Thomas Brenner

8/6/2019 Thomas Brenner

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Paper to be presented at the DRUID Summer Conference 2007

on

APPROPRIABILITY, PROXIMITY, ROUTINES AND INNOVATION

Copenhagen, CBS, Denmark, June 18 - 20, 2007

FACTORS AND MECHANISMS CAUSING THE EMERGENCE OF LOCALINDUSTRIAL CLUSTERS - A META-STUDY OF 159 CASES

Thomas Brenner

Max-Planck-Institute for Research into Economic [email protected]

Andr Mhlig

Max-Planck-Institute for Research into Economic Systems

Abstract:Local industrial clusters have attracted much attention in the recent economic and geographic literature. A hugenumber of case studies have been conducted. This paper offers a meta-analysis of the case studies of 159 localindustrial clusters in various countries and industries. Guided by a theory about the emergence of such clustersit analyses the involvement of many local conditions and processes. The paper offers an overview on theknowledge that is gathered in these case studies and a comparision between continents, new and old clusters,and high- and low-tech industries.

JEL - codes: , ,


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Factors and Mechanisms Causing the Emergence of

Local Industrial Clusters - A Meta-Study of 159 Cases

Thomas Brenner and Andre Muhlig

Max-Planck-Institute for Research into Economic Systems

Evolutionary Economics UnitKahlaische Str. 10

07745 Jena, Germany

ABSTRACT. Local industrial clusters have attracted much attention in the

recent economic and geographic literature. A huge number of case studies

have been conducted. This paper offers a meta-analysis of the case studies

of 159 local industrial clusters in various countries and industries. Guided

by a theory about the emergence of such clusters it analyses the involvement

of many local conditions and processes. The paper offers an overview on the

knowledge that is gathered in these case studies and a comparision between

continents, new and old clusters, and high- and low-tech industries.

KEYWORDS: local industrial clusters, case studies, meta study, local con-

ditions.

JEL classification: L60, O18, R12


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2 Thomas Brenner and Andre Muhlig

1. Introduction

In the recent economic and geographical literature, the phenomenon of local in-dustrial clusters has attracted much attention. Under the headings of industrial

districts, industrial local clusters, innovative milieu and regional innovative

systems, the reasons why certain regions are successful while others are not have

been extensively studied. Two kinds of approaches dominate this literature: case

studies of regions that are identified as being economically successful (more than

200 such studies are analysed here) and more general theoretical approaches that

aim to identify some of the circumstances that cause regions to be successful (such

approaches can, for example, be found in Becattini 1990, Porter 1990, Scott 1992,

Camagni 1995, Markusen 1996 and Brenner 2004). Furthermore, approaches in theso-called New Economic Geography explain the existence of local industrial clus-

ters on the basis of mathematically modelling or simulating economies of location

(Krugman 1991 and Fujita & Thisse 2002).

All this literature tries to examine, why local industrial clusters exist, how they

emerge and why they are successful in comparison to other locations. In this paper

we focus on the question of how local industrial clusters emerge. Especially we

address the question of what are the circumstances or prerequisites for the emer-

gence of industrial clusters in a region. This questions has been addressed in many

of the case studies that have been conducted. However, different case studies come

to different results. A study that combines the findings of the various case studies

is missing. Hence, the literature provides us with a huge number of single studies

and a few studies in which 2, 3, 4 or some more clusters are compared. In addition,

the case studies are conducted on the basis of different concepts and assumptions.

Thus, it is difficult to grasp a clear picture about what really causes the emergence

of local industrial clusters.

Only one large meta-study can be found in the literature (van der Linde 2003).

It is based on the theory of Porter (see Porter 1990). This means that van derLinde focuses the meta-study on the factors that Porter claimed to be important,

mainly four factors. He studies whether these factors play a role according to the

case studies of 833 local clusters. He finds evidence for three of the four factors.

Furthermore, he provides additional statistics about various characters, such as

age and size, of the analysed clusters. However, he does not provide more detailed

information about whether the factors have to occur together or on what their

importance depends. Furthermore, the meta-study by van der Linde is restricted

to the four factors claimed by Porter.


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A Meta-Study of Local Clusters 3

We use a different theoretical background and examine 35 factors in total. Fur-

thermore, we analyse the differences in the importance of these factors between

industries, countries and in time. As a consequence, the analysis is restricted to

159 local industrial clusters that are analysed in detail. In total 184 publications

are used to conduct this study, quite some of them containing several case studies.

The paper proceeds as follows. In Section 2 the theoretical background is out-

lined. In Section 3 the methodology used and the local characteristics and processes

that are included in the analysis are described and discussed. Section 4 describes

the sources for this study, including how the cases are selected and some basic

statistics about the local industrial clusters chosen. In Section 5 the basic findings

are presented and some fundamental issues discussed. The comparison between

countries, industries and points in time is conducted in Section 6. Section 7 con-

cludes.

2. Theoretical background

The literature provides us with various different concepts, such as clusters, indus-

trial districts or innovative milieux. Each of them is based on different assumptions

about the most important processes in the context of clustering. One of the main

aims of this paper is to analyse case studies independent of one specific concept.

The theory of local industrial clusters developed by Brenner (see Brenner 2001a

and 2004) provides such a general framework that embraces the different available

concepts.

This theory is based on the assumption that local self-augmenting processes

occur in certain industries (see Brenner 2001a). These self-augmenting processes

cause a situation in which for each region two stable stationary states exist: a

state with a low activity in the industry under consideration and a state with

an industrial cluster. This means that local industrial clusters only exist if the

self-augmenting processes are strong enough.In which of the two stable states a region is found in such a case is determined

by the history of the region. The history is determined by the initial conditions in

the region and by events. Therefore, Brenner (2004) identifies two fundamentally

different kinds of conditions for the emergence of a local industrial cluster. First,

sufficiently strong local self-augmenting processes have to take place. Second, the

region has to have sufficiently favourable characteristics and conditions and the

actors within the region have to be sufficiently active.

Each of the two conditions can be satisfied by quite a number of different factors


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and processes. For example, local self-augmenting processes might be provided by

the mutual positive impact of the number of firms and the number of spin-offs in

a region. A higher number of firms results, on average, in a higher number of spin-

offs, which in turn implies a faster increase of the number of firms. There are many

other such self-augmenting processes, such as the interaction between firms and

the local human capital or the mechanisms of spillovers and cooperation between

firms (see Brenner 2000 for a discussion of various self-augmenting processes).

Local self-augmenting processes might be provided by all mechanisms that are

characterised by a positive feedback loop between the local activity related to the

cluster, such as employment or firm numbers, and some local factor. Quite a num-

ber of mechanisms might satisfy this condition (see Brenner 2001a). These arespin-offs, the support of start-ups by firms, spillovers, cooperation among firms,

impact of other firms location on location choices, buyer-supplier-relations, and

interactions of the firm population with the accumulation of the local human cap-

ital, public education and research, local policy makers, local venture capitalists,

and the local public opinion. All these mechanisms might constitute a local self-

augmenting process but do not necessarily imply such a process. It depends on

the importance and the characteristics of these mechanisms whether a sufficiently

strong local self-augmenting process results. Hence, it depends on the industry and

location under consideration whether a self-augmenting process occurs and whichof the above mechanisms is most relevant. The theory only tells us that, at least,

one of these mechanisms has to be given if a local industrial cluster emerges.

The same holds for the favourable conditions in a region. Again many different

factors might influence the location of industrial clusters. These are public opinion

and cultural aspects, local policies, national policies, geographic location, type of

region, universities and public research, technology parks, industrial structure, local

capital market, local demand, suppliers, networks, wages, infrastructure, quality of

life, and qualified labour. In addition, the emergence of local industrial clusters

is often triggered or supported by specific actions. These actions depend on the

region because the region has to provide the adequate actors. Furthermore, chance

plays a role because these actors have to do the right thing at the right time.

Therefore, these actions can only be partly attributed to the region but often play

an important role. These actions are promoting activities, specific policy measures,

historical events or preconditions, specific innovations, and foundings of leading

firms.

Again, the theory does not tell us which of these factors and events has to be


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given for a local industrial cluster to emerge. It tells us that these factors and events

have to be seen as substitutes and that the likelihood of the emergence of a local

industrial cluster increases with the quality of these factors and the occurrence of

these events.

To sum up, there are two fundamentally different types of conditions for the

emergence of local industrial clusters. On the one hand, there are mechanisms that

cause local self-augmenting processes, which are necessary for the emergence of

clusters. On the other hand, various local factors and events increase the likelihood

of such an emergence of clusters. A large group of mechanisms, factors and events

exist that might be alternatively causal to the emergence of local industrial clusters.

Theory tells us that one of each type of conditions should be, at least, visible in

each emergence of a cluster.

3. Methodology

Which of the above mechanisms, factors and events cause each emergence of

a local industrial cluster is an empirical question. It has to be studied for each

cluster separately. However, only if we combine the knowledge obtained in such

studies of specific clusters, we are able to develop a comprehensive picture about

the emergence of local industrial clusters.

Therefore, this paper combines the findings of many case studies, 159 case studies

to be exact. The common framework that is necessary for such a study is provided

by the above theory. This theory offers a number of alternative explanations, in

the form of mechanisms, factors and events, for the emergence of local industrial

clusters. We check these alternative explanations in each case study. But one has to

keep in mind that until now no primary research case study was conducted testing

the concept used here. This means that the authors of the case studies might not

be aware of all possible causes and influences that are studied here. Hence, for each

case study and each potential explanation our examination results in one of three

outcomes:

Class I: The author of the case study states the potential explanation to be

present and important.

Class U: The author of the case study states the potential explanation as not

given or unimportant.

Class N: The case study does not provide any information about the potential

explanation.


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Reading a case study, we are able to classify the case study with respect to

each potential explanation into one of the three classes above. As a result, we

obtain a matrix in which each row presents a local industrial cluster and each

column presents a potential explanation for its emergence. Each cell of this matrix

contains either an I, a U, or a N.

For some local industrial cluster more than one case study is available. In these

cases all available case studies are used. If the results for the case studies vary for

a potential explanation, the following rules are applied:

If only the classes I and N appear, the cluster is classified as I.

If only the classes U and N appear, the cluster is classified as U.

If the classes I and U are mentioned by different authors, it is looked for furtherevidence in other sources. If further evidence is found, this evidence is used

for a classification into I or U (including a mark of uncertainty). If no further

evidence could be found, it was classified as N.

These rules imply that if a potential explanation is considered only in some of

the case studies for one local industrial clusters, only the case studies that mention

this explanation are considered.

In this way 159 local industrial clusters are classified with the help of 184 publi-

cations (see the appendix of this paper for a list of these publications). We studyin total 35 potential explanations for the emergence of local industrial clusters.

According to the above theory, these explanations are distinguished into mecha-

nisms, factors and events. The theory predicts that, at least, one mechanism has

to be given for the emergence of local clusters. Furthermore, the factors and events

have to be sufficiently positive for a cluster to emerge. In the following the 35 po-

tential explanations are described. Whether they are relevant in a specific case is

determined as described above.

The mechanisms that we consider in this study are

Spin-offs (SPIN): A higher or important spin-off activity within the cluster.

Support of start-ups by firms (SUP-START): Existing firms do activily sup-

port start-ups within the cluster.

Intra-industrial spillovers (INTRA-SPILL): Important intra-industrial spillovers

(including formal and informal spillovers as well as the moving of employees

between firms) take place within the cluster.

Will be added later.


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Inter-industrial spillovers (INTER-SPILL): Important inter-industrial spillovers

(including formal and informal spillovers as well as the moving of employees

between firms) take place within the cluster.

Cooperation among firms (COOP): Cooperation is common within the clus-

ter.

Choice of co-location with other firms (COLOC): Firms deliberately choose

to locate near to other firms of different industries, so that they end up in the

cluster.

Buyer-supplier-relations (BUYSUP): Cooperation between buyer and sup-

plier industries is common in the cluster.

Accumulation of the local human capital (F-HC): Accumulation of humancapital exists within the cluster.

Interaction with public education and research (F-EDU/RES): Cooperation

takes place between firms and public education or research institutes, such as

scientific spin-offs or joint research projects.

Interaction with local policy makers (F-POL): Interactions between local pol-

icy makers and the emergence of the cluster take place.

Interaction with local venture capitalists (F-VC): Interactions take place be-

tween venture capitalists and cluster firms, such as a specific co-location or a

focus of venture capitalists on cluster-specific activities.

Interaction with local public opinion (F-OPIN): Interactions take place be-

tween the public opinion and the cluster firms, such as interest and support for

the cluster in the region or the impact of existing cluster firms as role models

for further entrepreneurs in the region.

Local factors that might play a role are:

Public opinion and cultural aspects (CULT): Specific attitudes towards self-

employment, cooperation, or innovation are present within the cluster.

Local policies (LOC-POL): Local policy makers take measures that are favourable

for the cluster activities.

National policies (NAT-POL): National policy makers take measures that are

favourable for the cluster activities.

Geographic location (GEOGR): Location has certain characteristics that are

relevant for the activities in the cluster, such a natural resources, access to the

see, or climatic conditions.


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Type of region (TYPE): Regions can be classified according to their popula-

tion density in to classes such as metropolies, cities, or rural areas.

Universities and public research (UNI/RES): Relevant public research in-

stitutes exist in the region.

Qualified labour (LABOUR): Labour in necessary quality and quantity is avail-

able in the region.

Technology parks (TECH-PARK): A technology park exists in the region.

Industrial structure (IND): Other industries that are important for the clus-

tering industry are present in the region.

Local capital market (CAPITAL): Sufficient financial resource, through banks

or venture capitalists, for investments are available in the region.Local demand (DEMAND): The region is characterised by a high or technology-

/fashion-oriented demand for the products of the cluster.

Suppliers (SUPP): An adequate set of suppliers exists in the region.

Networks (NET): Networks (institutional, social or personal) exist in the region.

Wages (WAGE): Salaries are low in the region.

Infrastructure (INFRA): The region is characterised by an attractive infras-

tructure (including airports, ports, railway connections, and roads).

Quality of life (LIFE): The region offers favourable life conditions, such as high

air quality, attractive cultural infrastructure and good housing infrastructure.

Tradition and historical preconditions (TRAD): The history and tradition

of the region are favourable for the clustering industry.

Finally, the following events are included in the study:

Promoting activities (PROM): Influencial local promoters (single persons, groups

of people, different organisations, etc.) who support the cluster acitivies exist

in the region.

Specific policy measures (SPEC-POL): Policy makers take specific measures

that directly supported the development of the cluster.

Historical events (HIST): Historical events exist that had an influence on the

cluster development.

Specific innovations (INNO): Major innovations are discovered in the region

that are related to the cluster activities.

Founding of leading firms (LEAD-FIRM): A leading or important firm ex-

ists in the region.


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Chance (CHANCE): Other events occur in the region that are classified by the

authors of the case studies as happening by pure chance.

All these events are stochastic in nature. This means that the characterisics of a

region do only partly determine their occurrence. Nevertheless, we also include the

more general event CHANCE in our analyse because quite some authors simply

talk about the fact that the local industrial cluster emerged by chance without

stating a specific event that triggered this development.

In the above lists many aspects seem to appear several times. Policies are an ex-

ample, showing up as mechanism (F-POL), as factor (LOC-POL and NAT-POL)

and as event (SPEC-POL). It might be helpful to explain the differences between

these explanation-variables here in order to make the difference between mecha-

nisms, factors and events clearer. Variable F-POL represents the mechanism that

a large number of firms might influence local policy makers and thus change lo-

cal policy in such a way that the firm population is able to prosper. This means

that variable F-POL relates to changes in policies. Instead the factors LOC-POL

and NAT-POL represent the long-term political environment, meaning all policy

aspects that remain stable for longer periods of time and cannot be changed even

by a large number of firms in a short period of time. Events, in contrast to mech-

anisms, are not triggered by a large number of firms. They are, thus, exogenous

with respect to the emergence of the local industrial cluster. Hence, SPEC-POL

contains all short-run policy measures that are taken by policy makers independent

of the emergence of the local industrial, but maybe in order to support or trigger

such an emergence. For all other aspects that appear above in different lists, similar

distinctions are taken.

Through this, the different explanation-variables are defined such that they do

not overlap and the classification of statements in case studies with respect to the

above list can be easily done. More problematic is the fact that many of these

potential explanations are not mentioned by each author of a case study. As aconsequence, we obtain a matrix that contains many N-entries (no information).

The aims should be to have only I- (factor is important) and U-entries (factor is

unimportant) in the matrix. However, without repeating all case studies this is not

possible. For the analysis we have to decide how to deal with the N-entries. It can

be assumed that the authors of case studies focus their attention on the factors,

events and processes that are important for the emergence of the local industrial

cluster they study. Hence, they should point out most of the important variables,

while variables on which the case studies contain no information should be most


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dustries nor by low-tech industries. A classification of the analysed local industrial

clusters into one of the standard industry classifications is often not possible be-

cause many clusters span across these classes and we have to rely on the description

of the authors of the case studies. Therefore, we use categories that are frequently

used by the authors of the case studies themselves and build some prominent

classes. The frequencies are given in Table 3.

industry number of cases

Textile & leather 25

ICT 23

Life science 15Media 13

Electronics, instruments & optics 12

Furniture, juwelery & musical instruments 10

Automotive 9

Metals 9

Agriculture, fishery & food 8

Maschines 8

High-technology (general) 5

Tourism & Culture 5

Glas & ceramics 4

Banking & business services 4

Airspace 2

Ship building 2

Others 5

Table 3: Number of local industrial clusters from each industrial class that are in-

cluded in the analysis.

The distributions of the local industrial clusters that are included in this study in

time, space and industry shows that local clustering is not a specific phenomenon.It is very general and the cases that are included here seem to be adequate to

capture the wide range of this phenomenon.

5. Basic findings

The conduction of this meta-study, first of all, resulted for each of the above

mechanisms, factors and events in a count of the number of case studies that

mention these to be relevant. Hence, the most straight-forward way of presenting


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the results is to list these frequencies. According to these frequencies the different

mechanisms, factors and events can be ranked. However, we have to be aware of

the fact that the most often stated variable is not automatically the one that is

most often involved in the emergence of local clusters. It might well be a more

frequently important variable is less often mentioned because the authors of the

case studies do not believe in its importance or it was simply not studied (e.g.

because of different intentions of the case studies or an only narrow study of some

parts of the cluster). The source of our study, the 159 case studies, are the results of

a subjective evaluation of the situation and developments in the analysed regions.

Nevertheless, such a ranking of the variables should give us some information

about their importance. It can be expected that there is at least a positive corre-lation between the subjective accounts of the case study authors and reality. It is,

however, likely that some variables are systematically underestimated.

We list the frequencies of the various variables again in three lists because the

theory predicts that mechanisms, factors and events are within each group substi-

tutes while the groups are complementary to each other. For the mechanisms the

frequencies are presented in Table 4. The accumulation of human capital results

to be the most frequently mentioned of all variables. Human capital is also promi-

nently mentioned in the literature on local industrial clusters (see, e.g., Saxenian

1994). Hence, it can be assumed that most case study authors are well aware of thepotential influence of this factor, although in 33 of the 159 cases that are analysed

here it is unclear what role the accumulation of human capital plays. This implies

that the real value should not be much greater than the identified frequency of 116.

Due to our method it is also most likely not smaller. We can conclude that the

accumulation of human capital plays an important role in the emergence of many

local industrial clusters, but not in the emergence of all of them.

There are other mechanisms that seem to be nearly as important, such as the

cooperation of firms, the choice of firms to co-located and spillover. All other

mechanisms are also found to be important in some or quite a number of case

studies. Furthermore, we have to take into account that some mechanisms are not

mentioned in more than 120 of the studied 159 cases. It is unclear whether the

authors of the case studies simply do not take them into account due to different

reasons (like different intention of the case study) or whether they play no role in

these cases. This is the already mentioned drawback of such a secondary analysis.

However, the results above imply that there seems not to be one mechanism that

is responsible for the emergence of all local industrial clusters. Nevertheless, local


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events important unimportant not known

LEAD-FIRM 62 4 93SPEC-POL 53 10 96

HIST 52 0 107

PROM 22 7 130

INNO 15 4 140

CHANCE 14 1 144

Table 6: Frequency of each event to be mentioned as an important factor for the

emergence of the local industrial cluster in the respective case studies.

that each of them has to be represented in each emergence of a local industrial

cluster has been mainly confirmed. We also obtained a ranking for the average

importance of each variable. However, this has to be used with care. First, some

variables might be under represented because the authors of case studies tend to

ignore them. Second, some case studies contain no information because they were

conducted for a different intention. Third, we have seen that each local industrial

cluster is individual, so that it is unclear what such an average ranking tells us.

6. Differences in industry, time and space

Above it is discussed that the overall frequency with which the different variables

are mentioned to be important in the case studies have to be interpreted with care.

The various variables differ in their prominence, so that they are not all examined

with the same rigourness. However, this kind of bias in the case studies can be

expected to be relatively independent of the specific case study at hand. Hence,

the results of a comparison between different case studies is more reliable. Three

kinds of comparisons are of specific interest: a comparison between different pointsin time, a comparison between high- and low-tech industries, and a comparison

between different parts of the world.

First, we analyse whether the variables that play a role for the emergence of

local industrial clusters have changed in time. Especially we want to know whether

nowadays different variable play a role than in earlier times. Therefore, we compare

the relevance of each variable for the cluster emergence between the local industrial

clusters that developed before 1950 and after 1950. We also compare the times

before 1970 and after 1970 in order to study the very recent developments. All


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35 variables are examined. However, only the significant changes are presented in

Table 7.

Comparison: Comparison:

Variable post 1950 vs. post 1970 vs.

ante 1950 ante 1970

SUP-START > (0.020) > (0.012)

F-EDU/RES > (0.006) > (0.041)

F-POL > (0.011) > (0.044)

F-VC > (0.009) > (0.047)

LOC-POL > (0.007)

NAT-POL > (0.040) > (0.027)

GEOGR < (0.011) < (0.001)

UNI/RES > (0.039) > (0.010)

TECH-PARK > (0.003) > (0.001)

IND < (0.003)

CAPITAL > (0.032)

LIFE > (0.013)

SPEC-POL > (0.001) > (0.000)

HIST < (0.032)

CHANCE < (0.018) < (0.001)

Table 7: Significant differences (significance level: 5%, result that are significant on

a 1%-level are bold) in the importance of the variables for the emergence of local

industrial clusters during different periods of time (> (


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to have further decreased after 1970. Furthermore, the local industrial structure

has lost importance after 1970.

Nowadays, specific policy measures seem to be more important as triggering

events. Among the local conditions, policy (LOC-POL and NAT-POL), university

and public research (UNI/RES), technology parks (TECH-PARK), the availability

of venture capital (CAPITAL) and the quality of life (LIFE) have increased in

importance, the importance of NAT-POL, UNI/RES and TECH-PARK has espe-

cially increased after 1970. Most of these local factors depend on policy. Hence,

a much greater importance is assigned to policy in the case studies of newer lo-

cal industrial clusters than in the case studies of older clusters. Policy seems to

increasingly interfer with the development of local clusters.There are also some self-augmenting processes that have gained in importance

over time. Existing firms play a more important role for supporting new start-

ups (SUP-START). Furthermore, the interaction between firms and universities,

public research (F-UNI/RES), local policy (F-POL), and local venture capital (F-

VC) has increased in importance. This implies that the local interaction between

different local actors has become more important nowadays, as it is, e.g. argued in

the concept of the triple helix (see Leydesdorff 2000).

Let us now move to examining the differences between high- and low-tech in-

dustries. We use two sources for a distinction of the industries: the definition ofhigh- and low-tech industries according to Hatzichronoglou (1997) and the def-

inition of knowledge-intensive and not-knowledge-intensive industries by Grupp,

Jungmittag, Legler und Schmoch (2000). We find that the results for the two dis-

tinctions of industries are very similar. Again we list only the variables for which

the importance differs significantly between the industries. This is done in Table

8.

An obvious fact, comparing Table 7 and 8, is that there are more differences

between industries than in time. However, many of the differences that are found

between local industrial clusters that developed before 1950 and after 1950 are also

found between high- and low-tech industries, or between knowledge-intensive and

other industries. The reason for this is that local clusters in high-tech industries

are, on average, younger than local clusters in low-tech industries. Nevertheless, a

number of additional differences are found between high- and low-tech industries.

For the self-augmenting processes cooperation between firms (COOP) seems to

play a more important role in the low-tech industries. However, that might be

caused by the strong focus on cooperation in the Italian studies, where low-tech


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industries, which are more dependent of natural resources. The latter is related

to the fact that in low-tech industries location decisions are very much based on

labour costs. In high-tech industries, instead, the availability of qualified labour

plays a more important role (LABOUR). Furthermore, local factors, such as the

local infrastructure (INFRA), universities and public research (UNI/RES), and

technology parks (TECH-PARK), are more important in high-tech industries than

in low-tech industries. However, most of these factors are also more important for

newer local clusters than for older local clusters. This holds especially for the policy

related factors (LOC-POL, NAT-POL, LIFE, and TECH-PARK). For these factors

it is not clear whether they show up more often in high-tech clusters because they

are more relevant there or because they have become more relevant with time and

high-tech clusters are usually less old.

In the case of the importance of local events, specific policy measures (SPEC-

POL) are found to be more relevant in high-tech industries, while historical events

(HIST) matter more for low-tech industries. Again the same differences in impor-

tance are found between old and new local industrial clusters, so that the causal

relations are not clear. However, two new differences show up between high- and

low-tech industries: the emergence of high-tech local clusters is more often triggered

by local promoters (PROM) and the existence of leading firms (LEAD-FIRM). It

seems that specific persons and individual outstanding firms are of a higher impor-tance in high-tech industries than in low-tech industries. This might be caused by

the importance of innovations and technological leadership in high-tech industries.

Finally, we turn to differences between countries. We study two kinds of differ-

ences here: the differences between developed and developing countries and the

differences between the continents. Since, continental Europe differs from U.K.

more than the USA differs from U.K., we distinguish between the anglo-saxion

sphere (Australia, Canada, U.K. and USA), continental Europe, and Asia. The

other continents are not reflected by a sufficient number of case studies in this

analysis. The results of these comparisons are given in Table 9.

Again, some of the findings can be explained by the correlation with other charac-

teristics of the local industrial clusters. High-tech clusters are more frequent in the

developed countries. Thus, it is not surprising that some variables, such as SPIN,

F-EDU/RES, LOC-POL, UNI/RES, WAGE, LIFE, HIST and LEAD-FIRM, show

up in the same way in the comparison of developing and developed countries as in

the comparison of high- and low-tech industries. More interesting are the facts that

the decision to locate near to other firms of the same industry (COLOC) and the


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while the other two groups do not contain such countries. Therefore, it can be

expected that some differences between third world countries and developed coun-

tries show up also between Asia and the other two groups. However, the differences

should be much weaker because 19 of the 25 local clusters in Asia that are studied

are located in developed countries. Therefore, we discuss the differences between

the three groups of countries independent of the analysis above.

An interesting finding is that the anglo-saxian sphere does not differ from con-

tinental Europe with respect to the relevant local self-augmenting processes, while

Asia differs strongly in the relevant processes from the other two country groups.

In Asia spin-offs (SPIN) and the interaction of the existing firm population with

universities and public research (F-UNI/RES), the local policy makers (F-POL),local venture capitalists (F-VC), and the local public opinion (F-OPIN) play a less

important role. In the case of the interaction with local venture capitalists conti-

nental Europe seems to be somewhere between Asia and the anglo-saxian sphere.

In contrast, cooperation between firms (COOP) plays in Asia a more important

role, while continental Europe is again somewhere between. Especially the findings

for the variables F-VC and COOP are well in line with the general finding that

venture capitalists play a stronger role in the anglo-saxian sphere, while Asia has

a more cooperative attitude.

The results for the relevance of the various local factors are more mixed. The

anglo-saxian sphere is outstanding for the strong relevance of the type of region

(TYPE) and the infrastructure (INFRA). Continental Europe shows a comparable

strong influence of the tradition of regions (TRAD). Asia deviates by showing a

weeker dependence on the public opinion and cultural aspects (CULT), on uni-

versities and public research (UNI/RES), and on the quality of life in the region

(LIFE). In the case of the national policy (NAT-POL) and the availability of cap-

ital (CAPITAL) there is again a gradient in importance from the anglo-saxian

sphere to continental Europe and finally Asia with the lowest importance.

With respect to the relevance of specific events and dynamics in the regions, two

differences are detected between the three groups of countries. First, the anglo-

saxian sphere shows a strong importance of major innovations. This goes in line

with the argument in the literature that especially the USA economy is strong in

transferring major inventions into market products. Second, the founding of lead

firms plays a significantly stronger role in continental Europe than in the other two

groups of countries.


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7. Conclusions

This paper presents a meta-study of a large number of case studies of localclusters. Furthermore, it provides a theoretical framework on which different local

cluster can be compared. The framework consists of a distinction of three groups

of variables that influence the emergence of local clusters. In total 35 different

mechanisms, factors and events are considered. It is studied whether these variable

appear to be important in the local clusters that are examined in the literature.

As the theoretical framework predicts, we find that none of the mechanisms,

factors and events is found to be important in all case studies. The mechanisms of

the emergence of local clusters differ tremendously between the cases. Nevertheless,

some mechanisms, factors and events show up more often then others.

Finally, the analysis is used to detect differences between various kinds of clus-

ters, such as clusters in developed countries compared to clusters in developing

countries or clusters in high-tech industries compared to clusters in low-tech in-

dustries. Many interesting results are obtained. For example, it is found that with

time (comparing clusters before and after 1950 and 1970) the geographic location

and the present industrial composition have lost in importance while policy in-

tervention and location interactions of various kinds have gained importance. The

latter point is striking, given the fact that everybody talks about globalisation.Furthermore, high-tech local clusters are found to be based more than low-tech

local clusters on variables related to human capital, public research and policy

intervention.

Comparing different groups of countries with each other we found that coopera-

tion plays a more important role in Asia, while the local interaction between firms

and public research, policy makers, venture capitalists and the public opinion play

a less important role there. In continental Europe the emergence of local clusters

are characterised by a relatively stronger influence of the founding of leading firms

and the tradition of regions. The anglo-saxian sphere is characterised by a higher

importance of major innovations, national policies and venture capital.

The main problem of conducting this meta-study is the fact that the authors of

case studies might not consider all potential influences as they are, for example,

proposed in the Brenner concept. Thus, there might be important variables that are

not mentioned in all case study in which they should be mentioned. Therefore, we

hope that in the future authors of case studies use our list or any other common

list of potential variables in analysing specific local cluster. That would lead to


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more comparable results and would allow to judge more reliably the importance

of different variables.

References

Becattini, G. (1990). The Marshallian Industrial District as a Socio-economic Notion. In:

(F. Pyke, G. Becattini & W. Sengenberger (Eds.)) Industrial Districts and Inter-firm

Co-operation in Italy, pp. 37-51. Geneva: International Institute for Labour Studies.

Brenner, T. (2000). The Evolution of Localised Industrial Clusters: Identifying the Pro-

cesses of Self-organisation. Papers on Economics & Evolution #0011, Max Planck

Institute Jena.

Brenner, T. (2001a). Self-organisation, Local Symbiosis of Firms and the Life Cycle of

Localised Industrial Clusters. Papers on Economics & Evolution #0103, Max PlanckInstitute Jena.

Brenner, T. (2004). Local Industrial Clusters: Existence, Emergence and Evolution. Lon-

don: Routledge.

Camagni, R.P. (1995). The Concept of Innovative Milieu and Its Relevance for Public

Policies in European Lagging Regions. Papers in Regional Science 74, 317340.

Fujita, M. & Thisse, J.-F. (2002). Economics of Agglomeration - Cities, Industrial Loca-

tion, and Regional Growth. Cambridge: Cambridge University Press.

Grupp, H., Jungmittag, A., Legler, H. & Schmoch, U. (2000). Hochtechnologie 2000

- Neudefinition der Hochtechnologie fur die Berichterstattung zur technologischen

Leistungsfahigkeit Deutschlands. mimeo, Fraunhofer-Institut fur Systemtechnik undInnovationsforschung ISI, Karlsruhe.

Hatzichronoglou, T. (1997). Revision of the High-Technology Sector and Product Classi-

fication. STI Working Papers 1997/2, OCDE/GD(97)216, OECD, Paris.

Krugman, P. (1991). Increasing Returns and Economic Geography. Journal of Political

Economy 99, 483499.

Leydesdorff, L. (2000). The Triple Helix: An Evolutionary Model of Innovations. Research

Policy 29, 243-255.

Markusen, A. (1996). Sticky Places in Slippery Space: A Typology of Industrial Districts.

Economic Geography 72, 293-313.

Porter, M.E. (1990). The Competitive Advantage of Nations . New York: The Free Press.

Saxenian, A.-L. (1994). Regional Advantage . Cambridge: Harvard University Press.

Scott, A.J. (1992). The Role of Large Producers in Industrial Districts: A Case Study

of High Technology Systems Houses in Southern California. Regional Studies 26,

265-275.

van der Linde, C. (2003). The Demography of Clusters - Findings from the Cluster

Meta-Study. In: (J. Brocker, D. Dohse & R. Soltwedel (eds.)) Innovation Clusters

and Interregional Competition, pp. 130149. Berlin: Springer.

Thomas Brenner

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