PhD Project Theme: Trough Mountains and valleys – innovation...

PhD Project

Theme: Trough Mountains and valleys – innovation and

catching up dynamics

I – Introduction

The present document describes the proposed investigation PHD project.

The first section of this document is the present introduction. The second section

consists in a brief description of the project based in economic theories rooted in

evolutionary economics, emphasizing the importance of innovation in the studied

phenomena.

In the third section we try to define the problem behind the investigation. Using the

concept of innovation system (SI) we try to analyse the regional growth and

development differences through out different regions. In this sense, we’ll introduce the

concept of Regional System of Innovation, trying to analyse its importance in recent

scientific literature and the way it’s been applied in empiric studies. Afterwards, we’ll

analyse the role of innovation in regional economic growth (and its role in the rapid

growth phenomena known as catching up). Finally, we try to relate the defined concepts

with the Portuguese regions, accounting their disparities and similarities.

In the fourth section we define the investigation questions. In the fifth section we

present some objectives and an introduction to the methodology we’ll use in the

research.

The last section describes the bibliographic references used in this early work.

II – Contextualizing the investigation

Innovation is a prevailing concept in the modern times. It’s present in the news, in

political language, in the management language and in economic theory. The

importance of innovation can be observed in the recent growth of scientific research

using the concept and in the formulation of development politics, during the last to

decades, especially in European Union (EU) and in Asia (Fagerberg, 2004).

Despite that, innovation as socio-economic phenomenon is not something new, in fact,

as Fagerberg (2004) refers, innovation is something as old as mankind. Since the

beginning of history that wee use of inventions to produce new or better products,

services and processes, being them new metal swords or virtual prototyping machines

supported by TI services.

But the study of innovation itself, as an economic issue, didn’t exist until very recently.

Although Schumpeter (1934) as used the concept as something central to the economic

development process, something that thrives de growth cycles, it was only in 1960s that

innovation studies emerged as a independent field of research (Fagerberg, 2004).

In the last few years, those studies have been diversified, emphasising the role of

innovation in economic change (in the macro and micro levels). This view brings us

closer to what is known in the literature as evolutionary economics, a concept used to

relate a process of qualitative and economic change that occurs in certain period of time

(Fagerberg, 2002).

The role of innovation is essential in long term perspective of economic growth since its

function implies the introduction of novelty in the economic sphere. If the flux of

innovation was to stop the economies would fall in a steady state, as foreseen in

neoclassical economic literature (but contrary to the empiric evidence), with little or

zero growth (Fagerberg, 2004). This is obviously a very strong role in the definition of

the growth capabilities of different firms, sectors, regions and countries, since

innovation can be used as powerful variable in the explanation of performance

differences (Fagerberg, 2004). Therefore, the most innovative firms will tend to conquer

temporary monopolies or cost advantages or better differentiation skills, obtaining

higher market share and/or business volume.

These factors enlighten the importance of innovation, and how it is created, to both

political authorities and firm managers. But the innovation process is something that

reveals a certain complexity and that requires a multidisciplinary analysis (the academic

research reflects the fact that a single discipline can’t explain the total phenomenon)

(Fagerberg, 2004).

The complexity of the process of generating innovation is clear in its systemic form.

The innovation process involves a broad set of related activities whose results are many

times uncertain. Therefore, firms are not isolated in the production of innovation,

counting on the participation of several actors (publics institutions, laboratories,

universities, technological parks, financial agents, Government, etc.).

Rosenberg (1976, p.75) confirms this systemic vision:

““Not a single well defined act but a series of acts closely linked to the inventive

process. An innovation acquires economic significance only through an extensive

process of redesign, modification and a thousand small improvements”.

In a more actual analysis, innovation is understood as something that results from a

“model” that integrates a set of systems, composed by networks of firms and

institutions. Following this systemic conceptualization a broad array of literature was

born (despite that, as Edquist (2005) refers, it seems that the systemic perspective still

needs a refinement).

III – Defining the problem

3.1. Innovation Systems – the territory perspectives

In the framework of innovation theory the systemic perspective won, as we wrote, a

special place in the academic and political levels.

We can find several definitions of National Innovation System (NIS). One of the first

definitions was proposed by Freeman (1987) in his study about Japan: “a network pf

institutions in private and public sectors, whose activities and interaction initiate,

import, modify and diffuse new technologies”. Lundvall (1982) suggests that an

innovation system is composed by “…the elements and relationships which interact in

the production, diffusion and use of new, economically useful knowledge … and are

either located within or rooted inside the borders of a nation state”,

Nelson (1993) defines it as “… a set of institutions where interactions determine the

innovative performance…of national firms”. Another interesting definition is the one

proposed by Patel and Pavitt (1994) that sates that a NIS is composed by “… the

national institutions, their incentive structures and their competencies, that determine

the rate and direction of technological learning (or the value and composition of change

generating activities) in a country.”

Finally, Metcalfe(1995) defines NIS as “… that set of distinct institutions which jointly

and individually contribute to the development and diffusion of new technologies and

which provide the framework within which governments form and implement policies

to influence the innovation process. As such it is a system of interconnected institutions

to create, store and transfer the knowledge, skills and artefacts which define new

technologies.”

It’s difficult to find a consensual definition of NIS, but this one: “the system of

interacting private and public firms (either large or small), universities, and government

agencies aiming at the production of science and technology within national borders.

Interaction among these units may be technical, commercial, legal, social, and financial,

in as much as the goal of the interaction is the development, protection, financing, or

regulation of new science and technology”, proposed by Niosi et al. ((Niosi, Saviotti,

Bellon and Crow, 1993) is one of the most complete ones and one on which we can

build the theoretical approach of this research (whenever we refer to NIS). We could

also underline Porter (1990) and Edquist definitions’ (1997 and 2005) as relevant ones.

With the maturation and further development of the concept new forms of innovation

systems tend to emerge in the scientific literature, building upon the geographical

(Regional Innovation System – RIS) and economical (Sectoral Innovation System)

perspectives.

Since the beginning of 90’s the geographical perspective, the one will explore on this

work, has gained a rather large importance, both among the academic research and the

political authorities, as a building base to the formulation of new regional development

politics (this view is rather evident within the EU) (Doloreaux and Parto, 2004). This

approach delivers a promising analysis base to the understanding of the innovation

process in a regional economy (Asheim et al., 2003; Isaksen, 2002; Cooke et al., 2002)

(Doloreaux and Parto, 2004). The concept popularity is tightly connected to the

appearance and study of localized industrial clusters and to the emergence of innovation

policies that consider the region as the appropriate place to the implementation of

knowledge based economies (Asheim and Isaksen, 1997) (Doloreaux and Parto, 2004)..

3.2. Regional Innovation Systems

3.2.1. Defining the concept

In similarity with the concept of NIS, it’s also difficult to find a consensual definition of

RIS. In broad sense we can define RIS as a set of private and public agents, formal

institutions and other organizations that interact (trough formal and informal relations)

whose interactions lead to the production, diffusion and utilisation of knowledge

(Doloreux, 2003; Doloreaux and Parto, 2004). The main argument behind this concept

lies on the fact that this set of actors can generate systemic effects that encourage firms,

within a region, to develop specific forms of investment that derive from social

relations, norms, values and interactions form the community, supporting the regional

innovation capability and competitiveness (Gertler, 2003; Doloreaux and Parto, 2004;).

The concept enhances the fact that firm trajectories, in terms of learning and innovation

are results of social interaction. These interactions exceed the industrial environment

and reach the area linked to universities, research laboratories, learning and technology

transfer institutions (Cooke et al., 2000; Doloreaux and Parto, 2004). That is, within a

RIS knowledge flows through networks of innovators that work in proximity, supported

by institutions and regional policies (Ho, 2004). These characteristics, that comprise

knowledge itself and the interaction mechanisms, are accumulated from regional human

capital, building up to future technological developments (Lundvall, 1992). From a

systemic and dynamic perspective, “human resources” in output/input become essential

to the innovation capability of the region (Ho, 2004).

The origin of the concept can be traced back from two main fields of research

(Doloreaux and Parto, 2004). The first one accounts for innovation systems theory that

is based, as we mentioned before, on evolutionary theories of economic and

technological change, and tries to conceptualize innovation as an evolving complex

process (Edquist, 2004) (Doloreaux and Parto, 2004). The social aspect of innovation is

related with the collective learning process between several departments of a firm, as

well as external collaboration with other firms, financial institutions, universities,

technological centres, etc (Cooke et al. 2000; Doloreaux and Parto, 2004).

The second body of literature is connected to regional science and its main concern with

the understanding of the social-institutional environment where innovation arises

(Doloreaux and Parto, 2004). Regional science deals simultaneously with the role of

proximity (the benefits from local advantage and spatial concentration) and with the set

of norms through which the process of knowledge creation occurs (Kirat and Lung,

1999; Doloreaux and Parto, 2004).

As Doloreaux and Parto (2004) refer, in the past decade, the concept of RIS as become

more popular in geographic economics, in regional studies and among regional

authorities that are responsible for the formulation of politics (Doloreux, 2004; Asheim

et al.,2003; Wolfe, 2003, Asheim and Isaksen, 200; ACS, 2001; Cooke et al., 2000,

Braczyk et al., 1998). This growing popularity reflects the importance of social

interaction and learning in the economic growth. Since this approach focus in the

processes of knowledge flows in a regional scale it becomes important as basis for

economic policies at a regional scale (Doloreaux and Parto, 2004).

3.2.2. Empirical Approaches

One question that arises is, to what extent does the concept has empiric validity?

Despite we can argue on some aspects of the concept, the truth is that the RIS approach

has been developed with the support of a broad empirical research. In this research we

can also distinguish two lines of investigation (Doloreaux and Parto, 2004).

One of the investigation lines tries to explore some aspects of the regional innovation

capabilities, as a way to analyse the main elements of the system (the institutions, the

firms and the innovation profile of the region – accounting its levels of education, the

regional R&D, the technological outputs, etc.) (Doloreaux and Parto, 2004). Another

goal is to explain the regional differences in terms of innovation activities and

competitiveness. This type of studies is used by local authorities and governments,

defining what characterizes a region and the components that can make one a RIS

(Doloreaux and Parto, 2004).

The second line of research builds upon the notion that a RIS can exist in every region

(Doloreaux and Parto, 2004). A RIS is identified from a combination of key indicators

on several aspects that include from institutional capability, regional competence to

innovation capabilities and the interaction between actors (Cooke et al., 1998;

Doloreaux and Parto, 2004). On this approach the RIS concept corresponds, more or

less, to the one used by Cooke et al. (2000) and Asheim and Isaksen (2002), Wolfe

(2003), Todtling and Kaufman, (2001) Enright, (2001) (Doloreaux and Parto, 2004)..

According to these authors, every region is a RIS, even those with weak innovation

capabilities, and they tend to classify these RIS form weal to strong (Cooke et al., 1998;

Cooke, 2001) (Doloreaux and Parto, 2004).

Despite that, the concept still has a few limitations. On one hand, the diversity of RIS

types becomes a problem on the construction of a valid and universal accepted

definition, and this fact leads to another problem on the empiric validation of the

concept, which itself constitutes an impediment on the use of the concept by regional

politicians and researchers(Doloreaux and Parto, 2004)..

On the other hand, the emphasis on localized learning and the existence of informal

knowledge interdependence aren’t enough to understand the scale of a RIS (where can it

be identified and built). There is to much emphasis on local institutions without a

clarification of what are this institution and how do they work(Doloreaux and Parto,

2004).

Despite those faults, the concept has gained a large acceptance, and its being abundantly

used on studies and economic policies.

3.2.3. RIS Studies

Even though it isn’t a goal of this proposal to define all types of studies carried on RIS,

as Doloreaux and Parto (2004) refer we can summarize a lot of them in two main sets.

The first one is based on comparative and empiric studies of several regions, exploring

the key factors upon which innovation emerges at a regional scale. As referred by

Staber (2001) and Doloreux (2002a), it’s difficult to capture and fully understand a RIS

and its impact on industrial development, without this comparison (Doloreaux and

Parto, 2004).

The second set of studies offers us individual characterizations of regions, trying to

understand to which extent they are truly RIS (in the definition considered by the

authors) (Doloreaux and Parto, 2004). The study of individual RIS can lead to the

identification of the main factors responsible for the birth and development of a RIS,

and to the understanding of the role of social and institutional aspects and the

interaction between regional actors, in regional innovation processes (Doloreaux and

Parto, 2004).

This individual studies illustrate the set of unique characteristics of a certain

institutional context that can show us that there isn’t a unique model that can generalize

the dynamics of a successful RIS (Doloreaux and Parto, 2004).

3.2.4. Units of analysis

The debate on appropriate scale to study a RIS is still open (Doloreaux and Parto,

2004). Some authors point to metropolitan areas as the ideal scale (Diez, 2000 and

2002). And some research seems to confirm it (Audretsch and Felkdmann, 1999), or at

least it points out that this geographical unit as a high potential of innovation (Browner

et al., 1999) since it offers to firms specific resources and spatial, technological and

institutional proximity (Doloreaux and Parto, 2004).

Another unit of analysis is the EU view of Nomenclature of Territorial Units for

Statistics (NUTS) regions (Evangelista, 2001 and 2002), a type of classification that

was developed by Eurostat to study the different European regions (Doloreaux and

Parto, 2004). The use of this classification to regional analysis creates some problems

and limitations that lay on the geographical analysis, since a region defined by a NUT II

(we usually consider NUTS II as the unit closer to a definition of region) may not

correspond to a sufficiently homogeneous and unique region(Doloreaux and Parto,

2004). Despite that, this unit of analysis is widely used on several studies that use the

information provided by the different EU organizations (Doloreaux and Parto, 2004).

The use of several different units of analysis in different RIS studies stands as an

obstacle to the development of a conceptual framework that uses the “region” as a

theoretic study object (Doloreaux and Parto, 2004). In such a way, we find another

problem in the use and understating of the RIS concept, as in a definition of its frontiers.

3.3. Regions, Economic and Technological Development and “Catching Up”

We introduced the concept of RIS as a possible way to analyse and understand the

social-economic environment of a region and to understand the growth disparities

between those regions. But we still need to clarify some fundamental concepts

connected to regional economic growth using, once more, a framework linked to

innovation literature and research.

Regional economic growth theory shows us that there are basic dimensions that explain

economic growth that include economic and technological aspects (Ho, 2004).

Economic growth literature presents an inseparable relation between technological

progress and economic aspects, with the two factors expressing how a region or country

develops.

The traditional growth theory (Solow, 1956) and the new growth theory (Romer, 1990),

conclude that the growth path oh each region and country will converge (conditional or

unconditionally) to a certain point, even though they emphasize the role of

technological change in this process. Some more recent literature, with a

“Schumpeterian” foundation, argues that technological change is a strong distortion

factor on growth paths, opening the door to regional and country disparities’.

Verspagen(1998) summarized some of the different theories on technological change at

a regional scale and used concepts of economic geography to classify different

European regions in terms of technological development. Dalum, Laursen and

Verspagen (1999) examined the relation between technological development and

economic growth, demonstrating that specializing in different technologies and sectors

has an important impact in economic growth. In brief, economic literature tells us that

the pattern of technological development as a crucial importance in the comprehension

of regional development (Ho, 2004). In fact, the existence of several specialization

patterns in specific regions shows the preference and technology capacity as a trend to

development of RIS. Severally studies point to the necessity of accounting for

innovative activities, knowledge and specialization as determinants of regional

development (Ho, 2004).

Despite what we said above, the scientific literature that explains why some

regions/countries grow more than others is undeniably extensive (Godinho and

Fagerberg, 2004). One of the goals of this project is, besides focusing on RIS and its

characteristics, understanding the “catching up” phenomenon at a regional scale.

“Catch up” defines the country or region’s ability to shorten the difference of

productivity and income to another region or country more developed. (Godinho and

Fagerberg, 2004). Several authors tried to identify some situations where the catching

up process as occurred – USA and Germany in the second half of the XIX century seem

evident situations, as well as the Japan of the first half of the XXth century. Korea,

Taiwan and Ireland are other much cited cases.

Other authors try to explain why and how this phenomenon does occur. Abramovitz

(1986, 1994) is one of the lead authors of this approach, but being since this project is

rooted on Schumpeterian framework, we have to mention Fagerberg (197, 1988) and

Verspagen (1991) whose work integrate something that is crucial to this project – the

importance of innovation, imitation and other efforts linked to the commercialization of

technology, as producers of economic growth (Godinho and Fagerberg, 2004).

In this work well try to apply these notions to regions and RIS.

3.4. Portugal and RIS

3.4.1. Portuguese Regions

When we mention the concept of RIS we are implicitly using the concept of region.

This way the cultural and historic background (culture, language ands territory) can be

vital on the definition of region (for example, the Basque region). The definition of

region can also be built upon a distinctive political and administrative division (as the

German federalism and other regions inside centralized democracies like France and

Italy). In this context a RIS should include the notion of how can the institutional and

cultural environment speeds or delays the innovation process (Wolfe, 2002).

One of the critical components of a RIS is the infrastructure of R&D localized inside it

and the links between public and private organizations, like: life-long learning

institutions, companies that apply new knowledge on the production and marketing of

new improved processes and products, technological centres, business associations

universities and other higher education organizations, risk capital organizations,

business angels, local authorities.

The importance of culture and institutions derives from the difference between different

forms of codified knowledge and new learning dynamics that constitute this paradigm.

The networks of communication and new technologies tend to value more the

knowledge that can not be transferred, since it is tacit, than the explicit and codified

knowledge that can be easily transferred (we can emphasize its omnipresence).

Therefore, the more tacit is knowledge, more important is spatial proximity(with

proximity, the transfer process can sometimes actually succeed at lower costs and with

more confidence in the interaction). By extension, the process of learning and

knowledge production can be applied not only to an individual firm but a whole region.

Taking into account these definitions it’s difficult to find in a small and homogeneous

country like Portugal regions that can isolate themselves from the remaining portion of

the country, at a cultural, social and historic level. The autonomic regions can almost be

one oh these cases given its geographical isolation, but we can hardly find any other.

Therefore, we can only use the political administrative division perspectives.

In Portugal we can find the usual division within the EU, the NUTS classification.

Among these regions we’ll focus our attention on NUTSII and NUTSIII. This

classification includes smaller regions that have between 800 thousand inhabitants and 3

million, the NUTSII, and between 150 thousand and 800 thousand inhabitants

(NUTSIII). On our further work we’ll expand our analysis to a different kind of regions,

the “Local Administrative Units" (LAU) that include the districts and municipalities,

and that can be more useful when it comes to the study of urban areas.

We can find 7 NUTSII regions in Portugal that we’ll briefly characterize in the next

section.

3.4.2. The performance of Portuguese Regions

One of first questions that arise is, are there any significant differences of development

between Portuguese regions. If we were to find similar regions, in terms of economic

aspects, and productive and technological specialization, it would be difficult to

highlight the existence of various RIS.

But looking closely to the differences in regions can lead us to trace different paths of

economic and technological change.

To accomplish this analysis, that we’ll be important for the basis of our initial research,

we’ll use some data extracted from very recent empiric studies.

The following figure allows us to evaluate different regions in terms of wealth creation.

Figure 1- GDP per capita Evolution, 1995-2003

Source: Mateus (2006)

As shown in the graphic, there are large disparities in both the levels of GDP per capita

and the growth rates of different regions. The region Lisboa e Vale do Tejo(LVT) has a

per capita GDP more than 40% above the country mean, and only the Algarve and RA

Madeira are above country average. We can also detect that the majority of regions are

more or less stagnated in terms of growth, with the exception of Algarve and Ra

Madeira.

The regions with higher growth are specialized in tourism, as the regions were more

traditional industries prevail, register small or negative growths (the case of Centro and

Norte).

These ideas are somewhat confirmed by the next graphic on productivity.

Figure 2 – Productivity Evolution, 1995-2003

Source: Mateus (2006).

Once more the regions of Centro, Norte and Açores are in a bad situation, in termns of

growth and absolute values. We should emphasize the productivity growth on Madeira

and Algarve, and the performance of Alentejo (despite having a low value of per capita

GDP, the region performs close the country mean in terms of productivity).

Well, since this research will focus on the knowledge and technology creation

perspective, will draw our attention to some indicators connected to innovation.

Figure 3 – R&D intensity in the 90s

Source: Laranja (2006).

Analysing the all country, we realize the R&D intensity as grown more than 60%, with

the GDP percentage spent on R&D reaching more than 80% (when in the beginning of

the decade it reached only around 50%). Once more, the region of LVT registers a very

high absolute value and a high growth rate. The two regions that registered promising

values in terms of GDP and productivity, Algarve and RA Madeira, have weak

performances in R&D (growth and absolute values), with Madeira registering a negative

growth rate, since the middle of the decade. On the hand, Centro and Norte, register

positive growths, and Centro comes close to the country mean. Although we don´t want

to anticipate some future findings, an empiric analysis can stress out that the regions

with the best GDP growth performance are leveraging their growth in low intensity

R&D activities.

The next two graphics explore some of the differences between regions in terms of

industrial production on high R&D industries and employment on IT industries.

Figure 4 – Industrial GVA (Gross Value Added) in High/medium technology

sectors as a % of the total industry (1995 and 2000)

Source: Laranja (2006)

Figure 5 – Employment in Information and Communication Technologies (ITC) as a %

of the total employment (1995 200)


Both figures translate the idea that LVT region is somehow more specialized in higher

technological industries. The Alentejo region seems to undergo a change of

specialization, with the higher technological sectors gaining importance, making it the

second countries’ region in terms of technological intensity (this single factor is very

important, since this was always an agricultural region). Once more, the two regions

that seemed to “catch up” in terms of GDP don’t have any weight in terms of

technological industries.

These few figures are a starting point to analyse Portuguese regions in terms of their

growth capacity and to investigate what is behind their economic performances, with

relevance to the technological indicators. This is also a starting point of analysis to

investigate the RIS perspective.

But, in further research we want to look closely to urban growth phenomenon.

3.4.3. Lisbon’s Performance

Great Lisbon (the total urban area of Lisbon) GDP’s per capita, measured in purchasing

power standards (pps), was, in 2002, higher than the one from Madrid and Barcelona

(Godinho, 2006). In 2002, the values for those three cities were 28, 27 and 26 thousand

euros per inhabitant (Godinho, 2006).

This is a remarkable fact that surpasses the mere traditional regional analysis. What

explains that Lisbon excels so much, while other regions, areas fall behind? One might

argue that industrial specialization, human resources, levels of education technological

performance can be some of the reasons behind this fact, and our aim is to fully

understand this urban phenomenon

But this is not an isolated event. At a global scale we can find many other examples:

Shanghai’s GDP per capita is fifteen times higher than the poorest region in China

(Godinho, 2006). In India, the region of Banglore, in Brazil the area of São Paulo, in

Mexico the area of Monterey, are other examples of “mountains” of economic

performance that underwent a “catching up” process (Godinho, 2006).

What we aim to find is which factors that distinguish these regions and areas from

others and can we replicate this phenomena in other regions.

IV – Research Questions

Future work will be divided in three separate parts that we’ll focus on different spatial

notions, and that will constitute three different articles.

I – Regions and technological and economic progress

• What is the growth dynamic of Portuguese regions?

• Which factors explain their growth disparities?

• Are Portuguese regions examples of RIS?

II – Great Lisbon

• How to explain the fast growth of Great Lisbon?

• Which factors tend to explain the dynamics of catching up?

III – Urban regions of rapid growth

• How does Great Lisbon compare to other regions?

• Why can we find other regions similar to this one around the globe?

• What is the social-historical role of the city in innovation, and hence, in

economic growth?

• How to replicate these conditions in other less developed regions?

V – Methodology

The first phase of the research will try to analyse the Portuguese regions. As we saw

some of them have clearly fallen behind and others have moves ahead (at least slightly).

Our analysis builds upon the fact that we can classify regions from their systemic

indicators and then weigh the explanatory power of these indicators on different GDP

levels and growth rates.

Freeman (1995) and Cook (2003) point out five dimensions linked to Education,

Knowledge Transfer, Business R&D, Linkages, Investment and Communications, that

can be used as composite innovation systems indicators.

The European Commission on the Innovation Scoreboard follows this approach, where

from an array of key indicators the national innovation capability is determined.

Godinho et al. (2006) build up a framework of analysis that uses 30 variables in 8

dimensions (Market conditions, Institutional conditions, Investment climate, Scientific

Knowledge, Economic Structure, Openness & Absorption, Diffusion and Innovation) to

typify National Innovation systems. This methodology could be adapted to typify

regions instead of countries.

Muller et al. (2006) refer different regional typologies related to innovation used prior to

their work, such as the typology of Clarysse/Muldur (2001) used in the production of

the second European report on S&T indicators, the typology of regions developed by

PWC Consulting and Tsagaris Consult (2002) consult for DG Research, and the

typology of regional innovation needs by Muller et al. (2001) (on behalf of the DG

research). They use a similar method to classify the new entrant regions in the EU,

using five dimensions: knowledge creation, absorptive capacity, diffusion capacity,

demand and governance capacity. For each dimension they present a set of variables,

and then they proceed with a component analysis. In the end of the statistic process they

find five types of regions.

Similar methodologies have been used on other reports prepared for the European

Commission by various study groups. Usually the studies look through the scientific

literature and from an array of variables that characterize a region they perform a factor-

analysis and they build synthetic indicators that represent some dimension (that as also

some background in academic approaches). Then they use cluster analysis to group

regions, and classify then in terms of performance.

Our aim here is to combine this type of analysis, one that creates composite indicators

that represent the regions’ dynamic capabilities and then, in a second step, try to use

these dimensions as explanatory variables of the GDP per capita from the regions. If

necessary we would include some new variables that are more commonly used on other

type of models. Here we can follow the approach by Rodriguez Pose and Crescenzi

(2006) that tries to cross-fertilize 3 areas of regional studies (the analysis of the link

between investment in R&D, patents, and economic growth; the study of the existence

and efficiency of regional innovation systems; the examination of geographical

diffusion of regional knowledge spillovers) constructing a model of regional

performance (they conduct a multiple regression analysis for all regions of the EU-25,

including measures of R&D investment, proxies for regional innovation systems, and

knowledge and socio-economic spillovers). We have also to mention the Fagerber’s et

al. work (2004) that points out four dimensions to measure competitiveness

(technology, costs, capacity and demand) that can be used as an inspiration to build a

growth model closer to the “classical” econometric perspective. .

Since we proposed to study catching up, we’ll use the model to explain the

phenomenon. Taking inspiration from Fagerberg and Srolec (2005) important work on

catching up, well combine their approach with our model, identifying a set of

“capabilities” which, according to the literature, might be assumed to be of critical

importance for catch up (Fagerberg’s model is somewhat similar to the approaches

referred before since it formulates a synthetic empirical model and, with the help of

factor analysis, he identifies the dimensions and uses them to explain GDP levels and

GDP rates).

This analysis will use data from the Regio database from Eurostat on NUTSII regions

(and additional data from the Portuguese National Institute of Statistics - INE)

The second phase of the research will try to analyse the Great Lisbon urban area. This

will constitute a different article.

On this analysis we’ll explore innovation perspectives linked to the metropolitan

perspective. We’ll start to use our model from the first phase to explain the GDP and

GDP growth rate. If the model fails on this task we’ll try to adapt it to the urban

perspective. Network flows of social-habits, culture, economic factors and knowledge

tend to display a rather important role in the economy of the city (Simmie, 2005), and

will have to take into account these particular facts. Some further empiric work will be

needed, and qualitative data will be collected from interviews with specialists on urban

reality and networks.

We’ll use also quantitative data at the urban level, collected from Eurostat Sabe and

INE.

Finally, the last phase will consist on an extended analyse on metropolitan areas around

the world that stand out from the economic and technological point of view (the so

called mountains), that have some characteristics that allows them to grow rapidly, even

if the rest of the country seems a “depressed” valley. Using our reworked model we’ll

try to analyse some of the important factors that contribute to the catching up process

occurring in these areas.

Summing up the three analyses we should be ready to produce some policy

recommendations that can lead other regions to a catch up process in a near future.

VI – Work Plan

1st Phase – Research and bibliographical analysis on the state of the problem; data

collecting;

2nd Phase – Model construction and statistic validation;

3rd Phase – Interpretation of the first results; remarks on Portuguese regions and

catching up;

4th Phase –New data collection, possible field work on the social analysis of the city;

analysing the innovation urban perspective;

5th Phase - Re-writing the model at the city scale; remarks on Great Lisbon economic

and technological performance;

6th phase – Data collecting and reapplying the model at various “mountains” in the

world;

7th phase – Interpretation of results, remarks on urban catching up phenomenon around

the globe

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1

DIMETIC - Strasbourg – 29 March 2007

PhD ProjectTheme: Trough Mountains and valleys –

innovation and catching up dynamics

Vítor Hugo Ferreira

Technique Institute Leiria

ISEG – Technical University of Lisbon

2

Presentation Structure

1. Introduction

2. Situating the Research

3. Theoretical Background – a brief review

4. Research Motivation

5. Research Questions

6. Exploring the Methodologies

3

Introduction

The first phase of the Project aims to analyse theperformance of several regions in Portugal.

The second phase aims to analyse the urban areaof Lisbon, the Portuguese “region” with higherincome (about 140% of the mean).

The last goal is to build a theoretical model that wecan apply to the several “mountains” ofdevelopment, explaining the development of theseareas, comparing monocentric countries likePortugal to other similar countries.

4

Situating the ResearchInnovation – a systemic view Innovation process involves a broad set of related activities whose

results are many times uncertain; Firms are not isolated, counting on the participation of several

actors (publics institutions, laboratories, universities, technological parks, financialagents, Government, etc.);

Rosenberg (1976, p.75) confirms this systemic vision: “Not a single well defined act but a series of acts closely linked to

the inventive process. An innovation acquires economicsignificance only through an extensive process of redesign,modification and a thousand small improvements”.

Nowadays, innovation is understood as something that resultsfrom a “model” that integrates a set of systems, composed bynetworks of firms and institutions (innovation systems theory);

But... it seems that the systemic perspective still needs arefinement (Edquist, 2005).

5

Theoretical BackgroundInnovation Systems – the Territory Perspectives

National Innovation Systems (NIS) Some commonly cited definitions: Lundvall (1992), Freeman (1987)

Nelson (1993) Patel and Pavitt (1994), Metcalfe(1995), Edquist (2005);

“the system of interacting private and public firms (either large or small),universities, and government agencies aiming at the production of scienceand technology within national borders. Interaction among these units maybe technical, commercial, legal, social, and financial, in as much as thegoal of the interaction is the development, protection, financing, orregulation of new science and technology”,(Niosi, Saviotti, Bellon and Crow, 1993);

New forms of innovation systems tend to emerge- geographical(Regional Innovation System – RIS) and economical (SectoralInnovation System);

Since 90’s, the geographical perspective has gained a rather largeimportance, both among the academic research and the politicalauthorities, (this view is rather evident within the EU) (Doloreaux andParto, 2004).

6


Regional Innovation Systems - OriginsThe origin of the concept can be traced back from two main fields of

research (Doloreaux and Parto, 2004):1. The first one accounts for innovation systems theory that is based on

evolutionary theories of economic and technological change, and triesto conceptualize innovation as an evolving complex process (Edquist,2004). The social aspect of innovation is related with the collectivelearning process between several departments of a firm, as well asexternal collaboration with other firms, financial institutions, universities,technological centres, etc (Cooke et al. 2000);

2. The second body of literature is connected to regional science and itsmain concern with the understanding of the social-institutionalenvironment where innovation arises (Doloreaux and Parto, 2004).Regional science deals simultaneously with the role of proximity (thebenefits from local advantage and spatial concentration) and with theset of norms through which the process of knowledge creation occurs(Kirat and Lung, 1999; Doloreaux and Parto, 2004).

7


Regional Innovation Systems – the concept RIS: a set of private and public agents, formal institutions

and other organizations that interact (through formal andinformal relations) whose interactions lead to theproduction, diffusion and utilisation of knowledge(Doloreux, 2003; Doloreaux and Parto, 2004);

The concept enhances the fact that firm trajectories, interms of learning and innovation are results of socialinteraction. These interactions exceed the industrialenvironment and reach the area linked to universities,research laboratories, learning and technology transferinstitutions (Doloreaux and Parto, 2004).

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RIS - Empirical ApproachesWe can distinguish two lines of investigation (Doloreaux and Parto,

2004):1. One tries to explore some aspects of the regional innovation

capabilities, as a way to analyse the main elements of the system(the institutions, the firms and the innovation profile of the region –accounting its levels of education, the regional R&D, thetechnological outputs, etc.) (Doloreaux and Parto, 2004). Anothergoal is to explain the regional differences in terms of innovationactivities and competitiveness;

2. The second builds upon the notion that a RIS can exist in everyregion (Doloreaux and Parto, 2004). A RIS is identified from acombination of key indicators on several aspects that include frominstitutional capability, regional competence to innovation capabilitiesand the interaction between actors (Cooke et al., 1998; Doloreauxand Parto, 2004).

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Theoretical BackgroundRegions, Economic and Technological Development and“Catching Up”

Regional Growth Regional economic growth theory shows us that there are basic dimensions

that explain economic growth that include economic and technologicalaspects (Ho, 2004);

The traditional growth theory (Solow, 1956) and the new growth theory(Romer, 1990), conclude that the growth path oh each region and countrywill converge (conditional or unconditionally) to a certain point, even thoughthey emphasize the role of technological change in this process;

Some more recent literature, with a “Schumpeterian”/Evolutionaryfoundation, argues that technological change is a strong distortion factor ongrowth paths, opening the door to regional disparities;

Dalum, Laursen and Verspagen (1999) examined the relation betweentechnological development and economic growth, demonstrating thatspecializing in different technologies and sectors has an important impact ineconomic growth;

Economic literature tells us that the pattern of technological development asa crucial importance in the comprehension of regional development.

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Theoretical BackgroundRegions, Economic and Technological Development and“Catching Up”

Catch Up “Catch up” defines the country or region’s ability to shorten the difference of

productivity and income to another region or country more developed.(Godinho and Fagerberg, 2004);

Several authors tried to identify some situations where the catching upprocess as occurred – USA and Germany in the second half of the XIXcentury seem evident situations, as well as the Japan of the first half of theXXth century. Korea, Taiwan and Ireland are other much cited cases;

Other authors try to explain why and how this phenomenon does occur:- Abramovitz (1986, 1994) is one of the lead authors of this approach;- But Fagerberg (1988) and Verspagen (1991), whose work integrate the

importance of innovation, imitation and other efforts linked to thecommercialization of technology, as producers of economic growth, are thereference to this project.

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Research MotivationPortuguese Regions and their growth paths

Are there Portuguese Regions?

When we mention the concept of RIS we are implicitly using theconcept of region. This way the cultural and historic background(culture, language ands territory) can be vital on the definition ofregion (for example, the Basque region). The definition of region canalso be built upon a distinctive political and administrative division (asthe German federalism and other regions inside centralizeddemocracies like France and Italy). A RIS should include the notionof how can the institutional and cultural environment speeds ordelays the innovation process (Wolfe, 2002);

Taking into account these definitions, it’s difficult to find in a smalland homogeneous country like Portugal regions that can isolatethemselves from the remaining portion of the country, at a cultural,social and historic level. Therefore, we can only ´look at the politicaladministrative division perspectives.

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GDP per capita Evolution, 1995-2003

•Source: Mateus (2006)


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Productivity Evolution, 1995-2003

•Source: Mateus (2006)

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R&D intensity in the 90s

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Industrial GVA (Gross Value Added) in High/medium technology sectors as a % of the total industry (1995 and 2000)



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Research MotivationUrban Area of Lisbon

Some Facts: Great Lisbon GDP’s per capita, measured in purchasing power

standards (pps), was, in 2002, higher than the one from Madrid andBarcelona (Godinho, 2006). In 2002, the values for those three citieswere 28, 27 and 26 thousand euros per inhabitant (Godinho, 2006).

This is a remarkable fact that surpasses the mere traditional regionalanalysis. What explains that Lisbon excels so much?

One might argue that industrial specialization, human resources,levels of education technological performance can be some of thereasons behind this fact, and our aim is to fully understand thisurban phenomenon;

At a global scale we can find many other examples: Shanghai’s GDPper capita is fifteen times higher than the poorest region in China(Godinho, 2006). In India, the region of Banglore, in Brazil the areaof São Paulo, in Mexico the area of Monterey, are other examples of“mountains” of economic performance that underwent a “catching up”process.

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Research Questions

Regions and technological and economicprogress

What is the growth dynamic of Portugueseregions?

What type of RIS are the PortugueseRegions? (stagnating, growing?)

Which factors explain their growth disparities?(bottlenecks or good actors?)

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Research QuestionsExploring the Methods of analyses I

First step – shift-share analyses – Are the regions growing/stagnating because of their structureor because their industries perform better than the same industries in other regions?

We have a study area in which employment and population are evolving. Total employment inour area is e, and that in the ith activity is ei . We have a larger frame reference area, usually thenation, where total employment is E, and that in the ith activity is Ei . The shift-share model saysthat growth in the study area’s ith activity employment is a function of:

The study area’s share of national (or regional) growth. The mix change in activities. And the shift change of activities toward the study area. This says that change in employment in the study area’s ith activity from time t to time t+n can be

measured:

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Second Step – What type of RIS can we find? Our analysis builds upon the fact that we can classify regions from their

systemic indicators and then weigh the explanatory power of theseindicators on different GDP levels and growth rates.

Freeman (1995) and Cook (2003) point out five dimensions linked toEducation, Knowledge Transfer, Business R&D, Linkages, Investment andCommunications, that can be used as composite innovation systemsindicators.

Godinho et al. (2006) build up a framework of analysis that uses 30variables in 8 dimensions (Market conditions, Institutional conditions,Investment climate, Scientific Knowledge, Economic Structure, Openness &Absorption, Diffusion and Innovation) to typify National Innovation systems.This methodology could be adapted to typify regions instead of countries.

Muller et al. (2006) use a similar method using five dimensions: knowledgecreation, absorptive capacity, diffusion capacity, demand and governancecapacity. For each dimension they present a set of variables, and then theyproceed with a component analysis. In the end of the statistic process theyfind five types of regions.


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Research QuestionsExploring the Methods of analyses IMeasuring RIS - example

Source: Muller et al. (2006)

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Our aim here is to combine this type of analysis, one that createscomposite indicators that represent the regions’ dynamiccapabilities and then, in a second step, try to use these dimensionsas explanatory variables of the GDP per capita from the regions;

Since we proposed to study catching up, we’ll use the model toexplain the phenomenon. Taking inspiration from Fagerberg andSrolec (2005) important work on catching up, well combine theirapproach with our model, identifying a set of “capabilities” which,according to the literature, might be assumed to be of criticalimportance for catch up (Fagerberg’s model formulates a syntheticempirical model and, with the help of factor analysis, he identifies thedimensions and uses them to explain GDP levels and GDP rates);

This analysis will use data from the Regio database from Eurostaton NUTSII regions (and additional data from the INE)


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II – Great Lisbon II – Great Lisbon

How to explain the level of wealth of GreatLisbon?

What is the social-historical role of the city ininnovation, and hence, in economic growth?

Research Questions

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Research QuestionsExploring the Methods of analyses II Quantitative analyse with foundation on the urbanization economies

(i.e. the benefits coming to firms by being located in a heterogeneousindustrial environment – specialized suppliers, demanding market,experienced labours, knowledge organizations, etc) + RIS analysesbased on previous model;

Network flows of social-habits, culture, economic factors andknowledge tend to display a rather important role in the economy ofthe city (Simmie, 2005), and will have to take into account theseparticular facts. Some further empiric work will be needed, andqualitative data will be collected from interviews with specialists onurban reality and networks;

We’ll use also quantitative data at the urban level, collected fromEurostat Sabe and INE.

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Great cities and Growth

How does Great Lisbon compare to otherregions? (Lisbon within Portugal vis-à-visother capitals on small monocentriccountries).

How the growth in Lisbon can have an affecton other regions in Portugal?

Research Questions

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The first question can use themodel/methodology developed in theprevious moments.

The second question is difficult to analyseempirically. One idea can be to use data thatrelates firms in other regions with Lisbon, andsee if they use the previous as a source ofinnovation.

Research QuestionsExploring the Methods of analyses III

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Thank you for your attentionand for your comments

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PhD Project Theme: Trough Mountains and valleys – innovation...

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