1

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

2

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.

3

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 non-compensated 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 Characteristics

Knowledge spillovers Industry spillovers Growth spillovers

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.

4

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 See Arora and Vamvakidis, 2005; Holod and Reed, 2004; Cheshire, 1995; Cheshire and Carbonaro, 1996. 5 See Harrod, 1939; Domar, 1957.

5

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.

6

Fig. 1. The Spatial Approach

.

Spatial mechanisms: - spatial proximity

enhancing probability contacts

Transmission channels: - epidemiological contacts

Knowledge created

in one firm

Knowledge received by

another firm

Reasons for spatially-bounded channels: - easier information flows

in limited geographical areas

c) Growth spillovers b) Industry spillovers a) Knowledge spillovers

Transmission channels: - trade linkages - demand linkages - interregional mobility of

production factors Spatial mechanisms: - gravity type of spatial

mechanisms

Growth enhancing tools

created in one region

Growth enhancing tools

received by another region

Reasons for spatially-bounded channels: - reduced transport costs; - easier commuting

possibilities for the labour force.

Spatial mechanisms: - gravity type of spatial

mechanisms

Transmission channels: - input-output relationships - inter-industry linkages - labour market pooling effect

Productivity enhancing tools created in one firm (generally a MNE)

Productivity enhancing tools received by other (domestic)

firms

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.

7

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 spatially-bounded 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.

8

Fig. 2. The Functional Approach

Transmission channels: - epidemiological contacts

Knowledge created

in one firm

Knowledge received by

another firm

Territorial filters: - presence of R&D and university activities; - firms with absorptive capacity; - technological gap.

Transmission channels: - input-output relationships - inter-industry linkages - labour market pooling effect

Productivity enhancing tools created in one firm (generally a MNE)

Productivity enhancing tools received by other (domestic)

firms

Territorial filters: - highly specialised industrial tissue; - pool of high skilled employees; - pool if indigenous firms with a certain degree of

technological knowledge.

Transmission channels: - trade linkages - demand linkages - interregional mobility of

production factors

Growth enhancing tools

created in one region

Growth enhancing tools

received by another region

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.

c) Growth spillovers b) Industry spillovers a) Knowledge spillovers

9

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 growth-pole 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.

10

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).

11

Fig. 3. The Cognitive Approach

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 created in one firm (generally a MNE)

Productivity enhancing tools received by other (domestic)

firms

Transmission channels: - trade linkages - demand linkages - interregional mobility of

production factors

Growth enhancing tools

created in one region

Growth enhancing tools

received by another region

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.

c) Growth spillovers b) Industry spillovers a) Knowledge spillovers

Knowledge created

in one firm

Knowledge received by

another firm

Territorialised transmission channels: - presence of stable and

long lasting customers-suppliers relationships;

- spin-offs from firms and universities;

- high mobility in internal labour market

12

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 co-operation (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

Territorial settlement structure

Positive

Negative

Metropolitan city-regions

Concentrated development

Diffused development

Rural areas

Filtering down development

Desertification patterns

13

- 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):

rs = 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 self-

employees, 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 socio-economic data; (b) specific spatial effects indicators, built in order to capture proximity effects, in

14

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

Regional economic resources:

Share of human resources in S&T Predetermined % of people working in S&T on population at NUTS 2 in the year 2000

Average population growth rate (1995-2002)

Predetermined Average annual population growth rate at NUTS 2 in the period 1995-2002

Energy consumption by population in 2002

Predetermined Total energy consumption on population at NUTS 2 in the year 2002

Regional structural and sectoral characteristics:

Relative density of infrastructure endowment in 2001

Predetermined (intermediate policy

target)

Km of roads on surface at NUTS 2 on km of roads on surface at NUTS 0 in the year 2001

Share of self-employment

Predetermined Share of self-employment on total employment

Share of tertiary activity in 2001

Predetermined (intermediate policy

target)

Employment in services in 2001 in percentage of the total at NUTS 2

Territorial specificities

Dummy variables Rural, urban, agglomerated, megas

Pillar 2 expenses of CAP Policy instrument Total funds devoted to Pillar 2 on agricultural working units (awu)

Structural funds expenditures Policy instrument 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).

European integration process:

Regional integration potentials (1998-99)

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).

The originality of our database consists in: (a) specific and so far unavailable territorial and socio-economic 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.

15

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

j rj

jtjrt d

YwSP

1 (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:

jrd

YY

d

YY=IP

n

=j rj

rtjtn

=j rj

rtjtrt ≠∑

∆−∆−∑

∆−∆;

1 21 (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.

16

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 regional-national 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 See Anselin, 1988. 17 See Pisati, 2001, pp. 277-298.

17

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

Dependent variable Independent variables

Regional average annual differential GDP growth

rate 1999-2002 Constant -5.29

(-4.38) Economic resources: Regional share of human resources in S&T in urban areas in 2001

-0.008 (-0.4)

Regional share of human resources in S&T in Eastern Countries in 2001

0.076 (4.76)***

Regional average population growth rate (1995-2002)

0.646 (3.29)***

Regional energy consumption by population in 2002

0.006 (2.47)**

Regional energy consumption by population in tertiary regions in 2002

-0.000111 (-4.1)***

Structural and sectoral characteristics: Relative regional density of infrastructure endowment in 2001

-0.13 (-0.91)

Relative regional density of infrastructure endowment in mega areas in 2001

0.13 (0.34)

Regional share of self-employees

0.047 (2.90)***

Regional share of tertiary activity in 2001

0.058 (4.46)***

Dummy for mega regions

0.50 (2.27)**

Dummy for rural regions -0.43 (-1.13)

Pillar 2 expenses per agricultural working unit (awu) 0.03 (3.11)***

Spatial processes: Spatial growth spillovers (1997-98)

184.31 (2.21)**

Spatial growth spillovers in the agglomerated regions (1997-98)

-171.73 (-2.60)**

Spatial growth spillovers in urban areas in Eastern Countries (1997-98)

-201.68 (-1.92)*

European integration process: Regional integration potentials in Western Countries (1998-99)

23.5 (0.76)

Regional integration potentials in Eastern Countries (1998-99)

-27.38 (-0.60)

Number of observations 227 R-square 0.30 Spatial error test: Robust Lagrange multiplier (p-value)

0.42 (0.51)

Spatial lag test: Robust Lagrange multiplier (p-value)

0.38 (0.75)

* significant at 10%; ** significant at 5%; *** significant at 1%.

18

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. 21 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.

19

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) self-employment 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.

20

Fig. 4. MASST Model Specification Source: Capello, 2007; Capello et al, 2007.

Exogenous variables

Endogenous variables

Submodel 1: National component Submodel 2: Regional differential component

∆ internal consumption

∆ exports

Macroeconomic elements

∆ investments (including

FDI)

Differential shift

Regional differential component

Structural policies Regional structure

Spatial and territorial structure: - Spatial spillovers -geographical dummies - territorial dummies

National component

National growth

- attractiveness - economic success

Regional growth - attractiveness - economic success - spatial spillovers

Regional disparities

Final economic effect

∆ imports

Population growth

Migration flows

Migration Flows

Regional differential GDP

∆ in national GDP

Institutional elements: - economic integration potentials - changes in economic integration potentials

∆ efficiency wage

(inflation and productivity increases)

Macroeconomic policies

Inflation

∆ interest rates

Effective exchange rate

Stock of FDI

Structural and sectoral features: productive structure Infrastructure endowment

Human capital policies

Infrastructural policies

Technological development

policies

Structural funds and CAP reforms

Birth rate

Mortality rate

Unemployment rate

Settlement structure

∆ public expenditure

Local material

inputs and resources:

human capital energy

resource

21

Map 1. Spatial spillover component in regional growth

Politecnico di Milano - MASST model - December 2006Effects of spillovers

-0.418 - -0.127-0.127 - 0.0020.002 - 0.2670.267 - 0.5060.506 - 0.7140.714 - 0.9050.905 - 1.0491.049 - 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

22

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 co-opting 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.

23

Bibliographical References Abreu M., de Groot H. L. F : and Florax R. J.G.M. (2005), “Spatial Patterns of Technology

Diffusion: An Empirical Analysis Using TFP”, mimeo available on line Acs Z., Audretsch D. and Feldman M. (1994), “R&D Spillovers and Recipient Firm Size”, Review

of Econmics and Statistics, vol. 76, pp. 336-340 Almeida P. and Kogut B. (1999), “Localization of Knowledge and the Mobility of Engineers in

Regional Networks”, Management Science, Vol. 45, n. 7, pp. 905-927 Anselin L. (1988), Spatial Econometrics: Methods and Models, Kluwer Academic, Dordrecht Anselin L. and Florax R. (1995), New Directions in Spatial Econometrics, Springer Verlag, Berlin Anselin L., Varga A. and Acs Z. (2000), “Geographic and Sectoral Characteristics of Academic

Knowledge Externalities”, Papers in Regional Science, vol. 79, n. 4, pp. 435-443 Arbia G. (2006), Spatial Econometrics, Springer Verlag, Berlin Arora V. and Vamvakidis A. (2005), “Economic Spillovers. Exploring the Impact Trading Partners

have on Each Other’s Growth”, Finance and Development, Vol. 42, n. 3 Audretsch D. B. and Feldman M. (1996), “R&D Spillovers and the Geography of Innovation and

Production”, American Economic Review, vol. 86, pp. 630-640 Audretsch D. B. and Feldman M. P. (2004), “Knowledge Spillovers and the Geography of

Innovation”, in Henderson J. V. and Thisse J.-F. (2004), Handbook of Regional and Urban Economics, North-Holland, Amsterdam, pp. 2713-2739

Barrios S., Bertinelli L. and Strobl E. (2003), “Multinationals and Local Indigenous Development”, CORE Discussion Paper

Blomström M. (1989), Foreigner Investment and Spillovers, Routledge, London Camagni R. (1991), “Local Milieu, Uncertainty and Innovation Networks: Towards a New

Dynamic Theory of Economic Space”, in Camagni R. (ed.), Innovation Networks: Spatial Perspectives, Belhaven-Pinter, London, pp. 121-144

Camagni R. (2007), “Towards a Theory of Territorial Capital”, in Capello R., Camagni R., Chizzolini B. and Fratesi U., Modelling Regional Scenarios for the Enlarged Europe: European Competitiveness and Global Strategies, Springer Verlag, forthcoming

Camagni R. and Capello R. (2002), “Milieux Innovateurs and Collective Learning: From Concepts to Measurement”, in Acs Z., de Groot H. and Nijkamp P. (eds.), The Emergence of the Knowledge Economy: A Regional Perspective, Springer Verlag, Berlin, pp. 15-45

Camagni R. and Capello R. (2007), “Knowledge-based Economy and Knowledge Creation: the Role of Space”, in Fratesi U. and Senn L. (eds), Growth and Competitiveness in Innovative Regions: Balancing Internal and External Connections, Springer Verlag, Berlin, forthcoming

Capello R. (1999), “Spatial Transfer of Knowledge in High-technology Milieux: Learning vs. Collective Learning Processes”, Regional Studies, vol. 33, n. 4, pp. 353-365

Capello R. (2007), “A Forecasting Territorial Model of Regional Growth: the MASST Model”, Annals of Regional Science, Vol. 41, n. 3, forthcomong

Capello R. and Faggian A. (2005), “Collective Learning and Relational Capital in Local Innovation Processes”, Regional Studies, vol. 39, n. 1, pp. 75-87

Capello R., Camagni R., Chizzolini B. and Fratesi U. (2007), Modelling Regional Scenarios for the New Europe: European Competitiveness and Global Strategies, Springer Verlag, Berlin

Cappellin R. (2003), “Territorial Knowledge Management: Towards a Metrics of the Cognitive Dimension of Agglomeration Economies”, International Journal of Technology Management, vol. 26, n. 2-4, pp. 303-325

Chenery H. (1962), “Development Policies for Southern Italy”, Quarterly Journal of Economics, vol. 76, pp. 515-547

Chenery H., Clark P. and Pinna C. (1953), The Structure and Growth of the Italian Economy, U.S. Mutual Security Agency, Special Mission to Italy for Economic Co-operation

24

Cheshire P. (1995), “A New Phase of Urban Dispersion in Western Europe? The Evidence for the 1980s”, vol. 32, pp. 1045-1063

Cheshire P. and Carbonaro G. (1996), “Urban Economic Growth in Europe”, Urban Studies, vol. 33, no. 7, pp. 1111-1128

Coe D. T. and Helpman E. (1995), “International R&D Spillovers”, European Economic Review, Vol. 39, n. 5. pp. 859-887

Cohen W. M. and Levinthal D. A. (1989), “Innovation and Learning: the Two Faces of R&D”, Economic Journal, Vol. 99, pp. 569-596

Dixit A. and Stiglitz J. (1977), “Monopolistic Competition and Optimum Product Diversity”, American Economic Review, Vol. 67, n. 3, pp. 297-308

Domar E.D. (1957), Essays in the Theory of Economic Growth, Oxford University Press, London Dunning J. H. (1977), “Trade, Location of Economic Activity and MNE: a Search for an Eclectic

Approach”, in Ohlin B., Hesselborn P. O. and Wijkman P. M. (eds.), The International Allocation of Economic Activity, Macmillan, London

Feldman M. and Audretsch D. (1999), “Innovation in Cities: Science-Based Diversity, Specialisation and Localized Competition”, European Economic Review, vol. 43, pp. 409-429

Fischer M. (2006), Innovation, Networks and Knowledge Spillovers: Selected Essays, Springer Verlag, Berlin

Friedmann J. (1966), Regional Development Policy: a Case Study of Venezuela, MIT Press, Cambridge Mass.

Giersch H. (1949), “Economic Union Between Nations and the Location of Industries”, Review of Economic Studies, vol. 17, pp. 87-97

Girma S. and Wakelin K. (2000), “Are There Regional Spillovers from FDI in the UK?”, paper presented at the International Economics Association Conference, held in Nottingham, 7-8 July

Griliches Z. (1992), “The Search for R&D Spillovers”, Scandinavian Journal of Economics, vol. 94 (Supplement), pp. s29-s47

Grünfeld L. A. (2002), “Multinationals Searching for R&D Spillovers. A Survey of the Literature”, Norwgian Institute of International Affairs, n. 629

Harrod R.F. (1939), “An Essay in Dynamic Theory”, The Economic Journal, vol. 49, n. 193, pp. 14-33

Henderson R., Jaffe A. B. and Trajtenberg M. (1993), “Geographical Localization of Knowldge Spillovers as Evidence by Patent Citation”, Quaterly Journal of Economics, vol. 108, n. 3, pp. 577-598

Holod D. and Reed R. R. (2004), “Regional Spillovers, Economic Growth and the Effects of Economic Integration”, mimeo available on line, University of Kentucky

Hymer S. H. (1976), The International Operations of National Firms: a Study of Direct Investment, MIT Press, Boston

Isard W. (1954), “Location Theory and Trade Theory: Short Run Analysis”, Quarterly Journal of Economics, vol. 68, n. 2, pp. 305-320

Jaffe A.B. (1989), “Real Effects of Academic Research”, American Economic Review, vol. 79, pp. 957-970

Karlsson C. and Manducchi A. (2001), “Knowledge Sipplovers in a Spatial Context – A Critical Review”, in Fischer M. M. and Fröhlich J. (eds.), Knowledge, Colpexity and Innovation Systems, Springer Verlag, Berlin, pp. 101-123

Keeble D. and Wilkinson F. (1999), “Collective Learning and Knowledge Development in the Evolution of Regional Clusters of High-Technology SMS in Europe”, Regional Studies, vol. 33, pp. 295-303

Keeble D. and Wilkinson F. (2000), High Technology Clusters, Networking and Collective Learning in Europe, Aldershot, Ashgate

25

Kluger M. (2006), “Spillovers from Foreigner Direct Investment: Within or Between Industries?”, Journal of Development Economics, Vol. 80, pp. 444-477Domar E.D. (1957), Essays in the Theory of Economic Growth, Oxford University Press, London

Lundvall B.A. and Johnson B. (1994), “The Learning Economy”, Journal of Industry Studies, n. 1, pp. 23-42

Maier G. and Sedlacek S. (2005), Spillovers and Innovations, Springer Verlag, Wien Markusen J. R. and Venables A. J. (1999), “Foreigner Direct Investment as a Catalyst for Industrial

Development”, European Economic Review, vol. 43, n. 2, pp. 335-356 Marshall A. (1920), Principles of Economics, 8° ed., Macmillan, London Moreno R., Paci R. and Usai S. (2003), “Spatial Spillovers and Innovation Activity in European

Regions”, CRENoS Woring Paper, n. 2003/10 North D. (1955), “Location Theory and Regional Economic Growth”, Journal of Political

Economy, vol. 63, pp. 243-258 OECD (2001a), Measuring Globalisation: The Role of Multinationals in OECD Economies, OECD,

Paris OECD (2001b), OECD Territorial Outlook, Paris Paelink J. and Klaassen L. (1979), Spatial Econometrics, Saxon House, Farnborough Perrin J.-C. (1995), “Apprentissage Collectif, Territoire et Milieu Innovateur: un Nouveau

Paradigme pour le Développement”, in Ferrão J. (ed.), Políticas de Inovação e Desenvolvimento Regional et Local, Edição do Instituto de Ciencias Sociais de Universidade de Lisboa, republished in Camagni R. and Maillat D. (2006), Milieux Innovateurs, Economica-Anthropos, Paris, pp. 99-128

Perroux F. (1955), “Note sur la Notion de Pôle de Croissance”, Economie Appliquée, vol. 7, n. 1-2, pp. 307-320

Pisati M. (2001), “Tools for Spatial Data Analysis”, STATA Technical Bulletin Reprints, vol. 10, pp. 277-298

Romer P. (1986), “Increasing Returns and Long-Run Growth”, Journal of Political Economy, vol. 94, n. 5, pp. 1002-1037

Romer P. (1990), “Endogenous Technological Change”, Journal of Political Economy, vol. 98, pp. S71-S102

Sirkin G. (1959), “The Theory of Regional Economic Base”, The Review of Economics and Statistics, vol. 41, n. 4, pp. 426-429