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An evolutionary integrated view of regional systems of innovation. Concepts, measures and historical perspectives

Simona Iammarino*

ABSTRACT

The literature on geographical systems of innovation has traditionally shown a ‘national-bias’ that has strongly affected the identification of actors, relationships and attributes operating at the sub-national scale. Indeed, the historical evolution of the regional dimension has rarely been considered (implying that history really matters only at the national level). Modes of governance have also mostly been examined from a country perspective, which neglects the complexity, heterogeneity and path dependency of multi-level governance in current innovation systems. This paper reviews the main literature on the concept of Regional Systems of Innovation (RSI), adopting an integrated view that brings together both top-down and bottom-up characteristics and evolutionary mechanisms for the purpose of identifying RSIs. After discussing conceptual problems, and the relevance and applicability of an evolutionary integrated view of RSI, the case of Italy is employed to support the argument that the historical perspective on regional cultures is often unavoidable in order to assess future opportunities for regional development. JEL Classification: O3, R1, O1.

Keywords: Technological Change, Regional Systems of Innovation, Regional Development.

* SPRU, University of Sussex (UK), and University of Rome “La Sapienza” (Italy). Address for correspondence: SPRU, The Freeman Centre, University of Sussex, Brighton, BN1 9QE, United Kingdom. Tel: +44 (0)1273 877565. Fax: +44 (0)1273 685865. E-mail: s.iammarino@sussex.ac.uk. The author is grateful for helpful and valuable comments on earlier versions of this paper from Suma Athreye, Ron Boschma, Nick von Tunzelmann, and one anonymous referee. I would like also to thank the participants in the conference on “Regionalisation of Innovation Policy”, DIW, Berlin, 5-6 June 2004, for the lively and stimulating debate. The usual disclaimer applies.

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An evolutionary integrated view of regional systems of innovation. Concepts, measures and historical perspectives

Simona Iammarino

1. Introduction

Since the appearance of the first literature on National Systems of Innovation (NSIs) the idea of

applying tout court a similar conceptual perspective at a smaller geographical level – regional, or

even local – has been very tempting (Breschi, 1995; Cooke et al., 1997; Howells, 1999). However,

whilst a NSI cannot by any means be considered as being the simple sum of regional systems,

attempts to find some kind of standardisation of sub-national systems of innovation have so far

encountered two major problems.

The first one, theoretical in nature, arises from the fact that geographical innovation systems have

been conceptualised and applied by considering components, relationships and attributes, which

operate and are governed mainly at the national level. Indeed, the systems of innovation (SI)

approach assumed, at least in its original formulation, that key decision-making processes

regarding the aggregate of micro-founded innovation activities are taken at a macro (national)

level and, equally, that much micro-level activity is linked through a macro (national) web of

interconnections. Hence, in the SI literature the modes of governance – i.e. markets, corporate

hierarchies, political hierarchies and networks – have been regarded mostly from a country

perspective, which ignores the complexity, heterogeneity and path dependency of multi-level

governance in current SI settings (von Tunzelmann, 2004). This ‘national-bias’ has strongly

affected identification of the relevant actors, relationships and attributes operating at the meso

(sub-national) scale and very rarely has the regional dimension effectively been considered in

terms of its historical evolution, implying that history really matters only at the national level. This

bias has also contributed to creating some confusion in the policy debate, arguably leading to a

virtual distinction between ‘stylised’ and ‘actual’ Regional Systems of Innovations (RSIs) and to

an excessive criticism of the conceptual framework, thereby undermining the search for better

methods to test its validity.

The second and related problem is the well recognised and broad ‘regional measurement issue’.

The availability of data and indicators on the characteristics and behaviours not only of firms, but

also of the multitude of actors and sets of institutions whose interactions determine the innovative

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performance of the region, is much better at national than at sub-national level. This has further

constrained the possibility of exploring the nature and evolution of RSI based on statistically

robust evidence (see among others, Braczyk et al., 1998; Evangelista et al., 2002).

This paper takes the view that a top-down (macro-to-micro) conceptual perspective, i.e. the ‘shift’

of NSI features down to the regional scale, although it provides the necessary conditions to

distinguish RSIs is not by itself sufficient. It is necessary to integrate the top-down view with a

bottom-up (micro-to-meso) perspective – tackling also the internal dynamics of regionally

embedded social, economic and institutional structures, whose analysis in terms of historical

origins and evolution of regional cultures, may further help to refocus the nationally-biased

‘Listian’ view of innovation systems (Cooke, 2001; Dopfer et al., 2004). Moreover, whilst clear

thinking on RSIs is obviously a prerequisite for making sensible policy implications, adequate data

and methodologies are crucial to determine whether the conceptual approach is suited to

identifying structural regularities of regional systems and providing sound normative justifications

for public intervention.

The paper is organised in five sections. The next section reviews the main literature on the concept

of RSIs, adopting an integrated view that brings together top-down and bottom-up characteristics,

and evolutionary mechanisms for the purpose of identification of RSIs. Section 3 discusses the

relevance and applicability of the evolutionary integrated concept of RSIs by addressing such

questions as: to what extent is the system portrayed in such a framework applicable and replicable?

Do currently available data and indicators reproduce both structure and performance of RSIs?

Does the integration of concepts and measures actually provide a normative base for regional

policy? Section 4 attempts a first application of the evolutionary dynamic view of RSIs to the case

of Italy, which offers strong support for the argument that use of the historical perspective as a

filter is often unavoidable in assessing future opportunities for regional development. Section 5

presents some concluding remarks, and draws out the main implications and directions for future

research.

2. Top-down, bottom-up and integrated views of RSIs

The extensive literature on the advantages of geographical agglomeration with particular reference

to innovation and technology activities, has generally encompasses two distinct perspectives on

the relationship between innovation and space. The first approach, and antecedent to the second,

follows the Marshallian tradition in trying to identify these advantages and their implications for

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overall economic growth (see, for instance, Perroux, 1950; Pred, 1967, 1977; Malecki, 1977,

1983). The pioneering works and intuitions of Marshall suggested that the accumulation of skills

and know-how takes place within spatially bounded contexts, which create a kind of favourable

‘industrial atmosphere’ capable of enhancing economic growth and spurring the generation and

diffusion of new ideas. The empirical literature that flourished over the last decades confirmed that

spatially-specific factors strongly influence firms’ innovative performance and regional patterns of

technological specialisation. However, from such a perspective, the geographical dimension

represents a factor characterising economic development, in relation to which the localised

innovative potential is assumed to be an exogenously determined explanatory variable. The second

and more recent line of research has addressed the localised structural and institutional factors that

shape the innovation capacity of specific geographical contexts. This has given rise to

heterogeneous sub-national typologies, all pointing to a broadly defined model of spatial

organisation, that is the ‘innovative cluster’. Amongst the most popular units of analysis we find

milieux innovateurs (Aydalot, 1986), new industrial districts (Becattini, 1987), technological

districts (Markusen, 1985, 1996; Storper, 1992), learning regions (Asheim, 1995; Morgan, 1997)

and regional systems of innovation (Cooke, 1992; Cooke et al., 1997; Howells, 1996, 1999;

Braczyk et al., 1998).1

Focusing on this latter sub-national model, the importance of contextual elements and the presence

of systemic interactions in the process of generation and diffusion of innovation are recognised as

a key determinant of regional technological and economic performance. This is in line with the

prolific literature on NSI – introduced by evolutionary theorists in the late 1980s (Freeman, 1987;

Lundvall, 1992; Nelson, 1993; Nelson and Rosenberg, 1993; Edquist, 1997) – which argues that

the performance of national economies cannot be explained only in terms of the strategies and

performance of firms. There are other factors and actors that play vital roles in favouring the

generation and diffusion of knowledge, including: inter-organisation networks, financial and legal

institutions, technical agencies and research infrastructures, education and training systems,

governance structures, innovation policies, etc.

The notion of RSI has emerged as a territorially-focused perspective of analysis derived from the

broader concept of NSI: a RSI may thus be defined as ‘the localised network of actors and

institutions in the public and private sectors whose activities and interactions generate, import,

modify and diffuse new technologies within and outside the region’ (Howells, 1999; Evangelista

et al., 2002). Indeed, the highly uneven pattern and spread of innovation in space suggests that it 1 For the ‘fuzziness’ of the cluster concept itself, see Gordon and McCann (2000). For a critical review of territorial

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could be better depicted by assuming sub-national units of analysis, which can avoid the

distortions and the loss of information of hypothesising national systems as homogeneous entities

(Morgan, 2004). As Carlsson and Stankiewicz aptly remarked “high technological density and

diversity are properties of regions rather than countries” (Carlsson and Stankiewicz, 1991: 115).

It is therefore implicitly maintained that the elements characterising a national system can be

transposed to a smaller territorial scale to help define the RSI. Following Dopfer et al. (2004),

micro-structures refer to individual components (or actors, i.e. individuals, firms and other

organisations) that through their relationships (market and non-market) and their attributes

(competencies and functions) build up and maintain systems of rules (see also Carlsson, 2003).

Macro-structures, on the other hand, are comprised of the population of meso-systems and their

interdependence. The top-down (macro-to-micro) view of a RSI thus implies identification of the

following characteristics (Howells, 1999):

internal organisations of firms, the latter being the principal agents of innovation;

inter-organisation relationships and, more specifically, the type and intensity of interactions

between the business sector and the rest of the economic system;

role of public sector and innovation policy (assuming that, at the local level, formal

policies interact to a much greater extent with informal rules and conventions);

institutional framework (administrative, political, legal, fiscal, educational, etc.);

institutional set-up of the financial sector (i.e. whether based on a developed capital

market, on regulated and strictly controlled credit, or on relatively ‘free’ access to funds,

etc.);

industrial structure (i.e. average firm size, degree of competition and collaboration among

firms, sectoral specialisation models, demand patterns, etc.) and intensity and organisation

of R&D activities (both private and public);

spatial structure (e.g. relative geographical position, degree of urbanisation, extent of

regional network externalities) and intra-regional scale and scope of geographical

agglomerations (i.e. sub-regional clusters with specific advantages in local labour markets,

specialised suppliers and knowledge spillovers);

degree of openness, capacity to attract/absorb external resources, integration in global

innovation networks;

core/periphery hierarchical forces driven by the historical evolution of regional societies.

innovation models, see Moulaert and Sekia (2003).

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The above list is clearly not exhaustive and underlies a broad definition of territorial systems of

innovation, encompassing the specific institutions that deliberately promote innovation and

knowledge, as well as the wider socio-economic system in which political and cultural influences

and specific modes of governance determine innovation structures and performances (Lundvall,

1992; Cooke et al., 2000; Freeman, 2002). However, as discussed in section 3 below, the actual

identification of components, relationships and attributes, as well as that of the ‘purposive nature’

of geography, required to be a relevant context for a system of innovation, is more problematic in

regional systems than in other cases (Carlsson, 2003). Nonetheless, the top-down approach is

helpful for a comparative view of RSIs, both within and across national boundaries, in assessing

the nature of the innovation system in terms of space and networks of forces, which escape the

broad and generic national dimension insofar as they “can never be made precise by their outline

or by their container” (Perroux, 1950: 102).

Yet, within the systemic approach to the geographical dimension of innovation, the distinction

between aspects related to the agglomeration of innovative activity (i.e. Marshallian-type

agglomeration forces) and the contextual mechanisms for the generation and diffusion of

innovation (i.e. the impact of space on innovation through informal and formal social networks

Pred, 1967; Hagerstrand, 1967; Lundvall, 1988, 1996) has become increasingly blurred. The first

implication of the merging of these two aspects is that the top-down perspective may account for

the necessary but not the sufficient conditions needed to identify RSIs. Thus, integration of the

top-down view with a bottom-up (micro-to-meso) perspective, which also takes account of the

internal and dynamic regularities of territorially embedded socio-economic structures, has gained

momentum (Asheim, 1995; Asheim and Gertler, 2003; Dopfer et. al., 2004).

As highlighted by the evolutionary theories of technological change, the dynamism of an economic

system, which necessarily builds on access to and efficient use of knowledge, rests upon three

main functional dimensions:

1. absorption of new knowledge, technology and innovation for adaptation to local needs;

2. diffusion of innovations throughout all the constituent parts of the regional social fabric to

strengthen the existing knowledge base;

3. generation of new knowledge, technology and innovation.

All three functional dimensions involve micro and macro changes for adapting and maintaining the

system of rules for many distinct environments, so contributing to processes at the meso level,

which in turn account for local and regional variety (Dopfer et al., 2004). The dynamism of a

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regional system may therefore be sustained through several channels related to these different

dimensions and based on collective learning processes through which knowledge and technology

are used, diffused and created. Such learning processes are central to growth and competitiveness,

and have convincingly provided a strong argument in favour of meso perspectives of analysis

(Cooke and Memedovic, 2003; Morgan, 2004). Learning dynamics and the employment of

informal channels for the exchange of tacit and ‘sticky’ codified knowledge are in fact embedded

in distinct environments of interactions among various actors and organisations, the sharing of

common attitudes, habits and conventions, and institutional settings facilitating idiosyncratic (and

often context-specific) types of learning (see among others, Hägerstrand, 1967; Lundvall, 1988,

1992; Audretsch and Feldman, 1996; Cooke, 2001; Asheim and Isaksen, 1997, 2002).

According to technological gap theories (Abramovitz, 1986; Fagerberg, 1987, 1994; Fagerberg et

al., 1994), ‘social capability’ and ‘technological congruence’ concepts are particularly relevant

when considering the meso level insofar as both appear to be highly variable across space, even

within the same national economy. The first concept refers to the overall ability of the region to

engage in innovative and organisational processes and to make institutional change; the second

relates to the distance of the region from the technological frontier, or, in other words, its capacity

to implement the technical properties embedded in new technologies (Fagerberg et al., 1994).

Following this approach, therefore, regional systems with stronger social capabilities and a

stronger knowledge base will tend to also be better equipped to exploit new technological

opportunities, to adapt existing activities to emerging business environments, and to learn faster

about how to build new regional advantages. Even the most specialised forms of knowledge are

nowadays becoming a perishable resource due to the accelerating pace of technological change;

competencies and capabilities have to be accumulated quickly; continuous learning and adaptation

determine the innovative performance of individuals, firms and systems; economic growth

involves complex and simultaneous change in both micro and macro structures, whose relations

become apparent particularly when the meso domain is taken into account (Steinmueller, 2000;

van der Meer et al., 2003; Dopfer et al., 2004).2

Yet, on the one hand, being able to build new competencies quickly involves the ability to

establish links at all levels, from the ‘global’ to the ‘local’: the extent to which a region attracts

innovative resources from outside – i.e. spurring its external integration – depends first and

2 The remarkable regional divergence in growth rates observed within Europe in the last decades is largely attributable to the presence or absence of social capabilities for institutional change, especially those that stimulate high rates of technological change (see among others, Rodriguez-Pose, 1994, 1998; Fagerberg and Verspagen, 1996; Freeman, 2002).

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foremost upon its extant absorptive capacity (Asheim and Isaksen, 2002; Cantwell and Iammarino,

2003; Simmie, 2003). As learning curve advantages are mainly people- and institution-embodied,

human capital and skill upgrading raises the ability of a region to absorb, diffuse and generate new

knowledge. On the other hand, technology diffusion is proportional to the ability of a region to

absorb innovation: as large differences in terms of absorptive capacity spur geographical

agglomeration, knowledge will flow more easily and socio-economic development in general will

be more evenly spread if high absorptive capacity is even across space. Absorptive capacity

depends significantly on diversity: innovation occurs where there is a diverse (technological,

social, economic) culture, and the most dynamic capabilities lie in the combination of both

exploration and exploitation of new and existing assets (Rantisi, 2002).

Regional systemic advantages (or disadvantages) are therefore also supposed to depend on

attributes such as ‘untraded interdependencies’, informal flows of knowledge, interactive learning,

degree of embeddedness,3 which generate the bulk of territorial externalities at the meso level

(Saxenian, 1994; Storper, 1998). Moreover, dense social networks may prove to be critical

channels for knowledge diffusion and learning, recombining old and new pieces of knowledge. It

has been recently argued that geographical proximity would not produce knowledge spillovers per

se, but social proximity in the region would (Breschi and Lissoni, 2001; Faggian and McCann,

2004).

The above discussion may help to clarify what is meant by top-down versus bottom-up views. The

sum of micro units does not give a macro aggregate, in the same way that the decomposition of the

macro does not translate into many micro, due to the presence of meso units. As already argued,

socio-economic growth implies complex change in both micro and macro structures: however,

evolutionary mechanisms and structural regularities are mainly captured in terms of the

relationship between such structures, that is at the meso level (Dopfer et al., 2004). This is not to

deny the importance attributed by evolutionary economic thinking of the meso domain, but rather

to contribute an improved analytical framework – that of geographical systems of innovation –

which seems to be lagging with respect to the strong theoretical foundations and empirical testing

of other meso perspectives (such as those, for example, of sectoral and technological SIs). As

pointed out by Dopfer et al. (2004: 6): “The essential point to grasp here is that macro is not a

behavioural aggregation of micro, but, rather, it offers a systems perspective on meso viewed as a

3 Following Cooke (2001), embeddedness refers to a set of characteristics appropriate for systemic innovation and reflecting the extent to which a social community operates in terms of shared norms of cooperation, trustful interaction and untraded interdependencies (see also Dosi, 1988; Fritsch, 2001).

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whole. Similarly, micro is not the reduced essence of an economic system; it is a ‘bottom up’

systems perspective on meso when viewed in terms of its component parts.”

Adapting from Howells (1996, 1999), among the idiosyncratic characteristics of RSIs which

emerge from a bottom-up view are:

• localised communication patterns relating to innovation processes, both at individual

and corporate levels;

• localised invention and learning patterns (individual, organisational, institutional and

societal);

• localised knowledge sharing (inter-individual, intra- and inter-organisation);

• localised search and scanning procedures relating to innovation and technology;

• localised network integration (within and between networks, intra- and extra-region)

and consequent degree of alignment of governance modes;

• historical path dependency of localised innovation processes.

Again, this list may not be complete, but it helps to illustrate the degree of regional embeddedness

of the knowledge and technology generation and diffusion processes, as well as the type of

learning attitudes and the potential for capability building of a particular RSI. These are crucial

elements insofar as regions with similar responses to the top-down criteria may show different

abilities to accommodate innovation and adjust in ways that enhance innovative capabilities. Also,

if the top-down view has the merit that it emphasises the role of networks as forms of governance

between (and beyond) the market and the hierarchy (corporate or political), the bottom-up

approach brings to the fore the complexity and idiosyncratic nature of linking the two within and

between localised networks developed for different purposes, thereby determining the overall

direction of regional governance (von Tunzelmann, 2004).4

An integrated micro–meso–macro view can be a better lens for identifying differences among

RSIs. The more science-based, codified and hierarchical the mechanisms by which interdependent

pieces of knowledge are integrated and recombined, the better the overall process governing

technological change and the character of the regional knowledge base can be grasped by a top-

down view. Conversely, the more important the idiosyncratic knowledge and/or the more informal

the mechanisms for knowledge absorption, integration and diffusion, the more suitable is a

bottom-up outlook for picking up the process managing technological change and the nature of the

regional knowledge base (Patrucco, 2004).

4 Following von Tunzelmann (2003), we adopt here the broad definition of governance as ‘organising collective action’, first coined by Prakash and Hart (1999).

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The RSI integrated approach emphasises evolutionary mechanisms such as routines, technological

trajectories, selection environments, heterogeneity and path dependency. As argued by Boschma

and Lambooy (1999) and Lambooy and Boschma (2001), variety in characteristics, behaviours and

performance is a key assumption of evolutionary perspectives, formed by its surrounding selection

environment, which includes both market and non-market factors. Regional environments are

heterogeneous (due to chance) and path dependent (as a result of historical contingency):5 they act

as selection mechanisms that may, or may not, provide conditions favourable to meeting the new

requirements of technical change (i.e. social capabilities for institutional change). Hence, new

growth opportunities are shaped and constrained by meso path dependency, or, in other words, by

the inheritance of local structural regularities from past knowledge accumulation and learning.6 In

such a dynamic perspective, the interdependence between structures and actors should be regarded

as a ‘feedback’ mechanism: not only do the characteristics of the selection environment and their

changes influence the actors, but the latter also change the environment (Lambooy and Boschma,

2001).

When integrating and applying the two lists of identifying features of RSIs, however, related

questions emerge such as: to what extent is the RSI portrayed by the evolutionary integrated view

applicable? Are its actual manifestations replicable? Do available data and indicators reproduce

both RSI structure and performance? Does the combination of concepts and measures provide a

sound normative base for regional policy? The next section discusses the relevance and

applicability of the evolutionary integrated view of RSI by addressing some of these issues.

3. Applicability and replicability of the evolutionary integrated view of RSIs

3.1 ‘Stylised’ and ‘actual’ RSIs

The framework derived from the integration of top-down and bottom-up approaches undoubtedly

helps in the definition of a ‘stylised’ RSI. The identifying features, in practice, combine in

different ways, have different intensity, quantity and quality and depend on the historical paths of

regional cultures made up by socially accepted values, norms, routines and general customary

practices. It is intuitive that the more diverse is the cultural base of a national system, the more

5 Historical contingency refers to the actual existence of selection mechanisms in socio-economic processes: change is neither solely based on structural elements subject to general rules, nor purely driven by random effects. At each point in time in a system’s evolution, a number of paths is theoretically possible, but only a few will be chosen by the actors because each path must conform to the logic of socio-economic dynamics (Schwerin and Werker, 2003). 6 Indeed, as emphasised by Dopfer et al. (2004), path dependency is mainly a micro-meso concept.

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meaningful the meso analysis appears to be and the less the assumptions of a Listian view of

innovation systems hold.

Indeed, the presence of strong geographical asymmetries in socio-economic and innovative

variables raises the question of to what extent the top-down and bottom-up criteria can be applied

(even allowing for strong and weak RSIs) and the extent to which actual (and in particular

successful) RSIs are replicable. The answer points to some limitations: first of all because any

‘system’ to be distinguished as such, is required to have internal coherence, a collective identity

and conform to the ‘rules of the game’ which result in a whole or a model, trickier to grasp at the

meso level of observation (Cooke et al., 1997). However, paraphrasising Cooke (2001), the

hypothetical world is only a special case. The integration of the two views7 is arguably sufficiently

flexible and consistent to address specific and diverse empirical cases. Its somewhat restricted

applicability and replicability do not seem to challenge its positive scope, that is, to provide

guidelines for empirical analysis, which in principle are oriented to testing the goodness of the

theory in describing the world as it is, and not as it might hypothetically be. The frequent ‘non-

existence’ of RSIs in the real world is not grounds for rejecting the concept as such (this being a

misconception of the null hypothesis); it rather gives an indication of the fact that not all regions

work as (innovation) systems and that replicating RSIs is a difficult and hazardous venture.

Although in principle this could also apply to NSIs, it is apparent that the regional scale poses

additional difficulties insofar as: “there are a few fully functioning RSIs and even fewer where the

economic performance of such regions is outstanding, at least in Europe” (Cooke, 2001: 958).

On the other hand, if a normative interpretation is to be given to the integrated evolutionary

framework, then the question becomes one of what an RSI ought to be, and drawing implications

(particularly those for public policy) from stylised constructs should be done with caution because

the risk of providing inadequate answers to complex and diverse problems is considerable. Again

following Howells (1999), the lack of agglomeration and localisation advantages for innovative

activities in some regions may be attributable to a combination of top-down and bottom-up

failures, such as for example:

o low density of interactions, both at individual and organisation level;

o peripherality with respect to political and economic centres of power and decision sittings;

o presence of social and cultural marginalisation;

o weak social networks and capital;

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o lack of dynamic innovative firms and institutions;

o weak access to and weak attraction for external knowledge and information flows;

o inflexibility of organisational and institutional structures, which hampers the capacity to

adequately monitor, evaluate, absorb and diffuse innovation produced elsewhere.

The above circumstances are indicators of either weakness or lack of social capabilities, that is, as

previously mentioned, the capacity of the region as a ‘social system’ to make institutional change

for growth. The integration of top-down and bottom-up characteristics defining RSIs renders the

inclusion of weaker regions in a systemic vision highly questionable (for a good example see

Vilanova and Leydesdorff, 2001). It is not only the lack of a critical mass to generate, import,

modify and diffuse new technologies, but also the dearth of internal coherence within the societal

structure that makes up the regional governance that prevent the attribution of the distinctive status

of (innovation) ‘system’ to the region.

Following von Tunzelmann (2003, 2004), each of the conventional modes of governance may be

associated with a certain type of failure. Along with the typical institutional (market, corporate,

state) failures, pervasive network (social) failures may emerge when integration – both within and

between localised networks – falls short. Hence, whilst network governance ‘alignment’ arises

when top-down and bottom-up elements are pulling in similar directions even when their aims are

different, network governance ‘misalignment’ occurs when these same elements are pulling in

contrasting (and sometimes contradictory) directions, threatening the unity and coherence of the

regional whole. As a consequence, regional modes of governance, that is the combined and

interrelated roles of markets and hierarchies as intercoupled through various levels of networking,

account largely for systemic success and growth. The ‘purposive nature’ of the geographical

context that is required for a regional system of innovation to develop does not necessarily have to

rely on a complete and adamantine mix of characters (such as those in the top-down and bottom-up

lists above). It does however have implications for the functional dimensions based on collective

learning, that is absorption, diffusion and creation of new knowledge, technology and innovation.

The null hypothesis here is the Marshallian cluster as arising out of ‘something in the air’: for an

RSI, the air has to be blown about by some deliberate and integrated decision-making process.

Weak or vulnerable regions often show innovation barriers such as organisational ‘thinness’,

institutional lock-in, social fragmentation and overall anti-developmental network governance

(von Tunzelmann, 2004; Tödtling and Trippl, 2004). Moreover, they are bound to be highly 7 A different and complementary perspective is to integrate the ‘regionalisation approach’ with the ‘regionalism approach’, the first related to the region’s competence capacity, the second connected to the region’s cultural base

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dependent on actual innovation systems, particularly those belonging to the same NSI; their need

for new technologies is satisfied mainly by mere adaptation of imported innovation, and they have

limited or null capacity to recombine and integrate old and new pieces of knowledge; their high

dependency on external providers is usually coupled with a low degree of openness and

attractiveness towards external resources; etc.. Even intermediate regional cases are often not

eligible for full RSI status: this applies, for instance, to those regions whose industrial base is open

to innovation, but which have only a weak scientific system; or regions where, in spite of a

relatively strong local scientific and research infrastructure, this is not sufficient to ensure spillover

effects (Vilanova and Leydesdorff, 2001; Evangelista et al., 2002).

Furthermore, it is essential to underline the shifts towards new techno-economic paradigms, as

they are likely to have a significant impact upon the evolutionary concept of a RSI, as well as on

actual regions. The spread of Information and Communication Technology (ICT) is far from being

uniform across space; also it is affecting the boundaries between codified and tacit knowledge,

with corresponding implications for the rationale of geographical agglomeration (see among

others, Mansell and Steinmueller, 2000; Steinmueller, 2001; Ernst et al., 2002); it is likely to

change the nature and the relative balance of the top-down and bottom-up features of RSIs; it

might broaden (or help reduce?) the gap between full RSIs and regional peripheries. To give an

example of these on-going transformations, the rising techno-economic paradigm is having major

consequences for local labour markets, leading to more unstable environments, transformation of

work regulations, individualisation of labour processes, changing institutional protection and

unemployment structure. These changes, which will alter the links between individuals and

societies, not only may create opportunities for rising RSIs, but also increase the risk of lock-in for

‘low road’ regions (Iammarino et al., 2004).

The advent of new paradigms also casts doubts on the stability of RSIs, once identified as such. In

an evolutionary perspective, the capacity of regions to withstand the (macro) processes of

technological change and globalisation is determined by the comparative advantage upon which

they can rely; and, as seen above, the features of the region’s internal organisation and structure

are subject to (micro-meso) change over time (as learning processes are, by definition, not stable).

This may lead to the strengthening or the breaking down of systemic coherence, even where a

system has been identified as existing. In other words, network governance alignment does not

only imply alignment of objectives (market/non-market, private/public) and levels of decision

from which the degree of systemic potential stems (Cooke et al., 1997).

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making (micro, meso, macro); system integration increasingly also requires the alignment of old

and new technologies (von Tunzelmann, 2003).

To sum up, the conceptual framework as depicted here – that is the evolutionary integrated view of

RSIs – is believed to provide a suitable, though perhaps not perfect, interpretation tool for

analysing diverse regional configurations despite the fact that the replicability of RSIs on the basis

of stylised constructs has proved to be rather risky, because the idiosyncratic nature of

evolutionary mechanisms operating at the meso level rarely allows for problem-solving using

standardised procedures. As is argued below, to successfully accomplish the task of improving the

interpretative and normative framework to respond to the growing complexity and variety of the

actual world, the scope for testing its validity further should be considerably enhanced.

3.2 ‘Structures’ and ‘performance’ of RSIs

Measuring RSIs is one of the most discussed issues in this field of research. The conceptualisation

of geographical innovation systems has never overcome the serious drawbacks to data collection

and measurement with regard to smaller (than the national level) territorial units. It may be argued

that, somewhat surprisingly, in spite of the longstanding acknowledgement of the interactive

nature of innovative processes – which led to the substitution of the linear model by the chain-

linked model based on feedbacks, interactions and networks (Kline and Rosenberg, 1986) – the

empirical analysis of RSIs is still stuck in the consequential and oversimplified logic of the old

theoretical models of technology-push and demand-pull (Evangelista et al., 2002).

Two issues are worth highlighting here. The first is that generally available data and indicators are

appropriate for measuring RSI ‘performance’ – usually expressed in terms of economic

accomplishments, but they are of little help for examining regional ‘structures’, which reduces the

scope for testing the conceptual framework sketched above. Firm performance – as measured by

value added, exports, employment, innovative output, etc. –does not say much about the system, if

regional structural regularities have not been clearly identified: for instance, whose performance

are we measuring? The scant possibility to appraise, for example, interdependence at the inter-

regional level is due not only to the lack of sub-national data, but also to the shortage of indicators

appropriate to give account of the degree of attractiveness, dependence and openness of a region

or, in other words, of interregional innovation flows and networks. Turning to bottom-up

characteristics, the drawbacks become even more serious: the current state of the art on measures

and estimations of intra-regional innovative features and flows is, to say the least, not very

15

advanced in spite of the emphasis that regional evolutionary scholars have put on systemic

innovation, interactive learning processes and network modes of governance.

The second and related issue is that there are, in any case, inherent difficulties in drawing

inferences about performance from the data used in regional analysis. The concept of regional

competition is indeed well established as far as some aspects are concerned – i.e. conventional

economic strengths and weaknesses, but much less so in relation to others, such as innovation

processes, which are usually detected at the national level (and, at best, simply ‘regionalised’).

Moreover, in most of the existing empirical analyses the innovativeness of a specific region

corresponds simply to the sum of selected micro behaviours – e.g. the innovative activities of

resident firms – and not to that of the meso system as a whole. Hence, as argued by Dopfer et al.

(2004), we may well have good micro-macro arithmetic, but still we cannot capture the behaviour

of our unit of observation. What is depicted mainly is not the overall innovative performance of

the region, but rather the local reaction to often exogenous decisions and strategies. Therefore,

what performance are we measuring?

Data on performance in general do not reveal whether regional behaviour actually affects firm

behaviour, or whether it is simply an aggregation of it. There is evidence of regions identified as

RSIs displaying poor values in relation to traditional economic indicators, as well as of relatively

weak or even non-systems scoring rather well using the same standard measures (Cooke et al.,

1997; Evangelista et al., 2002).8 Yet, as stressed by Feldman and Martin (2004), firms’ success

and regional economic growth are mutually dependent and their interdependence may set up

virtuous (or vicious) historical cycles. Firms’ success depends on their external environment: there

is no unilateral causality nexus but, rather, a process of coevolution.

These shortcomings have so far prevented a complete integration between concepts and measures

of RSIs, thereby hampering the possibility of, on the one hand, enhancing the theoretical model

and, on the other, attaining a better normative justification for public policy. We very much agree

with Schwerin and Werker (2003) in that policy could learn from historical experience how to

identify localised patterns of socio-economic change. Although regional heterogeneity and the role

of chance will always provide margins for failure, a deeper understanding of regional path

dependency and historical contingency could strengthen the knowledge base upon which policy is

8 A further element that should not be disregarded given its increasing importance in innovative processes and technological change, particularly in the ICT paradigm, is the role of the demand for innovation. This implies that regions might be ranked according to their capacity to generate demand (and thus to create the conditions to provide a supply to meet this demand). The demand for innovation comes from a variety of actors (public sector, firms, social organisations) and their interactions, and it is usually difficult to detect using available information (Evangelista et al., 2002).

16

built and policy learning occurs. A prerequisite for any sound normative base is to dig up far more

data and devise more adequate indicators than those currently available. As Schwerin and Werker

maintain “policy is intelligent in the sense that learning from past experience takes place”

(Schwerin and Werker, 2003: 402). In this light, the effectiveness of policy will be constrained as

long as available methods and measures do not allow testing of the concepts, or the hypotheses, to

correctly identify regional structural regularities and coevolution processes.9

4. Historical perspectives of RSIs: the Italian example

According to the evolutionary perspective, economic growth and change are the outcomes of

cumulative processes that operate through non-linear and self-reinforcing feedbacks between

technological and structural change. An array of constraints and conditions that are basically

idiosyncratic to structures and the historical paths of regions affect learning and knowledge

accumulation (Patrucco, 2004). Such conditions and constraints cannot be overlooked when

considering how knowledge and technology are absorbed, diffused and generated across space.

Nonetheless, as argued above, little progress has been made in testing the definition of RSI on the

basis of the importance attributed to localised knowledge generation and diffusion and

idiosyncratic learning capabilities. Even less work has been devoted to considering this definition

from the historical perspective likely to be enlightening about critical questions such as: which

regions are RSIs? Why? What does this status imply? Where do the modes of regional governance

come from in such systems and how do they evolve?

As was emphasised by Freeman (1995, 2002), a long-term historical approach is essential in order

to establish a true link between socio-economic growth, and innovation systems. The very nature

of technical and institutional change implies that it could take centuries for gaps between different

countries, or regions within a country, to show up, and a similarly long time may be required to

close them. As Carlsson pointed out, only a small percentage of innovation systems studies “can

be considered ‘dynamic’ in the sense that they focus on a historical process or development over

time rather than on a snapshot of a system in a particular time period” (Carlsson, 2003: 11).10 In

9 It is worth highlighting the meagreness of socio-economic analyses at the regional level in a dynamic perspective, which should in principle be the basis of any effective policy. We are well aware of the huge costs associated with data collection. However, in spite of the magnificent efforts of the EU National Institutes of Statistics and Eurostat to provide territorial variables in many domains and, more rarely, to reconstruct historical time series of, at least, regional GDP, the fundamental problem of sub-national data on innovation in the Union seems, rather surprisingly, to be rather underestimated. For instance, it is still not clear whether and to what extent the regional dimension was one of the sampling requirements in the most recent version of the Community Innovation Survey (CIS3). 10 There have recently been a few interesting attempts to investigate regional and local systems of innovation from a historical perspective: see Rantisi (2002), Schwerin (2004), Boschma and Wenting (2005).

17

the main, the prevailing approach still adheres to a static view of the world (von Tunzelmann,

2003). The persistence of the empirical and historical ‘national bias’ is all the more serious in

countries characterised by a high degree of spatial imbalances, such as Italy, in which, therefore,

the scope for either the existence or the non-existence of RSIs is rather large.

Italy is a frequently cited example of a non-homogeneous NSI.11 It illustrates some crucial points

raised in the previous conceptual discussion, such as: sharp territorial differences in terms of both

heterogeneity and historical contingency; coexistence of actual RSIs and lack of a system among

its 20 administrative regions; highly problematic measurement issues (i.e. inadequacy of GDP-

based indicators to proxy socio-economic development due to persistent and significant inter- and

intra-regional differentiation; relatively low technological-intensity of specialisation patterns and

prevailing processes of ‘innovation without R&D’, which call into question the effectiveness of

standard indicators to grasp innovative phenomena); copious evidence of policy failure,

particularly with respect to innovation and technology policies.

The Italian ‘regional problem’ is arguably at least a thousand years old. It is interesting to contrast

some historical synopses with short descriptions of innovation features in the current Italian

regional panorama, outlining the long-term diversity of Italian regional trajectories in order to

support the case of history as the filter necessary to assess the existence of RSIs and, more

generally, opportunities for regional development. For the sake of brevity, a short overview by

current macro-regions (i.e. by aggregating the NUTS2 20 administrative regions in conventional

larger territorial units) is provided below.12

North-west:13 the Italian RSIs

Economic wealth and industrial orientation in the Italian north-west were historically rooted in the

substantial stability (since the 11th century), both political and administrative, of the Savoia family

in Piemonte, and the power of the Visconti and Sforza families (1300-1500) and the Asburgo

regime (up to Italian unification in 1860) in Lombardia. Agricultural development, incorporating

sophisticated irrigation techniques, was already advanced at the end of the Middle Ages; this

allowed intensive cropping, plant breeding and production of new cultivars. The abundance of

water in the area favoured the diffusion of water mills and looms, which played a crucial role in

the establishment of the first industrial settings for the production of wool, cotton, linen, silk and

manufactured textiles. Indeed, such activities stimulated diverse and related manufacturing

11 A number of studies have addressed the structure and performance of the Italian NSI as a whole. See, for example, Antonelli, 1988; Malerba, 1993; Archibugi et al., 1991; Evangelista et al., 1997. 12 Based mainly on Cipolla (1974) and Zamagni (1990). For a more detailed discussion see Iammarino (2005). 13 Piemonte, Valle d’Aosta, Liguria and Lombardia.

18

productions, and were the basis for what was to become a machinery and equipment industry (still

a distinctive strength in the national specialisation model). This industrial development

overlapped, particularly in Lombardia, with the merchanting activity that had flourished since the

11th century in autonomous and prosperous communes (e.g. Milan, Como, Lodi, Cremona, Pavia),

where social institutions and networks in the modern sense had been in operation since their

establishment. Moreover, since the 16th century the North-west underwent wide institutional

reform and restructuring processes aimed at reducing the property and power of the church and the

nobility and making education accessible across social classes, thus providing significant dynamic

and innovative impulse to the knowledge base of the area. Following its accession to the Austro-

Hungarian empire (1714), Lombardia benefited from the political and custom unification of

Asburgo leadership, becoming integrated in economic networks at the continental level. At the

same time, the institutional reorganisation carried out in 1720-30 by Vittorio Amedeo II had

transformed Piemonte into the most organised and efficient bureaucratical state in the peninsula; in

the 1850s, under the political leadership of Cavour, a British-type economic policy spurred

commercial liberalisation, promotion of education and the expansion of transport networks. The

features peculiar to the Liguria region were connected to the history of Genoa – a crucial naval,

merchant and financial centre at the European level – and to its strong economic integration with

Piemonte, which were the basis for its regional development up to the present time.

Today Lombardia and Piemonte (and to a lesser extent Liguria) represent the technological heart

of Italian industry. The full range of links and interactions that form the skeleton of an innovation

system is very apparent in these two regions in the form, for example of: structured relationships

among firms and between firms and other organisations (universities, research institutes, industry

associations, etc.); a good scientific and technological infrastructure; strong R&D intensity;

diffused networks of technological services; attractiveness for external sources of technology;

effective regional innovation policies; relatively high institutional flexibility and adaptability to

change; and structured social ties and networks.

North-east and Centre-north:14 the Learning Regions

The commercial, industrial and economic ‘core’ of the North-east was certainly Venice, which, in

terms of political influence and economic power, was among the top European cities through the

period 1200-1700. Under the Venetian influence, clusters of manufacturing activities were

established in the area, some mainly specialised in traditional skill-intensive sectors (Murano’s

glasses, Vicenza’s jewels, Bassano’s ceramic tiles); some oriented towards ‘human resources

14 Veneto, Friuli Venezia Giulia and Trentino Alto Adige; Emilia Romagna and Toscana.

19

building’ (the prestigious university of Padua (1222) and the art centre of Verona); and others

geared toward trade, with docks and construction activities (Trieste). It might be argued that the

pattern of development in the Italian North-east followed a kind of ‘urban driven’ path, although

apart from the few urban agglomerations, most of the area suffered from being a ‘boundary region’

ruled by Venetian, and later on, Austrian political powers, with a strong inclination towards

primary activities and with a rather narrow diversification of manufacturing production. However,

in the late 20th century the North-east went through one of the most impressive processes of socio-

economic change to occur in Italy – the so-called ‘miracle of the Third Italy’ – with the rapid

transformation of its areas into fast-growing industrial and entrepreneurial regions (from

essentially agricultural-based production systems and societies).

The two regions of the Centre-north show some similarities in the historical character of their local

knowledge bases. Local governance, entrepreneurship and indigenous culture were firmly rooted

in Emilia and Toscana, comparable to Lombardia’s communes. In Emilia, various types of

sophisticated activity flourished under the progressive traditions of the dukedoms of Parma and

Piacenza (Visconti-Sforza in 1300-1500; Farnese family until 1730), Modena and Reggio (Estensi,

1300-1800) and Ferrara; the autonomous commune of Bologna, with its university established in

1088, exerted a powerful influence on the historical evolution of regional knowledge linkages.15

The industrial districts of Carpi, Sassuolo and Faenza are among the oldest in Italy. The

agricultural production systems, favoured by the enlightened political leaders of the big families,

the rather anti-clerical, innovative and open local culture, and the natural fertility of the soil, led to

rapid industrialisation in the form of a food production.16 The economic prosperity of Toscana

started in the 11th century based on the unbeatable cultural traditions of its communes (Lucca, Pisa,

Pistoia, Siena, Arezzo), all fierce competitors for market share, product variety, trade intensity,

cultural creativity and social dynamism, until finally Florence prevailed and absorbed the most

positive elements of Toscana’s typical pattern of development. Resources such as iron from Elba

and marble from Carrara were the basis for related manufacturing activities; and the strength of the

local communities and social ties, together with deliberate actions from local authorities, gave rise

to specialised dynamic clusters, which became the archetype for the ‘industrial district’ (e.g. the

widely-studied case of Prato).

15 Actually, between 1513 and 1859 Bologna was still formally part of the Catholic Church State, but was ruled by an independent local authority. 16 It is interesting to note that the provinces of Emilia-Romagna that benefited least from the general wealth and openness of the region were those in the east that had been subject to the Catholic Church State since 1500.

20

Nowadays, in all these regions knowledge flows and systemic interactions in innovation take the

form mainly of inter-firm user-producer interactions, which are particularly dense in those

industrial areas organised as districts. Such technological links, largely informal in nature and

loosely structured, are enhanced by spatial proximity and by an economic and cultural

homogeneity based on localised competencies. The innovation process is accomplished by

relatively modest research and development (R&D) activities: the public R&D system is also

rather weak. However, the innovation activities of firms rely upon a mix of codified knowledge

(engineering skills) and locally embedded competencies based on long established cumulative

learning processes. Linkages are facilitated by the high product specialisation of firms, and by

tightly integrated organisational models of production and favourable context-specific conditions

represented by the plurality of active institutional actors (e.g. specialised business services,

technology-transfer agencies, private business associations, chambers of commerce, and training

agencies).17

Centre:18 the Conservative Regions

This whole area was under the conservative political influence of the Roman Catholic Church for

varying lengths of time: Lazio since its foundation, Umbria and Marche since 1500. Whilst in the

latter two, rather small regions agriculture and ceramics industries were the major points of

strength, economic and social activities in Lazio were basically linked to the demands of a peculiar

pattern of urbanisation and of a wide bureaucratic structure (administrative, legal and

representative services, paper industry, etc.). The tertiary-oriented nature of the region continued

throughout the period of Italian unification and was even reinforced after the choice of Rome as

the capital of the kingdom.

As a result of its past and present role of capital-region, a large proportion of the national public

R&D infrastructures and expenditure is currently concentrated in Lazio. The most frequent forms

of linkages are those between a restricted number of science-based firms and public and private

research institutes. Yet, in the whole area systemic interactions do not play a very critical role;

support from local governments is neither proactive nor particularly effective; collaborative

relationships between firms, as well as other forms of knowledge and technological linkages, are

far from being intense; and regional social environments, more generally, are not particularly

cohesive, nor are their economic structures oriented towards technological change and institutional

reformism. 17 See also Boschma (2003). The literature that emphasises the diversity of industrial district evolution is not included here for reasons of space.

21

South and Islands (Mezzogiorno):19 the Peripheral Regions

The so-called Italian Mezzogiorno had its golden age under the rule of the Normans (Frederick II),

who made the area economically and culturally advanced and promoted the magnificence of cities

like Naples and Palermo until 1266. Since then, the ‘Southern Kingdom’ was ruled by Spain: the

colony was administered on the basis of undisputed privileges given to the nobility, and the

exploitation of the local resources and the peasantry. The principal source of wealth was

agriculture, but this was often damaged by periods of famine and suffered from lack of any

innovations (under the long-lasting system of wide land estates); only in Puglia, positioned on the

eastern side, did agricultural and trading activities flourish. The Kingdom became ‘independent’

under the Bourbons (1734): the Bourbons have traditionally been blamed for the backwardness of

the area, but in fact were in power for only 126 years (roughly between 1734 and 1859, with the

inclusion of few years of French rule). Sardinia’s development was strongly affected by its long

colonisation, first by the Spanish, then by the Savoia from Piemonte who exploited the natural

resources of the island, without instituting any structural reforms or innovation processes (in 1840,

agricultural productivity in Sardinia was approximately 1/10 of that of Lombardia).

Various historical factors contributed to the weakness of the territorial systems and the scantiness

of social capabilities for institutional change in the whole of Southern Italy.20 Economic factors

include the late advent of a feudal system of agriculture (at a time when in the North the society of

communes was already established), a sharp separation between financial capitals and the

management and organisation of production, unstable and hierarchical economic relations, a rather

concentrated urban geography, and, more recently, a process of State-led industrialisation based on

resource- and scale-intensive sectors, with low degree of production interdependencies and limited

scope for either pecuniary or knowledge externalities.21

The current Mezzogiorno is a backward area, particularly in terms of innovation and technology

indicators, both in Italy and within the EU. Its vulnerability is related not only to the weak

technological performance of its firms, but also to the absence or weakness of any systemic

18 Lazio, Umbria, Marche. 19 Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardinia. 20 It is clearly beyond the scope of this paper to go into the details of the complex ‘Mezzogiorno problem’, although its relevance for the topic discussed here (whether and why some regions are RSIs and some are not even ‘systems’) is crucial. For a more extended discussion see Iammarino (2005). 21 One of the causes underlying poor historical accounts is indeed the scarcity of interdisciplinary studies carried out at the meso level. Excellent contributions, for examples, can be found in the work of Italian geographers on the historical evolution of the country territorial polarisation. See, for example, Muscarà (1967, 1992), Bagnasco (1977), Celant (1994).

22

dimension of innovation processes, at least at the level of administrative regions.22 Indeed, some

elements seem to suggest that firms in the main carry out innovation activities in isolation, and

have little contact with other firms, R&D institutes and the broader institutional context. The lack

of a critical mass of qualified components, relationships and attributes suggests that the status of

RSI cannot be identified in these regions. However, the emergence of “many Mezzogiorni”,

stressed by the economic literature particularly in the last decade, seems to suggest that both

catching up and falling behind processes are taking place in the area, giving rise to an increasing

intra-South differentiation (see, for instance, Guerrieri and Iammarino, 2002; Viesti, 2003).

This succinct picture serves the purpose of highlighting the relevance of history in understanding

the link between regional socio-economic and institutional trajectories and innovation systems.

Both heterogeneity, due to chance events, and path dependency, due to historical contingency,

underlie the longevity of Italian regions and their paths of development. Economic differentiation,

even more pronounced than political fragmentation, has been consolidating over the centuries,

preventing Italian economic development from achieving a single ‘natural dimension’ until quite

recently. The persistence of governance structures headed by communes, provincial dukedoms, the

Catholic Church, foreign leadership, etc., neatly indicates decision-making processes that are

overwhelmingly regionally, or even locally, based: in other words, the techno-economic aspects of

innovation have to be necessarily associated with some component of governance. There is a key

issue here about active and passive decisions concerning innovation: the extent to which the

accumulation of ‘surpluses’ (in the sense of economic development), and especially their

deployment to innovation-related or industrial-oriented activities, may be a crucial determinant of

either the presence/absence or the success/failure of geographical systems of innovation.

Hence, if on the one hand the long-term backwardness of the Mezzogiorno can be explained by

naturally poor resource endowments and unfavourable geo-climatic conditions, on the other it

definitely cannot be seen only as the outcome of accident or fate. Rather, it was coupled with the

accumulation of what small surpluses there were being in the hands of anti-developmental

governance, i.e. from southern barons in the 18th century to the more recent Mafia.23 In the Central

regions natural endowments were presumably richer, but the dominance of the Catholic Church –

22 Indeed, some elements (rules, incentives, structural and historical similarities, etc.) seem to suggest that the whole Mezzogiorno could be considered as a ‘uniform’ macro-region but, within it, the degree of differentiation, particularly in terms of performance, is indeed quite striking. 23 To quote an example of wealth concentration, at the end of 18th century 60% of the total income produced in the continental southern regions was owned by 650 families, of which 90 controlled two-thirds and 20 approximately a quarter of the total population respectively (Villani, 1973 not in refs, quoted in Zamagni, 1990).

23

universally condemned by historians as responsible for wealth concentration and anti-innovative

practices – took most of such surpluses. In northern Italy, and especially in the North-west, the

endowments were richer still and some were used ‘productively’ by both local community actions

and progressive foreign powers.

Indeed, what emerges from current attempts to apply the RSI concept to the Italian case (Silvani et

al., 1993; Iammarino et al., 1996, 1999; Camagni and Capello, 1999; Evangelista et al., 2001,

2002) is a variety of regional patterns, differing in terms not only of firms’ strategies and

performance, but also in terms of contextual and systemic characteristics, density and quality of

interactions and scope and effectiveness of functions. Thus, regional diversity depends on to

components, relationships and attributes, to social capabilities for institutional change and to the

degree of alignment in network governance.

The portrait of the contemporary Italian regional divide consistently reproduces the broad

historical trajectories. Though currently largely disregarded in academic and policy debates,

quantitative and qualitative historical research based on carefully collected data and in-depth case

studies may help reveal the structural regularities of socio-economic evolution and the degree of

integration of governance design controlling the efficiency of knowledge absorption, diffusion and

creation at both micro and meso levels.

5. Conclusions

It has been argued that by adopting an evolutionary integrated approach, the RSI framework does

provide an adequate conceptual base from which to investigate whether and why a region is an

innovation system. However, it is also argued that the severe constraints on empirical testing of the

significance of structures and performances of RSIs – due to both insufficient information and

methodological bottlenecks – have so far seriously hampered the achievement of a better

delineation of the concept, as well as of a sounder normative base for regional policy. Clear

thinking is a prerequisite for fruitful research efforts, but it is not a sufficient condition per se:

adequate data and robust indicators to identify structural regularities and patterns of socio-

economic change are urgently required. The extent of applicability and replicability of RSIs will

arguably widen only if supported by richer sets of both quantitative and qualitative variables and

by historical evidence on territorial paths of evolution.

Any attempt to analyse why a region is a particularly successful RSI must first, because systems

change over time, explain what are the factors underlying such status and their dynamics.

24

Therefore, an evolutionary approach may be the only approach of value to deal with the unsolved

questions highlighted by the recent literature on regional development in Europe: for example,

how can regional trajectories be abandoned? Is it possible to diverge from the social, economic

and institutional past? How can structural and localised change be planned and managed? Is there

a role for regional policy at all levels of governance? (Boschma, 2004).

A further issue is related to the ‘alignment of networks’ perspective, and concerns the top-down

versus bottom-up distinction. The question then becomes one of how the contrasting levels can (or

cannot) be integrated. The agents do not have to be the same in both cases, and may indeed have

different political or other objectives, but there has to be a joint and reasonably consistent set of

views about enhancing the regional knowledge base. Only advanced historical knowledge can

make visible the extent of the integration between and within networks as modes of governance in

the region. As evolutionary mechanisms like ‘technological trajectory’, ‘selection environment’,

‘heterogeneity’ and ‘path dependency’ are crucial determinants of the geography of innovation,

history in terms of inheritance of regional structures and governance often acts as a filter for

assessing new opportunities for social and techno-economic growth. As was effectively pointed

out by Lambooy and Boschma (2001), this is all the more important when policy implications are

to be drawn: regional policy – at local, national and supranational (EU) level – cannot start from

scratch, but rather it has to be based on a deep understanding of how historical trajectories affect

change. As Lambooy and Boschma have argued, in an evolutionary systemic environment, it is

unlikely that policy makers will fundamentally change the course of development of a region as:

Regional policy is likely to fail when local strategies deviate considerably from the local

context. In such circumstances, policy makers have to account for the fact that adaptation

to change is largely constrained by boundaries of the spatial system laid down in the past.

However, this also implies that the potential impact of regional policy may be quite large

when the policy objectives are strongly embedded in the surrounding environment

(Lambooy and Boschma, 2001: 113).

The Italian experience summarised in this paper gives some preliminary support to the fact that

regions differ not only according to the specific strategies and performance of firms, but also with

respect to the density of systemic interactions, quantity and quality of idiosyncratic factors, and

evolution of regional social capabilities and modes of governance, favourable (or unfavourable) to

innovation. This is not to deny the importance of the national system, but rather to stress the

complementarity between an NSI and its regions/RSIs. The importance of history, which is

emphasised here, goes against an a priori determination of the spatial level relevant for innovation

25

systems to exist and operate. It rather shows that there is an array of conditions and constraints

dictated by the heterogeneity and path dependency of localities, regions and countries, affecting

learning, knowledge accumulation and, ultimately, socio-economic growth.

Future research will be directed to exploring more in depth the link between regional socio-

economic growth and systems of innovation, as well as the complex relationship between

governance and technology, at both the historical and empirical levels, and devising indicators

able to reflect, at least to an extent, the region as a ‘social system’.

26

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