Spatial Spillovers and Regional Growth:
A Cognitive Approach
Roberta Capello
Politecnico di Milano
Abstract
Spatial spillover effects are widely studied in the literature, as an important source of externality and therefore of
discrepancy between private and social optimum, creating the emergence for ad-hoc policy interventions. Within the
literature, their spatially-bounded nature is highly emphasised, but the mechanisms for their spatial diffusion are
generally considered as a black box, with no reference to territorialized channels through which these externalities
spread around. This is true for knowledge spillovers, for which territorial channels for knowledge transmission are not
identified; it is true for industry spillovers, for which no clear territorially rooted mechanisms explain their diffusion
among local industries; it is even more true for regional spillovers, for which little attention is given to highlight the
territorial elements guaranteeing the inter-regional diffusion of growth enhancing tools. The paper aims to present a
literature review on the spatial spillover concept, emphasising two main stages of reflection, the so-called spatial
approach and the functional approach. The paper identifies in the cognitive approach a new perspective to the
interpretation of spatial spillovers and their conditions, and on this approach it elaborates theretically and empirically.
The empirical analysis is run on all NUTS 2 of the 27 European member countries, with the aim to capture the role of
growth spillovers in Europe; the results support the proposed conceptual approach; the magnitude of spatial spillovers
does not merely depend on spatial distance but also on each region’s cognitive receptivity to external growth
opportunities.
1. Introduction
Spatial spillovers have found a wide interest in the last decade among regional economists and
economic geographers, who are more and more aware of the strategic role played by – voluntary or
unintended – locally bounded and territorially rooted interactions among actors, institutions and
local economies in explaining regional growth dynamics. In particular, the interest in spatial
spillovers lies in the fact that they represent pure externalities, producing non compensated
advantages for receivers; a discrepancy between private and social optimum creates the emergence
for ad-hoc policy interventions.
A large variety of spatial spillovers has been identified by the literature. Knowledge spillovers are
the most popular, describing a situation in which knowledge created by a firm (or an institution)
does not solely remain within that firm (or institution), therefore creating values for other firms or
institutions (Fischer et al, 2002); industry (horizontal and vertical) spillovers refer to situations in
which the dynamics of one firm (generally a large firm) in a sector generates, through input/output
or competition linkages, domino effects on related (horizontally or vertically) industries; growth
spillovers refer to a situation in which the dynamics of one local economy (being a region, a
metropolitan area, a local district) influences growth of neighbouring local economies, through
trade linkages and market relationships.
Much scientific work has been produced since the beginning of the nineties around the idea of
spatial spillovers, and on the reasons for their locally-bounded nature. However, when one looks
carefully into the existing literature, two striking aspects emerge. On the one hand, it appears quite
evident that the spatial spillover concept is interpreted as a very well known concept which does not
need any conceptual specification given its straightforward meaning and its wide popularity. In
reality, this is not the case. Different kinds of spatial spillovers exist in the literature, each of them
hiding different economic mechanisms that may justify the channels through which they spread
1
around. Giving the concept as known does not help in identifying which kind of spillovers we are
confronted with and which are the channels through which these advantages spread around.
On the other hand, it appears quite evident that little attention is given also to the reasons for the
spatially-bounded nature of spillovers. The development of new spatial econometrics, mainly
concerned with spatial interactions among observations (Paelink and Klaassen, 1979; Anselin,
1988; Arbia, 2006) has pushed the conceptual interpretation of spatial spillovers in a specific
direction, that of highlighting the role played by the mere physical proximity in the complex
processes of local interactions. In the same line of what Mills said at the beginning of the nineties
with regard to empirical analyses on agglomeration economies and regional growth, we feel we can
state that this “is one of the few fields in which econometrics is ahead of theory” (Mills, 1993).
Over time one can identify in the literature an attempt to move away from a pure physical distance
approach to spatial spillovers. The aim of the paper is first of all to identify the different
interpretative paradigms explaining the channels through which spillovers spread around and the
conditions under which spatial spillovers are spatially-bounded. The main difference among these
interpretative paradigms lies on a different theoretical interpretation of space. In the recent
approaches, space is no longer measured through a simple geographical distance, but it either
becomes a geographical container of specific territorial functions, or, more recently, a relational
space, where functional and hierarchical, economic and social interactions take place and are
embedded into geographical space. A different interpretation of space leads to a different approach
to the identification of specific local conditions under which spillovers occur (Sec. 2 - 5). The
second aim of the paper is to validate empirically one of the most recent conceptual approaches to
spatial spillover (Sec. 6 and 7). The paper ends with some concluding remarks (Sec. 8).
2. A Variety of Spatial Spillovers Concepts
Despite the early use of the concept in many traditional regional development theories1, only at the
beginning of the nineties the spatial spillover concept began to spread in the scientific literature,
mainly thanks to the wide diffusion of advanced econometric techniques able to take into account
the spatial interactions among observations. Regional scientists, industrial economists and
geographers took up the challenge provided by the new advances in spatial econometrics, and
developed an important field of research with the aim to identify the presence, nature and
magnitude of the externalities stemming from spatial proximity of economic activities, linking
policy suggestions to the empirical results obtained.
Interestingly enough, a wide variety of spatial spillovers are treated in the literature, which have a
different nature. The most diffused concept of spillovers refers to knowledge spillover2. Given its
nature of a non-excludable and non-rivalrous good (Romer, 1990), knowledge does not solely
remain within that organisation, but spreads around, thereby creating value for other firms or
organisations without any compensation mechanism (Fischer, 2006, p. 1). In fact, the lack of
excludability implies that knowledge producers do not benefit from full appropriability of the
knowledge value and that – despite their will – knowledge spills over the firm and becomes
1
Although not explicitly mentioned, the concept of spatial spillover was present in many regional growth theories, like
the growth pole theory of Perroux (1955), in the export-base theory of North (1955), the potential development theory
of Isard (1954) and Giersch (1949), just to quote those theories in which the liason with the modern concept of spatial
spillover is evident.
2
See among others, Almenida and Kogut, 1999; Maier and Sedlacek, 2005; Coe and Helpman, 1995; Acs et al., 1994;
Anselin et al., 2000; Audretsch and Feldman, 1996; Feldman and Audretsch, 1999; Fischer, 2006; Henderson et al.
1993; Holod and Reed, 2004; Jaffe 1989; Karlsson and Manducchi 2001.
2
available to other firms and organisations. The absence of rivarly means that knowledge can be
applied by different users at the same time without deteriorating its value. Standard technological
externalities thus occur.
Examining the evidence for knowledge spillovers becomes crucial as it is the existence of noncompensated benefits which provides the rationale for government incentives in knowledge
creation; if spillovers occur, then the social rate of return to an investment exceeds the private rate
of return, justifying government intervention. From the normative point of view, the identification
of locally-bounded spillovers can be of support in identifying specific areas (metropolitan areas,
local districts, milieux) in which to invest (Girma and Wakelin, 2000).
Table 1. Spatial spillovers: nature and characteristics
Kinds of spillovers
Knowledge spillovers
Industry spillovers
Growth spillovers
Characteristics
Definition
Creation of values for a
firm or an organisation
thanks to knowledge
created by other firms or
organisations
Creation of values for a
firm or an organisation
thanks to the performance
of another firm in the same
or different sectors
Creation of growth
potentialities of a region
thanks to the growth for
other regions
Economic nature
Knowledge as a public
good
Technological externalities
Productivity enhancing
elements as technological
and pecuniary externalities
Growth enhancing
opportunities as public
goods
Technological externalities
Level of analysis
Firm level
Regional level
Firm level
Industry level
Regional level
Expected effects
Only positive
Positive and negative
Positive and negative
Industry spillovers are a second kind of spillovers widely studied in the literature3. With this term a
situation is referred to in which the presence of a productive and dynamic firm in an area – usually a
large firm – produces an increase in productivity to other vertically and horizontally linked firms,
without any compensation for the advantage generated. The concept of industry spillovers is usually
and widely applied with reference to the effects generated by multinational firms on indigenous
firms. The theoretical basis for the expectations of spillovers from foreigner firms is the level of
firm-specific assets (technical and managerial knowledge) MNEs are assumed to have in order to
overcome the higher costs they face in foreigner markets (Hymer, 1976; Dunning, 1977; Girma and
Wakelin, 2000). Industry spillover is a broader concept than the knowledge spillover one,
encompassing a wider variety of interaction mechanisms among firms (knowledge exchange being
only one of them), through which productivity and innovativeness enhancing tools are transferred
from MNEs to indigenous firms, recalling the well known “growth pole theory” of the French
economist Perroux, who identified the presence of an “industrie motrice” as one of the main catalyst
for regional development (Perroux, 1955).
3
See among others, Barrios et al., 2003; Grünfeld, 2002; Blomström, 1989; Kluger, 2006; Audretsch and Feldamn,
2004; Markusen and Venables, 1999; Henderson et al., 1993; Moreno et al., 2003.
3
Productivity enhancing tools can be identified as:
-
-
technological advances and good managerial practices. These are expected to characterise
MNEs which, through the above mentioned industry linkages, transfer them to domestic
firms. Griliches (1992) defines these kinds of spillovers as rent spillovers, i.e. positive
externalities that arise when the value of an input to the firm exceeds the input costs. These
spillovers re-call the knowledge spillover effects previously mentioned;
increase in labour force training. The presence of a technologically advanced and
productive firm guarantees the increase in technological and managerial skills of labour
force. Labour market pooling effects arise.
These productivity enhancing tools spill over the whole area. The transmission channels are
identified by the literature in:
-
input-output relationships, thanks to which productivity enhancing tools (like technological
advances, managerial and organisational capability, creativity, market innovativeness,
labour skill upgrading) of MNEs become an advantage also for domestic firms;
inter-industry linkages, through which productivity enhancing tools are made available to
other domestic firms producing complementary or substitute goods with respect to the goods
produced by MNEs.
Differently from the case of knowledge spillovers, in the case of industry spillovers there exist
possibilities that the spillover outcome assumes positive as well as negative values. Productivity
enhancing mechanisms described above lead to positive externalities, but it might also be the case
in which the presence of MNEs can generate negative externalities through market competitiveness
enhancing mechanisms. Through superior technologies and economies of scale due to its size,
MNEs may be able to produce lowering down their average cost, and thus increasing competition
for domestic firms; or MNEs may increase the costs of local inputs because of their higher demand,
and nevertheless producing under profitable conditions, enhancing the costs of competition for
domestic firms.
The most general concept of spillovers treated in the literature is the one of growth spillovers4. By
this term a situation is meant in which a region grows thanks to the behaviour of neighbouring
regions. A region is a geographically limited economic system, where it is rarely the case that all
necessary goods are produced locally; and, conversely, where those goods that are produced
frequently exceed local demand for them and are sold on domestic or even international markets.
Trade linkages are necessary for the survival and growth of the regional economic system, and
strong intertwined positive and negative feedbacks are expected to be transmitted via market and
trade linkages between local systems.
Growth in one region can generate increase in local income, and a consequent increase in demand
for goods, greater internal savings and greater employment opportunities. Greater demand involves
necessarily imported goods, with the well-known positive income multiplicative effects - suggested
by the export-growth theory (North, 1955) – on the income of the region producing and exporting
that good. A greater income is also expected to generate greater internal savings and greater job
opportunities. As explained by the regional version of the Harrod-Domar model5, neighbouring
regions can take advantage of capital and labour availability: in neighbouring regions capital may
grow at the same rate as output (thus guaranteeing the steady state) even if investments tend to
outstrip savings, provided that the gap between savings and investments is covered by a surplus of
4
5
See Arora and Vamvakidis, 2005; Holod and Reed, 2004; Cheshire, 1995; Cheshire and Carbonaro, 1996.
See Harrod, 1939; Domar, 1957.
4
net capital imports. Similarly, full employment in a region with an internal shortage of labour may
be maintained by an inflow of workers from other regions, while outflows of migrants to other
regions may off-set unemployment in the region.
Therefore, regional spillovers can be defined as those growth enhancing elements of one region
that, in their nature of public goods, exert positive (negative) effects on other regions, with visible
distance-decay effects. The transmission channels have been highlighted as:
-
trade linkages;
demand linkages;
interregional mobility of production factors.
3. The Reasons for the Spatially-Bounded Nature of Spillovers
3.1. The spatial-geographical approach
In the theoretical and empirical analyses on all kinds of spatial spillovers what became immediately
clear was the need to identify the conditions under which spatial spillovers have a spatially-bounded
nature. Under this respect, two main stages of reflections can be envisaged in the literature, built on
different theoretical conceptualisations of space; the spatial-geographical approach in an earlier
stage, and the territorial-functional approach in more recent time.
The first stage of conceptual reflections concerning spatial spillovers and the conditions for their
spatially-bounded nature conceptualises space in a widely abstract, indirect and stylised way: space
is purely geographical, a physical distance among actors.
With this concept of space in mind, the reasons explaining the spatially-bounded nature of
spillovers were mainly found in spatial proximity, facilitating exchange of information, contacts,
trade and market relationships, within a pure gravity type logic.
This is true for all three kinds of spatial spillovers (Fig. 1). In the case of knowledge spillovers, the
main explanation for the local effects was envisaged in spatial proximity, going back to Marshall’s
identification of high flows of information and ideas between firms of a certain region – what is “in
the air” – as one of the main reasons for concentration of activities in space (Marshall, 1920). In a
spatial approach, the knowledge transmission channels are epidemiological contacts among local
agents.
In the industry spillover concept, the need to understand the locally-bounded nature was even more
important, as the externalities were mainly pecuniary, mostly developed through market forces,
hardly embedded in any spatial dimension. The theoretical basis for the expectations of greater
advantages from the presence of MNEs to local firms was lying again on a simple reduction of the
concept of space to that of spatial proximity, which favours:
-
local input-output relationships thanks to transport costs minimisation between suppliers/
distributors and MNEs;
local exchange of technological advances and good managerial practices and demonstration
effects thanks to higher probability contacts, recalling the knowledge spillovers case.
5
Fig. 1. The Spatial Approach
a) Knowledge spillovers
Knowledge created
in one firm
Spatial mechanisms:
spatial proximity
enhancing probability
contacts
.
b) Industry spillovers
Productivity enhancing tools
created in one firm
(generally a MNE)
Transmission channels:
- epidemiological contacts
Reasons for spatially-bounded
channels:
easier information flows
in limited geographical
areas
Knowledge received by
another firm
Spatial mechanisms:
gravity type of spatial
mechanisms
Transmission channels:
- input-output relationships
- inter-industry linkages
- labour market pooling effect
Reasons for spatially-bounded
channels:
reduced transport costs;
larger pool of skilled
workers;
easier imitation of
success stories;
easier transfer of
knowledge and
information flows.
Productivity enhancing tools
received by other (domestic)
firms
c) Growth spillovers
Growth enhancing tools
created in one region
Spatial mechanisms:
gravity type of spatial
mechanisms
Transmission channels:
trade linkages
demand linkages
interregional mobility of
production factors
Reasons for spatially-bounded
channels:
reduced transport costs;
easier commuting
possibilities for the
labour force.
Growth enhancing tools
received by another region
6
Growth spillovers have always been expected to be stronger with neighbouring regions; this
assertion has always found an explanation in gravity approaches, having a great interpretative
power in trade partnership; if this is true for nations, it is even more true for regions, strongly linked
one another also by country-specific macroeconomic effects. The conceptual basis for the
expectations of regional growth spillovers to be spatially-bounded can be found in Walter Isard’s
1950s theory of development potential, and in H. Giersch’s analysis of the barycentric location of
core European regions, which was subsequently reprised by J. Friedmann in his theories of
underdevelopment6; in these conceptual approaches, proximity to growing regions is in itself a
factor fostering development, while distance hampers it. Access to information, technological
knowledge, outlet markets of goods, and markets for production factors is a necessary condition for
the growth of a local market, while distance from a hypothetical economic centre entails higher
transport costs for finished goods, raw materials, semi-finished goods, greater costs of information
acquisition, delays in the adoption of innovations: all of which features hamper income growth and
competitiveness. Cases exist in which the growth potentialities developed by one region negatively
influence the growth trajectories of neighbouring regions which – instead of attracting growth
opportunities – become donors of tangible and intangible resources, and therefore suffer from
gradual impoverishment and consequent economic decline.
3.2. The territorial-functional approach
It became evident soon that the spatial approach was too simplistic and had a too weak
interpretative power (Karlsson and Manduchi, 2002; Capello and Faggian, 2005). In the case of
knowledge spillovers, a spatial approach was able to explain the diffusion mechanisms, but was not
capable of identifying channels of direct knowledge interaction, and learning, an aspect of
overriding interest not only for scholars but also, and especially, for policy-makers, should they
wish to explore the possibilities of normative action to promote local development. In both the
industry and growth spillovers cases, the weakness of a spatial approach was demonstrated by
relying, on one side, on transport costs, whose weight on total production factors was constantly
decreasing over time; on the other, on spatial epidemiological diffusion processes of information
and knowledge, suffering from the same limits mentioned above of explaining spatial diffusion of
knowledge but unable to interpret knowledge creation mechanisms.
In a second stage of reflections, mainly developed at the end of the nineties, a functional approach
was preferred, even if with wide conceptual overlapping with the previous one, stressing the
importance of the presence of territorially-embedded functions in the explanation of spatiallybounded spillovers (Fig. 2). In the functional approach, space becomes a physical container of
territorial functions and the presence of these functions explains the spatially-bounded nature of
spatial spillovers. In the knowledge spillover concept, concentration and agglomeration of main
R&D facilities provided a conditional element for knowledge spillovers, together with the presence
of universities and research oriented firms, having a certain absorptive capacity, defined as the
ability of firms to learn, assimilate and use knowledge developed somewhere else (Cohen and
Levinthal, 1989). By the same token, at an aggregate regional level, the concept of technological
gap was identified as a good proxy for measuring the ability of a region to absorb new knowledge
(Abreu et al., 2005).
6
See Giersch, 1949; Isard, 1954; Isard and Peck, 1954; Friedmann, 1966.
7
Fig. 2. The Functional Approach
a) Knowledge spillovers
Knowledge created
in one firm
Transmission channels:
- epidemiological contacts
Territorial filters:
presence of R&D and university activities;
firms with absorptive capacity;
technological gap.
Knowledge received by
another firm
b) Industry spillovers
Productivity enhancing tools
created in one firm
(generally a MNE)
Transmission channels:
- input-output relationships
- inter-industry linkages
- labour market pooling effect
Territorial filters:
highly specialised industrial tissue;
pool of high skilled employees;
pool if indigenous firms with a certain degree of
technological knowledge.
Productivity enhancing tools
received by other (domestic)
firms
c) Growth spillovers
Growth enhancing tools
created in one region
Transmission channels:
- trade linkages
- demand linkages
- interregional mobility of
production factors
Regional filters, i.e. regional receptivity measured
through the presence of:
a specialised productive structure allowing the
region to be a net-exporter of locally produced final
goods;
an efficient productive structure requiring the
region to be a net-importers of production factors.
Growth enhancing tools
received by another region
8
In the industry spillover literature, once again, the presence of a territorial filter at the regional level,
mostly made of an highly specialised productive structure with a certain absorptive capacity, of a
receptive labour market (Fig. 2), became the conceptual explanation to anchor spatial spillovers to a
local area, and to avoid conceptually the possible leakages in income-multiplying effects, identified
as the main weaknesses - and therefore sources of strong criticisms - of the export-base and growthpole theories. A functional approach allowed to take into consideration the political suggestion of
the “minimum content of local value” suggested by the OECD to FDIs (OECD, 2001a).8
A similar attitude pervaded growth spillover literature, in search of functional elements explaining
the local conditions of a region to attract flows of resources and of demand requirements; these
elements were identified in the literature as a specialised and efficient productive structure, allowing
a region to be net-exporter of locally produced final goods, and net-importer of production factors.
4. Towards a Cognitive Approach
Where do reflections stand nowadays? Do we feel satisfied about all that has been said so far about
territorial conditions for spatially-bounded effects? The answer is, in my opinion, no. Despite the
attempt of the functional approach to anchor the spillover effects to a territorially-defined structure
for the explanation of their spatially-bounded nature, this explanation still lies on a rather simple
concept of space, and is therefore not fully convincing in its spatially-bounded nature. Space is in
fact a territorial container of value added functions and activities, mainly located inside large
agglomerations or city-regions to get benefits from a wide input and output market; because of their
value-added nature, these functions have an intrinsically greater probability to take advantage of
close proximity to knowledge, productivity and growth opportunity sources.
From what said, it is difficult to escape the impression that even if a step forward has been provided
by the functional approach to the identification of the local conditions, this approach produces once
again a simplified picture of the complex nature of spatial spillovers and of the territorial conditions
that needs to be addressed to exploit these externalities at the local level. The presence of advanced
research sectors and advanced functions like R&D and high education are special features of only
some of the possible innovative paths, and, especially, are not able to explain the mechanisms
through which agents and contexts learn; the presence of a highly specialised industrial tissue and
of a pool of indigenous firms with a certain absorptive capacity, though relevant, increases the
possibility for input-output relationships with a local MNEs to occur, but does not explain the local
nature of input-output relationships; the presence of a highly specialised and efficient productive
system explains only part of the story. In fact two regions with similar specialised productive
structures and at the same distance from a growing region may have completely different receptivity
to the external growth stimuli.
The attention focused on the presence of advanced sectors, of a specialised productive tissue, of
qualified labour force may run into the risk of overemphasising the mere physical presence of
activities and of overlooking the more complex elements that explain learning processes; learning
on how to translate knowledge into innovation, learning on how to get the highest benefits from the
presence of a MNEs, learning on how to attract resources and to apply them in a creative and
innovative way. Interactive processes between the different actors at the local level are increasingly
8
See Chenery et al., 1953; Chenery, 1962. Sirkin (1959) stressed that multiplier effects change not only according to
the sector of specialization but also according to the level of specialization. More specialized areas necessarily require
greater openness to interregional trade, especially if there exists highly diversified internal demand which stimulates
greater commercial exchanges. It is therefore highly unlikely that the multiplier effect will be the same in regions with
different structural features.
9
seen as the crucial element in learning processes. This element is typical of production contexts
characterised by the presence of SMEs but also of contexts where large firms develop their own
internal knowledge, culture and know-how enhancing internal interactions and boosting selective
external interaction with industrial partners, universities, professionals and research centres.
A shift in the conceptualisation of space taking interactive processes among actors into account
helps in going a step forward in the interpretation of spatial spillovers and of their spatially-bounded
nature. Space has in this case to be conceptualised as a relational space where functional and
hierarchical, economic and social interactions take place and are embedded into geographical space
(Camagni and Capello, 2007). Space becomes real territory, i.e. a “territory” identified by both a
geographical proximity (agglomeration economies, district economies) and a cognitive proximity
(shared behavioural codes, common culture, mutual trust and sense of belonging).
A third approach to spatial spillover can be highlighted, a cognitive approach, mainly focused on
the construction of knowledge through cooperative learning processes, nourished by spatial
proximity (“atmosphere” effects), network relations (long-distance, selective relationships),
interaction, creativity and recombination capability9. This fits very well the needs to identify the
territorial channels through which knowledge spreads around locally, focusing on the ability of
local actors to manage information in order to identify and solve problems, or, more precisely in the
economic sphere, to transform information and inventions into innovation, cooperative or market
interactions into productivity and growth increases. In such an approach, the channels through
which knowledge spreads around are territorialized, embedded into the socio-cultural structure of a
local system, and therefore anchored by definition to the local area; they do not call for the
identification of territorial filters to guarantee a spatially-bounded effect of spillovers (Fig. 3).
The cognitive approach also helps in understanding the territorialized transmission channels of the
productivity enhancing tools in the industry spillover case; input-output relationships and inter
industry linkages find a natural location in highly cooperative contexts, i.e. in contexts characterised
by an intrinsic attitude towards co-operation, by a governance structure supporting exchange of
experiences and combining existing but dispersed know-how with interpretations of market needs,
imitation of good practices with creativity. Also in this case, transmission channels of productivity
enhancing tools assume a strong territorialized nature, easily justifying the spatially-bounded nature
of industry spillovers effects.
Growth spillovers call for a different approach than the knowledge and industry spillovers; they are
by definition externalities spreading among regions, and therefore a pure territorialized approach
does not help the identification of the reasons for the spatially-bounded nature of spillovers; the
interregional transmission channels can be identified only in the interregional flows of goods and
input factors. What changes with this approach is the identification of the local conditions in which
regional receptivity is embedded. In a cognitive approach, regional receptivity, defined as the
capacity of a region to extract the highest benefit from access to information, knowledge, services
and other places in general, is no longer dependent on the presence of value added functions, of an
advanced and efficient industrial structure, but on the presence of more intangible factors, mainly
“untraded interdependences” such as understandings, customs and informal rules that enable
economic actors to work together under conditions of uncertainty.
9
A collective learning process of this kind was first hypothesized by the GREMI group (Camagni, 1991; Perrin, 1995)
and subsequently widely adopted as a sound theoretical concept for the interpretation of knowledge-based development
and innovation (Keeble and Wilkinson, 1999 and 2000; Capello 1999; Cappellin, 2003).
10
Fig. 3. The Cognitive Approach
a) Knowledge spillovers
Knowledge created
in one firm
Territorialised transmission
channels:
presence of stable and
long lasting customerssuppliers relationships;
spin-offs from firms and
universities;
high mobility in internal
labour market
Knowledge received by
another firm
b) Industry spillovers
Productivity enhancing tools
created in one firm
(generally a MNE)
Territorialised transmission
channels:
attitude towards
cooperation among local
firms;
governance structure
supporting cooperation;
creativity in imitating and
replicating good
practices;
open attitudes towards
novelties.
Productivity enhancing tools
received by other (domestic)
firms
c) Growth spillovers
Growth enhancing tools
created in one region
Transmission channels:
- trade linkages
- demand linkages
- interregional mobility of
production factors
Regional filters, i.e. regional receptivity measured
through the capacity of:
converting external inputs to the needs of local
firms;
transcoding success stories of other regions to
solving local problems;
searching and signalling interest to the external
environment for strategic complementary assets.
Growth enhancing tools
received by another region
11
The “learning” region is identified as the place where such cognitive processes play a crucial role,
combining existing but dispersed know-how, interpretations of market needs, information flows
with intellectual artifacts such as theories and models and allowing exchange of experiences and cooperation (Lundvall and Johnson,1994).
In the cognitive approach to space, regional receptivity, as well as regional competitiveness and
attractiveness, reside more in intangible, strongly territorialized and un-imitable assets, like local
trust and sense of belonging than in pure availability of capital, in creativity than in the presence of
skilled labour force, in connectivity than of pure accessibility (Camagni, 2007). The expertise to be
a “cognitive engine” resides on the ability of a region to enable economic actors to work together
under conditions of uncertainty, or solidarity, mutual assistance and co-opting of ideas that often
develop in clusters of small and medium-sized enterprises working in the same sector (social
capital), or in highly creative environments like cities; both cities and specialised areas provide an
intangible factor, ‘something in the air’, which is the outcome of a combination of institutions,
rules, practices, producers, researchers and policy makers that make a certain creativity and
innovation possible (OECD, 2001b, p. 15). Regional receptivity of growth opportunities resides in
the present of this intangible factor which allows:
-
to convert external inputs to the needs of local firms;
to transcod success stories of other regions to solve local problems, through a process of
creative adoption;
to search and signal interest to the external environment for strategic complementary assets.
Especially in contexts characterised by a plurality, variety, density and redundancy of agents – like
cities or industrial districts – one can expect to find the highest capability of translating virtual and
intangible elements into effective action, co-operation, public/private partnership, supply of
services, of translating potential relationality into effective relationality and linkages among
economic agents, by attracting the most effective and useful linkages and contacts for the area.
The territorial settlement structure can represent a good proxy to capture density interactions; the
latter does not always herald a positive receptivity, as diseconomies of agglomeration may produce
a negative reaction to growth opportunities. Table 2 presents the different development paths that
can be highlighted, according to the regional settlement structure and the growth spillover effects:
Table 2. Territorial structure and growth spillovers:
alternative development paths
Growth spillover effects
Positive
Negative
Metropolitan city-regions
Concentrated development
Diffused development
Rural areas
Filtering down development
Desertification patterns
Territorial settlement
structure
12
-
a concentrated development is the outcome of positive growth spillovers in metropolitan
city-regions;
a diffused development is expected when metropolitan city-regions get a negative advantage
of external growth opportunities;
a filtering down process of local development, involving at each step less dense regions
emerges if growth spillovers are positive in rural areas;
a desertification pattern is highlighted in the case rural areas are unable to grasp external
growth opportunities.
In the next section, we present an empirical analysis with the aim to measure he role of spatial
spillovers into account in explaining regional growth differentials; in particular, the aim is to
measure the role of different degrees of regional receptivity of growth spillovers on local
competitiveness. Empirical evidence on the geographical characteristics of regional growth in
Europe can be also highlighted.
5. Territorial Elements and Growth Spillovers: an Empirical Analysis
5.1. A regional differential growth model
The aim of the empirical part is to test the role of different regional receptivity of growth spillovers
on local competitiveness. The starting point of the analysis is the estimate of a differential shift
equation (sr):
s r = f (local material inputs and resources; structural and sectoral characteristics;
institutional elements, spatial spillovers and territorial structure)
(1)
The equation comprises:
-
local material inputs and resources, in particular: infrastructure endowment, share of selfemployees, external resources like CAP (Community Agricultural Policy) funds, share of
tertiary activity;
structural and sectoral resources: quantity and quality of human capital, availability of energy
resources;
the institutional elements, in particular economic integration processes which provide a larger
market potentials for regions;
the spatial spillovers, captured through growth opportunities of a region dependent on its
neighbouring regions’ dynamics (growth spillovers);
the settlement structure of region, a good proxy to capture the role of agglomeration and
urbanisation economies on regional performance, enabling parameters of the different
explicative variables to vary across different settlement structures present in space, again
emphasising agglomeration economies.
In Table 3 all the relevant explanatory variables for the estimation of the regional differential
growth equation are presented and classified.
The originality of our database consists in: (a) specific and so far unavailable territorial and socioeconomic data; (b) specific spatial effects indicators, built in order to capture proximity effects, in
13
line with the large and settled literature on this issue10; (c) a merged Eurostat and ESPON economic
data base, which allowed us filling gaps and checking for data consistency.
Table 3. List of variables in the regional differential shift equation
Classification
Type
Definition
Share of human resources in S&T
Predetermined
Average population growth rate (1995-2002)
Predetermined
Energy consumption by population in 2002
Predetermined
% of people working in S&T on population at
NUTS 2 in the year 2000
Average annual population growth rate at NUTS
2 in the period 1995-2002
Total energy consumption on population at
NUTS 2 in the year 2002
Regional economic resources:
Regional structural and sectoral
characteristics:
Relative density of infrastructure endowment
in 2001
Share of self-employment
Share of tertiary activity in 2001
Territorial specificities
Predetermined
Km of roads on surface at NUTS 2 on km of
(intermediate policy roads on surface at NUTS 0 in the year 2001
target)
Predetermined
Share of self-employment on total employment
Predetermined
Employment in services in 2001 in percentage
(intermediate policy of the total at NUTS 2
target)
Dummy variables Rural, urban, agglomerated, megas
Pillar 2 expenses of CAP
Policy instrument
Structural funds expenditures
Policy instrument
Total funds devoted to Pillar 2 on agricultural
working units (awu)
Total structural funds expenditures in the period
1994-1999 on population
Spatial processes:
Spatial spillovers (1997-98)
Predetermined
Weighted sum of the relative annual regional
growth rates of all regions j other than
region r divided by the distance between each
other region and region r (see equation 2).
Predetermined
A European integration potential indicator for a
generic region r, obtained as the difference
between the indicator of growth differential with
neighbouring regions described above and the
same indicator calculated by squaring distance
for those regions at the border between Eastern
and Western Countries (see equation 3).
European integration process:
Regional integration potentials (1998-99)
The originality of our database consists in: (a) specific and so far unavailable territorial and socioeconomic data; (b) specific spatial effects indicators, built in order to capture proximity effects, in
10
See among others Cheshire 1995; Cheshire and Carbonaro, 1996, and the wide literature on spatial econometrics. On
the latter, see, among others, Anselin 1988; Anselin and Florax, 1995.
13
See among others Cheshire 1995; Cheshire and Carbonaro, 1996, and the wide literature on spatial econometrics. On
the latter, see, among others, Anselin 1988; Anselin and Florax, 1995.
14
line with the large and settled literature on this issue13; (c) a merged Eurostat and ESPON economic
data base14, which allowed us filling gaps and checking for data consistency.
The new and original territorial variables are represented by: i) a typology of regions according to
their settlement structure. Regions are in fact divided into agglomerated, urban and rural regions, on
the basis of the type of urban system (dimension and density of cities) present in the region; ii) a
typology of best performing regions, defined MEGAs (Metropolitan European Growth Areas),
selected on the basis of five functional specialisation and performance indicators: population,
receptivity, manufacturing specialisation, degree of knowledge and distribution of headquarters of
top European firms. All these variables were collected at FUA (Functional Urban Area) level and
combined to give an overall ranking of FUAs; the 76 FUAs with the highest average score have
been labelled MEGAs15. MEGA regions are the NUTS2 level administrative areas with at least one
of the 76 FUAs located in it.
The settlement structure variable provides a very useful indicator for the presence of agglomeration
economies in the region. At present, it represents the best synthetic indicator available for regional
attractiveness. As we will see, it has a highly significant explicative power when inserted in the
estimation procedure, and will increase the interpretative power of the spillover variable,
highlighting some tendencies in the nature of the regional development nowadays.
Regional growth spillovers are calculated as:
n
∆Y jt
j =1
d rj
SPrt = ∑ w j
(2)
where:
∆Yjt = income growth
j = all neighbouring regions of region r
drj= physical distance between region r and j
n = number of neighbouring regions
w= weight of the economy of region j on the economy of Europe.
In this way, the magnitude of spatial spillovers are made dependent on: i) a spatial dimension
(geographical distance), ii) a dynamic dimension (growth rate of neighbouring regions), iii) an
absolute dimension (size of neighbouring regions).
The institutional barrier effect is a dependent variable, since it obtained as the difference between
the indicator of growth differential with neighbouring regions and the same indicator calculated by
squaring distance for those regions at the border between Eastern and Western Countries, as
follows:
n
∆Y jt − ∆Yrt
j=1
d rj
IPrt = ∑
n
∆Y jt − ∆Yrt
j=1
d rj2
−∑
;r ≠ j
(3)
14
ESPON is a wide research programme launched by the European Union with the aim to analyse and study the
European territory. Within the programme, the creation of a territorial dataset was one of the aims.
15
See ESPON project 1.1.1. for technical details, available on the Espon web-site www.espon.eu.
15
where all symbols have already been defined. This indicator was built for border regions between
the new and old member countries up to 2007 and for border regions between member countries
and Bulgaria and Romania after 2007. In fact, it was built with the aim to measure the effects of a
barrier fall on regional GDP growth rate; in particular, it was used to measure the effects of the
integration of Bulgaria and Romania in 2007, after the entrance of the two countries in the EU.
5.2. The Estimation Results
Equation (1) was estimated in cross-section, on 259 regions in one year, given the data availability.
Some of the variables needed to estimate the differential regional growth equation and the
population and migration equations were in fact available for almost all 259 regions in our sample
for years 1995 through 2002. However, some other relevant variables, such as human capital or
accessibility and infrastructure measured through the kilometers of available roads in regions, are
available only for one year, 2000 in most cases. Lastly, some territorial variables keep constant
through time because of their nature. Thus it was not possible to use panel techniques. The
information along the time dimension, whenever available, was not left aside, but it has been used
to solve some specification and strictly econometric problems relating to the possible correlation
between some of the regressors and the error term of the equations and to the likely presence of
spatial correlation in the estimated residuals16. The equation has been tested for spatial dependence
using the spatial regression and testing modules in STATA17, and a distance matrix consisting of
the distances in kilometres between all couples of regions in the sample. It results that the residuals
are not characterized by spatial dependence, and the appropriate maximum likelihood estimation
technique did not need to be used. Robust OLS estimates were instead performed.
Table 4 presents the estimated specification, discussed in detail in the following aspects. Regional
GDP at constant prices is available for years 1995 through 2002 for most regions, and GDP growth
rates and regional shift (sr) from 1996 on. Yearly growth rates measure, by definition, only short
term fluctuations, while our intention is in fact to explain the structural part of regional growth, due
to structural elements, like human capital, infrastructure endowment, population growth, the
settlement structures and agglomeration economies. Therefore, the choice of the average regionalnational differential GDP growth between 1999 and 2002 was chosen as the dependent variable, so
as to smooth out any abnormal short-term fluctuation in regional income.
The growth spillover and integration potential variables are computed for each region/observation
as weighted averages of the income growth rates of the other regions in the sample, using as
weights the distances between each couple of regions (formulas 2 and 3). Spatial econometrics
proves that regional growth rates are jointly determined and these spatially lagged regressors will be
contemporaneously correlated with the error term. To avoid this simultaneity problem, and given
the availability of data on regional income for years before 1999, the growth spillover and potential
integration variables were computed on lagged in time income growths. In econometrics terms, this
operation allows the use OLS to estimate the parameters of this equation, once again relying on the
property of consistency of OLS estimators that holds when regressors and error term are not
contemporaneously correlated and error terms are not serially correlated. From an economist’s
perspective, note that using as regressors lagged in time spillover and integration potential
variables, introduces a dynamic component into the specification that may yield useful information
on the speed of adjustment of each region’s growth to neighbouring regions’ growth, and on how
such speed may be affected by territorial characteristics. In fact these lagged in space and lagged in
16
17
See Anselin, 1988.
See Pisati, 2001, pp. 277-298.
16
time spillover variables enter the differential growth equation on their own and crossed with
territorial variables (see Table 4).
Table 4. Estimates of the regional differential shift
Independent variables
Constant
Dependent variable Regional average annual
differential GDP growth
rate 1999-2002
-5.29
(-4.38)
Economic resources:
Regional share of human resources in S&T in urban areas in 2001
Regional share of human resources in S&T in Eastern Countries in 2001
Regional average population growth rate (1995-2002)
Regional energy consumption by population in 2002
Regional energy consumption by population in tertiary regions in 2002
Structural and sectoral characteristics:
Relative regional density of infrastructure endowment in 2001
Relative regional density of infrastructure endowment in mega areas in 2001
Regional share of self-employees
Regional share of tertiary activity in 2001
Dummy for mega regions
Dummy for rural regions
Pillar 2 expenses per agricultural working unit (awu)
Spatial processes:
Spatial growth spillovers (1997-98)
Spatial growth spillovers in the agglomerated regions (1997-98)
Spatial growth spillovers in urban areas in Eastern Countries (1997-98)
European integration process:
Regional integration potentials in Western Countries (1998-99)
Regional integration potentials in Eastern Countries (1998-99)
Number of observations
R-square
Spatial error test: Robust Lagrange multiplier
(p-value)
Spatial lag test: Robust Lagrange multiplier
(p-value)
* significant at 10%; ** significant at 5%; *** significant at 1%.
-0.008
(-0.4)
0.076
(4.76)***
0.646
(3.29)***
0.006
(2.47)**
-0.000111
(-4.1)***
-0.13
(-0.91)
0.13
(0.34)
0.047
(2.90)***
0.058
(4.46)***
0.50
(2.27)**
-0.43
(-1.13)
0.03
(3.11)***
184.31
(2.21)**
-171.73
(-2.60)**
-201.68
(-1.92)*
23.5
(0.76)
-27.38
(-0.60)
227
0.30
0.42
(0.51)
0.38
(0.75)
17
Both these elements, the introduction of time dynamics into the specification and the possibility to
estimate the effects of spillovers crossed with territorial variables, were the reason we chose to
compute our own spillover and integration potential variables instead of using the available spatial
regression packages that automatically compute the spatially lagged variable and estimate by
maximum likelihood the spatial lag model in one point in time20.
While dealing with production functions, we must acknowledge that output and production factors
are actually jointly determined: output is a function of production factors, but the latter are
demanded by firms as a function of (planned) output (as well as of factor prices). In econometric
terms, production factors used as regressors in a production function will be correlated with the
error term of the equation, and induce inconsistent parameter estimates.
For this reason, in the regional shift equation the lagged in time proxies of the production factors
were introduced whenever possible. Labour growth rate, for instance, is proxied by the average
population growth rate between 1995 and 1998; this regressor may be defined as a predetermined
variable and will not be correlated with the error term in the equation, that we assume to be serially
independent21.
Unfortunately, for other production factors and sectoral characteristics (share of tertiary
employment, for example) only data on year 2000 are available (one of the years used to compute
the dependent variable). In this case, the assumption is made that their volume, although measured
for year 2000, was actually determined by previous years’ incomes and activity levels. Thus also
these variables are treated as predetermined variables and are assumed to be uncorrelated with the
error term in the equation.
Tests for spatial dependence were run and the null hypothesis of no spatial correlation in the error
terms was not rejected. The model was estimated with robust OLS.
Table 4 shows the estimation results. The R2 of the equation is 0.30, not large indeed in absolute
terms, but more than acceptable given the growth rate specification of the explanandum: it is worth
remembering that the dependent variable in this equation is a difference in growth rates, almost a
random variable itself. The coefficients of most of the relevant variables are however statistically
significant.
The results of the model witness that spatial spillovers do have an effect on regional differential
growth, and are in general positive; a faster growing area tends to attract resources (more productive
and highly paid workers, efficient and dynamic firms, more financial investments) from its
neighbouring regions, increasing the differential in economic growth rate with respect to them.
An interesting result emerges when growth spillovers are estimated for specific territorial areas,
therefore taking the different regional receptivity into account. In those agglomerated areas showing
a greater density of productive activities and population, and therefore expected to be characterised
by greater dynamic agglomeration economies, spatial spillovers are much lower, though still
positive; a weak but statistically significant diffused development is taking place in Western
Countries. The process of diffused development is even more accentuated in Eastern countries, in
which highly negative spatial spillovers and backwash effects occur in highly urbanised regions:
20
See fore example the module “spatreg” in STATA, with the spatial lag option.
It is not possibile to test for serial correlation within each region, given that, with the available data, we can only
estimate one crossection in time.
21
18
rural regions in Eastern Countries seem to be the only ones which clearly grow at the expenses of
neighbouring city-regions. This result is in line with other empirical studies on spatial effects on
regional differential growth22. Weak cumulative and self-reinforcing processes are present,
witnessing a change in the expected concentrated nature of regional development in Eastern
countries.
6. Growth Spillovers and Regional Growth Dynamics
The differential shift equation is the first equation of a more elaborated regional growth model,
called the MASST model23. The latter has also a national component part, and the link between the
national factors and regional ones concerning growth is assured by the structure of the model, which
interprets regional growth as the result a national growth component and a differential regional
growth component.
Fig. 4 presents the logic of the model, in which it is clear that the econometric model consists of
two intertwined blocks of equations, a national block and a regional one, giving to both the regional
and national component a role on local economic trajectories.
On the regional component side, not all explicative variables of the differential shift equation are
exogenous; three of them are endogenous and allow for cumulative processes, namely: i) selfemployment is in part dependent on structural funds expenditures, as the creation of new firms is
seeing as one of the most productive effects of structural funds expenditures; ii) demographic
changes (population growth rate) are dependent on birth and death rates and on in-migration; iii) on
its turn, in-migration is made dependent on regional income differentials, unemployment rate, and
on the different settlement structures of regions; iv) spatial spillovers are dependent on the regional
growth rates of neighbouring regions in previous years; v) an institutional barrier effect, since it is
obtained as the difference between the indicator of growth differential with neighbouring regions
and the same indicator calculated by squaring distance for those regions at the border between
Eastern and Western Countries.
Once estimated, the MASST model is used for simulation exercises on the way regional growth will
look like under certain scenario assumptions. An extrapolative scenario assumption of the present
situation has been built i.e. following the assumption that the present spontaneous and normative
trends up to 2015, with an extrapolative approach of the actual situation.24
What is of specific interest for our study is the relative importance of growth spillovers in the rate of
growth of single regions. Map 1 provides this information, by showing the weight of the spatial
spillover component on regional growth for each NUTS 2. Spatial spillovers play an important role
in the growth of central, “pentagon” Europe, spreading around mayor capital and “mega” regions
(London, Paris, Milan, Munich, Bruxelles, etc..), but are also scattered towards more peripheral
territories, contrary to what would have been expected by a spatial approach. The regions gaining
the most from growth of other regions are those located near strong or highly growing regions:
urban regions in Central Germany; rural and urban regions in France, Spain, Greece, Romania and
Bulgaria, Eastern Polish regions; all gain the most in terms of spatial spillovers. On the other hand,
“agglomerated” and “mega” regions show negative effects, being “donor” more than “receiving”
regions.
22
See Cheshire, 1995; Cheshire and Carbonaro, 1996.
23
For the explanation of the conceptual approach, the database, the results of the estimates and the simulation algorithm
of the MASST model, see Capello, 2007.
24
For the exact implementation of the scenario assumptions, see Capello et al., 2007.
19
Fig. 4. MASST Model Specification
Submodel 1: National component
Macroeconomic elements
∆ in national
GDP
∆ efficiency
wage
(inflation and
productivity
increases)
∆ internal
consumption
∆
investments
(including
FDI)
Submodel 2: Regional differential component
National
component
National
growth
- attractiveness
- economic
success
∆ imports
Inflation
Effective
exchange rate
Regional differential
component
∆ exports
Regional
structure
Structural and
sectoral
features:
productive
structure
Infrastructure
endowment
Local
material
inputs and
resources:
human capital
energy
resource
Structural policies
Human capital
policies
Infrastructural
policies
Technological
development
policies
Structural funds
and CAP reforms
Differential shift
∆ interest
rates
∆ public
expenditure
Stock of FDI
Final economic
effect
Macroeconomic
policies
Regional growth
- attractiveness
- economic
success
- spatial
spillovers
Regional disparities
Spatial and
territorial
structure:
- Spatial
spillovers
-geographical
dummies
- territorial
dummies
Population
growth
Birth rate
Mortality rate
Migration flows
Institutional
elements:
- economic
integration
potentials
- changes in
economic
integration
potentials
Migration Flows
Regional
differential GDP
Endogenous variables
Unemployment
rate
Exogenous variables
Settlement
structure
Source: Capello, 2007; Capello et al, 2007.
20
Map 1. Spatial spillover component in regional growth
Politecnico di Milano - MASST model - December 2006
Effects of spillovers
-0.418 - -0.127
-0.127 - 0.002
0.002 - 0.267
0.267 - 0.506
0.506 - 0.714
0.714 - 0.905
0.905 - 1.049
1.049 - 1.203
7. Conclusions
A large variety of concepts labelled under the common name of “spatial spllovers” are present in
the scientific literature and produced a plethora of empirical approaches; in the first stage of their
conceptualisation, all emphasised the role of spatial proximity as the main (and unique) reason for
these externalities to occur and to be spatially bounded.
It soon became clear that the explicative power of a spatial approach to spillovers, i.e. an approach
highlight spatial proximity as the main explanation for spillover effects to be spatially bounded was
a too weak interpretation of the phenomenon. A second stage of reflections can be envisaged in the
21
literature, mainly developed at the end of the nineties, in which a functional approach is preferred,
even if with wide conceptual overlapping with the previous one, stressing the importance of the
presence of territorially-embedded functions in the explanation of spatially-bounded spillovers. In
the functional approach, space becomes a physical container of territorial functions, whose presence
explains the spatially-bounded nature of spatial spillovers.
This paper stresses the limits also of this approach: the mere presence of advanced functions is
perhaps a necessary but not sufficient conditions to guarantee spatially-bounded spillovers: space is
a pure container of some functions, but nothing in the approach guarantees and explains the
mechsnims through which local agents and local context cumulate knowledge and learn to exploit
productivity enhancing tools and growth opportunities.
To overcome these interpretative limits, the paper has suggested a different approach to space: a
“cognitive approach” focuses on the ability of local actors to manage information in order to
identify and solve problems, or, more precisely in the economic sphere, to transform information
and inventions into innovation and productivity increases through cooperative or market
interactions. In such an approach, space becomes real territory, a relational space where functional
and hierarchical, economic and social interactions take place and are embedded into geographical
space.
For all three categories of spatial spillovers, a cognitive approach helps in identifying the ways in
which agents perceive economic reality, are receptive to external stimuli, can react creatively, are
able to co-operate and work in a synergic way. The identification of these additional elements can
explain part of the non-regularities that are generally found out in the empirical analyses. Growth
spillovers effects at regional level highly depend on regional receptivity, defined as the capacity of a
region to extract the highest benefit from access to information, knowledge, services and other
places in general.
The empirical part of the paper presents a regional growth model in which growth spillovers and the
territorialized conditions under which spillovers occur. The regional growth model is estimated on.
The role of regional growth spillovers and their territorial dimension on regional competitiveness is
highlighted in an empirical growth model on all 259 NUTS 2 regions of the 27 member countries; a
tendency of a diffused development in the Western part of Europe and of an even more decisive
diffused development in Eastern countries is the first main result. Taking the settlement structure
into account changes an expected outcome of growth spillovers, i.e. spatial proximity does not
solely explain the magnitude of growth spillovers effects; their magnitude can be very strong also in
peripheral areas of Europe, depending on the receptivity of each region to growth opportunities.
These results have normative implications. Regional development policies may try to address local
competitiveness, but a specific focus is also required in order to strengthen receptivity and
attractiveness; the latter have to interpreted in a modern way, by reinforcing all cognitive processes
combining existing but dispersed know-how, interpretations of market needs and allowing exchange
of experiences and co-operation, supporting work together under conditions of uncertainty, and coopting of ideas that often develop in clusters of small and medium-sized enterprises or in highly
creative environments like cities. A combination of institutions, rules, practices, producers,
researchers and policy makers that make a certain creativity and innovation possible is the best
recipe to increase receptivity and make the best use of external growth opportunities.
22
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