (Malhotra, 2003). Another example for OECD’s

classification would be the identification of four industry groups according to their

involvement levels in R&D: high, medium-high, medium-low, and low technology manufacturing (OECD, 1996). Thus it should be kept in that KE is a new manner

of production with more emphasis on the use of knowledge and technology in any

sectoral activity and not solely IT-related. It is a common mistake to see KE as a substitute for the IT sector.

5. Human capital

While the information and knowledge become more accessible and transferable, an

increasing need appeared for selecting and interpreting this information. Rapid diffusion

of communication technologies along with the intensive use of Internet resulted in the

need for people with know-how skills (OECD, 1996). This skill is commonly associated

with a highly trained workforce.

Human capital has always been a dominant component of any economy. Specific to KE,

additional skills are required in order to reply to the market needs. Besides possessing a

classical education, creativity, lifelong and being multi skilled became the necessary characteristics of a (OECD, 1996). Because it is almost

impossible to quantify such skills, the assessment is mostly done by examining

educational attainments (Gault, 2005).

21 6. Networks

The KE is a (Brinkley, 2006). Because knowledge is developed

collaboratively, networking is crucial for knowledge transfer and the flow of information.

Networks can be built between different knowledge entities as well as between

knowledge workers themselves. Any means that the knowledge is transferred through is deemed to create a network. In this respect, networks can be physical as well as virtual.

Operatives of knowledge organizations are highly educated knowledge workers. They keep well informed through their contacts with customers and vendors and through membership in informal virtual networks (Sveiby, 1997) such as free the content sharing networks like LinkedIn, Facebook, and Twitter, in which knowledge flows freely

(Leadbeater, 2005). There are also more specialized networks sharing knowledge of more specific areas (Leadbeater, 2004). Today, these virtual networks are getting increasingly important in encouraging collaborative work through knowledge transfer (Leadbeater,

2008).

The second type of network is the physical network among which techno-parks or science-parks can be mentioned. These networks, as industry-university collaborations, are entities transferring knowledge between one another. In this collaboration model, businesses benefit from university resources, especially R&D related activities, while the universities benefit from the businesses by raising funds or establishing practical training agreements for their students.

22 Although the KE literature is extensive, there is no unified definition or a finite list of features for the KE. Lists of features tend to differ significantly based on the practice area in which the studies are undertaken, though most of the literature appears to agree on the features mentioned above. Now, knowledgeable of the common features of KE’s, the next section will evaluate the measurement methods used at different geographical levels.

Measuring Knowledge Economy

Today, gaining global competitiveness in the new economy has become a popular

strategy worldwide. Many nations, regions, and cities are addressing this goal through

their development strategies by mostly using benchmarks or rankings based on a

combination of measures that addresses KE features. Thus, those measures are extremely

important in building relevant development strategies.

The literature suggests that knowledge is a complex issue, and so are its measurement

methods (Hodgson 2000, Gault 2005, Brinkley 2006). As seen in its features, KE’s

encompasses many practice areas, such as economics, sociology, education, technology,

business, human resources, planning and political . In addition there is a variety

of knowledge activities such as knowledge generation, knowledge transmission; and the

use of knowledge (Foray, Dominique and David 1995, OECD 1996). Each key feature

being critically important for different activities adds complexity to the measurement

issue. As a result, the measurement methods vary significantly depending on who approaches it for what purpose.

23 With regard to strategic development, the KE measurement methods can be categorized

based on the scales from which they are carried out. These methods include

measurements at international, national, and city/region levels, as well as organizational

levels (Arundel, 2005). For the purpose of this study we will mainly focus on

international measurement models, which has the most grounded content and .

International Measurement Measurement at Methods the Regional/City Level

Figure 3 Measurement Methods.

This section presents international measurement methods from the OECD and the WB.

By focusing on international measurement models, indicators’ relevance to KE will be

clarified, and some perspective about their strengths and limitations will be gained. It is

assumed that following a hierarchical approach will provide an understanding of the

possible implications in the planning practice for regions or cities of knowledge.

Assessment at International Level6

World development organizations such as the World Bank (WB), the OECD and the

United Nations (UN) have proposed several knowledge asset measurement models.

While these models provide assessment, comparison and benchmarking of knowledge

6 This section is primarily based on Malhotra’s research paper ‘Measuring Knowledge Assets of a Nation: Knowledge Systems for Development’ (2003) which is granted by the United Nations Department of Economic and Social Affairs (UNDESA) Division for Public Administration and Development . In his research, Malhotra reviews the theory, research, practices, and national policies related to KE. He critically analyzes and contrasts the most popular models available for measurement of Knowledge assets. His review includes Knowledge modeling and measurement frameworks and their applications by reputed developmental organizations and national governments.

24 economies around the world, they mostly use indicators that relate to the themes of

intellectual, social, and human capital (Malhotra, 2003).

World Bank’s Knowledge Assessment Methodology (KAM) and Scorecards

The WB’s Knowledge Assessment Methodology (KAM) is an interactive benchmarking

tool that aims to provide information to the countries in transition to the knowledge-based

economy. The tool tries to reveal how an economy compares with its neighbors,

competitors, or countries it wishes to follow. The tool identifies the challenges and

opportunities that those countries have compared to other benchmark countries that have

similar characteristics (WB, 2008a).

KAM measures a nation’s performance in KE in four areas; Economic Incentive and

Institutional Regime, Education, Innovation, and Information and Communications

Technologies. The model uses a total of 83 structural and qualitative variables7 that are normalized on a scale of zero to ten, relative to other countries in the comparison group

(WB, 2008a). Many of these variables are showing a direct relationship with the KE features investigated in the previous sections of this study. Fourteen of these variables are considered as the core variables and they are used in standardized scorecards (Table 2).

As the outputs, the model displays index scores to facilitate comparison across nations.

7 The complete set of KAM variables is included in the Appendix 1.

25 KAM Basic Scorecard Measures

Performance Average annual GDP growth (%) Human Development Index

Economic Incentive and Institutional Regime Tariff and non-tariff barriers Regulatory Quality Rule of

Education and Human Resources Adult literacy rate (% age 15 and above) Secondary enrolment Tertiary enrolment

Innovation System Researchers in R&D, per million population applications granted by the USPTO, per million population Scientific and technical journal articles, per million population

Information Infrastructure Telephones per 1,000 persons, (telephone mainlines + mobile phones) Computers per 1,000 persons Internet users per 10,000 persons Table 2 Variables Used in the “Standard” 14-Variable Scoreboards. Source: Measuring Knowledge Assets of a Nation: Knowledge Systems for Development, Malhorta (2003).

Malhotra (2003) states that the WB’s Knowledge Assessment Methodology (KAM) and

Scorecards, represent a very comprehensive tool for reviewing world development data.

The 14 variables listed above are expected to address overall performance in KE as well as four development areas:

• An economic incentive and institutional regime that provides policies to allocate

resources and stimulate creation, dissemination, and use of existing knowledge.

• Educated and skilled workers who adapts their skills to create and use knowledge.

• An effective innovation system built from firms, research centers, universities,

consultants, and other organizations that can keep up with the knowledge

revolution and tap into the growing stock of global knowledge while adapting it to

local needs.

26 • A modern and adequate information infrastructure that can facilitate the effective

communication, dissemination, and processing of information and knowledge

(WB, 2008b).

OECD Measurement Models for Knowledge Assets and

The Organisation for Economic Co-operation and Development (OECD) has been

carrying out several studies and producing several reports related to the development of

knowledge-based economies. The aim of OECD’s extensive efforts is to track progress in the developed economies and highlights the lessons learned to provide guidance for under

developed economies that are in transition to KE. Although OECD’s primary focus is

mostly on developed countries, their studies are also relevant for developing economies

(Malhotra, 2003).

The OECD (2007) Science, Technology, and Industry Scoreboard (STI) is a widely

known assessment of KE on the international platform. STI 2007, with a focus on

innovation and performance in the global economy, aims to inform policy makers on

areas such as: international mobility of researchers and , growth of the

, innovation by regions and industries, innovation strategies by

, internationalization of research, changing role of multinational enterprises,

and new patterns in competitiveness and productivity.

The STI study compares several nations from over 200 indices (OECD, 2007). Table 3

lists all these indices, which are categorized under nine groups. These groups are: R&D

27 and investment in knowledge, human resources in science and technology, innovation

policy, innovation performance, productivity and trade, ICT, particular technologies,

internationalization of science and technology, and global economic flows.

Within this section, we first defined key features of the KE and then investigated two widely recognized assessment models on an international scale; the assessment models were taken from OECD’s Science, Technology, and Industry Scoreboard and WB’s

Knowledge Assessment Methodology. These two methods are both found to show similarities for defining KE features. Although the literature suggests many other assessment methods, the investigation of these two models provides necessary guidance in looking at KE at regional/city levels. Accordingly, the following section introduces a recent concept in KE; the Knowledge City and reviews the assessments of that matter at this scale.

28 OECD Science, Technology and Industry Scoreboard 2007 Indices R&D and investment in knowledge ICT Investment in knowledge Investment in ICT equipment and software Trends in domestic R&D expenditure Telecommunications networks R&D financing and performance Internet subscribers and hosts R&D in non-OECD economies Broadband and security Business R&D ICT access by Business R&D by size classes of firms Internet use by individuals Business R&D by industry Internet access and use by businesses Health-related R&D Internet access and use in non-OECD economies Volume of electronic commerce Internet commerce activity Human resources in Science and Technology Telecommunications pricing New university graduates Occupations and skills in the information economy Foreign and international doctoral students International tr. in ICT goods S&E doctorates awarded International tr. in ICT goods in non-OECD economies Employment of tertiary-level graduates R&D in selected ICT industries Human resources in science and technology ICT-related International mobility of the highly skilled R&D personnel Particular technologies Researchers Biotechnology firms Foreign scholars in the United States Biotechnology R&D Human resources in S&T in non-OECD economies Public-sector biotechnology R&D Employment of HRST by industry Biotechnology applications Earnings by educational level Bioscience Biotechnology patents Innovation policy Nanoscience Public-private cross-funding of R&D Nanotechnology patents Government R&D budgets Environmental science Tax treatment of R&D Patents in environment-related technologies Patenting by universities and government Collaboration with public research organizations by innovating firms Internationalization of Science and Technology Science linkages in technology Foreign of domestic inventions Entrepreneurship Domestic ownership of inventions made abroad International co-operation in research Innovation performance Sources of R&D funding from abroad Triadic patent families International collaboration in science Patent intensity Internationalization of R&D Regional patenting Foreign collaboration on innovation Patenting by industry Scientific articles Global economic flows Innovation within companies Trends in international trade and investment flows Innovation and economic performance International trade Non-technological innovation Intra-firm trade Foreign direct investment flows Productivity and trade Activity of affiliates under foreign control in mfg. Income and productivity levels Activity of affiliates under foreign control in services Labor productivity growth Trends in employment in foreign affiliates Growth accounts for OECD countries Share of turnover in selected mfg and services sectors Labor productivity growth in the Import content of exports Technology- and knowledge-intensive industries Off shoring of intermediates International trade by technology intensity Technology Exports from high- and medium-high-technology industries Contributions to the manufacturing trade balance Table 3 OECD Science, Technology and Industry Scoreboard 2007 Indices. Source: OECD (2007) Science, Technology, and Industry Scoreboard.

29 The Knowledge City Concept and its Assessment

Recently, following the global trend towards KE’s, city level investigations have become

a topic of interest and discussion (Ergazakis, Metaxiotis and Psarras, 2006). One of the

first people who attracted attention to the emerging importance of cities in the KE was

Richard Florida. In his article, World is Spiky (2005), he avowed that even in the new

economy, location still holds importance. He found that some cities had a higher

concentration of talented workers, innovation, and science and that these cities were

competing globally in the KE. They are believed to be successful due to certain urban

characteristics such as tolerance, technology and creative talent. Florida’s spread quickly all over the world, causing cities to review their development strategies either to become a KC or to keep their competitive advantage in the KE (Martinez, 2004).

With regard to the improvements in ICT and transportation, the new development in economic theory suggests that the location of a talented workforce matters for economic prosperity. In today’s world talented people are more flexible in finding a job in places they desire to live (Florida, 2004; Florida, 2007). Richard Florida’s contribution to the literature is extensive in the subject matter. His major contributions to the literature included his success in attracting attention to the issue of creativity, and the cities’ ability to attract creative talent. In his bestselling book, The Rise of (2002), he gives some social characteristics of the “creative class” and “creative way of living” and its urban requirements. In another book, Flight of Creative Class (2007), Florida focuses more on the global competition among cities to attract the creative class. He highlights the importance of the subject by explaining how the United States became the first

30 destination for talent worldwide. He looks at socio-urban dynamics of regions and

regional requirements for attracting creative talent through the use of several indexes.

The indexes he used were: the Talent Index8, the Coolness Index9, and the Diversity

Index10 (Florida, 2002). Based on his findings cities that have high levels of technology

use, social tolerance, and quality of life are more likely to attract creative talent; therefore

they are more likely to be successful in the new economy.

Because of the importance of location in the new economy, local governments around the

world follow certain paths to become more competitive in the new global market.

Although the majority of people understand the key features of KE and its possible

effects on the cities, there seems to be no single clear-cut methodology to follow for

becoming more successful at the city scale. A small handful of researchers are working

on identifying common features, success factors, and assessment criteria at the city level.

The following section introduces some of these efforts.

8 The basic Talent Index is a measure of highly educated people, defined as those with a bachelor’s degree and above. Two additional measures are also used: professional and technical workers, and scientists and (Florida, 2002).

9 The Coolness Index is a measure adapted from the so-called coolness factor used by POV Magazine (December-January 1999). The measure is based on the percentage of population ages 22-29, nightlife (number of bars, nightclubs, and the like per capita) and culture (number of art galleries and museums per capita) (Florida, 2002).

10 The Diversity Index is also known as the Gay Index. It is a measure of the fraction of the gay population. The index is used as a proxy for diversity defined as lower barriers to entry for human capital (Florida, 2002b).

31 The Knowledge City Concept

“Knowledge cities (KCs) are creations of the new millennium”, Carrillo states. Even at

the early stages of their development, cities have always been strongly associated with

knowledge and wisdom (Carrillo, 2004). It was at the dawn of the 21st century that the

cities paid more attention to the Knowledge-based economy (Ovalle, Marquez &

Salomon, 2004).

Cities or regions play a crucial role in KE’s mainly for two reasons. First, cities are the

hubs where the Knowledge activities take place. They provide the necessary spatial

environment, KE features, and citizens to create Knowledge activities. Second, cities or

regions are defined self-jurisdictions. Therefore, they are the places where local

strategies, initiatives and plans that respect the hierarchical structure of international or

national policies are implemented.

The KC concept, as well as its methods of assessment, frameworks for KCs, and design

requirements are relatively new and still under investigation (Carrillo, 2004). There are

many terms that have been created specifically for the KC concept. For this reason, it is

important to define and have an understanding of the various terms used in the literature.

Martinez (2004) mentions some of the most common terms used by different authors to describe a KC or closely related initiatives:

• Technopolis

As Horn and Henson suggest, a Technopolis is a new form of city centered around the high-tech industry, where a liberal economic and social vision is reflected (Martinez,

32 2004). Okubo, as cited by Martinez (2004), defines a Technopolis as:

Incorporates technological advances in a basic infrastructure and ; comprises

institutions and resources that hasten the application and diffusion of technological

innovation; enhances or protects the quality of life and overall human condition; and

links the inhabitants of the technopolis globally for the widest possible range of

forms of communication and transaction.

• Intelligent City

Toh and Kominos define the Intelligent City as an urban area in which the urban community, businesses and prosper together in the

(Martinez, 2004).

• Learning City

Holden and Connelly define a Learning City as a city that ties its sustainable development to that of an educational process. The educational process, as a lifelong learning activity, is the essential social function of the city. The most important element of the city is considered to be the school around which the four critical dimensions of the

Learning City evolve. Martinez (2004) notes these critical dimensions as: partnering, serving, designing, and teaching.

• Knowledge City

The term KC was created to indicate a regional economy driven by value-added exports created through research, technology and brainpower. Edvinsson defines KC as “a city that is purposefully designed to encourage the nurturing of knowledge.” Michaud asserts that a KC is primarily notable for the wealth of knowledge contained within the city, which is generated from its learning institutions, research centers, businesses, and

33 creators (Martinez, 2004).

There are also some integrated terms such as; “Ideopolis,” “Brainport,” and

“Technopole,” along with other concepts such as “Cross-city,” “Meta-city,” and “Global city” (Martinez. 2004). Although each of these terms offers slight differences, all in all, they tend to refer to the same globally competitive cities where the knowledge activities are concentrated. For practical purposes, this study will refer to such areas as

“Knowledge Cities.”

Although literature on KCs is currently limited, it is rapidly emerging. Many scholars approach the issue from different perspectives changing from design to economic and strategic development. Ergazakis et al. (2004) portrays some aspects and benefits of the

KCs concept, the key success factors related to it, as well as some case studies. Berg, Pol,

Winden, and Woets (2005) have also conducted similar research including an assessment framework for KCs and applied case studies. Their research, however, mostly concentrated on European cities of Knowledge and built their framework on the key success factors of KCs of Europe.

Baqir and Kathawala (2004) present a highly theoretical KC model by constructing

“knowledge homes”. They consider these homes as the basis for a KC model that outlines the building blocks of futuristic technologies. They believe the knowledge homes are necessary to implement the concept of a place or a platform in KCs.

Chen & Choi (2004) underline the importance of in KE and how it can effectively be tied to Knowledge based cities through science parks. They stress the role

34 of three interrelated processes that create and transfer tacit knowledge for the creation of

successful KCs. They identify the processes as local knowledge creation, transfer of

knowledge from external sources, and transfer of that knowledge into productive activities. Chen & Choi (2004) also draw exclusive attention to the importance of

planning practice in the subject matter. They believe that in a KC, researchers need to

focus on processes for the creation and transfer of tacit knowledge, while planners,

designers, and policy makers, need to focus on ways of creating tacit knowledge for these

cities.

Garcia (2004) examines the theoretical background behind the concept of KCs and

knowledge-based development. She focuses on Manchester, UK and establishes its

potential to become a KC through regeneration. She indicates that she is following an

international Knowledge Based Development model in her study to understand how local

solutions are being found for global challenges. For one of her most notable outcomes,

she states that the most problematic practicality of the knowledge based models and KC

proposals have been to operationalize the theoretical variables into measurable

equivalences or quantifiable units. She argues that there is a need for the local authorities

to develop their own custom system to measure their own intangible capital.

Chatzkel (2004) provides a strategic perspective for professionals and practitioners to

better understand the necessary elements for building successful knowledge capital, by

conducting research through interviews with the different actors of Knowledge based

activities. Such actors would include key leaders from businesses and policy makers.

35 Chatzkel draws the conceptual scope of his study by mapping out the essential principals and practices for the creation of knowledge capital in general. Chatzkel’s findings are relevant to planners for practical reasons are that there is “a split between those people interested in developing and implementing a strategy for Knowledge capitals and those

concerned with transforming existing organizations to become Knowledge-based

enterprises. The broader reality is that both efforts need to be co-joint and co-equal if a

region is to become a true Knowledge capital over time.”

Dvir and Pasher (2004) investigate the concept of urban innovation engines and their

meaning for the development of KCs. Dvir and Pasher define innovation as the process of

turning knowledge and ideas into values. In this context, an urban innovation engine is

defined as “a system, which can trigger, generate, foster and catalyze innovation in the

city.” Through their research they describe examples of urban innovation engines such as

the museum, the library, the stock exchange, the café, the brown field, the grand fair, the

outlook tower, and the industrial district. Their study conceptualizes the notion of ‘urban

innovation engines” and provides a set of guidelines for creating a KC using innovation

engines as its building blocks.

After taking a brief look at the literature on KCs, the next step is the assessment of the

Knowledge potential. As mentioned before, the KC concept and its methods of

assessment have extensively been under investigation (Carrillo, 2004). Only a couple of

frameworks approach the issue at the city or regional level. Two alternative frameworks,

Ergazakis’ and EURICUR’s, will be presented briefly in the following section.

36 Ergazakis’ Framework

Ergazakis et al. (2004) defines the KC as “A KC is a city that aims at a knowledge-based

development, by encouraging the continuous creation, sharing, evaluation, renewal and

update of knowledge.” He explains that this can be achieved through the permanent

interaction between the KC’s citizens, both among themselves and the citizens from other cities. This knowledge-sharing culture is supported by appropriatly designed IT networks and infrastructures of the city (Figure 3).

As it has been pointed out in previous sections, KE is a highly interdisciplinary subject.

This multi-structural characteristic also applies to all of the Knowledge activities in KCs.

Knowledge activities at the city level can be interpreted as a mix of global activities of

Knowledge occurring at a local scale but combined with urban dynamics. Following the characteristics of the KE, several hypotheses may be built in order to create successful

KC’s (Ergazakis et al., 2006).

Figure 3 Ergazakis’ Knowledge City Concept. Source: Coherent framework for building successful KCs, Kostas Ergazakis et al. 2006

37

Design and Development of a KC

Political and societal will Financial support and investments

Strategic vision and development plan

Agencies to promote the International muti-ethnic Metropolitan Value creation Creation of urban Assurance of knowledge development of knowledge- character of the city website to citizens innovation engines society rights based regions

Operation of a KC

Low-cost access to advanced Research excellence Experience of public libraries communication networks network

Figure 4 Developmental and Operational Functions of a KC. Source: Coherent framework for building successful KCs, (Ergazakis, 2006).

Ergazakis, Metaxiotis and Psarras (2006) suggest a set of hypotheses to identify KCs.

Their framework classifies the KC hypotheses in two categories: developmental and operational (Figure 4). The following section lists these hypotheses.

Developmental Hypotheses11

1. Political and societal will

Political and societal will provides the basis for all the knowledge activities in a city. It is

crucial for a KC to be able to adjust to change and adapt to new socio-economic

environments. KC responds to the decline of traditional industries or the of local resources with a sense of urgency in the society and a belief in the necessity for change.

This societal will for change is the basis for any further action that should be translated

11 This section lists the Developmental Hypotheses of a KC. It is a part of Ergazakis, Metaxiotis and Psarras’ KC assessment framework. The authors declare that most of the hypotheses are based on the facts in Montreal KC and Advisory Committee (2003) Report.

38 into political will. A city cannot become a KC without clear support from higher-level

governments and local leaders. Therefore public-private triggered by an active governmental body are among the first requirements of KCs.

2. Strategic vision and development plan

Any attempt to transform a city into a KC should be guided by a clear strategic vision set forth by community leaders and actors that are responsible for the city’s future. Actors such as local governments, specific agencies and organizations should compile this strategic vision, which should also include a set of specific objectives and a series of measures and actions.

3. Financial support & strong investments

As in any other big scale development project, suitable funding of the initiatives must be guaranteed before any actions related to the strategic plan are implemented. For this reason, marketing efforts must be implemented in order to draw needed financial support, meaning, local governments should provide incentives to attract both outside and local investors.

4. Setting-up of agencies to promote the development of Knowledge-based regions

According to Engels, as cited in Ergazakis et al. (2006), agencies endorsing the

development of knowledge-based regions are vital for a KC to do well. These agencies

can be technology foundations, research centers, technology parks, universities, etc.

These agencies may be involved in various activities such as: designing and

implementing projects, conducting research and strengthening scientific cooperation and

knowledge sharing, attracting and retaining knowledge workers, sustaining economical

development, marketing of the KC concept, etc (Erganzakis et al., 2006).

39 5. International, multi-ethnic character of the city and open, inclusive society

Erganzakis et al (2006), also states that a KC must build its openness to diversity in order to succeed. Florida, as cited by Ergazakis et al (2006), underlines that creative and talented individuals prefer to live in cities with diverse . This diversity allows for an atmosphere that is more tolerant and open to new ideas. This allows for a more fluid transfer of knowledge and practices, and in return, results in an increase in entrepreneurship.

6. Metropolitan websites

In response to the citizens’ needs and expectations in their search for information and in their desire to assimilate into different communities of their own KC, the development of an effective metropolitan website is crucial (Ergazakis et al, 2006). The site may also include effective e-government services that would provide easier access to local zoning regulations, municipal policies, and general business information; which, and all in all, would provide a better civil service to boost entrepreneurship.

7. Value creation to citizens

This hypothesis states that it is also important for a KC to offer opportunities of ‘value creation’ to its citizens in order to be successful. Examples given for such practices are the creation of ‘microcosms of creativity,’ establishment of spaces for ongoing societal dialogue, building of comprehensive and high-quality websites, and networks.

8. Creation of urban innovation engines

According to Dvir “an urban innovation engine is a system that can trigger, generate, foster and catalyze innovation in the city” (Ergazakis, 2006). This complex system includes people, relationships, values, processes, a financial infrastructure, and tools both

40 technological & physical. Some examples of these ‘urban innovation engines’ are:

Libraries, Cafés, Town Halls, Universities, Museums, and the Stock Exchange. Examples such as these, however, must have a strategic intention and an explicit vision before they

can be used as an innovation engine.

9. Assurance of knowledge society rights of citizens

According to Viale, a KC must guarantee, accessibility, information, education, and participation rights to all its citizens. Accessibility, information, and participation rights

mostly refers to access to up-to-date transparent public information through easily understandable and virtually maneuverable broadband networks. The citizens must also be sufficiently educated and trained in order to effectively benefit from these networks,

while user security, protection, and user privacy is maintained to uphold trust and

confidence in ICT (Ergazakis et al, 2006).

Operational Hypotheses12

10. Low cost access to advanced communication networks

KCs must ensure low-cost and integrated access to advanced communication networks

and broadband services for all citizens. Wide scale connectivity is important for a KC,

therefore, low-cost broadband services and communication networks should be

maintained to make universal connectivity more feasible for both individuals and

organizations.

12 This section lists the Operational Hypotheses of a KC. It is a part of Ergazakis, Metaxiotis and Psarras` KC assessment framework. The authors declare that most of the hypotheses are based on the facts in Montreal KC and Advisory Committee (2003) Report.

41 11. Research excellence

A successful KC stands out primarily due to the amount of wealth of its acquired

knowledge, which essentially evolves from its research centers and learning institutions.

Knowledge production proceeds stem largely from what are known as a city’s economic

development engines. These engines would include research centers and universities that

are principally, but not exclusively, in the areas of science and technology.

12. Existence public library networks

Libraries should not be seen only as an archive of old intellectual achievements, but also

as a place for innovation. They can be active, lively places, where knowledge may be

created and exchanged. It is in such environments that ideas may be produced through

conversations, and is where innovation can arise (Dvir & Pasher, 2004).

Once the hypotheses were set, the framework was followed by an investigation of each fact that is identified by the hypothesis. During the assessment process, researchers use several different indicators, for all of their case studies: Barcelona, Stockholm, Munich,

Montreal, Dublin, and Delft.

Carrillo is another researcher who worked on constructing a similar framework for the

KCs. In his framework Carillo (2004) outlines a theoretical and methodological approach for the design, assessment and benchmarking of KCs. His framework is based on capital accounts as the common ground for work between (KM) and the field of urban studies and planning. He identifies three basic categories of a generic

KC capital system. These categories include: meta-capitals, human capitals, and

42 instrumental capitals. Meta-capitals refer to the issue of identity that is assumed to impact

the economic structuring of the city. Human capitals are found in the areas of health and education at the level of an individual. Carillo (2004) argues that the collective activities that are believed to strengthen the human capital of a city are rooted in the city’s culture and life style. Finally the instrumental capitals are described as the natural and artificial organizational capital of the city including infrastructure and production systems. Overall

Carillo’s (2004) approach is different from Ergazakis’ framework due to the fact that he emphasizes the existence of fundamental capitals in a KC rather than the knowledge activities that the city generates.

Both Ergazakis’s and Carillo’s frameworks are assessments of existing characteristics of

KCs. In other words they review the necessary requirements for KC’s by learning form experiences of successful ones. Therefore these methods are useful in identifying the existence or the absence of these characteristics in a city but they offer a limited comparison across other cities to better assess relative strengths and weaknesses.

EURICUR Framework

The European Institute for Comparative Urban Research (EURICUR) uses an emerging

framework to identify KE in European cities. The book European Cities in the

Knowledge Economy; The Cases of Amsterdam, Dortmund, Eindhoven, Helsinki,

Manchester, Munich, Rotterdam and Zaragoza by Leo Van Den Berg, Peter M.J. Pol,

Willem Van Winden and Paulus Woets (2005) presents the research framework and its

43 application to these cities. With regard to indicators, the EURICUR framework shows similarities with the international measurement methods that are reviewed in this chapter.

The research framework identifies two major categories in KE: i) the foundations, and ii) the progress indicators of a knowledge city, and a bridge between the two; ‘Organizing

Capacity’ (Figure 5). The EURICUR identifies KC as a place where many Knowledge activities such as attracting and retaining knowledge workers, creating new knowledge, applying new knowledge and making new combinations, and developing new growth

clusters occur. All of these activities are referred as either development of human capital

or development of knowledge-based industries. For a city to become a KC and to develop

human capital and knowledge-based industries, the foundations should be present.

Foundations Progress towards of the urban region a knowledge-based city

Knowledge base

Industry structure Development of human capital Quality of life Diversity

Accessibility Development of knowledge-based Urban Scale industries

Social quality

Organizing capacity

Figure 5 The EURICUR’s Approach to Knowledge City. Source: European cities in the knowledge economy: Towards a typology, 2007.

44

The foundations, namely Knowledge base, industrial structure, quality of life/amenities,

accessibility, diversity, scale, and social equity refer to an existing potential providing the

basis for the activities. However, having the necessary foundations is not enough to prove

the existence of a KE. It is the ‘Organizing Capacity’ that links the existing potential to

economic activities. ‘Organizing capacity’ fills the gap between potentials and progress;

therefore; it has critical importance. The key aspects of organizing capacity are vision,

strategic networks, leadership, political support, societal support and communication

(Berg, Pol, Winden and Woets, 2005). The EURICUR framework offers comparable

measures only for its foundational aspects; neither the organizational capacity nor the

activities are easily measurable. The foundations of this framework will be described in

further detail in the next chapter.

Key Findings of the Literature Review

In this literature review, we first identified the common features and characteristics of

KE, i.e. R&D, technology, and human capital. Next, we reviewed OECD and WB’s measurement methods for KE assessment purposes at the international level. Then, we

introduced the Knowledge City concept and examined the assessment methods at this

level.

Throughout the review we observed that the measurement methods at the international

levels tend to focus on certain areas that relates to the key features of the KE. After we

investigated two emerging measurement methods for cities, we found the greatest value

in using the EURICUR framework for our assessment purposes.

45 The reasons for the use of the EURICUR framework are as follows:

• As it has been captured by the use of separate indicators in the international

methods, the EURICUR framework addresses the existing capital (knowledge

foundations) and the knowledge activities separately.

• The EURICUR framework tackles similar key features as the international

methods yet its comparable set of indicators is only in foundational areas.

• Compared to the other methods at the city scale, the EURICUR framework is

found to be more grounded, well defined and therefore more practical for the

purpose of the study.

The following chapter examines the EURICUR framework more in depth, identifies the seven foundational areas of knowledge at the city level and lists the suggested indicators for comparison along with their assessment measures.

46 METHODOLOGY

In the literature review, we noted that the measurement methods vary based on the

geographical area being observed, whether it be national, regional or municipal.

Compared to measurements at regional and national levels, there is far less literature

available for assessment at city levels. City level assessments are particularly challenging

because in most cases, an economic activity encompasses city boundaries. Yet the KE,

relying on the intensive use of technology and knowledge dissemination, primarily takes

place in the urban environment.

The European Research Institute for Comparative Studies (EURICUR) recognizes the

emerging importance of cities in the new economy. Their work focuses on knowledge

economy in an urban context and its role in attracting knowledge workers, creating knowledge, applying knowledge and developing growth clusters. The scholars from the

center have conducted studies on KE in several European cities using their own

framework.

The EURICUR13 research framework is, one of the few city level assessment

methodologies. It is comprehensive and easily applicable to cities across the world. It

covers basics of the KE concept in an urban context and uses measures that relate to those

of greater geographical areas. This chapter introduces the overall EURICUR framework,

13 To fully understand the framework we investigated two studies carefully: The book European cities in the knowledge economy, 2005, and the article European cities in the knowledge economy: Towards a typology, 2007. We observed that the Research framework has been updated slightly in the latter 2007 publishing. For this reason, this study uses the most recent version of the framework, namely the version in the article.

47 seven foundational areas, and presents the measures used to evaluate the seven

foundations.

Research Framework and the Seven Foundations

The EURICUR research framework recognizes two bodies of action for a city to be

successful in KE:

i) KC Activities

ii) KC Foundations.

Knowledge activities start to occur only if the city has enough foundations to support the

activities. The following figure conceptualizes this relationship as a house with all the

activities taking place inside the structure and with the foundations on the bottom, laying the basis from which the activities occur.

Figure 6 The Foundations and Activities of a Knowledge City. Source: European Cities in the Knowledge Economy, 2005.

48 As explained in the literature review, KC activities are assumed to be: knowledge worker attraction, knowledge creation, knowledge application, and growth cluster development.

In order to generate knowledge activities, the city should previously establish some knowledge foundations such as the existence of a qualified workforce, a strong economic structure, a socially diverse population and a certain level of quality of life in the urban environment. Different from the activities, each of these seven foundations refer to a state of existing potentials and represent the city’s ability to create, disseminate, and use knowledge for economic growth. Each of the seven foundations’ relevance to the knowledge economy and their measurement methods are explained below.

1. Knowledge base

The knowledge base relates to knowledge creation in the city, which can come from many different sources. Knowledge can either be gained through personal experience, transferred through social contacts, or be codified and gained through various forms of educational institutes and/or research institutes (Lever, 2002). Whatever the source of knowledge is, its creation is fundamental for the existence of knowledge activities

(Florida, 2004).

Knowledge generation, through universities and/or research institutes, is used extensively in KE literature. It is believed that the knowledge generation through research and development leads to new products, services and processes that form the basis of the new economy. For this reason, attempts to correlate knowledge creation with economic growth tend to use R&D measures which quantify the number of research establishments,

49 number of patents, R&D investments, and the number of highly educated workers (Lever,

2002).

Similarly, the EURICUR framework suggests a codified knowledge-oriented approach to knowledge base measurement. Absolute number of university students, diversity of disciplines in universities, and the percentage of population with tertiary education

(bachelor, graduate, and post-graduate) are the primary measures to assess a city’s

knowledge base.

2. Industrial Structure

The industrial structure is measured based on the assessment of sectoral economic

activities in the city. Identifying a legacy of declining industries, a dominant sector, and

GDP per capita are the three indicators to investigate a city’s industrial structure.

A legacy of declining industries is believed to be a negative attribute for KCs. In most of

the case studies studied by the EURICUR scholars, the legacy of historical industries is

associated with the dependence on the manufacturing industry. It is believed that the

legacy shows a never-ending reliance on one single industry that has always been the

dominant economic activity for that particular city. As the city’s economic structure

remains unchanged over the time, the city is believed to be less flexible in adapting to

economic changes. Thus, the cities with a high degree of economic activity in several

sectors are found to be more successful in transition to KE.

50 Although the EURICUR scholars suggest that an equal distribution of economic activity

across industries is theoretically the most favorable situation, they underline that the

existence of a dominant service sector can also be deemed favorable. Economic diversity

allows an easier transition to the new economy but many cities fail to have such a

structure. The scholars argue that a dominant service sector can also create a positive

condition in this transition.

The economic base theory suggests that a city’s industrial structure is defined by its most

dominant economic activity that is characterized by its level of exports. The EURICUR

framework however, uses the number of employees as the main measure. For our

analysis, we used both the EURICUR method and the economic base theory by analyzing

the share of national economic activity in different sectors to illustrate a national/regional

economic reliance on the city.

3. Quality of life

Van den Berg (2005) and Florida (2004) stress that the quality of life is important for a city to attract and retain knowledge workers. It is not possible to retain or attract young and talented workers unless the city offers a viable and livable urban environment through its services and amenities (Florida, 2004).

The EURICUR framework measures the quality of life based on two indicators: urban vs. provincial feel and the attractiveness to residents. While urban vs. provincial feel is

51 determined by the share of residents living in each of these areas, the attractiveness to residents is determined by resident satisfaction.

In our analysis we used two different surveys to assess the quality of life in Istanbul. The first public survey was conducted by the Metropolitan Municipality of Istanbul, which focuses on the level of services provided by the municipality. The second survey was an independent study that measures the resident satisfaction of amenities in the city’s different neighborhoods.

4. Accessibility

KC accessibility is associated with the ability to build networks. We previously mentioned that networks in a KE can either be virtual or physical. With regard to the physical networks, face-to-face contact is an essential component of KE since they result in knowledge dissemination and (Simmie, Sennett, Wood, & Hart, 2002).

Accordingly, the KC’s ability to build networks, translates into a high level of intra- national/regional accessibility.

The EURICUR research framework concentrates on international and regional connectivity through air and rail transportation. The number of international airports and the existence of regional high-speed-train connections are used as the main indications.

Their framework, however, does not provide any insights to which extent these networks are used. Thus, in our analysis, we found it more valuable to observe passenger volume on international flights in order to better assess the accessibility.

52 5. Urban diversity

Many studies argue that diversity in an urban population generates creativity and

knowledge accumulation in a city. Cities with lower social barriers are believed to be

more likely to attract both new residents and businesses (Jacobs, 1961; Florida, 2004).

The economic success of many knowledge cities can be attributed to their positive

attitudes towards immigration (Carrillo, 2004). Lee, Florida, and Acs (2004) suggest that

diversity and creativity result in a concentration of human capital, which correlates to the formation of new firms. Both low and high skilled or extremely well educated immigrants are the highest risk takers who are very likely to engage in entrepreneurial activities.

The EURICUR framework uses the number of various ethnic backgrounds as a measure for social diversity. Although, the context of diversity can easily be broadened to include several social groups based on race, sex, religion, etc. as long as the data is readily available.

6. Urban scale

A knowledge city’s urban scale relates to the intensity of knowledge activities. As previously stated, knowledge activities are more likely to concentrate in larger cities.

Because larger cities have more specialized services and a greater number of knowledge workers, they enjoy the advantage of . The EURICUR framework uses the absolute number of population to measure the city’s urban scale.

53 7. Social equity

Social equity among resident groups is another component of KCs. Because these cities should have lower social barriers to their diverse populations, they also must ensure that the rights of each individual are protected and provide access to social securities such as education and healthcare. Yet, because of the absence of comparable measures we only examined social equity in terms of unemployment rates.

Foundations Indicators (EURICUR) Categories (EURICUR)

1. Knowledge Base High (>68,000) Absolute number of students Medium (33,000 – 68,000) Low (<33,000) Specialized (highly specialized university) Degree of specialization of the Medium knowledge infrastructure (several academic disciplines) Diversified (full range of disciplines) High (>20%) Percentage of population with Medium (12%-20%) tertiary education Love (<12%) 2. Industrial structure Legacy of declining industries High, Medium, Low Yes; which one? Dominant sector No 3. Quality of life / Feel: Urban vs. provincial Urban vs. provincial Amenities Attractiveness as a place to live High / Medium / Low 4. Accessibility High: international airport and HST connection Access to international airport Medium: international airport or and/or HST connection HST connection Low: neither international airport nor HST connection 5. Diversity High (>12%) Percentage of foreign Medium (4%-12%) population Low (<4%) 6. Scale Large (>1.5 million) Absolute population in urban Medium (0.7 0 1.5 million) region / network Small (<0.7 million) 7. Social equity Percentage on benefits High / Medium / Low

High (>10%) Unemployment rate Medium (5% - 10%) Low (0% - 5%) Table 4 The Seven Foundations and Their Comparison Measures. Source: European cities in the knowledge economy: Towards a typology, 2007.

54 As mentioned previously, the foundations prepare the basis for knowledge activities to

occur. Knowledge activities as a whole, is a separate subject for investigation. This study

will focus solely on the foundations of Istanbul as a KC, compare them to European averages, and investigate trends where possible. The table 4 above shows the original

EURICUR knowledge foundation indicators along with their comparison measures.

Where the existing indicators were not applicable for Istanbul, we have suggested certain

additional information that is found to be more meaningful comparison to Istanbul. In

table 5 below we have summarized all the indicators used in the analysis with our

suggestions indicated in italics.

The 7 Foundations EURICUR Indicators & Suggested Indicators Number of Students in Tertiary Education Number of Students per 1000 Residents Trend in Graduate and PhD Students 1. Knowledge base Students per 100 Resident Population (20-34) Degree of Specialization in Universities Percent of Population with Tertiary Education Legacy of Declining Industries 2. Industrial Structure Dominant Sector Feel: Urban vs. Provincial 3. Quality of life Attractiveness as a Place to Live 4. Accessibility Access to International Airports and HST 5. Diversity Percent of Foreign Population 6. Scale Absolute Population Percent on Benefits 7. Social Equity Health Indicators Unemployment Rate Table 5 The Seven Foundations and Their Indicators. Source: Adapted from European Cities in the Knowledge Economy, 2005.

Due to the variety of indicators used in the methodology, it was necessary for EURICUR

scholars to utilize several data sources. The scholars primarily relied on the Urban

55 Audit14, which provides comparable data for European cities. They have also utilized individual city statistics and interviews. Our study uses the same Urban Audit dataset for

comparable indicators. We have also applied some additional information from the

Turkish Statistical Institute (TURKSTAT) to look at the trends where the data is readily

available. With the use of these data sources, the next section analyzes Istanbul’s

performance in each of the seven knowledge foundations adapted from EURICUR’s

assessment methodology.

10 The Urban Audit is a joint effort by the Directorate-General for Regional Policy (DG REGIO) and Eurostat to provide reliable and comparative information on selected urban areas in Member States (MS) of the European Union (EU) and Candidate Countries.

56 ANALYSIS: ASSESSING ISTANBUL’S KNOWLEDGE FOUNDATIONS

The aim of this study is to assess Istanbul’s readiness in becoming a Knowledge City

(KC). As many suggest, Istanbul is on its way to become a globally competitive city in

the following decade. For this reason, it is worthwhile to investigate Istanbul’s potential in the new economy and reveal its strengths and weaknesses for establishing the KE foundations. This study aims to accomplish this task with the guidance of the EURICUR research framework, which was designed for KE assessment in European cities. The framework itself, its seven knowledge foundations, and the indicators and criteria used for their assessment, have all been introduced in the methodology section of this study.

Knowledge Economy Foundations in Istanbul

As underlined by the EURICUR framework, the city should establish a mix of seven

foundations in order to create, disseminate and use knowledge for economic growth. The

next section investigates each of the seven foundations and assesses Istanbul’s

performance in these foundations as high, medium, or low based on the criteria15 displayed in the methodology section (Table 4).

1. Knowledge Base

The knowledge base refers to the ability to create codified knowledge through universities. There are two types of universities in Turkey; state and private. Out of the

15 The criteria table includes original indicators from the EURICUR framework as well as additional indicators where framework measures are either not readily available or not applicable to the case of Istanbul.

57 22 universities in Istanbul, 7 belong to the state and 15 are owned by private entities.

Although, the state universities are less in number, a majority of students attend the state

universities because they are larger, have lower tuition fees and are relatively older and

more reputable.

Number of students in tertiary education16

In 2000, there were 181,406 college students in both state and public universities of

Istanbul (YOK, 2008). In 2007, the total number of students reached 230,735 showing an

increase of 27.2 percent since 2000 (Figure 6). Most of this increase was due to the

population growth and the establishment of private universities. In 2000, state universities accounted for about 98 percent of students in tertiary education. In 2006, their share of students decreased to 69 percent (Ministry of National Education, 2008).

The 27.2 percent growth in student population within the seven years did not occur in a linear fashion. The following graphs show the numbers of student in both types of universities, and the number of graduate and PhD students between the years 2000-2007.

The total number of students had fluctuated until the educational year 2003-2004. After

2003, large increases were observed year to year until 2007. Similarly, there was an increase in capacity for both graduate (31%) and PhD students (28%) between 2004 and

2005.

16 Tertiary education or “ISCED Level 5-6” as classified in the Urban Audit Database includes college education in Bachelor, Graduate, and Doctoral levels.

58 Total Number of Students in Universities 2000-2007

300

230.735 Thousands 200 216.328 220.618 195.419 181.406 185.124 182.177 186.321

100

0 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08

Figure 7 Total Number of Students in Universities 2000-2007. Source: Own calculation based on data from YOK, 2008. Note: Includes medical students in training hospitals.

Graduate and PhD Students 2000-2007

40

35

30 33.836 32.755 32.773

Thousands 25 28.043 26.176 24.9 25.634 25.797 20

15

10 9.67 5 9.322 9.769 6.676 6.463 6.344 6.768 7.262 0 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08

Graduate PhD.

Figure 8 The Trend in Graduate and PhD Students 2000-2007. Source: Own calculation based on data from YOK, 2008. Note: Does not include medical students.

59 Once the total number of students is compared to that of European cities of knowledge,

due to its size, Istanbul seems to perform far beyond the interval17 of the highest category

as specified by the EURICUR framework. This is due to Istanbul’s population, which is

much greater than that of European cities. For this case it also important to consider the

ratio of students to totals population, which is more meaningful for comparison purposes

than the absolute values.

The following figures 9 and 10 provide ratios such as “number of university students per

1000 resident” and “students in higher education per 100 resident population, aged 20-

34” for the cities of knowledge studied by the EURICUR scholars. The graphs compare

them to Istanbul. Based on the Urban Audit data provided by the EUROSTAT,18 Istanbul

ranks last among the comparison cities both in the share of university students per 1000

residents and the share of population aged 20-34.

Based on EUROSTAT, in 2004 there were an average of 78 university students for every

1000 population, where as in Istanbul, this number was as low as 31 per 1000, meaning

Istanbul was 60 percent below the average (Figure 8). Student percentage in the

population between the ages of 20 to 34, tells a similar story. In the same year, only 10

percent of the population between the ages of 20 to 34 was attending a college in

Istanbul, while average attendance in comparable cities was 28 percent (Figure 10).

17 For the absolute number of students in tertiary education, EURICUR Research framework identifies the following performance levels: High (x > 68,000), Medium (68,000 > x > 33,000), and Low (33,000 > x).

18 EUROSTAT is the official provider of statistics in the European Union.

60 Number of Students in Universities and Further Education Establishments per 1000 Resident Population, 2004

160

140 Manchester 120 Helsinki 100

80

60 Enschede München Zaragoza Eindhoven 40 Amsterdam Rotterdam 20 Istanbul

0

Figure 9 The Number of Students in Universities and Further Education Establishments per 1000 Resident Population, 2004. Source: Urban Audit Database by EUROSTAT, available online (http://epp.eurostat.ec.europa.eu). Note: Included are cities where comparable data is readily available.

Students in Higher Education per 100 Resident Population Aged 20-34, 2004 50

45 Manchester 40

35

30 München 25 Dortmund Enschede Zaragoza 20 Amsterdam Eindhoven 15 Rotterdam 10 Istanbul 5

0

Figure 10 Students in Tertiary Education per 100 Resident Population Aged 20-34, 2004. Source: Urban Audit Database by EUROSTAT, available online (http://epp.eurostat.ec.europa.eu). Note: Included are cities where comparable data is readily available.

61 Regardless of which ratio we consider the most relevant (the number of students per 1000 residents or the number of students per 100 residents 20-34), compared with the other cities of knowledge, Istanbul performs significantly below the averages. Yet in terms of progress, Istanbul appears to be building its knowledge base at a notable pace.

Degree of specialization of knowledge infrastructure

Another criterion for knowledge base is the specialization measured by the range of

available academic disciplines in universities. In 2000, state universities were offering a

wide range of disciplines but private universities had recently been established, so the

number of academic disciplines in these organizations was significantly less than it is

today.

Another way of assessing specialization might be to note the existence of medical schools

in Istanbul’s case. Istanbul has been well recognized as having a high concentration of

medical schools and university hospitals. Based on the trends in student numbers, this

concentration became more significant from 2000 to 2007. The table below compares the

increase in total college students to the increase in medical students. The data suggest that

the medical students’ share rose from 1.8 percent in 2000 to 2.5 percent in 2007.

The Share of medical students 2000-2007 Educational Year College Students Medical Students Percentage 2000-2001 181,406 3,319 1.83% 2001-2002 185,124 3,149 1.70% 2002-2003 182,177 3,032 1.66% 2003-2004 186,321 3,798 2.04% 2004-2005 195,419 4,596 2.35% 2005-2006 216,328 4,596 2.12% 2006-2007 220,618 5,062 2.29% 2007-2008 230,735 5,749 2.49% Table 6 The Share of Medical Students 2000-2007. Source: Own calculation based on data from YOK, 2008.

62 When the specialization of knowledge infrastructure is assessed with regard to the diversity of disciplines, we can suggest that Istanbul performs at medium level19. When the specialization within a certain practice is taken into account, Istanbul appears to be getting increasingly important in .

Percentage of population with tertiary education20

The percentage of population with a college degree is the final indicator for the knowledge base. In 2000, 7.06 percent of Istanbul’s population had a higher education degree21. According to the standards provided by the EURICUR framework (less than 12

%22), Istanbul places in the lowest category.

2. Industrial Structure

Economically, Istanbul is the most developed city in Turkey. As seen in the table below from 1995 to 2004, Istanbul has been representing 21 percent to 23 percent (22.7%) of the national Gross Domestic Product (GDP) (IMM, 2007). Also, the city has the largest

19 The framework identifies medium level specialization as having several academic disciplines.

20 Percent of population with tertiary education indicates the proportion of the population with tertiary education to the total population of the city.

21 The information is provided by the Turkish Statistical Institute (TURKSTAT) as the Urban Audit dataset. The Urban Audit is a joint effort by the Directorate-General for Regional Policy (DG REGIO) and the EUROSTAT to provide reliable and comparative information on selected urban areas in Member States of the European Union (EU) and the Candidate Countries. According to criterion defined by European Union Statistics Office, Turkey participated in this study for 26 province including Istanbul. This set of data is only available for the year 2000, and accessible through TURKSTAT’s website: www.tuik.gov.tr.

22 For the percentage of population with tertiary education, the EURICUR Research framework identifies the following performance levels: High (x > 20%), Medium (20% > x > 12%), and Low (12% > x).

63 shares of national production in many industries and accommodates the largest numbers of employees nationwide.

Year Share of GDP 1995 21.1% 1996 21.3% 1997 22.8% 1998 21.7% 1999 21.8% 2000 22.1% 2001 21.3% 2002 21.8% 2003 22.5% 2004 22.7% Table 7 Istanbul’s Share of the National GDP. Source: Istanbul Metropolitan Municipality (IMM) 2007-2011 Strategic Plan of Istanbul based on 2005 TURKSTAT data.

In terms of the industry sectors, Istanbul is the center for banking and finance, and the . Istanbul’s largest shares of national GDP lies in banking and finance

(47.5% in 2004), and in services (41% in 2004). These shares have been fairly steady

from 1995 through 2004 (Table 8).

Industry Sectors 1995 1999 2001 2004 Agriculture 1.8% 0.8% 1.2% 1.0% Manufacturing 26.2% 26.4% 24.0% 25.0% Construction 16.3% 18.6% 17.1% 18.0% Retail 28.7% 34.8% 34.1% 35.5% Information 22.9% 22.9% 21.1% 21.0% Banking & Finance 44.6% 50.5% 48.0% 47.5% Services 39.3% 41.4% 41.0% 41.0% Table 8 Istanbul’s Share of GDP by Industry Sectors. Source: Istanbul Metropolitan Municipality (IMM) 2007-2011 Strategic Plan of Istanbul based on 2005 TURKSTAT data.

With regard to the employment distribution in Istanbul, the city has the greatest number

of employment in services (53.3 percent in 2000). Although the percentage only

increased from 51.2 in 1980 to 53.3 in 2000, the absolute number of employees in

services had grown by almost 1,000,000 during this period (Table 9).

64 Sectors 1980 1985 1990 2000

Number Percent Number Percent Number Percent Number Percent

Agriculture 85,730 5.5 97,439 5.2 130,322 5.1 282,317 8.1 Manufacturing 538,440 34.4 652,044 34.8 853,625 33.6 1,116,126 32.2

Construction 111,690 7.1 122,936 6.6 224,126 8.8 215,925 6.2

Services 800,930 51.2 973,118 51.9 1,289,447 50.8 1,851,030 53.3

Other 27,149 1.7 28,060 1.5 42,443 1.7 6,002 0.2

Total 1,563,939 100 1,873,597 100 2,539,963 100 3,471,400 100 Table 9 Employment Shares in Istanbul by Industry Sectors. Source: Based on data from the Turkish Statistical Institute (TURKSTAT, 2000).

Legacy of declining industries

The framework assesses the legacy of declining industries (mostly referring to manufacturing) as a negative indicator for KE. It is also mentioned that a diversified distribution of employment and economic activity is a favorable condition in transition to knowledge base.

As shown in table 9, the employment share of manufacturing decreased by 2.2 percent between 1980 and 2000. The manufacturing industry is still important for Istanbul, since it represents 32 percent of its workforce in 2000, and ¼ of the nation’s total manufacturing production (Table 9). These two facts combined make it clear that

Istanbul’s economy still relies, to a certain extent, on its rooted industries, namely the manufacturing industry.

Dominant sector

As mentioned previously, an even distribution of economic activity across industries is the most favorable situation for transition to a KE. In real world experience, however, it is not easy to achieve such a balance. Based on their studies, the EURICUR scholars

65 emphasize the importance of a dominant service sector as a common feature for KCs of

Europe.

The dominant sector can either be defined by looking at employment distribution or

export levels as the Economic Base Theory suggests. Table 9 shows that the majority of

Istanbul’s employment has been in services since 1980. This share had increased by 2.5

percent from 1990 to 2000. With regard to export levels23, the nation’s reliance on

Istanbul can be used as a measure. Since Istanbul accounted for 47.5 percent of the

service industry’s national production in 2000, we can say that the service industry can be

claimed as the dominant sector in the city.

3. Quality of life/amenities

The two proxies used to evaluate the quality of life are: the atmosphere of the city

“metropolitan” or “provincial”, and the “attractiveness as a place to live”. It is already

pointed out that none of these proxies are meant to provide comparable data across the

cities. Here the analysis becomes a case-by-case investigation of the related proxies.

Feel: Urban vs. Provincial

Istanbul contains various districts of very diverse characteristics. High physical density

and high population density, however, ensure an overall metropolitan feeling throughout

the city.

23 Here, we assume that the industries with largest shares of national GDP are more likely to export the their to the rest of the nation.

66 Attractiveness as a place to live

There are two recent studies on Istanbul’s perception by its residents, both of which were

conducted for the Strategic Plan of the city. The first one is a Service Satisfaction Survey

(2007), which measures the level of satisfaction from the services provided by the city

government. The second survey measures Neighborhood Satisfaction (2005), which

compares the overall satisfaction from the services and amenities in neighborhoods with

varying population densities and land values on the two continents: Europe and Asia.

According to the Service Satisfaction Survey, public satisfaction is highest in protection and control of the physical environment, and historic preservation. This survey reveals

the largest problems of the city in physical infrastructure, transportation, and especially in

disaster (Figure 11).

Env. Protection and control Cultural activities Historic preservation Public health Public relations

Services Social services Physical infrastructure Transportation Disaster risk management

2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 Level of satisfaction Figure 11 IMM Public Satisfaction Survey. Source: Istanbul Metropolitan Municipality (IMM) 2007-2011 Strategic Plan of Istanbul. Note: The level of satisfaction is graded on 5 with 1 indicating the lowest level.

The Neighborhood Satisfaction Survey (Figure 12) reveals a positive relationship between the land and overall neighborhood satisfaction. The highest rates are observed in low and medium density neighborhoods with high land values. Not

67 surprisingly, the lowest rates are observed in neighborhoods with high density and lower

land values.

Figure 12 Neighborhood Satisfaction in Istanbul. Source: Turkoglu, Bölen, Baran, & Marans, 2006. Note: 1) Rating scale: 1 = not satisfies at all, 7 = very satisfied. 2) LD, MD, and HD indicate Low, Medium and High Densities. Similarly, LLV, MLV, and HLV indicate Low, Medium, and High Land Values.

4. Accessibility

In terms of accessibility, the EURICUR framework focuses primarily on the international

connections through international airports and/or high-speed train (HST) connections.

Access to international airports/HST

Istanbul acts as Turkey’s gateway to both Europe and Asia. The city has sufficient

capacity to function as an import/export hub with its international transport connections

(OECD, 2008). Istanbul has one international airport on each continent. The Ataturk

International Airport, on the European continent, is 24 km from the city center, while the

Sabiha Gokcen International Airport, on the Asian continent, is 45 km east of the

European city center.

68 Number of travelers passing through the two international airports had been increasing

significantly within recent years. Figure 13 below reveals the number of passengers carried by the international flights through the two airports of Istanbul. Based on the data from 2002 to 2005, the number of international passengers had been increasing incrementally with an annual average of 4 percent. By 2005, the number of traveling passengers reached 11,781,000, an increase of 16 percent.

Number of Passengers Carried Through the International Flights

14,000

12,000

11,781 10,000 10,170 8,000 8,828 8,978 8,506

6,000

Passengers (thousands) 4,000 2001 2002 2003 2004 2005

Figure 13 Number of Passengers Carried Through the International Flights. Source: TURKSTAT, 2007.

In addition to airports, Istanbul is equipped with for water ports. Istanbul has four ports.

The Haydarpasa port and the large Ambarlı port are both important for international logistics. The two others ports, Zeyport and Galata, located close to the business centers; are only used as ports for passenger vessels. The use of light rail transportation or HST, however, is fairly new to Istanbul. Although there are some undergoing projects such as the Marmaray, an underwater light rail connecting the Asian and European continents,

69 the city cannot currently be considered as having a developed light rail transportation

system.

Although Istanbul has a great advantage in being able to develop different kinds of transportation systems (air, sea…etc.), integration of these systems has always been one

of the main concerns for the city. Compared to the other European cities, Istanbul seems

to have a competitive advantage in air and water transportation, but the same argument

cannot be made for light rail transportation.

5. Diversity

The proportion of foreign-born population to the city’s total population is used as the

primary indicator for the social diversity foundation. Due to the common ground created

by the EU, EU immigration policies allow European citizens many opportunities to

travel, live, and work throughout the Union territory. Since Turkey has still not officially been inducted into the EU, Istanbul cannot benefit from the EU incentive policies in the

subject matter.

Percentage of foreign population

According to Urban Audit data, in 2000, there were a total of 48,663 foreign people

living in Istanbul (0.5% of the total population). Of this foreign population, only 0.08

percent of the total population has moved to the city from the EU countries, while the remaining 0.41 percent is a non-EU national. These percentages appear to be very low once compared to the European knowledge cities. The cities investigated by EURICUR have an average of 10.5 percent of foreign population (Figure 14). When compared to the

70 measures of the framework Istanbul is identified under the lowest performance category24

for this indicator.

Percentage of Foreign Population, 2000

25.0%

Munchen 20.0% Dortmund

15.0% Amsterdam

Rotterdam 10.0% Eindhoven

Enschede Helsinki 5.0% Zaragoza

Istanbul 0.0%

Figure 14 Percentage of Foreign Population, 2000. Source: Urban Audit Database by EUROSTAT, available online (http://epp.eurostat.ec.europa.eu). Note: Included are cities where comparable data is readily available.

6. Urban Scale

To measure the urban scale foundation, the EURICUR framework quantifies the number

of people living within the city. The absolute number of population is the only

comparable indicator for this foundational area. In 2001, Istanbul had 10,018,735 people

living within the city. In terms of absolute number of population, it is by far, larger than

any European city with its population found to be four times greater than that of the

average population of the cities investigated (Figure 15).

24 For the Percentage of the foreign population, the EURICUR Research framework identifies the following performance levels: High (x > 12%), Medium (12% > x > 4%), and Low (4% > x).

71

12

Istanbul 10

(Millions) 8

6 Population 4 Manchester Munchen Rotterdam 2 Amsterdam Helsinki Zaragoza Eindhoven Enschede Dortmund

0

Figure 15 Population of Cities, 2001. Source: Based on Urban Audit 2001 Data, available online (http://www.urbanaudit.org). Note: Included are cities where comparable data is readily available.

7. Social Equity

The framework assesses social equity by using two proxies; the level of social benefits,

and the unemployment rate. Because there is no comparable data available for social

benefits (health, unemployment, and social security benefits...etc) in Istanbul, we will use

some health measures as a substitute.

Health indicators

Throughout Turkey, the application of general health insurance for people with low income constitutes a comprehensive health policy. In 2005, there were 51 hospitals in

Istanbul, 23 of which were training research hospitals (IMM, 2007). The figure below compares Istanbul to the European KCs in number of hospital beds per 1000 residents.

72 Number of Hospital Beds per 1000 Residents

12.0

10.0 Munchen

8.0 Eindhoven Helsinki 6.0 Dortmund Zaragoza Enschede Rotterdam 4.0 Amsterdam

Istanbul 2.0

0.0

Figure 16 Number of Hospital Beds per 1000 Residents. Source: Based on Urban Audit 2004 Data25, available online (http://www.urbanaudit.org). Note: Included are cities where comparable data is readily available.

Based on the information above, Istanbul performs below the averages of KCs of Europe.

The average number of beds per 1000 resident in KCs (6.9) is found to be two times as much as it is in Istanbul (3.4).

Unemployment rate

According to the Urban Audit data, Istanbul’s unemployment rate was 14.57 in 2001.

When compared to European standards, Istanbul greatly exceeds the highest limits of unemployment. The average unemployment rate in European cities was 5.5 percent in

2001. A comparison of unemployment rates between Istanbul and the European cities is shown in the following figure (Figure 17).

25 Except for Istanbul. The data is for the year 2005 and its source is Istanbul Metropolitan Municipality (IMM) 2007-2011 Strategic Plan of Istanbul.

73 Unemployment rate 16% Istanbul 14%

12%

10%

8%

6%

4%

2%

0%

Figure 17 Unemployment Rates, 2001. Source: Based on Urban Audit 2001 Data, available online (http://www.urbanaudit.org). Note: Included are cities where comparable data is readily available.

Limitations and Findings

The EURICUR research framework is used to assess Istanbul’s performance in each of

the knowledge foundations, in order to reveal its strengths and weaknesses in becoming a

KC. This as done in accordance with the original indicators to the greatest extent possible

to evaluate Istanbul’s performance. During the investigation certain indicators were not

found to be applicable for Istanbul. These restraints were caused either by data limitations

or by characteristics relating to Istanbul’s population size that differs significantly from

the European cities. In these instances, other comparable substitute data was used. All

indicators including the ones that are not applicable to the case of Istanbul are listed in

the summary table below along with their suggested substitutes and the performance levels.

74

The 7 EURICUR Indicators & Suggested Istanbul’s Expected Foundations Indicators Performance Impact on KE Number of Students in Tertiary Education Very High Very Positive

Number of Students per 1000 Residents Low Negative

1. Knowledge Trend in Graduate and PhD Students Increasing Positive base Students per 100 Resident Population (20-34) Low Negative

Degree of Specialization in Universities Medium Moderate

Percent of Population with Tertiary Education Low Negative

2. Industrial Legacy of Declining Industries Medium Moderate Structure Dominant Sector Services Positive

3. Quality of Feel: Urban vs. Provincial Urban Positive life Attractiveness as a Place to Live High Positive

4. Accessibility Access to International Airports and HST Medium Moderate

5. Diversity Percent of Foreign Population Very Low Very Negative

6. Scale Absolute Population Very High Positive

Percent on Benefits Not Applicable Not Applicable 7. Social Health Indicators Low Negative Equity Unemployment Rate Very High Very Negative Table 10 Summary of the Findings. Note: The items in italic are the suggested indicators.

Overall, Istanbul is found to be performing at or below medium level for the majority of

foundational areas. In knowledge base indicators Istanbul performs extremely high in the

number of students in tertiary education. The city is found to be building its knowledge

base as a result of the increase in number of universities and their degree programs. The

trends in number of graduate and PhD students also showed an incremental increase

within the recent years. Yet due to its population the ratio of tertiary education students

still remains below the European averages. The city’s industrial structure is moderately

diversified in terms of employment distribution. As a positive aspect, the largest share of

75 employment is observed to be in the service sector but the reliance on historically strong

industries such as manufacturing is also noted. This is analyzed further by looking at

Istanbul’s share of national GDP. Istanbul is found to supply ¼ of Turkey’s total manufacturing GDP, showing regional disparities and Turkey’s high reliance on the manufacturing industry in Istanbul. Quality of life is observed to be high, especially as being an attractive place to live. The overall satisfaction from public services is found to be high with the exception of transportation, and disaster management. In the area of accessibility, the city’s international connection ability is observed to be high but the regional connectivity through high-speed trains was absent. Istanbul performed very low in diversity, which was measured by the percentage of foreign-born population. This might be due to Turkey’s status in the EU and its deprivation of EU incentive policies. In terms of the urban scale, Istanbul was an outlier. Its population size is found to be four times larger than the European knowledge cities’ average. With regard to social equity, the city is once again found to perform way below the average. Finally, in the area of social benefits, the only comparable health measure indicated a performance much lower than the European average, and unemployment was above the limits of the lowest performance category.

To summarize, Istanbul’s greatest strengths for becoming a KC lay in knowledge base due to the large number of students with higher education, and the increasing trends in graduates. Another strength appears to be in its quality of life, especially as being an attractive place to live. On the other hand, Istanbul’s largest weaknesses are shown in its

76 lack of social diversity and equity due to low levels of foreign residents, poor social benefits and its extremely high unemployment rates.

77 CONCLUSION

Today, knowledge is widely recognized as an economic driver. Due to changes in information and communication technologies, along with the use of the Internet, knowledge becomes easily transferable at lower costs. All together, these changes result in reformation of existing production processes and supply chains. Intangible assets, such as knowledge, are increasing in importance. This in turn intensifies the need for higher skilled workers in order to apply the knowledge, and become more globally competitive.

The term Knowledge Economy was coined to define and give recognition to this new knowledge driven environment.

This study aimed to reveal Istanbul’s strengths and weaknesses in becoming competitive in the KE. Because the KE is a fairly new concept in planning practice, we began our research with an investigation from an international level. We first identified the key features and characteristics of this new economy, then investigated measurement methods and indicators used by global organization such as the OECD and WB. Once similarities between their approaches were identified, we used them to select an appropriate assessment framework at the city level. Among the two frameworks that we have investigated, we found the greatest value in using the EURICUR framework for our

purposes. Lastly, we applied the EURICUR framework to Istanbul’s case and assessed its

readiness to become a knowledge city.

Specified performance bounds applied within the EURICUR framework were used to

expose Istanbul’s strengths and weaknesses for each of the listed knowledge foundations.

78 These foundations are believed to prepare the basis for the knowledge activities to occur

in a city. They cover seven areas: knowledge base, industrial structure, quality of life, accessibility, urban scale, and social equity.

For the majority of the foundational areas, Istanbul was not found to be performing higher than European knowledge city averages. Some strengths, however, were observed in the area of knowledge base. Istanbul accommodates a large number of graduate students. In terms of the share of tertiary students to total population, Istanbul ranked below the studied European cities; yet this is a result of Istanbul’s large population.

Istanbul is roughly four times more populated than the average population of the

European cities studied.

Its situation is also observed to be promising as far as the increasing trends in graduates is concerned. Another strength of the city appears to be in its quality of life; it has been seen as an attractive place to live with the exception of the inconvenience in transportation and disaster risk management services. On the other hand Istanbul’s biggest weaknesses are observed to be in the areas of social diversity and equity. Compared to the European knowledge cities, Istanbul is found to perform significantly lower in accommodating foreign population. This difference is partially due to Turkey’s non-member status with the European Union (EU). Because EU policies facilitate citizens of member states to travel, live, and work in other member countries more easily, the European cities are more likely to accommodate larger numbers of foreign populations.

79 Although some of the trends indicated progress towards becoming a KC, Istanbul still does not qualify for a majority of the foundational knowledge areas. Based on these findings, we conclude that Istanbul has a very promising status in terms of knowledge creation through its universities. It also has an advantage of having a young and talented workforce. Yet in terms of attracting knowledge workers from the global pool, the city seems to offer a certain level of quality of life, but does not offer social benefits nor enough accessibility to compete with the European knowledge cities.

In terms of future research, it would be interesting and useful to deepen the KE measures at the city level. Furthermore, KE methods are generally criticized for measuring inputs rather than outputs (Malthotra, 2003). This statement seems to be valid for emerging assessment methods at the city level as well. Perhaps developing measures to quantify knowledge activities such as dissemination and use may reveal more insightful results.

The development of such a framework would provide a more sound and in depth assessment.

80

APPENDIX

81 APPENDIX 1

Variables Available in the KAM

Performance Indicators Innovation System Average Annual GDP growth (%) Foreign Direct Investment (FDI) as percentage of GDP GDP per capita (International Current PPP) Royalty and license fees payments ($ millions) Royalty and license fees payments in US$ millions / million Human Development Index population Poverty index Royalty and license fees receipts in US$ millions Composite ICRG risk rating Royalty and license fees receipts in US$ millions / million population Average unemployment rate, % of total labor force Science & engineering enrolment ratio (% of tertiary level students) Employment in industry (% of total employment) Researchers in R&D Employment in services (% of total employment) Researchers in R&D / million GDP (current US$ bill) Total expenditure for R&D as percentage of GDP Manufacturing. Trade as % of GDP Economic Regime Research collaboration between companies and universities Average Gross capital formation as % of GDP Cost to register a business (% of GNI per capita) General government budget balance as % of GDP Cost to enforce a contract (% of GNI per capita) Trade as % of GDP Scientific and technical journal articles Tariff & nontariff barriers Scientific and technical journal articles per million people is well protected Administrative burden for start-ups Soundness of banks Availability of venture capital Exports of goods and services as % of GDP Patent Applications granted by the USPTO spread (lending minus deposit rate) Patent Applications granted by the USPTO (per million pop.) Intensity of local competition State of cluster development Domestic credit to the private sector (% of GDP) High-technology as percentage of manufactured exports Private sector spending on R&D Institutions Regulatory quality Information Infrastructure Rule of law Telephones per 1,000 people (telephone mainlines + mobile phones) Government Effectiveness Main Telephone lines per 1,000 people Voice and accountability Mobile phones per 1,000 people Political stability Computers per 1,000 persons Control of corruption TV Sets per 1,000 people Press freedom Radios per 1,000 people Daily newspapers per 1,000 people Education and Human Resources Internet hosts per 10,000 people Adult literacy rate (% age 15 and above) Internet users per 10,000 people International telecommunications: cost of call to US in $ per 3 Average years of schooling minutes Secondary enrolment E-government Tertiary enrolment ICT Expenditures as a % of GDP Life expectancy at birth, years Internet access in schools Gender Equality Public spending on education as % of GDP Gender development Index Professional and technical workers as % of the labor force Females in labor force (% of total labor force) 8th grade achievement in mathematics Seats in Parliament held by women (as % of total) 8th grade achievement in science Females Literacy Rate (% of females ages 15 and above) Quality of science and math education School enrolment, secondary, female (% gross) Extent of staff training School enrolment, tertiary, female (% gross) Management education in first class business schools Well educated people do not emigrate abroad Source: The Knowledge Assessment Methodology (KAM), WB 2008. Available from www.worldbank.org/kam.

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