A Network-based Approach to Regional Borders: The Case of Belgium

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A Network-based Approach to Regional Borders: TheCase of BelgiumAnnemarie Strihan aa City and Regional Planning , 14 Pravat Street, bl. P7, apt. 146, 061565, Bucharest,Romania E-mail:Published online: 12 May 2008.

To cite this article: Annemarie Strihan (2008) A Network-based Approach to Regional Borders: The Case of Belgium,Regional Studies, 42:4, 539-554, DOI: 10.1080/00343400701541813

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A Network-based Approach to Regional Borders:The Case of Belgium

ANNEMARIE STRIHANCity and Regional Planning, 14 Pravat Street, bl. P7, apt. 146, 061565 Bucharest, Romania.

Email: [email protected]

(Received September 2004; in revised form April 2006)

STRIHAN A. A network-based approach to regional borders: the case of Belgium, Regional Studies. This article puts forward a

network-based representation of border regions. It argues for and then employs a network approach to better understand the

relation between socio-economic linkages and ethnic divisions in the Flemish–Walloon border region. A segment of the

border region is described through polycentric networks of firms that cluster around multiple axes of solidarity. This article

focuses on the ethnic axis of solidarity and shows that ethnicity is more significant than geographical distance for the network

formation. It concludes with remarks on the place of this approach within border studies and the possibilities of using it as a

border management tool.

Regional borders Social networks Belgium

STRIHAN A. Une facon d’aborder les frontieres regionales basee sur les reseaux: etude de cas de la Belgique, Regional Studies. Cet

article cherche a proposer une representation des regions frontalieres basee sur les reseaux. On prone et puis emploie une facon

basee sur les reseaux afin de mieux comprendre le rapport entre les liens socioeconomiques et les clivages ethniques dans la region

frontaliere flamande–wallonne. On presente un secteur de la region frontaliere en termes de reseaux polycentriques d’entreprises

qui se regroupent autour de multiples axes de solidarite. Cet article porte sur l’axe de solidarite ethnique et montre que l’ethnicite

s’avere plus importante que ne l’est la distance geographique pour ce qui est de l’etablissement des reseaux. Pour conclure, on

commente la place de cette facon dans les etudes des regions frontalieres et les possibilites de l’employer comme outil de

gestion des regions frontalieres.

Regions frontalieres Reseaux sociaux Belgique

STRIHAN A. Ein netzwerkbasierter Ansatz fur Regionalgrenzen: der Fall Belgien, Regional Studies. In diesem Beitrag wird fur eine

netzwerkbasierte Darstellung von Grenzregionen pladiert. Es werden Argumente fur einen netzwerkbasierten Ansatz vorgebracht;

anschließend wird dieser Ansatz fur ein besseres Verstandnis der Beziehungen zwischen den soziookonomischen Verknupfungen

und den ethnischen Teilungen in der flamisch–wallonischen Grenzregion genutzt. Ein Segment der Grenzregion wird uber poly-

zentrische Netzwerke aus Firmen beschrieben, die sich entlang mehrerer Solidaritatsachsen gruppieren. Im Mittelpunkt dieses

Artikels steht die ethnische Solidaritatsachse; es wird gezeigt, dass die ethnische Herkunft zur Bildung eines Netzwerks von

großerer Bedeutung ist als der geografische Abstand. Der Artikel schließt mit Anmerkungen uber den Platz dieses Ansatzes inner-

halb der Grenzstudien sowie uber die Moglichkeiten der Verwendung eines solchen Ansatzes als Instrument zur Grenzverwaltung.

Regionalgrenzen Soziale Netzwerke Belgien

STRIHAN A. Un enfoque de redes para las fronteras regionales: El caso de Belgica, Regional Studies. En este artıculo se propone una

representacion de las regiones fronterizas a traves de redes. Se defiende un enfoque de redes que se emplea para comprender mejor

la relacion entre los vınculos socioeconomicos y las divisiones etnicas en la region fronteriza flamenco–valon. Se describe un seg-

mento de la region fronteriza a traves de redes policentricas de empresas que se aglutinan a lo largo de muchos ejes de solidaridad.

En este artıculo se presta especial atencion al eje etnico de solidaridad y se demuestra que para la formacion de redes la etnicidad es

Regional Studies, Vol. 42.4, pp. 539–554, May 2008

0034-3404 print/1360-0591 online/08/040539-16 # 2008 Regional Studies Association DOI: 10.1080/00343400701541813http://www.regional-studies-assoc.ac.uk

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mas importante que la distancia geografica. El artıculo concluye con observaciones del lugar de este enfoque en los estudios sobre

fronteras y las posibilidades de usarlo como una herramienta de gestion fronteriza.

Fronteras regionales Redes sociales Belgica

JEL classifications: R12, R19

INTRODUCTION

In the last two decades, the interest in regionalism hasgrown in Europe. Matters of regional identity, regionalautonomy, regional difference come out in policydebates, scholarly research, as well as in everyday dis-courses. Yet, in most cases, the borders of regionsremain implicitly assumed and conceptually underdeve-loped. The aim of this study is to introduce and contex-tualize a way of representing regional borders.

Regional borders take on multiple meanings,unlike most international borders. In WesternEurope, they refer predominantly to sub-nationaldemarcations based on political–administrative cri-teria that may overlap functional, cultural and econ-omic criteria. The regional borders considered hereare sub-national, political–administrative divisionsbetween ‘robust regions’ – that is, administrative ter-ritories with vigorously asserted cultural identities( JONNSON et al., 2000, p. 140). These bordersoverlap historically constituted, ethnic divisionsembedding political and social tensions that led, in agreat part, to regional mobilizations.

Focused mostly on international borders, borderresearch has used extensively the core–periphery rep-resentation of space that is increasingly challenged bythe socio-political reality of Western Europe in whichclear-cut divisions between core regions and peripheralterritories lose their relevance. New governance prac-tices have transformed territories demarcated bynational borders into territories articulated at differentscales by regional, national and supra-national borders.These articulations are constructed as pivotal connec-tors within territory through socio-economic inter-actions, political practices and discourses. Traditionallygrasped as divisions, borders (in particular, nationalborders) are thus currently understood as connections,a meaning sustained by border organizations and pro-grammes. In this transformation of national borders,regional borders entangle tightly, especially when theoverlap between ethnic and political–administrativedivisions preserves vigorous representations of cross-border differences, which is the case of the Flemish–Walloon border. Border research needs therefore newmethodological approaches able to grasp this newmeaning of borders as connectors, as interface areas.The proposed approach to regional borders employsconcepts and methods advanced in network theory todescribe border regions as structures of socialinteractions.

OUTLINE

This article compares networks of firms across theFlemish–Walloon border in order to suggest thelimits of the core–periphery model and to provide analternative to this: the network approach to analysingborders. It draws on regional planning, political geogra-phy, and border anthropology research concerning bothinternational and ethnic borders. With the breakingdown of rigid notions of territorial sovereignty, tra-ditional distinctions between the study of internationaland other kinds of borders have blurred (NEWMAN,1999, p. 129).

The first section of this article examines the core–periphery model. The second section introduces astructuralist representation of borders that challengesthree main notions of the core–periphery model: themonocentric structure of space, the periphericalnature of borders, and the divisive role of borders.The third section analyses a segment of the Flemish–Walloon border. The core–periphery representationof space assumes that spatial divisions follow homo-geneous economic, political, social and cultural cat-egories. By concentrating on categories, analysesoverlook the interactions within and, to some extent,between categories. These interactions actuallyproduce divisions. The network approach helps under-stand borders not as areas separating homogenouslydefined groups but as areas with specific patterns ofinteraction that exclude or integrate certain groups. Itprovides thus important insights into the local sourcesof division.

The proposed representation describes the borders aspolycentric networks of linkages between social enti-ties. In the analysed case, the nodes of the networksare firms; two nodes have a tie if they have a commondirector. The focus here is not on the economic com-ponent of ties, but on their social component under-stood in a broad sense as social contact between socialentities across borders. The analysis of the network-based representation uses concepts advanced in socialnetwork analysis – centrality and cohesion – to revealspecific patterns of interaction that are correlated to eth-nicity and geographical distance. The assumption is thatspecific configurations of relations influence the for-mation and maintenance of bridges across borders.

This study does not aim to provide for explanationsof border phenomena but to put forward a new wayof conceptualizing borders. It takes on a differentapproach from the approaches commonly used in

540 Annemarie Strihan

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border investigations that assume the isolating nature ofborders, although it does not exclude them. Furtherresearch can introduce other socio-economic and cul-tural variables into this new approach in order toexplain patterns of cross-border interactions and to(re)define social clustering categories.

THE CORE–PERIPHERY MODEL

Representations of political borders are grounded incoherent ideas about the socio-economic and politicalorganization of space. They span various disciplines,times and geographies, and undergo significant trans-formations. A major representation of space used inmany disciplines to explore border phenomena is thecore–periphery model. At the heart of thecore–periphery model lies a fundamental cleavagethat takes on economic, social and political dimensions:the cleavage between places identified as dominant andplaces identified as dependent. Space is thus envisionedas a monocentric structure with a centre and a border.Under this representation, a border divides twosystems, confronts them and becomes a new systemrelying on the systems it separates (GUICHONNET andRAFFESTIN, 1974).

Peripherality, the main characteristic of bordersunder this view, reflects uneven distributions of cultural,economic and political capital. The focus of borderanalysis is therefore on dependency relations with thecore, relations that explain in great part the interactionsbetween the sides of the border. The increase of theborders’ permeability is directly proportional with thedecrease of the borders’ dependency on cores. Inquiriesas varied as economic theories of locations and geopo-litical studies have assumed the core–periphery rep-resentation of space and made thus salient the divisiveand disadvantaged nature of borders. An earlyexample is Losch’s theory of location (LOSCH 1954/1932) that, although not directly related to borders,points out that political borders disrupt trade flowsand change movement patterns that would ‘normally’follow the economic logic of spatial distribution. Yet,using complementary factors (e.g. cheaper labour onthe other side of the border) and avoiding customstariffs (e.g. by locating firms’ branches across borders)constitute advantages for settling the firms in borderregions. In a related vein, CHRISTALLER (1966/1933)has represented the space as organized around centralplaces. Central places are urban agglomerations thatevolve in interdependence with their complementaryregions. In border areas, central places lack complemen-tary regions, but they may well extend their comp-lementary regions into the neighbouring territory inorder to supplement certain goods.

The barrier effects of political borders have beendescribed to some extent by modified gravity models.Gravity models interpret Newton’s law of gravitation in

social space and explain patterns of settlement throughlaws of attraction between places, on the one hand, andpeople, capital, and information, on the other hand.The underlying hypothesis is that larger places attractmore people, capital and information. The barriereffects of borders are introduced into these modelseither in the origin and destination variables as size orsocio-economic characteristics of places, or in thechannel variables as costs, language differences, infra-structure. Although modified gravity models candescribe locally the barrier effects of borders, theyprove insufficient for multilayered networks of flowsthat span international spaces (BATTEN and TORNQVIST,1990). As HANSEN (1977, p. 11) points out, theseapproaches failed to provide a reliable model for borderregions, because they stressed their ‘fragile and threa-tened’ nature that no longer constituted the Westernreality. The socio-political change in Western Europeshifted the emphasis from disjunctive to conjunctivefunctions of borders and triggered thus researchapproaches focused more on cross-border bridges(explored with network-based approaches) than onbarriers (explored in gravity models).

Core–periphery representations rely on the prin-ciple of territorial homogeneity that identifiesgeographical areas by means of cultural, political,socio-economic features generated by unitary groupsof people. Polycentric representations envision spatialdelimitations as resulting from interactions betweensocial groups. Border regions come thus to be under-stood as zones of interferences. An example is the‘active space approach’ applicable to the study ofregions and their borders (VAN GEENHUIZEN andRATTI, 2001). This approach integrates the logic ofnetwork cooperation/competition with instrumentsfrom new institutional economy, decision-making andgovernance models, and the economy of strategicresources. This new paradigm of understandingborders sees borders as specific cultural milieux con-structed through interactions between groups ofpeople. The next section works out the idea ofborders as constructed structured of interactions.

BORDERS AS STRUCTURES

OF INTERACTIONS

Homogeneity is commonly considered to be preservedthrough isolation and clear-cut boundaries. Yet, socialboundaries persist despite the presence of interactionsacross borders; they actually emerge from interactionsthat exclude certain groups and include others. Thiscompletely different view on borders has been devel-oped in anthropology by Frederik Barth. The majortheoretical points of his theory revolve around thestraightforward, though pioneering idea of borders asstructured interactions. The traditional definition ofthe ethnic group – predominantly based on recognized

Network-based Approach to Regional Borders: Belgium 541

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membership and common culture manifested as unityof cultural forms – implies that borders emerged fromisolating characteristics such as culture, race andlanguage. Under this view, differences betweengroups’ characteristics explain divisions betweengroups, but sharing a common culture is more a resultrather than a defining trait of the ethnic group, arguesBARTH (1969, p. 200), a weak factor therefore in iden-tifying membership in groups. Groups are defined bytheir social boundaries, not by the ‘substance’ enclosed.As a result, analysis should not centre on categories ofethnic characteristics and relations between them, buton borders’ social structures that form and preservedifferences between groups. In the Western Europeancontext, borders often entail tensioned overlapsbetween political and ethnic identities. Understandingborders as structured patterns of cross-border inter-actions helps to identify clustering patterns basedmore on the actual relationships between actors ratherthan on the characteristics of actors.

Thinking about borders commonly implies a processof categorization: in ethnic-, political-, gender-, reli-gious-, activity-based groups. . . . Borders demarcatedifferent categories of people. Categorization underwell-constructed criteria helps to reduce social complex-ity to manageable representations, but explaining borderphenomena through characteristics of categories neglectsthe very essence of borders, namely their relational nature.Borders emerge from processes of self-differentiation thatrelate to a more or less defined otherness. An effectiveapproach to borders calls for perspectives that emphasizerelationships among social units (individuals, groups,organizations) over categories of such units.

Recognizing the relational nature of borders alreadyinvolves a structuralist way of interpretation – that is,the focus moves from social categories to social relationsand thus to social structures. Structural analysis – ornetwork analysis (see WELLMAN, 1997, for a discussionof the use of these terms) – enables a direct look intostructures of relations. It identifies patterns of relations,depicts them and links the individual behaviour to thepatterns engaging it. As a result, it connects the microwith the macro level of analysis and offers an understand-ing of the social structure that moves away from thedeterministic role of structure within classical structural-ism. Action continuously renews the structure that, inturn, shapes action. The interdependency betweenstructure and action is visible, for instance, as structuralopportunities, which enable certain types of actionsand inhibit others, and which emerge from the network-ing capabilities of actors (BURT, 1992; WHITE, 2002).Contrary to traditional sociological approaches that cat-egorize entities, structural analysis relates entities in orderto explain phenomena. This does not deny the existenceof categories, but assumes that categories emerge out ofcertain patterns of social interaction. Changing thereforethe perspective from categories to linkages may uncoverthe social structure of cross-border differences.

Although structuralist analysis is commonly associatedwith formal network theory and thus reduced to a meremethodology, the perspectives it generates offer novelconceptualizations of the social phenomena. AsWELLMAN and BERKOWITZ (1997, p. 4) point out,‘network analysis is neither a method nor a metaphor,but a fundamental intellectual tool for the study ofsocial structures’. In border research, the network-basedapproach opens up possibilities for designing networksas strategies for border management. In the following,theoretical points are highlighted within structuralistanalysis, particularly significant for border analysis:

. Social relationships bear at least as much explanatorypower as characteristics of related people. Assumingthe relational nature of borders, the understandingof border phenomena requires a profound look intothe social interactions in border areas. The structural-ist analysis of borders can identify and interpret pat-terns of cross-border interactions.

. A structuralist approach de-emphasizes core–periphery representations of territory and emphasizespolycentric representations. This does not imply thatterritorial representations do not convey hierarchies,but that these are multiple and included in complexpatterns as networks of interactions spanningvarious geographical and social layers.

. A structuralist approach can offer perspectives on pat-terns of relations, not only on disparate pairs of relations.One of the main assumptions of network analysis is thatpeople can indirectly access other people in thenetwork. The patterns of ties affect the flows ofresources and information. The patterns’ researchopens the way to the design of the border structure.

Concepts and methods advanced along these lines canbe operationalized meaningfully in the empiricalresearch of borders so as to yield new interpretationsand to add to the understanding of border phenomena.The next section is such an attempt.

A NETWORK-BASED ANALYSIS

OF BORDER REGIONS

This section describes a segment of the Flemish–Walloonborder through networks of firms in order to uncover axesof solidarity, which can be family, ethnicity, physical proxi-mity, activity, psychological proximity, membership incertain organizations, among others. Within business net-works, GRANOVETTER (2001) highlights such axes –region, ethnicity, religion, kinship – that act as principlesof clustering business relations. Family ties (WONG, 1985),ethnicity and region (GONZALEZ, 1992; PORTES andMANNING, 1986) provide trust, based on which businessties are formed and maintained. Specific axes of solidarityin Belgium are ethnicity, language and region. Theyoverlap to a great extent and mix in Brussels and severalareas of the border region that include the other side’slanguage communities. These axes of solidarity have


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been investigated in relation to aspects of socio-economic(DELEECK, 1992/1959), political (MADDENS et al.,1998), and cultural life (ZOLBERG, 1974). In regionalscience, Klassen employed a gravity model to show thatlanguage differences, assimilated to cultural and psycho-logical differences, decreased the flow of interregionalcalls (KLAASSEN et al., 1972). Pivotal to these inquiriesof Belgium’s regional border is ethnicity, which is analysedin the next section as an axis of solidarity.

DATA

A network is a collection of nodes and ties. In this study,the networks depict ties between firms: two firms have atie if they have at least one common director (who isoften in small- and medium-sized firms both ownerand manager). The data set is built from a database of300 000 Belgian firms (BUREAU VAN DIJK, 2005) thatincludes information about firms such as their econ-omic profile, geographical location, language andpresent directors. The snowball sampling method usedhere follows three steps:

. Defining the border region. Four types of administrativejurisdictions organize the Belgian territory: regions,provinces, arrondissements and municipalities. Theborder region is defined in this analysis through thearrondissements adjacent to the political borderbetween two regions of the Belgian federation:Flanders and Wallonia.

. Selecting focal nodes. The analysed networks start from85 focal nodes located in a randomly chosen munici-pality adjacent to the borderline, Gingelom. Thesenodes represent 70% of the total number of small-and medium-sized firms (less or equal to 50 employ-ees) in Gingelom.

. Identifying nodes connected to focal nodes. From each focalnode, a network emerges. Thus, 85 focal nodes gen-erate 85 networks that partially overlap and that reachapproximately 1000 firms of various sizes, spanningthe whole Belgium, and engaging Belgian, as wellas international directors from Europe, the Americas,and Asia. The networks are bounded methodologi-cally to five tiers of nodes, considering that indirectties that surpass five ties are too loose. The majorityof sampled networks do not surpass this limit; onlyfour networks are not whole networks because ofthis methodological boundary.

The data consist of one structural variable that recordsdirectorate ties between firms. The firms and theirties result in a one-mode network – that is, one set ofnodes – which is dichotomous (the ties measureeither the presence or the absence of relations amongnodes) and non-directional (the ties do not differentiatebetween origin and destination). The nodes can be con-sidered as either the firms or their directors; in the lattercase, the directors and their ties result in a one-modenetwork that is non-directional and valued (the ties

measure the number of firms in which a pair of directorsengages).

SOCIO-ECONOMIC CONTEXT

The Belgian economy is one of the most open econom-ies in the EU in terms of imports and exports, in par-ticular with its three main neighbours: France,Germany and The Netherlands (ECODATA, 2005).French and Dutch companies invested heavily in themanufacturing sector in Belgium and, from the mid1990s on in the service sector. These strong linkagesare evident in all but one of the large analysed networks,in which directorate ties cross the international borderseven more often than they cross the regional Flemish–Walloon border, and subgroups of directors includemany international directors, mostly from TheNetherlands. Cross-border cooperation programmeshave contributed to this dense international web byenhancing interactions between SMEs in the EuregioMaas–Rhein that includes the Flemish provinceLimburg, the Walloon province Liege, the DutchLimburg and the German region Aachen.

The Belgian economy is not territorially homo-geneous. Flanders’ exports represent 77% of theBelgian exports, whereas Wallonia’s exports representonly 14%. The decline of the heavy industry sectors(coal, steel and heavy engineering) that triggered theBelgian economy at the turn of the twentieth centurydeeply affected the Southern region, Wallonia. Newindustries (light engineering, food, chemicals) and theservice sector did not succeed in reaching the level ofeconomic growth in Flanders, which is based in a greatpart on high-tech and research industries. Beside theseeconomic factors, analysts point at the lack of socialcapital in Wallonia as a source of economic regional dis-parity (REID and MUSYCK, 2000). This has led to inqui-ries into the socio-cultural factors that differentiate theinstitutional performance of the two regions, despitetheir identical constitutional powers. DE RYNCK

(1998) points at differences in policymaking (morestate-dependent in Wallonia than Flanders), clientelism(more pronounced in Wallonia than Flanders), andpolicy networks (more open in Flanders than Wallonia).

The Flemish province Limburg – where the focalnodes are located, namely in Hasselt province/Ginge-lom municipality – has major economic problemsdue to the decline of its coal mining industry. Socio-economic reconversion plans are on the way in theformer mining areas in Hasselt province and ruralareas in Tongeren. Gingelom is not an eligible area forthese programmes. Parts of the EU Structural Funds,under Objective 2, are used to help SMEs, whichprovide 69% of jobs in industry and services and over90% in construction and public works in Limburg(EUROPEAN COMMISSION, 2005). In Gingelom, agri-cultural enterprises constitute approximately 37% ofall active enterprises, followed by commercial enter-prises, enterprises in construction and in the real


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estate sector (BUREAU VAN DIJK, 2005). This is visiblein the analysed networks, where agricultural enterprisesare numerous (e.g. around 10% of the small networkshave activities related to agriculture) and form small-sized networks that cluster geographically around thefocal node, within the border region. Gingelom is aDutch-speaking municipality with no communitieswith French language facilities.

ANALYSIS

A comprehensive network-based approach followsthree stages: examination of network formation, ofpresent patterns and of possibilities of change. Basedon the available data, this analysis focuses on presentpatterns of networking. The SMEs in the analysedsegment of the border have usually small networks:

51 focal nodes do not form networks; 19 focal nodesform networks with fewer than ten nodes; sevenfocal nodes form large networks that reach between17 and almost 300 nodes (the maximum number islimited by the sampling method that stops at the fifthtier of networking). The remaining focal nodes formnetworks that overlap; therefore they have beencounted only once.

Geographical distance counts to some extent only insmall- and medium-sized networks – with up to tennodes. Large networks – with more than ten nodes –do not cluster according to physical distance and ofteninclude transnational ties with directors in Europe(Luxembourg, The Netherlands, France, Germany,Italy, Monaco, Spain) and beyond (Egypt, USA,China and Hong Kong). Unlike distance, ethnicityand family1 are salient axes of solidarity in the large net-works that show densely connected, ethnically homo-geneous groups. In the following a large network isused, as an example, to analyse the ethnic axis of solidar-ity. This network records the direct and indirect (up tofive) ties of the focal node, which is a notary/consul-tancy firm located in Gingelom. The networks’ activi-ties are related to those of the focal node and includealso real estate and financial intermediation activities,which appear in all large networks, on average at thethird tier of networking. The focal node reaches 59firms distributed as Table 1 shows.

Table 1. Distribution of firms according to region and language

Flanders

(22 localities)

Wallonia

(five localities) Brussels

Dutch-speaking 41 – 2

French-speaking – 6 –

Language data not

available

7 3 –

Total firms 82% (48 firms) 15% (9 firms) 3% (2 firms)

Fig. 1. Network of firms that shows the tendency to cluster according to ethnicityNote: Square nodes are firms located in Flanders; triangle nodes are firms (Dutch-speaking) located in Brussels; circle

nodes are French-speaking firms located in Wallonia


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Fig. 2. Network of directorsNote: Square and down triangle nodes are Dutch-speaking directors in Flanders and The Netherlands, respectively;circle nodes are French-speaking directors in Wallonia; up triangle nodes are directors in Germany and Switzerland

Table 2. Comparison of firms (described in Fig. 1) according to location and network properties

Wallonia Flanders Brussels

Number of firms (as percentage) 82% 15% 3%

Sum of normalized degrees 56.144 962.500 35.593

Sum of closeness degrees 306.048 2235.313 82.367

Cliques (as percentage from the total number of cliques: 29) 14% 28% 48% 10%

Table 3. Comparison of directors (described in Fig. 1) according to language and network properties

French-speaking

in Wallonia

Dutch-speaking

in Flanders

Dutch-speaking in

The Netherlands

Others (Germany

and Switzerland)

Number of directors (as percentage) 9% 63% 25% 3%

Sum of normalized degrees 38.762 356.4 88.709 19.355

Sum of closeness degrees 197.014 815.561 782.434 41.528

Cliques (as percentage from the total number of cliques: 38) 5% 32% 32% 21%

8%

2%


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In this network of firms, 64 directors/managersparticipate: 86% are Dutch-speaking directors fromFlanders, Brussels and The Netherlands; 9% areFrench-speaking directors from Wallonia; 3% are inter-national directors from Germany and Switzerland. Figs1 and 2 as well as Tables 2 and 3 describe the network offirms and its corresponding network of directors. Thenetwork of firms shows two major, ethnic-based clus-ters and one mixed subgroup of Flemish and Walloonfirms that connect these clusters. Other subgroups canbe identified within the two major clusters. Theyfollow various axes of solidarity – activity, location,membership in organization – along with the ethnicone, which is visible because the clusters do not crossthe border. The corresponding network of directorshas a densely connected Dutch-speaking nucleus ofdirectors from Flanders and The Netherlands.

Two concepts in network analysis are very helpful inanalysing the cohesion and connectedness betweenfirms: centrality and subgroups. Centrality measures

how much involved a node is in relationships withother nodes. Subgroups assess groups of nodes thathave direct, strong, or relatively frequent ties. Thenetwork of firms has 16 cohesive subgroups (cliques)– that is, groups of three or more nodes, in which allnodes link between them and no outside node has tieswith the whole group (WASSERMAN and FAUST,1994, p. 154). Only five of them include, each, onefirm located across the border, a pattern that indicatesclearly the regional border. The network of firms inFig. 1 can be reduced to the diagram in Fig. 4, inwhich subgroup 5, which has both Flemish andWalloon firms, connects the Walloon cluster of firms(1) with the Flemish clusters (2, 3 and 4).

The mediating clique (5) between Flemish andWalloon firms includes the best connected nodes(central nodes), which access all other nodes throughshort paths of a maximum of three ties. Their removalwould disconnect the network in two ethnic-basedblocks. Connectedness can be measured in variousways, from which use is made of: (1) degree centralitymeasured as number of direct ties of a node:(2) closeness centrality measured as number of directand indirect ties of a node; (3) cross-border centralitymeasured as number of ties across the border (it hasno directly corresponding concept in network analysis,but it derives from notions that define partitions ofnodes and analyse relations between partitions such asblockmodels). Tables 2 and 3 compare these measuresfor the network described in Fig. 1 and, respectively,Fig. 2:

. The degree of centrality can be measured when a firm ihas at least one director in common with a firm j. Itdescribes thus the position of firms in terms of direc-torate linkages. Let the matrix describing the analysednetwork be A ¼ [aij], where aij ¼ 1 if firm i is con-nected to firm j on the basis of a common director,aij ¼ 0 if they do not have a common director,and aii ¼ 0. Based on this matrix, the degree of cen-trality can be calculated by adding up the number ofdirect ties per node; the higher the degree of central-ity, the more connected the firm. In order to differen-tiate between nodes according to their centralitydegrees, a threshold value is set: one standard

Fig. 3. Diagram of the network in Fig. 1Note: Subgroup 1 includes predominantly firms locatedin Wallonia; subgroups 2, 3 and 4 include predomi-nantly firms located in Flanders; subgroup 5 includes

firms from both Flanders and Wallonia

Fig. 4. Identified patterns of interactions in the analysed networks


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deviation from the mean degree of centrality. Thus,18 firms located in Flanders, one firm in Brusselsand one firm in Wallonia are central in the detailednetwork (Fig. 1) because they have at least 18 ties –the threshold value of the direct connectedness.These nodes are included in subgroups 5 and 3(Fig. 3). Peripheral nodes – that is, nodes withunique ties – are two firms located in Flanders.Only for one node, peripherality emerges from theimposed network boundary so that the fifth tier ofnodes has further ties, but they are not recorded.Comparing all sampled networks, the majority ofthe most connected firms are located on the side ofthe border of the focal node, in Flanders. Highvalues of the degree centrality do not necessarilyimply that the nodes reach quickly other nodeslocated across the border. For instance, the centralnodes in Fig. 1 reach the other side’s nodes in threeto seven steps. Two other measures are used toidentify the nodes that are central for cross-bordernetworking: closeness centrality and cross-bordercentrality.

. Closeness centrality can be measured when a firm i canreach a firm j through firms k, l, . . ., as long as thereare ties between i and k, between k and l, between land . . . j. This potential network of contactsassumes that information spreads between directorsmanaging the same firm, so that if firm i has twodirectors A and B, firm k has two directors B andC, and firm j has two directors C and D, then directorB can potentially reach D through C so that firm i canpotentially reach firm j. If the matrix describing theanalysed network is B ¼ [bij], then all terms exceptfor diagonals equal 1 (in all sampled networks)because all networks are potentially reachable inone or more steps (owing to the snowball samplingmethod). The longer the chain from one firm tothe other (that is, the longer the chain between direc-tors), the weaker the probability of real interaction.The closeness centrality is measured as distancebetween nodes by calculating the reciprocal of thesum of distances between nodes; the highest the indi-cator, the highest the possibility to get involved ininteractions with other nodes. Closeness centralitycaptures thus the potential of firms (and of their direc-tors) to reach other firms (and their directors) throughmediated ties. The nodes with high closeness central-ity have high potential to mediate further inter-actions. In the detailed network, the clique 5 (inFig. 3) contains mediator nodes which have alsohigh values of degree centrality. Exceptions are twoWalloon-based firms that have low degree centralitybut large closeness centrality, suggesting that althoughthey do not have many direct ties, they can reach theentire network indirectly by linking to a few centralnodes.

. Cross-border centrality can be measured when a firm ihas a tie to a firm j located on the other side of the

border. Let the matrix describing the analysednetwork be C ¼ [cij], where cij ¼ 1 if a firm i has atie to a firm j located on the other side of theborder, cij ¼ 0 otherwise and cii ¼ 0. In the detailednetwork (Fig. 1) there are big discrepancies betweencross-border centrality on one hand, and degree cen-trality and closeness centrality on the other hand. Inother words, nodes with high degree centrality havelow cross-border centrality and nodes with highcross-border centrality have medium or low degreecentrality and closeness centrality. Thus, firms withcross-border ties are not central in their networks;they are reachable indirectly, through medium orlong paths. This suggests that a high number ofdirect ties does not imply a higher probability of con-necting across the border. The topology of networksmay explain partially this statistical mismatch. Thenext subsection investigates the configurations ofties in which firms engage.

Configurations of ties

Two main configurations are salient in the sampled net-works: stars and webs (Fig. 4). A star is a group of nodeswith one node tied to all other nodes – their numberis defined here as at least the number of nodes equal tothe threshold value of the degree centrality. The coresof stars have thus high degree centrality. Polycentric pat-terns emerge when two or more cores of stars are tied.In this pattern, the cores of stars have high closenesscentrality since they mediate connections betweensubgroups. A web is defined here as a densely con-nected subgroup of nodes that has at least half of allpossible ties. Webs can act as cores of stars in largenetworks.

These configurations do not follow exclusively theethnic axis of clustering. They can be identified bothwithin and across the two ethnic groups. However,there is a major difference between these two contexts.In the latter case, summarized in Fig. 5, there are only afew ties across the border, which do not develop furtherdirect, cross-border ties. Mediator nodes – that can be

Fig. 5. Pattern of cross-border connection


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individual nodes or webs – act as cores of stars with tiesto peripheral nodes.

Fig. 6 presents the ways in which stars and webs crossthe border in the analysed networks. They either stop

immediately after crossing the border or develop tiesexclusively on the other side of the border:

† Stars that develop only on one side of the border. In thiscase, stars have a few peripheral nodes across the border,which do not connect further. Star-shaped patterns – ofboth firms and directors – favour thus regional hom-ogeneity. The few firms located across the borderhave, in general, directors from the region where thenetwork is predominantly formed. Fig. 7 shows such apattern in a sampled network, which includes17 firms located in and around the focal node(a freight transport firm in Montenaken) and involves12 Flemish directors. The activities of networks aremainly related to recycling and freight transport byroad. This network shows a core (consisting of aclique of firms) which has a tie to one peripheral firmlocated in Wallonia, in the border region. This firm ismanaged by a Flemish director. The correspondingnetwork of directors is entirely Flemish and shows afamily-based cluster. Table 4 compares the firmsdescribed in Fig. 7.

† Stars that develop on both sides of the border. In this case,stars cross the border through a few ties to other nodes,which can be peripheral (case A) or cores of other stars.

Fig. 6. Types of configurations that cross the border

Table 4. Comparison of firms (described in Fig. 7) according tolocation and network properties

Wallonia Flanders

Number of firms (as percentage) 5% 95%

Sum of normalized degrees 6.25 643.75

Sum of closeness degrees 37.209 896.111

Cliques (as percentage

from the total number of cliques: 5)

0% 100%

Fig. 7. Network of firms (up) and corresponding network ofdirectors (down)

Note: Square nodes are Flemish firms/directors, circlenodes are Walloon firms/directors


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The pattern of crossing the border is through one direc-tor engaged in boards of directors on both sides of theborder. This common director acts as the core of amixed star (or mediates the link to a core), whichevolves separately on each side of the border. Anexample is the network in Fig. 8 (summarized by

Table 5) that consists of two groups of firms connectedby a unique tie. The complete network includes 26firms located on both sides of the border, but, in thegreat part, outside of the border region. The activitiesof the network are mainly related to selling/repairingvehicles. The two major components are connected

Fig. 8. Network of firms (up) and the corresponding network of directors (down)Note: Square nodes are Flemish firms/directors; circle nodes are Walloon firms/directors; down triangle nodes are

firms/directors located in Brussels (French-speaking); up triangle nodes are international directors (France)


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by a Walloon director engaged in the board of directorsof a Flemish firm. The corresponding network of direc-tors (summarized by Table 6) also maintains two maincomponents: the Dutch-speaking subgroup represent-ing 36% of all directors includes 17 directors in Flandersand one in Brussels; the French-speaking subgroup,representing 62% of all directors, includes 29 directorsin Wallonia, one in Brussels and one in France.Besides, the network includes one German-speaking

Table 5. Comparison of firms (described in Fig. 8) according tolocation and network properties

Wallonia Brussels Flanders

Number of firms (as percentage) 54% 8% 38%

Sum of normalized degrees 252 48 140

Sum of closeness degrees 485.869 63.292 300.471

Cliques (as percentage from the

total number of cliques: 6)

50 17 33

Table 6. Comparison of directors (described in Fig. 8) according to region and network properties

Flanders

(Dutch-speaking)

Wallonia

(French-speaking

and one

German-speaking

Other

(French)

Brussels

(French-speaking)

Number of directors (as percentage) 17% 77% 3% 3%

Sum of normalized degrees 141.304 326.087 4.348 2.174

Sum of closeness degrees 414.809 820.277 25.698 25.698

Cliques (as percentage from the

total number of cliques: 23)

31 8 57 4 0

Fig. 9. Network of firmsNote: Black nodes are Flemish-speaking firms located in Flanders; grey nodes are French-speaking firms located inWallonia (clustered) and Brussels (within a Dutch-speaking web); the white node is a Dutch-speaking firm located in

Wallonia


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director located in the German-speaking arrondissementsin Wallonia. Strong ties in this network (strengthmeasures the frequency of directors’ participations inthe same board) do not cross the border.

† Webs that develop only on one side of the border. In thiscase, webs include a few nodes across the border, butthese nodes do not connect further. Networks offirms tend thus to be regionally homogeneous. Anexample is the network in Fig. 9 that consists a denseweb, like a core, which radiates ties to other nodesand smaller webs. One of these small webs, peripheralto the core, includes five firms located in Wallonia;this web is tied to the core through a Flemish firm.The networks’ activities include sales of electricalgoods, construction industry, real estate and consul-tancy; the activities of the Walloon web – consultancy

and real estate – do not relate directly to the activity ofthe focal node. The Walloon web is connected to thecore through two Flemish firms that are also part ofthe web. The entire network includes 216 firmslocated, in majority, in Flanders and only six firms inWallonia. One of the Walloon-based firms is Dutch-speaking and part of a Flemish web. Its director isWalloon and manages two other Flemish firms locatedin the border region (in the western segment of theborder). These three firms are geographically close.

The network also includes two Brussels-based,French-speaking firms in a cluster of Flemish firms.Firms in Brussels, although not numerous and notcentral in the sampled networks, do not tend tocluster according to region or language. Brusselsis also the only place where bilingual firms arerecorded.

Fig. 10. Network of firmsNote: Black nodes are firms located in Flanders; light grey nodes are firms located in Wallonia; dark grey nodes are

firms located in Brussels


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†Webs that extend on both sides of the border. This case issimilar to (A), in which cores are replaced by very cohe-sive groups. An example is the network in Fig. 10 thatincludes 254 firms with activities related mainly toagriculture and food, real estate, consultancy, andmedical services. The network spans Belgium and,through its directors, extends internationally throughThe Netherlands, France, Germany, Switzerland,Luxembourg, Monaco, Spain, Austria, UK, Swedenand the USA. Twenty per cent of the network of firmsis located in Wallonia, 9% in Brussels (23 Brussels-based firms, among them six Dutch-speaking), 71% inFlanders. Walloon firms have similar activities to thefocal firm (agriculture, food). Firms cluster accordingto three regions (Flanders, Wallonia, Brussels), but notto geographical distance. The network has a polycentricpattern with central firms (either individual or clusteredin cohesive subgroups) as cores of star-shaped subgroups.Walloon clusters have common Walloon directors andeach include one or two Flemish directors.

CONCLUSION

This article challenges the traditional view on bordersbased on the core–periphery representation of spacein order to propose a model for border regionsadapted to the current socio-political reality ofWestern Europe. The core–periphery representationassumes the monocentric structure of space, the per-ipherical nature of borders and the divisive role ofborders. Yet, these premises do no longer characterizethis reality. Therefore, this article builds a representationof borders which considers that space is polycentric andthat spatial demarcations result from social interactions,not from isolation. Border regions are thus understoodas zones of interferences, not as divisions or disadvan-taged areas. The proposed representation emphasizesthe relational nature of borders and focuses thus onsocial relations in border regions.

Border regions can be thus represented as networksof linkages and examined with the network analysistoolbox. The analysed border regions are describedhere through networks of firms, but networks ofother social entities, from individuals to cities, canmodel them. This representation introduces a way ofthinking border permeability in terms of patternsof relations, which complements the common way ofthinking it in terms of quantity of interactions.2

The network-based approach to studying bordershelps analyse axes of solidarity that overlap or contra-dict political borders. These axes have a dual effect:they both integrate and segregate social entities. Indi-viduals cluster according to family, ethnicity, religion,geographical location, activity, among others. As Wattsputs it, people divide the world into layers, with thetop layer corresponding to the whole world and suc-cessive layers corresponding to cognitive divisions

based on individuals’ identities – where identities are‘sets of characteristics which they [individuals] attri-bute to themselves and others by virtue of their associ-ation with, and participation in, social groups’ (WATTS

et al., 2003). In cases where political borders coincideto some extent with cultural or socio-economic div-isions, the network approach highlights the axes ofsolidarity that maintain the border. In cases where pol-itical borders cut artificially cohesive social structures,the network approach can stress out layers that aremore susceptible to develop new axes of solidarityable to enforce political borders.

This study examines networks of firms in a segmentof the Flemish–Walloon border region in order toassess the geographical spread of linkages and the roleof the ethnic axis of solidarity. The analysis showsthat firms cluster less according to geographical dis-tance and more according to region and ethnicity.Directorate ties cross international borders (mostlythe neighbours’ borders: The Netherlands, France,Luxembourg and Germany) almost as often as theycross the Flemish–Walloon border, although thesampled focal nodes are SMEs in an area that neitherborders the country (thus, it is not part of transnationalborder programmes) nor includes main urban centres.Brussels-based firms appear rarely in the sampled net-works and activate predominantly in real estate, con-sultancy and financial intermediation, which appearon average at the third tier of networking in all thelarge sampled networks.

Language and ethnicity/region are visible as majorclusters in the sampled networks. This does not meanthat networks do not have ties across the borders(almost 40% of the networks with at least two nodescross the border), but that cross-border ties are few(compared with the total number of ties in a network)and form configurations that maintain visibly theethnic axis of solidarity. The general pattern of crossingthe border is through a node that acts as a mediatorbetween the sides of the border. Further ties from themediator node only rarely cross the border, so that thetwo major ethnic-based components of the networkare preserved. Mediator nodes have high cross-bordercentrality and, usually, medium or low direct centralityand closeness centrality; this indicates that there are nocentral nodes with many ties across the border.Mediator firms have directors from both sides of theborder. The small number of mediator nodes indicatesthe small number of mixed boards of directors.

The apparent paradox of matching a network-based representation to borders – in other words, ofenvisioning divisions as channels – becomes consist-ent considering that borders do not emerge fromsocial isolation, but from structured interactionsbetween social entities. This representation aims toopen a different way of understanding borderphenomena – a way that stresses characteristics ofties – and, evidently, needs to be mixed with


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approaches based on characteristics of nodes. Thestructuralist approach brings in a qualitative variableneglected in border approaches, namely the configur-ation of cross-border relations. It can help thus strat-egies of border management which use the design ofthese configurations. Such strategies are already usedto some extent in international cross-border pro-grammes in the EU (Interreg) that include as amajor component the development of cross-bordersocio-economic linkages. The emphasis in these pro-grammes is on initiating and maintaining interactionsacross borders. Despite several successful endeavours,numerous cross-border contacts do not outlive theEU programmes’ funds and expertise. In part, this isdue to the fact that the permeability of borders isnot assessed in terms of social structures in borderregions. This way of evaluating permeability stressesmore the type and quality of interactions than thequantity of interactions, and strategically orients theattention to parts of the existent cross-border socialstructures that convey potential to generate furtherinteractions in certain fields of activity.

Further research can add multiple sets of variouskinds of social relationships, besides the ones pre-sented here, and overlap them in order to offer anew perspective on the permeability of borders,which highlights patterns of interactions that favouror hinder cross-border relations. This perspective

centres on what keeps the difference across bordersas difference despite the fact that people interactacross borders. This exploration proposes a line ofinquiry that points out the very essence of borders:‘created by contacts, the points of differentiationbetween two bodies are also their common points’(DE CERTEAU, 1984, p. 127).

Acknowledgements – The author thanks the two

anonymous referees for very helpful comments. An earlier

version of this paper was presented at the Fulbright

Seminar in Regional Development, Vienna University of

Economics and Business Administration, 2005.

NOTES

1. This opinion relies only on the high number of identical

surnames encountered in the same board of directors.

Because of the lack of data recording family ties, the analy-

sis does not investigate the overlap of firms and family

networks.

2. Here reference is made to many classifications of borders

in terms of presence and quantity of cross-border contacts.

One of the most used (MARTINEZ, 1993) differentiates

between alienated borderlands (no contact across

borders); co-existent borderlands (a few contacts); interde-

pendent borderlands (stable contacts); integrated border-

lands (frequent and stable contacts).

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Documents

A Network-based Approach to Regional Borders: The Case of Belgium