STUDY PROGRAMME ON EUROPEAN SPATIAL PLANNING

STRAND 1.4

SPATIAL INTEGRATION

A paper presented by the co-ordinating workgroup 1.4: Belgium, France, Portugal, United Kingdom

Authors: Ph. De Boe, King Baudouin Foundation (B)

C. Grasland, UMR Géographie-Cités / Equipe P.A.R.I.S. (F) A. Healy, ECOTEC (UK)

with specific contributions of:

Th. Hanquet, King Baudouin Foundation (B) D. Robert, UMR Géographie-Cités / Equipe P.A.R.I.S. (F)

Final version: Ph. De Boe and Th. Hanquet

23 December 1999

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TABLE OF CONTENTS

TABLE OF CONTENTS.....................................................................................................................3

PREAMBLE ........................................................................................................................................4

PART I THE CONCEPT OF SPATIAL INTEGRATION .............................................................. 5

1. WHAT IS SPATIAL INTEGRATION? ..........................................................................................7 1.1. SPATIAL INTEGRATION IN ITS DIFFERENT FRAMEWORKS ..........................................................7 1.2. A REVIEW OF THE POSSIBLE MEANINGS OF SPATIAL INTEGRATION ........................................15 1.3. LINKS BETWEEN THE CONCEPT OF SPATIAL INTEGRATION AND OTHER FIELDS OF

RESEARCH................................................................................................................................22

2. HOW CAN WE FORMALIZE SPATIAL INTEGRATION?.......................................................27 2.1. A RELATIONAL APPROACH ......................................................................................................27 2.2. A MULTIDIMENSIONAL APPROACH..........................................................................................28 2.3. A DYNAMIC APPROACH ...........................................................................................................28 2.4. A MULTISCALAR APPROACH....................................................................................................29 2.5. A SYSTEMIC APPROACH...........................................................................................................30 2.6. SYNTHESIS: FURTHER STEPS TOWARD A BETTER UNDERSTANDING........................................30

3. HOW CAN WE MEASURE SPATIAL INTEGRATION? ..........................................................33 3.1. ANALYSIS OF THE DISCOURSES ON SPATIAL INTEGRATION .....................................................34 3.2. ANALYSIS OF SPATIAL INTERACTION (FLOWS AND BARRIERS) ...............................................34 3.3. ANALYSIS OF SPATIAL PATTERNS (HOMOGENEITY, DISCONTINUITIES, MULTISCALAR

ANALYSIS) ...............................................................................................................................38 3.4. ANALYSIS OF SPATIAL COOPERATION .....................................................................................42

CONCLUSION ..................................................................................................................................44

PART II EXPLORATORY STUDIES ON SPATIAL INTEGRATION...................................... 47

EXPLORATORY STUDIES 1: SYNTHESIS OF PROPOSALS MADE BY NATIONAL FOCAL POINTS................................................................................................................................49

1.1. SYNTHESIS OF THE GENERAL COMMENTS ...............................................................................50 1.2. SYNTHESIS OF THE PROPOSALS FOR PHENOMENA AND INDICATORS.......................................51 1.3. EXAMPLES OF PHENOMENA AND POSSIBLE INDICATORS - SYNOPSIS......................................52

EXPLORATORY STUDIES 2: FLOWS AND BARRIERS ............................................................54 2.1. THE MEASUREMENT OF FLOWS ...............................................................................................54 2.2. BARRIER EFFECTS....................................................................................................................76

EXPLORATORY STUDIES 3: ANALYSIS OF SPATIAL PATTERNS (HOMOGENEITY, DISCONTINUITIES, MULTISCALAR ANALYSIS) .....................................................................89

3.1. SPATIAL HOMOGENEITY AND TERRITORIAL DISCONTINUITIES................................................89 3.2. MULTISCALAR APPROACH OF SPATIAL COHESION ................................................................118

EXPLORATORY STUDIES 4: CROSS-BORDER COOPERATION...........................................130 4.1. INTERREG II A PROGRAMMES ...............................................................................................130 4.2. TWINNING ARRANGEMENTS BETWEEN MUNICIPALITIES .......................................................143

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PREAMBLE

This paper presents the results of the work of the Belgian, British and French partners on the criterion "Spatial integration". The three involved partners have tried to produce a common vision of the topic, but also to explore it in different but complementary ways.

As the aim is to come to a vision shared by all the national focal points (NFP's), different contributions are considered, although all the available material is not exposed here in detail. In particular we take on board:

- the views expressed by the national focal points in their response to the call for proposals launched by the European Commission (see Exploratory studies 1);

- the comments expressed by participants during the meeting of 07/12/1998 in Brussels and during the three workshops, in Stockholm between 22nd and 24th February 1999, in Nijmegen between 14th and 16th June 1999 and in Rome between 18th and 20th October 1999;

- several written and oral contributions provided by partners of the members of the workgroup; - the results of the meeting of the workgroup of 22/04/1999 in Paris; - the most useful comments made on earlier drafts by NFPs.

This report is divided in two parts:

- Part I is an attempt to define the concept of spatial integration and to propose some general guidelines for the construction of spatial integration indexes or for the development of further work on this subject;

- Part II is a set of exploratory studies on spatial integration which have been realised by members of the Belgian, French and UK NFP’s during the last months. Those original research papers try to develop new concepts and new methods for the measure and the cartographic representation of spatial integration. They try also to apply those concepts and methods to empirical situations (at European scale when possible) in order to validate their relevance and practicability.

A summary report dated 29 November 1999 has been produced (25 pages, including a selection of figures and maps).

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PART I

THE CONCEPT OF SPATIAL INTEGRATION

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1. WHAT IS SPATIAL INTEGRATION?

According to the complexity and the importance of the concept of spatial integration, it is necessary to start the analysis with an exploration of the different possible meanings which should be as wide as possible. In a first section (1.1) we present briefly the importance of the concept of spatial integration, firstly by framing it in the general context of the concepts of integration developed at the European level, then by exploring its meaning and contents in the frameworks of the European Spatial Development Perspective (ESDP) and of the present Study Programme on European Spatial Planning (SPESP). In a second section (1.2) we explore different possible meanings of spatial integration and related concepts like interaction, cohesion, cooperation, etc. Finally (1.3) we explore privileged links of the concept of spatial integration with other fields of research in order to delineate its specificity.

1.1. Spatial integration in its different frameworks

1.1.1. The emergence of the spatial dimension in the European views of integration

The idea of integration (social, economic and political) underpins the formation of the European Union. Integration tends to be regarded as a positive response to the disintegration of traditional structures caused by globalisation. The EU is widely perceived as being on the right scale to counterbalance the influence of the other major integrated regions in the world.

Within the EU several distinct concepts of integration can currently be identified: - The original concept, based around economic integration, is bound up with the Single Market

and is promoted through the Structural Funds. This concept is linked to the notion of "economic and social cohesion" promoted in the Treaty on European Union (TEU) and implies a need to address disparities in these two areas.

- Support for social integration has also been promoted by bringing together different nationalities and the exchange of experience between countries (e.g. ERASMUS and INTERREG or RECITE projects).

- The Amsterdam Treaty introduced the concept of "territorial cohesion" in the Treaty on European Union through Article 7D, which addresses the role of services of economic general interest. The inclusion of this article suggests that a new concept of cohesion, which is based more on relationships and exchanges, is emerging.

- The EU has recently acknowledged the need to increase the integration between policies operating within a given territory. This promotes an emphasis on the spatial dimension of integration.

- It is also possible to identify one final emerging concept. This involves the use of spatial integration as a means of identifying functional territorial units, which might be promoted as efficient spaces in which to live and work. These may span administrative boundaries and may require pro-active policies to realise their potential.

Even though there is little explicit mention of spatial integration in European documents, the allied concept of "territorial cohesion" is increasingly used in documents drawn up at a supra-national scale. The most prominent example is the Council of Europe, which puts this concept at the very heart of its current pan-European perspective. The OECD has also started to show more interest in the concept in recent years.

Since there is a will to consider the spatial dimension in several European policies, it is only natural to also focus on spatial integration. It is therefore not surprising to find the concept of

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spatial integration included within the seven criteria for territorial differentiation which are gradually being introduced and described through the ESDP process.

1.1.2. The evolution of the concept of “spatial integration” in the ESDP

The first document on European spatial planning issues, Europe 2000+ (1994), considers that territorial disparity, spatial imbalance and a lack of coherence across borders and between countries might undermine the European objectives of social and economic cohesion. The ESDP approved by the European ministers of spatial planning in Potsdam in 1999 also reflects this concern with its twin emphasis on balanced competitiveness.

The concept of spatial integration as an important criterion to assess the situation of the various parts of the European territory emerged progressively during the works on the first project of ESDP. When it first appeared in 1995 (under the term "Spatial articulation"), it was centred on the specific aspect of cross-border relationships, but gradually it extended to a more comprehensive vision, summarised in the first official project of ESDP (Noordwijk, 1997):

"Spatial integration expresses the opportunities for and level of (economic, cultural) interaction within and between areas and may reflect the willingness to co-operate. It also indicates, for example, levels of connectivity between transport systems of different geographical scales. Spatial integration is positively influenced by the presence of efficient administrative bodies, physical and functional complementarity between areas and the absence of cultural and political controversies."

The next version of the ESDP project (Glasgow, 1998) maintained the definition while removing the terms "(economic, cultural)" that related to the interaction, suggesting that the meaning was even broader than previously considered, and could extend to all kinds of relationships.

Work on the criterion "Spatial integration" in the framework of the study programme started on basis of the above mentioned definition, but it became gradually clear that even in the field of spatial development, the words could refer to several other meanings, some slightly and some quite different.

Meanwhile, the official version of the ESDP was adopted in Potsdam in May 1999. Its text largely differs from the projects of Noordwijk and Glasgow. In particular, there are no more indications on the definition of spatial integration (nor on definitions of the six other criteria). Furthermore, in contrast with the Noordwijk project, there is no more explicit reference to the concept of spatial integration in the Potsdam document. In other terms, the "final" version of the ESDP does not provide any reference allowing to orient the work.

This shows - if needed - that even if the Noordwijk document gave the impulse to the research on the seven criteria, research on the criterion "Spatial integration" and its related concepts can not be based on one reference document, but must adopt a broad approach, based on confrontation of ideas, dialogue and creativity.

In the Postdam ESDP, the term "integration" is most of the time associated with the "economic" qualifier rather with the "spatial" qualifier, or points toward transport issues. Even if the Potsdam document does not provide indications on the concept of spatial integration, the fact that an European spatial planning document places considerations on economic linkages and on transport networks at the heart of the concept "integration" is probably an interesting indication about commonly agreed views on the concept of (spatial) integration, even if there are many other possible points of view.

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The term "integration" also appears in the ESDP when it is question of integration of non-member countries into the European Union. Integration is seen at the same time as a challenge and an opportunity for the balanced development of the European territory.

The CEMAT, in its Guidelines for Sustainable Spatial Planning in Europe, develops a similar idea, recognising that sustainable development implies a long-term spatial balance, choosing to emphasise the importance of trans-continental relationships and the internal spatial development of the European continent.

The balanced development of the European territory promoted by both documents seems difficult to achieve without spatial integration. In its absence we will encounter islands of economic advantage surrounded by less well developed areas. This applies particularly to the polycentric development model such as proposed by the ESDP. Paradoxically though, in some cases the deepening of spatial integration in some areas in application of a polycentric development model may weaken relations outside of this area. For example proposals to promote increased internal trade linkages within the Baltic Sea territories may well displace external trade orientations. A polycentric model might result in ‘Islands of integration’ if it is not based on a global and balanced vision of spatial integration. This is probably why the ESDP also emphasises the role of partnerships in balanced spatial development.

As the ESDP recognises, merely making areas more accessible has both benefits and disbenefits – captured by the twin concept of pump and tunnel effects1. An accessible region may not be economically integrated with the rest of Europe but may merely be a dependent market. Spatial integration may therefore imply a degree of spatial balance between linked areas, characterised by the presence of two-way relationships.

Whereas the concept of (spatial) integration is generally recognised as a crucial one for spatial planning, it is not easy to delineate. If the aim is to reach a common definition, the task is even more difficult, because the views on this point are various, while awareness of all the possible meanings of this concept is not generalised.

1.1.3 The criterion "spatial integration" in the context of the Study Program (SPESP)

When the call for proposals of the Study Program on European Spatial Planning (SPESP) was launched in May 1998 for a deepening and improvement of the European Spatial Development Perspective (ESDP), it was asked to the research team from each of the 15 Member States of the European Union (EU) to make propositions related to three themes: I. Development of spatial indicators II. Strategic study on new urban - rural partnership III. Cartographic illustrations of policy options of the ESDP

In the framework of the theme 1, it was asked to propose indexes of spatial differentiation related to seven criteria: 1.1. Geographical Position 1.2. Economic Strength 1.3. Social Integration 1.4. Spatial Integration 1.5. Land-Use Pressure 1.6. Natural Assets 1.7. Cultural Assets

1 Pump effects occur where new or improved transport links enable dominant economies to extend their sphere of influence. Tunnel

effects reflect the impact of introducing High Speed Services through reducing the number of secondary connections, so reducing the

integration of territories no longer connected.

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It was also asked to produce synthetic indexes which should be able to take into account the seven dimensions of spatial differentiation in a global approach. These criteria, it is argued, provide a starting point for recognising and assessing the spatial dimension of the ESDP and, in combination, have a particular value for the purpose of spatial analysis.

During the meeting of 07/12/1998 in Brussels, the NFPs were asked to rank their interest for each of the seven criteria. The classification made on basis of their answers reflects a general interest in spatial integration. But obviously the challenge to try to capture the reality of the concept remains, because only a few NFPs have expressed specific views on the concept and these are very diverse.

A tentative synthesis of the NFPs contributions on spatial integration (Exploratory studies 1) shows indeed how great a variety of views exist in these matters. It also shows that some phenomena are recognised as important for spatial integration, although the indicators to assess them are difficult to define.

All this indicates that one of the first tasks related to the work on this study strand is a deepening of the concepts, taking into account not only the concept of spatial integration itself, but also some other related concepts that may help to delineate the field of the study.

For a first approach, the coordinators of the workgroup decided to refer to the first official project of ESDP (Noordwijk, 1997)2 that defines the criterion "Spatial integration" as: "Opportunities for and level of interaction within and between areas".

This definition is precise in the sense that it centres on spatial interaction, but rather comprehensive in the sense that it does not focus on any type of interaction or on any spatial level. Moreover, it mixes two different aspects of spatial interaction, "opportunities" and "level". While the level of interaction seems specific to spatial integration, opportunities may cover a very large range of issues, from physical conditions and constraints to demographic and economic resources and to political and social circumstances.

As it was recognised by all participants of the 1st and 2nd meetings of the NFPs, spatial integration is, perhaps, one of the criteria which is most directly related to the concept of spatial planning itself. Consequently, and by virtue of its comprehensive nature, it may often overlap the fields of interest of the other criteria. From the start it was noticed that that criterion has an overlay with the criteria "Geographical position" (1.1) but also with all the other criteria of spatial differentiation and probably with all the other parts of the call for proposals. In other words, the question of spatial integration is present everywhere in the SPESP and it is well known that "what is everywhere is also nowhere".

For example, there is a potential overlap between spatial integration and geographical position on the questions of distances and of transport and communication infrastructures. Some less obvious overlaps may occur with social integration (about integration factors such as language, culture, political sensitivity), with economic strength (economic functions generating relationships), with land use pressure (impact on migration moves through effects on land prices) or with natural and cultural assets (common resources that can account for spatial relationships). Links exist also with work on the urban - rural relationships typology and urban - rural partnerships considered under theme 2.

Exploring these inter-relationships and building linkages with national teams developing other areas of work is thus crucial. Most of the identified fields of potential overlap concern the potential and conditions for spatial interactions but not interactions in themselves. In that sense, there is no actual overlap, provided the study of spatial integration concentrates on its specific "interaction"

2 The first ESDP has been adopted only in May 1999.

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dimension. In that case, the six other criteria can be seen as providers of inputs for the spatial integration criterion.

Accordingly, the coordinators of the workgroup on spatial integration decided early that the final report could not be limited to the production of a simple list of indicators, nor that these would enable a precise determination of the degree of spatial integration of an area. Rather the indicators should enable a more informed analysis of key questions (which remain to be determined) and the final report should also identify a number of areas where it considers further work might be justified.

1.1.4. First questions encountered in the context of the SPESP

The exercise of synthesising the proposals of the research teams concerning the criterion "Spatial integration" – as it was agreed as a first step during the first meeting of the NFPs - confirmed from the very beginning that the work on that topic would be a difficult one, for different reasons:

- owing to its short duration, the study programme had to be based on an inventory of previous analysis, together with some new activity. However, it has to be acknowledged that the concept of spatial integration, whether on a European scale or at a lower level, has never been examined in detail previously. It is therefore necessary for the analysis to assume an exploratory nature;

- the examination of the answers given by the NFPs to the call for proposals, as well as the comments received during the course of the study programme, reveal the breadth of the concept of spatial integration and the very diverse, and sometimes divergent, expectations that it generates. The diversity of meanings even appears within individual workgroups as a result of the differing 'national' contexts and different professional profiles of the partners. But it must be said that although this sometimes presents management difficulties, this diversity has to be regarded as an asset. After all, it is only a reflection of reality (something which should always be kept in mind) and offers a genuine opportunity to test the feasibility of a transnational approach.

It thus proved that it would be vain to propose any indexes of spatial integration at European scale without a clarification of this very large and fuzzy concept. Of course it is obvious that this clarification could not be wholly achieved in the one-year period of the SPESP, but it seemed a valuable challenge to try to progress in this way.

Challenge seems an appropriate term, because even a simple question such as: "how to measure the level of spatial integration of a given territory?" raises numerous other questions. The workgroup reached a consensus that it would be best not to base all work from the start on a definition that would limit the significance of such a new, sensitive and politically important concept.

One of the first tasks was to delineate the field of research taking into account those covered by the other workgroups. As the issue of the potential overlay with the criterion "Geographical position" had been raised raised, the workgroup on spatial integration made a proposal of delimitation between the two criteria based on the distinction between potential relations (related to accessibility and geographical position) and effective relations (related to flows and spatial integration).

At this occasion, it was noticed that the French team could provide an interesting input to the work on spatial integration thanks to its expertise in the fields of discontinuities and barrier effects. French researchers were thus invited to join the workgroup. A similar proposal was made to any team interested in the topic.

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Geographical Position and Spatial Integration in the framework of the SPESP

GEOGRAPHICAL POSITION

SPATIAL INTEGRATION

SOCIAL INTEGRATION

ECONOMIC STRENGTH LAND USE PRESSURE

NATURAL ASSETS

CULTURAL ASSETS

Accessibility,Transportation network,Distance to ressources,Potential relation

Flows,Will to cooperate,Complementarities,Effective relation

I

I

Global analysis of potential/effective relations (barriers & discontinuities)

Accessibility to economic ressources & barriers to international trade

Accessibility to social ressources & barriers to social interaction

Accessibility to cultural ressource & barriers to touristic relationships

Accessibility to natural ressources & influence of physical constraint to relationships

Negative consequence of network infrastructure & sustainable development of a multimodal transportation system

Accessibility in urban network, transportation nodes & gates, metropolisation process

Cartography of accessibility, barriers & discontinuities at different scales

IDENTIFICATION OF SOME TRANSVERSAL TOPICS RELATED TO GEOGRAPHICAL POSITION

III V

II VI

IV

II

III

IV

V

VI

E.E

.A.

EU

RO

ST

AT

A

URBAN-RURALPARTNERSHIP

A

CARTOGRAPHY

B

B

O.E.C.D.

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1.1.5. Spatial integration, flows and willingness to cooperate

As it was discussed during the Stockholm's meeting the importance of flows between places is not sufficient to define the existence of spatial integration. In many cases the importance of flows is not symmetric and is related to social or economic discontinuities between both sides of a boundary (e.g. inverse flows of residential location and active population between France, Germany and Switzerland in the Regio Basiliensis). The dissymmetry of flows implies a dissymmetry of social and economic relations which can produce political tensions between the members of territorial communities located on both sides of a boundary.

For the spatial integration workgroup, it is clear that the analysis of flows between places is an important part of the work to be realised, but that it is certainly not sufficient for the construction of indexes of spatial integration in a wide sense.

It must also be kept in mind that flows help to define a material dimension of spatial integration (objective approach) but that they do not give directly information on the mental dimension of integration (subjective approach), which is probably the most important in a long-term perspective. If we admit (along with the Noordwijk project of ESDP) that one of the most important aspects of spatial integration in an European development perspective is the "willingness to cooperate", then the flows alone are not necessarily a good measure of the target phenomenon, neither are barriers effects (difference between observed and potential relations) which can be generated by a large range of parameters, physical as well as economic, social or institutional. We can feel that the mental dimension expressed in the terms "willingness to co-operate" must be taken into account not only because it is a motor to integration but - before all - because it can largely influence its results for the concerned entities and their population.

The practical problem is that information on flows (of goods, persons, information,…) and information on the "willingness to cooperate" are both difficult to find at the European scale.

- There are very few harmonised databases on flows between territories of the EU at a scale smaller than the States as a whole. It is not impossible to find some harmonised matrixes of exchanges at different periods of time which could allow to measure the progress of European integration through the comparison of (for example) indexes of barriers effects related to the crossing of international boundaries. But for other types of interactions at other spatial levels the available information is quite scarce.

- In the case of "willingness to cooperate" the situation is even more difficult because there are no previous studies providing "ready-made" indexes for the measure of such a phenomenon - if we admit that it can be measured -, and there are no databases dedicated to this type of topic, nor at a European scale nor at smaller scales. However this did not discourage the workgroup from trying to provide some indicators linked to co-operation (see Exploratory studies 4).

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Territorial and sectoral dimensions of spatial integration

Spatial systems :

Territory A Territory BF

orm

s o

f in

teg

rati

on

:

Env.

Eco.

Soc.

Env.

Eco.

Soc.

Territorial integrationof sectorial policies

Spatial harmonizationof sectorial policies

Gobal (sectorial and spatial)integration of policies

Eco. : economy Env. : environmentSoc. : social issues

Elements of the patial systems :

Env.

Eco.

Soc.

Env.

Eco.

Soc.

Env.

Eco.

Soc.

Env.

Eco.

Soc.

Consider two territories (States, regions) and three domains of political action (economical, social, environmental) which are not integrated to each other (e.g. one agency for each State and each sector):

(1) territorial integration of sectoral policies is an attempt to harmonize the political action between different sectors inside a given territory.

(2) spatial harmonisation of sectoral policies is the reverse attempt to harmonize the political action inside a given sector but between different territories.

(3) global harmonisation (sectoral and spatial) is the combination of the two previous approaches and leads to an harmonisation of political action between different sectors and between different territories.

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1.2. A review of the possible meanings of spatial integration

The following sections are as far as possible based on recent literature on spatial integration and neighbouring concepts.

1.2.1. Territorial and sectoral dimensions of spatial integration

Among the various interpretations of the concept of spatial integration, one can identify two main sets of meanings, that can be summarised respectively as "integration between different domains on the territory" and "integration between territories in different domains".

Those two different meanings appear both in our first reference, the ESDP project of Noordwijk. The definition mentioned under point 1.1.2 seems to belong to the second meaning ("integration between territories in different domains"). Another mention of the terms "spatial integration" seems to points toward a similar sense, although more focused on cross-border relationships: "There are also clusters in cross-border regions. There, however, the spatial integration is lacking. Even if there is a need and a willingness to co-operate, the administrative conditions for developing such geographically closely-knit networks are still difficult." (II.C.1.a)

This is however far from being the main meaning of "spatial integration" in the ESDP project. In most cases, the document refers to spatial integration as a process aiming to integrate different sectoral policies or objectives on a territorial basis. In other words, spatial integration points toward the first meaning, that is integration between sectoral approaches on a given territory. The ideas of a "framework for spatial integration" and of a "strategy for spatial integration" also mentioned in the text seem to go along with this meaning. A similar idea can be found under the terms "geographic integration" several times mentioned in part IV of the document. In this sense, spatial integration can be seen as one of the main objectives of the ESDP approach.3

In the Potsdam ESDP, even if spatial integration is not mentioned as such, one can find in chapter 4 of Part A a few mentions of "integrated spatial development policy" that seem to point toward the same idea.

Work on the spatial integration criterion in the framework of the study programme has since the start been based on the second meaning, that is "integration between territories". This option seems natural as the call for proposals describes the work on the criteria by reference to part II.E of the Noordwijk project of ESDP. Another (a posteriori) justification is that the aim of the work is to differentiate areas, which is not easy with the second meaning but would be even more difficult with the first one, which seems quite uneasy to assess and quantify.

This does not mean however that the concept of "integration of approaches / policies on a territory" is not worth interest in the context of European spatial planning. At the contrary, it seems particularly promising, and several parts of the work in theme 2 - in particular the study strand 2.4 "Policy and Governance implications" - may constitute privileged fields for such an approach in the particular field of rural - urban relationships. Moreover, the Potsdam ESDP recommends in its chapter A.4 global approaches of this type for areas such as networks of urban regions, Eurocorridors, cities and regions at the external borders of the European Union, natural areas contributing to biodiversity, areas of particular significance for the heritage and coastal areas.

3 The article "Beleidsvoering in de ruimtelijkeplanning via dynamische netwerken en onderhandelingsprocessen" (Prof. J. Van den Broeck, KUL (Belgium), 1999) is related to the same perspective, focusing on integration between different actors involved in projects on a given territory.

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1.2.2. Spatial integration and spatial interaction(s)

As the Noordwijk project of ESDP sets the main focus on spatial interaction ("spatial integration expresses the opportunities for and level of (economic, cultural) interaction within and between areas"), one may question this concept.

In itself, the term "interaction" can seem at least as global as the one of "integration". However, this term, often coupled with the "spatial" or "territorial" qualifier is often used in scientific literature, in particular by geographers.

In a wide sense, the concept of spatial interaction can be related to any kind of relationship between places (connexity, similarity, flows, proximity) and one could assimilate the analysis of spatial interaction to spatial analysis in itself, or even to geography.

In the practice of (mainly geographical) research, spatial interaction often takes a more limited and technical meaning and may refer to a phenomenon described as "decreasing of the intensity of flows with distance". Different spatial interaction models have been built in order to give account of this phenomenon, many of them relying on the general gravity model, based on distance and on relative weights of the considered entities.

Taking into account the ESDP definition, its global approach as well as the history of the criterion, it seems that "interaction" must be understood here as a rather comprehensive concept, that can not be directly limited to some domains of relationships, nor even to spatial interaction as it is generally understood. For example:

- the definition mentions "interaction between areas" rather than "spatial interaction", which could indicate that distance is not necessarily seen as central (maybe because the concept of distance is more specific of another criterion, "Geographic position");

- the fact that in the Glasgow draft of the ESDP project (1998), the definition of the criterion "spatial integration" is enlarged through the suppression of the terms "(social, economic)" seems to indicate that the range of interactions to take into account must not be limited to some pre-defined domains.

The ESDP definition, through its reference to "willingness to co-operate" and to "absence of cultural and political controversies", also indicates that the social and human aspects play an important role in an open minded approach of the concept of spatial interaction.

Besides its various interpretations, use of the term "interaction" in the definition of the Noordwijk draft of ESDP can also be seen as conveying some implicit ideas through its etymology. The two components of the word, "inter" and "action" seem to point respectively toward the idea of interrelation (going both ways and producing "feedback effects") and toward the idea of action (dynamic, process-oriented). Those two points of view are useful to keep in mind when exploring approaches that rely on more "static" foundations, such as those mentioned in the next point.

1.2.3. Spatial integration and territorial homogeneity

A delicate question in the study of spatial integration is the link with spatial patterns such as the spatial distribution of men and activities, often translated in terms of spatial distribution of parameters such as wealth, demographic parameters, education or living conditions. The workgroup could observe that there are several potentially different points of view about this question, among which some seem more spontaneously explored than others.

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One facet of spatial patterns is the question of homogeneity and discontinuities, which is often mentioned in relation with spatial integration. Several works on spatial organisation rely on the analysis of spatial homogeneity and territorial discontinuities. Various parameters are examined in this perspective, such as demographic parameters (population density, fertility rates, age structures) or economic parameters (GDP). When data are available at different periods, the evolution of those discontinuities (or homogeneity) can be assessed and provide a more dynamic vision.

The workgroup sees several reasons to include the study of homogeneity and discontinuities in the framework of the work on spatial integration: - a first reason linked to the need to clarify the concept, as links of similarity are evoked as a

possible feature of spatial integration (see tentative definition under point 2.6), a point that certainly deserves discussion;

- a second reason linked to the frequent association between the concept of integration and the concept of cohesion, often viewed in terms of equalization of (generally socio-economic) parameters in the European documents (this concept is further discussed under next point);

- a third practical reason linked to the lack of data in matter of flows that suggests a "work-around" through the analysis of the evolution of discontinuities.

However two important questions were raised in the course of the study: - are spatial homogeneity / discontinuities a cause and/or a result of spatial interactions? - in which way do interactions and homogeneity/discontinuities influence each other?

Analysis of spatial homogeneity or discontinuities related to analysis of flows and barrier effects suggests that there are no simple answer to those two questions:

- discontinuities can be seen as generating difficulties for spatial interaction / integration, in the sense that differences in behaviour, cultural and linguistic background or in socio-economic level can reduce the possibilities of relationships and interaction;

- they can either be seen as potential for increased spatial interaction / integration, in the sense that the "differential" they provide or the complementarity they show can generate flows between areas (in the example about Italy (see Exploratory study 3.1.2), homogenisation trends in matter of GDP go along with population redistribution, which might point toward such type of mechanism);

- discontinuities can also be seen as the result of a lack of spatial interaction / integration (in the sense that they imply a weak level of exchanges and flows between areas, revealing for example barrier effects) as well as the result of strong flows generating a differentiation between areas (what can be observed in the so-called phenomenon of "regionalisation");

- reversely homogeneity can be related either to the absence of interaction (in cases where borders "protect" spatial entities which could otherwise be submitted to pump or tunnel effects for example) or to flows tending to equalize the situations in a sort of "communicating vessels" effect;

- but finally homogeneity and discontinuities can be seen as neither cause or effect but as simple indicators of less visible phenomena that also have some link with spatial interaction and / or integration (for example, discontinuities in population density do not necessarily indicate a low level of spatial interaction / integration, but may reveal some (social, economic, environmental) difficulties of a given area that also act on spatial integration).

The link between homogeneity and integration is thus far from being simple, particularly if considered at a given moment in time. Analysis of the trends is often required (but not necessarily sufficient) in order to better assess this link. Besides it seems very important to deepen the analysis of its nature in a political perspective, because promoting integration in terms of increased interactions can not be seen as a simple way to generate more homogeneity between territories. It

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may even result in generating more inequalities if the concerned spatial entities are not strong enough to make the best of a more open situation.

Finally it must be said that if integration can not be viewed as an aim in itself, nor can homogeneity for its own sake. The ESDP emphasizes that the diversity of the European territory and of its cultural features are one of its assets. It seems difficult to consider that territorial and cultural diversity can survive totally independently from other dimensions of social life. The main challenge may be to achieve a balance - or even better, a synergy - between spatial equity and spatial diversity.

1.2.4. Spatial integration and (spatial) cohesion

In the same range of ideas, European documents often refer to a term whose meaning seems close to integration: cohesion. It is most often used in the locution "economic and social cohesion", one of the fundamental aims of the European Union (Treaty on European Union, title I, article B).

(Economic and social) cohesion is not formally defined in the Treaty, but reference is made to the reduction of disparities: "the Community shall aim at reducing disparities between the levels of development of the various regions and the backwardness of the least favoured regions, including rural areas" (article 130a).

The "First report on social and economic cohesion" published by the European Commission (1996) analyses the situation of the European regions in the same perspective of assessment of inequalities and of their trends. In that sense, cohesion seems closely related to the idea of homogeneity, even if reduction of disparities is not seen as a purpose in itself, but as a way to attain a better "functioning" of the whole European Union by giving more opportunities to all of its components.

Spatial (or territorial) cohesion is much less often mentioned than social and economic cohesion, although it appears in some documents related to spatial planning. In the Potsdam ESDP, spatial cohesion is mentioned once (§ 108) about the necessity to provide an adequate access to infrastructure for all regions. The Congress of Local and Regional Authorities in Europe (Council of Europe) considers territorial cohesion as the final aim of the pan-European perspective under way4, and recommends the establishment of a Pan-European Observatory of Territorial Cohesion of the New Europe5.

By analogy with the concept of social and economic cohesion as meant by the European Union, one could see spatial / territorial cohesion as a situation where all parts of the territory would have equivalent opportunities in matter of territorial features. This seems confirmed by the fact that the terms "territorial cohesion" were added in the Amsterdam treaty in an article treating of the role of services of economic general interest (among which transport and postal services).

Of course, the role of territorial parameters such as location, altitude and climate indicates that reduction of such disparities is in some way an inaccessible aim, even if efforts can be made in order to alleviate the constraints of areas that are particularly disadvantaged from this point of view (ultra-peripheric areas, areas with harsh climatic or soil conditions for example). Some criteria studied in the framework of the study programme have a link with this idea of "spatial equity", notably "Natural assets" and "Geographic position", and also, if one includes more "man-made" territorial features, "Cultural assets" and "Land -use pressure".

4 Recommendation 41 (1998) of the Congress of Local and Regional Authorities in Europe on the new prospects for regional/spatial planning policy in Greater Europe. 5 Resolution 63 (1998) of the Congress of Local and Regional Authorities in Europe on new prospects for regional/spatial planning policy in Greater Europe.

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However this vision is linked to a particular point of view centred on the "equity" facet of cohesion. The works of the Council of Europe suggest that territorial cohesion can have a more dynamic meaning, apparently more closely related to the concept of spatial integration as we are trying to explore it.

Whatever the meaning given to it, the concept of territorial cohesion seems to be mostly used in political documents rather than in studies. But at the same time the project of establishing a dedicated Pan-European Observatory tends to show that it is considered as a reality deserving in-depth analysis.

1.2.5. Spatial integration and co-operation

The Noordwijk project of ESDP introduces in its definition the concept of "willingness to co-operate", as a basis for spatial integration. This adds an important dynamic element to our understanding of territorial (or spatial) integration. Co-operation is often associated to integration, although there are some fundamental differences between the two concepts.

A priori, the concept of spatial interaction, which is at the core of the definition of spatial integration, has no positive or negative sense. Spatial interaction generally relies on human motivation (even if natural phenomena such as floods may cause spatial interaction), but these motivations might not be shared by all actors nor lead to win-win situations.

If co-operation appears as one of the important aspects for discussion, it is probably because of the context of the project of ESDP, which tends to emphasise the positive aspects of spatial integration in the view of a balanced development. Co-operation appears as one of the conditions to channel spatial interactions toward fruitful developments.

In contrast, absence of co-operation may result either in a limited level of spatial interaction, as relationships will not be supported by all actors, or in unbalanced relationships solely ruled by the law of the strongest. Lack of co-operation can be observed in practice in some cases where actual relations are less than one would expect in view of shared interests, physical possibilities available, or presence of spatial systems to manage (e.g. river basins). But absence of co-operation may also exist where integration (in terms of interactions) is strong, and in those cases it may have harmful effects for some of the partners. This reminds of the danger of the pump effect pointed by the ESDP. Urban rural relationships for example may show situations where the urban areas are emptied of their population and of their financial resources through strong integration with the surrounding rural areas, as well as situations where the reverse happens. Other similar situations are likely to happen at other spatial scales.

Although the ESDP mentions the "willingness to co-operate", it must be said that co-operation does not always rely on willingness but also on need to co-operate. This allows us to distinguish between situations that require co-operation (even if the partners are somewhat reluctant) and situations where spatial integration is actually weak but the "willingness to co-operate" exists. In the second case, certain material organisational changes may lead to an increase in the significance of relations between areas.

Attempting to translate the concept of "spatial co-operation" into indicators is recognised as being a very difficult task, as co-operation mainly relies on a "state of mind" and on organisational patterns that do not necessarily imply easily measurable phenomena.

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1.2.6. Networks of places

For many authors, it is useful to reflect further on how places are linked, in order that integration might occur. Places are no longer considered as simple geographical constructs, rather they are defined through social analysis (for a review, see Amin and Graham, 1998). These stress that places are "articulated moments in networks of social relations and understandings" rather than "areas with boundaries around" (Massey, 1993; 66). In this configuration socially-constructed places are "non-contiguous, diverse, dynamic and superimposed. As well as being bound to place-based relations, cultural, social, economic, political and environmental links and relations can be stretched across space" (Graham and Healey, 1999 (forthcoming)).

This partially reflects a more sophisticated analysis of how networks of cities and towns interact, an area that Theme 2 explores further. As Dematteis notes we have witnessed the "passage from a functional organisation in which the centres are graded with a multi-level hierarchy (as in the models of Christaller and Lösch) to interconnected networks organised on the basis of the corresponding complementarities of the nodes and the synergies produced" (Dematteis, 1994). It also reflects an understanding that it is not places which interact but the people and organisations (actors) which inhabit that space.

Geoff Mulgan (1997) labels the current era one of 'Connexity'. This reflects the ESDP’s definition of spatial integration as: "Opportunities for and level of interaction within and between areas". In Mulgan’s view cultures, economies, social worlds, politics and environments all become driven by logics of increasingly intense interconnections and flows, over larger and larger geographical scales. A growing range of economic, social, and cultural interactions which are "both in place and out of place" (Adams, 1996; 279) are being supported by modern communications technologies (Graham and Healey, ibid.). Of course, it is possible that connections may only occur between specific sections of society. We may therefore witness different social geographies of spatial integration.

The Noordwijk project of ESDP suggests that measures of spatial integration will include levels of linkage between transport systems at different geographical scales. However, a fuller interpretation of the conception offered above implies that spatial integration is wider than simply transport linkages but includes all transactions (or flows) between areas.

To Mulgan, the growing importance of network-based connections, means that economies are increasingly driven by "the logical or 'virtual' regularities of electronic communication, a new geography of nodes and hubs, processing and control centres. The nineteenth century's physical infrastructures of railways, canals and roads are now overshadowed by the networks of computers, cables and radio links that govern where things go, how they are paid for, and who has access to what. The physical manifestation of power, walls, boundaries, highways and cities, are overlaid with a 'virtual' world of information hubs, databases and networks" (Mulgan, 1991;3).

1.2.7. Networks of actors

The societal networks which facilitate spatial integration are also an important consideration for the Noordwijk project of ESDP considers that spatial integration will be positively influenced by the presence of:

- efficient administrative bodies; - physical and functional complementarity between areas; and - the absence of cultural and political controversies.

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Those conditions may be viewed not only as potential for efficient integration but also as fundamental requirements in order that the concerned spatial entities may interact on an sufficiently equal and constructive basis as to produce beneficial effects for all of them or at least as to avoid globally negative effects for any of them.

These societal networks can be conceptualised by what Boden and Molotch (1994; 259) call the "thickness of co-present interaction", where "intense, recursive, face-to-face interactions are supported within urban space, with growing mediated flows of communication and contact via technical media, to the broader city and beyond" (Graham and Healey, ibid., also see Thrift, 1996b).

For if places are social constructs then it is the strength of social relations which sustains them, and any consideration of spatial integration will need to review the presence of linkages between different actors. This question is further explored under point 1.3.3.

Different types of actors may be distinguished (individuals, more or less structured groups, enterprises, political and administrative authorities) leading to different social geographies. This may imply choices in order to select indicators that (together) may be considered as providing a good representation of the different implicated actors.

In the same range of ideas, one could focus on some specific agents of spatial integration. Those may be multilocational firms, but also the so-called "transnational communities", in other words groups of migrants that establish links not only between their country of origin and their country of settlement, but also between their different countries of settlement. Research has been carried on that subject by Alejandro Portes and by Thomas Faist.6

6 From the comments on the first draft report made by Robert Kloosterman (Netherlands).

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1.3. Links between the concept of spatial integration and other fields of research

As it appears clearly in the previous review of literature it is difficult to find an agreement on a common definition of spatial integration. The reason of this difficulty can be explained in some way by the history of the concept of spatial (and social) integration in the field of social sciences. This historical background reminds of the discussion about integration vs homogeneity carried out under point 1.2.3. It helps to understand why similarity links are sometimes viewed as features of spatial integration, and also validates the choice of the workgroup to focus on the "organical" facet of integration as described hereafter.

1.3.1. "Mechanical" integration and "organical" integration

Even if it is decided to limit the investigations on spatial integration to an "objective approach" based on existing information, the problem remains very difficult because the structure of flows between territories is not independent from other spatial dimensions of social life. Spatial integration may be regarded as a "vicious" concept because, as it was established one century ago at the same time by geographers and sociologists, it has two very different meanings.

In Durkheim's Suicide as well as in Ratzel's Politische Geographie (both published in 1897), a clear distinction is made between two distinct forms of integration which may be called "mechanical" and "organical" integration.

(a) The "mechanical integration" refers to the structure of a system (social or spatial) and is a measure of its internal homogeneity (i.e. the level of similarity of peoples or places involved in the system). For example, we can consider that a society has a high level of mechanic integration if all individuals speak the same language, believe in the same religion, agree with the same norms, etc. In the spatial case, we could consider that the level of mechanical integration is high if all regions have more or less the same level of GNP per inhabitant, unemployment, access to infrastructure, etc. The Structural Funds, the Objectives and other policies developed by the EU in order to reduce the inequalities between regions are typically an attempt to improve the level of mechanical integration of the European territory in the sense proposed by Durkheim and Ratzel.

(b) The "organical integration" refers to the flows between members of a system (social or spatial) and is a measure of the intensity of relations between the sub-systems which can be defined as existing at a given time. This definition is much more complicated than the previous one because it implies the existence of 3 levels of analysis: (1) the individuals between which relations can be defined; (2) the sub-systems which realise a partition of individuals in different groups; (3) the whole system which is the sum of all the subsystems (and of all individuals involved in those subsystems). In sociological case, Durkheim considers for example (1) the case of individuals (individus) which are member of (2) different social groups (segments sociaux) and which are involved in (3) a society (société). Accordingly, he defines the "organical integration" (also called social concentration in his work) as the "degree of interrelation of social segments" ("degré de coalescence des segments sociaux") and demonstrates that the progress of the division of work in modern society is related to a progressive decrease of mechanical integration and a parallel increase of organical integration.

A very similar approach is proposed by Ratzel in spatial case. Through an analysis of the political stability of the great European empires and States of the XIXth century (Austria, Russia, Germany,…), Ratzel argues that the importance of commercial flows between each region of a State is more important for modern States than the homogeneity of social, political

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or economical conditions: "In order to transform the mechanical juxtaposition of regions of very different size and population into an organical growth, the task of the State is to favour the bringing together [of parts], the exchanges and the mixing of populations […]. A central power which is really strong can endure, without prejudice for its unity, the most diversified [social] life, as far as it benefits from developped and expanding commercial relations". The development of European policies for the development of flows at European scale between individuals (e.g. Erasmus) and between territories (e.g. Interreg) is clearly an attempt to develop the organical social and spatial integration of E.U. in Durkheim's and Ratzel's sense.

The concept of spatial integration becomes clearer if we distinguish between a mechanical integration (based on the homogeneity of the different parts of the territory) and an organical integration (based on the intensity of flows between the different parts of the territory). But as it was suggested by Durkheim and Ratzel one century ago, those two forms of integration do not have the same meaning and they can, in certain cases, be contradictory.

1.3.2. Spatial integration and social integration

Despite the apparent parallelism of Durkheim's and Ratzel's reflections about mechanical and organical integration, it is necessary to be very cautious when we apply their common framework to social and spatial situations. Indeed, as it was observed by Durkheim (but also by other sociologists like G. Simmel), spatial integration (mechanical or organical) is a necessary but not sufficient condition of social integration.

A good example of this point is provided by the question of what Durkheim calls the "material density" but in fact points toward what we would actually call the spatial accessibility. According to the definition proposed by Durkheim in the Règles de la méthode sociologique (1895), the material density which is "not only the number of inhabitants per square kilometres but also the development of networks of communication and transmission" is generally related to what he calls the "dynamic density" and which is in fact another term for the designation of organical social integration. The dynamic density can be defined "not only as the purely material closeness of the [social] aggregate, which has no effects if individuals or rather group of individuals remain separated by moral emptiness, but the moral bringing together for which the previous one is only an auxiliary and more generally a consequence. All things being equal to the size [of society], the moral density can be defined as the number of individuals which are in relation, not only commercial but also spiritual [in French: "moral"]; it is to say, which has exchanges of services and relationships of concurrency but will share a common life. That is why the best definition of the dynamic density of a people is the degree of interrelation of social segments".

In Durkheim's opinion, the spatial concentration is more a consequence than a cause of social integration, even if he suspects the possibility of positive retroaction between both categories of phenomena: "Material density […] is related to dynamic density and can, generally speaking, help to measure it. Because if the different parts of the population tend to be closer [in a spiritual sense], they will necessary build the ways which will favour this increasing closeness; and, on an other side, [social] relationships can be established between different points of the social mass only if [geographical] distance is not an obstacle, i.e is in fact suppressed.".

But if Durkheim admits the existence of correlations between social integration and spatial accessibility he has also pointed the existence of many exceptions and, according to the conflict between social sciences at the end of the XIXth century, he was very suspicious to the possible contribution of human geography (especially the Anthropogeography of Ratzel) to the constitution of a global science of material condition of the life in society that he called Social Morphology (Morphologie Sociale).

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One sentence of Durkheim can be considered as very significant for the activity of the SPESP program and more precisely for the researches engaged by the workgroups on spatial integration and on social integration: "If we want to know the ways a society is politically divided, the way those divisions are connected, the more or less important degree of fusion which exists between the parts of these society, it can not be done through a material inspection and by geographical observations: because those divisions are spiritual, even if they have some basis in physical nature". Another reflection of Durkheim about commercial relationships can be considered as a very accurate and modern contribution to the debate on the overestimation of the social and cultural effects of economic integration and the insufficiency of Maastricht and Amsterdam treaty: "As purely economic relationships let people separated to each other (en dehors les uns des autres), it is possible to have very important [economic relationships] without participating for this reason to a common existence. Trade flows over the boundaries which separate the nations do not imply that those boundaries no more exist".

According to Durkheim's reflection on the differences between social and spatial integration we can feel that the task engaged by the workgroup on spatial integration can take its full sense only if it takes into account the social and mental dimension of the concept, and we perceive that there can be an interesting complementarity inside the theme 1 of the SPESP between the studies of spatial integration and of social integration. It seems thus appropriate to focus one of the facets of the study on an aspect that may be seen as providing a link between both, that is co-operation between spatial entities.

1.3.3. Is it meaningful to speak about "links between places"?

Many approaches developed by geographers and spatial planners who want to measure spatial integration suppose that it is possible to analyse links between places, towns, regions without consideration of which actors, individuals, resources or social groups are located in those territorial divisions. The question of the vocabulary is important because it has very deep theoretical, methodological and also political consequences. When we are speaking about "links between places" we introduce a kind of reification or neutralisation of social, economic, political or cultural relations between individuals or social groups which are located in those places and we base our research on the implicit assumption that the internal social differentiations of territories has no effect on the emerging level of interrelationships between aggregates of people. From a statistical point of view, the analysis of "links between places" is generally based on the overoptimistic and generally false assumption that the relations between individuals can be summarised through synthetic parameters like mean or maximum values of interaction, accessibility or similarity computed on the whole populations or resources of territorial units.

In many studies about globalisation or metropolisation, the authors underline the growing importance of economic or individual relations which are established between people at long distance. It is argued that the decrease of the price of air-transport on one side and the development of new tools of communication like Internet on the other side will favour the development of new relation between actors for which the physical (geographical) distance will be less and less important. In the 1980's, some authors have even predicted the End of Geography in a near future. But the same predictions have been made at the beginning of the XXth century when telecommunications started to develop. The arguments developed at that time concerning the abolition of physical distance are exactly the same than the ones which are developed by the actual "gurus" of the electronic communication.

It has recently7 been established on empirical basis that the relationships developed in the "Information Space" are not likely to replace but rather to complete face-to-face contact in a near

7 NCGIA, 1999, Measuring and Representing Accessibility in the Information Age, Research Conference Report Asilomar Conference Centre, Pacific grove, California, Nov. 19-22 1998, 61 p.

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future. What is really interesting is the development of hybrid forms of accessibility where synergies can be observed between distance-based and electronic-based relationships between individuals and social groups. But in a first step those synergies concern only the richest part of population (the one which is able to pay and use new channels of communication). And even when they are diffused through the whole population, they can produce negative effects on global social integration because they make more easy the development of close community based on sex, gender, ethnicity or whatever. In this case, the concurrency between physical accessibility and electronic accessibility can produce a global decrease of social integration related to a partial decrease of neighbourhood effects in physical space.

This example shows clearly how an aggregated analysis of information flows between places could lead to criticable conclusions concerning the influence of electronic accessibility and physical accessibility on social integration.

What is usually called "links between places" is in fact an aggregate of relationships between individuals or social groups which do not have necessarily the same interests, the same capital of money or communication, the same opportunity to access to new technologies of transport or communication. Accordingly, the measures of integration which are based on the analysis of those aggregated level should be very cautiously interpreted and completed by surveys at the local scale of individuals and actors.

1.3.4. Sociological and geographical dimensions of interaction

Despite the importance of Durkheim's criticism against a spatially oriented approach of the question of integration, it is possible to perceive all the interest of a workgroup on the subject of spatial integration for the analysis of other dimensions of integration if conceptual clarification is clearly made.

In the works of G. Simmel and especially in the 1897's paper called "How do social forms keep on?" ("Comment les formes sociales se maintiennent-elles?") it is possible to find the same criticisms than in Durkheim's work against the possible influence of geography on the stability of societies. In a sense, Simmel appears to be more radical than Durkheim when he asserts that "The fact that individuals are distant to each other and, as a consequence, external to each other, is not an obstacle to social unity; the spiritual union of mens surmount their spatial separation. When people are separated by space, the unity is the result of actions and reactions that they exchange to each other; because the unity of a complex whole does not mean anything else than the cohesion, and the cohesion can only be obtained through the mutual cooperation between common forces". For Simmel, the location of a society on a common territory and the proximity between the members of the society is not a necessary condition of social unit, even if it can be a useful help for the constitution of a social group and its duration over a long period of time.

But Simmel also demonstrated in the same paper that the unity of a society can not be analysed at individual scale and should necessary be based on an abstract analysis of the form of social association. This abstraction of social forms which is the necessary condition for a science of society must be based on concepts of different types (e.g. concentric and cross-cutting social circles) and can involve a spatial dimension (e.g. the Town or the State).

The research of Simmel on urban sociology led him to develop a "geometry of social life" which is based on the analysis of the comparison between various types of social distances. The American followers of Simmel which developed his ideas and formed the School of Chicago (Park, Bogardus, Burgess,…) introduced more explicit assumptions on the possible relationships between social distance and spatial distance inside the towns (urban ecology). But many of the members of this group focus on the determinant of social structure on spatial structure and they do not examine the reverse assumption of a possible influence of spatial organisation on social dynamics of

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integration or exclusion. Recent researches suggest that the idea of a possible retroaction between social distances and spatial distances was present in Simmel's work but was deformed by its followers which focus on the unilateral determination of spatial structures by competition and conflicts between social groups8.

If we admit with Simmel that sociological position (permanent attribute of individuals or groups) and geographical position (attribute of the place where individuals or groups are located) are not independent dimensions of social life, it is possible to suggest a theoretical framework where the analysis of spatial integration can contribute to a better understanding of the dynamics of societies.

1.3.5. Necessity to delineate the specificity of spatial integration

The previous points all go to show how interrelated the concept of spatial integration is with concepts such as economic and social integration. Far from serving to narrow its field of application, the body of literature concerned with spatial integration is in fact serving to widen its influence.

This raises the question of identifying the specific nature of spatial integration, and reminds of another similar question, that is identification of the specific nature of spatial planning / spatial development. In both cases, the multi-facetted nature generates a difficulty to focus on specific issues, notably because isolating the spatial dimension of a reality is a rather abstract exercise whose practical purpose is not always obvious to perceive.

Faced at the same time to this difficulty and to a limited amount of available time and resources, the choice of the workgroup is to work along two tracks: - an attempt to formalize the concept of spatial integration, in order to provide material for

further discussion; - an analysis focused on a set of elements of spatial integration considered as specific and

significant: flows between places and willingness to co-operate, to which analysis of spatial patterns is added in order to complement the analysis of flows.

8 Ethington P.J., 1997, "The intellectual construction of 'social distance': toward a recovery of Georg Simmel's Social Geometry", Cybergéo, n°30, 20 p. [http://www.cybergeo.presse.fr]

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2. HOW CAN WE FORMALIZE SPATIAL INTEGRATION?

In this chapter we try to define briefly the basic principles of a global and scientific approach of spatial integration. Those different points can be regarded as guidelines for a formalisation of the notion of spatial integration. Five basic principles have been identified:

- a relational approach; - a multi-dimensional approach; - a dynamic approach; - a multi-scalar approach; and - a systemic approach.

Finally a tentative and provisional definition is worked out as a base for further research.

2.1. A relational approach

Proposal 1: The starting point of an analysis of spatial integration is (1) the definition of a set of territorial units 1..i..N and (2) the definition of various matrixes (N.N) describing the links between the territorial units.

Comments: - The choice of the territorial units is a crucial question because the structure of the links

between places can be very different according to the size, the shape or the nature of territorial units. For example the measures of integration based on the ratio between internal and external flows will be very different according to the scale of territorial units which is used for the measure of internal flows.

- The fact to use matrixes Place.Place instead of matrixes Place.Attributs indicates clearly that the aim of the approach is not to classify the territories (statistical oriented approach) but to classify the relationships between territories (spatial oriented approach).

Territorial integration

Internal flows

External flows

Territory

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2.2. A multidimensional approach

Proposal 2: Different types of links between places should be analysed in order to take into account the various dimensions of the concept of spatial integration. We can distinguish some basic families of links which are related to different families of matrixes Place.Place:

- Matrixes of Interactions (flows of individuals, goods, information, energy,…) - Matrixes of Similarity and Complementarity (social, economic, linguistic, demographic) - Matrixes of Spatial Proximity (distance in time, cost, kilometres) - Matrixes of Territorial Proximity (common belonging, contiguity ) - Matrixes of Network Proximity (connexity, distance in urban hierarchy) …

Comments: - Matrixes of interaction are certainly the most important way to evaluate spatial integration, but

the analysis of flow-matrixes (a measure of effective relations) can be significantly enriched by taking into account the other matrixes of similarity, complementarity or proximity (measures of opportunities of relations).

For example, the analysis of a matrix of migrations between different regions of Europe is related to questions of spatial proximity (decrease of the probability of migration with distance), territorial proximity (barrier effect along international boundaries), network proximity (specific migratory exchanges between certain levels of urban hierarchy), similarity (common language) and complementarity (differentials of wages, of labour force,…).

- A multidimensional approach is necessary if we want to both describe and understand the processes of spatial integration. Indeed, an increase (or decrease) of flows can be related to a great variety of changes in the other structural factors, and it is not possible to assess their relative effects without considering the global system of interrelations between all types of links between places.

2.3. A dynamic approach

Proposal 3: As relations and links are subject to modifications through time, spatial integration should be regarded as a process rather than a state. Accordingly, the analysis of spatial integration should be based on the analysis of links over different periods of time.

Comments: - In the case of interaction links, the evaluation is necessarily based on a period of time [t0,t1]

which is not the case for similarity or proximity links which can be measured at a given date (t). But in any case, the spatial integration process should be based on the comparison of flows at different periods of time [t0;t1], [t1,t2] … [tn;tn+1] or the comparison of similarities and proximities at different dates t0 …tn.

- Comparison through time is especially necessary when we try to compute synthetic indexes of barriers or homogeneity which are based on complex statistical and mathematical procedures. Generally, those indexes are subject to important variations according to the methodology used by the observer and what is important is not the absolute value of those indexes but the trends of their evolution (increasing or decreasing barrier effects, increasing or decreasing territorial homogeneity, etc.).

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2.4. A multiscalar approach

Proposal 4: Spatial integration processes do not occur at one scale of territorial organisation without consequences at the upper or lower levels of territorial organisation. Accordingly, it is necessary to develop a multiscalar approach in order to understand the conflicts or contradictions between the evolutions observed at each scale (European, national, regional, local).

- In some cases, the spatial integration process can be regarded as a zero-sum game where increasing integration at one level is accompanied by a (at least relative) decrease of integration at other levels. For example, the development of flows between regions belonging to different States (European integration) may be associated with a reduction of flows between regions belonging to the same State (National integration).

- In other cases, the spatial integration process is a win-win situation where the development of relations at one level is directly related to similar increases of relations at upper or lower levels. For example, the construction of the Trans-European-Networks is increasing the international accessibility of various places but may also have a positive impact on accessibility of towns and regions within a Member State.

- The geographical form of spatial integration may vary because it will occur differently at different spatial scales. World cities such as Paris, Frankfurt and London, are ‘integrated’ in a different manner than smaller economic centres, which may connect to the global markets through their relationships to such cities, or may form the hub of regional networks.9

- Several scales (international, inter-regional, intra-regional) can theoretically be considered. As the 'general' level used for the study of the criteria is the NUTS 2 level (in order to enable the combination of indicators), the inter-regional level appears to be the most appropriate to analyse spatial integration in this framework. However, for some aspects, the issue of intra-regional integration is critical, especially because of the emphasis on urban-rural inter-relationships in Theme 2. Sub-regional conceptions of spatial integration are not explored further, even if at that scale households and firms are now likely to have complex relations, which are less related to physical proximity.

- Territories outside the European Union should be taken into account. The Union is not a closed system and integration occurs both with neighbouring territories and more distant areas with shared economic or cultural relationships.

- Multi-level relationships between territories can be quite complex (as is conceptually illustrated hereafter). Hierarchical relationships clearly exist between different territorial scales which combine with horizontal relationships between similar territorial units. This complexity may not always be reflected in the analysis.

Territorial integration at different scales

9 For further discussion of this area see for example the work on "Global Cities" by Saskia Sassen (1991).

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2.5. A systemic approach

Proposal 5: Spatial integration should be based on a systemic approach in order to combine the analysis of spatial structures (which defines the opportunities of interaction between places), the analysis of spatial relations (which defines the level of effective interactions) and the analysis of spatial processes (which defines the consequences of realised or unrealised interactions).

- Spatial integration has a clear link with the concept of spatial systems10. Spatial systems can be conceptualised as areas or other forms of territorial organisation (natural as well as human) where a form of integration (interaction) is present. Spatial systems may belong to different types of domains (economic, social / cultural, ecological) and may take various forms according to the issue, such as for example river basins, urban networks, Eurocorridors.

- Delimitation of spatial systems is linked to the issue and type of relationships concerned (for example, a river basin can be considered as a significant spatial system for issues like floods, water pollution or water transport). Spatial systems do not necessarily coincide with geographical or administrative areas. This may make it difficult to combine indicators of spatial integration with other indicators.

- Inside spatial systems, there can be various types of integration such as: - de facto integration which might be generated for example by nature (ecological systems) or

by some kinds of pressures or "objective" interactions; - integration operated through policies in response to specific issues, and which may (or must)

include co-operation. The first type of integration may generate a "need to co-operate" (common issues to manage),

which is to be distinguished from the "desire to co-operate", although the latter may add to the efficiency of the action and lead to a more "pro-active" approach, making the most of common opportunities.

2.6. Synthesis: further steps toward a better understanding

The complexity revealed by this attempt to define basic principles of spatial integration, along with the frequent use of this concept in political documents, emphasize the interest to further deepen its meaning and contents, not only from a scientific standpoint but also from a political standpoint.

In order to provide a basis of discussion, an initial tentative definition of spatial integration has been identified (C. Grasland):

"Spatial integration is a system of links (flux, similarities, proximity, territoriality, connexity,...) between territories which is the emerging result of concrete social, economic, and cultural relationships, but this system is also a structure which influences and sometimes determines the further development of social, economic and cultural links."

This definition does not pretend to integrate all aspects discussed previously, which anyway would probably be an impossible task. The aim is to provide a first material for a discussion among scientific and political actors. Even if it proves impossible to achieve a consensus on a definition, the discussion about it and the awareness it could increase would probably be very significant results of the approach.

Another element to progress in the reflection on spatial integration is to work on selected fields for a systemic approach of spatial integration, notably in cross-border regions. The following

10 From the comments on the first draft report made by Peter Janssens (Belgium).

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illustration presents some of those potential fields with for each the representation of the two extreme situations of non-integration and integration, and the integration dynamics implied by going from the first situation toward the second.

Selected fields for a systemic approach of spatial integration in cross-border regions

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1. Non-integration 2. Integration 1-2. Integration dynamics

Density

Administrativeand policy grid

Territorialhomogeneity

Flows

Transportnetwork

Urbannetwork

Relative growth of densityin the border area

Built of new links to connectthe two national networksand to improve their connexity

Connection of the two territorialurban networks and increaseof cities influences

Relative growth of transborder flows

Homogenization of characteristics

Adoption of a similar politicaland administrative spatial division

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3. HOW CAN WE MEASURE SPATIAL INTEGRATION?

The purpose of this study is to make a contribution to assessing the situation in different parts of Europe to identify their degree of spatial integration. More specifically, the terms of reference for Theme 1 refer to differentiating between territories according to their level of spatial integration.

It appears that there will be neither a global indicator nor a standard ‘basket’ of indicators to measure spatial integration, but rather combinations of indicators which take into account the various facets of the concept. The indicators presented here are therefore envisaged as an initial approach, and should be considered within the context of being used in conjunction with other indicators.

Many different indicators have been proposed by the different national focal points for the theme "Spatial integration". They are commented under the first point and illustrate a variety of points of view, which demonstrates how large the potential field of research in this area could be.

As mentioned under point 1.3.6, the members of the workgroup decided to focus on two elements of spatial integration considered as specific and significant: flows between places and willingness to co-operate, and to work on a limited range of indicators related to those aspects of spatial integration. This is because of several factors including the available time, the necessity to deepen the concepts and also the fact that many components of "opportunities for interaction" are integral to areas studied by other workgroups – especially "Geographical position", "Economic strength" and "Social integration". A third complementary point of view - analysis of the dynamics of homogeneity and discontinuities - is added notably in order to respond to the relative lack of data on the topic of flows.

The study of flows includes the analysis of "barrier effects" as cases where interactions are weaker or stronger than could be expected in view of the existing potential, which may indicate respectively a lack of or a high level of spatial integration.

The main aspects explored and their associated indicators are summarised in the table below:

Main aspects Potential indicators explored Spatial interaction measured through flows and barriers

Goods transport flows Inter-regional migration flows Barriers to trade flows, flows of goods, residential migration flows

Spatial patterns: homogeneity, discontinuities and multiscalar position

Wealth differential between neighbouring regions Multiscalar profile and dynamics of regions (based on GNP per capita)

Spatial co-operation National financing of the Interreg II A programmes Town and city twinning activities

The identified potential indicators are not considered as capable of giving a complete picture of the subject. The workgroup is working on the assumption that an "indicator" gives a sign about the presence of a certain state rather than describing it (in the same way as the presence of a particular plant does not provide the basis for a description of the soil but may well give some sort of indication about its chemical composition).

For several reasons (lack of data, limited time and the need for a more comprehensive methodology), the potential indicators have not all been computed nor mapped for the whole

34

European territory, but the key parts of the approach (modelling, methods, significance) have been explored.

The main features of the analyses are summarised in the next sections. Full details are given in the Exploratory studies in Part II.

3.1. Analysis of the discourses on spatial integration

One of the first tasks carried out by the workgroup has been the gathering and summarising of the points of view about spatial integration expressed in the answers made by the National Focal Points (NFP's) to the call for proposals made by the European Commission in the framework of the SPESP.

The three tables in the Exploratory studies 1 summarize the results of this analysis and try to draw topics from it that should be further explored: - the first table groups several general comments made on the criterion and its framework; - the second table details proposals of phenomena and of potential indicators related to the

criterion; - the third combines those proposals with those of the authors of the report according to the

structure retained for the analysis.

Those tables show a great variety of potential indicators that illustrate a similar variety of points of view. Of course it is impossible in the course of the SPESP to explore all of them, be it only to test their relevance and practicability.

This adds an argument to the proposal made under the previous section to deepen the knowledge about the concept of spatial integration and to widen the awareness of all its dimensions. Besides discussion and debate, comparative research could be made about the different ways to define this notion in different States and at different levels of political decision (international, national, regional, local). We may indeed suppose that the different actors of territorial planning have different opinions on this question of spatial integration, and it could be very useful to enlarge the comparison of points of view which has been undertaken in the framework of this Study Program.

⇒ See EXPLORATORY STUDIES 1

3.2. Analysis of spatial interaction (flows and barriers)

Compared to conceptual discussion, analysis of flows may seem rather simple at first glance, but also raises a number of questions, some of which are fundamental to the interpretation of flows in terms of spatial integration: - what is the area under consideration? - which flows are to be measured? - how are they to be measured?

All these aspects are of course interwoven.

3.2.1. What area?

Clearly the size and composition of the area(s) being considered is crucial both to the depth of integration which might be anticipated and to the data which might be available. The ‘shape’ of the area may also be critical, for example administrative areas may be artificial constructs which

35

overlie a different pattern of social and economic interactions. The level of spatial integration within such an artificial area may then be quite limited.

A priori, levels of internal interaction might be expected to increase as the area under consideration gradually increases in size, before beginning to decline again once an optimum size has been reached. In contrast, the extent to which a defined area integrates with places outside of its boundaries may be expected to decline as the defined area gets larger, before beginning to increase again beyond a certain optimal size. It is likely that these two patterns will reflect each other.

As said before, spatial interaction will often be measured on basis of spatial systems, and rely on other geographical bases than areas, for example axes, networks, points of transit or origin / destination links. This will require some transformations of data in a way that makes sense in terms of spatial integration.

Another potential difficulty is due to the fact that by definition flows concern at least two areas and are thus influenced by the features of both. In some cases where there are large differences in terms of size, potential, features or political / administrative competencies, this may lead to difficulties in interpreting the results.

3.2.2. Which flows?

Assuming that the global level of all types of flows is not necessarily the most significant factor to assess spatial integration, relevant types of flows must be selected and combined, in order to provide a balanced picture. One can think of an approach mixing global indicators, giving a global picture, and selective indicators (selected types of actors, of modes of transport, of goods transported,… according to availability of sources) allowing to refine the picture and enhance its relevance. Such an approach would obviously require much more time than allowed in the framework of the SPESP.

The selection of flows must also take into account their significance for the concerned area. Transit flows, for example, may be of little or no use to assess spatial integration of an area, unless they have some kind of interaction with this area. If they just go through, their significance for the area may be limited to some negative features such as pollution, congestion and costs for maintenance of the infrastructure. Recent trends such as globalisation (tending to increase the spatial range of exchanges) and a relative change in the nature of flows (growing volumes of informational and financial flows obeying to other laws than material flows) tend to indicate that the share of transit flows is likely to increase at least for a number of areas. This emphasises the need to deepen the understanding of this phenomenon in order to make correct interpretations in terms of spatial integration.

Flows may be measured along transport links in a broad sense. Two main categories of links may provide measures of flows to be analysed: - transport links: they may include passenger movements by air, rail, boat, car or bus, possibly

distributed according to the purpose of the trip (leisure or business), as well as freight movement by air, road, sea, rail or inland water, measured for example by tonnage;

- telecommunications links: whilst ICT links might be seen as a crucial component of spatial integration in the knowledge-based economy, most traffic measures are not obtainable owing to confidentiality constraints, and the level of technology penetration may provide a proxy for the extent to which a region is ‘digitally connected’ (relative number of telephones, of computers, of ISDN subscribers, of Internet subscribers, as well at home than at business).

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Of course other types of flows can also provide interesting angles of view, such as residential migrations (analysed in Exploratory study 2.1), flows of information, financial flows. As discussed under next point there can also be various ways to measure them.

3.2.3. Which measure?

Measurement of flows always relies on some hypotheses and imply choices, not only in matter of unit but also in matter of place of measurement (a parameter that can be very significant for road flows).

Another question concerns the way to take into account spatial patterns of interactions. A priori, spatial interaction might not be considered as isotropic. Some areas may have privileged one-to-one relationships while others may have a more moderate level of spatial interaction with more numerous other areas. Besides, some relationships may be one-way and others two-way. The extent to which data sources for flows provide both origin and destination details is currently an unresolved question.

These aspects may lead to difficulties when combined to the necessity to characterise each area of the territory by its level of spatial integration. For each indicator, measurement must lead to a single value for each area, which requires us to find a way to "summarise" spatial patterns. Methodological choices relying on conceptual significance are thus required while selecting the indicators.

3.2.4. Analysis of flows

Relevance and significance of the analysis of flows are explored. Two main types of flows are analysed: transport flows and interregional migrations.

Transport flows

The analysis focuses on three countries: the UK and the Netherlands for rail and road transport and Portugal for road transport. The factors taken into account are the number of interactions within each country, the level of interregional interaction, the role of geographical distance, the effect of market strength and well as the presence of a border.

To stimulate more comprehensive consideration of transport measures as indicators of spatial integration a regression analysis for road transport flows is tested for the UK.

Interregional migrations

The analysis of inter-regional migration patterns demonstrates the importance of variables such as economic strength, spatial proximity and the size of the regions concerned on inter-regional migration levels. For this reason, migration figures alone are not sufficient to demonstrate spatial integration. A simple regression analysis has been developed to attempt to control for the interaction between these variables. In order to try to understand which regional dissimilarities seem to explain the migration flows statistically, a regression model similar to a gravity model is constructed for migration flows between provinces in the Netherlands and between regions within the UK.

The explanatory power of this model is limited until absolute GDP/capita data for the destination is used as an additional explanatory variable. This improves the regression results. It is possible that this configuration, along the lines of the traditional gravity model, is a better specification for the model.

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3.2.5. Barrier effects

Like modelling flows, measuring barrier effect(s) provides a significant idea of the degree of present-day spatial integration (static) and enables the monitoring of the integration process (dynamic).

The barrier effect is the effect on exchanges of a boundary separating two territories. Associated with the territorial limits, this effect expresses differences between these territories and, finally, their lack of spatial integration. This effect is called frontier effect when it refers to political and administrative areas.

The reasons why territorial boundaries give rise to frontier effect lie partly in three explanations corresponding with European policies aiming to reduce those factors:

(1) Because of customs dues and exchange control regulations, it is a fact that a frontier can express a reduction of the exchanges. This reduction may not only be associated with the limit in itself. It rather reflects on the territory a willingness to control and, sometimes, limit exchanges (economic protectionism, control of persons migration, etc.). In response, the European Union aims to create a single European market (for instance: Customs union of 1968, Single European Market of 1993, etc.) and to establish a European citizenship (Schengen agreement).

(2) Territories are by definition areas with a set of different characteristics: differences of language, differences of economical practices, differences of currency units, etc. All these differences frequently make more difficult and more expensive the exchanges between places located in different territories. These factors are counteracted for instance by the European policies of monetary union and development of cultural and educational cooperations (ERASMUS, SOCRATES, etc.).

(3) The building of the transport networks, material supports of the exchange, arises from territorial - often national - policy. So, the main purpose assigned to the network is to link the places of the territory, of the nation, and the links between this territory and the bordering countries are secondary purposes. Furthermore, sometimes, for strategic reasons, these types of links are thoroughly avoided. That is why the European Union is interested in the development of Trans-European networks.

These factors, components of the frontier effect, are difficult to identify. They together play a role, more or less important, and they could remain during a long period of time after a spatial integration process. However, some authors have proposed a method to measure the frontier effect on exchanges (J.Bröcker and H.Rohweder, 1990, C. Grasland, 1996). This method relies upon the use of a spatial interaction model (see point 1.2.2).

The study of spatial integration, considering spatial interactions and barrier effects, has to take into account important nuances and limits. Some of them are general problems linked to the analysis of flows and are discussed under points 3.2.1 to 3.2.3. Others are specific to the phenomenon of barrier effect: for example the type of barrier (physical, lack of network connectivity, impact of social networks, of economic policies, etc.) must be identified if the aim is to remove it, reduce it or limit its impact in order to promote spatial integration.

Three studies present the approach of spatial integration based on the modelling of exchanges and the identification of barrier effects. D. Robert has analysed economic exchanges between areas at a supranational level (trade flows between European countries), and at an international level (goods flows between Belgian and French regions). C. Grasland has analysed barriers at an intra-national level (persons mobility in Belgian regions). These studies present a way to assess barrier effect(s)

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based on the gravity models. They also show the limits of partial analysis and the difficulties to clearly define to what a barrier effect refers (lack of network connectivity, linguistic differences, etc.).

⇒ See EXPLORATORY STUDIES 2

3.3. Analysis of spatial patterns (homogeneity, discontinuities, multiscalar analysis)

3.3.1. Homogeneity and discontinuities

Many indexes have been proposed in order to summarise the regional variation of demographic, social or economical indexes at European scale. Some of those indexes are based on purely statistical considerations (standard deviation, coefficient of variation, entropy, …) and some others introduce some economical or sociological assumptions (Duncan, Hoover, Gini, …).

Most of the usual indexes are not spatial because they are independent from the spatial organisation of territories in terms of distance or contiguity. The implicit assumption of non spatial indexes of regional inequality is that the proximity between rich and poor regions has no effects. In the case of GNP per capita, the use of non spatial indexes implies the assumption that the potential redistribution of GNP is independent from the distance between regions.

The existence of a high level of positive spatial autocorrelation in a given spatial system implies the existence of a kind of geographical organisation of differences between territorial units. We propose to distinguish two basic types of geographical organisation of differences, which are related to different processes of homogenisation through time:

(1) The spatial organisation is related to the lack of discontinuities between contiguous areas. The existence of spatial organisations is generally related to spatial diffusion processes without barrier effects. The regional differences which can exist at a given time period are reduced under the influence of local effects of redistribution (of population, of wealth) and it is possible to simulate the evolution of a system which increases its spatial organisation through an iterative process of local smoothing of the values.

(2) The territorial organisation is related to the existence of a hierarchy of levels of spatial organisation, i.e. a regionalisation of the differences. In this case, we can observe groups of contiguous territorial units with internal homogeneity and external heterogeneity. The process which can explain this type of geographical distribution is not related to a continuous effect of distance but rather to the discrete effect of the common belonging of territorial units to the same region. In the example of GNP per capita, a territorial organisation could be explained by internal redistribution of population or wealth between the territorial units of the same region whatever their distance and by the existence of a lack of redistribution between territorial units which belong to different regions (even if they are very close). Thus, a territorial organisation can be the cross result of a positive internal effect (regional integration) and/or a negative external effect (barrier between regions).

The territorial discontinuities are related to a particular combination of spatial and territorial organisation. We propose to define territorial discontinuities as "lines of high dissimilarities between sets of contiguous territorial units, which can be considered as local exception in a context of global spatial organisation" (Grasland, 1997). This definition is based on a set of assumptions detailed in Exploratory studies 3, which include also illustrations of the different cases.

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According to this definition it is possible to distinguish different types of territorial discontinuities corresponding to different types of geographical organisation. The first type of discontinuity is related to a strong territorial organisation: discontinuities are defined as a brutal change between homogeneous sets of territorial units (i.e. regions defined on a criterion of global homogeneity). It is also possible to define discontinuity related to a strong spatial organisation: discontinuities are not limits between homogeneous areas but brutal changes between regular gradients of change (i.e. regions defined on a criterion of local homogeneity).

This distinction is crucial because the processes which can explain the discontinuities are not the same according to the levels of spatial and territorial organisation. In the first case we may suppose that the discontinuities are the consequence of a process of territorial homogenisation. In the second case we may rather suppose that the discontinuities are the consequence of a process of spatial diffusion with barrier effects (general reduction of the inequalities between contiguous regions except if they are separated by a barrier).

In order to look at this issue in more detail, a study into spatial patterns of wealth (measured in terms of GNP per capita) has been undertaken. The topic is close to criterion 1.2. "Economic strength", but it is considered here as an indicator of spatial integration; the same approach could apply to other variables that are regarded as relevant for spatial integration.

The approach aims to produce a measure which differentiates regions according to their local situation, i.e. according to the relative levels of a given indicator in neighbouring regions. The "index of dissimilarity" is the ratio between the difference and the mean of GNP per capita (in the present case) of neighbouring regions. The analysis can be refined by weighting the index by the length of the common border between units or by the level of their potential interaction (measured by the output of their population).

Analysis of the evolution of dissimilarities over time can give indications over processes of spatial integration. Distinctions can be made between contiguous units belonging to the same State and contiguous units belonging to different States in order to identify the effect of territorial organisation on integration processes.

3.3.2. Multiscalar analysis

Despite the interest of a territorial approach of discontinuities for the measure of the evolution of spatial integration, some limits of an analysis in terms of couples of places must be underlined.

The previous analysis produces indexes which can describe the situation of the limits between regions but not the situation of the regions themselves. As the purpose of the study is precisely to produce indexes of regional differentiation, we have to to examine how the analysis of territorial discontinuities can help to produce specific measures of territorial integration for each region at level Nuts 2.

The basic question is to assess the consequences of territorial discontinuities for each region and to produce a measure which differentiates the regions according to their local situation. For a given criteria (e.g. GNP/Inh.) we want to differentiate:

(a) regions surrounded by regions with equivalent wealth levels (b) regions surrounded by regions with higher wealth levels (c) regions surrounded by regions with lower wealth levels (d) regions surrounded by regions with higher and lower wealth levels

Different theoretical solutions are available in order to produce such a classification. However, evaluation of local situations can not be isolated from a general evaluation of relative position of

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the regions at European and national levels. That is the reason why we propose to integrate the analysis of local situations in a wider concept of multiscalar analysis of the relative positions of European regions.

Figure 6: Multiscalar profile of selected border regions

Figure 2: Regional levels 50 65 50 60 55 50

65 80 65 55 50 45

50 65 50 50 45 40

45 45 50 50 35 25

60 45 45 35 25 20

Figure 3: Relative deviations to international context +2% +33% +2% +22%

+12% +2%

+33% +63% +33% +12% +2% -8%

+2% +33% +2% +2% -8% -18%

-8% -8% +2% +2% -29% -49%

+22% -8% -8% -29% -49% -59%

Figure 1: Territorial organisation A1 A2 A3 B1 B2 B3

A4 A5 A6 B4 B5 B6

A7 A8 A9 B7 B8 B9

C1 C2 C3 C4 C5 C6

C7 C8 C9 C10 C11 C12

Figure 4: Relative deviations to national context -17% +8% -17% +20% +10% +0%

+8% +33% +8% +10% +0% -10%

-17% +8% -17% +0% -10% -20%

+13% +13% +25% +25% -13% -38%

+50% +13% +13% -13% -38% -50%

Figure 5: Relative deviations to regional context -23% +8% -21% +13% +3% +0%

+8% +23% +11% -2% +0% -4%

-14% +16% -13% +0% +3% +4%

-13% -12% +5% +18% -3% -21%

+33% -10% +4% -13% -17% -20%

-20%

-10%

0%

10%

20%

30%

A9 B7 C3 C4

International National Inter-regional

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Whatever the criterion used for the measure of the relative level of development of European regions (GNP, unemployment, population increase, etc.), a measure of territorial inequalities or territorial heterogeneity should be based on different norms of comparison which can be called territorial contexts. A territorial context is an area where the value of the target index is used as reference for the evaluation of each local situation. The choice of the territorial context should be justified by the fact that it is a level of organisation for the target phenomena, an area where some processes of regulation can take place and where precise dynamics can be identified. In other words, a territorial context is a spatial system or a spatial sub-system identified as significant and available for the study of the target phenomena.

A theoretical example illustrates the differences of significance of the three indexes when taking into account the three different territorial contexts: European, national and inter-regional.

Thus, consider the theoretical case of an economic union between three States (A,B,C) divided in 30 regions (A1..A9, B1..B9, C1..C12) (Figure 1) characterised by various levels of development quoted on a scale between 0 and 100 (Figure 2). We suppose that a project of transborder co-operation is launched between the regions A9, B7, C3 and C4 (represented in grey) and that policymakers and regional planners try to evaluate the relative potential of development of the four regions which are involved in the project.

This project seems to be relatively easy to undertake because the four regions involved have the same level of development (50), so that there is no territorial discontinuity between them (which is not the case on other boundary lines between the States A, B and C). However, may we assess that the regions of the border area have exactly the same opportunities and potential of development if we consider their relative position in a multiscalar framework?

In order to answer to this question, we have computed for each region three indexes as relative deviation to the mean value of:

(1) the international context (deviation to the mean value of the 30 regions) (2) the national context (deviation to the mean value of the regions of the same State) (3) the neighbourhood context (deviation to the mean value of neighbouring regions)

The Figures 3 to 6 show that the relative situation of the four regions involved in the project is not equivalent if we consider their position in different territorial contexts:

- region A9 has a medium level according to the international context (+2%), a low level according to the national context (-17%) of the State A (the richest of the study area) and a low level according to the inter-regional context (-13%). This profile has many potential consequences: positive fiscal transfers or subsidies at national level, competition from richer neighbouring regions at local level, lack of subsidies at international level, etc.;

- region B7 has a perfect mean level, whatever the context (+2% at international level, 0% at national and inter-regional level). This situation is possible only if the mean level of the State is equal to the mean level of the international context and if the neighbouring regions have the same level of development;

- regions C3 and C4 have profiles which are more or less opposite to the profile of region A9. They are characterised by a mean level at international scale and by a very high level at national scale. But they are not strictly equivalent: region C3 is surrounded by regions with equivalent levels of development while region C4 is in contact with less developed regions. Accordingly, their respective levels in the inter-regional context are very different (+5% and +18%). This means that the strategy of local actors will not be the same in both regions. In the case of region C3, we may suppose that the relations with neighbouring regions concerning the labour market will be symmetrical (according to the homogeneity of the levels of development). In the case of region C4, the relations will probably be asymmetrical with flows of labour-force

42

from less developed neighbouring regions and/or flows of capitals or investment toward those regions with lowest incomes and wages.

This theoretical example illustrates that taking into account different levels of analysis can deeply improve the interpretation of the relative position of regions. Moreover, the interest of a multiscalar approach is more evident when we try to evaluate the evolution of relative positions in time.

This approach is developed into a proposition of a multiscalar index for spatial differentiation. The method is tested on GNP per capita of Italian provinces, and then applied to all the NUTS 2 regions of the European Union.

⇒ See EXPLORATORY STUDIES 3

3.4. Analysis of spatial cooperation

A large range of indicators has been proposed by the NFPs on this subject (see Exploratory studies 1), that might be divided into two sets: - those which measure the conditions supporting co-operative relationships between areas; and - those which measure the extent of actual co-operation between different bodies.

The work programme proposed for theme 1.4 concentrates at this stage on the extent of actual co-operation. This means that the willingness and need to co-operate will not be explored for themselves but in the frame of effective co-operation, although others ways could be explored, for example through calculating ratios between some indicators proposed under Strands 1.1 and 1.4 respectively (for example, ratio between the flows and the potential in terms of demographic or economic resources).

Indicators of effective co-operation between spatial entities are not easy to identify, as co-operation is intrinsically difficult to quantify. Effective co-operation may be assessed for example through the existence of co-operation structures and projects. Such structures and projects at the cross-border or transnational level are particularly significant, because they face constraints of different political and administrative contexts: - cross-border initiatives - cross-border management agreements - agreements between different levels of spatial entities (international, inter-regional, etc.) - networks of cities and regions - twinning arrangements

In order to provide significant measurements, some kind of typology should be retained in order to select structures that are, if not homogeneous, at least comparable11. Priority could be given to structures that develop some kind of global project, such as the elaboration of a spatial vision for example. Such a work goes however beyond the constraints set by the framework of the SPESP.

In this framework, and given that there are no available specific methods or data for the measurement of co-operation, the work focuses on the development of simple indicators. In order to provide a balanced picture, two conditions need to be met by those potential indicators: - to express (at least a form of) co-operation in a way that is relevant at the European level; - to use data that is sufficiently homogeneous across the European territory.

11 A study made in 1995 on behalf of the Benelux Economic Union (Nijmeegs Instituut voor Planologie en Omgevingsstudies) and attempting to classify existing cross-border projects and co-operation structures around Benelux could provide a starting point, that could probably be extended to the whole European context and to structures other than cross-border ones.

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Co-operation does obviously not apply only to cross-border relations. The concept of urban network can be seen - at least in its ideal form - as a very relevant example of spatial integration, in the sense that a functioning urban network combines interactions and co-operation. The lack of readily accessible and objective data is however an obstacle to further analysis of this concept in the framework of the SPESP.

Nevertheless, as the study of barrier effects and other studies demonstrate (notably 1.2 "Economic strength"), spatial integration within the Nation State remains strong. It thus makes sense to focus on cross-border co-operative relationships.

Two indicators that meet the conditions laid down have been studied, based respectively on: - the INTERREG II A Programme (cross-border co-operation); - twinning activities co-financed by DG X.

3.4.1. Co-operation in the Interreg framework

The Interreg II A Community Initiative is aimed at encouraging cross-border co-operation and helping border areas to face their specific problems through the co-financing of programmes by the Structural Funds. It focuses on NUTS 3 areas with a national border (including some areas with maritime borders). Under certain conditions contiguous NUTS 3 areas may also be involved in the programme.

As the Interreg II A programme relies on voluntary participation, the indicator chosen could have been based on the level of participation by the eligible units. However, as almost all eligible areas take part in the initiative, it is more useful to consider the extent to which they take part. The chosen indicator is thus the financial investment made by the national, regional and local authorities and the private sector (labelled as "national co-financing") related to population. A method of weighting through national GDP has been introduced because it is assumed that when investment is at the same absolute level, relative investment is higher in less wealthy areas. An index is calculated dividing the amount of national financing/ inhabitant by the GDP/ inhabitant, or national financing/ GDP.

A disadvantage of such an indicator is that by definition it relates only to some NUTS 3 areas, and consequently it is difficult to combine with other indicators.

3.4.2. Twinning activities

Twinning between municipalities offers an interesting and complementary point of view on co-operation, because it focuses on long distance relationships (twinned municipalities must be located in different countries and must be located more than 250 km away from each other) and also because it touches many different actors, from individual citizens to organisations and local authorities.

The analysis is based on data provided by DG X about all European municipalities that have received financial assistance to host twinning activities (such as exchanges between citizens, conferences and seminars) during the period 1990-1998.

The chosen indicator is the number of municipalities that have received assistance (regardless of the number or type of activities organised over the period). The indicator is applied at the NUTS 2 level. In order to take into account the different sizes of areas, the number of host municipalities is related to the total number of municipalities within the area.

⇒ See EXPLORATORY STUDIES 4

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CONCLUSION

Certainly, there are no "simple" indicators available with which to measure spatial integration. It is necessary to combine a number of different variables just to build a simplistic understanding of aspects of spatial integration. In this respect, spatial integration can be seen as a synthetic criterion, summarising the spatial dimension of the thematic domains covered by other criteria, i.e. the links between territories in those different domains.

The different approaches described above all have the common feature of an exploratory character. Measurable results for the whole of Europe could not be expected for all aspects at this stage. Despite a good, albeit limited, descriptive work, it is clear that the analytical power of what has been done is not yet satisfactory. Results are difficult to interpret, not only because each approach explores only a partial element of reality but also because the conceptual and methodological bases for studying spatial integration are not yet firmly established.

The study demonstrates that our understanding of spatial integration requires further examination with respect to a number of different features, in particular: - the dynamic and relative character of spatial integration which evolves not only according to

the features of the concerned area but also according to the relative situations of the other areas (this issue is probably common to the criterion "Geographical position);

- the effects of spatial division into political/administrative entities, reflecting different national/regional systems (linked with the sub-themes 2.2. and 2.4).

Results and possible implications for policy applications

Differentiating between territories according to their level of spatial integration does not imply identifying specific areas that are well integrated with other particular regions. This might of course be interesting - for example to identify parts of Europe that demonstrate good "internal" spatial integration - but it would only give a partial view of spatial integration, i.e. integration between contiguous or neighbouring areas. If we want to examine integration with more distant areas it would be technically impossible to fully consider all the various potential groupings of areas. In the present framework we focus thus on the measurement of integration of each area.

The results of the computation and mapping of potential indicators show that they do not present any obvious pattern of spatial integration. According to the approach taken, we can observe that there:

- is a general trend toward a lowering of international disparities and barriers within the Union; - is a similar trend towards the development of more integrated spaces at different scales; - is an effect of different types of borders that may generate different patterns according to the

scale and type of interactions; - are various spatial trends according to the aspect studied (long-distance or short-distance

relationships, nature of the relationships, effective flows or will to co-operate,…) and to the method of measurement.

This absence of convergence means that at this stage - where the considered indicators are not yet usable - we are not able to infer which areas of Europe could be considered as more integrated than others. It also shows that any measurement will require arbitration around the relative importance of the various dimensions of spatial integration in order to give them their respective weight in the global assessment.

Nevertheless, it is possible to observe a process toward "regionalisation", with increasing contrasts between regions or groups of regions in the same state while the effects of national borders tend to weaken. This evolution can be seen as a positive feature from a European point of view, but it is

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not necessarily positive from a national perspective as it may generate islands of wealth or interaction.

Other possible implications for policies are linked to the features of the concept itself, that were enlightened in the course of the study:

- the political significance of the concept is emphasised notably by the fact that political actors use it frequently. One could even say that spatial integration/territorial cohesion appear as keywords in political terminology, particularly at the international level. Even within the SPESP, territorial cohesion is viewed as somewhat of a political concept, as we observe that it is evoked as an underlying theme in the working documents about strand 2.4 ("Possible fields of policy implications for rural – urban partnership");

- the relative nature of spatial integration underlines the fact that spatial integration is not an overall positive value whose increase will always be globally beneficial. Therefore, choices are necessary, as stronger integration at one level, or in one area or domain, may result in relatively lower integration at another level, domain or elsewhere. This is why it is perhaps desirable to aim for 'balanced' spatial integration, in a similar sense to the 'balanced' polycentric urban development promoted by the ESDP;

- the dynamic nature of spatial integration leads to an emphasis on the importance of the state of mind of the concerned actors. While for some aspects the market forces go spontaneously toward integration, for other aspects the desire to co-operate seems to be significantly influenced by European encouragement (for example the distribution of values of the "Interreg" indicator clearly shows the effect of the rate of financial assistance linked to Objective 1 of the Structural Funds);

- the multi-faceted nature of spatial integration appears as a feature of the work on other criteria, which sometimes make reference to a link with spatial integration without going into detail about it. This strengthens the impression that spatial integration often provides a "shell" for many different types of visions that would probably deserve further debate at the European level not only among scientific actors but also with political actors;

- the way political and administrative systems divide the territory into areas with different size, competencies and financial means (translated notably in the division into NUTS units) varies strongly from one country to another. This draws significant discontinuities between them, which can not be forgotten when talking about spatial integration and particularly about cross-border co-operation. This underlines the fact that national borders still exert an important effect as limits between different systems, despite the progress of European integration.

Further work

- Obviously much work remains to be done to conceptualise the field more fully. It is necessary to go beyond the conventional image of an overall positive but vague notion of spatial integration and take into account all of its aspects and implications. The fact that political actors frequently use this concept suggests that they should be actively brought into the debate. The development of a common definition could help to focus conceptual work.

- On the basis that the work has shown that the NUTS division does not always provide the most appropriate basis to assess spatial integration, it could be interesting to consider functional territorial units. Delimitation of functional territorial units is still a matter of discussion. A clear link can be established with the work on typologies undertaken in the frame of Theme 2 of the SPESP.

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- Studies on cross-border areas are very helpful in providing a better understanding of the integration processes, as these areas appear to be real laboratories. The considerable financial resources allocated to cross-border programmes within the framework of European policies underline the significance of the issue.

- Some aspects of spatial integration could not be studied in the present framework because they would require a significant investment of time in order to define a methodology and collect the data. This is especially the case for the "networks" dimension: influence of the spatial organisation of infrastructure networks and of their features ("interconnectivity") on spatial integration, co-operation inside networks of cities and regions. This would supply an interesting and complementary point of view.

- An apparently minor but in fact essential contribution to integration is the improvement of consistency of the European databases (Eurostat, EEA etc.), as well as statistical and cartographic advancements. Availability of a reliable and homogenous source of information that transcends national borders is a basic prerequisite for analysis of spatial integration within Europe and its implementation.

- Work on spatial integration, and especially on indicators of spatial integration, cannot rely exclusively on traditional data available through Eurostat or provided by other studies. Our experience shows that:

- data on flows are very sparse and difficult to compare; - homogeneous data on spatial patterns (for example, bassin d’emploi /travel to work areas)

are not yet available; - for aspects such as co-operation, specific data can be found through other sources but are

not always easy to identify and collect.

Further efforts could be made to increase the awareness of the potential usefulness of data collected for various purposes by different services, even outside the European institutions. More homogeneous methods in organising databases (for example using a defined NUTS level for all countries) would significantly enhance their usability.

= = =

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PART II

EXPLORATORY STUDIES ON SPATIAL INTEGRATION

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EXPLORATORY STUDIES 1: SYNTHESIS OF PROPOSALS MADE BY NATIONAL FOCAL POINTS

(Ph. De Boe, Th. Hanquet & A. Healy)

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1.1. Synthesis of the general comments

Domain Contents General comments about the criteria Two groups of criteria must be distinguished: those which describe a relatively stable spatial structure, and those (including spatial integration) which reflect evolutions and

developments and which are influenced by policies. Work of the existing European institutions must be complemented rather than duplicated. Concept of spatial The concept of spatial integration is set out comprehensively in the ESDP. integration Spatial integration is a multifaceted concept with many intersections with other indicators Spatial diversity inside the European Union can be used as [cum recurso] and integrated as a major dimension of the development strategies. Urban networks, co-operation across boundaries, regional co-operation etc. provide examples for models of spatial integration. Spatial integration is one of the means to apply the subsidiarity principle and to strengthen co-operation inside the territory of the European Union. Giving responsibilities

to the regional actors can be decisive in this regard. Phenomena and choice The establishing of indicators requires preliminary conceptual and analytical work (that will require time). of the indicators There must be an agreement about definitions, collection methods, measurement periods and imputation methods. (in general) Selection of the indicators must be oriented towards the aims of the ESDP. The assumptions associated with the indicators and their intended use should be specified from the beginning All the main phenomena that take part in the specific indicator have to be pointed out. The indicators must be built in an understandable way, easy to be communicated and accepted by the political actors. Selection of the indicators must take into account availability of data at the regional level for the whole territory of the Union. Selection of the indicators must take into account the countries of the enlargement area. Compilations of statistical heterogeneous sources must be avoided. Temporal series must exist and be continuous. There must be static indicators but also dynamic indicators. It must be possible to give different weights to the indicators in order to give a representation of the criterion. Territorial aggregations for data on neighbouring areas must be possible, even if they do not correspond to administrative borders. Selection of the indicators must allow multivariate analysis. Modern spatial analysis tools allow fruitful use of simple indicators. The Driving force - State - Response (DSR) framework seems appropriate provided it is adapted to the context of the study Phenomena and choice Indicators will be chosen taking into account a subsequent integration on a European level. of the indicators The phenomena (political or administrative limits, barriers and discontinuities) must be studied simultaneously and from a systemic viewpoint (spatial integration) A distinction has to be made between internal processes of integration (examples of the language, the culture, the history, the length of existence of relations) and external

processes of separation or fragmentation. Scale / study areas The scale of analysis is a central element. (in general) The scale must allow to take into account geographical levels as region, city, axis and spot (including point and linear information) There must be an agreement on appropriate ways to represent the indicators on map form, operative at the smallest praticable spatial scale Scale / study areas A multiscalar approach is needed (about spatial discontinuities and barrier effects) (spatial integration) It will be important to individuate areas, not necessarily adjacent, that interact strongly or could potentially do it. The studied areas will be, if not homogeneous, at least similar and comparable.

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1.2. Synthesis of the proposals for phenomena and indicators

Domains Phenomena Proposed indicators Demographic, Spatial discontinuities Demographic discontinuities (age structure and sex ratio, level and of changes of fertility and of the death rate social, Territorial complementarity (in particular in cross-border regions) economic, Language, culture, history, Percentage of pendular moves cultural Length of time of relationships structures Absence of cultural controversies Transport and Morphology of the territory Geographic factors and natural barriers influencing the transport networks telecommunication (with regard to transport networks) Main works completed or under construction (tunnels, passes, etc) networks Spatial discontinuities Average density of rapid transport infrastructures in cross-border regions Networks efficiency Transport or telematic networks density Barrier effects Restrictive constraints of transport at a break-point (border, break, physical obstacle) / for direct transport Accessibility (speed, saturation, corridor) Presence of logistic nodes Political and Territorial grids Number of public administration offices with front-office services administrative State intervention Number of public organisms of central administration de-concentrated to the regional level structures Presence of efficient administrative Main territorial limits of the public service, of management and of spatial planning (repetitions / discrepancies) bodies Percentage of employment in the public sector De-concentration of Presence and followers of political parties of national importance public administration Absence of political controversies Flows of persons, Migration flows Barrier effect affecting air and rail exchanges of passengers at European cities of over 200,000 inhabitants goods, Pendular migrations Percentage of pendular moves information Goods traffic Volume of goods traffic Telecommunication traffic Passenger transportation traffic Barrier effect affecting inter-regional transport of goods Telephonic traffic between districts Presence of research centres Co-operation Regional co-operation Number of co-operation agreements relationships Cross-border co-operation

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1.3. Examples of phenomena and possible indicators - Synopsis

This synoptic table presents the "indicators" mentioned in the NFPs proposals as well as those proposed by the authors of the report. Choices have to be made in order to retain for example one very representative indicator for each phenomenon. Some indicators may also be shared with other criteria.

Dimension Phenomena Possible indicators (or "subjects" for indicators) Links

Centres of employment and catchment areas 1.2 Retail and services catchment areas Location and catchment areas of call centres or commercial helpdesks

(Re)-Location of Inward Investment 1.2

Functional complementarity

Demographic discontinuities 1.3 Cities linked by HST 1.1 River basins 1.6 Large natural areas 1.6

Existence of spatial systems (need to cooperate)

Areas of common cultural structures 1.7 Geographic factors and natural barriers influencing the transport networks

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Main works completed or under construction 1.1 Transport or telematic networks density 1.2 Average density of rapid transport infrastructures in cross-border regions

Restrictive constraints of transport at a break-point / for direct transport

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Discontinuities in transport inter-operability 1.1 Logistic hubs 1.1/1.2 Intermodal links 1.1/1.2 Links between transport systems of different levels 1.1 Distance from an airport, railway station, seaport 1.1

Transport infrastructures

Utilities connections 1.2 Fixed / mobile phones Computers linked to the Internet Points of access to the Internet

Potential for physical and functional relationships

Telecommunications infrastructures

Computer hardware and software retailers Commuting patterns and flows Migration patterns and flows 1.3 Passenger movements by air / rail / bus / road

Flows of persons

Barrier effect affecting air and rail exchanges of passengers between cities

1.1

Freight movements by air / rail / road / waterway 1.2 Flows of goods Barrier effect affecting transport of goods 1.1 Telephone traffic Flows of information Internet traffic (Re-)location of inward investment 1.2 Financial flows 1.2

Actual extent of relationships

Other flows

Commercial links 1.2

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Dimension Phenomena Possible indicators (or "subjects" for indicators) Links

Areas with political competencies Main territorial limits of the public service, of management and of spatial planning

Changes in organisational scale of administrative bodies

Number of public administration offices with front-office services

Number of public organisms of central administration de-concentrated to the regional level

Percentage of employment in the public sector

Political and administrative organisation

Differences in social / fiscal legislations 1.2/1.3 Presence and followers of political parties of national importance

Frequency of elections Political resignations Linguistic / cultural differences 1.3

Absence of political / cultural controversies

Development of a ‘regional’ identity Willingness to cooperate Flows related to relationships potential

Absence of restrictions to travel Stability of exchange rates

Conditions supporting relationships

Other conditions

Presence of research centres 1.2 Agreements between spatial entities at different levels Cross-border management agreements Networks of cities and regions

Cooperation structures

Twinning arrangements Cross-border, interregional and transnational initiatives

National financial investment in such initiatives

Effective cooperation

Cooperation projects

Common spatial projects

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EXPLORATORY STUDIES 2: FLOWS AND BARRIERS

2.1. The measurement of flows

(A. Healy & al.)

Introduction

The following section develops the analysis carried in the main body of the report through consideration of potential indicators by which to measure spatial integration. From the range of potential indicators suggested by the preceding work we have concentrated on the following for reasons of pragmatism and data availability.

At this stage we have not attempted to collect a comprehensive set of data for all parts of the Union. Instead we have concentrated in illustrating the strengths and weaknesses of different approaches in order that key indicators can be identified and data collection embarked upon within the second stage.

For the purposes of this paper we concentrate on flows within Member States (see Exploratory study 2.2 for work which develops flows between Member States). We have examined the UK, Netherlands and Portugal to illustrate the points being made.

For each of the following indicators we undertake a range of analytical work to test their suitability as measures of spatial integration. The study concentrates upon the interactions and will discuss how they are connected with positions; it is after all still unclear whether the interactions are spatial integration itself, are a sign of spatial integration or are a driving force in changing spatial integration through the regional dissimilarities. At the same time this study will seek to show what can be done practically and what can not, where assumptions have to be made and what sorts of datasets might be useful for future work.

The indicators considered are:

- The transportation of goods by road - The transportation of goods by rail - Inter-regional migration - Trans-national patterns of student migration for study purposes - Travel to Work Areas

Air traffic flows were discounted from the analysis owing to problems with accessing representative data in the time available. We point to Cattan’s work for an example of how this might operate.

Hopefully this work complements that undertaken by others in the ESDP, recognising that an analysis of spatial integration can not be considered without knowing the wider context - social, economical and cultural. Overall, the work demonstrates the difficulties involved in using simple measures of flows of goods or people where considering even spatial interaction.

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1. The transport of goods

The transport of goods between regions is essentially an economic activity and therefore one would expect the main factors driving the flows to be economic in terms of suppliers and markets. There is also the possibility of logistic hubs which could increase flows from domestic or international sources into the regions, and thence out to other regions. With the increasing globalisation of trade and the development of improved logistic networks, supported in part by TENS-Transport, it is likely that this trend will increase.

Separate data is available on the transport of goods by road and rail for the UK at NUTS1 level for 1982-1994 for rail and 1980-1996 for road and for the Netherlands (NL) at NUTS1 for both between 1978 and 1996. Naturally data at NUTS2 would be welcome, as it would give a better idea of integration, and for the NL NUTS1 does not allow for much analysis as it breaks the country into only 4 regions. We examine four factors:

- the number of interactions within each country - the level of inter-regional interaction - the role of geographic distance - the effect of market strength

The number of interactions

In order to gain an overall idea of the absolute level of inter-regional interactions the mean transport flows (thousand Tonnes) per thousand of population were calculated for the periods 1986-89 and 1993-1996 for each country as a whole.

Road Rail 1986-89 1993-1996 1986-89 1993-96

NL 25.8 25.5 0.29 0.35 UK 28.9 28.2 2.51 1.70

According to this data it seems that there are relatively more interactions in the UK than in the Netherlands, particularly in terms of rail. However, this is too simplistic for it fails to take into consideration the differences between the two countries: for example it is possible that scale plays a role so that there are more goods transported in a larger country (the UK). The fact that the UK is an island may also affect observed transport patterns.

In the UK the figures also suggest that the level of goods transported has fallen between the earlier and later period. It is unlikely, but not impossible, that spatial interactions (and by implication spatial integration) may have decreased in recent years, rather that the dates reflect different stages of the economic cycle. Over the same period flows in Netherlands remained relatively stable with degree of substitution from road to rail.

Inter- regional interaction

Interaction between regions is likely to offer a stronger measure of spatial integration.

The development of the flows from each of the regions can be shown as in Chart 1. This reflects road transport in the NL. The flows are compared with the index 100 which is equal to the value of the flow form region x in year 1978. This shows whether the interactions are increasing from the individual regions and to what extent.

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Chart 1: Development in road transport in the NL by originating region

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Overall the level of goods transported by road has increased by about 10% since 1976. Flows from Eastern Netherlands have grown particularly strongly; it is possible that this may be a result of increased European integration since the region holds a geographically central position for Europe and perhaps European logistics. Flows from the western Netherlands have grown the least (only around 5%). This region contains Rotterdam and the Netherlands’ other main ports and therefore one could expect quite a high level of interactions through such an area used as a ‘hub’. In this way changes in international transport patterns may have affected the internal interactions.

In the UK (see Chart 2), overall flows have increased by about 20% since 1980 (although the level is below that of the late 1980s when the UK enjoyed an economic boom). Herein lies a problem with the construction of indices for interaction. Any index must have one value and therefore mean values, or the values in periods, have to be used to create an index. Therefore the values earlier for the periods 1986-89 and 1993-96 were perhaps not representative of the overall trend over 15+ years.

Chart 2: Development of UK road transport flows by destination

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Chart 2 examines flow by destination. The flows to Northern Ireland have grown by approximately 150% over the time frame (the line has been taken off the graph because otherwise the scale is too large to show any of the other variations) demonstrating an enormous increase in spatial interaction between Northern Ireland and Great Britain. Other regions that have experienced relatively strong growth in road transport inflows are Scotland, the North west, West Midlands and South West. Below average growth has been experienced by both East Anglia and the South East.

Chart 3 shows the share of rail goods traffic in the UK which is inter-regional according to which region it departs from. On average the share is around 40% and has stayed more or less constant since 1982 but the shares between the individual regions differ sharply. The shares for Scotland and Northern Ireland are very low suggesting a higher level of spatial interaction within these regions than with the rest of the UK, or the existence of barriers between these regions and the rest of the UK.

In contrast almost all the transported goods that originate in East Anglia leave East Anglia, other regions showing a high share of inter-regional traffic are the East and West Midlands and the South East.

Chart 3: % share of UK rail goods transport that is inter-regional by originating region

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The share of inter-regional flows for the NL are much closer to being homogeneous (see Chart 4). The overall share of inter-regional flows is increasing and lies currently at around 30% (significantly lower than the level for the UK). This could suggest a higher level of interaction between regions in the UK, but could be a symptom of different geographical attributes of the individual regions or the country as a whole. Northern Netherlands has the highest share of inter-regional goods traffic but this lies far below that of regions in the UK. The flows from the Southern Netherlands show a degree of volatility from 1990-97.

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Chart 4: % share of NL road transport flows that are inter-regional (by originating region)

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However, none of the above data takes into account network availability. Despite this, the measure does appear to provide a first indication of regions which might be more highly integrated internally, and those which have a higher degree of external interactions. This appears to have some relationship to centrality and, potentially, differential economic strength.

The importance of distance

It is possible to consider transport flows between bordering regions12 in order to try to discover whether geographical distance plays an important role in spatial integration or to see whether there are spatially integrated regions made up of several bordering NUTS1 areas. Owing to the limitations of such an analysis in the Netherlands we concentrate on the UK here.

Two possible measures are used to analyse the extent to which spatial interactions are affected by proximity. Firstly the share of the total inter-regional flows that are between bordering regions is calculated. Then the mean flow from each source region to bordering regions is divided by the mean for all inter-regional flows to give a ratio (mean border flow ratio or MBFR). Another possibility would be an indicator comparing the flows across borders with the flows that are not between bordering regions, hence giving a multiplier for the effect of sharing common border on transport flows.

Chart 5 shows the development in the share of inter-regional goods traffic (road) going to bordering regions according to source region in the UK. The overall level is about 75% and appears fairly constant. This does suggest that spatial integration or interactions at least is greater for regions close to one another. The share for Scotland is markedly the lowest and by some distance; in addition the share going to bordering regions seems to be falling further below 30%. This could be read as suggesting a barrier effect between Scotland and the rest of the UK. Interestingly, though, the value of the mean border flow ratio (MBFR) for Scotland is one of the highest in the UK (though it has fallen since the early 1980s).

12 Bordering regions were defined for the UK as regions with a land boundary. For the Netherlands those regions that are ‘next to each other’ even if separated by some water are counted as bordering (e.g. Friesland and Noord-Holland).

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Chart 5: % share of UK inter-regional road transport flows that go to bordering regions (classified by originating region)

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Chart 6 shows the MBFR for the rail flows in the UK.

Chart 6: MBFR ratio for UK rail transport flows by originating region

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North of England. The West Midlands and Wales have the lowest ratios with both values falling below 1 for several years. Flows to other parts of the country (Scotland, Yorkshire and the South East in the case of Wales) have dominated the outward goods traffic and therefore it seems that there is no particular spatial integration in this Western part of Great Britain.

The effect of market strength

In order to highlight particularly large (out-)flows of goods, the flows per thousand population (in the source region) were calculated for all NL and UK regions. Other possible weighting methodologies would be to divide by the destination population or the sum of the two populations, or use a measure of GDP perhaps. Undoubtedly some of the largest flows are not highlighted since they originate from a region with a particularly large population (for example the South East in the UK). This analysis can be used however to show a pattern of large flows or larger than expected flows and thus to an opportunity or constraint analysis. The table below shows values where the average road transport of goods divided by the population in the source region was greater than 2 during 1986-89 and 1993-96. 2 is chosen heuristically to select a reasonable sized group of the largest flows.

The Netherlands

1986-1989 1993-96 Source Destination Source Destination North East

West North East

West East West

South East West

South West South

South West South East West

The UK

1986-89 1993-96

Source Destination Source Destination North Yorkshire/Humber North Yorkshire/Humber

North West Yorkshire/Humber East Midlands Yorkshire/Humber East Midlands

North West East Midlands Yorkshire/Humber

West Midlands East Midlands Yorkshire/Humber

West Midlands East Anglia East Midlands

South East East Anglia East Midlands

South East South West South East South West South East

West Midlands East Midlands West Midlands East Midlands North West

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For rail transport the following flows were particularly large (in this case the criterion was that the transport flow per capita was greater than 0.1.

The Netherlands

The UK

1986-89 1993-96

Source Destination Source Destination North Yorkshire/Humber

South West North West Scotland

North Yorkshire/Humber North West

Yorkshire/Humber North East Midlands North West

Yorkshire/Humber North East Midlands

East Midlands North Yorkshire/Humber

East Midlands North Yorkshire/Humber

East Anglia East Midlands South East North West Scotland

East Anglia East Midlands North West Scotland

South East Yorkshire/Humber East Midlands South West

South East Yorkshire/Humber East Midlands South West

West Midlands North Yorkshire/Humber East Midlands Wales

West Midlands North Yorkshire/Humber East Midlands

North West Yorkshire/Humber South East West Midlands

North West Yorkshire/Humber

Wales South East North West Scotland

Scotland North Scotland North

The tables themselves give an overall view of the ways that flows have changed between the two periods represented diagrammatically by Maps 1a and 1b for the UK. Comparing the top 10 flows in the UK from 1993-96 for road and rail, rail flows tend to occur over longer distances (owing to the economics of this mode of transportation) with large road flows occurring across a border.

1986-89 1993-96 Source Destination Source Destination North East

West South

North East West

West North South

West North South

South West South West

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Maps 1: UK transport 1993-96 top 10 flows 1a: Road 1b: Rail

In terms of road transport the four central regions (the East and West Midlands, the North West and Yorkshire/Humber) have the highest level of spatial interaction in goods traffic and therefore perhaps greater spatial integration. Alternatively it may be a function of the geography of industrial structure within the UK. In contrast rail transport seems focussed on Yorkshire/Humberside and the East Midlands.

Comparing developments in time for the transport of goods by rail, it is striking how the number of large non-border flows fell between 1986 and 1989 (see Maps 2a and 2b) although all rail transport fell during this period as shown by the table above. Therefore one has to be careful about suggesting that the level of spatial interaction has fallen per se. It is possible for example that economic factors have played a role or that road transport substituted for rail transport (as a result of better road infrastructure). This clearly demonstrates the danger of using a single modal indicator of transport flow for the purposes of measuring spatial interaction, particularly when the different economics of modal choice are considered (with air transport more economical for longer journeys, road movements for short trips and rail for medium distances). Modal shift can also occur as a result of changes in the external policy environment. One key influence on transport flows is the transport network which may affect spatial interactions by constraining them or providing new economic opportunities. Consideration of this factor will involve further integrated working with strand 1.1.

In terms of methodology it is difficult to measure the efficacy or scope of a network. The problem with considering an indicator such as the motorway km per capita, or per sq km is that the links between regions are not well represented. Spatial interactions are constrained or supported by the level of ‘distance’ between actors or regions. It is important that the network is efficient as well in the sense that it serves those areas that are most in need. Further work on distance in the distance study group may lead to the construction of a more solid indicator (for example the time or cost of travelling between two regions) and this is developed further below.

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Maps 2: UK rail transport large non-border flows 2a: 1986-89 2b: 1993-96

The case of Portugal

In order to test the constraints we identified in the use of simple transport flow data as measures of spatial integration we also examined their application at the NUTS 2 level in Portugal.

For Portugal there is data (though patchy) from 1987-94 at NUTS 2 level for interregional transport of goods by road. Non-Continental Portugal (Madeira and the Azores) will not be considered since the magnitude of road transport from these islands to the rest of Portugal is probably minimal and the data definitely is minimal.

The average goods transported by 1000 population in Portugal were:

Therefore the level of inter-regional road interactions seems to have increased quite substantially between the two periods. In comparison with the NL and the UK there are less inter-regional goods flows per population, although the flow in the later period is very comparable with that of the NL. The rail transport flows also grew over the period and at a comparable rate to the road flows. There is apparently more rail transport than in the NL at this level and less than in the UK.

An analysis dividing road flows by GDP rather than population gives the following indicators:

In contrast this suggests that there is a higher concentration of spatial interactions in relation to economic developments in Portugal than in the NL and the UK. In addition the ‘interactivity’ of

1986-89 1993-96 Road 20.38 25.63 Rail 0.50 0.68

1986-89 1993-96

NL 1.96 1.35 UK 2.43 1.93 Portugal 3.48 3.21

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these economies seems to be decreasing (though again there is the possibility of substitution to other methods of transport or simply transport at a lower NUTS level due to better logistics).

Looking at the development of flows from (Chart 7) and to (Chart 8) particular regions it seems that the continental Portuguese regions have more or less all followed the same pattern of development. Centro and Lisboa seem to have shown slightly stronger than average growth in incoming goods flows while Norte has experienced somewhat weaker growth. In terms of flows originating from the regions Centro and Lisboa have again experienced the strongest growth with the Southern pair of Alentejeo and the Algarve having had the weakest.

Chart 7: Development in road transport flows in Portugal by destination

0

20

40

60

80

100

120

140

160

180

1994a001993a001992a001991a001990a001989a001988a001987a00

pt1

pt11

pt12

pt13

pt14

pt15

Chart 8: Development in road goods transport flows in Portugal by originating region

0

20

40

60

80

100

120

140

160

180

1994a001993a001992a001991a001990a001989a001988a001987a00

pt1

pt11

pt12

pt13

pt14

pt15

The share of transport of goods originating in each region that is inter-regional is shown in Chart 9. Alentejeo has by far the highest share with around 30% the shares for the other regions all lie between 8 and 15%. This is quite low in comparison to what was found in the UK (average 25-30%) or the Netherlands (average 20-30%). This would suggest less spatial interactions (and hence integration) at the NUTS1 level in Portugal, though it is possible that it is more integrated at a lower regional level. A lower share of inter-regional goods transport means that there is a higher

65

share of goods transport that originates in one NUTS1 region and never leaves this region. It could therefore be that the regions are well-integrated at NUTS2 level and be more self-sufficient at NUTS 2 level.

Chart 9: % share of road transport flows in Portugal that are inter-regional (by originating region)

0

5

10

15

20

25

30

35

1993a001992a001991a001990a001989a001988a001987a00

pt11

pt12

pt13

pt14

pt15

The impact of common borders on flows between regions is shown in the two Charts 10 and 11.

Chart 10: % share of inter-regional road transport flows in Portugal that go to bordering regions (classified by originating region) region)

0

5

10

15

20

25

30

1993a001992a001991a001990a001989a001988a001987a00

pt11

pt12

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The share of goods being transported in bordering regions is highest for Alentejeo suggesting that it is perhaps better integrated across its borders. Somewhat surprisingly Lisboa has a low share of flows that go to bordering regions (less than 10%). This is surprising owing to its geographically central position and the fact that it contains the country’s capital and largest city, Lisbon. Norte has the highest consistent MBFR suggesting that its flows with Centro are very sizeable despite the share of flows going to bordering regions not being that large (but as above with the case of Scotland it only borders one region). In contrast the MBFR for the Algarve is close to 1 and sometimes below suggesting that its flows to Alentejo are not always more important than with the

66

rest of Portugal. This is at first sight a little surprising given that Alentejeo is large and seems to have a relatively high level of interactions with its bordering regions. However close analysis shows that these two southern regions are much the poorest in terms of GDP which probably explains the lack of strong flows between the two.

Chart 11: Mean border flow ratio for road transport flows in Portugal (classified by originating region) (classified by originating region)

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

1995a001994a001993a001992a001991a001990a001989a001988a001987a00

pt11

pt12

pt13

pt14

pt15

Map 3: Large road good flows (related to GDP) in Continental Portugal

Calculating the transport flows per GDP (in source region) in Portugal one can highlight the largest flows. These are shown on Map 3. The results are unremarkable. The largest flows occur between the bordering regions. The flows originating from the Algarve give the impression that there are large goods flows leaving the region and one could imagine some spatial integration at the NUTS2 level. This may be true but may also be affected by the low level of GDP in this region. Norte and Lisboa both have GDPs which are approximately ten times that of the Algarve and Alentejo. Therefore one must be careful using GDP as a way of weighting flow. One can consider the ‘interaction level’ of the economy but spatial integration is not higher simply because a region is poorer. One has therefore to be careful with the specifications of the indicators.

67

Constructing a regression model

To initiate thinking of more comprehensive considerations of transport measures as indicators of spatial integration we have run a regression on UK road transport flows using the following hypothesized model:

Ln(fl) = a1* ln(gdp) + a2*ln(gdpdes) + a3*ln(bor) + c

Where:

fl is the goods transport flow from region x to region y gdp is the GDP in region x gdpdes is the GDP in region y bor is a dummy for bordering regions taking the value 2 or 1 if there is or is not respectively a

common border c is a constant to be defined in the regression procedure

The model specification is that of a simple gravity model assuming that transport flows are driven by economic forces and therefore larger flows will occur between areas of higher economic activity. The dummy variable for bordering regions is a simple way to include a distance measure into the model.

The regression is carried out for the years 1996 and 1986 and for the UK regions at NUTS2 level. The results are the following:

Variable Coefficient t-value Gdp 0.961 8.324***

Gdpdes 0.897 7.831*** Bor 2.991 10.652*** C -13.365 -7.590***

Observations 214 R2 0.596

The majority of outliers are flows that are smaller than expected – in particular in connection with Northern Ireland (see Maps 4a and 4b). There is therefore a clear barrier between Northern Ireland and the rest of the UK in terms of road transport flows.

There also seems to be something of a barrier between Scotland and the South of England, which may be a reflection of rail transport becoming more important over these longer distances. The problem with drawing conclusions from this model regarding spatial interactions or spatial integration is with the specification of the model and the size of the NUTS regions. The region ‘the South East’ has by far the largest population and GDP and could be broken up in smaller regions for a more profitable analysis while regions such as Wales and Scotland contain a wide variety of levels of population density, GDP and motorway network.

Further analysis may include adding a dummy variable in for Northern Ireland in order to improve the quality of the results and to reflect its special position geographically. Adding a dummy variable for Northern Ireland results in R2 reaching 0.869 (excellent) with the variables staying at a similar level of significance. The t-value for the Northern Ireland dummy is by far the largest and is greater than 20. The outliers from this model can also be seen in Maps 5a and 5b.

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Maps 4: Road transport outliers from regression 4a: larger than predicted 4b: smaller than predicted

Maps 5: Outliers from regression with NI dummy

5a: larger than predicted 5b: smaller than predicted

The flows that are smaller than predicted are now focussed around Scotland (and to a lesser extent Wales and Northern Ireland). This points to another barrier with Scotland. Flows larger than expected are distributed among various regions. The West Midlands seems to receive some flows larger than expected. This may be due to its central position or the relatively high level of industry

69

and hence goods transport according to the rest of the UK. Consequently we might argue that the West Midlands has a high degree of spatial integration.

2. Interregional migration

Our analysis of inter-regional migration patterns has demonstrated the importance of variables such as economic strength, shared culture (potentially identified by proximity) and the size of the regions concerned as affecting inter-regional migration levels. For this reason, migration figures alone are not sufficient to demonstrate spatial integration and so we have developed a simple regression analysis to attempt to control for the interaction between these variables. For example, people migrating from Scotland to England may be closer to the North of England but the economic power of the South East acts as a more powerful magnet. Does this make the South East and Scotland more spatially integrated than Scotland and the North East?

Student migration

The limitations of using simple migration figures are illustrated by the following example of non-permanent migration of students supported by the ERASMUS study programme.

As tables 1 and 2 demonstrate UK students are more likely to study in France, Spain and Germany, a factor perhaps of the larger choice of Universities in these countries and the greater familiarity of the languages. In contrast very few UK students choose to study in the Republic of Ireland, a country which might be thought to be more spatially integrated with the UK than are other Member States.

Patterns of foreign students studying in the UK are different in two important respects, the number of Irish and Greek students studying in the UK are substantially higher than UK students studying in these countries. Their position at the top of the table suggests that these two countries are more closely integrated with the UK than are France and Germany for example, particularly when the size of the respective student populations are considered.

Table 1: UK students overseas through the ERASMUS programme Host Country 1996/97 1997/98 change (%) Austria 201 176 -12.4 Belgium 307 259 -15.6 Germany 1583 1863 17.7 Denmark 188 182 -3.2 Spain 1641 1706 4.0 France 3794 3890 2.5 Greece 175 149 -14.9 Italy 711 818 15.0 Ireland 129 88 -31.8 Iceland 2 5 150.0 Norway 81 64 -21.0 Netherlands 698 684 -2.0 Portugal 144 111 -22.9 Sweden 313 303 -3.2 Finland 301 294 -2.3 Total 10268 10592 3.2

Source: UK Socrates-Erasmus

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Table 2: Overseas students in the UK Country of origin 1994/95 1997/98 change (%) EU 65716 89606 36.4 Belgium 2093 2169 3.6 Denmark 1179 1744 47.9 France 9916 12844 29.5 Germany 11054 13037 17.9 Gibraltar 384 536 39.6 Greece 12247 25602 109.0 Republic of Ireland 12858 15894 23.6 Italy 3897 5254 34.8 Luxembourg 438 509 16.2 Netherlands 2887 2817 -2.4 Portugal 1426 1980 38.8 Spain 5705 7220 26.6 Other countries 97997 123658 26.2 Total 163713 213264 30.3

Source: HESA "Students in Higher Education Institutions" 1994/95 and 1997/98

Regression results for a linear regression for regional migration

In order to try to understand which regional dissimilarities seem to explain the migration flows statistically, a regression model similar to a gravity model was constructed for migration flows between provinces in the Netherlands and the UK. Such a model not only shows which regional dissimilarities are important structurally but it can also be used to predict migration flows and hence highlight outliers from the general spatial integration pattern, indicating integrated regions or barrier effects (opportunities and constraints). The model has been used to analyse flows in the year 1988 and 1996 (i.e. we have carried out a cross-section regression with two time values). It would be possible to carry out a full pooled regression for all years but it seems unlikely that the results would merit the work. A yearly series would probably succumb to the volatility of the data; in fact it could be better to carry out a regression based upon means 1988-1991 and 1994-1996 for example.

A model was built assuming that migration flows are driven by comparative wealth and employment chances with bordering regions experiencing higher growth than non-bordering ones. In addition it is assumed that large provinces (in terms of absolute GDP) will experience more emigration through scale effects.

FLPC is the log of the migration flow per capita (in source region) BOR is a dummy for a border. BOR takes value 0 when no border and log 2 otherwise GDPC is the log of (GDP per capita in destination/GDP in source region). I.e. attraction of GDP

in destination GDPT is the log of the GDP in origin region (i.e. like size/wealth of origin region) UN is the log of (Unemployment rate in source/unemployment rate in destination) i.e.

employment attraction.

The model takes the form:

Ln (FLPC) = a1*ln(GDPC) + a2*ln(GDPT) + a3*ln(UN) + a4*ln(BOR) + c

i.e. FLPC = GDPCa1*GDPTa2*Una3*BORa4*ec

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In the Netherlands, using the least squares procedure the results were the following:

R2 = 0.447 Observations: 264 ** significant to 5% *** significant to 1%

The signs of the coefficients are all positive as expected in the model. R2 is disappointing, however, only about 0.5 of the variance is explained. But GDPC, UN and BOR in particular seem to be highly significant factors (high t-values) and GDPT as well. The value of 2 for BOR suggests that bordering regions can expect flows about 4 times as large as those between non-bordering regions.

Regressions were also carried out just for 1988 and 1996. The model for 1996 is slightly more successful, that for 1988 less.

The following flows were outliers in the sense that the absolute size of the residue from the linear regression model (in log form) was greater than 1. A positive residue means that the observed flow is smaller than the predicted flow and a negative residue vice versa.

Source Destination Residue +ve or –ve?

Groningen Friesland Drenthe Zeeland

- - +

Friesland Zeeland + Drenthe Gelderland

Zeeland - +

Overijssel Zeeland + Gelderland Zuid-Holland - Flevoland Noord-Holland

Zuid-Holland - -

Utrecht Overijssel Gelderland

- -

Noord-Holland Gelderland Zeeland

- +

Noord-Brabant Groningen Zeeland

+ +

Limburg (NL) Groningen Zeeland

+ +

These flows are shown on Maps 6a and 6b.

Flows to Zeeland seem to be largely overestimated by the model, and from a geographical point of view this could be due to its declining GDP, peripherality, rural economy, distinct culture and problems of internal movement – demonstrating the contingent nature of this analysis. However, this does suggest that this model may be able to identify areas which are not spatially integrated. It is also interesting to note that flows are underestimated from Groningen (another peripheral

Variable Coefficient t-value GDPC 1.825 6.656*** GDPT 0.578 2.337**

UN 1.037 4.729*** BOR 1.946 11.502***

C -5.994 -2.505**

72

province) to its neighbours. There is a suggestion that the provinces of Drenthe, Groningen and possibly Friesland are quite spatially integrated. Something similar seems true for the Randstad region.

Maps 6: Outliers from migration model 6a: bigger than predicted 6b: smaller than predicted

However, the limits of the model are numerous. Firstly the model explains less than half of the variance in the data. Nonetheless the large t-values do suggest that the factors chosen for the specification in the model play an important role. The use of BOR as a border could be more highly developed by using a continuous or discrete metric for distance, but such methodological questions are difficult to solve. In addition the inclusion of the term GDPT is controversial and perhaps unnecessary but it seems important according to the model. If such a regression were to be developed more formally it would be no doubt sensible to follow the steps suggested in Chapter I.2 (i.e. decide more formally which factors are important in the particular case as opportunities or constraints).

In the UK, the explanatory power of the model is, however, somewhat disappointing as are the t-values for the majority of variables.

Variable Coefficient t-value C 0.119 0.154

LGdpc 1.013 7.068 LBor 1.316 10.314

LGdpt -0.0007 -.011 LUn 0.00109 0.082

Datapoints 175 R2 0.511

It seems that the comparative GDP per capita and borders do however play an influential role. When one looks at the outliers they all involve flows larger than expected to the South East and smaller than expected to Northern Ireland. This demonstrates the poor spatial integration between

73

Northern Ireland and the rest of the UK and the economic power of the South East. It may also be the case that the data underestimates the GDP of the South East. Not all data is at NUTS 95 level (the new NUTS1 regions) and therefore we have had to use some transformations to get comparable data. Another major problem however is that NUTS1 level does not reflect the nature of the South East. Central London has a huge GDP and this may have a magnetic blinding effect above and beyond the overall average picture that there are rich and poorer areas in the South East and London.

If one uses absolute GDP/capita data for the destination as an additional explanatory variable then the regression results improve somewhat. It is possible that this is a better specification for the model, along the lines of the classical gravity model. Including the GDP per capita in the destination region (GDPCDEST) the results are:

Variable Coefficient t-value BOR 1.359 13.629***

GDPC 0.002 0.492 GDPCDEST 0.920 18.704***

UN -0.110 -1.262 C -10.401 -14.262***

Datapoints 208 R2 0.777

So the flows are influenced almost exclusively by the border effect and the ‘pull’ of the GDP in the destination region. Outliers from this model are shown on Maps 7a and 7b. Flows which are smaller than expected are very focussed on Northern Ireland. However the effect of the South East seems to have been relativized in this new model. There are also a number of flows involving Scotland suggesting a barrier between this region and the rest of the UK. These results are not surprising but they show the complexity of using a regression model to analyse migration flows. It would be necessary to include a more exact continuous metric for distance and perhaps dummy variables (as is done below for road flows) to take into account the cultural diversity of certain regions.

Maps 7: UK migration outliers using GDPdest 7a: larger than predicted 7b: smaller than predicted

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Travel to work areas

In analysing levels of spatial integration it is also useful to consider daily movements of population. This helps to assess over what immediate area a territory might be classified as ‘spatially integrated’. To do so we have used the Travel To Work Area (TTWA) maps of the UK. In the UK the 1991 TTWA map was revised in 1998. This clearly demonstrates the effect of increasing spatial integration as people are willing to travel further each day to their place of employment. To the extent that workers are moving to reside in areas which previously had a different labour market geography, and perhaps different occupational characteristics, this may lead to greater integration of areas in the social and cultural spheres as well as economic.

= = =

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Appendix: NUTS regions

NL1 North Netherlands NL11 Groningen NL12 Friesland NL13 Drenthe NL2 West Netherlands NL21 Overijssel NL22 Gelderland NL23 Flevoland NL3 West Netherlands NL31 Utrecht NL32 Nord-Holland NL33 Zuid-Holland NL34 Zeeland NL4 South Netherlands NL41 Noord-Brabant NL42 Limburg (NL)

UK1 North UK2 Yorkshire and Humberside UK3 East Midlands UK4 East Anglia UK5 South East UK6 South West UK7 West Midlands UK8 North West UK9 Wales UKA Scotland UKB Northern Ireland

PT11 Norte PT12 Centro PT13 Lisboa e Vale do Tejo PT14 Alentejo PT15 Algarve

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2.2. Barrier effects

Barrier effects can be highlighted and analysed with the help of a mathematical model. This model is a recent refinement of the gravity model (Brocker J., Rohweder HC. 1990, Flowerdew R., 1991.). The three following studies demonstrate the possibilities of such a model, each analysing the barrier effect at a different spatial level: - supra-national: trade flows between European countries, the frontier associates one group of

countries, those of the EEC of 1957; - inter-national and inter-regional: goods flows between a group of Belgian and French regions,

the limit is the frontier between Belgium and France; - intra-national and inter-regional: persons migrations between Belgian regions, the limit

separating Wallonia and Flanders regions.

2.2.1. Modelling the frontier effect

(D. Robert)

The geographical analysis of flows recognizes two fundamental hypotheses as explicative of an exchange pattern: - The flows between two places are in proportion with their capacities of imports/immigrations

and exports/emigrations. In this form, this hypothesis does not allow for the influence of the economical structure and specialization of the local production and consumption, "taking the level of exports and imports in each country as given and living it unexplained", as J.Bröcker and H. Rohweder said. Concerning the flows of persons, it does not account for the internal factors of migration: wealth, quality of life, etc. Moreover, this model does not consider the relation between the capacity of exchanges of a place and the initial importance of population or activities.

- A second hypothesis introduces the importance of the spatial factor in the analysis of the exchanges behaviours. This hypothesis defines as negative the role of the distance: the greater the distance between territories is, the smaller their exchanges are.

The last hypothesis, which led to the construction of a model called spatial interaction model, is in fact more complex. Distance is not only physical distance. It also means cultural distance, political distance, linguistic distance, etc. (1.3.4). Frequently, these differences are relevant of a territorial belonging structure. Thus, a part of the frontier effect could be integrated in the measure of the distance. What we propose is to modelize the frontier effect as a specific parameter. According to the previous definition of this frontier effect, this parameter constitutes a third hypothesis (Figure 1). It measures the reduction of flows which cross a boundary and the proportional increase of internal flows, those which do not cross a boundary.

Combining step by step the three hypotheses we obtain a set of three models:

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Table 1. Models formulations – Simplified forms

Models Explanatory variables and hypothesis Capacities interaction models F*ij = ai.Oi.bj.Dj Spatial interaction models F*ij = ai.Oi.bj.Dj.dij

α Geographical interaction models F*ij = ai.Oi.bj.Dj.dij

α.(1/γ)Aij

Capacities of exchange : the good/individuals flow between two places is in proportion with capacities of imports/immigrations and exports/emigrations of these places

Capacities of exchanges and distance :if capacities of exchanges are considered as equal, the greater the distance between two places is, the smaller their exchanges are

Capacities of exchanges, distance and discontinuity : if capacities of exchanges and distance are considered as equal, the overstepping of a limit reduces the exchanges

where : Fij : flow between two places, i and j Oi : propulsiveness of i measured by the total of exports/emigrations; Dj : attractiveness of j measured by the total of imports/immigrations; ai and bj : balancing factors of i and j; dij : distance between i and j; α : distance-decay parameter; Aij : territorial belonging parameter; γ : border effect.

In the following study, these models take the mathematical form of a Poisson regression (R.Flowerdew, 1991).

Figure 1. The geographical modelization of flows

km

0

1000

100

10

Capacities of imports/emigrations and exports/emigrations

(sum of flows)

Flows

km

Distance

Frontier

Hypothesis 3. The overstepping of a territorial limitreduces the exchanges

Hypothesis 2. The greater the distance between two territories is, thesmaller their exchanges are

Hypothesis 1. The exchanges between two places are inproportion with capacities of imports/immigrationsand exports/emigrations of these places

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2.2.2. The effects of the European integration on the trade flows between countries

(D. Robert)

The spatial organization of trade flows in Europe

The french institute CEPII has built a database of international trade flows. This base relies on a work of harmonization of States balances of trade. To study the impact on exchanges of the European integration process, we can consider a part of these informations, those which concern the exchanges between the seventeen countries of the western Europe: the fifteen countries of the actual European Union, Norway and Switzerland.

From 1971 to 1994, an important increase of these exchanges can be observed. In 1994, as in 1971, the more important economical relations link the main European economies: France, Germany, Italy and United Kingdom (Figure 2). Because of the importance of the international trade in their economical activities (Belgium, Netherlands, Sweden) or because they have established intense political and economical links with a main economy (Austria with Germany, Ireland with United Kingdom), important exchanges also concern smaller economies.

Thus, taking into account the explanatory power of the capacities of exports and imports of each country, the economical interaction model « explains » more than 80 % of the spatial organization of exchanges (Figure 3). Concerning the trade flows between countries, the importance of the first hypothesis is strongly verified.

If we consider simultaneously the first and the second hypothesis, the explanatory power of the model – the spatial interaction model – becomes up to 90 %. What is more interesting to analyse with this model, are the residuals, relative differences between observed and predicted flows. Residuals determine which countries have intense relations, for capacities of exports and imports, and for distance estimated as equal. A specific geographic organization of these residuals can be observed in 1971 (Figure 4). Two groups of countries were clearly separated: the EEC and the other western European States, in particular those which made up the EFTA. Important exchanges within each group of countries and low exchanges between these groups - in relation to their spatial proximity and economical importance - characterize the European integration process in 1971. Among the thirty more important negative residuals, 28 were exchanges between these two groups. The main positive residuals concerned, above all, exchanges inside each group. In 1994, this territorial organization of exchanges has disappeared. The exchanges are not structured by the former or present territorial belonging of States (Figure 5).

If we consider, for each country, the geographic orientation of its European imports and exports, the disappearance of the 1971’s frontier - discontinuity between two supranational territories - is obvious. All the countries are integrated in the same set in 1994 (Figure 6)

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Figure 2. Main trade exchanges between European countries in 1971 and 1994

0 500 km

2,2 % to 4,9%

1,3 % to 2,1 %

0,8 % to 1,2 %

Part of the exchanges between westerneuropean countries in 1971

-

2,2 % to 3,9%

1,6 % to 2,1 %

1 % to 1,5 %

Part of the exchanges between westerneuropean countries in 1994

0 500 km

Figure 3. Explanatory power of the models

80

85

90

95

100

1971 1974 1977 1980 1983 1986 1989 1992

Economical interaction Spatial interaction

Geographical interaction (EEC 6)

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Figure 4. Residuals of the spatial interaction model in 1971

0 500 km

medium

very important

important

Significant negative residuals in 1971 :

medium

very important

important

0 500 km

Significant positive residuals in 1971 :

81

Figure 5. Residuals of the spatial interaction model in 1994

medium

very important

important

Significant negative residuals in 1994 :

0 500 km

medium

very important

important

Significant positive residuals in 1994 :

0 500 km

82

Figure 6. Economical exchanges and integration process in Western Europe

1971

19941986

1981

0 500 km

Specific orientation of exchangesbetween european countries :

EEC 6

European Union

EFTA

The frontier effect: a measure of the European integration process

To demonstrate the reality of the European integration process from 1971 to 1994, we can analyse the evolution of the frontier effect during this period. For this study, the EEC of 1971 (Belgium, France, Germany, Italy, Luxembourg and Netherlands) is considered as a territory. The geographical interaction model measures the effect of the limit of this territory on the exchanges between western European countries. This frontier effect was important in 1971 (Figure 7). It has continuously decreased since this time and becomes non-existent in 1994. Furthermore, the statistical significance of this parameter has been diminishing since 1986, year of Portugal and Spain integration. We obtain similar results considering the other partition of the European space: the one which gathers the EFTA countries.

Figure 7. Frontier effects predicted with geographical interaction models 0,0

0,2

0,4

0,6

0,8

1,0

1971 1974 1977 1980 1983 1986 1989 1992

γγ

E E C 6 E F T A

83

Conclusion

The analysis of economical exchanges between European countries offers a reliable measure of the importance of the European integration process. Thus, this paper demonstrates the possibility and the interest of studies of the territories interdependences, of spatial integration, which relies on the observation of their exchanges. However, this type of research has to overpass two difficulties. The first difficulty is relative to the method itself. It has to be improved, developed with the intention of identifying the specific contribution of each component of the frontier effect. The second difficulty concerns the lack of reliable data considering the exchanges in Europe, at a regional level and by types of goods for instance, which constitutes indeed a strong limit to such study.

84

2.2.3. Application to the goods flows between Belgian and French regions

(D. Robert)

The creation of a matrix of good flows by road between Belgian and French regions offers the possibility to measure the existing barrier effect. It is noteworthy that this matrix was built with homogeneous data from Belgian and French statistical offices. So, it is a rare case of a real international matrix available at a regional level of information.

The first model, called capacities interaction model (or economic interaction model), shows a low power of adjustment: capacities of imports and exports only explain 21 % of the exchanges pattern, as demonstrated by the deviance reduction.

The introduction of the road distance between places (capitals of regions) increases the goodness-of-fit. As showed by the reduction of the deviance, the distance accounts for more than seventy percents of the observed organization of flows. In this case, the distance-decay parameter is important: 1,8. Furthermore, the spatial interaction model offers other information. A structured geographical organization of the residuals of this model, relative differences between observed flows and estimated flows, can be observed. It brings out the negative role on the exchanges played by the frontier between Belgium and France. Thus, the thirty more important positive residuals all concern flows in the same country. Among the thirty more important negative residuals, twenty-three are international flows. It means that, other thing being equal, the frontier impedes the exchanges between regions. The residuals matrix leads to a same conclusion concerning all the flows.

Map 1. Differences between observed and estimated residuals

Significant negative residuals Significant positive residualsvery important

medium

less important

frontier

0 100 km

Spatial interaction model – 30 most significant residuals

85

The geographical interaction model makes it possible to obtain a direct measure of the frontier effect. The estimated coefficient γ of the barrier effect shows that, ceteris paribus, exchanges between regions of a same country are about seven times as high as exchanges between regions of different countries. This model offers the best fitting power. The frontier - explanatory variable - contributes to an important improvement of the goodness-of-fit and, thus, to the explanation of the exchanges geographical organization.

Table 2. Results Models Distance decay α Barrier effect γ Deviance %

deviance Mean interaction

(model 0: mean flow) Capacities interaction

(masses) Spatial interaction

(masses and distance) Geographical interaction

(masses, distance and barrier)

- -

-1,84

-1,64

- - -

0,14

9 357 790

- 7 398 691

20,9 % 1 808 043

80,7 % 894 107

90,4 %

Table 3. Residuals of spatial interaction model

Ile-d

e-Fr

ance

Cha

mpa

gne-

Ard

en.

Pica

rdie

Hau

te-N

orm

andi

e

Cen

tre

Bas

se-N

orm

andi

e

Bou

rgog

ne

Nor

d-Pa

s-de

-Cal

ais

Lor

rain

e

Als

ace

Fran

che-

Com

té

Pays

-de-

la-L

oire

Bre

tagn

e

Poit

ou-C

hare

ntes

Aqu

itain

e

Mid

i-Py

réné

es

Lim

ousi

n

Rhô

ne-A

lpes

Auv

ergn

e

Lan

gued

oc-R

ous.

PAC

A

Anv

ers

Bra

bant

fla

man

d

Bra

b. w

all.(

+Bru

x.)

Flan

dres

occ

.

Flan

dres

or.

Hai

naut

Liè

ge

Lim

bour

g

Lux

embo

urg

Nam

ur

Ile-de-France O O O O O O O O O O O O OOO O OOO OOO OOO OOO OOO OOO

Champagne-Arden. O O O O O O O O O O O O OOO OOO O O O OOO O O OOO OOO O O

Picardie O O O O O O O O O O O OOO OOO O O OOO OOO OOO OOO O OOO

Haute-Normandie O O O O O O O O O O O O OOO O OOO OOO OOO O OOO OOO

Centre O O O O O O O O O O O O OOO OOO OOO O O OOO OOO OOO OOO OOO

Basse-Normandie O O O OOO O O O O O O O O O OOO O O OOO OOO OOO OOO OOO OOO OOO

Bourgogne O O O O O O O O O O O O O O O O O O OOO O OOO OOO OOO OOO OOO OOO

Nord-Pas-de-Calais OOO OOO OOO OOO O O O OOO OOO O O

Lorraine O O I I O O O O O O OOO OOO O O O O OOO OOO OOO OOO OOO

Alsace O O O O O O O O O O O O O O O O O O O OOO OOO O O OOO OOO OOO OOO OOO OOO

Franche-Comté O O O O O O O O O O O O O O O O O O O O O O O O OOO OOO O O

Pays-de-la-Loire O O O O O O O O O O O O O O O O O O O O O OOO OOO O OOO OOO OOO OOO OOO OOO

Bretagne O O O O O O O O O O O O O O OOO OOO O OOO OOO OOO OOO O O OOO

Poitou-Charentes O O OOO O O O O O O O O O O O O O O O O O O O O O O OOO O O OOO O O OOO OOO OOO OOO OOO O

Aquitaine O O O O O O O O O O O O O O O O O O O O OOO O OOO OOO OOO OOO OOO O

Midi-Pyrénées O O O O O O O O O O O O O O O O O O O O O O O O OOO O O OOO OOO OOO OOO O

Limousin OOO O O O OOO O O OOO O O O O O OOO O OOO O OOO OOO OOO OOO OOO O

Rhône-Alpes O O O O O O O O O O O OOO O O OOO OOO OOO OOO OOO OOO

Auvergne O O O O O O O O O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO

Languedoc-Rous. O O O O O O O O O O O O O O O O O O OOO O O OOO OOO OOO OOO

PACA O O O O O O O O O O O O O O O O OOO O O OOO O O OOO OOO OOO OOO

Anvers O O O O O O O O O O O O O O O O O OOO O O O O O O OOO O OOO O O O O

Brabant flamand O O O OOO OOO OOO OOO OOO O O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O

Brab. wall.(+Brux.) OOO OOO OOO OOO OOO OOO OOO OOO O O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O O O O

Flandres occ. O O O O O O O O OOO O O O O O O O O OOO O

Flandres or. O O O O O O O O O O O O O O O O O O O O O O OOO O O OOO OOO O O O O O O O O

Hainaut OOO OOO OOO OOO OOO OOO OOO OOO OOO O O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O O O O O O O O

Liège OOO O O O O OOO OOO OOO O O O OOO OOO O O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O O

Limbourg OOO OOO OOO OOO O O OOO OOO O O OOO OOO OOO OOO OOO OOO O O OOO OOO OOO OOO OOO OOO O O O O

Luxembourg OOO OOO OOO OOO OOO OOO OOO O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O

Namur OOO OOO OOO OOO OOO OOO OOO O OOO OOO O OOO OOO OOO OOO OOO OOO OOO OOO OOO OOO O O

Positive residuals:

very important Negative residuals: OOO very important

medium O O medium

less important O less important

86

If we consider different types of goods, for instance chemical and mineral and building materials, we can notice differences of flows geographical organization. Thus, chemical products are less influenced by distance and by the frontier. On the contrary, the impediment effect of the frontier is more important concerning flows of minerals and building materials: it corresponds to a fourteen times reduction of the international flows with respect to intra-national flows.

Table 4. Results for two types of goods Type of goods – models Distance decay α Barrier effect γ Deviance (1-% model0) Minerals and building materials

Mean interaction Economical interaction Spatial interaction Geographical interaction

- -

-2,28 -2,11

- - -

0,07

3 623 431 2 793 431 23 % 915 230 75 % 652 130 82 %

Chemical

Mean interaction Economical interaction Spatial interaction Geographical interaction

- -

-1,49 -1,56

- - -

0,21

541 934 340 772 37 % 129 258 76 % 78 629 85,5 %

All results conclude to a still important effect of the frontier on the exchanges between Belgium and France, even if these countries have been joined in the same economical and political process of integration since 1957. From a methodological point of view, this study demonstrates the interest of a specific extent of the well known spatial interaction model to the problematic of the frontier and thus to the study of geographical integration.

Elements of bibliography

Brocker J., Rohweder HC. 1990. Barriers to international trade. Methods of measurement and empirical evidence. The Annals of Regional Science, pp.289-305.

Cattan N. 1995. Barrier effects: the case of air and rail flows. International Political Science Review, vol.16, n°3, pp.237-248.

Flowerdew R. 1991. Poisson regression modelling of migration. In Congdom P., Stillwell J., Migration models: macro and micro approaches, chap.6, Behaven Press London, pp.92-112.

Fotheringham As., O’kelly Me. 1989. Spatial interaction models: formulation and applications. Kluwer Academic Press, Dordrecht-Boston-London. 221p.

Grasland C. Octobre 1996. La mesure des effets-frontière. Notes de synthèse du SES.

Robert D. Mai 1997. Une approche dynamique de l’intégration européenne par la mesure de l’"effet de barrière". Notes de synthèse du SES, pp.19-24.

87

2.2.4. Application to residential migrations between Belgian Provinces

(C. Grasland & M. Poulain)

The study of the migration of population in Belgium 1989 asks about an existing barrier effect between regions of different linguistic practices.

Figure 1. Positive residuals of migrations between Belgian regions in 1989

The positive residuals of a doubly constrained spatial interaction model outlines important surplus of migration within Flanders and Wallonia. Concerning the Bruxelles area (peopled with 80 % of French-speaking persons), important positive residuals can be observed with the Wallonia, except the North-Brabant in Flanders which is its nearly suburbs. Figure 2. Negative residuals of migrations between Belgian regions in 1989

The negative residuals of the spatial interaction model show the relative lack of migrations between Flanders and Wallonia and also between Bruxelles and Flanders.

The following figure presents for each pair of regions, the ratio between observed flows of migration (F) and expected flows (estimated with the doubly constrained spatial interaction model). This ratio is up to 1 when residuals are positive, otherwise (negative residuals) it is less than 1.

The result matrix can be classified, clustering regions with similar types of relations. Thus, two integrated regions are opposed: Flanders on one hand, Bruxelles and Wallonia on the other hand. More than the correspondence with the linguistic separation of Belgium, what is most surprising is the absence of exception to the rule. This was not the case at the beginning of the sixties. Positives residuals could be observed between regions of different linguistic practice.

88

Figure 3. Regional typology of migration (residuals in 1989)

89

EXPLORATORY STUDIES 3: ANALYSIS OF SPATIAL PATTERNS (HOMOGENEITY, DISCONTINUITIES, MULTISCALAR ANALYSIS)

(C. Grasland)

3.1. Spatial homogeneity and territorial discontinuities

3.1.1. Operational concepts: spatial homogeneity, territorial homogeneity, territorial discontinuities

Criticism of non-spatial indexes of regional inequality

Many indexes have been proposed in order to summarize the regional variation of demographical, social or economical indexes at European scale. Some of those indexes are based on purely statistical consideration (standard deviation, coefficient of variation, entropy, …) and some others introduce some economical or sociological assumptions (Duncan, Hoover, Gini, …).

But most of the usual indexes which are employed for the measure of regional heterogeneity or regional inequality are not spatial because they are absolutely independent from the spatial organisation of territories in terms of distance or contiguity.

If we decide, for example, to measure the inequalities of the distribution of wealth at regional level with the Gini index and the Lorenz curve, the only information which is required is the population and the GNP of each territorial unit and we absolutely do not take into consideration the fact that the region with high and low GNP by inhabitant could be spatially distributed in very different ways.

The implicit assumption of non spatial indexes of regional inequality is that the proximity between reach and poor region does not matter and has absolutely no effects. In the case of GNP per capita the use of non spatial indexes implies the assumption that the potential redistribution of GNP are independent from the distance between regions. Under the assumptions of the regulation theory, it means that the people which will migrate from the poorest to the richest region will choose their destination without consideration of proximity and territoriality. Conversely, it implies that the economic actors which try to relocate their activity from richest to poorest region will give no priority to the opportunities located at a shorter distance to their origin plant than the others.

Global heterogeneity and local heterogeneity: the significance of spatial autocorrelation

Before to go further in the discussion of the theoretical framework, we will consider a very simple empirical example and analyse a sample of the distribution of GNP per capita in Italia. Instead of considering the whole distribution of GNP per capita between the 95 provinces, we will first limit our investigation to a sample of 22 regions located on a « path » of 1300 kilometres between Aosta and Reggio di Calabria (Figure 1 and Table 1).

90

Figure 1: Location of the north-south sample path of 22 Italian provinces

AOSTAMilano

Livorno

Roma

Napoli

REGGIO

Table 1: Distribution of GNP per capita of 22 Italian provinces located on a sample path between Aosta and Reggio di Calabria (1951-1991)

n° Name of the province GNP per capita: 100 = Italian mean Dist 1951 1963 1971 1981 1991 (km)

1 VALLE D'AOSTA 154.6 149.5 134.5 143.0 126.7 0 2 VERCELLI 177.3 130.5 117.2 122.2 121.4 68

3 NOVARA 148.3 117.3 114.8 125.5 119.6 113

4 VARESE 181.1 153.9 137.6 128.3 121.2 149

5 MILANO 186.6 170.7 150.6 132.6 130.3 203

6 PIACENZA 117.9 115.6 113.8 117.7 117.9 270

7 PARMA 107.3 110.7 121.7 124.2 125.7 307

8 MASSA-CARRARA 89.6 88.7 102.3 103.5 84.0 357

9 LUCCA 87.1 88.5 94.1 104.6 105.5 397

10 PISA 99.1 108.8 102.7 117.5 109.7 450

11 LIVORNO 129.6 118.7 114.3 110.8 103.5 486

12 GROSSETO 112.9 99.4 108.5 96.8 89.4 557

13 VITERBO 82.7 88.9 98.6 104.9 97.5 629

14 ROMA 136.4 124.5 119.8 102.9 118.2 704

15 LATINA 66.3 83.5 94.6 112.1 110.9 781

16 CASERTA 53.0 59.2 64.5 74.8 67.1 869

17 NAPOLI 85.2 79.7 72.7 68.9 69.0 913

18 SALERNO 60.0 67.9 65.3 73.3 71.3 996

19 POTENZA 51.8 68.6 54.3 61.6 61.4 1051

20 COSENZA 56.1 54.3 55.1 63.9 60.8 1166

21 CATANZARO 58.7 55.9 56.4 62.2 60.1 1243

22 REGGIO DI CALABRIA 55.6 55.4 56.8 65.1 55.3 1325

The most simple way to evaluate the evolution of the global level of heterogeneity of the GNP per capita between the 22 regions of the sample is to compute at each period of time the mean absolute difference of level between any couple of regions (22 x 21 / 2 = 231 couples of different regions). This index of heterogeneity is purely statistical and does not take into account the relative location of the 22 regions. The examination of the results (line A, Table 2) reveals an important reduction of the global level of heterogeneity between 1951 and 1971 and a stabilisation of this level during the 1971-1991 period.

91

Table 3: Evolution of global and local heterogeneity of the distribution of GNP for the sample of the 22 regions located on the sample path Aosta-Reggio di Calabria

1951 1963 1971 1981 1991 (A) mean difference between 51.3 39.0 33.9 29.9 29.9 two provinces (n = 231) (B) mean difference between 23.4 18.0 12.8 9.2 10.8 two contiguous provinces (n=21)

(A/B) spatial autocorrelation 2.19 2.17 2.64 3.24 2.78 An alternative and complementary approach is to examine the evolution of the local level of heterogeneity of the GNP per capita of the 22 provinces located on the path Aosta-Reggio di Calabria. This local level of heterogeneity is simply define as the mean absolute difference of level between contiguous couple of regions (21 cases). What we try to measure with this index is the type of spatial organisation of the heterogeneity.

- If the transition between regions of high and low level of GNP per capita is smooth and gradual, the local level of heterogeneity will be lower than the global level (positive spatial autocorrelation).

- If we observe, on the contrary, a regular alternance of regions with high and low level of GNP per capita the local level of heterogeneity will be greater than the global level of heterogeneity (negative spatial autocorrelation).

- Finally, if the regions with high and low level of GNP per capita are random spatially distributed, the local level of heterogeneity will be more or less equal to the global level (null spatial autocorrelation).

In our example, the local heterogeneity is lower to the global heterogeneity during the whole time period (1951-1991), which reveals the presence of an important positive spatial autocorrelation. This positive spatial autocorrelation is the consequence of a more or less regular gradient of decrease of GNP per capita from north to south in Italia and it is not really surprising to observe that region with equivalent levels of GNP are generally closer and more frequently contiguous than regions with very different levels of GNP. But they are many exceptions to this rule and at each time period it is possible to observe important differences between contiguous region: between Milano (187) and Piacenza (118) or between Roma (136) and Latina (66) in 1951.

Spatial organisation, territorial organisation and discontinuities: theoretical examples

The existence of a high level of positive spatial autocorrelation in a given spatial system imply the existence of a kind of geographical organisation of differences between territorial units. But it is important to precise the type of this geographical organisation before to go further in the analysis.

We propose to distinguish two basic type of geographical organisation of differences which are related to different processes of homogenisation through time:

(1) The spatial organisation is related to the lack of discontinuities between contiguous area. The spatial organisation can be related to a regular gradient (Figure 2-a) or more generally to gradual transitions between different minima and maxima (Figure 2-b). The existence of spatial organisation is generally related to spatial diffusion processes without barrier effects. The regional differences which can exist at a given time period are reduced under the influence of local effects of redistribution (of population, of wealth) and it is possible to simulate the evolution of a system which increase its spatial organisation through an iterative process of local smoothing of the values

92

(2) The territorial organisation is related to the existence of a hierarchy of levels of spatial organisation, i.e. a regionalisation of the differences. In this case, we can observe groups of contiguous territorial units with internal homogeneity and external heterogeneity. In other words, the territorial organisation is simply defined by the existence of homogeneous regions which are cluster of territorial units which are similar and contiguous (Figure 4). The process which can explain this type of geographical distribution is not related to a continuous effect of distance but rather to the discrete effect of the common belonging of territorial units to the same region. In the example of GNP per capita, a territorial organisation could be explained by internal redistribution of population or wealth between the territorial units of the same region whatever their distance and by the existence of a lack of redistribution between territorial units which belong to different regions (even if they are very close). Thus, a territorial organisation can be the cross result of a positive internal effect (regional integration) and/or a negative external effect (barrier between regions).

(3) The territorial discontinuities are related to a particular combination of spatial and territorial organisation. It is not possible to propose here a complete discussion of the concept of territorial discontinuity (Brunet, François, Grasland, 1997) but it is important to precise the definition which will be used in the research. We propose to define territorial discontinuities as "Lines of high dissimilarities between sets of contiguous territorial units, which can be considered as local exception in a context of global spatial organisation" (Grasland, 1997). This definition is based on the assumptions that:

- if a geographical system is not spatially organised (null or negative spatial autocorrelation) it is impossible to define discontinuities because high differences are distributed between too many territorial units;

- if a geographical system has a perfect spatial organisation (maximum positive spatial autocorrelation) it is also impossible to define discontinuities because the differences between territorial units are always gradual and smoothed;

- the existence of a high level dissimilarity between two single territorial units is not sufficient for the definition of a territorial discontinuity;

- it is only when we observe a line of high differences between two regions (sets of contiguous and homogeneous territorial units) that we can call this line "discontinuity".

According to this definition it is possible to distinguish different types of territorial discontinuities corresponding to different type of geographical organisation. The first type of discontinuity is related to a strong territorial organisation (Figure 5-a): discontinuities are defined as brutal change between homogeneous set of territorial units (i.e. regions defined on a criterion of global homogeneity). But it is also possible to define discontinuity related to a strong spatial organisation (Figure 5-b): discontinuities are not limits between homogeneous area but brutal changes between regular gradients of change (i.e. regions defined on a criterion of local homogeneity).

This distinction is crucial because the process which can explain the discontinuities are not the same according to the levels of spatial and territorial organisation. In Figure 5-a, we can suppose that the discontinuities are the consequence of a process of territorial homogenisation (reduction of the inequalities inside each region). In Figure 5-b we can rather suppose that the discontinuities are the consequence of a process of spatial diffusion with barrier effects (general reduction of the inequalities between contiguous regions except if they are separated by a barrier).

93

Theoretical examples of spatial organisation, territorial organisation and discontinuities

Figure 3: Spatial organisation of heterogeneity

Figure 3-a: single gradients

50

100

150

0 200 400 600 800 1000 1200 1400

Figure 3-b: multiple gradients

50

100

150

0 200 400 600 800 1000 1200 1400

Figure 4: Territorial organisation of heterogeneity

50

100

150

0 200 400 600 800 1000 1200 1400

Region A Region B

Region C

III

III

Figure 5: Spatial and territorial organisation => Discontinuities

Figure 5-a: discontinuities related to territorial processes (e.g. regional homogenisation)

50

100

150

0 200 400 600 800 1000 1200 1400

Region A Region B

Region C

III

III

Figure 5-b: discontinuities related to spatial processes (e.g. spatial diffusion with barrier effects)

50

100

150

0 200 400 600 800 1000 1200 1400

Region A Region B

Region C

III

III

94

Spatial organisation, territorial organisation and discontinuities: an empirical example

If we examine the evolution of the level of GNP per capita of the 22 Italian provinces located situation on the sample path Aosta-Reggio di Calabria between 1951 and 1991 (Figures 6.1 to 6.5), it is possible to recognize most of the theoretical situations defined above:

In 1951, it is possible to observe a general gradient of decrease of GNP per capita between North and South, but this gradient is not regular and many « peaks » or « valleys » can be observed along the path. A very important discontinuity can be observed between the provinces of Varese or Milano (GNP per capita > 180) and the following provinces of Piacenza and Parma (GNP per capita < 120). An important level of local heterogeneity can be also observed in central Italia with important peaks in Livorno, Roma or Napoli. But the rest of the Mezzogiorno is rather homogeneous with levels of GNP per capita comprise between 50 and 70 in most cases.

The evolution between 1951 and 1971 is characterised by an important increase of spatial organisation, i.e. a strong reduction of differences of level between contiguous provinces. This reduction of local heterogeneity is associated to a reduction of global heterogeneity, but this second trend is apparently not so important (the level of the provinces of Mezzogiorno remain more or less the same ) and we can suppose that the redistribution process was limited to provinces of northern and central Italy.

The evolution between 1971 and 1981 is more complex. It is characterised by an important reduction of differences between north and south (convergence to national level) but also by the beginning of a territorialisation process of Mezzogiorno one the one hand, Northern and Central Italy on the other hand. As a consequence the North-South gradient becomes less regular and a discontinuity appears in the south of Roma between the provinces of Latina and Caserta.

The evolution between 1981 and 1991 reveals a strong increase of the territorialisation process, i.e. a more and more evident opposition between two homogeneous area in Italia. If we except the province of Massa Carara (GNP per capita = 84), we can notice that all provinces of northern and central Italy has more or less equivalent levels of GNP per capita in 1991 (comprise between 90 and 130) and the same is true for Mezzogiorno but with lower values (comprise between 50 and 70). As a consequence, the discontinuity between both homogeneous area has increased and the transition between both parts of Italy appears more brutal than before.

Different interpretations and explanations can be given from the processes observed on the sample path Aosta - Reggio di Calabria. But before to go further it is necessary to develop the analysis on the whole Italian territory (95 provinces). The aim of this first part was just to precise the definitions, concepts and assumptions which can be used for the analysis of homogeneity and discontinuity.

95

The example of 22 Italian Provinces on the sample path Aosta-Reggio

Figure 6.1: GNP per Capita in 1951 (index 100 = italian mean in 1951)

0

50

100

150

200

0 200 400 600 800 1000 1200

Milano Roma Napoli ReggioAosta Livorno

NORTHERN ITALIA "THIRD ITALIA" MEZZOGIORNO

Figure 6.2: GNP per Capita in 1963 (index 100 = italian mean in 1963)

0

50

100

150

200

0 200 400 600 800 1000 1200

Milano Roma Napoli ReggioAosta Livorno

NORTHERN ITALIA "THIRD ITALIA" MEZZOGIORNO

Figure 6.3: GNP per Capita in 1971 (index 100 = italian mean in 1971)

0

50

100

150

200

0 200 400 600 800 1000 1200

Milano Roma Napoli ReggioAosta Livorno

NORTHERN ITALIA "THIRD ITALIA" MEZZOGIORNO

Figure 6.4: GNP per Capita in 1981 (index 100 = italian mean in 1981)

0

50

100

150

200

0 200 400 600 800 1000 1200

Milano Roma Napoli ReggioAosta Livorno

NORTHERN ITALIA "THIRD ITALIA" MEZZOGIORNO

Figure 6.5: GNP per Capita in 1991 (index 100 = italian mean in 1991)

0

50

100

150

200

0 200 400 600 800 1000 1200

Milano Roma Napoli ReggioAosta Livorno

NORTHERN ITALIA "THIRD ITALIA" MEZZOGIORNO

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3.1.2. Application to the distribution of GNP/inh. of the 95 Italian Provinces (1951-1991)

The choice of a territorial approach

Before to go further in the analysis of heterogeneity and discontinuity of GNP per capita in Italy between 1951 and 1991, it is important to precise the differences between two different family of tools and methods which can be used for this purpose.

- The territorial analysis of heterogeneity relies on the assumption that the geographical units which will be used in the analysis (i.e. the 95 provinces) are based on a significant division of space and society. In other words, the territorial analysis consider that the limits of territorial units are generally related to significant changes in the nature and level of the target phenomena and, conversely, that the internal differences of territorial units can be neglected. Accordingly, the territorial analysis will produce maps of discontinuities or heterogeneity which will be strictly based on the limits of territorial units.

- The spatial analysis of heterogeneity relies on the reverse assumption that the geographical units which are used in the analysis are purely conventional and are not necessary based on significant division of space and society. According to this assumption, it is possible to imagine that important discontinuities of the target phenomenum can appear inside the territoriale units. But if the territorial units used in the analysis define the maximum level of information, it is impossible to recognize those internal discontinuity. Accordingly, the spatial analysis of discontinuity will consider that with an information of level N, it is impossible to recognise discontinuities at the same level N. And it is only after a transformation (aggregation or smoothing methods) that it is possible to observe discontinuities at a higher level N+1 (with a lose of information).

A very simple example of both approach can be provided by the construction of two series of maps of GNP per capita in Italia between 1951 and 1991.

In Figure 7, we have choosen a territorial cartographic representation of the values of GNP per capita of each Italian province are represented with a classical choropleth map. Accordingly, we can observe geographical differences at a local scale (50-100 km) if we suppose that each province is more or less homogeneous.

In Figure 8, we have choosen a spatial cartographic representation of the values of GNP per capita are smoothed by a gaussian function of neighbourhood with span 50 km. As we consider that local differences are not necessary significant, we agree to a loose of information (generalisation) and we produce a map which would have been more or less the same if the limits of the 95 provinces has been changed. This stability of the resulting map (now independant from the original division of space in territorial units) is obtained through the choose of the span of neighbourhood which is greater than the mean radius of provinces.

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Figure 7: A territorial representation of the GNP per capita in Italy (1951-1991)

1951

1963

1971

1981

1991

EVOLUTION OF G.N.P./ CAPITA IN ITALIA BY ADMINISTRATIVE PROVINCESFROM 1951 TO 1991

Source : Celante A. (1994)

(c) Grasland C., UMR Géographie-Cités (1999)

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Figure 8: A spatial representation of the GNP per capita in Italy (1951-1991)

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EVOLUTION OF G.N.P. / CAPITA IN ITALIAIN A GAUSSIAN NEIGHBOURHOOD (span 50 km)FROM 1951 TO 1991

100 = mean value in Italia

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The choice of provinces

In this working paper, we have chosen a territorial approach of the question of discontinuities, but each cartographical or statistical method which will present has in fact an equivalent in the framework of the spatial approach of heterogeneity13. As it is not possible to present both approaches in this working paper, it is important to precise briefly the reasons and the consequence of the choice which has been made between spatial and territorial approach.

The 95 Italian provinces are a strong and old territorial division. Even if it is possible to point small changes in the number and limits of italian provinces, they can be considered as a relatively stable and permanent division of space since the end of the XIXth century. As in the case of French departements, the Italian provinces has been organised around and administrative centre which was generally an urban centre with polarisation effect on its whole territory. The adequation between the size of urban centre and its administrative area generally increase through time in a process of self organisation. Thus, even if the greatest towns has bigger functional areas than the province where they are located, we can consider Italian provinces as areas of cohesion for most basic functions and services.

The 95 Italian provinces can be considered as the most adequate division for the analysis of the variation of GNP per capita between 1951 and 1991. As most studies on economic development in Italy have demonstrated the importance of local effects (industrial districts) the choose of a higher level of aggregation (like the 20 regions) would hide most interesting differences and produce mean values which will not be relevant for the analysis of the spread of technological, social or economic innovation. At the contrary, the choose of a lower level of aggregation (like the communes) would produce a too complex picture of differences and the size of territorial samples would not be sufficient for a correct computation of economic indexes like GNP per capita. Another reason of the choice of provinces is the fact that their size is more or less equal to the mean distance of journey to works that can be observed around most poles of employment (even if this distance was subject to important variations between 1951 and 1991).

The 95 italian provinces are relatively homogeneous in size and shape which is a recognise advantages for all statistical methods. This homogeneity is certainly stronger for superficy than for population14 but this is precisely the homogeneity of superficy which is necessary for a correct evaluation and cartography of territorial discontinuities.

The 95 provinces define the NUTS3 level of aggregation of the European Union for Italy and this NUTS3 level is the target level for the construction of indexes of differentiation in the 1st stream of the Study Program on ESDP. This is not a scientific justification but as the purpose of this working paper is to demonstrate the interest of a method which could be further applied to the whole European Union, it is logical to use NUTS3 level in the example of Italy, in order to view a sample of the maps that could be obtained at the scale of the whole E.U.

The choice of an index of dissimilarity

The choice of an index of dissimilarity between provinces is a crucial question for the measure of heterogeneity and the cartographical representation of territorial discontinuities. Indeed, this choice can produce important modifications of the results and it is not possible to base the choice of this index of dissimilarity on purely statistical considerations.

13 For a comparison of both approaches, see. Grasland C., 1998, "La composante d'échelle dans l'analyse des distributions spatiales", in Contribution à l'analyse géographique des maillages territoriaux, Habilitation à diriger des recherches en géographie, Université Paris 1, Volume D:4, 24 p. 14 The mean superficy of Italian provinces is equal to 3170 km2 with a standard deviation of 1710 km2 (C.V.= 54%). The mean values of population in 1991 is 600 000 inhabitants with a standard deviation of 640 000 inhabitants (C.V. = 107%)

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As a very simple example (Table 3), we will examine the differences of GNP per capita in 1951 between two couples of contiguous provinces located in Northern Italy (Milano and Piacenza) or in Southern Italy (Caserta and Napoli). The question to be solved is "For which couple of regions (Milano-Piacenza or Napoli-Caserta) can we observed the highest dissimilarities of GNP per capita in 1951?"

Table 3: Influence of the criterium of dissimilarity on the perception of discontinuities First territorial unit (i) Second territorial unit (j) dissimilarity between i and j

Name Xi Rank(Xi) name Xj rank(Xj) DAXij DVXij DRXij DOXij MILANO 186.6 1st PIACENZA 117.9 20th 68.7 4720 45.1% 19 NAPOLI 85.2 50th CASERTA 53.0 88th 32.2 1037 46.6% 33

(a) DAXij=|Xi -Xj| (b) DSXij=(Xi -Xj)2 (c) DRXij=2.|Xi-Xj|/(Xi+Xj) (d) DOXij=|rank(Xi)-rank(Xj)|

- The criterion of absolute differences (DAX) is based on the single differences of level. It indicates that the level of dissimilarity between Milano and Napoli (68.7) is two time greater than the level of dissimilarity between Napoli and Caserta (32.2). This criterion was chosen in the first part of the working paper and we can notice that the value of dissimilarity is proportional to the height of the "steps" between contiguous area on the graphical representation of the sample path Aosta-Reggio (Figures 6).

- The criterion of square differences (DSX) is used in most cases by statisticians because it can be directly related to variance analysis and is associated to well known statistical tests (e.g. Geary index of spatial autocorrelation or variogrammes). This criterion has a tendency to increase the high dissimilarities and we can notice that if we choose it, we will conclude that the level of dissimilarity between Milano and Napoli (4720) is now four time greater than the level of dissimilarity between Napoli and Caserta (1037).

- The criterion of relative differencies (DRX) appears more interesting than the previous one if we consider that, for the process to be analysed, it is not the same thing to grow from 50 to 100 (+100%) than from 100 to 150 (+50%) or from 150 to 200 (33%). In order to obtain a symmetric measure of dissimilarity we use an index which is the absolute difference divided of level of two provinces divided by the average value of those two provinces. According to this criterion of dissimilarity, we will conclude that the level of dissimilarity between Milano and Napoli (45.1%) is more or less equal to the level of dissimilarity between Napoli and Caserta (46.6%).

- The criterion of ordinal differencies (DOX) tries to eliminate the possible influence of exceptional values in the distribution of GNP per capita and measure the differences of rank instead of the differences of level between the 95 provinces. We obtain thus a less sensible but more robust measure of dissimilarity because the distribution of GNP is transformed into an uniform distribution. As Milano is at the first place for GNP per capita in 1951 and Piacenza at the 20th rank, their level of dissimilarity is equal to 19 for this criteria. As Napoli is at the 50th rank and Caserta at the 88th rank, their level of dissimilarity is equal to 33. We will now conclude that the level of dissimilarity between Milano and Piacenza is more or less two much lower than the level of dissimilarity between Napoli and Caserta

The very simple example developed above indicates clearly that the maps of discontinuities and the index of homogeneity which can be established are not independent from the choice of the dissimilarity index.

Different choices are possible according to the assumptions made by the observer on the target phenomenon but it is important to justify this choice from an empirical or theoretical point of view

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and not only under methodological considerations of statistical commodity15. In our own research with J.M. Decroly and A. Bopda, we have demonstrated that it was possible to find general solutions for the measure of geographical organisation of heterogeneity, whatever the criterion of dissimilarity which has been chosen by the researcher and to produce statistical tests which are equivalent to the classical test used in the framework of variance analysis16.

In the particular case of an analysis of variation of GNP per capita between the Italian provinces from 1951 to 1991, it appears that the best solution is probably given by the index of relative differences (DRX) because we assume that the perception of economic inequalities by actors or inhabitants is probably more relative than absolute. In other words, we assume that in 1951 the attraction of the province of Napoli on the inhabitants of the province of Caserta (related to a relative increase of 45% of the GNP per Capita) is more or less equivalent to the attraction of the province of Milano on the inhabitants of the provinces of Piacenza at the same time period.

An important consequence of this choice for our study is the fact that the probability to find high dissimilarities and discontinuities between provinces with low incomes (Mezzogiorno) will be higher than if we had made the choice to use an index of absolute differences. With the indexes DAX or DVX, we would found much more differences and discontinuities in Northern than in Southern Italy. It is important for the reader to be aware of this fact when he/she will analyse the results and especially the maps of local heterogeneity and discontinuities.

The evolution of economic heterogeneity in Italy between 1951 and 1991

Before to analyse the precise location of heterogeneity and discontinuities on maps, it is important to evaluate the global evolution of the geographical organisation of economic heterogeneity in Italy with synthetic parameters of spatial organisation and territorial organisation.

The spatial organisation of heterogeneity can be measured with a spatial autocorrelation index which is the ratio between a measure of global heterogeneity and a measure of local heterogeneity. If we decide to measure the global heterogeneity as the mean dissimilarity between two places (whatever their location) and the local heterogeneity as the mean dissimilarity between two contiguous places (provinces with a common border), we can demonstrate that, whatever the criterion of dissimilarity the level of spatial organisation of economic heterogeneity was clearly improved during the 1951-1991 period (Table 4).

If we examine more precisely the results concerning the criterion of relative differences (Table 4-c) we can observe a regular reduction of global economic heterogeneity between provinces from 1951 (38%) to 1981 (28%) but a stabilisation at this level in the 1981-91 period. It is difficult to determine if this reduction of economic inequalities between Italian provinces is mainly the effect of political decisions (e.g. interregional transfers related to the action of Cassa per il Mezzogiorno) or the emerging result of individual choices (e.g. migration of working force from North to South) but it is interesting to observe that the reduction of economic inequalities has been a very regular process during the period of economic expansion 1951-1981. The stabilisation of the global economic heterogeneity after 1981 can be interpreted as consequences of the beginning of the economic crisis and as the beginning of a reduction of the interregional transfers (even if the "extraordinary" intervention of Cassa per il Mezzogiorno was only abolished after 1991).

15 In many books and papers about spatial analysis of heterogeneity (autocorrelation, variogrammes), the authors choose the index of square differences (DSX) because it is the most usual (?) or because it is the only index for which it is possible to find statistical tests (??) or because the results would probably be the same with other indexes of dissimilarity (???). 16 Decroly J.M., Grasland C., 1997, "Organisation spatiale et organisation territoriale des comportements démographiques: une approche subjective", in Bocquet-Appel & al., Analyse spatiale des données biodémographiques: approches récentes, John-Libbey/Ined, Eurotext Bopda A.., Grasland C., 1997, "Noyaux régionaux et limites territoriales au Cameroun", in Bocquet-Appel & al., Analyse spatiale des données biodémographiques: approches récentes, John-Libbey/Ined, Eurotext

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The decrease of local economic heterogeneity has been more important but also less regular in time than the reduction of global heterogeneity. It is indeed mainly between 1963 (18.4%) and 1981 (9.6%) that the main reduction of differences between contiguous provinces took place. The reduction of local heterogeneity was quite lower in the 1951-1963 or the 1981-1991 time periods.

Finally, if we consider the cross evolution of local and global heterogeneity (spatial autocorrelation) we can state that their evolution was parallel except between 1971 and 1981 when the spatial autocorrelation coefficient jumped from 2.1 to 2.9 in less than 10 years. In conclusion, we can say that it is during the 1971-1981 period that the process of spatial organisation of differences has been the most effective and we can suspect the existence of very strong process of local redistribution between contiguous provinces during this 10 years period.

Table 4: Evolution of the heterogeneity of GNP per capita of the 95 Italian provinces (1951-1991) at local and global level according to different criteria of dissimilarity

(a) absolute differences (DAX) DAXij = |Xi-Xj| 1951 1963 1971 1981 1991

(A) mean dissimilarity between 37.3 32.4 28.0 26.7 26.0 Two provinces (n = 4465)

(B) mean dissimilarity between 20.3 17.5 13.8 9.4 9.1 Contiguous provinces (n=205) (A/B) spatial autocorrelation 1.84 1.85 2.03 2.84 2.86

(b) square differencies (DSX)

DSXij = (Xi - Xj)2 1951 1963 1971 1981 1991

(A) mean dissimilarity between 2294 1626 1184 1084 1045 Two provinces (n = 4465)

(B) mean dissimilarity between 800 543 310 142 142 Contiguous provinces (n=205) (A/B) spatial autocorrelation 2.87 2.99 3.82 7.63 7.36

(c) relative differencies (DRX)

DRXij=2. |Xi-Xj| / (Xi+Xj) 1951 1963 1971 1981 1991 (A) mean dissimilarity between 37.9% 34.8% 30.2% 27.9% 27.6%

two provinces (n = 4465) (B) mean dissimilarity between 20.3% 18.4% 14.6% 9.6% 9.3% contiguous provinces (n=205) (A/B) spatial autocorrelation 1.87 1.89 2.07 2.91 2.97

(d) ordinal differencies (DOX)

DOXij = |rank(Xi)- rank(Xj)| 1951 1963 1971 1981 1991 (A) mean dissimilarity between 32.0 32.0 32.0 32.0 32.0

two provinces (n = 4465) (B) mean dissimilarity between 16.0 15.9 15.3 12.7 13.7 contiguous provinces (n=205) (A/B) spatial autocorrelation 2.00 2.01 2.09 2.52 2.34

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Table 5: Distribution of dissimilarities (DRX) between all Italian provinces (1951-1991)

0%

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125%

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Table 6: Distribution of dissimilarities (DRX) between contiguous Italian provinces (1951-1991)

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Map of homogeneous areas and territorial discontinuities

The global process of global/local réduction of economic heterogeneity which has been described before is not easy to explain if we do not precise the regions or provinces which was the most concerned by this reduction of heterogeneity during 1951 and 1991. Thus, it appears necessary to propose different cartographical solution in order to visualize the location of heterogeneous areas and their evolution.

A map of highest and lowest dissimilarities between contiguous provinces (Figure 9) can be established in order to visualise the homogeneous regions (set of provinces which are linked together by gradual transition) and the territorial discontinuities (lines of brutal change which separate two homogeneous areas). In order to visualise those two types of geographical objects, we have chosen a particular cartographic solution which combine three types of information.

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Figure 9: Discontinuities and homogeneous areas according to the distribution of GNP per capita in Italy (1951-1991)

0 - 5 %

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(highest dissimilarities between contiguous areas)

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10 - 20 %

more than 20 %

Claude GRASLAND , GDR-Libergéo (1999)

HOMOGENEOUS AREAS

TERRITORIAL DISCONTINUITIES

Cartographie : Eugénie DUMAS

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- The centres of contiguous provinces with a high level of similarity are linked by a red line.

Accordingly, when many provinces are similar and connex, they will form a red sub-graph which can be interpreted as the extension of an homogeneous region.

- The borders of contiguous provinces with a high level of dissimilarity are underlined by a blue polyline. Accordingly, when two homogeneous area are located on both side on a limit of brutal change, they will be separated by a blue line which is a territorial discontinuity.

- The mean value of GNP per capita of provinces is represented in a gray scale in order to precise the economic type of the homogeneous regions (high, medium or low level of GNP per capita) and of the discontinuities which are located between those homogeneous regions (high-high, high-medium, medium-medium,…).

A difficult question for the realisation of those maps is the choice of the classes of dissimilarity which will be used for the representation of homogeneity and discontinuity. Different solutions are possible according to the objectives of the research and it is not possible, in the framework of this working paper, to discuss all possibilities 17. In the present application we have decided to define four classes of dissimilarity according to the quartiles of the distribution of DRX between contiguous regions (Table 6). But as we wanted to show the progress of homogenisation at local level, we decided to used the quartiles of the distribution of dissimilarity of regions for the five periods considered as a whole (205 x 5 values of dissimilarity). With this solution the proportion of discontinuities and barriers is not necessary the same each year and it is possible to analyse their respective increase or decrease (proportion of "red line" and "blue borders").

The analysis of the evolution of homogeneous areas and discontinuities in Italy from 1951 to 1991 provides very interesting results from a theoretical and empirical point of view. Our purpose is not to comment in details but to point some major trends which can be observed in different parts of the Italian territory.

In Northern Italy, the heterogeneity is very important in 1951 but it is yet possible to observe the existence of a major homogeneous area in the centre of the Po valley. This central homogeneous area is composed from provinces of different regions (Lombardia, Emilia-Romagna, Trentino, Toscana) and is characterised by a medium value of GNP per capita. It is surrounded by discontinuities which separate it from less homogeneous areas, characterised by higher or lower levels of economic development. During the 1951-1991 period, this central homogeneous area increases progressively in size and cover all the northern part of Italy at the beginning of the 1990's. Some differences of level can always be observed between eastern and western part, but the transition are now very gradual and it is quite impossible to detect discontinuities at local level.

In Central Italy, the situation is more complicated and it is not possible to observe such a local homogenisation process as in Northern Italy. At each time period, it is possible to observe different homogenous area but they are separated by important discontinuities and there location is not always the same. Nevertheless, we can observe a general reduction of discontinuities and a proportional increase of the size of homogenous area through time. In 1991, most part of West central Italy define an homogeneous area which is clearly separated from Northern Italy by a line of discontinuity. Another homogeneous area, smaller in size is established between Roma and the neighbouring provinces. This area is surrounded by a circular discontinuity which isolate it from the less developed provinces of Central Italy (to north) and from Mezzogiorno (to east and south).

In Southern Italy, a general tendency to homogenisation can be observed between 1951 and 1991, but many exceptions can be observed at each time period. Many of those exceptions are related to the great industrial plants (Catania, Bari, …) which produced a brief increase of GNP at local level

17 For an overview of this problem, see Grasland C., 1997, "L'analyse géographique des discontinuités territoriales", Espace Géographique, 4, pp. 309-326

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(with a circular discontinuity around the province where the industry where localised) followed by a return to the mean value of the rest of the Mezzogiorno (disparition of the discontinuity). We can notice the integration of Napoli in a big homogeneous area after 1971, when the level of GNP of the town decrease and became more or less equal to the one of the other provinces of Campania.

Map of local economic heterogeneity

As a complement to the previous map of discontinuity, it is possible to propose a synthetic map of heterogenity at local level in Italy. This map of local heterogenity can be considered as an evaluation of the density of territorial discontinuities in a given neighbourhood of 50 km around each point 18.

The different maps of the Figure 10 indicate the location of the most heterogeneous area at local level, i.e. the places in Italy where it is possible to find regions with very different economic levels close to each other. The delimitation of those heterogeneous area is particulary interesting for territorial planners and policy makers because heterogeneous areas are places where it is possible to organise redistributions at local scale, without necessary migrations of population or re-location of industrial plants at a long distance. Local heterogeneity can produce political, economical and social tensions, but they can also favour an equalisation of chance. It is thus possible to consider local heterogeneity as an opportunity for the regional development on the short term, and as a factor of reduction of unequalites on the long term.

18 The basic idea is to measure in a given neighbourhood a local coefficient of variation which is the ratio between the local mean and the local standard deviation of the GNP per capita between provinces. This local level of heterogeneity has been established in a gaussian neighbourhood with a small span (50 km), in order to focus on the local variation of the economic heterogeneity. For more details, see. Grasland C., 1998, "La composante d'échelle dans l'analyse des distributions spatiales", in Contribution à l'analyse géographique des maillages territoriaux, Habilitation à diriger des recherches en géographie, Université Paris 1, Volume D:4, 24 p.

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Figure 10: Economic heterogeneity and spatial opportunities for local development in Italy (1951-1991)

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EVOLUTION OF THE LOCAL HETEROGENEITYOF G.N.P. / CAPITA OF THE 95 PROVINCIES OF ITALIA FROM 1951 TO 1991

Coefficient of variation of the GNP/inh.of italian provincies in a gaussian neighbourhood (span 50 km)

homogeneity

heterogeneity

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3.1.3. Application to the distribution of GNP/Inh. of the 198 Nuts2 regions of the European Union in 1981 and 1996

The previous concepts and application (Annex 3.1 and 3.2) was discussed during the Third Meeting of National Focal Point of the SPESP in Nijmegen (June 1999) and the discussion revealed a general agreement about the interest of the proposed methodology for the analysis of spatial integration but some criticisms concerning the choice of the scale of analysis which was used in the case study. Moreover, the participants of the debate underline that the interest of the proposed concepts and methods could be better evaluate through an application at the scale of the whole European Union.

As at the same time it was decided to focus the analysis of the indexes of spatial differentiation on a particular level of analysis (Nuts 2 regions in 1995 delimitation), we decided to propose a new application of the method in this framework.

In order to insure opportunities of comparison with the results obtained through the Italian case study, we decided to focus another time the analysis on the evolution of territorial discontinuities of wealth and to choose GNP per capita in 1981 and 1996 as data for the application at the scale of the European Union.

The statistical information (population and GNP of regions in 1981 and 1996) and the cartographic information (1995 delimitation of regions Nuts 2 ) was received from the German national focal point (BBR and IRPUD). We suppose that the original source of the information are the EUROSTAT's products Regio (for statistics) and Gisco (for delimitation of region).

The measure of the magnitude of territorial discontinuities

According to the theoretical and methodological discussion of the previous sections (Annex 3.1 and 3.2), we have decided to focus the analysis on relative differences of wealth between contiguous regions (presence of a common border) and to use a symmetric measure of dissimilarity which is the ratio between the difference and the mean of GNP/inh. of neighbouring regions.

)(/

./

./

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SymetricjandiregionsbetweenInhGNPofdifferenceRelativeDS

jregionfromInhGNPX

iregionfromInhGNPX

with

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This dissimilarity index assume that economic differences between regions produce effects (migrations, economic flows, etc.) which are related to the relative gradient of wealth and not to the absolute value of the difference.

Example: Two neighbouring regions A and B with levels of GNP equal to 10000 and 15000 Ecus/Inh. are separated by a discontinuity of the same magnitude than two regions C and D with respective values of 20000 and 30000 Ecus/Inh. In both case, the dissimilarity index is equal to 0.4 which indicates that the absolute difference of GNP/Inh (5000 for A-B and 10000 for C-D) is equal to 40% of the mean value of the neighbouring regions (12500 for A-B and 25000 for C-D).

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The choice of a symmetric measure of dissimilarity is interesting from a theoretical point of view because it provide a solution to avoid the classical ambiguity of indexes of relative differences which are not equivalent if maximum or minimum value are chosen as reference value.

Example: In previous example of two regions A and B with GNP/Inh. equal to 10000 and 15000 Ecus, we can produce two different measure of relative differences according to maximum (A is 33% lower than B) and to minimum (B is 50% greater than A).

The use of logarithmic differences could provide an interesting alternative solution for the production of a symmetric measure of relative differences but the results would be more difficult to understand for people which are not very familiar with mathematics. That is the reason why the solution which has been adopted appears to us as the best compromise between scientific and political preoccupations.

The cartographic representation of territorial discontinuities

Different solutions have been proposed for the cartographic representation of dissimilarities between contiguous regions. In previous working paper, we proposed to combine a representation of homogeneous area (red links between centre of contiguous regions which are very similar according to the chosen criteria) and territorial discontinuities (blue lines along the boundaries of regions which are very different according to the chosen criteria).

This solution is very interesting from a theoretical point because it gives the opportunity to examine either the fragmentation or the cohesion of the territorial distribution on a single map. But internal discussion from the French national focal point has also lead to important criticism against the choice of this solution in the framework of the ESDP project:

- The representation is too complex for people which are not very familiar with spatial analysis of heterogeneity and discontinuity. The information on the map is too abundant.

- Their is an important risk of mistake in the interpretation of the "network" of highest similarities. Indeed, many people can interpret the "red links" as material flows or physical networks.

- It is difficult to distinguish between discontinuities (or homogeneous area) which are located in regions of high level and low level of GNP per capita. The grey-scale which was used in order to give this information is not well perceived when blue limits and red lines are juxtaposed.

After different attempts, we have choosen a cartographic solution which is much more efficient for the transmission of the results to non-scientific people and for political decision:

- Only discontinuities are represented as black boundaries between regions with high (DSij >23%) or medium (12%<DSij<23%) levels of dissimilarity. Links of similarity (DSij<12%) are not represented on the map.

- Level of region is represented with a blue-red double colour-scale which is much more easy to read: red for regions that are above the mean level of the European Union and blue for the others; the intensity of the colours represents the importance of the deviation to this mean level (dark blue or red for important deviations to European mean and light blue or red for small deviations).

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- The limits of classes which define the level of regions are based on a logarithmic progression19

in order to insure a full comparability with the level of discontinuities (which is measured in relative terms).

According to this method, we propose two maps which indicates the localisation of discontinuities of GNP/Inh. between European regions at level Nuts 2 in 1981 (Map 1) and 1996 (Map 2).

Localisation of the main interregional discontinuities in 1981 and 1996

According to the chosen criterion (relative differences of GNP/Inh) the distribution of the main inter-regional discontinuities in 1981 and 1996 seems to be related to four great families of factors:

- Contact between metropolitan areas and neighbouring regions: In most States of European union the region where the political capital is located is fully surrounded by a very important discontinuity of wealth. It is especially true in France (Ile-de-France), Greece (Athens), Portugal (Lisboa), Spain (Madrid), Belgium (Brussels), Austria (Wien), Sweden (Stockholm) and Finland (Helsinki). More generally, the regions where the most important urban areas are located are also surrounded by economic discontinuities, even if the towns are not political capitals of the State (Frankfurt, Köln, München, Hamburg, Antwerpen, …).

- Boundaries of actual or former States are also related in many cases to important discontinuities of wealth. It is for example the case of the France-Spain boundary in 1981 (and at a less important degree in 1996) and the case of the former political division between eastern and western Germany.

- Limits between central and peripheral regions are also frequently related to important discontinuities at different scales. (1) At the scale of the European Union, it is possible to observe more or less continuous lines of discontinuities between the core-area (blue banana) and the other regions. It is especially true in 1996 where it is possible to define a long line of discontinuity from Groningue to Montpellier (following the German boundary but crossing France) and another one from Hambourg to Trieste (across Germany and Austria). (2) Inside certain States, it is possible to observe similar discontinuities related to brutal change of GNP level between groups of regions: this is the case for example in Belgium (between Flanders and Wallonia), Italia (between Northern and Mezzogiorno), Greece (between Eastern and Western part) and Spain (between NE and SW). In some cases (Austria, Germany, France) it is difficult to distinguish the discontinuities which are related to international and intranational core-periphery oppositions.

- Specific local situations are also causes of discontinuities, especially in the case of region which have specific advantages (e.g. the production of gas - Groningen - and oil - Scotland- or the fiscal exemptions - Luxembourg) or specific disadvantages (e.g. W. Ireland or N.W. Greece which are characterised by a low level of accessibility to transportation).

This first classification of discontinuities of wealth is of great interest and may be completed with a summary of the observations in a more synthetic way. It leads to propose synthetic measures of homogeneity or heterogeneity of local situations at the national and international scales, for the whole European Union.

19 If 100 is equal to the mean level of the European Union and α the parameter of the geometric progression, the limits of classes for a partition in 8 classes will be: Min, 100/(1+α)3, 100/(1+α)2, 100/(1+α), 100, 100.(1+α), 100.(1+α)2, 100.(1+α)3, Max. In the example of Maps 1 and 2 we have chosen α=0.26 and the limits of classes are equal to the following indexes of GNP/Inh: Min, 50, 63, 79, 100, 126, 159, 200, Max.

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Map 1

112

Map 2

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Global reduction of international discontinuities and stability of intra-national discontinuities

If we compute the mean value of discontinuities (1) for all neighbouring regions, (2) for neighbouring regions located in the same State and (3) for neighbouring regions located in different States in 1981 and 1996, we can evaluate the structural effect of political boundaries on differences of wealth at each time period and measure its evolution during the 1981-1996 period. Accordingly, we will be able to answer to the following questions:

- Are territorial discontinuities higher between regions of the same States than between regions of different States in 1981? in 1996?

- Can we observe a global reduction of territorial discontinuities (international and intra-national) during the 1981-1996 period?

- Can we observe the same tendency in the evolution of international and intranational territorial discontinuities?

The most simple way to answer to those questions is to compute the mean value of dissimilarity of contiguous regions according to the time period (1981 and 1996) and the type of the boundary (intra-national or international).

Table 4: Political boundaries and territorial discontinuities of wealth in 1981 and 1996 (unweighted)

DS 1981

DS 1996

Evolution 1981-96

International 0.208 0.181 -13.0% Intranational 0.165 0.167 1.3% Total 0.172 0.170 -1.5%

The table 3 indicates that in 1981 as in 1996 the discontinuities of GNP/Inh are higher for neighbouring regions located on both side of an international boundary than for neighbouring regions located in the same State. But the analysis of the dynamic of the period 1981-1996 reveals that the very small reduction of territorial discontinuities which can be observed at the European level (-1.5%) is the combination of an important decrease of international discontinuities (-13.0%) and an increase of intra-national discontinuities (+1.3%). In other words, the convergence of the economic level of regions at the local scale has been much more important across the international boundaries than inside the States.

This result is very interesting from political (correct …) point of view because it supports the idea that the differences of wealth inside the European territory remain important but are less and less related to national divisions. We can imagine that the European Commission could use such a result as a proof of the efficiency of various actions developed for the reduction of territorial inequalities (Feder, Interreg,…).

The validation of this conclusion leads to examine the results in more details.

First, the results presented in table 3 can be criticised because they are related to a particular territorial division of European Union (Nuts 2, 1995) which is not necessary the most significant for the target phenomenon (inequality of wealth). We can argue that this level Nuts 2 is the most commonly used for territorial planning at European scale but it is not the only one (some objectives are related to State level or to Nuts 3 divisions) and the action of national State can also be based on various territorial divisions, different from the Nuts 2 level used in this study. In other words, the results presented here should be cautiously interpreted because a reduction of territorial

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discontinuities at one level of observation (Nuts 2) is not necessary related to an equivalent reduction of territorial discontinuities at a greater or lower scale of analysis.

But even if we decided to focus our research on the territorial discontinuities at level Nuts 2, we should take into account the fact that the mean value of dissimilarity for contiguous regions is not necessary the best solution to summarise the results in a synthetic way.

For example, the length of the boundary with contiguous regions could be introduced in the computation of the mean value at dissimilarity at international or intra-national level. Indeed, the length of the border between two regions can vary between one kilometre and several hundreds of kilometres. The introduction of a weight in the computation of the mean value of dissimilarities appears thus as a relatively simple way to improve the previous results.

Table 5: Political boundaries and territorial discontinuities of wealth in 1981 and 1996 (weighted by the length of regional borders)

DS 1981

DS 1996

Evolution 1981-96

International 0.234 0.200 -14.4% Intra-national 0.156 0.158 1.4% Total 0.165 0.163 -1.3%

As we can observe in Table 5, when mean is weighted by the length of the boundaries between region, that produces some changes in the values of discontinuities at each time period. It is interesting to notice a significant increase of the mean value of international discontinuities, which reveals that the longest international boundaries between regions are related to higher levels of dissimilarity than the shortest international boundary. This fact could indicate a statistical bias (scale effect) but we do not observe the same effect for intra-national boundary. On the contrary the weighted mean produces a small decrease of intra-national differences between contiguous regions (probably because the most important intra-national differences are related to differences between the capitals - of smaller size than the other regions - and their neighbourhoods).

An alternative solution is to weight the mean value by the level of potential interaction between contiguous regions, defined as the product of their respective population. We can indeed consider that the social and political consequences of territorial discontinuities are proportional to the amount of people which are concerned by its potential consequences. If we suppose that territorial discontinuities of wealth can induce positive or negative effects on the opportunities of relation between people located on each side of a boundary, it is logical to weight their effects by the number of interactions which are potentially affected by the discontinuity. Thus social and political constraints may be integrated in the indexes, which appeared very interesting according to the question analysed.

Table 6: Political boundaries and territorial discontinuities of wealth in 1981 and 1996 (weighted by the potential interactions between regions)

DS 1981

DS 1996

Evolution 1981-96

International 0.232 0.174 -25.2% Intra-national 0.180 0.181 1.0% Total 0.187 0.180 -3.4%

The Table 6 reveals the same tendencies than the previous analysis (strong reduction of territorial discontinuities along the boundaries, stability or increase of territorial discontinuities inside the States) but with a much more important intensity of the international convergence. According to this criterion, we can notice an inversion of the level of discontinuities for international and intra-

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national couple of places: in 1981 the intensity of territorial discontinuities was lower between contiguous regions of the same State than between contiguous regions belonging to different States; in 1996 it is the contrary.

In conclusion, we can observe that, whatever the criterion used in the analysis (unweighted, weighted by length, weighted by potential interactions), it is possible to observe the same tendencies in the evolution of territorial discontinuities during the period 1981-1996:

(1) A small diminution of territorial discontinuities of wealth (international or international). (2) A small increase of local intra-national territorial discontinuities of wealth. (3) A very important diminution of international territorial discontinuities of wealth.

Typology of political boundaries according to the evolution of international discontinuities

The general reduction of territorial discontinuities between region located on each side of a boundary can be precise by Table 6 which indicates the mean value of territorial discontinuities between neighbouring regions in 1981 and 1996 and their evolution. The computation of the mean values of discontinuities has been weighted by the potential interaction (product of population of neighbouring regions) which appears to us as the most objective and interesting solution. The results are more or less the same when the mean values are unweighted or weighted by the length of regional boundaries.

Table 7: Evolution of international discontinuities of wealth along State boundaries (weighted by potential interactions between contiguous regions)

State i State j DS 1981

DS 1996

Evolution 1981-96

ES FR 0.366 0.202 -45% DE NL 0.399 0.254 -36% ES PT 0.326 0.211 -35% FI SE 0.346 0.241 -30% BE NL 0.249 0.177 -29% AT IT 0.102 0.079 -23% FR IT 0.062 0.053 -13% DE FR 0.156 0.146 -7% BE DE 0.199 0.195 -2% BE FR 0.162 0.166 2% IE UK 0.227 0.295 30% AT DE 0.239 0.313 31% DE DK 0.113 0.175 54% BE LU 0.280 0.532 90% FR LU 0.211 0.596 183% DE LU 0.113 0.445 295%

The table 6 reveals clearly that the general tendency of diminution of international discontinuities (-25%) is not equally spatially distributed. Using this means level as a reference value, we can distinguish three types of evolutions of discontinuities between two States:

- A strong international convergence is observed in the case of the regions located on each side of the boundary between France and Spain (-45%), Germany and Netherlands (-36%), Spain and Portugal (-30%), Finland and Sweden (-30%), Belgium and Netherlands (-29%).

- A weak international convergence is also observed between the regions of Austria and Italy (-23%), France and Italy (-13%), France and Germany (-7%), Belgium and Germany (-2%).

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- On the contrary, we can observe a tendency of international ldivergence of the economic level of regions located on each side of certain boundaries. This increase of discontinuity is rather small between France and Belgium (+2%), but very important between U.K. and Ireland (+30%), Austria and Germany (+31%), Austria and Denmark (+54%) and Luxembourg with all neighbouring countries.

Typology of States according to the evolution of intra-national discontinuities

If we compute for each State the evolution of the dissimilarity index between contiguous regions in 1981 and 1996, we can observe very important differences in the level and the evolution of territorial discontinuities of wealth.

Table 8: Evolution of intra-national discontinuities of wealth (weighted by potential interactions between contiguous regions)

State Number of regions

DS 1981

DS 1996

Evolution 1981-96

NL 12 0.143 0.095 -33% PT 5 0.291 0.240 -18% IT 20 0.135 0.120 -11% DE 38 0.225 0.206 -9% BE 11 0.223 0.218 -2% AT 9 0.210 0.211 1% UK 35 0.198 0.202 2% ES 16 0.181 0.188 4% SE 8 0.100 0.109 9% FR 22 0.164 0.202 23% GR 13 0.099 0.146 48% FI 6 0.124 0.202 63% IE 3 0.065 0.269 316%

N.B. Territorial discontinuities based on contiguity can not be computed for Denmark (divided in 2 regions but without land boundary) and Luxembourg (1 region) at this scale of analysis.

As it is clearly demonstrated by Table 8 the slow increase of intra-national territorial discontinuities at European level (+1.0%) is the result of very different evolution in each State. We can distinguish three type of evolutions:

- An important decrease of internal territorial discontinuities of wealth can be observed in Netherlands (-33%), Portugal (-18%), Italy (-11%) and Germany (-9%).

- A relative stability of internal territorial discontinuities of wealth can be observed in Belgium (-2%), Austria (+1%), United Kingdom (+2%) and Spain (+4%).

- An important increase of internal territorial discontinuities of wealth can be observed in Sweden (+9%), France (+23 %), Greece (+48%) and Finland (+63%). The dramatic increase of differences between regions of Ireland (+316 %) is not significant because of the too limited number of territorial divisions used for computation (only 3 regions).

It is important to keep in mind that the DS index is a measure of economic differences between contiguous regions (local heterogeneity) and not between all couple of regions (global heterogeneity). Accordingly, the evolution of territorial discontinuities is not necessarily correlated with the evolution of global differences between regions of the same State.

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Typology of States according to the evolution of global and local heterogeneity

It is interesting to compare the local level of heterogeneity (relative differences between contiguous regions of the same State) to the global level of heterogeneity (relative differences between any regions of the same State). This global level of heterogeneity can be estimated in a preliminary step as the coefficient of variation of the GNP/inh of the regions of the same State multiplied by two20.

Table 9: Evolution of global interregional differences of wealth (weighted by potential interactions between contiguous regions)

State Number of regions

2*CV 1981

2*CV 1996

Evolution 1981-96

NL 12 0.776 0.279 -64% PT 5 0.495 0.397 -20% BE 11 0.586 0.485 -17% GR 13 0.315 0.292 -7% DE 38 0.596 0.579 -3% ES 16 0.397 0.386 -3% FI 6 0.421 0.427 1% IT 20 0.505 0.518 3% AT 9 0.448 0.496 11% UK 35 0.278 0.321 15% SE 8 0.174 0.206 19% FR 22 0.307 0.367 20% IE 3 0.110 0.380 246%

The data related to the evolution of heterogeneity at local level (Table 5) and global level (Table 6) can be combined in order to build a typology of the evolutions at local and global level according to the situations of divergence (evolution of heterogeneity > + 5%), convergence (evolution of heterogeneity < -5%) and stability (evolution of heterogeneity comprise between -5% and +5%) of regional economic differences at local or global level.

Table 10: Classification of national evolutions of interregional economic heterogeneity at local and global level.

Divergence GR FI FR, SE, IE

Local level Stability BE ES AT,UK

Convergence NL, PT DE, IT

Convergence Stability Divergence Global level

20 As the C.V. is defined as a relative deviation to the mean value of regions, it must be multiplied by two in order to obtain an estimation of the mean relative difference between two regions. A more efficient way to estimate the value of global heterogeneity (and to compare it to local heterogeneity) would be the computation of the DS index for all couples of regions and the computation of the mean value of DS for the couples of regions in each State (weighted by the amount of potential interaction).

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3.2. Multiscalar approach of spatial cohesion

3.2.1. Application to the distribution of GNP/inh. Of the 95 Italian Provinces (1951-1991)

In this first application, we propose to established a typology of the economic level of Italian provinces based on the cross-combination of their local and global level of GNP per capita.

The global economic level of provinces is defined by their relative deviation to the mean value of Italia at each time period. For example, the GNP per capita of the province of Napoli in 1951 is equal to 85.2 (indx 100 = mean value for Italia) and its relative deviation is negative and equal to –14.8. It means that the province of Napoli is 15% under the global level of economic development of Italy in 1951.

The local level of economic development of provinces is defined as the relative deviation to the weighted mean of GNP per capita of the neighbouring provinces. In the case of Napoli, the mean weighted value of GNP per capita of the four neighbouring provinces (Caserta, Benevent, Salerno, Avelino) is equal to 54.6 which is quite lower from the level of GNP per capita of the province of Napoli (85.2). Accordingly the local level of economic development of the province of Napoli is characterised by a very important positive deviation equal to +44%. It means that the province of Napoli can be considered as an attractive area for neighbouring provinces, even if its global level is below the national level of Italia.

If we make a classification of each criterion in three classes "low", "medium", "high" (according to the levels of deviation –10% and +10%) we can finally produce a typology of economic levels in 9 classes. The province of Napoli in 1951 can be considered, for example, as "a local economic centre from the Italian economic periphery" according to this classification. Conversely, the province of Piacenza which is located near the province of Milano has in 1951 a positive global deviation (+18) and a negative local deviation (-38) for the same criterion: accordingly, Piacenza can be considered as "a local economic periphery from the Italian economic centre".

What is interesting to observe in the case of the distribution of GNP per capita in Italy between 1951 and 1991 is the progressive elimination of the local positive and negative deviations and the greater stability of global deviation (Carte 11). It is also interesting to observe that the distribution of the local economic centre (positive deviation at local level) has been strongly modified between 1951 and 1991. In Northern Italy, for example, the province of Modena appeared has a local economic periphery in 1951 and as a local economic centre in 1981. And the province of Milano which was a very strong local economic centre in 1951 (+30% of GNP per capita as compared to neighbouring provinces) loosed slowly its advance at local level (only +5% in 1981 and +6% in 1991).

We do not go further in the analysis of the results but we hope that our colleagues of the Italian NFP can help us to develop the comments and explanations of the dynamics which are revealed by the Figure 9, 10 and 11.

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Figure 11: Typology of Italian Provinces according to local and global levels of GNP per capita (1951-1991)

GNP PER CAPITA OF ITALIAN PROVINCES (1951-1991)

-10% +10%

+10%

-10%

* : index 100 = mean value of Italy ** : index 100 = mean value of contiguous provinces

1951

1971

1981

1991

Claude Grasland, GDR-Libergéo (1999)

1963

Cartographie : Eugénie Dumas

DEVIATION TONATIONAL

LEVEL*

DEVIATION TO LOCAL

LEVEL**

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3.2.2. Application to the distribution of GNP/Inh. of the 198 Nuts2 regions of the European Union in 1981 and 1996

In the case of the distribution of GNP/inh. between European regions it is possible to propose three territorial contexts which seems to be relevant for the analysis of structures and dynamics of economic heterogeneity.

(1) The European Union context is clearly relevant, as far as it is subject to regulations by external barriers (with State located out of the E.U.) and internal free circulation (of population, capitals,…). It is also particularly relevant because of political actions developed by the European Union in order to reduce economic differences according to this criterion of GNP/Inh. (Objectives).

(2) The national context is also relevant because, according to the principle of subsidiarity inscribed in the treaties of the E.U., the action of States is still very important in the fields of territorial planning and reduction of inter-regional disparities. National delimitation remains a major area of redistribution of wealth in all States of Europe. Specific actions of the States for the reduction of regional economic differences are based on different axis than the action of the European Union

(3) The inter-regional context (or neighbourhood context) is based on the increasing development of local relations between regions of the same State or of different State. It can be impulse by political decisions (INTERREG programs) but also on spontaneous decisions of private actors (firms, migrants) which decide to relocate in neighbouring regions according to local opportunities (jobs, wages, …). Those local dynamics are not necessary easy to measure (what is the scale of the processes?) but are probably of increasing importance for the understanding of regional dynamics.

If we admit that public and private decisions which influence the regional development are taken at those different scales, we can not evaluate the regional positions with a single index and we are obliged to follow a multiscalar approach based on indexes of relative position at different scales (European Union, National, Inter-Regional). Accordingly, each region will be described by a set of values of relative position which define multiscalar territorial profiles for the target index introduced in the analysis and a multiscalar territorial dynamics according to the evolution of relative positions according to each territorial context during a given period of time.

For each of the regions of the European Union at level Nuts 2, we have computed the 9 following indexes:

GNPE81: relative deviation to mean level of GNP/Inh of E.U. in 1981

GNPE96: relative deviation to mean level of GNP/Inh of E.U. in 1996

GNPE81-96 = (GNPE

96 - GNPE81): Variation of international position

GNPN81: relative deviation to national level of GNP/Inh in 1981

GNPN96: relative deviation to national level of GNP/Inh in 1996

GNPN81-96 = (GNPN

96 - GNPN81): Variation of national position

GNPR81: relative deviation to mean level of GNP/Inh of neighbouring regions in 1981

GNPR96: relative deviation to mean level of GNP/Inh of neighbouring regions in 1996

GNPR81-96 = (GNPR

96 - GNPR81): Variation of inter-regional position

N.B. 1: When the States coincide with an unique region of the level Nuts 2 (ex:Luxembourg) the values of relative deviation at national level are equal to 0 and the variation of national position is of course also equal to 0. In a further step, the analysis could be developed at other scales of analysis (Nuts 3 or Nuts 5) in order to avoid this situation.

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N.B. 2: When regions do not have a land-boundary (islands), the relative deviation to regional level in 1981 and 1996 and its evolution is also equal to 0. In a further step, the notion of contiguity could be extend to a more general concept of accessibility including maritime links and/or opportunity of contacts based on times of travel between regions (daily accessibility). N.B.3: The inter-regional context of a region i is defined here by the single mean of the values of GNP/Inh. of neighbouring regions (excluding the region itself). In further steps, we could experiment different variant based on (1) the inclusion of the region in the definition of its own context (2) the introduction of weights (by population, superficies, length of boundaries, …) in the computation of the mean value of neighbourhood.

Example 1: Comparison of two neighbouring regions of the same State

Alsace Lorraine

-25%

-15%

-5%

5%

15%

25%

1981 1996 V81-96

International National Inter-regional

-25%

-15%

-5%

5%

15%

25%

1981 1996 V81-96

International National Inter-regional

It is well known that those two neighbouring regions of N.E. France had very different trends in the evolution of their GNP/Inh. during the last 20 years. The multiscalar approach help to precise the dynamics observed in each of them.

- In Alsace, we can observe a relative decrease of the European level (-4) which is mainly related to the relative decrease of the situation of France according to the chosen criteria. Indeed, the national position of Alsace has been improved during the same period (+2) and we can observe an increasing advantage at the interregional level (+8). This increase of the local position is partly related to the decline of the neighbouring regions of Lorraine and Franche-Comté.

- In Lorraine, indeed, we can observe a dramatic decrease of all the indexes of relative position at international, national and interregional scales of analysis (-15, -8 and -11). Even if the GNP/Inh of Lorraine remains relatively near from the European mean in 1996 (-8%), it is clear that the decline of the industry in this region has been related to very deep modifications in the territorial positions at each scale.

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Example 2: Comparison of two neighbouring regions of different States

Midi Pyrénées Cataluna

-40%

-20%

0%

20%

40%

1981 1996 V8196

international intranational interregional

-40%

-20%

0%

20%

40%

1981 1996 V8196

international intranational interregional

The regions from Midi-Pyrénées (FR) and Cataluna (SP) are a classical example of the potential contradictions which can appear between European, national and inter-regional policies spatial integration and economic cohesion.

- According to the European level, we can observe that both regions have a GNP/Inh. lower than European mean. But the situation of Midi-Pyrénées was clearly much better than the one of Cataluna in 1981, even if the differences has been strongly reduced between 1981 and 1996. As a consequence, the help from the FEDER to Cataluna has been strongly reduced because many conditions of eligibility to the objective are no more available.

- According to the national level, the situation is reversed because the GNP/Inh. of Cataluna is much higher than the mean value of Spain (+14% in 1981 and +23% in 1996). It is the contrary for Midi-Pyrénées where the GNP/Inh. is clearly below the national mean of France in 1981 (-18%) as in 1996 (-16%). This difference with the national position has very important consequences in terms of fiscal flows between regions belonging to the same State: as the result of national policy of redistribution is negative for Cataluna and positive for Midi-Pyrénées, the political consequences are not the same (perception of the role of national State).

- According to the inter-regional level we can observe the stability of the position of Midi-Pyrénées (GNP/Inh. higher than the mean of neighbouring regions) and the important improve of the position of Cataluna which had a GNP/Inh. lower than the mean of neighbouring regions in 1981 (-13%) but more or less equal (-1%) in 1996.

In conclusion, we can observe that during the period 1981-1996 the situation of Cataluna for the criterion of GNP/Inh. has been improved according to all parameters of relative position (international: +9; national: +9; inter-regional: +12). In Midi-Pyrénées the situation is characterised on the contrary by a full stability, whatever the territorial context used for the measure of the evolution (international: -3; national: +2; inter-regional: -1).

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Example 3: Comparison of metropolitan regions from Northern Europe (I)

Ile de France (FR) Greater London (UK)

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

Uusimaa (FI) Noord-Holland (NL)

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

In the four examples of metropolitan regions presented above, the location of important cities inside regional boundaries (Paris, London, Helsinki, Amsterdam) insures very high level of GNP/Inh whatever the territorial context. In all cases, the metropolitan regions from northern region are characterised by positive deviations to European, national and inter-regional levels.

But those specific structural advantages of metropolitan regions can be modified by specific trends of concentration (metropolisation) or deconcentration (suburbanisation, diffusion) which can modify the situation on the long term.

- In the cases of France (Paris) and Finland (Helsinki) we have clearly a tendency of increasing concentration of GNP inside the limits of the metropolitan regions. As a consequence of this process of concentration we can observe a small increase or a stability of the European position (+4% in Ile-de-France and 0% in Uusimaa), an important increase of national position (+12% in Ile-de-France and +7% in Uusimaa) and a dramatic increase of inter-regional position (+23% in Ile-de-France and +9% in Uusimaa).

- In the cases of U.K. (London) and Netherlands (Noord-Holland) we observe the reverse situation of deconcentration of GNP outside from the limits of the metropolitan regions. As a consequence, we observe a stability or small decrease of their European position of (0% in G.London and -4% in N.Holland), a medium decrease of their national position (-8% in G.London and -2% in N.Holland) and an important decrease of their inter-regional position (-19% in G.London and -9% in N.Holland).

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Example 4: Comparison of metropolitan regions from Southern and Western Europe (II)

Madrid (ES) Lisboa e vale do Tej (PT)

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

Lazio (IT) Eastern Ireland (IE)

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

-75%

-50%

-25%

0%

25%

50%

75%

1981 1996 V8196

international intranational interregional

The metropolitan regions which are localised in less developed part of the territory of European Union are characterised by very important differences in their level according to the territorial context. Generally we observe an inverse relation between the level of development and the size of the territorial context, which means that those metropolitan areas define very strong polarisation effects in their neighbourhood. A good example is given by the situation of the region of Madrid: In 1981, the GNP/Inh of this region was 33% below the mean of the European union but 16% above the mean of Spain and 32% above the mean of the neighbouring regions of Castilla-Leon and Castilla-La Mancha.

Generally, the tendency of the period 1981-1996 is characterised by a global improvement of the position of those metropolitan areas from peripheral Europe at all levels of territorial observation (international, national and interregional). This general improvement of all levels is probably related to a process of concentration and metropolisation which is similar to the situation described in France or Finland. But we can also point some exceptions, such as in the case of the region of Lisboa where the growth of GNP/Inh has been lower than in other regions of Portugal and especially lower than in neighbouring regions.

Those results suggest that the tendencies to concentration and deconcentration are not necessary connected to "steps" of economic development because they can the same situations can be observed in area with very different levels of development.

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Proposals for a multiscalar typology of regions

1. For any criterion of spatial differentiation (GNP/Inh. has been used only as an example) it appears very interesting to transform the absolute indexes in multiscalar deviations to different territorial contexts which are relevant for this criterion (in certain cases - natural assets - the relevant territorial contexts can be different from the one which has been used for GNP - river basin, mountain areas, …).

2. The interest of multiscalar approach is particularly obvious applied to the analysis of trends: it helps to precise the level and the scale of the dynamics which tend to modify the structure of European territory. The policymakers and territorial planners should obtain therefore precious indications on the potential levels of efficiency of their action and be aware of the possible contradictions between actions developed at each level of decision (Europe, State, Regions).

3. The realisation of a classification of profiles according to each of the contexts (European, national, inter-regional) in order to identify first the qualitative types of situations in 1981 and in 1996 and secondly a typology of the regions according to the evolution of the indexes between the two dates.

4. Whatever the methodology chosen for the synthesis, it is important to underline that the choice of variables, time-period, territorial divisions and territorial context can heavily modify the results of a multiscalar territorial analysis. In other words, the multiscalar approach is only a useful complement to the analysis which has been developed by the workgroups engaged in the 1st Stream of the SPESP.

Definition of variables

Code: Code of the Nuts2 region Name: Name of the Nuts2 region Neighb.: number of contiguous regions used for the measure of interegional level 81E: relative deviation to mean level of GNP/Inh of E.U. in 1981 81N: relative deviation to national level of GNP/Inh in 1981 81R: relative deviation to mean level of GNP/Inh of neighbouring regions in 1981 96E: relative deviation to mean level of GNP/Inh of E.U. in 1996 96N: relative deviation to national level of GNP/Inh in 1996 96R: relative deviation to mean level of GNP/Inh of neighbouring regions in 1996 V_E: Variation of international position V_N: Variation of national position V_R: Variation of inter-regional position

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CODE Name neighb. 81E 81N 81R 96E 96N 96R V_E V_N V_R

AT11 BURGENLAND 2 -27 -37 -24 -23 -34 -20 3 3 5

AT12 NIEDEROESTERREICH 4 -1 -15 -11 -2 -16 -13 -1 -1 -2

AT13 WIEN 1 60 37 62 72 48 76 12 11 14

AT21 KAERNTEN 5 -3 -17 -11 -6 -19 -16 -3 -3 -5

AT22 STEIERMARK 5 -5 -19 -7 -7 -20 -8 -2 -2 0

AT31 OBEROESTERREICH 5 15 -2 -2 7 -8 -9 -7 -6 -8

AT32 SALZBURG 6 29 10 12 26 8 9 -3 -2 -2

AT33 TIROL 7 18 2 -1 14 -2 -7 -5 -4 -6

AT34 VORARLBERG 2 28 10 7 19 2 2 -9 -8 -5

BE1 REG.BRUXELLES-CAP./B 1 82 67 116 64 51 78 -18 -16 -38

BE21 ANTWERPEN 5 35 23 44 34 23 34 -1 0 -10

BE22 LIMBURG (B) 5 -7 -15 -7 3 -6 -1 10 10 6

BE23 OOST-VLAANDEREN 5 -1 -9 -3 4 -5 0 5 5 4

BE24 VLAAMS BRABANT 7 -16 -23 -24 -8 -15 -16 8 8 8

BE25 WEST-VLAANDEREN 4 8 -1 12 15 5 23 7 7 11

BE31 BRABANT WALLON 4 -18 -25 -8 -15 -22 -3 2 2 5

BE32 HAINAUT 8 -16 -23 -14 -20 -27 -15 -5 -4 -2

BE33 LIEGE 9 3 -5 5 -1 -9 -5 -4 -4 -10

BE34 LUXEMBOURG (B) 5 -16 -23 -23 -5 -12 -11 11 11 11

BE35 NAMUR 5 -15 -22 -9 -20 -27 -13 -6 -5 -4

DE11 STUTTGART 5 57 32 22 46 25 17 -11 -7 -6

DE12 KARLSRUHE 7 44 20 7 35 16 4 -8 -5 -3

DE13 FREIBURG 3 28 7 -2 19 1 -5 -9 -6 -3

DE14 TUEBINGEN 4 30 9 -6 24 6 -4 -5 -3 1

DE21 OBERBAYERN 7 58 33 34 69 44 45 11 11 11

DE22 NIEDERBAYERN 3 2 -14 -19 6 -9 -17 4 5 1

DE23 OBERPFALZ 4 4 -13 -19 8 -8 -17 4 5 2

DE24 OBERFRANKEN 5 14 -5 21 16 -1 20 2 4 -1

DE25 MITTELFRANKEN 6 39 16 9 33 14 3 -5 -2 -5

DE26 UNTERFRANKEN 7 9 -8 -14 14 -3 -12 4 5 2

DE27 SCHWABEN 6 21 2 -12 20 2 -11 -1 1 2

DE3 BERLIN 1 46 22 128 11 -5 60 -35 -28 -68

DE4 BRANDENBURG 5 -36 -46 -18 -31 -41 -3 5 5 15

DE5 BREMEN 3 81 51 63 61 37 46 -20 -14 -17

DE6 HAMBURG 2 114 79 109 106 76 101 -7 -3 -9

DE71 DARMSTADT 6 66 39 40 84 57 58 18 18 18

DE72 GIESSEN 4 6 -11 -18 14 -3 -13 8 8 5

DE73 KASSEL 7 16 -2 0 25 7 7 9 10 7

DE8 MECKLENBURG-VORPOMME 4 -48 -57 -37 -36 -46 -24 12 11 12

DE91 BRAUNSCHWEIG 6 25 4 29 15 -2 18 -9 -6 -11

DE92 HANNOVER 5 31 10 4 27 9 7 -3 -1 3

DE93 LUENEBURG 8 -5 -21 -23 -7 -21 -22 -2 0 0

DE94 WESER-EMS 8 7 -10 -21 10 -6 -3 3 4 18

DEA1 DUESSELDORF 5 46 22 33 29 10 22 -17 -12 -11

DEA2 KOELN 6 32 11 18 25 7 19 -7 -4 1

DEA3 MUENSTER 6 18 -1 4 4 -11 -5 -14 -10 -8

DEA4 DETMOLD 6 24 4 3 17 0 1 -7 -4 -2

DEA5 ARNSBERG 7 26 6 4 14 -3 -2 -12 -8 -6

DEB1 KOBLENZ 7 7 -11 -16 0 -15 -18 -7 -4 -2

DEB2 TRIER 5 5 -12 -13 -5 -19 -22 -10 -7 -9

DEB3 RHEINHESSEN-PFALZ 6 32 11 4 12 -4 -9 -20 -15 -13

127

CODE Name neighb. 81E 81N 81R 96E 96N 96R V_E V_N V_R

DEC SAARLAND 5 24 4 7 14 -3 0 -10 -7 -6

DED SACHSEN 5 -40 -50 -24 -34 -44 -12 7 7 11

DEE1 DESSAU 4 -23 -36 18 -42 -51 -14 -19 -15 -32

DEE2 HALLE 4 -23 -35 23 -29 -39 15 -6 -4 -8

DEE3 MAGDEBURG 7 -42 -51 -26 -39 -48 -20 3 3 6

DEF SCHLESWIG-HOLSTEIN 4 10 -8 -9 13 -4 -9 3 4 0

DEG THUERINGEN 7 -42 -51 -38 -37 -46 -34 5 5 4

DK11 DANMARK (EAST) 0 50 11 0 41 3 0 -9 -8 0

DK12 DANMARK (WEST) 1 23 -9 12 34 -2 19 11 7 7

ES11 GALICIA 3 -51 -14 -3 -53 -23 -7 -2 -9 -4

ES12 ASTURIAS 3 -37 10 13 -44 -8 4 -7 -18 -8

ES13 CANTABRIA 3 -36 12 1 -41 -3 -3 -5 -15 -4

ES21 PAIS VASCO 5 -25 30 0 -28 19 2 -2 -11 2

ES22 NAVARRA 4 -26 28 -7 -26 22 -3 0 -6 3

ES23 RIOJA 4 -26 28 12 -30 15 3 -4 -13 -9

ES24 ARAGON 8 -40 5 -16 -31 14 -3 9 9 13

ES3 MADRID 2 -33 16 32 -25 24 42 9 8 10

ES41 CASTILLA-LEON 11 -46 -6 -1 -45 -9 -3 1 -4 -1

ES42 CASTILLA-LA MANCHA 7 -53 -19 -11 -49 -16 -11 4 3 0

ES43 EXTREMADURA 5 -64 -37 -15 -57 -29 -5 7 8 10

ES51 CATALUNA 4 -34 14 -13 -25 23 -1 9 9 12

ES52 COMUNIDAD VALENCIANA 4 -42 1 4 -41 -2 -5 1 -3 -8

ES53 BALEARES 0 -27 27 0 -22 29 0 5 2 0

ES61 ANDALUCIA 5 -56 -23 7 -55 -26 -2 1 -2 -9

ES62 MURCIA 3 -49 -12 2 -46 -11 4 3 1 2

FI11 UUSIMAA 1 30 23 27 29 30 36 0 7 9

FI12 ETELAE-SUOMI 3 2 -3 -4 -5 -5 -2 -7 -1 1

FI13 ITAE-SUOMI 3 -7 -12 -4 -24 -23 -16 -16 -11 -12

FI14 VAELI-SUOMI 3 -4 -9 0 -14 -13 -1 -10 -4 -2

FI15 POHJOIS-SUOMI 3 -8 -12 -13 -9 -8 -2 -1 4 11

FI2 AHVENANMAA/AALAND 0 48 40 0 24 25 0 -24 -15 0

FR1 ILE DE FRANCE 5 65 42 51 69 55 74 4 12 23

FR21 CHAMPAGNE-ARDENNE 8 16 0 10 -2 -11 -2 -18 -11 -11

FR22 PICARDIE 5 8 -6 -6 -12 -19 -19 -20 -13 -13

FR23 HAUTE-NORMANDIE 4 14 -1 -4 11 2 -1 -3 3 3

FR24 CENTRE 8 5 -9 -1 -4 -12 -6 -9 -3 -5

FR25 BASSE-NORMANDIE 4 -2 -16 -7 -5 -13 -3 -2 3 4

FR26 BOURGOGNE 6 2 -12 -13 -6 -14 -14 -8 -2 -1

FR3 NORD-PAS-DE-CALAIS 3 -1 -14 -1 -10 -17 -4 -9 -3 -3

FR41 LORRAINE 7 7 -8 -8 -8 -16 -20 -15 -8 -11

FR42 ALSACE 5 18 2 -4 14 5 3 -4 2 8

FR43 FRANCHE-COMTE 5 8 -7 -3 -4 -12 -5 -12 -6 -1

FR51 PAYS DE LA LOIRE 4 1 -13 3 -4 -12 4 -5 1 2

FR52 BRETAGNE 2 -2 -16 -2 -11 -19 -7 -9 -3 -5

FR53 POITOU-CHARENTES 4 -8 -20 -8 -13 -21 -7 -6 -1 1

FR61 AQUITAINE 6 7 -8 32 -5 -13 19 -12 -5 -13

FR62 MIDI-PYRENEES 6 -5 -18 13 -8 -16 11 -3 2 -1

FR63 LIMOUSIN 5 -11 -23 -10 -16 -23 -8 -5 0 2

FR71 RHONE-ALPES 7 15 0 10 6 -3 9 -9 -3 -1

FR72 AUVERGNE 6 -7 -20 -7 -13 -21 -6 -6 -1 0

FR81 LANGUEDOC-ROUSSILLON 5 -10 -22 -7 -16 -23 -8 -6 -1 -1

FR82 PROVENCE-ALPES-COTE 4 13 -2 8 -3 -11 -4 -16 -9 -13

128

CODE Name neighb. 81E 81N 81R 96E 96N 96R V_E V_N V_R

FR83 CORSE 0 -13 -24 0 -15 -22 0 -3 2 0

GR11 ANATOLIKI MAKEDONIA, 1 -57 -11 -8 -55 -11 -11 2 0 -3

GR12 KENTRIKI MAKEDONIA 3 -53 -3 4 -49 1 11 4 3 7

GR13 DYTIKI MAKEDONIA 3 -55 -6 4 -54 -9 6 1 -3 2

GR14 THESSALIA 5 -54 -3 -2 -54 -8 3 0 -5 5

GR21 IPEIROS 3 -62 -21 -16 -67 -33 -26 -4 -12 -10

GR22 IONIA NISIA 0 -58 -11 0 -53 -7 0 4 4 0

GR23 DYTIKI ELLADA 4 -56 -9 -13 -57 -14 -2 0 -5 11

GR24 STEREA ELLADA 3 -37 32 35 -49 2 6 -12 -30 -29

GR25 PELOPONNISOS 2 -48 8 10 -55 -10 -9 -7 -19 -19

GR3 ATTIKI 2 -49 6 -12 -44 11 16 5 5 28

GR41 VOREIO AIGAIO 0 -66 -29 0 -63 -26 0 3 3 0

GR42 NOTIO AIGAIO 0 -54 -4 0 -43 14 0 11 18 0

GR43 KRITI 0 -58 -13 0 -46 8 0 13 22 0

IE11 IRELAND (EAST) 2 -35 6 9 -6 15 29 29 10 20

IE12 IRELAND (WEST) 3 -39 -1 -9 -36 -22 -27 3 -21 -18

IE13 IRELAND (SOUTH) 2 -42 -5 -7 -18 0 4 23 5 11

IT11 PIEMONTE 6 8 17 -7 6 14 -5 -2 -2 2

IT12 VALLE D'AOSTA 2 19 28 6 18 27 11 -1 -1 5

IT13 LIGURIA 4 4 12 -7 8 16 2 4 4 10

IT2 LOMBARDIA 4 21 31 11 20 29 6 -1 -2 -4

IT31 TRENTINO-ALTO ADIGE 4 7 15 -9 15 23 -3 8 8 6

IT32 VENETO 6 1 9 -9 12 20 -1 11 11 9

IT33 FRIULI-VENEZIA GIULI 2 3 12 4 14 23 11 11 11 7

IT4 EMILIA-ROMAGNA 6 23 32 15 19 28 12 -3 -4 -4

IT51 TOSCANA 5 5 13 2 0 7 -3 -5 -6 -5

IT52 UMBRIA 3 -8 -1 -8 -10 -4 -9 -2 -3 -2

IT53 MARCHE 5 -2 6 0 -5 2 -4 -3 -4 -4

IT6 LAZIO 6 -4 3 14 3 10 24 7 7 10

IT71 ABRUZZO 3 -22 -15 -11 -18 -12 -8 4 3 2

IT72 MOLISE 4 -31 -26 -9 -30 -25 -10 1 1 -1

IT8 CAMPANIA 4 -39 -34 -15 -40 -35 -19 -1 -1 -4

IT91 PUGLIA 3 -35 -30 2 -36 -31 1 0 -1 -1

IT92 BASILICATA 3 -40 -35 -1 -38 -34 4 2 1 5

IT93 CALABRIA 1 -43 -39 -6 -46 -42 -13 -3 -3 -7

ITA SICILIA 0 -38 -33 0 -39 -35 0 -1 -1 0

ITB SARDEGNA 0 -34 -29 0 -32 -27 0 2 2 0

LU LUXEMBOURG (GRAND-DU 5 32 0 26 70 0 71 38 0 45

NL11 GRONINGEN 3 131 119 134 25 21 32 -106 -98 -102

NL12 FRIESLAND 4 -17 -21 -40 -12 -15 -17 5 6 23

NL13 DRENTHE 4 6 1 -17 -13 -16 -16 -19 -16 1

NL21 OVERIJSSEL 6 -8 -13 -11 -8 -11 -8 0 2 4

NL22 GELDERLAND 7 -12 -16 -17 -7 -10 -12 5 6 5

NL23 FLEVOLAND 0 -30 -34 0 -21 -24 0 9 10 0

NL31 UTRECHT 3 3 -2 -2 15 12 9 12 14 11

NL32 NOORD-HOLLAND 4 20 14 25 16 12 15 -4 -2 -9

NL33 ZUID-HOLLAND 4 8 3 7 7 4 1 -1 2 -6

NL34 ZEELAND 4 1 -4 -6 -2 -5 -14 -4 -2 -8

NL41 NOORD-BRABANT 6 -9 -14 -11 2 -1 -2 11 12 8

NL42 LIMBURG (NL) 6 -14 -19 -21 -7 -10 -15 7 8 7

PT11 NORTE 3 -64 -16 -22 -59 -13 -13 5 4 9

PT12 CENTRO (P) 5 -66 -21 -22 -61 -17 -17 5 5 5

129

CODE Name neighb. 81E 81N 81R 96E 96N 96R V_E V_N V_R

PT13 LISBOA E VALE DO TEJ 2 -43 33 70 -41 26 55 2 -7 -15

PT14 ALENTEJO 5 -67 -22 -21 -63 -21 -21 3 1 0

PT15 ALGARVE 2 -61 -8 2 -54 -1 13 7 7 11

SE01 STOCKHOLM 1 56 19 29 48 23 36 -7 4 6

SE02 OESTRA MELLANSVERIGE 4 20 -8 -9 9 -9 -12 -11 -1 -3

SE03 SMAALAND MED OEARNA 3 27 -4 1 18 -3 5 -9 1 4

SE04 SYDSVERIGE 2 27 -3 -1 11 -8 -5 -16 -5 -5

SE05 VAESTSVERIGE 4 29 -2 5 16 -4 2 -13 -2 -2

SE06 NORRA MELLANSVERIGE 3 20 -9 -6 16 -4 1 -3 5 7

SE07 MELLERSTA NORRLAND 2 32 0 5 19 -1 3 -13 -2 -3

SE08 OEVRE NORRLAND 2 31 0 17 16 -4 10 -15 -4 -6

UK11 CLEVELAND, DURHAM 2 -9 -9 -7 -14 -19 -19 -6 -10 -12

UK12 CUMBRIA 6 2 2 10 7 1 11 5 0 1

UK13 NORTHUMBERLAND,TYNE 2 -9 -10 -11 -12 -16 -19 -2 -7 -8

UK21 HUMBERSIDE 4 -14 -14 -8 -4 -9 3 10 5 11

UK22 NORTH YORKSHIRE 6 -5 -5 3 6 0 13 10 5 9

UK23 SOUTH YORKSHIRE 4 -8 -9 -1 -22 -26 -21 -13 -17 -20

UK24 WEST YORKSHIRE 5 -7 -7 -1 3 -3 10 10 4 11

UK31 DERBYSHIRE, NOTTINGH 8 -5 -5 3 -7 -12 -5 -2 -7 -7

UK32 LEICS., NORTHAMPTONS 7 -2 -2 6 11 5 8 12 7 2

UK33 LINCOLNSHIRE 4 -10 -10 -4 -5 -10 -7 5 0 -3

UK4 EAST ANGLIA 4 -4 -4 2 6 1 6 10 5 5

UK51 BEDFORDSHIRE, HERTFO 5 1 1 -6 3 -3 -13 2 -3 -7

UK52 BERKS.,BUCKS., OXFOR 7 1 0 -4 29 22 13 29 22 17

UK53 SURREY, EAST-WEST SU 4 -10 -10 -19 5 -1 -15 15 9 5

UK54 ESSEX 4 -12 -12 -19 -8 -13 -20 3 -1 -1

UK55 GREATER LONDON 5 54 54 65 54 45 46 0 -8 -19

UK56 HAMPSHIRE, ISLE OF W 4 4 3 9 10 4 -2 6 0 -11

UK57 KENT 3 -12 -13 -21 -2 -8 -16 10 5 5

UK61 AVON, GLOUCS., WILTS 5 2 2 11 17 10 11 14 8 0

UK62 CORNWALL, DEVON 1 -15 -15 -5 -13 -18 -11 2 -3 -6

UK63 DORSET, SOMERSET 3 -11 -11 -8 -3 -8 -7 8 3 1

UK71 HEREFORD-WORCS., WAR 7 -17 -17 -10 3 -3 0 20 15 10

UK72 SHROPSHIRE, STAFFORD 6 -17 -17 -11 -11 -16 -12 6 1 -2

UK73 WEST MIDLANDS (COUNT 2 -3 -3 17 1 -5 5 4 -2 -12

UK81 CHESHIRE 5 5 5 20 17 10 33 11 5 14

UK82 GREATER MANCHESTER 5 -4 -4 2 -4 -10 -1 -1 -6 -2

UK83 LANCASHIRE 5 -10 -10 -5 -8 -13 -5 2 -3 0

UK84 MERSEYSIDE 3 -11 -11 -8 -23 -27 -24 -13 -17 -16

UK91 CLWYD, DYFED, GWYNED 4 -23 -23 -14 -17 -21 -16 6 2 -2

UK92 GWENT, MID-S-W GLAMO 3 -14 -14 -1 -10 -15 -11 3 -1 -10

UKA1 BORD.-CENTR.-FIFE-LO 5 0 0 1 11 5 8 10 5 8

UKA2 DUMFR.-GALLOWAY, STR 3 -9 -10 -10 -3 -9 -4 6 1 6

UKA3 HIGHLANDS, ISLANDS 3 -1 -2 -4 -16 -21 -27 -15 -19 -23

UKA4 GRAMPIAN 2 18 17 18 37 29 40 19 12 22

UKB NORTHERN IRELAND 1 -23 -24 26 -14 -19 35 9 5 9

130

EXPLORATORY STUDIES 4: CROSS-BORDER COOPERATION

(Ph. De Boe & Th. Hanquet)

4.1. Interreg II A programmes

1. Introduction

In matter of spatial integration, it appears that there will be no global indicators but rather combinations of indicators in order to take into account the various facets of the concept (which include co-operation between spatial entities). The indicator discussed here is thus envisaged in the perspective of combinations with other indicators.

Indicators of effective co-operation between spatial entities are not easy to identify, as co-operation is intrinsically difficult to quantify. The aim is not to find "measures" of co-operation but to give indications about the relative intensity of co-operation on the European territory.

In order to provide a balanced picture, two conditions are to be met by potential indicators of the facet "co-operation": - to express (at least a form of) co-operation in a way that is relevant at the European level; - to use data that are sufficiently homogeneous on the whole European territory.

The European initiative Interreg, and particularly the Interreg II A strand, oriented toward cross-border co-operation, may therefore be considered as an interesting field to provide an indicator, because: - it is based on voluntary co-operation between spatial entities; - it concerns all the Member States of the European Union; - it implies general rules and modes of computation common to the whole European territory.

2. The Interreg (II A) initiative

The Interreg initiative was launched by the Commission in 1990 in order to promote cross-border co-operation and to help internal and external border areas to face specific problems due to their relative isolation by way of co-financing of programmes by the Structural Funds. Eligible measures concerned many different domains such as economic development, work, environment, rural development, education, culture and health.

The initiative was renewed and extended in 1994, when the European Union still counted 12 Member States. It is assumed that the delay between the launching of Interreg II and the accession of the three new Member States (Austria, Finland and Sweden) in 1995 has no significant effect on the budget of the programmes involving the new Member States.

Interreg now counts three strands, among which the first one, Interreg II A, still concerns cross-border co-operation, while the other ones regard respectively completion of the energy networks (Interreg II B) and transnational co-operation (Interreg II C). The Interreg II A initiative is by far the most important European initiative in terms of available budget (2.617 M€ for the 1994-1999 period, value of 1996). We focus here on that II A strand.

131

Eligibility to the Interreg II A initiative is based on the NUTS 3 level and concerns border areas21. Among the 1032 NUTS 3 areas of the European Union22, 289 are eligible to the Interreg II A initiative, because they are located along a land border or because they have been recognised as eligible maritime border areas.

59 Interreg II A programmes were approved in 1994 and 1995, for a period extending until 1999. 35 concern internal borders, in the sense that they involve two or more countries belonging to the European Union. 28 concern external borders, involving one or more countries outside the European Union, belonging or not to the "enlargement countries" (respectively 12 and 17 programmes, one programme mixing the two cases). 4 programmes have both internal and external borders.

50 programmes associate two countries, and 9 associate three countries or more.

In principle, eligible areas are administrative areas of the NUTS 3 level. However, sometimes only small parts of a NUTS 3 unit are involved in the programme. For example, the programme Finland / Sweden "Islands Region" concerns six NUTS 3 units which have together more than 4.000.000 inhabitants, but the target of the programme is an area formed by the islands of these six areas that represent only 65.000 inhabitants. However, for comparability aims and in order to avoid discrepancies, all populations are computed on base of whole NUTS 3 units.

The most populated co-operation area has more than 5,5 millions inhabitants (France / United Kingdom "Nord-Pas-de-Calais - Kent"), while the smallest has about 30.000 inhabitants (United Kingdom / Morocco "Gibraltar"). The ratio is thus more than 100 to 1 between the two extremes.

The ratio is even higher for what concerns the implied budgets: the programme with the largest total budget (Spain / Portugal: 755,3 M€) has 500 times more than the programme with the lowest budget ("Gibraltar": 1,7 M€).

On the whole, on basis of the data used (complete accuracy must still be verified - see further), 310 NUTS 3 units are involved in Interreg II A programmes. The discrepancy with the number of participating eligible areas (274) is only apparent, as the European Commission indicates that under precise conditions, non eligible areas bordering eligible areas may be taken into account (36 NUTS 3 are concerned).

258 NUTS 3 units take part in one Interreg II A programme, 49 in two programmes, and 3 in three programmes. For these 52 areas, amounts of national financing per head are cumulated.

3. Description of the indicator

As the Interreg II A initiative relies on voluntary participation, a first exploitation of the data could be to assess which eligible areas are actually taking part or not in the programme.

274 eligible NUTS 3 entities take part in at least one Interreg II A programme. This rate of participation (95 %) indicates that taking part in an Interreg II A initiative may not be considered by itself as an indicator to differentiate the areas from the point of view of their co-operation with bordering entities.

On the other side, non participation of eligible areas could be considered as significant of a problem in matter of cross-border co-operation, but this is difficult to assess, because the concerned areas are very few and some absences may derive from inaccuracies in the description of the area, and also because some of these cases are particular cases.

21 The complete list of eligible areas was published in the Official Journal on 01/07/94 (n° C 180). 22 1995 definition.

132

3.1. National financing / inhabitant

As almost all eligible areas take part in the initiative, it is more significant to consider the way they take part. The financial investment is a convenient parameter that could provide a valuable picture of the relative investment in cross-border co-operation.

Some facts must however be kept in mind for the interpretation: - the guidelines for the Interreg II A indicate that a large part of the European means must be

devoted to border areas eligible to the Objectives 1 or 6, while the areas non eligible to one of the Objectives (1, 2, 5b and 6) must represent a limited part of those means;

- rates of European intervention are higher for areas eligible to the Objective 1 than for other areas;

- documents from DG XVI show that an indicative repartition of the financial envelope of the Interreg initiative is defined form the start between the Member States (at least it is the case for the period 2000 - 2006);

- the programmes are proposed to the Commission by the Member States and not directly by the actors of the concerned areas, which may imply some degree of arbitration at the national level (moreover if this level takes a large part in the budget);

- among the various measures to finance, some may be costlier than others, such as infrastructure works, even if they do not necessarily imply more cross-border co-operation.

The total budget of the different programmes is not a relevant indicator of cross-border co-operation because the part taken by the Commission in the financing introduces a bias that will give more weight to some areas according to their eligibility to one of the Objectives.

The amount of financing provided by the national, regional and local authorities and the private sector (further designated as "national financing") seems more significant, because: - it represents an effective financial investment from the national / regional authorities and from

the concerned area, although the NUTS 3 units do not correspond in all the countries with entities having decisional and financing capabilities; but even if the decision is taken at a higher level, it may express a priority or a choice related to cross-border co-operation in the concerned area;

- it allows to alleviate somewhat the bias of the eligibility to an Objective even if it does not eliminate it, as it may be more interesting to invest in a project of the same total cost in an 'Objective 1' area than elsewhere (lever effect).

The "national financing" is calculated by subtracting the European co-financing from the total budget of the programmes.

In order to eliminate as much as possible the size effect generated by the large differences between the largest and smallest co-operation areas (figures below), the financing is related to the number of inhabitants of the concerned area. The amount of national financing / inhabitant is indicated for each Interreg II A programme in table 1.

The distribution of the variable "national financing per head" is represented in the graph.

0

5

10

15

20

25

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191

€ / inhabitant

Num

ber

of N

UT

S 3

uni

ts

133

In spite of the effect of the population parameter, differences between the areas with the smallest and largest amounts per head remain quite broad. If the two largest values (relative to Greek areas) are not taken into account, the range is more reasonable.

The distribution of values does not indicate a clear division in classes. However, a split in four classes as indicated by the hatched lines (X <15 €/inh., 15 €/inh. ≤ X <30 €/inh., 30 €/inh. ≤ X < 50 €/inh. and X ≥ 50 €/inh.) seems sensible, even if there are much more units in the two lower classes (131 and 85 respectively) than in the two upper classes (62 and 32 respectively).

Representation by choropleth maps gives a distorted picture of reality, as NUTS units have quite different sizes and configurations. Therefore, a "linear" representation is preferred, with the colour representing the class of the NUTS unit allocated to the national border. Map 1 represents the amount of national financing / inhabitant in such a way.

As the NUTS 2 level is retained to combine a set of indicators belonging to the seven study themes, an exercise has been made to apply the same approach at this level. The translation from NUTS 3 to NUTS 2 raises the fact that only some of the NUTS 3 units are eligible for Interreg II A. Consequently the indicator is meaningless for the other ones. Aggregation and computation of the data for the whole territory of the NUTS 2 units would introduce strong bias linked to the different compositions of the NUTS 2 units.

The option taken is to attribute to each unit the value of the NUTS 3 unit(s) included in it when all those units have the same value (which is the most current case: 67 of 106 NUTS 2 units concerned). When it is not the case, the value attributed to the NUTS 2 unit is the mean value of those of the included NUTS 3 units weighted according to their respective numbers of inhabitants.

The resulting values of amount of national financing per inhabitant at NUTS 2 level are indicated in table 2.

3.2. National financing / GDP

Relevance could be improved taking into account the GDP / inhabitant because for a same amount the relative investment is higher in less wealthy areas. As the main financing is probably not provided at the local level (there are no comprehensive published data on that point), it seems more appropriate to consider the GDP at a higher level. This raises another question, as the main source of "national" financing varies according to the Member States. In most cases it probably is the national level, but there are may be cases where it is the regional level. However even in the second case there is a form of redistribution at the national level. The national level (NUTS 0) is thus retained here.

An index is calculated dividing the amount of national financing / inhabitant by the GDP / inhabitant, or national financing / GDP. In order to take into account possible fluctuations of the GDP, the value taken for the GDP / inhabitant is the mean value of this parameter over the three years of the program for which data are available (1994, 1995 and 1996).

134

As it could be expected, the range of values is much more extended (table 2). There is also a greater concentration of values in the lower part of the range (see next graph).

As the distribution is far from being symetrical, we choose to represent it with 5 classes, the first below the average (94) and the four others computed on basis of the standard deviation (409).

For technical reasons, the mapping at NUTS 2 level could not be realised in a "linear" way. Map 2 represents the index national financing / GDP through circles, where the shade of the color is graded according to the value of the index, each circle having a size proportional to the population of the border NUTS 3 units included in the NUTS 2. This representation gives a more qualified picture than would chloropleth maps, as it appears that some of the highest values of the index concern a relatively limited number of inhabitants.

4. Methodological issues

4.1. Data

- Data concerning the programmes (aims, budget, areas involved) are those provided by the DG XVI on its Web site and in various publications23. In a few cases, complementary information has been taken from other sources24.

- Description of the programmes by the DG XVI always include some information about the areas concerned, but the form of this information is not always the same:

- the areas are not always referred to in terms of NUTS 3, and when NUTS 3 units are mentioned it is by name and not by code. In some cases, this requires some interpretation. In a few cases, such as the programmes "Greece - Cross-border" and "Spain / Portugal", it would be useful to have more details;

- in some cases, the area is defined by reference to a NUTS 2 unit including several not eligible NUTS 3 units. In that case, only the eligible NUTS 3 units are considered, unless explicit mention of the participation of other units. On the opposite, when only parts of a NUTS 3 unit are mentioned, the whole unit is taken into consideration.

The case of the programme Gibraltar - Morocco is left out of the analysis as it can not be linked to a NUTS 3 unit.

- The list and cartographic representation of NUTS 3 units used here are the ones published by Eurostat in March 199525. The same units are apparently used by the DG XVI in order to identify the programme areas (published list of eligible areas).

The cartographic data are provided by the GISCO database (limits of 1995).

23 Such as the two editions of the "Guide of Community Initiatives" (1994 and 1998). 24 Notably the Nordic Internet site Boreas and the Nordregio web site 25 "Nomenclature of territorial units for statistics - NUTS".

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Map 1: National financing / inhabitant (NUTS 3)

136

Map 2: National financing / GDP (NUTS 2)

- The population data (1994 to 1996) and the GDP data (1994 to 1996) are provided by Eurostat (Regio - New Cronos). The statistics for 1996 are based on a list of NUTS units that differs from the list of 1995 for what concerns the eastern part of Germany, Finland, Ireland, and particularly the United Kingdom, where the administrative limits have been for a large part redefined. As the areas eligible to Interreg II A are moderately concerned, correspondences between the old and new areas can be established without too many difficulties.

137

4.2. Treatment

- Figures for the programmes budgets (total budget, part taken by the Structural Funds) are the ones calculated before the start of the programme (in 1994 or 1995). Published data relate to the whole programme areas and are not detailed by NUTS 3 units nor by countries. Consequently the figures are computed for the whole programme area.

This limits the accuracy in the sense that it leads to allocate to each NUTS 3 unit belonging to a

programme area the average value of the whole area (total national financing divided by the total population of the area). This simplification relies on the implicit hypothesis that the ratio between national financing and population is homogeneous on the programme area.

The bias introduced is difficult to assess. It is likely to be stronger when NUTS 3 units included in an Objective 1 area are associated with other ones. 6 programmes (out of 59) feature such an association.

Although it would be consistent with the frequent use of the NUTS 3 level for studies at European scale, allocation of national financing at the NUTS 3 level would probably be irrelevant:

- some actions imply several units and may not be localised (structures, networks); - at least a part of the national financing generally comes from a higher level (regional,

national) and may be difficult to allocate accurately to the concerned NUTS 3 entities.

- For programme areas concerning external borders, a problem arises to take the concerned population into account.

The communication of the Commission on the Interreg II initiative26 states that only areas located on the territory of the European Union may benefit from its co-financing. However, in some cases, areas located outside the borders of the Union are involved in the programme in some way. Two main cases may be distinguished:

- the area is located in an "enlargement country": in most of those cases, the Interreg II A programme is coupled with the PHARE programme, which co-finances the projects located on the external side of the border;

- the area overlaps another bordering country which does not benefit from an European programme (such as Norway, Switzerland) but which is willing to take part in the programme and to financially contribute to the projects.

A few rules have been applied in order to take into account the ratio between the "national financing" and the population in a comparable way:

- when the PHARE programme is indicated as active for the external side of the border, this external side is not taken into account (the Interreg strand being considered as "autonomous");

- when there are indications that a "non-accession" country is taking part in the programme budget, the concerned population of this country is taken into account on bases as far as possible comparable to the bases used for the European countries (for example, Swiss cantons);

- when there is no mention of participation of the bordering external country, it is not taken into account;

- a few cases remain dubious, such as programmes on the border with Albania or Morocco. For the moment, these countries have not been taken into account for the population, given the lack of details about their participation.

26 94/C 180/13.

138

5. Results

At this stage, the approach and the results provide a basis to allow an assessment of the relevance of an indicator based on the Interreg II A programmes, as a facet of the spatial integration criterion. Discussion on the results can be helpful to reveal particular aspects or specific situations that could have not been taken into account, as well as to go further into the interpretation.

This exploratory work enlightens several limitations of a quantitative approach in this domain. Some of these limitations may be common to all the studied themes while others are more specific of the subject.

The relevance of the indicator in the general framework of the study programme depends also on the ability to combine it with the other indicators selected for the same criterion and for the other criteria. The indicator presented here applies by definition to national border areas, what could raise problems for combination with other indicators.

In any cases, the spatial distribution shows that the highest values are located along the external borders of the European Union, while the core areas have generally lower values.

High values on the external borders seem to be in line with the aims of the Interreg initiative. The fact that some external border areas are also Objective 1 areas reinforces that pattern. National financing per head is indeed generally higher in the areas eligible to Objective 1, what can be explained by the greater lever effect. At the scale of individual countries, the effect appears clearly when only a part of the eligible areas are located in an Objective 1 area. If the map was based on total budget of the programmes rather than on national financing, the Objective 1 areas would appear even more prominently.

It must be kept in mind that low values of financial investments in Interreg II A programmes do not necessarily mean weak cross-border cooperation. In some cases the contrary may perhaps be true also.

It is not easy to state on statistical basis whether high figures effectively correspond to stronger spatial integration and in particular to stronger co-operation, or whether they express a will to progress in that way in regions encountering particular difficulties. Combinations with some other indicators may however give more indications.

But in either case high values can be considered as significant: - spatial integration and co-operation can be seen from a dynamic point of view (process, trends)

as well as from a static one, all the more because processes toward more integration as well as toward the reverse can go relatively fast;

- a trend toward more cooperation and integration can in some cases have more important consequences in terms of spatial planning than a stable situation of strong (or weak) co-operation and integration; moreover, as spatial integration is not an absolute but a relative concept, progress in some areas can result in a relative weakening of the links between other areas.

In that sense, it is clear that the efforts made toward external borders where the tradition of cooperation is less developed must not result in forgetting the other areas, which also need attention in order to maintain and develop their capital of co-operation and integration. Cross-border cooperation is also needed in more prosperous areas where growing competition could threaten the sustainable long term development.

= = =

139

Table 1: National financing / inhabitant (Interreg II A Programmes)

Interreg II A programme Total budget

in € National

financing in € Population

1996

National financing in

€ / inh. Austria / Czech Republic 12.114.000 7.614.000 2.138.700 3 Austria / Hungary 28.156.000 17.156.000 1.870.600 9 Austria / Slovak Republic 16.027.000 10.527.000 2.546.300 4 Austria / Slovenia 22.560.000 13.560.000 890.500 15 Belgium / France - Hainaut/Nord-Pas-de-Calais/Picardie 148.399.000 76.881.000 3.718.900 20 Belgium / France - West-Vlaanderen/Nord-Pas-de-Calais 38.310.000 20.320.000 3.002.000 6 Belgium / France / Luxemburg -Wallonia/Lorraine/Luxemburg

62.233.000 32.033.000 2.535.100 12

Belgium / France Ardennes 27.770.000 15.320.000 697.200 21 Belgium / Netherlands - Euregio Middengebied 66.330.000 33.920.000 4.320.500 7 Belgium / Netherlands - Euregio Scheldemond 22.786.000 11.696.000 2.684.400 4 Denmark – Bornholm 4.610.000 2.610.000 45.100 57 Denmark / Sweden Øresund 28.000.000 15.000.000 2.647.100 5 France / Italy - Alpes 160.265.000 103.295.000 5.391.600 19 France / Italy - Corsica/Sardinia 74.169.000 40.490.000 584.900 69 France / Italy - Corsica/Tuscany 58.245.000 39.657.000 472.800 83 France / Switzerland - Rhône-Alpes 11.610.000 6.237.000 2.390.100 2 France / Switzerland -Franche-Comté 14.192.000 7.096.000 2.657.500 2 France / United Kingdom Nord-Pas-de-Calais/Kent 95.268.000 50.166.000 5.561.700 9 Germany / Austria -Bayern/Oberösterreich/Salzburg/Tirol/Vorarlberg

56.258.000 31.658.000 3.736.500 8

Germany / Czech Republic 42.202.000 25.402.000 1.143.300 22 Germany / Denmark - KERN and Fyns Amt 3.600.000 1.800.000 1.057.200 1 Germany / Denmark - Ostholstein, Lübeck and Storstrøms Amt

10.400.000 5.200.000 671.700 7

Germany / Denmark - Planungsraum V and Sønderjyllands Amt

22.200.000 11.100.000 689.600 16

Germany / France - Pamina 22.110.000 11.056.000 2.291.200 4 Germany / France - Saar/Lorraine/Palatinat 46.530.000 23.265.000 1.996.400 11 Germany / France / Switzerland - Upper Rhine and Centre/South

49.899.000 25.320.000 3.591.600 7

Germany / Luxemburg DeLux 30.936.000 22.901.000 851.500 26 Germany / Netherlands - Ems-Dollart 62.730.000 40.260.000 986.200 40 Germany / Netherlands - Euregio 53.610.000 31.600.000 2.632.500 12 Germany / Netherlands - Euregio Rhein-Waal 23.060.000 11.529.000 2.073.600 5 Germany / Netherlands - Rhein-Maas-Nord 12.760.000 6.380.000 1.065.200 5 Germany / Netherlands / Belgium - Euregio Meuse-Rhine 71.910.000 36.205.000 3.509.500 10 Germany / Poland - Brandenburg 120.041.000 55.021.000 722.200 76 Germany / Poland - Mecklenburg - Vorpommern 84.107.000 21.037.000 840.500 25 Germany / Poland / Czech Republic - Saxony 215.705.000 69.255.000 1.557.200 44 Germany / Switzerland -Bodensee-Hochrein 9.710.000 4.855.000 901.800 5 Greece - Cross-Border 492.220.000 182.420.000 1.133.800 160 Greece / Italy 304.895.000 135.664.000 4.094.600 33 Ireland / United Kingdom - Northern Ireland 262.000.000 105.100.000 2.070.600 50

140

Interreg II A programme Total budget

in € National

financing in € Population

1996

National financing in

€ / inh. Ireland / United Kingdom - Wales 142.760.000 58.679.000 2.267.300 25 Italy / Albania 178.227.000 96.697.000 4.085.300 23 Italy / Austria 27.426.000 15.578.000 2.372.400 6 Italy / Slovenia - Friuli-Venezia Giulia and Veneto 31.350.000 15.770.000 1.728.500 9 Italy / Switzerland 52.734.000 32.734.000 1.798.700 18 Spain / France 142.640.000 80.196.000 4.637.400 17 Spain / Morocco 185.317.000 83.946.000 2.487.200 33 Spain / Portugal 755.265.000 203.265.000 5.393.100 37 Suomi-Finland - Karelia 31.847.000 17.962.000 629.100 28 Suomi-Finland - South Finland Coastal Zone 22.676.000 16.596.000 2.145.200 7 Suomi-Finland / Russie - South-East Finland Region 40.669.000 31.042.000 504.800 61 Suomi-Finland / Sweden - Barents 23.361.000 12.904.000 4.400.000 2 Suomi-Finland / Sweden - Islands Region 9.500.000 5.465.000 4.071.400 1 Suomi-Finland / Sweden - Kvarken MittSkandia 14.614.000 7.995.000 963.700 8 Suomi-Finland / Sweden - North Calotte Region 29.216.000 17.004.000 889.359 19 Sweden / Norway - Borderless co-operation 13.000.000 7.500.000 352.100 21 Sweden / Norway - Inner Scandinavia 10.780.000 6.280.000 812.776 7 Sweden / Norway - The Nordic Green Belt 13.040.000 7.540.000 519.000 14 United Kingdom / France - East-Sussex / Haute Normandie / Picardie

80.769.000 46.709.000 3.070.700 15

United Kingdom / Morocco Gibraltar 1.709.000 999.000 30.000 33

141

Table 2: National financing / inhabitant and / GDP (NUTS 2)

NUTS GDP/inh. 1994-1996 (€) Financing/inh. (€) Financing/GDP X 100 000

AT11 Burgenland 21578 13 61

AT12 Niederösterreich 21578 4 22

AT13 Wien 21578 16 78

AT21 Kaernten 21578 18 83

AT22 Steiermark 21578 15 70

AT31 Oberösterreich 21578 10 49

AT32 Salzburg 21578 11 53

AT33 Tirol 21578 14 67

AT34 Vorarlberg 21578 8 39

BE21 Antwerpen 20049 7 39

BE22 Limburg (B) 20049 18 90

BE23 Oost-Vlaanderen 20049 4 21

BE25 West-Vlaanderen 20049 7 36

BE32 Hainaut 20049 29 144

BE33 Liège 20049 10 51

BE34 Luxembourg (B) 20049 27 139

BE35 Namur 20049 21 109

DE12 Karlsruhe 22219 4 21

DE13 Freiburg 22219 10 46

DE14 Tübingen 22219 12 55

DE21 Oberbayern 22219 8 38

DE22 Niederbayern 22219 26 120

DE23 Oberpfalz 22219 22 99

DE24 Oberfranken 22219 22 99

DE27 Schwaben 22219 10 45

DE4 Brandenburg 22219 60 273

DE8 Mecklenburg-Vorpommern 22219 25 112

DE94 Weser-Ems 22219 43 197

DEA1 Düsseldorf 22219 8 37

DEA2 Köln 22219 10 46

DEA3 Münster 22219 12 54

DEB2 Trier 22219 27 125

DEB3 Rheinhessen-Pfalz 22219 7 32

DEC Saarland 22219 17 79

DED Sachsen 22219 44 200

DEF Schleswig-Holstein 22219 7 35

DK Danemark 25190 7 28

ES11 Galicia 11223 37 335

ES21 Pais Vasco 11223 17 154

ES22 Comunidad Foral de Navarra 11223 17 154

ES24 Aragon 11223 17 154

ES41 Castilla-Leon 11223 37 335

ES43 Extremadura 11223 37 335

ES51 Catalunia 11223 17 154

ES61 Andalucia 11223 34 306

ES63 Ceuta y Melilla 11223 33 300

FI11 Uusimaa 18099 9 50

FI12 Etelä-Suomi 18099 27 154

FI13 Itä-Suomi 18099 41 228

FI14 Väli-Suomi 18099 8 45

FI15 Pohjois-Suomi 18099 25 138

FI2 Ahvenanmaa/Åland 18099 1 7

FR21 Champagne-Ardenne 20008 21 109

142

NUTS GDP/inh. 1994-1996 (€) Financing/inh. (€) Financing/GDP X 100 000

FR22 Picardie 20008 28 144

FR23 Haute-Normandie 20008 15 76

FR3 Nord-Pas-de-Calais 20008 32 160

FR41 Lorraine 20008 20 103

FR42 Alsace 20008 5 28

FR43 Franche-Comté 20008 2 13

FR61 Aquitaine 20008 17 86

FR62 Midi-Pyrénées 20008 17 86

FR71 Rhône-Alpes 20008 16 83

FR81 Languedoc-Roussillon 20008 17 86

FR82 Provence-Alpes-Côte-d'Azur 20008 19 95

FR83 Corse 20008 76 384

GR11 Anatoliki Makedonia, Thraki 8584 160 1874

GR12 Kentriki Makedonia 8584 160 1874

GR13 Dytiki Makedonia 8584 160 1874

GR21 Ipeiros 8584 154 1799

GR22 Ionia Nisia 8584 33 385

GR23 Dytiki Ellada 8584 33 385

GR41 Voreio Aigaio 8584 160 1874

IE Irlande 14487 30 210

IT11 Piemonte 15291 19 124

IT12 Valle d'Aosta 15291 37 244

IT13 Liguria 15291 19 125

IT2 Lombardia 15291 18 119

IT31 Trentino-Alto Adige 15291 6 42

IT32 Veneto 15291 8 56 IT33 Friulia-Venezia Gi 15291 13 90

IT51 Toscana 15291 83 548

IT91 Puglia 15291 50 330

ITB Sardegna 15291 69 452

LU Luxembourg (Grand-Duché) 32466 39 121

NL11 Groningen 19320 40 211

NL13 Drenthe 19320 52 273

NL21 Overijssel 19320 12 62

NL22 Gelderland 19320 11 61

NL34 Zeeland 19320 4 22

NL41 Noord-Brabant 19320 7 40

NL42 Limburg (NL) 19320 18 94

PT11 Norte 8091 37 465

PT12 Centro 8091 37 465

PT14 Alentejo 8091 37 465

PT15 Algarve 8091 37 465

SE01 Stockholm 20561 1 6

SE02 östra Mellansverige 20561 1 6

SE04 Sydsverige 20561 5 27

SE05 Västsverige 20561 21 103

SE06 Norra Mellansveri 20561 7 37

SE07 Mellersta Norrlan 20561 10 50

SE08 Övre Norrland 20561 17 85

UK53 Surrey, East-West 14870 15 102

UK57 Kent 14870 9 60

UK91 Clwyd, Dyfed, Gwynedd 14870 25 174

UKB Northern Ireland 14870 50 341

143

4.2. Twinning arrangements between municipalities

1. Introduction

Twinnings between municipalities were mentioned among potential indicators of effective co-operation in the draft report on Spatial Integration discussed at the Stockholm meeting.

The concept of twinning was born after the Second World War and launched by the Council of European Municipalities, that later became the Council of European Municipalities and Regions (C.E.M.R.), based in Strasbourg27.

2. Definition

Jean Bareth, one of the founding fathers of the C.E.M.R., defines this form of co-operation as follows: "A twinning is the meeting between two municipalities to act together within a European perspective, confronting problems and developing increasingly closer and friendlier ties between one another."

Some specific features of this form of co-operation: - twinnings are intended to promote contact between municipalities of different countries at two

levels, between citizens and between local authorities; - twinnings may be conceptualised as a sort of wedding between municipalities, with an

engagement period followed by a solemn oath of the partners; - as for weddings, the twinning oath is the starting point of co-operation in many domains:

exchanges between citizens and between local organisations as well as co-operation between political authorities in order to find coherent solutions to their respective problems;

- this co-operation fits in the global framework of the stimulation and strengthening of European identity.

These are the aims, even if probably not all of them are entirely achieved in all the cases.

3. Facts and figures

- According to the C.E.M.R., more than 8000 municipalities belonging to the countries of the Council of Europe are twinned, and there are about as many twinnings, as many municipalities have concluded more than one twinning.

- The same source indicates that France and Germany account for more than a half of the number of twinnings, which played an important role in the reconciliation of those two countries after the Second World War.

- Since 1989, the European Commission supports twinnings through a specific programme of the DG X "Town-twinning". With a budget of 10 MECU in 1998, it is one of the major programmes managed by DG X. The DG X also gives an annual award ("The Golden Stars of Town Twinning") to the towns which have best contributed to European integration with the help of the previous year grants.

27 Documented information about the concept of twinning can be found on the Web site of the C.E.M.R., notably on the following page: http://www.ccre.org/jumelages/origins.html.

144

4. Sources and data

The C.E.M.R. edits a "Directory of European Twinnings" that should constitute an interesting source for an indicator. A first volume concerning the 12 countries belonging to the European Union in 1995 is published (but it seems not easy to find, according to our contacts with the National Section of C.E.M.R.), while a second one relative to the other countries of the Council of Europe is under way.

Another potential source for an indicator is provided by data relative to the "town-twinning" programme of DG X. In this case, the lists do not include all twinned municipalities, but all municipalities that have received a subvention in the framework of the programme.

At first view, these data should provide a different point of view, due to some specific conditions of the programme:

- it focuses on three types of projects: - exchanges between citizens from towns that are twinned or are setting up a twinning

scheme; - conferences and meetings on European subjects, and activities designed to give a fresh

impetus to the twining concept; - training seminars for organisers of town-twinning schemes;

- the action must be related to an European theme - under an adequate form implying all participants - and must contribute to improve knowledge about the European political, social and cultural contexts;

- it gives priority to exchanges: - relative to the preparation of new twinnings in countries where there are weakly developed; - implying twinned towns from several countries; - implying disadvantaged twinned towns (due to their geographical location or to their

belonging to a weakly developed area); - with the Baltic republics or with some Central and East European countries; - between municipalities of less than 5.000 inhabitants; - involving mainly people under 25 years; - between socially disadvantaged groups of population; - between municipalities that have not yet received a subvention in the framework of the

programme;

- it does not allocate subventions to exchanges: - between municipalities of a same country; - between municipalities located less than 250 km away from each other; - implying less than 10 persons from the invited municipality; - with a touristic, folkloric, commercial character; - involving mainly local political or administrative authorities; - similar to exchanges that have already received a subvention during the same year28 or

during the previous year29; - that can benefit from financial aid within another Community programme such as Socrates,

Leonardo,…; - for which the subvention required is less than 750 Euros.

- the subvention may not exceed 2/3 of the expenses and is limited to a maximum of 50.000 Euros.

28 When the partners are the same and meet at the same place. 29 When the theme and the concerned group of population are the same.

145

5. Work under way

DG X has provided a list (paper copy) of all municipalities that received a subvention as "inviting municipality" in the framework of the town-twinning programme, from the beginning of the programme (1990) to September 1998.

The list is structured by "activity files", each file relating to an action in a municipality that has benefited from a financial help from DG X over a given year. Each file mentions: - the name of the inviting municipality; - references of the file (indicating the year of the subvention); - the region (NUTS unit); - the number of inhabitants of the municipality; - the amount of the subvention. On the other hand, it does not mention which are the invited municipality(ies). These are mentioned in another list, but it is not possible to link the two lists.

All the information concerning municipalities located inside the European Union has been stocked in a database for the purpose of the study programme. This represents 8327 files and 3976 municipalities. The average number of files for a same municipality is thus a little more than 2 (the maximum being 14 files for the same municipality).

The first problem encountered in the building of an indicator is the fact that the attribution to a NUTS unit has been made differently according to the countries concerned: - in most countries the reference level is the NUTS 2 level; - however in some cases (Denmark, Finland, Ireland, Sweden) the reference is the NUTS 3 level

(this is not a problem as NUTS 3 units can be related to the including NUTS 2 units); - in Germany, the level of reference is the NUTS 1 level (this causes some more problems, as the

information about the NUTS 2 can only be found through the name of the municipality - this had to be done for 1759 files).

The table at the end of the note lists all results summarised by NUTS 2.

A first exercise has been made in order to represent the number of files and the number of different host municipalities by NUTS 2 (maps 1 and 2).

However two factors must be taken into account: - the different weights of the concerned NUTS 2 units; - the very different demographic size of the municipalities according to the countries: from an

average of a little more than 1000 inhabitants to more than 30.000 (figures of 1996).

As the idea is to give a picture of the relative will to co-operate through the particular way of twinning activities, a more relevant indicator seems to be the proportion of municipalities of a NUTS 2 unit that have introduced at least a file over the given period.

In order to compute this ratio, the number of municipalities per NUTS 2 is assumed to be the number of NUTS 5 units of those NUTS 2 units (1991 nomenclature included in the data provided by Eurostat), except in Portugal where, according to the mode of administrative division30 and to the actual list of municipalities concerned, the NUTS 4 level seems to be more relevant than the NUTS 5 level.

30 "The EU Compendium of spatial planning systems and policies", 1997

146

Map 3 has been drawn on basis of a representation by circles. Ratio of host municipalities is indicated by the colour of the circle, while its size indicates the total number of municipalities of the NUTS 2 unit. The distribution of the variable "ratio of host municipalities" is based on the average (6,6 %) and the standard deviation (9,88) and uses the four classes indicated in the next graph.

This map gives a quite different picture than the maps 1 and 2, due to the influence of the characteristics of the division into NUTS units, which reflects the various administrative structures.

One can observe that in general the NUTS 2 units that have the most numerous municipalities (often of small size) generally show a weak ratio of host municipalities. This could tend to indicate that it is more difficult for a small municipality to undertake such cooperative actions, be it only for a lack of financial and organisation means. This underlines the relevance to give preference to small municipalities while attributing aids to twinning activities (see also the conclusion of part I).

This very simple exercise also shows clearly the influence of the type of cartographic representation on the perception of reality. This problem is of course not specific of the present indicator and underlines the interest of developing harmonised methods.

Maps could have been drawn over different periods of time, for example in order to take into account the accession of three Member States in 1995 and assess its effect. However as the aid of DG X is not limited to municipalities of the European Union, it is not sure that the fact to take into account the whole period of recording (1990-1998) changes the general picture. This exercise has not yet been done due to time constraints.

= = =

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% of host municipalities

Num

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147

Map 1: Number of files

148

Map 2: Number of host municipalities

149

Map 3: Ratio of host municipalities

150

Files and host municipalities by NUTS 2

NUTS 2 Name Number of files

Number of host municipalities

Number of municipalities

Ratio of host municipalities (%)

AT11 Burgenland 6 5 153 3,3 AT12 Niederösterreich 4 4 569 0,7 AT13 Wien 0 0 1 0,0 AT21 Kaernten 8 5 128 3,9 AT22 Steiermark 7 7 544 1,3 AT31 Oberösterreich 8 7 445 1,6 AT32 Salzburg 1 1 119 0,8 AT33 Tirol 7 6 278 2,2 AT34 Vorarlberg 0 0 96 0,0 BE1 Région de Bruxelles-Capitale 4 4 19 21,1 BE21 Antwerpen 13 7 70 10,0 BE22 Limburg (B) 8 6 44 13,6 BE23 Oost-Vlaanderen 19 5 65 7,7 BE24 Vlaams Brabant 12 7 65 10,8 BE25 West-Vlaanderen 35 16 64 25,0 BE31 Brabant wallon 11 5 27 18,5 BE32 Hainaut 35 23 69 33,3 BE33 Liège 30 20 84 23,8 BE34 Luxembourg (B) 14 6 44 13,6 BE35 Namur 21 11 38 28,9 DE11 Stuttgart 109 52 343 15,2 DE12 Karlsruhe 73 39 211 18,5 DE13 Freiburg 65 39 303 12,9 DE14 Tübingen 88 39 255 15,3 DE21 Oberbayern 72 40 511 7,8 DE22 Niederbayern 38 18 261 6,9 DE23 Oberpfalz 20 13 231 5,6 DE24 Oberfranken 39 18 223 8,1 DE25 Mittelfranken 73 29 215 13,5 DE26 Unterfranken 65 38 315 12,1 DE27 Schwaben 119 49 344 14,2 DE3 Berlin 1 1 2 50,0 DE4 Brandenburg 12 8 1793 0,4 DE5 Bremen 0 0 2 0,0 DE6 Hamburg 0 0 1 0,0 DE71 Darmstadt 157 67 189 35,4 DE72 Giessen 71 30 101 29,7 DE73 Kassel 63 29 140 20,7 DE8 Mecklenburg-Vorpommern 9 5 1123 0,4 DE91 Braunschweig 45 25 199 12,6 DE92 Hannover 57 26 229 11,4 DE93 Lüneburg 48 25 369 6,8 DE94 Weser-Ems 47 28 256 10,9 DEA1 Düsseldorf 21 15 66 22,7 DEA2 Köln 113 44 99 44,4 DEA3 Münster 35 14 78 17,9 DEA4 Detmold 53 25 70 35,7 DEA5 Arnsberg 61 23 83 27,7 DEB1 Koblenz 79 37 1108 3,3 DEB2 Trier 50 24 558 4,3 DEB3 Rheinhessen-Pfalz 76 37 637 5,8 DEC Saarland 38 14 52 26,9

151

NUTS 2 Name Number of files

Number of host municipalities

Number of municipalities

Ratio of host municipalities (%)

DED Sachsen 23 19 1623 1,2 DEE1 Dessau 11 5 293 1,7 DEE2 Halle 5 3 419 0,7 DEE3 Magdeburg 6 5 649 0,8 DEF Schleswig-Holstein 56 26 1131 2,3 DEG Thüringen 39 24 1694 1,4 DK Danemark 61 38 276 13,8 ES11 Galicia 19 12 313 3,8 ES12 Principado de Asturias 12 5 78 6,4 ES13 Cantabria 14 9 102 8,8 ES21 Pais Vasco 4 4 247 1,6 ES22 Comunidad Foral de Navarra 12 6 265 2,3 ES23 La Rioja 20 7 174 4,0 ES24 Aragon 15 10 729 1,4 ES3 Comunidad de Madrid 16 7 178 3,9 ES41 Castilla-Leon 42 21 2248 0,9 ES42 Castilla-La Manch 33 12 915 1,3 ES43 Extremadura 3 2 380 0,5 ES51 Catalunia 90 45 942 4,8 ES52 Comunidad Valencia 100 44 539 8,2 ES53 Baleares 2 2 67 3,0 ES61 Andalucia 48 22 766 2,9 ES62 Murcia 9 5 45 11,1 ES63 Ceuta y Melilla 0 0 2 0,0 ES7 Canarias 1 1 87 1,1 FI11 Uusimaa 5 4 39 10,3 FI12 Etelä-Suomi 18 16 174 9,2 FI13 Itä-Suomi 6 6 78 7,7 FI14 Väli-Suomi 11 9 89 10,1 FI15 Pohjois-Suomi 7 5 64 7,8 FI2 Ahvenanmaa/Åland 1 1 16 6,3 FR1 Ile de France 334 134 1281 10,5 FR21 Champagne-Ardenne 38 22 1936 1,1 FR22 Picardie 87 43 2293 1,9 FR23 Haute-Normandie 77 39 1421 2,7 FR24 Centre 165 75 1842 4,1 FR25 Basse-Normandie 219 99 1814 5,5 FR26 Bourgogne 145 75 2044 3,7 FR3 Nord-Pas-de-Calais 103 51 1549 3,3 FR41 Lorraine 22 21 2335 0,9 FR42 Alsace 13 8 899 0,9 FR43 Franche-Comté 23 15 1786 0,8 FR51 Pays de la Loire 354 135 1504 9,0 FR52 Bretagne 439 152 1269 12,0 FR53 Poitou-Charente 196 67 1465 4,6 FR61 Aquitaine 206 78 2290 3,4 FR62 Midi-Pyrénées 93 39 3019 1,3 FR63 Limousin 77 33 747 4,4 FR71 Rhône-Alpes 266 135 2879 4,7 FR72 Auvergne 100 41 1310 3,1 FR81 Languedoc-Roussillon 100 47 1545 3,0 FR82 Provence-Alpes-Côte-d'Azur 151 72 963 7,5 FR83 Corse 0 0 360 0,0 FR91 Guadeloupe 0 0 34 0,0

152

NUTS 2 Name Number of files

Number of host municipalities

Number of municipalities

Ratio of host municipalities (%)

FR92 Martinique 0 0 34 0,0 FR93 Guyane 0 0 21 0,0 FR94 Réunion 0 0 24 0,0 GR11 Anatoliki Makedonia, Thraki 0 0 304 0,0 GR12 Kentriki Makedonia 7 6 625 1,0 GR13 Dytiki Makedonia 0 0 361 0,0 GR14 Thessalia 10 4 533 0,8 GR21 Ipeiros 1 1 566 0,2 GR22 Ionia Nisia 2 2 266 0,8 GR23 Dytiki Ellada 0 0 681 0,0 GR24 Sterea Ellada 0 0 597 0,0 GR25 Peloponnisos 8 5 885 0,6 GR3 Attiki 21 15 150 10,0 GR41 Voreio Aigaio 4 3 195 1,5 GR42 Notio Aigaio 3 1 191 0,5 GR43 Kriti 6 5 567 0,9 IE Irlande 259 102 3445 3,0 IT11 Piemonte 41 26 1209 2,2 IT12 Valle d'Aosta 1 1 74 1,4 IT13 Liguria 3 3 235 1,3 IT2 Lombardia 48 32 1546 2,1 IT31 Trentino-Alto Adige 14 11 339 3,2 IT32 Veneto 80 46 582 7,9 IT33 Friulia-Venezia Gi 15 11 219 5,0 IT4 Emilia-Romagna 51 26 341 7,6 IT51 Toscana 114 54 287 18,8 IT52 Umbria 42 20 92 21,7 IT53 Marche 22 12 246 4,9 IT6 Lazio 60 33 376 8,8 IT71 Abruzzo 6 4 305 1,3 IT72 Molise 0 0 136 0,0 IT8 Campania 2 2 551 0,4 IT91 Puglia 9 8 257 3,1 IT92 Basilicata 0 0 131 0,0 IT93 Calabria 5 3 409 0,7 ITA Sicilia 17 7 390 1,8 ITB Sardegna 2 2 375 0,5 LU Luxembourg (Grand-Duché) 22 9 118 7,6 NL11 Groningen 3 3 25 12,0 NL12 Friesland 3 1 31 3,2 NL13 Drenthe 10 7 34 20,6 NL21 Overijssel 5 4 45 8,9 NL22 Gelderland 15 9 86 10,5 NL23 Flevoland 4 2 6 33,3 NL31 Utrecht 3 3 38 7,9 NL32 Noord-Holland 27 13 76 17,1 NL33 Zuid-Holland 21 11 101 10,9 NL34 Zeeland 3 3 30 10,0 NL41 Noord-Brabant 24 13 131 9,9 NL42 Limburg (NL) 10 7 69 10,1 PT11 Norte 52 20 84 23,8 PT12 Centro 44 21 78 26,9 PT13 Lisboa e Vale Do 12 8 51 15,7 PT14 Alentejo 6 2 46 4,3

153

NUTS 2 Name Number of files

Number of host municipalities

Number of municipalities

Ratio of host municipalities (%)

PT15 Algarve 3 3 16 18,8 PT2 Açores 0 0 19 0,0 PT3 Madeira 1 1 11 9,1 SE01 Stockholm 2 2 25 8,0 SE02 östra Mellansverige 7 7 48 14,6 SE03 Smaalands med öarna 5 4 32 12,5 SE04 Sydsverige 6 5 38 13,2 SE05 Västsverige 8 7 56 12,5 SE06 Norra Mellansveri 12 12 41 29,3 SE07 Mellersta Norrlan 5 4 15 26,7 SE08 Övre Norrland 1 1 29 3,4 UK11 Cleveland, Durham 21 10 287 3,5 UK12 Cumbria 0 0 177 0,0 UK13 Northumberland, Tyne and Wear 7 5 243 2,1 UK21 Humberside 21 7 198 3,5 UK22 North Yorkshire 14 7 225 3,1 UK23 South Yorkshire 9 5 98 5,1 UK24 West Yorkshire 27 8 131 6,1 UK31 Derbyshire, Nottinghamshire 40 17 417 4,1 UK32 Leicestershire, Northamptonshire 44 25 351 7,1 UK33 Lincolnshire 65 25 196 12,8 UK4 East Anglia 83 41 596 6,9 UK51 Bedfordshire, Hertfordshire 30 15 310 4,8 UK52 Berkshire, Buckinghamshire, Oxfordshire 68 26 426 6,1 UK53 Surrey, East-West 44 32 525 6,1 UK54 Essex 46 22 326 6,7 UK55 Greater London 16 10 815 1,2 UK56 Hampshire, Isle of Wight 50 28 315 8,9 UK57 Kent 13 9 379 2,4 UK61 Avon, Gloucestershire, Wiltshire 80 36 495 7,3 UK62 Cornwall, Devon 139 75 413 18,2 UK63 Dorset, Somerset 65 34 323 10,5 UK71 Herefords & Worcestershire, Warwickshire 44 18 325 5,5 UK72 Shropshire, Staffordshire 35 16 368 4,3 UK73 West Midlands (Co) 0 0 169 0,0 UK81 Cheshire 13 6 212 2,8 UK82 Greater Manchester 12 5 224 2,2 UK83 Lancashire 10 6 318 1,9 UK84 Merseyside 2 1 123 0,8 UK91 Clwyd, Dyfed, Gwynedd 50 26 568 4,6 UK92 Gwent, Mid-South-West Glamorgan 31 15 377 4,0 UKA1 Borders-Central-Fife-Lothian-Tayside 74 26 358 7,3 UKA2 Dumfries & Galloway, Strathclyde 28 15 448 3,3 UKA3 Highlands, Islands 1 1 110 0,9 UKA4 Grampian 19 8 87 9,2 UKB Northern Ireland 12 10 566 1,8