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Entrepreneurship and Development:The Role of Clusters Hector O. Rocha
Small Business Economics 23: 363–400, 2004. 2004 Kluwer Academic Publishers. Printed in the Netherlands.
ABSTRACT. Defining entrepreneurship as the creation ofnew organisations, this paper explores, from a literature reviewstandpoint, the moderating effect of clusters on the impact ofentrepreneurship on development. To identify potential causesof this moderating effect, the paper focuses on three differentimpacts: entrepreneurship on development, clusters on devel-opment, and clusters on entrepreneurship. The findings of thepaper are threefold. First, entrepreneurship is positively asso-ciated with economic growth. Given the importance of entre-preneurship in changing the economic and social structure ofthe economy, more research on the impact of entrepreneurshipon development – i.e. focus on capabilities rather than onoutput – is needed. Second, it is difficult to reach empiricalgeneralisations on the impact of clusters on developmentand entrepreneurship given conceptual and methodologicalconstraints. Both positive results and caveats are found at dif-ferent levels of analysis and at different stages of developmentof a cluster. Finally, given the previous finding, it is difficultto generalise on the impact of clusters on the associationbetween entrepreneurship and development. Consensus on andvalidity between conceptual and operational definitions ofclusters; consideration of context as well as process and, there-fore, quantitative and qualitative methods; and differentiationbetween levels of analysis controlling for cluster stage andstrength are the main criteria for future studies to consider todisentangle the impact of clusters on entrepreneurship, devel-opment and the association between entrepreneurship anddevelopment.
1. Introduction
Does clustered entrepreneurial activity contributeto development more than non-clustered? Definingentrepreneurship as “the creation of new organi-sations” (Gartner, 1989, p. 62) and cluster as ageographically proximate group of firms and asso-ciated institutions in related industries, linked by
economic and social interdependences (adaptedfrom Porter, 1998), this research question arisesfor several reasons.1 Firstly, it targets one of themost important challenges and contributions ofthe entrepreneurship field, i.e. the role of newenterprises in furthering economic progress(Low and MacMillan, 1988). Secondly, bothclusters and entrepreneurship face high visibilityamong academics and policymakers, given theircommon historical resurgence and potentialto retain and increase employment after thedrastic changes in the economic, institutional,and technological environments since the 1970s(Birch, 1981; OECD, 1996a, 1999, 2000, 2001;Arzeni and Pellegrin, 1997; Porter, 1998; Bergmanand Feser, 1999; Reynolds et al., 2001). In effect,the intrinsic rigidity of the independent large firm-based system was incompatible with the fast paceof change in the environment resulting in anincrease of unemployment. Consequently, a shiftof emphasis from mass to flexible production,from independent firm-based to regional network-based system, and from established firms tonew firms emerged as part of the solution (Pioreand Sabel, 1984; Saxenian, 1994; Nohria, 1992,1996; Castells, 2000). Finally, from a publicpolicy standpoint, several authors highlight theimportance of the entrepreneurial climate infostering economic development through thecreation of new companies (Malecki, 1994;Reynolds et al., 2001). Given the spatialvariations of entrepreneurial activity acrossregions (Reynolds et al., 1994), it is worthstudying how clusters, a special regional context,moderate the relationship between entrepreneur-ship and development.
Although all of these reasons invite to analysethe moderating effect of clusters on the relation-ship between entrepreneurship and development,there are no studies on this cluster moderating
Final version accepted on October 9, 2002
London Business School Regent’s ParkLondon NW1 4SA United KingdomE-mail: [email protected]
effect. Researchers have studied specific kindsof clusters, such as industrial districts (Visser,1999; Fabiani et al., 2000) and scientific parks(Westhead and Storey, 1994), using as unit ofanalysis established small and medium sized com-panies (focus on size) rather than entrepreneurship(focus on new firms). Those studying foundingand failure rates (Hannan and Freeman, 1989;Baum and Mezias, 1992; Lomi, 2000; Sorensonand Audia, 2000) have focused on only oneindustry and one dimension of clusters – i.e.agglomeration of economic activity, withoutanalysing societal level outcomes of the entrepre-neurial activity.
Given this research need, the main thrust of thispaper is to explore, from a literature review stand-point, whether clustered entrepreneurial activitygenerates more development than non-clustered.However, any answer to this question will dependon how development, entrepreneurship, andclusters are defined and measured. Also, in orderto identify the potential causes of the moderatingeffect of clusters on the relationship betweenentrepreneurship and development, it is necessaryto isolate three different impacts: entrepreneurshipon development, clusters on development, andclusters on entrepreneurship. Therefore, the aimof this paper is threefold: to review the conceptualand operational definitions of entrepreneurship,clusters and development; to review the literatureon the impact of entrepreneurship on developmentas well as that on the impact of clusters on devel-opment and entrepreneurship; and to put forwardsuggestions for future research. These aims set thescope of the paper (Figure 1).
The focus is to analyse whether entrepreneurialactivity develops its potential contribution todevelopment better inside clusters rather thanoutside them (discontinuous line in Figure 1). Toaddress this research need, this paper analysesthe relationships indicated with a continuous linein Figure 1. Given the focus on clusters, otherinteresting relationships such as the impact ofdevelopment on entrepreneurship and the effectof entrepreneurship on clusters are out of thepaper’s scope.2 Theories behind each concept areexplained briefly and representative bibliographyis given in the appropriate place. The most exten-sive analysis will be that devoted to clusters, onwhich subject there is neither consensus nor avail-able data to accomplish cross-sectoral and longi-tudinal research as there is in the cases ofentrepreneurship and development.
Given this focus, the paper brings togetherrelevant literature from the development, entre-preneurship and cluster fields. The link betweenconcepts was derived from combined keywordsearches in several search engines and specialisedjournals. Additionally, due to the policy nature ofthe topic, there was a search of publications andwebsites of multilateral organisations. AppendixA shows the sources of information analysedduring the literature review.
The findings of the paper are threefold. First,entrepreneurship is positively associated witheconomic growth. Given the importance of entre-preneurship in changing the economic and socialstructure of the economy, more research on theimpact of entrepreneurship on development – i.e.focus on capabilities rather than on output – is
364 Hector O. Rocha
Figure 1. Research focus.
needed. Second, it is difficult to reach empiricalgeneralisations on the impact of clusters on devel-opment and entrepreneurship given conceptual andmethodological constraints. Both positive resultsand caveats are found at different levels ofanalysis and at different stages of development ofa cluster. Finally, given the previous finding, it isdifficult to generalise on the impact of clusters onthe association between entrepreneurship anddevelopment. Consensus on and validity betweenconceptual and operational definitions of clusters;consideration of context as well as process and,therefore, quantitative and qualitative methods;and differentiation between levels of analysis con-trolling for cluster stage and strength are the maincriteria for future studies to consider to disentanglethe impact of clusters on entrepreneurship, devel-opment and the association between entrepre-neurship and development.
This paper is organized as follows: the nextsection reviews the concept of development andits operational definition in the entrepreneurshipfield; section three and four review the conceptsand measures of entrepreneurship and clusters,respectively, as well as their impact on develop-ment. Section five reviews the impact of clusterson entrepreneurship. Section six concludes.
2. Development – conceptual definition and 2. measurement
Several books have undertaken the task of classi-fying and explaining the theories of development.3
A review of the literature reveals that there is agreat deal of variation in the way development isdefined and measured. One reason for this is thatthe term “development” has strong policy impli-cations for society as a whole. Therefore, differentstakeholders with different views (economists,business leaders, labour leaders, and public offi-cials) intervene in its conceptualisation and mea-surement. However, the historical evolution of theconcept shows three main conceptualisations:economic growth, economic development, anddevelopment (Allen and Thomas, 2000; UNPD,1992; Sen, 1990). Table I shows different defini-tions and their associated measures according tothe literature.
Two main distinctions are in order. The first oneis between economic growth (Table I reference 1)
and economic development (Table I references 2and 3). While economic growth is a quantitativechange in the scale of the economy in terms ofinvestment, output, consumption, and income,economic development is a qualitative change,which entails changes in the structure of theeconomy including innovations in institutions,behaviour, and technology (U.S. Department ofCommerce, 2000). The second important distinc-tion is between traditional economic developmentdefinitions and measures (Table I references 1 to3) and the new view of development (Table Ireferences 4 to 6). The traditional view under-stands development as the capacity of a nationaleconomy to generate and sustain an annualincrease in its gross national product (GNP) and/orincome per capita. This view prevailed until the1970s and defined development as an “economicphenomenon in which rapid gains in overall andper capita GNP growth would either ‘trickle down’to the masses in the form of jobs and othereconomic opportunities or create the necessaryconditions for the wider distribution of theeconomic and social benefits of growth” (Todaro,2000, p. 14). The new view of developmentemerged during the 1970s when many under-developed countries had realised their economicgrowth-targets during the 1960s but the levelsof living of the masses of people remainedunchanged. The situation worsened during the1980s, when the distribution of the benefits ofdevelopment was concentrated in the richer scaleboth within and across countries (PNUD, 1992;Todaro, 2000). Thus, economic development wasredefined as reduction of poverty, inequality, andunemployment within the context of a growingeconomy.4
The literature uses the previous conceptualisa-tion and measures of development at both nationaland regional levels. However, regional economicsfocuses on explaining how regional disparities,especially in unemployment rates, arise and whythey persist over time. This focus has been thecentre of regional policy since the 1950s and willcontinue to dominate discussion of regional policyissues in the future (Armstrong and Taylor, 2000,p. 3).
The literature on entrepreneurship and devel-opment takes GNP/capita and job creation asindicators by which to measure economic devel-
Entrepreneurship and Development: The Role of Clusters 365
opment (Birch, 1981; Brock and Evans, 1989;OECD, 1996b; Arzeni, 1998; Reynolds and White,1997; Reynolds, 1999; Reynolds et al., 2001).Strictly speaking, the first indicator is a measureof economic growth while the second one is anindicator more related to the new view of devel-opment given the human, social, and economicimplications of getting a job. The validity of job
creation as a measure of development increases ifit is related to both outputs – in order to measureeconomic productivity – and quality jobs – inorder to include the human and social dimensionsof development. Quality jobs encompass not onlyeconomic (wage level, pension provision, carallowances) and social (holiday entitlements, sickpay, safety and health, working hours, security of
366 Hector O. Rocha
TABLE IAlternative definitions of development
Reference Term Definition Measurement
1 Allen and Economic “A continued increase in the size of an Variation in GDPThomas, 2000 growth economy, i.e. a sustained increase in
output over a period”
2 U.S. Economic “Economic development is (. . .) about GNPDepartment of development enhancing the factors of productive Job creationCommerce, capacity – land, labour, capital, and 2000: 1 technology – of a national, state or
local economy”
3 Bernstein, Economic “Raising the productive capacities Raising in the 1983 (cited in development of societies, in terms of their productivity of labourAllen and technologies (more efficient tools Thomas, 2000) and machines), technical cultures
(knowledge of nature, research and capacity to develop improved technologies), and the physical, technical and organisational capacities and skills of those engaged in production”.
4 Sen, 1990, Development “Expansion of human freedom to live Literacy rate1997, 1999 the kind of lives that people have Life expectancy rate
reason to value” (Sen, 1997: 21). This Health ratefreedom is achieved by the expansion Agricultural expansionof people capabilities Industrial development
People’s political participationReal income per capita
5 United Nations Human “The purpose of development is to Human Development IndexDevelopment development create an environment in which all (HDI): a weighted index that Programme, 1992 people can expand their capabilities, combines three indicators:
and opportunities can be enlarged for life expectancy at birth, both present and future generations” educationalattainment, and
per capita income
6 UK Department Sustainable “Ensuring a better quality of life for Three broad themes:of the Environment, development everyone, now and for generations – Environment (ex. Transport, and the to come” local air quality)Regions, 1997 – Society (ex. road
traffic accident/1000) – The economy (ex. rate of long term unemployment)
employment, child care) benefits but also moraleand job satisfaction (OECD, 1996b). Futureresearch can benefit from this simpler measure asopposed to the more complex ones suggested bythe new view of development.5
3. Entrepreneurship – definition, 2. measurement, and impact on development
3.1. Defining and measuring entrepreneurship
Entrepreneurship as a field of study is relativelyyoung (Cooper et al., 1997).6 The definition ofentrepreneurship has evolved from a trait orsupply side (who is the entrepreneur) to a contextor demand side approach (the influence of firmsand markets on how, where, and why new enter-prises are founded) (Thornton, 1999). The litera-ture on entrepreneurship and development definesentrepreneurship as either the creation of neweconomic activity (Low and MacMillan, 1988;Shane and Venkataraman, 2000), often resultingin the creation of new organisations (Schumpeter,1934, p. 66; Gartner, 1989; Reynolds, 1999), orthe pursuit of innovation (Schumpeter, 1934; fora review, see Wennekers and Thurik, 1999;Davidsson et al., 2001).
From the work of Birch (1981), entrepreneur-ship was measured in terms of size – i.e., smalland medium sized enterprises (SMEs). Yet, ifentrepreneurship is the creation of new organisa-tions, it is not consistent to measure it in terms ofexisting firms. Now the focus is on the phenom-enon itself given data availability not only on newfirms creation (Reynolds et al., 2001) but also onthe entrepreneurial process – i.e. the gestation,birth, and growth of firms7 (Reynolds, 2000).
3.2. Entrepreneurship and development
The link between entrepreneurship and develop-ment has been approached mainly from aneconomic standpoint, theoretically as well asempirically, focussing on economic growth ratherthan on development.
From the theoretical standpoint, Todaroanalyses five leading theories of economic devel-opment, named stages of growth, structuralpatterns of development, dependence, neoclas-sical, and new endogenous growth theory (Todaro,
2000, p. 78). Entrepreneurship falls in the latter,which includes technological innovation as wellas human capital as endogenous variables in themodel that explains economic growth (Morris,1998, p. 38; Wennekers and Thurik, 1999, p. 36;Todaro, 2000, p. 101; Porter et al., 2000, p. 14).Endogenous growth theory emerged to overcomethe shortcomings of the traditional neo-classicaltheory. This theory attributes economic growth tolabour and capital, but leaves unexplained 50%of historical growth in the industrialised nations,which is called “Solow residual”. Therefore, “neo-classical theory credits the bulk of economicgrowth to an exogenous or completely indepen-dent process of technological process” (Todaro,2000, p. 99). The origin of the new endogenousgrowth theory can be traced to the work ofSchumpeter, who introduced the idea thatchanges in technology introduced by entrepreneurscontribute to development (Schumpeter, 1934),through a process of “creative destruction”.Baumol, based on Solow’s finding that grossoutput per man-hour variation was due to technicalchange (Solow, 1957), was one of the first toargue that despite the role of the entrepreneur infostering innovations that drive technical change,the economic formal models are “entrepreneur-less” (Baumol, 1968, p. 51). He concluded that topromote economic growth it is key to examine thedeterminants of the payoff to entrepreneurialactivity and to encourage it. In a later work he con-cludes that the entrepreneur acts according to theprevailing “rules of the game” – the reward struc-ture in the economy –, and that these rules undergosignificant changes from one period to another andhelp to dictate the effect on the economy via theallocation of entrepreneurial resources (Baumol,1990, p. 25). This line of reasoning is endorsedby Kent (1982) and Kirzner (1982), who vieweconomic development not as an increase in theper capita production of goods and services butrather as a change in the total economic and socialstructure in a population’s standard of living.Kent summarises the role of the entrepreneur ineconomic development through not only his influ-ence on both the supply and demand sides of thegrowth equation but also on the phases of the stagetheories. Kirzner concludes that entrepreneurshipwill most thrive where rewards are paid to thosewith sufficient insight to exploit opportunity
Entrepreneurship and Development: The Role of Clusters 367
(Kirzner, 1982). Reynolds and White (1997) andReynolds et al. (2001) build a bridge between theprevious theoretical arguments and their empiricaldemonstration. Reynolds and White (1997) backthe argument that entrepreneurial activity is gen-erally not included in formal economic models,refusing empirically each of the assumptions onwhich they are built. The explicit inclusion ofentrepreneurship in a formal model to explaineconomic growth takes place in the GEM model(Reynolds et al., 1999).
From the empirical standpoint, the linkbetween entrepreneurship and economic growthhas been explored and demonstrated since thepioneering work of Birch, in which he showed,through a longitudinal analysis, that SMEs werethe main factor of job creation in the U.S.A.(Birch, 1981). This impulse was followed by suc-cessive research on the topic, which confirmedBirch’s findings and extended them to the creationof new enterprises as well as the “creative destruc-tion” of established ones (Reynolds, 1999) throughnot only cross-sectoral and cross-national analyses(Brock and Evans, 1989; OECD, 1996b; Arzeni,1998; Reynolds, 1999; Reynolds et al., 2001;Audretsch and Thurik, 2000), but also longitudinalones (Audretsch and Fritsch, 2000, cited inWennekers and Thurik, 2001). Longitudinalstudies are the most important ones in the studyof entrepreneurship and development given thetime lag between the creation of new enterprisesand the variation in rates of economic growth anddevelopment.
The increasing support for the positive associ-ation between entrepreneurship and economicgrowth seems to diminish the importance of somenegative impacts attributed to entrepreneurshipsuch as disrupting research and developmentprojects and reducing the incentive for firms toinvest in human capital (Florida and Keeney,1990).8 However, given the importance of entre-preneurship in changing the economic and socialstructure of the economy, the entrepreneurshipfield would benefit from more research on theimpact of entrepreneurship on development – i.e.focus on capabilities rather than on output. Thisis a major challenge for future studies on societallevel outcomes of entrepreneurship.
4. Clusters – definition, measurement, and 4. impact on development
Economic geographers, economists, sociologists,researchers in business and management, andpolicy makers have witnessed an increased interestin the study of clusters during the 1990s. Evidenceof this interest are the bulk of books (Weiss, 1988;Porter, 1990; Pyke and Sengenberger, 1992;Saxenian, 1994; Van Dijk and Rabellotti, 1997;Steiner, 1998; Crouch et al., 2001), publicationsof national and international organisations (Nadvi,1995; OECD, 1996a; OECD, 1999; Ceglie andDini, 1999; World Bank, 2000; UNIDO, 2001;Porter et al., 2001; Schwab et al., 2001; DTI,2001; OECD, 2001a; OECD, 2001b; Observatoryof European SMEs, 2002), and papers publishedsince 1990 that are related to clusters and similarconcepts.
One of the main reasons for this increasedinterest in clusters is the presumed impact ofclusters on firm performance, regional economicdevelopment, and country competitiveness. As aconsequence, several multilateral organisations,such as the OECD, UNIDO, the World Bank,UNCTAD, the European Commission, and othersare assessing and using cluster strategies as toolsfor economic development (Enright and Fflowcs-Williams, 2001).
This section analyses whether this renewedinterest in clusters is justified and how clustersrelate to development. Given that the answer tothis latter point will depend on the conceptual andoperational definitions of clusters as well as thespace over which development is measured, thefollowing sections address these issues, focusingon key contributions around which significantbodies of literature have evolved and yielded dif-ferent answers to the question about clusters anddevelopment.
4.1. Clusters – Evolution of the concept and 4.1. potential impact on development
Clusters existed long before the industrial revolu-tion – silk in China and trade services in the citiesof the Hanse are some examples. “During and afterthe industrial revolution clusters magnified andmultiplied: steel and shipbuilding in Glasgow, carsin Detroit, watches in Switzerland, machinery in
368 Hector O. Rocha
Southern Germany, to name but a few” (Steiner,1998, p. 2). Nowadays, clusters are found in bothdeveloped and developing countries, including allindustry types and typical placeless ones such astelemarketing in Omaha, call centres in Sydney,and software in Bangalore (Enright, 2001).
This variety of clusters poses a problem ofdefinition.9 For example, clusters have beendefined (either implicitly or explicitly) by someauthors as a geographically proximate group offirms producing basically the same product orservice (Marshall, 1966 (1890); Arthur, 1990;Sorenson and Audia, 2000); by others, as a groupof interrelated industries (Porter, 1990) located inclose geographic proximity (Porter, 1998); byothers as networks of firms, specially SMEs(Becattini, 1989), and related institutions withingeographical boundaries (Saxenian, 1994); by stillothers as groups of firms using the same core tech-nology and linked to other groups of firms on thebasis of technology (Tushman and Rosenkopf,1992; Wade, 1995).10 However, it is necessaryto have a framework to link these differentdimensions of clusters. To that end, this sectionaddresses the historical evolution of the clusterconcept and reviews the main schools of thoughtthat both gave shape to current conceptualisationsof clusters and provided different answers to thequestion about clusters and development. Table IIsummarises the evolution of the cluster concept,the associated schools of thought, the context inwhich the theoretical development took place, andthe antecedents and consequences of clusters. Thefollowing sections group different schools ofthought around the most important stages in theevolution of the cluster concept.
Genesis – Industrial Districts and ExternalEconomies11 (1890–1920). The work of Marshall(Marshall, 1966 (1890)) on localisation economiesis recognised as the point of departure of thecurrent literature on clusters. Marshall’s rationalefor what he called “industrial districts” (Marshall,1966, p. 225) is associated with the role of thelocalisation of industry – i.e. “concentration ofsmall businesses of a similar character in partic-ular localities” (Marshall, 1966, p. 230) – in gen-erating external economies of scale. Theseeconomies are external to the firm but internal tothe geographic area, and increase the efficiency of
each individual firm. Four main forms of externaleconomies can be found in Marshall’s work:economies of specialisation arising from inter-firmdivision of labour in complementary activities;economies of labour supply arising from the localpool of specialised labour; economies of infor-mation and communication arising from the jointproduction of no-standardised commodities andthe presence of local subsidiary trades; and theacquisition of specialised skills and the promotionof innovation and innovation diffusions – inmodern terminology technological spillovers –arising from both the mutual knowledge and trustand the industrial atmosphere created within thedistrict through frequent interchange between localactors (Marshall, 1966, pp. 225–230, 264; Zeitlin,1992, p. 280; Martin and Sunley, 2001, p. 6;Malmberg and Maskell, 1997, p. 31; Asheim,2000, p. 415).12 Marshall’s industrial districtperspective has five main features. First, the his-torical reference of comparison is the internaleconomies of scale of large firms that spawnedafter the second industrial revolution. Thisexplains why only interdependent small firms,which through an extensive division of labour incomplementary activities generate economies ofspecialisation, integrate Marshallian districts.Second, these economies of specialisation increasethe efficiency of the SMEs. Although Marshalllinks his macro-analysis of growth to his micro-analysis of increasing returns to firms and indus-tries (Rostow, 1990, p. 170), the focus is on theindividual small firm’s efficiency as a result of theexternal economies created within the district.Third, proximity is a precondition for the emer-gence of small firms’ interlinked activities thatgenerate economies of specialisation, which, inturn, increase SMEs’ efficiency. Fourth, spillovers,mutual knowledge and trust that emerge frominterdependences among specialised actors inclose proximity are the socio-cultural factors ofthe district. Amazingly, the founder of neo-clas-sical economics has set the basis to analyse thenon-economic dimension of clusters that hasreceived much attention since the resurgence ofthe concept of cluster in the 1970s. Fifth andfinally, there is neither indication of how theprocess of industrial localisation starts, normention of why it starts in certain places and notin others (Martin and Sunley, 2002).
Entrepreneurship and Development: The Role of Clusters 369
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Impasse – The prevalence of mass production(1920s–1970s). Marshall’s seminal work on indus-trial districts is the starting point of almost all ofthe subsequent theoretical proposals on clusters.However, there was an impasse of more thanhalf a century between Marshall’s work and therenewed interest in clusters in the early 1970s.This lack of interest can be explained by thepreponderance, between 1920s and 1960s, of thevertically integrated corporation drawing on inter-nally generated scale economies to produce stan-dardised goods for a predictable market (Amin,2000, p. 149). As Scott and Storper point out,“many leadings industrial sectors were convertedto mass production methods” and “various insti-tutions and practices were constructed to regulatethe social and economic effects of mass produc-tion” (Scott and Storper, 1992, p. 3).
Although important in their respective areas,only few studies were tangentially related to thecluster phenomenon. One of them is Perroux’swork on development and growth poles, consid-ered as one of the antecedent of the filière –value chain – approach in the 1970s in France(Steiner, 1998); the other one is related to agglom-erative and spatial complexes (Czamanski andCzamanski, 1977; Czamanski and de Ablas, 1979).The first stream focuses on the economic dimen-sion of clusters – i.e. economic linkages amongfirms – and is intrinsically non-spatial. ForPerroux there is no reason to link the spatial spaceand the economic space (Feser, 1998). However,Perroux’s idea of propulsive industries for growthis an antecedent of Porter’s focus on successfulindustries in international markets to define themost competitive clusters (Porter, 1990). Thesecond stream – i.e. spatial complexes – focuseson the concept of industry complex – i.e. groupof industries connected by important flows ofgoods and services – and shows that industrialagglomerations emerge as a result of not only acommon attraction to urban centres but also inter-action among several industries (Steiner, 1998).Both streams differ from the Marshallian one inthat the latter is characterised by independentsmall firms, while both growth poles and indus-trial complexes encompass large companies. Inparticular, large firms are the motors of growthpoles due to their supposed greater innovativecapacity than that of small firms, generating a pole
output larger than in the rest of the economy(Asheim, 2000).
New Industrial Districts – The crisis of mass pro-duction (1970s–1980s). In the late 1970s and early1980s there was a renewed interest in industrialdistricts. The main reason for this interest was theimpact of the drastic changes in the economic,technological, institutional, and political contextssince the mid 1970s on the prevalent industrialorganisation model at that time – i.e. mass pro-duction or independent large firm-based industrialsystem. The suspension of free convertibility ofdollar into gold in 1971, the oil crisis in 1973, theIT revolution initiated with the invention of themicroprocessor in 1971, the declining power oforganised labour, and the liberalisation process ini-tiated by neo-liberal governments in U.S. and U.K.during the 1980s, are only some of the key factsthat impassed pressure on the Keynesian model ofgrowth and its related industrial organisationsystem based on large firms and mass production– i.e. “Fordism”. The intrinsic rigidity of thissystem was incompatible with the fast pace ofchange in the environment, resulting in an increaseof unemployment. At the same time, several indus-trial regions such as the Central and NortheastItalian regions and Southern California and theBay Area in U.S., based on vertical disintegration,inter-industrial networks, and local labour marketsarouse outside the heartlands of mass production(Storper and Scott, 1989). Therefore, a shift ofemphasis from mass to flexible production andfrom independent firm-based to regional network-based systems – i.e. clusters – took place. All thesechanges, together with their social, economic,institutional, and geographical consequences arewell documented in the literature (Piore and Sabel,1984; Scott, 1988; Storper and Scott, 1992;Saxenian, 1994; Nohria, 1996; Castells, 2000),and were analysed through several theoreticalperspectives or schools of thought. Each of themhas shaped the cluster concept and its relationshipto development in different ways. The mostimportant schools are the Italian School, theInstitutional or flexible specialisation school andthe Californian School.
The Italian School (Becattini, 1979, 1989;Brusco, 1992; for a review, see Pyke et al., 1990;Pyke and Sengenberger, 1992; Cosentino et al.,
Entrepreneurship and Development: The Role of Clusters 371
1996) applied Marshall’s framework to interpretthe small-firm development in the Central andNortheast Italian regions. However, this schoolextended and modified Marshall’s original ideas,specially the historical and territorial specificsocio-cultural factors driving external economies.Becattini contends that the unit of analysisshould change from firms and sectors to industrialdistricts – cluster of interconnected firms locatedin a small area (Becattini, 1989). In other workhe emphasises the socio-economic dimension ofindustrial districts, in which “community andfirms tend to emerge” (Becattini, 1990, p. 38).From this perspective, the industrial district is a“socio-territorial entity which is characterised bythe active presence of both a community of peopleand a population of firms in one naturally and his-torically bounded area” (Becattini, 1990, p. 38).Both the change of the unit of analysis from firmsand industries to industrial districts and thefocus on socio-economic factors underlying theiremergence have led to emphasise the impact ofindustrial districts not only on firm efficiency butalso on local economic development. Given thatindustrial districts are composed mainly bySMEs, firm efficiency is increased due to thebenefits of external economies of scale and scope.Regarding local development, it is fostered byboth SMEs’ shared vision and organisationaccording to several principles. Among theseprinciples are local networks, entrepreneurship,flexibility, collective efficiency, and the existenceof trust (Sengenberger and Pyke, 1992). Thisendogenous development view differs from theneo-classical one, in which financial resources andimported technology is seen as the key sources ofdevelopment. The socio-economic notion of thedistrict was extended by sociologists such asBagnasco and Trigilia who highlighted the impactof historical family and political inheritances(Zeiltin, 1992, p. 281).
In short, the Italian school emphasised twodimensions. First, the focus is on both the successof the community of firms and the individual smallfirms efficiency. Second, the success of the dis-tricts lies not only on economic factors but alsoand mainly on historical and territorial specificsocio-cultural ones. However, the Italian Schoolfaces two main weaknesses. First, its generalisa-tions are based on Italian examples that have long
historical roots difficult to replicate (Amin andRobins, 1990; Zeiltin, 1992, p. 283). Second, thediversity of industrial districts both inside andoutside Italy challenges the idea of a canonicalmodel based on successful localised Italian SMEs(Zeiltin, 1992; Rabellotti, 1995; Rabellotti andSchmitz, 1999).
A second school of thought analysed the resur-gence of industrial districts from an institutionalperspective (Piore and Sabel, 1984; Sabel andZeitlin, 1985). Its central claim is that “we areliving through the second industrial divide. (. . .)[We] see two potential contradictory strategies forrelaunching growth in the advanced countries. Thefirst strategy builds on the dominant principles ofmass-production technology (. . .). The secondstrategy veers sharply from established techno-logical principles and leads back to those craftmethods of production that lost out at thefirst industrial divide” (Piore and Sabel, 1984,p. 6). This second strategy is called “flexiblespecialisation” (Piore and Sabel, 1984, p. 17).Generalising from the industrial districts of Italyto other cases – especially West Germany –, Pioreand Sabel argue that small innovative andsectorally focused firms are an alternative to themass production model and its resulting depen-dence on big firms, and therefore a solution tofoster growth and employment. Although Pioreand Sabel acknowledge the convergence betweenbig and small firms, they associate their flexiblespecialisation model to the vertically disintegrated,small firm industrial system, as in the case ofMarshall and the Italian School. The main contri-bution of the flexible specialisation school to theevolution of the cluster concept is the argumentthat the industrial district is an important spatialmanifestation of the flexible specialisation model.The need for inter-firm collaboration and trustgives rise to the tendency for spatial agglomera-tion. Therefore, it acknowledges that dynamicsforces for economic growth such as technologicallearning are localised and territorially specific,with specific institutions playing an important role(Storper, 1997). This latter line of reasoning wasdeveloped some years later by Amin and Thrift(Amin and Thrift, 1994), who developed theconcept of institutional thickness to refer to theexistence of relations between development insti-tutions, firms and organisations, and politicians
372 Hector O. Rocha
at the local and regional level whose close asso-ciations are thought to be instrumental in creatinggrowth.
A third school of thought that emerged in the1980s was the Californian School (Scott, 1988;Storper and Scott, 1989). Focussing on the pecu-liarities of the industrial geography of SouthernCalifornia and the Bay Area in U.S., this schoolproposes a transaction cost view of clustering. Theargument is that uncertainty is met via externali-sation of activities leading to vertical disintegra-tion of production chains either to minimise risksor to maximise the benefits of specialisation.However, this vertical disintegration increasestransactions among firms leading to an increase intransactions costs. To overcome this issue, firmscluster geographically materialising flexible pro-duction complexes. Therefore, agglomeration offirms is the result of the minimization of inter-firmtransaction costs (Scott, 1988; Storper, 1997). Thismodel extends the original flexible specialisationmodel and contributes to the evolution of thecluster concept in two dimensions. First, itincludes not only SMEs but also large firms.Second, it allows any mix of sectors ratherthan only manufacturing (Storper, 1997, p. 11).However, one of the main shortcomings of thetransaction cost explanation of the clusteringprocess is its focus on traded interdependences.These input-output relations between firms are notenough to explain clustering in some capital-inten-sive and high technology sectors. For example,Liebeskind et al. study of the biotechnology sectorin California shows that the sourcing of the mostcritical input in this industry – i.e. knowledge –is based on social networks rather than on markettransactions (Liebeskind et al., 1996). Some yearslater, Storper will argue that there is another andmore important reason than traded interdepen-dencies for the agglomeration of firms: the exis-tence of untraded interdependencies, “which takethe form of conventions, informal rules, and habitsthat coordinate economic actors under conditionsof uncertainty” (Storper, 1977, p. 5) and constituteregion-specific assets in production. Thus, theoriginal transaction cost economics frameworkthat focused on localised input-output transactionsis complemented with a sociological approach toanalyse localised untraded relations. This isanother application of the sociological factors –
i.e. the “industrial atmosphere”, mutual knowledgeand trust – mentioned by Marshall.
Clusters – Territories amid globalisation andrapid technological change (1990s onward). Inthe late 1990s two contextual features extendedthe importance of the cluster phenomenon: theheightening of the globalisation process (Held etal., 1999, pp. 13, 431) and radical technologicalchange (Longhi and Keeble, 2000, p. 45). Bothprocesses have made the geographical andnetwork-innovation dimensions of clusters moreprominent. This increasing interest in clustersamid globalisation and technological change is,at a first glance, counter intuitive. How are clustersexplained in a context of increasing globalisation?Why should geographic location matter whendrastic technological changes have reduced trans-portation and communication costs and barriers?
The traditional explanation for the co-existenceof globalisation and clustering of economicactivity hinges its roots in the title of the thirdchapter of Book I of the Wealth of Nations: “thedivision of labour is limited by the extent of themarket” (Smith, 1999 (1776)). This means thatregional specialisation depends on globalisation(Steiner, 1998). Therefore, “globalisation will beaccompanied by more, rather than less, speciali-sation; and hence, by implication, will lead tofurther spatial concentration of such activity”(Dunning, 1998, p. 15).
Although important, the principle of speciali-sation does not explain the kind of activities thatwill be concentrated in clusters. To get a morespecific explanation, it is useful to distinguishbetween traditional and modern theories of trade.Traditional trade theory – i.e. Heckscher-Ohlinmodel – is based on Ricardian comparative advan-tage, and argues that nations will specialise inthose industries in which they have comparativefactor advantages. The relative factor endowmentsof different countries are thus the main reason forinternational trade and specialisation. Therefore,the principle of comparative advantage states thatcountries with dissimilar resource endowmentswill exchange dissimilar goods. This theory, whichis based on conditions of perfect competition andrelative immobility of factors of production, helpsto explain only part of world trade – inter-industrytrade between developed and developing coun-
Entrepreneurship and Development: The Role of Clusters 373
tries. However, much of the world trade is betweencountries with similar factor endowments; besides,they exchange very similar products (Storper andChen, 2000), which are based on knowledge-inten-sive activities (Dunning, 1998). This intra-industrytrade is essentially a result of both consumerdesires for diversity in the choice of products andinternal economies of scale (Armstrong andTaylor, 2000). This means that competition isbased on innovation, quality and dynamic effi-ciencies – i.e. those depending of the rate oflearning and the capacity for innovation – ratherthan on low cost. Given that knowledge spillovers,a key element of the innovative activity, tend tobe spatially restricted (Audretsch and Feldman,1996), especially when they are based on informalor social ties (Audretsch and Stephan, 1996), itturns out that globalisation triggers the clusteringof economic activity via the concentration of inno-vation, making local regions a key source ofadvantage (Audretsch, 2000).
In sum, globalisation triggers regional special-isation and concentration of innovative activity.These, in turn, have a positive impact on trade.This process relies on competitive advantage,external economies, increasing returns to scale,and non-economic factors rather than on compar-ative advantage, low costs, and perfect competi-tion. Within this context, the cluster literaturehas divided into two streams in the 1990s: theeconomic one, which highlights the economicexternalities mentioned by Marshall; and thesocio-economic and innovation one, which high-lights the territorial, social, institutional, andcultural factors underpinning cluster dynamics.This latter approach is called the networkparadigm (Powell, 1990; Conti et al., 1995) andis characterised by both the opening of the blackbox of territorial specificities and the measuringof innovation externalities that occur withinclusters. Porter’s theory of competitiveness(Porter, 1990, 1998, 2001) and Krugman’s neweconomic geography (Krugman, 1991) fall into theeconomic stream. The second stream encompassesthe innovative milieu school (Aydalot, 1986;Camagni, 1991; Maillat, 1996), the Nordic Schoolof innovation and learning (Lundvall and Johnson,1994; Malmberg and Maskell, 1997; Lundvall andMaskell, 2000), the geography of innovationapproach (Jaffe, 1989; Feldman, 1994; Audretsch
and Feldman, 1996; Audretsch and Stephan, 1996;Zucker, Darby and Armstrong, 1998; Zucker,Darby and Brewer, 1998), and the cultural-insti-tutional approach (DiMaggio and Powell, 1983;Powell, 1990; Saxenian, 1994; Ingram andRoberts, 2000).13 Each of these schools of thoughtare analysed below.
Porter’s theory of competitiveness (Porter,1990), which some authors consider as the startingpoint of the current renewed interest in clusters(Rosenfeld, 1997; Steiner, 1998; Martin andSunley, 2002), has been adopted by severalregional and national governments and interna-tional organisms to foster competitiveness. Porterdefines clusters as “a geographically proximategroup of interconnected companies and associatedinstitutions in a particular field, linked by com-monalities and complementarities” (Porter, 1998,p. 199). He proposes a framework to analyse firmproductivity and regional/national competitivenesswhere location is a main source of competitiveadvantage within a context of a global economy.In effect, “the roots of productivity lie in thenational and regional environment for competi-tion” (Porter, 1998, p. 7); “(the) presence ofclusters suggests that much of competitive advan-tage lies outside a given company or even outsideits industry, residing instead in the locationsof its business units” (Porter, 1998, p. 198).How location affects firm’s productivity andregional/national competitiveness? Porter arguesthat these outcomes are strongly influenced by thequality of the business environment (Porter, 1998,p. 198). This business environment is createdthrough the interactions between four factors – i.e.Porter’s diamond: context for firm strategy andrivalry; factor (input) conditions; demand condi-tions; and related and supporting industries(Porter, 1990, 1998). These factors are enhancedwhen the concerned firms are geographicallylocalised (Porter, 1990, p. 157). The developmentof and the interaction between the factors of thecompetitive diamond enhance competitiveness inthree ways: improving productivity, fosteringinnovation, and facilitating the commercialisationof innovation by easing the creation of new firms(Porter, 1998, p. 213; 2001). It is important todistinguish Porter’s view of clusters in 1990 fromhis conceptualisation of clusters in the late 1990s.In his 1990’s book Porter defines clusters in
374 Hector O. Rocha
sectoral terms – i.e. industries related by verticaland horizontal links. The literature calls this con-ceptualisation sectoral cluster (OECD, 1999; DTI,2001; Sternberg, 1991). Although Porter acknowl-edges the importance of regions, clusters aredefined mainly as an industrial rather than aterritorial phenomenon.14 In contrast, Porter’sdefinition in 1998 is more comprehensive andincludes three main dimensions: the sectoral, thegeographical, and the network ones. Porter’sreference to economic geography (Porter, 1998,pp. 227–230) and to socio economics (1998, pp.225–227) stresses the importance of these lattertwo new dimensions. Despite the inclusion of theregional and network dimensions in his concep-tualisation of clusters, the methodology to defineclusters is still similar to that of 1990: the first stepis the creation of an industrial cluster templatebased on industrial interdependences and thesecond one is the application of this clustertemplate to different regional levels (Porter et al.,2001). Therefore, although territorial as well associo cultural specificities are acknowledged inPorter’s conceptualisation, these important factorsare exogenous in his model. In other words, thespecific causal mechanisms that link territorial andsocio-cultural factors to both the process of clus-tering and the generation of competitive advantageare not included in the model.
The second school of thought that belongs tothe economic stream of clusters is the new eco-nomic geography of Krugman (Krugman, 1991).Stressing that the “most striking feature of thegeography of economic activity” is concentration(Krugman, 1991, p. 5), Krugman argues thatincreasing returns to scale have a “pervasive influ-ence on the economy, and [they] give a decisiverole to history in determining the geography ofreal economies” (Krugman, 1991, p. 10). Increas-ing returns affect economic geography at localscale – via the location of particular industries –,urban scale – via the emergence of cities –, andnational scale – producing the uneven develop-ment of whole regions (the core-periphery argu-ment). The existence of increasing returns to scaleat the plant level means that individual producersare motivated to concentrate geographically theirproduction in order to benefit from the resultinginternal economies. Krugman explains the reasonsfor localisation of industries in terms of Marshall’s
sources of external economies – i.e. local pool ofspecialised labour, local subsidiary industries, andtechnological spillovers (Krugman, 1991, p. 36).These factors lead to the clustering of economicactivity at the local level. At a higher level ofanalysis, assuming that upstream and downstreamproducers are subject to increasing returns, asbarriers to trade are reduced, “backward andforward linkages tend to concentrate the upstreamand downstream producers in a single location”(Krugman, 2000, p. 55). This market-size effectleads to centre-periphery patterns within nations,which produce regional divergence due to aprocess of cumulative causation. Also, increasingreturns at the level of industry or externaleconomies can lead similar countries in terms offactor endowments to specialise in the productionof different goods. Therefore, Krugman explainsregional specialisation and trade in terms ofincreasing returns and imperfect competitionrather than in terms of comparative advantages andperfect competition. However, clustering forcesare not the only ones at work. In a latter work,Krugman explains the countervailing dispersion orcentrifugal forces: immobile factors of production,land rents, and pure external diseconomies(Krugman, 1998). Therefore, the combination ofclustering or centripetal forces and dispersion orcentrifugal forces will determine either the con-centration or the dispersion of industries. A clearcontribution of Krugman to the cluster literatureis the formalisation of the causes for agglomera-tion, trade, and regional growth. However, thissame emphasis on formal economic models hasled him to set aside important clustering factorssuch as technological spillovers or flows, which“are invisible; they leave no paper trail by whichthey may be measured and tracked (. . .)”(Krugman, 1991, p. 53). In fact, of all the above-mentioned centripetal and centrifugal forces,formal models only include the market-size effectsand immobile factors (Armstrong and Taylor,1999). So far, these models show that geographymatters “when it comes to trade, despite thedecline over time of transport costs and barriersto trade” (Armstrong and Taylor, 2000, p. 138).However, as in the case of Porter, there is no indi-cation of the territorial and socio-cultural speci-ficities that are conductive to the clusteringprocess.
Entrepreneurship and Development: The Role of Clusters 375
The focus on innovation and the role of terri-torial and socio-cultural specificities together withthe network dimension highlighted by the ItalianSchool is further developed by the networkapproach to clusters, which includes sociologicalconstructs such as embeddedness (Polanyi, 1944;Granovetter, 1985), social networks (Powell, 1990;Nohria and Eccles, 1992), and untraded interde-pendencies (Storper, 1997).
A first approach within this network stream isthe geography of innovation one. Its main thrustis to measure knowledge spillovers, which werenot analysed by Krugman. This literature linksknowledge spillovers to the geography of innov-ative activity and demonstrates both theoreticallyand empirically that knowledge spillovers areimportant to innovation and tend to be spatiallyrestricted (Jaffe, 1989; Patel and Pavitt, 1991;Feldman, 1994; Audretsch and Feldman, 1996),especially when they are based on informal ties(Audretsch and Stephan, 1996). The spatial linkbetween knowledge spillovers and innovationbased on the microeconomic linkages across actorssuch as scientists and firms is the main contribu-tion of this literature to the cluster approach.However, it says little about how economicactivity is organised within a given geographicspace (Audretsch, 1998, p. 24).
The second school of thought within thenetwork approach is the innovative milieu intro-duced by the GREMI group. An innovative milieuis a territorially based system of relationshipsbetween different economic and social actors thatleads to innovation (Aydalot, 1986; Camagni,1991, p. 130). This approach emphasises theimportance of inter-firm relationships, territorialsocio-economic embeddedness, and dynamic localcollective learning process to firm innovativeactivity (Keeble and Wilkinson, 2000). The inno-vative milieu approach contributes to the evolu-tion of the cluster concept stressing the territorialdimension of networks of multiple actors (firms,governmental agencies and not-for profit organi-sations such as universities) to foster innovation.However, as Storper points out, it does not identifythe economic logic by which territorial specificitymakes technological and organisational dynamicsbetter (Storper, 1997).
Close related to the innovative milieu approach
is the Nordic School of innovation and learning(Lundvall and Johnson, 1994; Malmberg andMaskell, 1997; Lundvall and Maskell, 2000). Thisschool stresses the concepts of learning economies(Lundvall and Johnson, 1994; Lundvall andBorras, 1998) and regions (Asheim, 1997; Maskelland Malmberg, 1999), which overlap with theconcept of national innovation systems (Freeman,1987; Lundvall, 1992; Lundvall and Maskell,2000). In fact, it is proposed to see clusters asreduced-scale national (OECD, 1999) and regional(OECD, 2001a, p. 8) innovation systems. Thesestreams of the literature stress the role of knowl-edge as a strategic resource and learning as a keyprocess of economic development. The argumentis that knowledge has an informal and tacit(Polanyi, 1967) dimension. This type of knowl-edge is embedded in the social and territorialcontext and therefore is difficult to codify andtransfer through formal mechanisms. This meansthat informal personal contact is necessary inorder to transfer knowledge, which leads tothe geographical concentration of innovators.Therefore, non-market factors such as socio-cultural, institutional, and political ones appearas paramount for cluster dynamics (Saxenian,1994; Malmberg and Maskell, 1997). Sociologicalconstructs such as embeddedness of economicactivity (Polanyi, 1944; Granovetter, 1985) andsocial capital (Coleman, 1988; Putman, 1993)appears to be the major driving forces underlyinginnovation.
These sociological factors are the foci ofthe cultural-institutional approach to clusters(DiMaggio and Powell, 1983; Powell, 1990;Saxenian, 1994; Ingram and Roberts, 2000).Clusters or local industrial systems are conceptu-alised as networks of firms and related institutionswithin geographical boundaries (Saxenian, 1994,p. 4). This school of thought argues that the social,institutional, and cultural factors underlying theinterdependence of economic actors are moreimportant than the economic and technical – i.e.external economies – ones to firm performanceand local economic development (Saxenian,1994). Therefore, a systemic and relational viewreplaces the atomistic view of interdependencesamong firms. These interdependences do not occurin a non-spatial dimension, but they are embedded
376 Hector O. Rocha
in the social and institutional setting of the cluster.In analysing the impact of clusters on firm per-formance and economic development, this streamemphasises a typical trade-off between the advan-tage of being embedded in the local economicstructure and the disadvantage of being locatednear competitors (Baum and Haveman, 1997;Sorenson and Audia, 2000; Ingram and Roberts,2000). There is no conclusive evidence, but giventhe importance of the social structure for the clus-tering process, balancing cooperation and compe-tition seems to be the way of getting the most ofthe above-mentioned trade-off. In particular, it isargued that ties embedded in social relationshipsenhance collaboration, mitigate competition, andfoster information exchange, which in turnimprove the performance of organisations (Ingramand Roberts, 2000). Additionally, in clusters witha strong division of labour, the differentiationamong clustered firms leads to functional com-plementarities that create mutualistic effects andtherefore neutralise the negative effect of sourcingfrom the same resource pool.
A main critic to the network approach to clusteris its emphasis on the socio-territorial embedded-ness of knowledge and innovation. The individualor human capital dimension is also importantand therefore it is not necessary to be locallyembedded to transfer knowledge. Zucker et al.(1998) demonstrated this in the biotechnologyindustry analysing star scientists. Audretsch andStephan (1996) found that 70% of knowledge istransferred via formal arrangements that do notneed a territorial dimension. Finally, Rallet andTorre (1998) found that organisational proximitycontributes to technology transfer and innovationdiffusion more than geographical proximity.Despite the importance of these studies, their con-clusions do not mean that arms length and non-territorial social networks are the only factors thatmatter to innovation diffusion. A more balancedapproach, answering under what conditions terri-torial embeddedness matters is required. It seemsthat proximity matters when knowledge spilloversare informal. On the other hand, when knowledgeis transmitted through formal mechanisms suchas participation in boards or joint ventures, prox-imity appears to be less important (Audretsch andStephan, 1996).
4.2. Summing up – Do clusters matter to 4.2. development?
The previous section showed that the differentschools of thought share the idea that economicactivity tends to be sectorally concentrated andgeographically clustered. However, as Table IIshows, each school of thought has contributed itsown vocabulary and set of assumptions, stresseddifferent cluster dimensions and components, andidentified different causal chains to associateclusters to firm efficiency and local development.To complicate the picture, different researchershave elaborated different definitions and typolo-gies of clusters. This conceptual variety introducesa source of noise in studies on clusters and devel-opment because different units of analysis aredealt as they were only one – i.e. clusters.
An additional obstacle to reach conclusionsabout the relation between clusters and develop-ment is the variety of research designs in empir-ical studies.15 There is an increasing number ofcase-based studies that have used different con-ceptual and operational definitions under the samelabel. The same problem appears in more quanti-tative designs. In effect, Table III summarisesrepresentative quantitative studies on clusters,showing that researchers have studied the effectsof clusters at different levels of analysis, adoptedmultiples measures of this concept, and chosendifferent performance criteria. Particularly prob-lematic is the mixing of different units and levelsof analysis, given that several studies take thepositive impact of clusters on firm performanceas evidence of the contribution of clusters todevelopment, assuming that firm-level outcomestranslate directly to regional and national levels.This is not only a methodological flaw but alsoan oversimplification, as both economic historyand theory demonstrates (Aghion and Williamson,1998). This section aims to clarify this secondsource of variation – i.e. the impact of clusters atdifferent levels – in the answer to the questionabout clusters and development. The review ofthe impact of clusters at the firm, regional,and national levels will help to avoid fruitlessdebates that either justify or critic cluster initia-tives mixing arguments at different levels ofanalysis.
Entrepreneurship and Development: The Role of Clusters 377
378 Hector O. Rocha
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tudy
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hod
of m
easu
rem
ent
Sco
peD
epen
dent
var
iabl
e R
esul
tan
alys
isT
ime
Spa
ce
Fir
mA
gglo
mer
atio
n of
In
gram
and
P
olit
ical
bou
ndar
ies
1998
Syd
ney
Hot
el
Fir
st m
odel
–
Pos
itiv
eco
mpe
tito
rsR
ober
ts,
2000
Indu
stry
Hot
el p
erfo
rman
ce
“Hot
els
perf
orm
bet
ter
if t
heir
(r
even
ue p
er
man
ager
s ha
ve f
rien
dshi
ps w
ith
avai
labl
e ro
om)
com
peti
tors
and
if
thos
e S
econ
d m
odel
–
com
peti
tors
are
the
mse
lves
E
xist
ence
of
frie
nds”
(In
gram
and
Rob
erts
, fr
iend
ship
2000
, p.
390
)“F
rien
dshi
ps a
re m
ore
like
ly
betw
een
man
ager
s w
hose
hot
els
are
clos
e co
mpe
tito
rs (
. .
.).
Exi
stin
g fr
iend
ship
s ar
e m
ore
like
ly t
o pe
rsis
t, an
d ne
w
frie
ndsh
ips
are
mor
e li
kely
to
form
, if
the
oth
er i
ndiv
idua
l m
anag
es a
com
peti
ng h
otel
” (I
ngra
m a
nd R
ober
ts,
2000
,p.
390
).
Fir
mM
arsh
alli
an
Fab
iani
et
al.,
LQ
em
ploy
men
t an
d 19
82–
Ital
yR
OE
and
RO
IP
osit
ive
Indu
stri
al d
istr
ict
2000
expe
rt o
pini
on b
ased
19
95A
ll t
he
Lab
our
cost
F
irm
s in
ind
ustr
ial
dist
rict
s on
Loc
al L
abou
r L
LM
As,
199
pe
r ca
pita
have
hig
her
prof
itab
ilit
y (R
OE
M
arke
t A
reas
in
dust
rial
and
RO
I) a
nd h
ighe
r pr
oduc
tivi
ty
(LL
MA
s)di
stri
cts
(lab
our
cost
per
cap
ita)
acr
oss
tim
e th
an n
on-d
istr
icts
fir
ms
cont
roll
ing
for
size
and
sec
tor
Fir
mC
lust
er
Bap
tist
a an
d R
egio
nal
empl
oym
ent
1975
–U
KIn
nova
tion
in
term
s P
osit
ive
(geo
grap
hica
lly
Sw
ann,
199
8in
a f
irm
’s o
wn
1982
of t
echn
olog
ical
ly
Str
ong
stat
isti
call
y as
soci
atio
n lo
cali
sed
indu
stry
sign
ific
ant
and
betw
een
inno
vati
on a
nd c
lust
er
empl
oym
ent)
com
mer
cial
ly
stre
ngth
mea
sure
d in
ter
ms
of
succ
essf
ul
own-
sect
or e
mpl
oym
ent
inno
vati
ons
Fir
mIn
dust
rial
Dis
tric
tV
isse
r, 1
999
Exp
ert
judg
emen
t –
1993
Lim
a –
Per
uF
irm
per
form
ance
P
osit
ive
boun
dari
es o
f th
e G
arm
ent
(sev
eral
ind
icat
ors
Clu
ster
ed f
irm
s pe
rfor
m b
ette
r cl
uste
r de
fine
d by
in
dust
rysu
ch a
s em
ploy
men
t th
an n
on-c
lust
ered
one
s du
e to
the
mai
n ro
ads
size
and
gro
wth
, cl
uste
ring
adv
anta
ges
such
su
rrou
ndin
g it
sale
s an
d w
ages
)as
low
er c
osts
and
in
form
atio
n sp
illo
vers
Loc
al l
abou
r M
arsh
alli
anS
forz
i, 19
92L
Q e
mpl
oym
ent
and
1971
–A
ll i
ndus
trie
s G
row
th i
n P
osit
ive
mar
ket
area
s In
dust
rial
dis
tric
tex
pert
opi
nion
bas
ed
1981
acro
ss I
taly
man
ufac
turi
ng
“Mar
shal
lian
Ind
ustr
ial
dist
rict
s (L
LM
As)
on L
ocal
Lab
our
61 M
arsh
alli
anem
ploy
men
t ex
peri
ence
d th
e fa
stes
t in
crea
se
Mar
ket
Are
asIn
dust
rial
G
row
th i
n in
em
ploy
men
t bo
th i
n di
stri
cts
tota
l em
ploy
men
tm
anuf
actu
ring
[36
.7%
] an
d in
to
tal
empl
oym
ent
[37.
6%]
betw
een
1971
and
198
1”
(Sfo
rzi,
1992
: 10
6)
Entrepreneurship and Development: The Role of Clusters 379T
AB
LE
III
Clu
ster
s an
d de
velo
pmen
t –
Em
piri
cal
Stu
dies
Lev
el o
f C
lust
er c
once
ptS
tudy
Met
hod
of m
easu
rem
ent
Sco
peD
epen
dent
var
iabl
e R
esul
tan
alys
isT
ime
Spa
ce
Reg
iona
l C
lust
erD
TI
UK
, L
Q e
mpl
oym
ent
1991
–U
KE
xpor
tM
ixed
(Sta
te l
evel
)20
01In
put/
outp
ut a
naly
sis
1998
A
ll i
ndus
trie
s pe
rfor
man
ce
Job
crea
tion
in
the
clus
ters
for
E
mpl
oym
ent
revi
ew(p
age
(man
ufac
turi
ng(i
t is
bas
ed o
n m
ost
of t
he r
egio
ns i
s no
bet
ter
Act
ivit
y an
alys
is17
)an
d se
rvic
es)
sect
ors
rath
er
than
the
reg
iona
l av
erag
e an
d E
xper
t op
inio
nan
d re
gion
sth
an r
egio
nal
in s
ame
case
s si
gnif
ican
tly
wor
se.
154
clus
ters
clus
ters
).E
xcep
tion
s in
clud
e L
ondo
n, t
he
Job
crea
tion
Sou
th E
ast,
and
Eas
tern
reg
ions
(p
age
43)
whe
re o
vera
ll j
ob c
reat
ion
is
sign
ific
antl
y be
tter
tha
n re
gion
al
aver
age
(pag
e 9)
.
NU
Ts
2 an
d H
igh
tech
nolo
gyK
eebl
e an
d S
elec
tion
of
wel
l-19
99E
urop
e In
nova
tive
cap
abil
ity
Pos
itiv
e–
Inno
vati
ve c
apab
ilit
y N
UT
s 3
clus
ters
W
ilki
nson
, kn
own
clus
ters
of
10 h
igh
Com
peti
tive
ness
and
com
peti
tive
ness
2000
high
tec
hnol
ogy
SM
Es
tech
nolo
gy
Reg
iona
l di
spar
itie
s“C
lust
ers
of h
igh-
tech
nolo
gy
usin
g cl
uste
r an
alys
is
clus
ters
SM
Es
play
an
impo
rtan
t ro
le
and
base
d on
the
in
str
engt
heni
ng i
nnov
ativ
e N
UT
S2
and
NU
TS
3 ca
pabi
lity
and
com
peti
tive
ness
cl
assi
fica
tion
at t
he r
egio
nal/
loca
l, na
tion
al,
and
EU
lev
els”
(p.
230
)
Neg
ativ
e–
Reg
iona
l di
spar
itie
sC
lust
ers
of h
igh-
tech
nolo
gy
SM
Es
can
inte
nsif
y ex
isti
ng
econ
omic
and
soc
ial
regi
onal
an
d na
tion
al d
ispa
riti
es
Reg
iona
lL
ocal
Pro
duct
ion
Deb
ru a
nd
LQ
for
loc
al c
once
n-19
99F
ranc
eG
row
thP
osit
ive
Sys
tem
Sag
et,
1999
trat
ion
of S
ME
s A
ll i
ndus
trie
s R
egio
nal
clus
ters
hav
e on
E
xper
t fo
r co
oper
ativ
e an
d re
gion
s –
aver
age
an e
qual
or
bett
er
and
com
peti
tion
pat
tern
s,ex
cept
Ile
-de-
grow
th t
han
the
Fre
nch
aver
age
rela
ted
acti
viti
es,
and
Fra
nce
shar
ed c
ultu
re22
6 cl
uste
rs
Reg
iona
l G
eogr
aphi
call
y Ja
ffe
(198
9);
Pol
itic
al b
ound
arie
s19
82U
.S.
Inno
vati
on m
easu
red
Pos
itiv
e(S
tate
lev
el)
loca
lise
d F
eldm
an (
1994
);
in t
erm
s of
pat
ents
In
nova
tion
pro
cess
ten
ds t
okn
owle
dge
Aud
rets
ch a
nd
(Jaf
fe,
1989
) or
new
be
hig
hly
loca
lise
d.F
eldm
an (
1996
)pr
oduc
t in
nova
tion
s (F
eldm
an,
1994
; A
udre
tsch
and
F
eldm
an,
1996
)
Cou
ntry
Clu
ster
sP
orte
r, 2
001
Pol
itic
al b
ound
arie
s19
99–
75 c
ount
ries
P
rodu
ctiv
ity
mea
sure
d P
osit
ive
– C
ount
ry20
01(G
loba
lin
ter
ms
of
The
mic
ro b
usin
ess
envi
ronm
ent
Com
peti
tive
ness
GD
P/c
apit
a–
Por
ter’
s di
amon
d –
expl
ains
R
epor
t)m
uch
of t
he v
aria
tion
in
over
all
nati
onal
pro
duct
ivit
y.
380 Hector O. Rocha
TA
BL
E I
IIC
onti
nued
Lev
el o
f C
lust
er c
once
ptS
tudy
Met
hod
of m
easu
rem
ent
Sco
peD
epen
dent
var
iabl
e R
esul
tan
alys
isT
ime
Spa
ce
Cou
ntry
Sec
tora
l C
lust
ers
Por
ter,
199
0U
se o
f se
ctor
al
1978
–U
.S.,
Sw
itze
rlan
d,S
hare
of
the
sect
or
Pos
itiv
ecl
uste
r te
mpl
ates
to
1985
Sw
eden
,in
wor
ld e
xpor
tsT
he m
ost
inte
rnat
iona
l id
enti
fy i
nter
nati
onal
ly
Ger
man
y,co
mpe
titi
ve i
ndus
trie
s co
mpe
titi
ve s
ecto
rs
Japa
n, I
taly
, ar
e fo
und
in c
lust
ers
–ba
sed
on e
xpor
ts/F
DI
Kor
ea,
and
UK
indu
stri
es r
elat
ed b
y ho
rizo
ntal
U
se o
f I/
O t
able
s an
d an
d ve
rtic
al l
inks
– w
ithi
n ex
pert
opi
nion
to
each
cou
ntry
. T
he s
yste
mic
id
enti
fy l
inka
ges
natu
re o
f th
e di
amon
d am
ong
sect
ors
prod
uces
clu
ster
ing
(199
0: 1
57)
Mul
tile
vel
Loc
al
Rod
rigu
ez-
Pol
itic
al b
ound
arie
s19
77–
Fra
nce,
Cha
nge
in
Mix
edR
egio
nal
Pro
duct
ion
Pos
e, 2
001
1994
Ger
man
y,R
egio
nal
shar
es
Pos
itiv
e as
soci
atio
n be
twee
n th
e L
ocal
Sys
tem
–
Ital
y, a
nd
in n
atio
nal
GD
Pde
nsit
y of
LP
S a
nd g
row
th (
p. 4
3)cl
uste
rs o
f U
KC
hang
e in
S
ome
regi
ons
incr
ease
d th
eir
SM
Es
Loc
al S
hare
s in
sh
are
of n
atio
nal
GD
P (
Bav
aria
,na
tion
al G
DP
Eas
t A
ngli
a, V
enet
o, e
tc)
whi
leot
hers
not
(B
aden
-Wur
ttem
berg
, E
mil
ia-R
omag
na)
Som
e lo
cali
ties
inc
reas
ed t
heir
sh
are
of n
atio
nal
GD
P (
Bol
ogna
, C
ambr
idge
, L
ower
Bav
aria
, et
c)
whi
le o
ther
s no
t (B
resc
ia,
Par
ma,
S
ttut
gart
)C
oncl
usio
ns:
LP
S b
ased
on
netw
orks
of
SM
E a
re n
ot a
pa
nace
a fo
r hi
gh g
row
th.
Thi
s is
mai
nly
an u
rban
phe
nom
enon
(p
p. 4
3, 4
5) a
nd th
e be
st e
cono
mic
perf
orm
ance
s oc
cur
whe
n m
etro
poli
tan
spac
es a
nd L
PS
mee
t
Mul
tile
vel:
Tra
ded
Clu
ster
s P
orte
r et
al.,
Iden
tifi
cati
on o
f tr
aded
Y
ear
ofU
S –
Atl
anta
, O
vera
ll E
cono
my:
Pos
itiv
eR
egio
n(c
once
ntra
ted
in
2001
.in
dust
ries
and
cre
atio
n or
igin
Pit
tsbu
rgh,
the
E
mpl
oym
ent,
“Reg
ions
wit
h a
high
er
Met
ropo
lita
n so
me
area
s an
d C
lust
ers
ofof
an
inte
r-in
dust
ry
of t
he
Res
earc
h U
nem
ploy
men
t, pe
rcen
tage
of
wor
kers
in
Are
ase
llin
g to
oth
er
Inno
vati
on:
clus
ter
tem
plat
e us
ing
clus
ter
Tri
angl
e,W
age,
Wag
e tr
aded
ind
ustr
ies,
and
wit
h E
cono
mic
re
gion
s or
R
egio
nal
corr
elat
ion
of i
ndus
try
– ye
ar
San
Die
go,
grow
th,
Cos
t of
a la
rger
por
tfol
io o
f re
lati
vely
A
rea
nati
ons)
Fou
ndat
ions
em
ploy
men
t or
2000
and
Wic
hita
li
ving
, ex
port
sst
rong
and
gro
win
g cl
uste
rs,
for
US
lo
cati
onal
cor
rela
tion
s 15
clu
ster
sIn
nova
tion
te
nd t
o be
mor
e pr
ospe
rous
” C
ompe
titi
vene
ssto
ide
ntif
y th
e m
ost
outp
ut:
pate
nts,
(P
orte
r et
al.,
200
1:A
-2).
inte
rrel
ated
ind
ustr
ies
esta
blis
hmen
tA
ppli
cati
on o
f th
e fo
rmat
ion,
VC
tem
plat
e to
a
inve
stm
ent,
IPO
s,sp
ecif
ic r
egio
n us
ing
fast
gro
wth
fir
ms
empl
oym
ent
and
firm
da
ta –
i.e
. L
Q –
Clusters and firm performance. The surveyedschools of thought argue that, at the firm level ofanalysis, firms within clusters are better off thanfirms not within them. Both external economies(Marshall, 1966; Krugman, 1991; Porter, 1998)and the special competitive (Porter, 1998) andsocio cultural (Becattini, 1989; Saxenian, 1994)environments within clusters foster firm effi-ciency, innovation, and performance. Table IIIshows that quantitative studies at the firm level,although employing different conceptual defini-tions and measurements of clusters, support thehypothesis that clusters foster firm performance(Ingram and Roberts, 2000; Fabiani et al., 2000;Visser, 1999) and innovation (Baptista and Swann,1998). However, different answers to the questionabout clusters and firm performance might resultaccording to the stage of the life cycle (Pouder andSt John, 1996; Porter, 1998; Enright, 2001) as wellas the degree of development (Arthur, 1990;Baptista and Swann, 1998; Enright, 2001; Porteret al., 2001) of a cluster. Regarding the former, thesame forces promoting firm productivity and inno-vation in an initial stage can offset clusters’positive impact in a later stage due to congestionand competition effects. For example, physicalinfrastructure within clusters contributes to firmsproductivity lowering transaction costs andincreasing the quality of services. However, thisargument seems to be true either for clusters intheir initial stage of development or for non-highgrowth clusters. As clusters grow, saturationwithin the cluster may generate diseconomies ofscale, reflected in higher cost of living, real estateprices, and salaries of technical personnel (Pouderand St. John, 1996). The dynamic reasoning usedfor physical infrastructure could be applied toother causal mechanisms such as the existence ofentrepreneurial profits (Schumpeter, 1934), insti-tutional forces (DiMaggio and Powel, 1983), thequality of the environment (Raco, 2000), andmanagers’ mental models (Prahalad and Bettis,1986). Besides cluster stage of life cycle, a secondimportant variable that could yield differentanswers to the question about clusters and firmperformance is the degree of development of thecluster, given that firm performance is expected tobe higher in strong clusters compared to weakclusters (Baptista and Swann, 1998; Porter, 1998,2001; Enright, 2001).
Clusters and local development. The majority ofthe revised schools of thought relates the presenceof clusters to local development. However, withthe exception of Krugman, none of them aretheories of regional growth (Feser, 1998).Therefore, what follows is an explanation of theimpact of clusters on local development placingthe arguments of the revised perspectives withinthe framework of mainstream theories.
Marshall provides the basis to understand howmicro-level business relationship could influenceregional development; however, he focusesmainly on firm efficiency, without an explicitexplanation of how clusters contribute to localeconomic development. This explanation is foundin Hirshman who, coining the concept of growthcentres, proposes a regional extension of Perroux’snon-spatial growth poles (Feser, 1998). Accordingto Hirshman, regional growth is promoted viapublic directed capital in few key propulsivesectors in underdeveloped areas. This growth-centre strategy was applied in the 1960s and 1970sand it was a failure given the little attention paidto the economic and social prerequisites necessaryfor growth centres to work (Feser, 1998). Thegrowth-centre strategy is one of the variants oftraditional regional policy (Armstrong and Taylor,2000) or what is known as exogenous develop-ment or development from above perspective inthe regional development literature. This regionalpolicy approach aims to achieve functional inte-gration wherein leading regions expand intolagging regions and resources of lagging regionsare made more accessible to leading regions.Therefore, the source of development relies onfactors external to the local system, emphasisingthe mobility of capital and labour. In this approachthe key is the pursuit of growth through centralgovernment policies and urban and large-scaleenterprises based on standardisation and capitalintensiveness.
The 1980s witnessed a shift of emphasisfrom this exogenous development approach tothe opposite strategy – i.e. endogenous or indige-nous development or development-from-below(Garofoli, 1992; Nelson, 1993; Armstrong andTaylor, 1999). This endogenous developmentstrategy aims to create regional autonomy throughintegration of all aspects of life within a territorydefined by its culture, resources, landscape and
Entrepreneurship and Development: The Role of Clusters 381
institutions. The source of development relies onthe local economic and social system where entre-preneurship, SMEs, and innovation play a key rolefor competitive advantage. Clearly, the schools ofthought that stressed both the territorial specifici-ties and SMEs composition of clusters – i.e.the Marshallian, Italian, Flexible specialisation,Innovative Milieu, and Cultural-institutionalschools – fit within this indigenous developmentstrategy. As Table III shows, quantitative studieswithin either of the above-mentioned schoolssupport this strategy (Keeble and Wilkinson, 2000;Debru and Saget, 1999).
Another mainstream theory that sheds lighton the potential contribution of clusters tolocal development is the endogenous growththeory.16 While the endogenous development per-spective stresses that the key factors promotinglocal development are found within the region,endogenous growth theory stresses that techno-logical change or productivity increase, consideredan exogenous factor by neoclassical economics, isdetermined within the growth model. In otherwords, what was previously taken as given – i.e.technological change – is now explained (Todaro,2000; Fine, 2000). Therefore, the level of growthis a function of not only the stock of capital butalso the rate of technological change, assumed asgiven in old growth models. New growth theoryextends the old one in two ways. First, it deter-mines not only the level of growth but also andmainly the rate of growth of an economy becausetechnology is variable. Second, investments inhuman capital and R&D are the two main strate-gies to affect the rate of technological change orproductivity improvements, which offset dimin-ishing returns to capital investment. This is themain point of departure from old theories ofgrowth, which assume diminishing returns. Theacceptance of increasing returns to scale impliesthat non-pecuniary externalities and thereforemarket imperfections are acknowledged. Here itlays the most important link between new growththeory and clusters. If physical proximity andnetworks, two main components of clusters, fosterexternalities – and therefore knowledge spilloversas a special kind of externalities –, and these exter-nalities foster growth – as the new growth theoryargues –, therefore clusters foster growth. Clearly,
all the schools of thought analysed in this paperwithout any exception are consistent with thisendogenous growth theory explanation and there-fore offer an interesting theoretical argument tosupport the relationship between clusters andregional growth. In particular, as Table III shows,the works of Jaffe (1989), Feldman (1994), andAudretsch and Feldman (1996) have found thatknowledge spillovers are important to innovationand tend to be spatially restricted.
Finally, the last mainstream theory that helps toexplain the link between clusters and local devel-opment is Krugman’s New Economic Geography.As it was seen in the analysis of this school,Krugman argues that increasing returns affecteconomic geography at several levels. At theregional level increasing returns lead to the clus-tering of economic activity and the concentrationof development in specific areas where the processstarted due to chance or historical accident(Krugman, 1991). Then, a process of cumulativecausation and inflexibility starts: “once anoutcome (. . .) begins to emerge it becomesprogressively more ‘locked in’” (Arthur, 1989,p. 117). Cumulative causation and lock ineffects are not always positive in terms of localdevelopment. At least five potential negative casescan be identified: a region with few clusters;clusters specialised in only one industry; conges-tion effects; disparities within the region; and dis-parities between regions. The first four cases areexplained below; the issue of regional disparitieswill be analysed in the next section – i.e. clustersand national development.
The first case of potential negative impact ofclusters is that in which a region has only one ortwo clusters as drivers for growth. In this case, theregion has a higher risk of regional depressionbefore economic or competitive shocks than amore diversified region. The decline of the coal,iron, and steel complex of the Ruhr (Grabher,1993) and of the Swiss watch industry (Glasmeier,1994) are only two examples. While it is true thatthe largest places will develop multiple clusters(Porter, 1998), the majority of regions have littleprospect of developing more than one or twoviable clusters (Bergman and Feser, 1999). Yet,this argument does not take into account the abilityof clusters to overcome economic crisis. Some
382 Hector O. Rocha
authors argue that the failure cases could be attrib-uted either to specific cluster features or othercausal processes rather than to the intrinsic natureof clusters. For example, the issue of regionaldepression due to cluster failure has severalcounter-examples, such as the cases of SiliconValley and the Ruhr Valley in Germany(Rosenfeld, 1997). In the former case the industryshifted into the personal computer and equipmentindustry, while in the later case the industrytook advantage of the local expertise to build anew cluster around environmental technologies.These examples show that clusters, like industries,are able to respond to competitive shocks andnew demands. Yet, the specific capabilities andprocesses that lead to the revitalisation of clustersremain unknown.
A close related negative case appears whenclusters include only one industry, showing ahighly specialised pattern. This makes a clustermore vulnerable to industry shocks. Also, asGlaeser et al. (1992) have shown, regional diver-sity is more important than regional specialisa-tion to industry growth. This can explain themixed results of some specific quantitative clusterstudies shown in Table III. For example, the exis-tence of deep clusters – i.e. those with the mostindustrial and institutional linkages – is associatedwith better regional employment growth in U.K.(DTI, 2001, p. 9). Also, better regional perfor-mance in Europe is related to the intersectionbetween clusters and metropolitan spaces, whichcomprise several industries (Rodriguez-Pose,2001). This latter case shows that although clustersentail a richer industrial dimension than singleindustries and cities (Porter, 1996), it is difficultto distinguish between urban, industry, and clusterspecific externalities.
A third negative effect appears in high-tech-nology clusters. Although they help to increase thewealth of the region, they also create socialdivides within it (Keeble and Wilkinson, 2000), asin the case of Telecom City, Bangalore (OECD,2002), and Silicon Valley (Harrison, 1994, p. 114).
Finally, the cumulative process of clusteringcan harm the environment, given that in manygrowth regions economic and social concentrationhas created environmental problems, which mayundercut future competitiveness (Raco, 2000).
In sum, although there are several theoreticalarguments and empirical evidence to support thepositive association between clusters and localdevelopment, this association seems to be contin-gent to some cluster features, such as the inten-sity of inter-organizational networks within thecluster and its industry span. Also, it is importantto analyse the association between clusters andlocal development within the appropriate timehorizon. For example, Tuscany and EmiliaRomagna’s productivity growth and employmentwere higher than the national average during the1980s. However, the same regions showed anaverage annual income growth rate below theaverage Italian rate during the 1990s (Capello,1996; Rodriguez Pose, 2001).
Clusters and national development. Four of thediscussed schools of thought relate clusters tonational development, although in different ways.First, Piore and Sabel propose the regional versionof flexible specialisation – i.e. industrial districts– as an alternative to mass production to generateeconomic growth and employment (Piore andSabel, 1984). Second, Porter argues that nationalcompetitiveness is based on the quality of thebusiness environment – i.e. his competitivediamond. These factors are enhanced when theconcerned firms are geographically localised, as itis demonstrated by the concentration of the mostinternational competitive industries in strongclusters (Porter, 1990; DTI, 2001). Porter hasfound evidence for this argument testing statisti-cally across a broad sample of countries (Porter,2001). Specifically, using his conceptual frame-work for the study of clusters to measure themicroeconomic context, he demonstrated that thiscontext explains much of the variation in overallnational productivity, measured in terms of GDPper capita (Porter, 2001). Porter argues that thisstudy challenges the notion that microeconomicimprovement is automatic if proper macroeco-nomic policies are instituted. Third, Krugmanargues that increasing returns affect economicgeography at different levels. At the large level,he develops a core-periphery model in “which theinteraction of demand, increasing returns, andtransportation costs drives a cumulative process orregional divergence” (Krugman, 1991, p. 11).
Entrepreneurship and Development: The Role of Cluster 383
Therefore, contrary to the constant returns modelsof neoclassical economics that deny externalities,Krugman argues that divergence rather than con-vergence between regions is the norm. Finally, theNordic School emphasises the knowledge andlearning dimensions of economic development(Lundvall, 1992) and their embeddedness inspecific social and institutional national environ-ments. This school of thought stresses the systemicnature of clusters and acknowledges both virtuousand vicious circles as a function of the fit or misfit,respectively, between the economic, institutional,and social elements of the innovation system(Lundvall and Maskell, 2000). Therefore, it is theworking of systemic interrelation of factors rootedin specific environments what makes developmentpossible. The Nordic School offers an extensionof endogenous growth models, which highlightthat complementary investments in human capitaland R&D are needed in order to financial andphysical capital produce their expected benefits(Todaro, 2000, p. 101). This can be seen in devel-oping countries, where lower levels of investmentsin human capital, R&D, and supporting institu-tions offset the potential high rates of return ofinvestments in financial and physical capital(Ranis et al., 2000)
Two main issues arise from the previousarguments the association between clusters andregional disparities, and the transferability ofcluster initiatives across regions and countries.Regarding the first issue, cluster initiatives implya policy-led attempt to strengthen regional con-centrations. As a consequence, cluster initiativesdo not take fully into account the minimisation ofregional disparities in growth and income, whichis not only one of the indicators of development(UNDP, 1992) but also a traditional goal inregional policy (Armstrong and Taylor, 2000).However, from a neoclassical economics perspec-tive and contrary to Krugman’s cumulative cau-sation process, this should not be an issue giventhat the free flow of factors of production will leadto a convergence of development across regions.General empirical studies do not provide a definiteanswer on this debate (Armstrong and Taylor,2000), although one of the few cluster specificstudies that addresses the divergence problem con-cludes that even when clusters of high-technology
SMEs in Europe contribute to regional competi-tiveness they also can intensify existing economicand social regional and national disparities(Keeble and Wilkinson, 2000). Some argue thatthese regional disparities may result from inade-quate national policies to balance regional devel-opment rather than from the nature of clusters(Sengenberger and Pyke, 1992). In this view, it isargued that national socio-economic policies andclusters policies should go hand in hand becausethey complement and reinforce one another.Regarding the issue of transferability, there is con-sensus that clusters are not transferable amongregions (OECD, 2001a, p. 9), or more broadly,from one society to another, for clusters areembedded in social systems of production dis-tinctive to their particular society (Hollingsworth,1997).
4.3. Measuring clusters
Both the different cluster’s conceptualisation andthe different levels of analysis described in theprevious sections explain the varying argumentsregarding the methodology to identify clusters.This third source of potential variation in findingsin clusters studies is the focus of this section.
The approaches so far include both quantitativeand qualitative techniques. The proposed method-ologies and their evaluations are shown in TableIV.17 To overcome the pitfalls of each method-ology, there is a general consensus in the litera-ture that in order to truly identify clusters it isnecessary to conduct both qualitative and quanti-tative analyses (Rosenfeld, 1997; DTI, 2001). Forinstance, “although inter-industry transactions(. . .) can sometimes be detected in input-outputtables, neither the character of relationships amongfirms nor the benefits of clustering can be dis-cerned in this way” (Doeringer and Terkla, 1999).Traditional quantitative measures are inadequateto discover important features present in someclusters such as social infrastructure, entrepre-neurial energy, shared vision, and level of collab-oration, and therefore are unable to “distinguish asimple industry concentration from workingclusters” (Rosenfeld, 1997).
Yet, combining quantitative and qualitativeapproaches faces a number of methodological bot-
384 Hector O. Rocha
tlenecks and complexities that complicate thecomparability of cluster studies. From a qualita-tive standpoint, the rich reality comprised in theconcept of cluster makes it difficult to agree ondescriptors of the cluster concept. From a quanti-tative standpoint, existing official national andinternational data sources for cluster analyses arelimited by conventions on official classificationsystems of economic activities and industries.These sources were not designed to cover inter-industry and inter-firm linkages. Besides, clusteranalysis needs input-output data at very low levelsof aggregation (three or four-digit industry codelevel), and only a few countries such as theU.S.A., Canada and Denmark have detailed input-output tables (Roelandt and Hertog, 1999).
An additional source of complexity is the lackof correspondence between conceptual and oper-ational definitions of clusters, as Table V shows.This table permits to discover the phenomenonunder study from the method employed to identifyit. In effect, independently of the label they use,some authors are studying agglomerations, basedeither on firms (Baum and Mezias, 1992; Lomi,2000; Sorenson and Audia, 2000) or on employ-ment (Baptista and Swann, 1998; Glassman andVoelzkow, 2001) within a single industry; othersfocus on interrelated industries without consid-ering regional boundaries – i.e. sectoral or valuechain clusters – (Porter, 1990; Roelandt et al.,1999); yet others study single or interrelatedindustries within specific geographical boundaries,including concentrations of either SMEs – i.e.industrial districts – or firms of different sizes –i.e. clusters. Clearly, the methods used in thesestudies do not converge to capture similar attrib-utes of the cluster concept, indicating a much-needed dialogue on the definition and dimensionsof clusters. In this regard, Figure 2 attempts to linkdifferent concepts used in the cluster literature andtheir associated techniques to identify and measurethem. It is argued that future studies will not addto the current confusion if they clearly specify thetype of cluster under study and use the appropriatetechnique associated with that type of cluster. Thisis one of the major methodological challengescluster studies face in order to have a strongerempirical foundation to support both theoreticalarguments and policy designs.
5. The impact of clusters on entrepreneurship
The existence of national (Audretsch andThurik, 2000; Reynolds et al., 2001) and regional(Reynolds et al. 1994) variations in entrepreneur-ship means that territorial specificities matter tofirm creation. Given that clusters comprise morethan this geographical dimension, several authorsargue that regions where strong clusters operatebenefit from higher start-up rates. This sectionanalyses different arguments and associated empir-ical evidence of the impact of clusters on entre-preneurship.
It is argued that clusters foster entrepreneurshipproviding established relationships and betterinformation about opportunities; lowering entryand exit barriers; opening up niches of special-ization due to the low degree of vertical integra-tion; fostering a competitive climate and strongrivalry among firms that impose pressure toinnovate due to the presence of close competitors;providing role models and the presence of otherlocal firms that have “made it”; capturing impor-tant linkages, complementarities and spilloversfrom technology, skills, information, marketingand customer needs that cut across firms andindustries, which is key to the direction and paceof new business formation and innovation; pro-viding access to physical, financial, and commer-cial infrastructure; easing the spin offs of newcompanies from existing ones; reducing risk anduncertainty for aspiring entrepreneurs; and pro-viding a cultural environment where establishingone’s own business is normal and failure is nota social stigma (see for example Pyke andSengenberger, 1992, p. 20; Saxenian, 1994, pp.30–41, 111–118; Rosenfeld, 1997; OECD, 1998,p. 93; Porter, 1990, 1998, pp. 205, 224). Despitethe plausibility of these arguments and someempirical evidence supporting them (Saxenian,1995, p. 125; Baptista and Swann, 1999; Oakey,1995), they present two main weaknesses. First,the main18 focus is on the absolute creation offirms rather than the net start-up rate – i.e. birthsminus deaths or churning rate; second, clusteradvantages to entrepreneurship are assumed aspermanent, with independence of the clusterstage.
Taking a more dynamic view, some authors
Entrepreneurship and Development: The Role of Cluste 385
386 Hector O. RochaT
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387
388 Hector O. Rocha
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argue that the start-up rate increases during theinitial stage of a cluster and then decreases in amore mature stage. The reasons behind thisprocess are different, though. Schumpeter (1934)argues that successful pioneer entrepreneursremove the obstacles faced by entrepreneurialactivity in its early stages. This produces the “clus-tering of the followers” up to the point of elimi-nating entrepreneurial profit. Pouder and St. John(1996), referring to high growth clusters in theirorigination phase of evolution, argue that clustersmay be viewed as an incubator for start-ups andspin-offs. At a later stage, congestion effects,mimetic behaviour and homogeneity in managers’mental models stabilise entry. Finally, organisa-tional ecology theory argues that at low levels oforganisational density legitimation processesdominate and, therefore, the net founding rateis positive. However, at high levels of density,
competition processes dominate and therefore thenet founding rate decreases (Hannan and Carroll,1992). Despite the strong initial empirical supportfor this argument, results differ according tothe level of analysis at which the model isspecified (Carroll and Wade, 1991; Lomi, 1995,2000).
The dynamic view analyses the net start-up rateand provides different answers to the questionabout the impact of clusters on entrepreneurshipbased on the stage of the cluster. However, it facestwo limitations. First, from the cluster point ofview, it is based on only one industry and onedimension of clusters – i.e. agglomeration ofeconomic activity. The inter-industrial, inter-organisational, and network dimensions of clusterscould produce different patterns of start-up evo-lution. Second, from the entrepreneurship point ofview, the analysed dynamic perspectives focus
Entrepreneurship and Development: The Role of Cluste 389
1 = Standard industrial classification codes (SIC, NAICS, NACE, etc)2 = Specialisation indexes (location quotients, Gini coefficients; inverse Herfindhal index)3 = Political and Administrative boundaries at several levels (States, Metropolitan Areas, Economic Areas, Counties, NUTs, etc)4 = I/O tables and industrial cluster templates – i.e. inter-industry trade patterns using I/O data at national level and applying either
correlation (Porter, 2001) or factor analysis (Feser and Bergman, 2000) to identify the most interrelated industries5 = Cluster template applied to an specific region6 = Local Labour Market Areas (LLMAs) and expert opinion. LLMAs are travel-to-work distance areas where the majority of
the resident populations social and economic relations takes place (Sforzi, 1992)7 = Some of the previous quantitative techniques complemented with in depth case studies and expert opinion
Figure 2. Matching conceptual and operational definitions.
only on the context of entrepreneurship, withoutconsidering firm specificities. In particular, pop-ulation ecology studies take as unit of analysis thepopulation and thus treat foundings as identicaladditions to homogeneous organisational popula-tions, without considering the characteristics ofnew organisations (Baum and Haveman, 1997).This misses two key attributes of entrepreneurship:the role of human volition and organisationallearning, and the generation of different outputs atthe firm level (Bygrave and Hofer, 1991).
In sum, the previous analysis suggests thatlinking process and context at different levels ofanalysis, considering both organisational and rela-tional density (Baum and Oliver, 1996; Aldrichand Martinez, 2001), are two main criteria toconsider in future studies on the impact of clusterson entrepreneurship.19
6. Conclusions and directions for future 6. research
Does clustered entrepreneurial activity contributeto development more than non-clustered? Giventhe promises of entrepreneurship and clusters tofoster development as well as the lack of studieson the moderating effect of clusters on the rela-tionship between entrepreneurship and develop-ment, this paper aimed to set the basis to answerthe above question. The terms were defined asfollows: entrepreneurship is the creation of neworganisations; cluster is a geographically proxi-mate group of firms and associated institutions inrelated industries, linked by economic and socialinterdependences; and development is the expan-sion of capabilities rather than the increase inoutput. To identify potential causes of the moder-ating effect of clusters on the impact of entrepre-neurship on development, the paper focused onthree different impacts: entrepreneurship on devel-opment, clusters on development, and clusters onentrepreneurship. Thus, the paper reviewed theliterature related to these relationships, aroundwhich the following conclusions and suggestionsfor future research are grouped.
First, the literature review on the impact ofentrepreneurship on development reveals thatthere is a positive association, theoretically aswell as empirically, between entrepreneurship
and economic growth. Given the importance ofentrepreneurship in changing the economic andsocial structure of the economy, more research onthe impact of entrepreneurship on development –i.e. focus on capabilities rather than on output –is needed. This is a major challenge of futurestudies on societal level outcomes of entrepre-neurship.
Second, despite some preliminary positivefindings, several factors do not permit empiricalgeneralisations on the impact of clusters ondevelopment. The reviewed schools of thoughtrelated to clusters have contributed their ownvocabulary and set of assumptions, stressed dif-ferent cluster dimensions and components, andidentified different causal chains to associateclusters to development. Additionally, method-ological bottlenecks and data constraints makecase studies the most used research method, com-plicating the comparability and generalisability ofresults. Also, researchers have not been consistentin matching the label they attach to the phenom-enon under study and the techniques they use toidentify it. Finally, researchers have studied theeffects of clusters at different levels of analysis,adopted multiples measures of this concept, andchosen different performance criteria. Particularlyproblematic is the mixing of different unitsand levels of analysis, assuming that firm-leveloutcomes translate directly to regional andnational levels.
Separating the impact of clusters at differentlevels is a first step in reaching conclusions onthe relationship between clusters and development.At the firm level, although using different con-ceptual and operational definitions, empiricalresults show a positive effect of clusters on firmperformance and innovation. However, differentresults might be expected in future empiricalstudies that control by both the stage and strengthof the clusters under analysis. At the regionallevel, both endogenous development perspectiveand endogenous growth theory provide thebasis to explain the positive results of clusters onregional development. These results come fromstudies based mainly on the schools of thoughtthat stress territorial specificities and SMEscomposition of clusters as well as the schoolsof thought that focus on knowledge spillovers.
390 Hector O. Rocha
However, some negative impacts of clusters ondevelopment have been documented in four cases:regions with few clusters, clusters specialised inonly one industry, and clusters producing conges-tion effects and social divides within a region.Whether these negative effects are connatural toclusters or a matter of inadequate policy design isstill an issue under debate. A positive step to con-tribute to the debate is to agree on what kind ofcluster the discussion is about – see Figure 2. Forexample, the current interest in clusters makespolicymakers call cluster policy what in fact isa mere industrial policy based on sectors. Thesame mistake occurs when clusters are selectedaccording to what policymakers wish for theireconomies rather than to the capabilities –physical, human, and social – that are present inthe region. These cases are not cluster policies atall and it would be wrong to attribute their resultsto a cluster approach to regional development.Finally, at the national level, there is quantitativesupport for the hypothesis that the business envi-ronment created by clusters fosters national com-petitiveness. However, given that cluster initiativesimply a policy-led attempt to strengthen regionalconcentrations, the issue of regional disparities isnot taken into account. This suggests that regionaland national policies should be coordinated toavoid both regional disparities and destructivecompetition between regions.
Third, it is argued that clusters positively affectentrepreneurship given informational, relational,competitive, economic, cultural, and institutionaladvantages. However, when considering a moredynamic perspective the impact of clusters onentrepreneurship seems to be a function of thestage of the cluster. Yet, the empirical test of thishypothesis has been based on the organisationalecology perspective, which focuses on densitywithin a given industry rather than on clusters.Also, its choice of populations as unit of analysisand its focus on context does not consider the indi-
vidual characteristics of new organisations, a keyelement in studying the entrepreneurial process.
Fourth and finally, the second and third findingsregarding the impact of clusters on both develop-ment and entrepreneurship, respectively, implythat it is difficult to generalise on the moderatingeffect of clusters on the association between entre-preneurship and development. In effect, bothpositive results and caveats are found at differentlevels of analysis and at different stages of devel-opment of a cluster.
Defining clearly the phenomenon understudy, associating to it an appropriate operationaldefinition, linking context and process and,therefore, using quantitative and qualitativemethods at different levels of analysis controllingfor the stage and the strength of clusters arethe main criteria for future studies to considerto disentangle the impact of clusters on entre-preneurship, development, and the relationshipbetween entrepreneurship and development.
Acknowledgements
For their useful comments and suggestions duringthe presentation of the seminal ideas of this paperI thank the participants at the EntrepreneurshipSeminar at London Business School. I am espe-cially grateful to Professor Paul D. Reynolds forhis deep and insightful comments, suggestionsand, above all, example as researcher oriented toimprove the economic and social well being ofpeople through the study of enterpreneurship. Iam also grateful to editor David Audretsch andanonymous reviewers at the Small BusinessEconomic Journal whose generous critique greatlyimproved the paper. The research is funded witha grant from IAE – Business and ManagementSchool of Austral University (Argentina). Theresponsibility for everything said in this paper ismine alone.
Entrepreneurship and Development: The Role of Clusters 391
Notes
1 Although several researchers have carefully tried toestimate the economic impact of clusters, the lack of consensusaround how to define and measure them has prevented me tobegin with some striking figures to demonstrate the impor-tance of clusters. For example, it has been said that in theUnited States 380 clusters of firms employ 57% of the U.S.workforce, produce 61% of the country’s output and gener-ates 78% of the nation’s exports; or that local industrial dis-tricts account for some 30% of total employment and 43% oftotal exports in Italy (OECD, 1998, p. 93; see Observatory of
European SMEs (2002) for a review). Despite the effort inobtaining these figures, the reasons given above suggest tryingfirst to develop a framework to analyse the impact of clusters,setting the basis for both empirically based theories and policydesigns on the relationship among clusters, entrepreneurship,and development.2 For analyses on the impact of development on entrepre-neurship see Reynolds et al. (1994), Verheul et al. (2001), andSternberg (2001). In short, the basic argument is that growthimplies a demand effect, which in turn creates new opportu-nities for the creation of new firms. A more innovation-oriented argument is that customers place new demands on
392 Hector O. Rocha
Appendix ALiterature review – Sources of information
SourceTopic Entrepreneurship Development ClustersSource
Books See References
Journals and Frontiers of Economic Publications Entrepreneurship Development
Research QuarterlyEntrepreneurship American Theory and Practice Economic ReviewSmall Business Economic Economics Development Journal of Business ReviewVenturing
Regional StudiesUrban StudiesUrban and Regional Development Economic GeographyWorld Development
Entrepreneurship and Regional Development
Multilateral OECD OECD OECDOrganizations World Bank World Bank
UNPD
International Entrepreneurial The Global Competitiveness Report Research Research Consortium (World Economic Forum)Projects (Babson College) Decentralised Development (World Bank)
Global Entrepreneurship Monitor (Babson College – London Business School)
Local Economic and Employment Development (OECD)
Bibliographic Dow Jones InteractiveSearch Engines LBS Journal Finder
Business Source PremierProQuest DirectICE – Information Centre for Entrepreneurship BibEcWeb of Science
products and services creating opportunities for new techno-logical developments. This increasing demand for newproducts and services trigger the entrepreneurial process inorder to discover and exploit the new opportunities. Foranalyses on the impact of entrepreneurship on clusters, seeSengenberger and Pyke (1992), Rosenfeld (1997), and Porter(1990, 1998). The basic argument is that entrepreneurship isone of the driving forces of cluster development via spinoffs (Sengenberger and Pyke, 1992; Rosenfeld, 1997) andincreasing rivalry, one of the four components of Porter’s com-petitive diamond, due to the entry of new competitors (Porter,1990, 1998). 3 For a review of these theories see for example Cowen andShenton (1996) who distinguish between imminent develop-ment (a spontaneous and unconscious process of developmentfrom within) and intentional development (deliberate effortsto achieve higher levels in term of set objectives); Allen andThomas (2000: chapter 2), who develop a framework thatencompasses the different meanings and views of develop-ment; Todaro (2000, Chapter 3), who makes a comparativeanalysis of the theories of development since World War II;Coates (2000, Appendix), who explains the theories ofeconomic growth; and U.S. Department of Commerce (2000),which classifies the theories of economic developmentaccording to their basic categories, definition of development,essential dynamic, strengths and weaknesses, and applications.Although this paper does not cover their discussion, thesetheories are implicit in the following analysis of the meaningand measurement of development.4 Even the World Bank, which during the 1980s championedeconomic growth as the goal of development, has joined thenew current. In effect, the World Bank defines its mission as“a world free of poverty” (www.worldbank.org) and empha-sises that “development” implies quality of life and “attackingpoverty” (World Bank, 1991, 2000).5 Based on a positive correlation between income percapita and alternative development indicators such as literacylevel, some authors conclude that although taking a wider andmultidimensional view of development is conceptually correct,per capita GDP still works as a fairly good proxy for mostaspect of development (Ravallion, 1997; Ray, 1998). Despitethe sound of this argument, I prefer job creation rather thanincome per capita as a proxy for development. The former ismore related to capabilities than the latter. Besides, simplecorrelation analysis does not take into account how manypeople are excluded from the benefits of development and howlong they have to wait to receive these benefits. Finally,income per capita can be a misleading indicator for develop-ment, given that at least in developing countries the appro-priate sequence of investment is human development –economic growth, not vice-versa (Ranis et al., 2000).6 For a review of the evolution of the entrepreneurship fieldover time and its relation with other disciplines refer toLivesay (1982), who reviews historical definitions of entre-preneurship and the theories of entrepreneurship; Gartner(1989), who after a literature review and critic of the traitapproach (who the entrepreneur is) proposes that entrepre-neurship is the creation of new organisations (what the entre-preneur does); the two special issues of EntrepreneurshipTheory and Practice (1991, vol. 16 (2), and 1992, vol. 16 (3),
which focus on the entrepreneurship field from an interdisci-plinary perspective and the contribution of different disciplinesto the field, and vice-versa; Bechard (1997), who studies themost often quoted references in five academic journals, cate-gorises the contributions in three levels: praxeology, scientificdisciplines, and epistemology, and draws two paradigms: thatof the economy of entrepreneurs and that of the society ofentrepreneurs; Cooper, Hornaday, and Vesper (1997), whopresent an informal history of the field of entrepreneurship;Thornton (1999), who traces the evolution of entrepreneurshiptheory to Weber and contrast the supply side to the demandside approach to entrepreneurship, and proposes to integrateboth approaches using sociological frameworks; and Shaneand Venkataraman (2000), who draws upon previous researchto create a conceptual framework for the entrepreneurshipfield.7 This information is gathered by the EntrepreneurialResearch Consortium, which is a panel study of business start-ups in 10 countries (Reynolds, 2000). 8 The basis of this argument can be found in Schumpeter,who in Capitalism, Socialism and Democracy (1950) shifts thefocus of innovative activity from entrepreneurs to large firms.He describes a virtuous and cumulative process between R&Dand innovation in large firms, which increases the gap betweenthe innovative capability and outputs between large and smallfirms.9 For a review of different current definitions of clusters andrelated concepts see Bergman and Feser (1999), OECD (1999),and Martin and Sunley, 2002. Given the vagueness of thecluster concept, many authors have proposed typologies ofclusters outlining different criteria to classify different formsthat clusters may take. These typologies can be found inRoelandt and Hertog (1999), who use different levels ofanalysis; Gordon and McCann (2000), who use different the-oretical perspectives; Markusen (1996), who uses differentcluster’s configuration; Asheim (1997), who uses differentdegrees of innovative capabilities; Cullen (1998) who definesdifferent elements in organisational learning within SMEclusters; and Rosenfeld (1997), Porter (1998), and Enright(2001), who employ different stages of cluster development.Finally, although typologies can be seen as a form of theory(Doty and Glick, 1994), some of the previous types of clusteroverlap and are difficult to measure empirically. For thisreason, many authors argue that clusters should be charac-terised along relevant dimensions if they are to be distin-guished. This latter approach is found in Jacobs and de Man(1996); Maillat (1996); DTI (2001) and Enright (2001).10 I am grateful to a reviewer for the idea of summarisingthematic differences in the cluster literature in this way 11 Economists, geographers, and planners distinguish locali-sation economies – i.e. those that result from proximity amongfirms belonging to the same industry or close related indus-tries – from urbanisation economies – i.e. those that resultfrom general urban advantages (Hoover, 1937). Within theformer, there is a distinction between agglomeration orlocation theory and external economies perspective. Locationtheory calls the benefits of co-location agglomerationeconomies and argues that they are the result of either the sizeof the industry (Hoover, 1937; Isard, 1956) or the structureof the industry (Chinitz, 1961). External economies perspec-
Entrepreneurship and Development: The Role of Cluster 393
tive is rooted in Marshall’s work on industrial district (Feser,1998). While agglomeration economies are a kind of externaleconomies that emerge from large concentrations of economicactivity, external economies not necessary emerge fromagglomerations. Researchers who define clusters as concen-tration of firms within single or close related industries(Sorenson and Audia, 2000; Lomi, 2000) follow an agglom-eration approach. By contrast, researchers who define clustersemphasising their regional, social and inter-industrial dimen-sions (Camagni, 1991; Saxenian, 1994) use components ofboth the agglomeration and external economies approachesas well as sociological constructs such as embeddedness ofeconomic activity. For a review of the literature on differentexplanations of the clustering of economic activity seeHarrison (1992), Feser (1998), and Glasmeier (2000); for areview on the debate between specialisation vs. urbanisationeffects see Glaeser et al. (1992), Audretsch (1998), Glasmeier(2000), Feldman (2000), and Rodriguez Pose (2001). I haveavoided including these distinctions and debates as part ofthe evolution of the cluster concept given that, as noted above,different conceptualisations of clusters make it unreal toencapsulate them within a single perspective. Importantelements of clusters are not only spatial proximity but alsointer-organisational relations and the knowledge and socialbase underlying clusters dynamics. In this sense, every clusteris an agglomeration, but not every agglomeration – such ascities or a single concentration of firms – is a cluster (DTI,2001).12 These external economies are often referred to as the supplyside of the benefits of clustering (Baptista and Swann, 1998).However, Marshall also mentions some demand side benefitsof clustering as a function of the type of products. In effect,“shops which deal in expensive and choice objects tend to con-gregate together; and those which supply ordinary domesticneeds do not” (Marshall, 1966, p. 227).13 It is hard to differentiate these four schools of thought giventhat they share several assumptions regarding territorial speci-ficities and the role of socio-economic factors in the workingof clusters. For example both the innovative milieu and theNordic schools argue that innovation, which is key to fostercompetitiveness, is an interactive learning process in whichcooperation and mutual trust is enhanced by proximity. Thisinteraction between innovation and territorial proximity gen-erates learning regions where knowledge spillovers, the centralfocus of the geography of innovation approach, play an impor-tant role. The tacit nature of knowledge makes the social andcultural features of the local environment an important factorto explain innovation and entrepreneurial dynamics’ differ-entials across regions, which is explained by the cultural-insti-tutional approach to clusters. However, in an effort todifferentiate the different schools, it could be said that whilethe innovation approach to cluster analyses geographical prox-imity in terms of its impact on innovative activity, the cultural-institutional approach stresses the embeddedness of economicactivity in particular social and institutional settings to explainthe ability of firms to adapt to increasing globalisation andtechnological change. 14 See especially his methodology to define clusters in Porter,1990 Appendix A, where there is no reference to geograph-
ical boundaries. One reason is that with the exception of partof Chapter IV, his analysis is done at the country level. 15 Empirical studies are defined as those that include somekind of data or data analysis. These include both qualitativeand quantitative or statistical procedures. The former includesmethods such as case studies. The later includes any studyusing statistical techniques either in a descriptive or explana-tory way using empirical data (see Singleton and Strait, 1999;Chandler and Lyon, 2001).16 The building blocks of this recent theory are the works ofRomer (1986) and Lucas (1988). For a comprehensive treat-ment of this theory refer to Barro and Sala-i-Martin (1995).For a critical assessment see Fine (2000). The application ofnew growth theory concepts to development can be found inMorris (1998) and Todaro (2000). The application of newgrowth theory concepts to competitiveness can be found inPorter et al. (2000, p. 14).17 A detailed description of each methodology is beyond thescope of this paper. For a deeper understanding of their foun-dations and applications refer to Peneder (1995); OECD(1999); DeBresson and Hu (1999); Bergman and Feser (1999);Lichty and Knudsen (1999); Hill and Brennan (2000); Feserand Bergman, 2000; and Austrian, 2000.18 Porter recognises that intense competition within a clusterplus lower exit barriers promote not only births but alsodeaths. This process is argued to be positive for survivingfirms, which will be better positioned compared to rivals inother locations (Porter, 1998, p. 225; 2000, p. 25). This rea-soning does not explain why the net effect is positive. Mostimportantly, the net start-up rate is not the central part ofPorter’s explanation, which is focused on how clusterspromote new business formation. 19 Some studies have applied one or two of these criteria, butthe focus has been either on populations of firms belongingto the same industry or on metropolitan vs. rural areas ratherthan on clusters. For theoretical studies, see Aldrich(1999:Chapter 9), who links processes and context at differentlevels of analysis from an evolutionary perspective; andKleppler (1995) who takes a more technical approach and linkprocess and context based on the product life cycle. For empir-ical studies see Stearns et al. (1995) who propose a model toexamine the interaction effects between location, industry, andstrategy; and Baum and Oliver (1996), who consider bothorganisational and relational density at different levels ofanalysis.
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