Business, Brokers and Borders: The Structure ofWest African Trade Networks

OLIVIER J. WALTHERDepartment of Border Region Studies, University of Southern Denmark, Sønderborg, Denmark

(Received 7 April 2014; accepted 3 November 2014)

ABSTRACT The objective of this paper is to show how a formal approach to networks can make a significantcontribution to the study of cross-border trade in West Africa. Building on the formal tools and theories developedby social network analysis, we examine the network organisation of 136 large traders in two border regionsbetween Niger, Nigeria and Benin. In a business environment where transaction costs are extremely high, we findthat decentralised networks are well adapted to the various uncertainties induced by long-distance trade. We alsofind that long-distance trade relies both on the trust and cooperation shared among local traders, and on thedistant ties developed with foreign partners from a different origin, religion or culture. Studying the spatialstructure of trade networks, we find that in those markets where trade is recent and where most of the traders arenot native of the region, national borders are likely to exert a greater influence than in those regions where tradehas pre-colonial roots. Combining formal network analysis and ethnographic studies, we argue, can make asignificant contribution to the current revival of interest in cross-border trade in the policy field.

1. Introduction

Chief Ibrahim Hassan looked out at the new developments being built around his compound andtalked about the days when the city of Gaya was little more than a village along the Niger River.‘There were no houses or warehouses around here,’ he said, pointing at a businessman dressed in along white robe supervising the loading of a Nigerian truck. ‘I have waited for seven years until thetraditional authorities of old Gaya appointed me as District Chief. Nobody lived here on theses wildhillsides.’ That was 30 years ago. A little more than 8,000 people lived in this Nigerien border townfacing Benin and Nigeria, most of them engaged in agriculture and fishing. They are now more than45,000. Every day, hundreds of trucks loaded with food stuff, manufactured products, oil and uraniumconcentrates find their way along the crowded one-lane road that crosses Gaya, en route to the Gulf ofGuinea or to the major cities of the Sahel. The Cotonou corridor, where Gaya is located, accounts forhalf of the imported goods from Niger and three-quarters of total Nigerien exports in terms of value. Inthree decades, dozens of warehouses have been built by wholesalers attracted by the strategic locationof the city, at the crossroad between three states. The city now stretches out over a length of fourkilometres and the urban area – originally concentrated around old Gaya – has been extended morethan 10 times.

The spectacular economic development of Gaya – among many other border cities in West Africa –would certainly not have been possible without the existence of national borders. Without the price

Correspondence Address: Olivier J. Walther, Department of Border Region Studies, University of Southern Denmark, Alsion 2,DK-6400 Sønderborg, Denmark. Email: ow@sam.sdu.dkAn Online Appendix is available for this article which can be accessed via the online version of this journal available athttp://dx.doi.org/10.1080/00220388.2015.1010152

The Journal of Development Studies, 2015http://dx.doi.org/10.1080/00220388.2015.1010152

© 2015 Taylor & Francis

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differences across countries and the bans affecting the imports or exports of certain agricultural ormanufactured products, West African traders would have fewer incentives to move their goods acrossthe continent and avoid the taxes collected by customs authorities. Considering that the cost of movinggoods in Africa is the highest in the world (Teravaninthorn & Raballand, 2009), second-hand clothes,for example, would probably not be imported through Benin and then stored in a warehouse in Gayabefore being re-exported illegally to Nigeria, where they are subject to an import ban – a journey ofabout 1,800 kilometres across three countries (Brooks & Simon, 2012). However, the existence ofnational borders is not a sufficient condition for trade to flourish. Not all border cities are importantmarket places. Some of them lack market and road infrastructure or are predominantly dominated byadministrative or political rather than business activities. Many border cities also lack one of the mostimportant ingredients of trade: entrepreneurs willing to develop transnational networks.

The organisation and evolution of these networks have been interpreted in conflicting ways. In mostof the African studies literature, social networks are seen as a heuristic device or as a metaphor forinformal economic relations. Qualitative approaches, it is argued, are best suited to study the ethno-graphic dimensions of social networks, that is, how their organisational and institutional dimensionsresult from historical, cultural or geographical factors. Social networks are rarely – if ever – mapped,formally described or modelled. In most of the network science literature, by contrast, new conceptualand computational developments have been used to represent, describe, and model social networks inan increasingly sophisticated way. However, and despite Mitchell’s (1969) advocacy for an analyticalrather than metaphorical approach to networks, most formal social network analysis has thus farlargely ignored Africa. The objective of this article is to fill this gap by showing how a formalapproach to networks can make a significant contribution to the study of cross-border trade and to thecurrent revival of interest in cross-border trade in the policy field. Building on the formal tools andtheories developed by social network analysis (SNA) that allows researchers to map the nodes and tiesthat compose a network, we examine the network organisation of traders in two border marketcomplexes on the Nigeria–Niger–Benin border and on the Nigeria–Niger border.

The first part of the article aims to understand the centralisation of cross-border trade networks. Wewish to know whether traders are organised in a hierarchical structure in which a few central actorstransmit information, orders and resources, or in a rather decentralised way in which every actor isconnected to everyone else. This question is crucial for the efficiency of trade networks, as centralisednetworks are generally regarded as more efficient, but less resilient to threats, than decentralisedstructures. Our two case studies show that trade networks tend to be rather decentralised, with few tiesbetween the actors and few highly connected actors. We also study the trade-offs faced by tradersbetween embeddedness and brokerage for the purpose of determining whether traders are stronglyembedded within their group of peers or if they play the role of brokers beyond their own businesscommunity. Our work shows that combining embeddedness and brokerage is a determinant factor ofindividual economic performance because each of the two dimensions contributes to the accumulationof social capital among and between groups.

The second part of the article considers the spatial structure of trade networks. We are particularlyinterested in analysing the influence of national borders on the development of social ties. We studywhether traders are organised with a multiplicity of cross-border ties or according to several nationallyorganised markets with only a few brokers able to bridge the markets. Our aim here is to determinehow the social structure of trade networks has adapted to the constraints and opportunities offered bythe existence of national borders. In contrast to what may be initially assumed from a setting wheremarkets are located at a very small distance from the border, not all traders are engaged in cross-bordertrade. Those who carry out a cross-border activity occupy a position that makes them highly importantfor the functioning of the overall structure.

In a third part, we discuss certain important differences between the formal and the ethnographicapproaches, especially the ways in which social network analysis defines brokers and central actors,and argue that formal network analysis should be seen as complementary to conventional ethnographicstudies. We also show how combining formal network analysis with ethnographic studies maycontribute to support the recent revival of interest in cross-border trade in the policy field, which

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contrast with the views of the 1990s, during which traders were rarely rewarded for their role ofsuppliers of an increasingly urbanised continent and informal activities were perceived as a threat toeconomic growth and political stability (Meagher, 2010; Taylor, 2012).

2. Social Networks and Economic Performance

A certain consensus has emerged among historians, geographers, anthropologists and political scien-tists to consider that social networks can potentially enhance economic efficiency or undermineeconomic development, depending on the structural position and underlying strategies of the actors(Bähre, 2012; Goyal, 2012; Meagher, 2005; Urwin, Di Pietro, Sturgis, & Jack, 2008).

In Africa, this fundamental duality of social networks has been demonstrated in several historicalstudies that looked at the social composition of trade networks. One the one hand, these studies showthat the structure of trade networks was reliant on factors such as birth, kin and ethnicity. In manyAfrican regions, traders recruited their sons, not their clients, into their businesses as junior partners,with birth providing legitimacy and allowing the creation of a group of obligors bound by family linksand alliances (Grégoire, 1992). For example, family ties were particularly important in long-distancekola trade between Asante and Hausa and for Kooroko merchants during pre-colonial times (Amselle,1977; Lovejoy, 1980). In other cases, the transmission of business knowledge between fathers andsons was disrupted by the brutal decline of trade cities or the establishment of national borders andrailway roads (Howard, 2014). In Eastern Niger, for example, economic hardship in the early years ofcolonial rule, uncertainty in the cattle business, and inheritance law division of estates between heirsprevented the emergence of family dynasties (Baier, 1980).

On the other hand, empirical studies also indicate that kin was frequently not the preferred means ofdoing business due to the social pressure that resulted from economic transactions between familymembers. Family ties offer comfort, but social obligations can also undermine prosperous businessesor prevent enterprising individuals from developing innovative activities, pushing traders to developoriginal ways to accumulate wealth (Whitehouse, 2012). When kin relations constitute a burden,traders can choose to conceal their prosperity from their families by adopting a modest lifestyle or joingroups with different internal norms, such as reformist religious movements that promoted austerityand a reduction of social obligations. Numerous large traders from the Niger–Nigeria border, forexample, have joined the Society for the Removal of Innovation and Reinstatement of Tradition in thelast decades, not only because the Islamist movement provided trust, but also because social obliga-tions were reduced among its members (Masquelier, 2009). Another strategy adopted by traders is toemigrate and establish trade diasporas far enough from their community of origin to escape from thedaily pressure from kin, but close enough to play the role of cross-cultural brokers. Since Cohen’s(1969 [2004]) study on the Hausa trade diaspora of Ibadan, many studies have documented how thesespatially dispersed communities provided a suitable ground for reducing uncertainty and ensuring trustamong their members (see Pellow, 2002; Whitehouse, 2012).

Econometric studies have also documented the duality of social networks. On the one hand, socialnetworks appear to enhance trader’s productivity due to better information on prices, the trustfulness ofclients and suppliers, and potential access to commercial credit (Fafchamps & Minten, 2002). In abusiness environment characterised by weak formal institutions, being connected to numerous otherstraders, suppliers and clients positively influences the economic performances of the traders and providesthe trust that neither the state nor formal business institutions can provide (Fafchamps, 2004). On theother hand, social networks do not benefit all equally. Because they promote the well connected ratherthan the most qualified, they make it hard for newcomers to enter a market. Being too embedded in a setof interpersonal relationships has disadvantages because it decreases the ability of traders to reachexternal resources, such as foreign business partners of a different ethnic or religious background.

Another negative aspect of social networks is that their propensity to work across sectors andadministrative levels can potentially lead them to destabilise the fiscal and monetary control of nationstates. Competing with formal institutions, social networks have frequently been accused of impeding

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their development, leading to the criminalisation of states, corruption, and instability (Bayart, Ellis, &Hibou, 1999). These social liabilities are especially true when interpersonal networks include not onlybusinessmen but also politicians and civil servants. In this case, market-oriented firms have a hard timecompeting with network-based firms, unless they also rely on interpersonal links within the state. Inthe particular case of cross-border trade, the difficulty of predicting which of the formal or informalrules will be applied when dealing with trade agreements, blockade of key ports, or border conflicts,has led many traders to develop clientelist ties with state officials, or engage in politics themselves(Boone, 2006; Kraus, 2002; Tijani Alou, 2012).

In parallel to the ethnographic approach, recent research conducted in a variety of disciplinary andgeographical contexts by network scientists has reached similar conclusions regarding the duality ofsocial networks (Burt, 2005; Everton, 2012; Fleming, King, & Juda, 2007; Uzzi, 1996). As shown inTable 1, maximum performance is attained when an actor is simultaneously embedded in a cohesivegroup while being able to create diverse external contacts between actors that are not themselvesconnected. A strong degree of embeddedness can be beneficial to the activity of social actors, since itprovides trust between peers and reduces the risks associated with business activities. Strongly embeddedactors are therefore regarded as very central, in the sense that they are surrounded by a large number ofother actors with whom they frequently interact to exchange information, draw resources or communicateorders. This particular form of centrality is known as degree centrality because it refers to the number ofties of each actor or degree. In many rural societies, for example, traditional chiefs have a high degreecentrality, because they are the centre of a large network of family, ethnic and political ties within the localcommunity. Degree centrality is a local measure of centrality, in the sense that it only takes into accountthe immediate connections of an actor. It captures only a partial aspect of the centrality of social actors,which can also arise from their ability to reach other actors beyond their own group and play the role ofstructural brokers (Scott & Carrington, 2012).

The importance of brokers is formally measured through betweenness centrality, which refers to theimportance of bridging actors that would otherwise not be connected. It is a global centrality measurecalculated on the entire network and based on the number of shortest paths between actors. In theformal network literature, brokers are defined as actors who bridge several disconnected groups andbenefit from the existence of areas of low density in the networks, known as ‘structural holes’ (Burt,1992). Traders connected to business partners in distant markets or returning immigrants can play suchbrokerage role between their own community and the outside world.

As Spiro, Acton, and Butts (2013) demonstrate, brokerage is a fundamentally dynamic process,which can generate value in three different ways. Firstly, brokers can transfer resources between twodisconnected parties, a situation known as tertius gaudens or ‘rejoicing third’, as when traders act as abridge between importers and final consumers along transnational routes. Secondly, brokers canfacilitate matchmaking between two actors to the benefit of each, a situation known as tertius iungens,or ‘the third who joins’ (Obstfeld, 2005). Finally, the structural advantage of a broker can come fromhis ability to coordinate the activities of third parties without creating a direct relationship betweenthem, which reinforce their dependence on the broker.

Some actors may not be aware of their structural role as broker. As Morselli (2009, p. 17) shows,‘being a broker is not simply a role that can easily be identified, as would be the case in occupations

Table 1. Combining embeddedness and brokerage

Low degree of embeddedness High degree of embeddedness

High brokerage beyondgroup

Divisive group with diverse externalcontacts

Cohesive group with diverse externalcontacts (maximum performance)

Low brokerage beyondgroup

Divisive group with homogeneousexternal contacts (minimumperformance)

Cohesive group with homogeneousexternal contacts

Source: Burt (2005, p. 139).

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such as a stock broker, a real-estate broker, or even a power broker – in such cases, the occupation orrole defines the position’. Rather than a recognised professional occupation, brokers occupy a positionthat can vary according to the kind of information or resources conveyed by the network. Thestructural definition of brokers significantly differs from the one commonly found in the Africanstudies literature (Walther, 2014), which regards brokers as a specialised institution whose aim is towork between buyers and sellers on a market (Brooks, 1993; Little, 1992). In their study of the Hausatrade, for example, Grégoire and Labazée (1993, p. 22) identify a number of socially recognised status(such as courtiers, rabatteurs or coxeurs), which all work at facilitating business transactions.

Similarly, the formal network literature defines powerful actors according to their degree centrality.Actors with a high degree centrality are assumed to be powerful because, being at the centre of thenetwork, they can better control information flows, give advice and orders, and influence outcomesthan peripheral actors. This definition differs from the ethnographic definition of big men which takesinto account the ability of certain actors to achieve economic and political objectives through socialpatronage and redistribution (Nugent, 1995; Utas, 2012).

3. Case Study and Methodology

3.1. Two Border Regions

Data were collected between January and December 2012 on five border markets located betweenNiger, Nigeria and Benin: Gaya–Malanville–Kamba (GaMaKa) and Birni N’Konni–Illela (BNI)(Figure 1).

Figure 1. Location of case studies.Cartography: author.

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The two border regions were selected because their historical development of trade is ratherdissimilar, which, we assume, should result in two structurally different trade networks. In theGaMaKa region – known as the Dendi – long-distance trade activities have been encouraged by theliberalisation of trade that occurred since the 1980s in Benin and Niger. As mentioned in theintroduction, the small city of Gaya, whose estimated population was 45,000 in 2010, has progres-sively transformed into a regional hub for wholesalers willing to trade with Nigeria, where the importsof numerous goods, such as second-hand clothes, are prohibited. A few kilometres south, the city ofMalanville (population 60,000) is a regional centre for agricultural goods produced in the River NigerValley, such as onion, cassava and cereals. A colonial creation, the market of Malanville is one of thelargest in Benin and attracts a considerable crowd of foreign traders from neighbouring countries(Walther, 2009). The busy commercial activities of Gaya and Malanville contrast with the relativedecline of the neighbouring Nigerian city of Kamba (population 27,000), whose economic develop-ment has been slowed by the diminishing comparative price advantage of Nigerian products, poor roadconditions and insecurity.

The Dendi region was historically located on one of the main trade routes developed betweenHausaland and Asante in the nineteenth century. Caravans of Hausa traders from Kano, Sokotoand Jega stopped in Gaya before crossing the Niger River on their way to Sansanne Mango,Yendi-Gamaji and Salaga, where kola nuts were purchased. The region was also a productioncentre of salt, extracted in the fossil valleys of the Niger River and exported regionally by Dendimerchants (Lovejoy, 1986). These merchants exerted such a considerable influence that, by thenineteenth century, Dendi had become the dominant trade language of the northern regions ofpresent day Togo and Benin.

Today, the Dendi population of Gaya, Malanville and Kamba is predominantly engaged in agri-cultural activities. Up to 80 per cent of the large traders surveyed in this article do not come from thecity in which they work. They are alien entrepreneurs of Songhay, Zarma, Tuareg, Igbo, Fulani andHausa origin attracted by the opportunities of cross-border trade. The reason why trade is currentlydominated by foreign traders if the Dendi used to have a pre-colonial history of long-distance trade isnot clear from empirical evidence. Trade could have suffered from major disruptions in the nineteenthcentury, perhaps due to the Fulani jihad of 1804–1808, which has destroyed the business of manytraders in Hausaland and in the neighbouring regions (Lovejoy, 1980). The historical decline of theDendi could also have come from the reorganisation of trade networks, from the interior of WestAfrica to the Gulf of Guinea, which would have made the commercial route passing through Gayaobsolete.

Three hundred kilometres further north-east, the border area between Birni N’Konni (population63,000) in Niger and Illela (population 32,000) in Nigeria is characterised by the highly informalnature of economic activities and the historical Hausa ethnic networks that developed before colonialtimes (Amselle & Grégoire, 1998). Birni N’Konni was the capital of a small Hausa state beforebecoming a tributary of Gobir in the middle of the eighteenth century, and of the Sokoto Caliphate inthe nineteenth century. Both Birni N’Konni and Illela were integrated in the trade route that linkedSokoto to Agades in the nineteenth century, along which livestock, dairy products and salt from thenorth were traded against millet and cloth from the south. The enforcement of customs regulationsbetween French and British colonies led to a major disruption of trade but local traders quicklyadjusted to the new economic order imposed by colonisation. In the post-colonial era, the existence ofnational discontinuities became even more conducive to the establishment of transnational tradenetworks and to the rise of a new generation of businessmen enriched by the re-export of cigarettes,textiles and other manufactured and agricultural products (Grégoire & Labazée, 1993). Today, BirniN’Konni and Illela constitute an important border post on the road between Sokoto and Niger, as wellas on the east–west N1 highway which cross the Republic of Niger. Contrasting with the GaMaKaregion, a large majority of the traders come from the cities where they do business and are of Hausaorigin.

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3.2. A Network Approach

This study builds on social network analysis (SNA), which is both a paradigm of social interactionsbased on graph theory and a methodology using statistical tools to formally describe, represent andmodel structures that are not easily visualised (Newman, 2010). This approach regards social networksas a finite set or sets of actors linked to one another by social ties. It is particularly appropriate tounderstand trade, which is a fundamentally relational activity in which the constraints imposed on anindividual and his outcomes are explained by the structure of the social network (Spiro et al., 2013).

The first step of our survey was to identify a number of products that would be important for theeconomic development of the border regions. The identification of the relevant products was based ona preliminary macro-analysis of international trade conducted on the basis of longitudinal data fromnational customs authorities, pre-existing surveys made in the border regions in the mid-2000s and ananalysis of the goods banned in Nigeria, which are of high importance for re-export trade in WestAfrica (Raballand & Mjekiqi, 2010; Walther, 2012). This combination of data led us to identify fourproducts whose trade would be highly important for the traders located in the two border regions underconsideration: building materials; cereals and flour; textile; and used clothing. The trade of some ofthese products is sometimes considered illegal on one side of the border and legal on the other, such astextiles. As in previous studies (Igué & Soulé, 1993), we considered these trade flows as relevant forour survey, insofar as they did not generate criminal activities comparable to those of traffickingweapons, drugs or precious metals, but were merely the result of different legislation within the region.

Once these products were identified, we identified the actors involved in trading them. Socialnetwork analysis differs from most of the surveys conducted in Africa in the sense that we areprimarily interested in collecting data on the relations – rather than the attributes – of social actors.In contrast to traditional surveys that consider social actors as independent units that can be added untilthey constitute a representative sample of the population, our data refer to non-independent observa-tions. Sampling a population would not work in our case because we don’t know how the social actorsare intertwined with each other before we start our analysis and, by randomly selecting some of them,we would miss a large number of relevant connections. In order to address this issue, we used snow-balling techniques, a recognised alternative that allows identifying new economic agents from amongthe subjects’ existing acquaintances. Snowball sampling is particularly adapted to the study of cross-border traders, who don’t necessarily belong to a formal professional institution in which insiderscould easily be distinguished from outsiders, and whose number and activities are extremely difficultto evaluate from the investigator’s perspective.

With the help of local freight agents, we selected the traders whose annual turnover was over 100 FCFAmillion (€152,000) in 2010. This threshold is used by freight agents to distinguish between small traderswhose activities are predominantly limited to petty business and large traders who can potentially employ agreater number of employees and conduct much more ambitious trade activities. Focusing on large traders,we conducted a first wave of interviews with 43 of them, during which they were asked to nominatewhoever they considered as business partners, whatever their age, gender, ethnic group, nationality orreligious membership. The interviews were conducted in French and Hausa by the author and severalcolleagues from the University of Niamey. We collected data on the existence and frequency of businessinteraction between the actors, as well as on the type of relationships between them (family, neighbour,organisationmember, work, other friend).We concluded that a tie existed if two actors had been in businessin the last two years (2010–2012). These interviews produced a list of 114 nominees. We conducted twoadditional waves of interviews with the actors mentioned at least three times by the first wave. After threewaves of interviews, the same names started appearing again and again, which meant that populationsaturation was reached. With a response rate of 87.9 per cent in the GaMaKa trade network and 88.9 percent in the BNI trade network, we work on an almost complete population and our results are notconsidered to be negatively affected by missing data. Contrarily to the Gulf of Guinea, where some femaletraders have acquired prominent positions in the textile business, all the traders interviewed in this article –except for one – are male.

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4. The Structure of Trade Networks

This section investigates the social structure of the networks found between Gaya-Malanville, andKamba (GaMaKa) and Birni N’Konni-Illela (BNI). We start by testing whether the trade networks aremore centralised or more decentralised and studying the relative importance of central actors andbrokers, before turning to the spatial structure of the networks and look for a border effect.

4.1. Two Decentralised Trade Networks

Centralised networks are composed of a small number of actors with many ties and tend to be moreefficient in terms of coordination than decentralised structures, because information, orders andresources can be more easily transferred from central nodes to the rest of the network. The starnetwork in which a single actor occupies the centre of the network and where peripheral actors have noties between each other is the most extreme example of a centralised network. Its opposite is the fullyconnected network, a completely decentralised structure where every actor is connected to every otheractor. This structure is, however, much more resilient to threats than the star network because of itsredundancy of ties.

Our results show that the structure of both trade networks is composed of few actors (or nodes) withfew business relations (or ties) (Table 2). The BNI network is composed of 53 nodes and 64 ties, whilethe GaMaKa network is a little bit larger, with 83 nodes and 104 ties, including two isolated dyads ofactors. Both networks have a low density: only 4.6 per cent and 3.1 per cent of the potential tiesbetween the actors are actually present in the network. The clustering coefficients of 0.094 and 0.062,which ranges from 0 (no cluster) to 1 (one single cluster), also indicate that both networks are highlydecentralised. Most of the actors have relations with a limited number of contacts: the average numberof business partners is 2.4 in both regions. This result is consistent with previous studies that showedthat African traders engage in repetitive transactions with existing business partners and that pastcollaborations exert a considerable influence in shaping the structure of current trade networks(Fafchamps, 2004). Both networks have a relatively short characteristic path length – every actorcan be connected to every other by an average of 3.7 steps in the BNI region and 4.2 steps in theGaMaKa region. Short path lengths theoretically designate a rather compact network where informa-tion spreads easily, as opposed to networks with long path lengths, which tend to be linear and less

Table 2. Key metrics for the two networks

Birni N’Konni-Illela(BNI) network

Gaya–Malanville–Kamba(GaMaKa) network

Number of nodes 53 83Number of links 64 104Number of dyadic isolates 0 2Density 0.046 0.031Average number of ties 2.385 2.357Clustering coefficient 0.094 0.062Average path length distance 3.712 4.203Average degree 0.046 0.031Average betweenness 0.053 0.036Degree centralisation 0.172 0.181Betweenness centralisation 0.403 0.320

Calculations are the author’s; dyadic isolates are sub-groups of two actors separated fromthe rest of the network; the clustering coefficient refers to the extent to which the actors tendto cluster together; average measures refer to the average centrality of the actors involved inthe network; centralisation measures highlight whether certain nodes are exceptional interms of centrality. Individual centrality measures for top-scoring actors can be found in theOnline appendix.

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efficient in the spread of information. On the basis of these structural characteristics, both networksapproximate a random network, a structure with a low degree of clustering and short paths. They differfrom a regular network, an ordered structure where every actor has the same number of ties and iscomposed of long paths and strong clusters. They are also different from a small-world network, wheremost nodes can be reached from every other by a small number of steps even if they are notneighbours of one another (Watts & Strogatz, 1998).

In addition to being loosely connected, the actors of both networks have relatively little centralisa-tion, as can be seen from their low average measures of centrality. The average degree measureswhether nodes are linked to a particularly high number of actors and the average betweennessmeasures whether a node plays the role of a gatekeeper by controlling access to other nodes. Thedecentralised nature of the networks is also confirmed by further centralisation measures, which testwhether the network contains potentially exceptional nodes whose centrality would strongly differfrom the rest of the network. Table 1 presents two measures: degree centralisation and betweennesscentralisation. All measures vary from 0, a situation where no node is atypical in terms of centrality, to1, a situation where the centrality of one node exceeds all nodes. Both networks have low values fordegree centralisation (0.172 in BNI and 0.185 in GaMaKa), which makes them less sensitive tofragmentation, since no node seems to be particularly highly connected. The relatively highermeasures found for betweenness centralisation – 0.403 in BNI and 0.320 in GaMaKa – confirm,however, the existence of prominent brokers, which makes sense in a border setting where much profitcan be expected from bridging different partners from various countries.

4.2. Embeddedness and Brokerage

We now turn to the trade-off between degree centrality (embeddedness) and betweenness centrality(brokerage), two centrality measures which, when combined, produce social capital. As discussed inthe previous section, both networks are highly decentralised, with few actors having a high degreecentrality, which measures immediate influence. The relative absence of actors with high degreecentrality is particularly evident on Figure 2, which maps business relations between traders in theGaMaKa region. The size of the nodes reflects the number of ties developed by each actor and thecolour refers to each of the border markets. A part from El Hadj Harouna, a Nigerian textile wholesalerwho entertain numerous business ties with relatively minor actors, most of the traders involved in theGaMaKa network are not particularly central. The same applies to the trade network of the BNIregion.

What makes the two networks interesting is how certain actors have managed to occupy a strongbrokerage position. In both regions, trade is organised through certain brokers, who bridge several sub-groups of actors. The sociogram presented in Figure 3 provides a visual representation of thebetweenness centrality scores in the BNI network. Betweenness centrality is based on the number ofshortest paths that pass through a given actor, and therefore gives an indication about its ability tocontrol crucial flows of information and resources between groups. Each actor is represented accordingto his importance as a broker (size) and according to his country membership (colour). Figure 2 showsthat business relations between traders are mediated through a limited number of prominent brokers,such as El Hadj Zainou, a successful Hausa trader from Birni N’Konni, whose professional contactsdeveloped with traders in Kano helped him become the Nigerien representative of a large Nigerianwholesaler. El Hadj Talatu, a native of Sokoto active in the business of sugar and cooking oil, isanother example of a prominent broker. Without these actors, the network would break into severalisolated components, some of them organised by nationality.

Brokers can play different structural roles depending on whether they bridge actors from within orbeyond their own group. In our case, we hypothesise that borders create several groups of tradersaccording to their country of residence and test whether their brokerage roles vary according to theircountry membership. To do so, we draw on the typology developed by Gould and Fernandez (1989),which identified several types of brokers based on their structural position: coordinator; itinerant;gatekeeper; representative; and liaison brokers. In a triad composed of a broker (Ab) and two other

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nodes, coordinators belong to the same group as the nodes they bridge (A–Ab–A). In our case, this wouldrefer to traders coordinating economic activities within Niger, Nigeria or Benin respectively. For theirpart, itinerant brokers connect two nodes from a different group than their own (B–Ab–B), for examplewhen a trader from Nigeria connects two Nigerien or two Beninese traders. These two categories ofbrokers are known as within group roles, because the A or B belong to the same group, contrarily togatekeepers, representatives and liaison brokers, who all connect individual between groups. In a non-directed network such as ours, gatekeepers and representatives have identical values and connect asource or a recipient to a different group (A–Ab–B or B–Ab–A). Liaison brokers connect two nodes fromdifferent countries (B–Ab–C). This latter brokerage role is only relevant if there are more than twocountries, which is the case for the GaMaKa region, located between Niger, Nigeria and Benin.

Table 3 summarises the relative importance of each of the brokerage scores according to the countryof residence of the actors. These are relative scores, which means that the raw scores obtained bycounting the number of times each actor occupies a brokerage position within each network have beendivided by expected values given group sizes. The results for the within group roles show thatcoordinators bridging traders who belong to the same country are by far the most represented brokersin both networks and in all countries. Coordinators are particularly prominent in the GaMaKa network,which stresses the importance of national – rather than cross-border – business relations. Itinerantbrokers bridging two actors from the same country are rare, except in the Nigerien part of the BNInetwork (0.87). As for the between group brokerage roles, our results indicate that gatekeepers/representatives are particularly represented in the Nigerian (1.00) and Nigerien (0.80) parts of the

é

Actors from Gaya (Niger)

Actors from Kamba (Nigeria)

Actors from Malanville (Benin)

Figure 2. Gaya–Malanville–Kamba trade network: degree centrality.Note: the size of the nodes is proportional to their relative importance in terms of degree centrality. The more ties

an actor has, the bigger it appears.Source: author.

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GaMaKa network. Liaison brokers, whose role can only be analysed in a trinational setting becausethey imply three nationalities of actors, are surprisingly weakly represented in the GaMaKa region,where the conditions for bridging nodes that belong to different groups appeared theoretically to havebeen met. These results suggest that the most frequent brokers in the region bridge people from theirown country.

Table 3. Relative brokerage scores by country and case study

Coordinator Itinerant Gatekeeper/representative Liaison(A–Ab–A) (B–Ab–B) (A–Ab–B, B–Ab–A) (B–Ab–C)

BNI networkNiger 2.04 0.87 0.58 –Nigeria 3.10 0.19 0.42 –Whole network 2.66 0.47 0.49 –GaMaKa networkNiger 5.59 0.16 0.80 0.73Benin 7.12 0.38 0.35 0.04Nigeria 4.38 0.13 1.00 0.27Whole network 6.22 0.27 0.59 0.08

Notes: liaison brokers imply at least three groups (countries).Source: author’s calculation.

Actors from Birni N’Konni (Niger)

Actors from Illela (Nigeria)

Figure 3. Birni N’Konni–Illela trade network: betweenness centrality.Note: the size of the nodes is proportional to their relative importance in terms of betweenness centrality. Brokers

are characterised by larger nodes.Source: author.

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Thus far, we have separately described how large traders of the BNI and GaMaKa regions differedin terms of embeddedness and brokerage. In what follows, building on Burt’s (2005) theory of socialcapital, we combine the centrality scores obtained earlier and show how social capital results from acombination of embeddedness and brokerage. In Figure 4, we plot the degree and betweennesscentrality scores of all traders. In each region, we use the mathematical limit of one standard deviationfrom the mean to split the actors into four quadrants. Because the size and the composition of the twonetworks vary, the mathematical limits between the quadrants are slightly different: the one concerningactors from the BNI region appears with a continuous line; the one concerning the GaMaKa regionwith a discontinuous line.

Actors whose degree and betweenness centrality scores are higher than normal values occupythe upper-right quadrant, such as Elh Hadj Yacine, a Zarma trader whose connections with othersuppliers in Niamey, China, and Dubai make it one of the most successful businessmen of theregion. Actors whose centrality scores are below normal values occupy the lower-left quadrant.Deprived of both dimensions of social capital, these actors struggle to build a dense cluster ofclose business partners and, at the same time, have few connections beyond their group. Actorswho are very central on degree centrality only occupy the lower-right quadrant of the scatter plot.El Hadj Adams is a good example of such actor: his position as president of the traders of Gayahas contributed to reinforce his degree centrality rather than his broker role. Actors who are verycentral on betweenness centrality only occupy the upper-left quadrant. El Hadj Babangida, forexample, is a structural broker with relatively little degree centrality: established in the market ofIllela since 1979, this Hausa trader has become the chairman of the cereal market and a wealthybusinessman connecting Niger and Nigeria. As the next section shows, the profile of these actorsis strongly constrained by the existence of national borders and historical development of theregion.

0.00

0.10

0.20

0.30

0.40

0.50

0.00 0.05 0.10 0.15 0.20 0.25

Actors from BNI region

1 Std. Dev. above mean in BNI region

1 Std. Dev. above mean in GaMaKa region

Actors from GaMaKa region

Degree centrality

Bet

wee

nn

ess

cen

tral

ity

Elh Yacine

Elh Babangida

ElhAdams

Figure 4. Combining embeddedness and brokerage.Source: author.

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4.3. The Effect of National Borders

The major difference between the two trade networks comes from the fact that the proportion of tieswithin a group compared with the ties outside of the group – called homophily – is much larger in theGaMaKa network than in the BNI network. Despite the fact that local traders are located in theimmediate vicinity of a national boundary, 86.6 per cent of the ties exchanged within the GaMaKanetwork are with business partners from the same country, which is the sign of a highly homophilousnetwork (Table 4). Between Birni N’Konni and Illela, on the contrary, Nigerien and Nigerian tradersare not strictly segmented according to their country of residence: far more cross-border ties havedeveloped and only 67.6 per cent of the ties are within traders from the same country. This funda-mental difference between the two networks is confirmed by the fact that the E/I index, calculated asthe difference between external (E) and internal (I) ties for each country, divided by the total number ofties, is high and negative in the case of GaMaKa (−0.727**). This indicates a preference forhomophilous ties. The E/I index is neutral in the BNI network (0.108), signalling that countrymembership is a not a relevant attribute for explaining the structure of the network. In sum, most ofthe large traders in the BNI region are used to travelling across the national border to do business withtheir foreign correspondents and none of them occupies a strong brokerage position between countriesas in the GaMaKa region, where we find few cross-border ties.

The difference in the spatiality of the two networks reflects the historical development of bothregions. Between Birni N’Konni and Illela, in the heart of Hausaland, historical ethnic networkshave constantly evolved since precolonial times, resulting in a fine mesh of business ties. Incontrast, the development of the trade activities between Gaya, Malanville and Kamba is muchmore recent and linked to the liberalisation of international trade in Niger and Benin. In thisregion, trade policies adopted by Benin in 1973 encouraged re-export trade, by maintaining lowerimport barriers than Nigeria (Golub, 2012). These favourable customs regulations allowed goodsto be imported to Benin and then re-exported to neighbouring countries, stimulating the develop-ment of a foreign trade diaspora in Malanville. The situation changed in the mid-1990s, duringwhich a number of Nigerien importers established in Malanville found out that it was moreprofitable to import goods from the world market through Benin free of tax and stock them inthe city of Gaya before eventually re-exporting them to Nigeria. Despite being more circuitous, theroute Benin–Niger–Nigeria is preferred to the route between Benin and Nigeria because goodsimported duty free into Benin have to be declared as in transit for Niger rather than Nigeria, wherethe import of textile is banned. As a consequence, Benin imports a very large volume of goodsdeclared as in transit to land-locked countries, when in fact everyone knows the goods are mostlyultimately destined for Nigeria. Many experienced traders that had initially chosen to settle inMalanville have subsequently built large warehouses in Niger and continue to commute frequentlybetween the two markets.

There are a number of interesting differences within each region: the proportion of cross-border tiesand nodes is always higher for actors located in Niger, as can be seen in the border markets of Gayaand Birni N’Konni, and the lowest proportion for traders located in Nigeria, as in Kamba and Illela

Table 4. Cross-border ties and nodes

BNI network GaMaKa network

Cross-border ties 31.3% 18.3%Nodes with cross-border ties 43.4% 31.6%In Gaya (Niger) – 40.0%In Malanville (Benin) – 34.5%In Kamba (Nigeria) – 20.0%In Birni N’Konni (Niger) 56.0% –In Illela (Nigeria) 32.1% –

Source: author’s calculation.

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(Table 4). This element suggests macro dependence of Niger towards Nigeria, as traders located inNiger are obliged to develop transnational business interactions.

These results show that social relationships in trade can vary dramatically in terms of structure,which speak to the importance of using social approaches as a complement to other methods ofunderstanding cross-border trade.

5. Discussion

A formal approach to social networks can contribute to a better understanding of cross-border trade inmany ways. At the individual level, it can highlight how traders are related to each other, which tradersare prominent and which are not, what make some traders different from others, and, if temporal dataare available, how the centrality of traders has changed over time. Social network analysis is alsoparticularly well adapted to study the overall architecture of a network (its topology), which has directeffects on individual behaviour. The two case studies discussed above have made clear that cross-border trade was organised in a rather decentralised way. This particularity is in line with previousethnographic studies, which noted that transnational transit networks that import manufactured goodsinto Nigeria were less populated and less hierarchised than local networks because they didn’t rely onlarge physical and human infrastructures (Grégoire & Labazée, 1993). Compared to highly centralisedstructures, decentralised ones are less efficient, because information and resources flow less easilybetween the actors. They are, however, well adapted to the constant variations in the volume anddirection of business activities due to the seasonal and cyclical food shortages that affect West Africa.Depending on the season or on the availability of supplies, economic flows can abruptly decline orchange directions, which make the establishment of a network of business partners strategicallylocated on both sides of national borders indispensable.

A formal approach to networks can also help understand the spatiality of cross-border trade, that is,how social actors are spatially connected and the impact of national borders. Our results show thatthere is a correspondence between the type of trade (domestic or cross-border) and the structure of thenetwork that underlies it. This is congruent with the existing ethnographic literature, which showedthat four types of networks could be observed in West Africa depending on their geography (Grégoire,2003): (1) the mesh network typical of short distance trade powered by ecological complementarities,the landlocked situation of many Sahelian countries, market size differences, disparities in economicand monetary policy, and currency disparities, which corresponds to the one studied in this article; (2)the star network typical of certain ethnic groups whose trade is centralised in a regional centre orcapital city; (3) the string network typical of pre-colonial trans-Saharan trade, which has beenmodernised to include smuggling of cigarettes, drugs, and migrants; (4) the bipolar network whichcharacterise long-distance relationships between Africa and the Persian Gulf.

Mapping the exact topology of these networks could also contribute to support the recent revival ofinterest in cross-border trade in the policy field. In recent years, numerous international financialinstitutions, regional bodies, and aid organisations have aimed to promote informal trade activities,now regarded as ‘the most efficient, organised and deep-rooted system of trade in the region’ (AfricanDevelopment Bank, 2012a, p. 9). The commercial skills of traders are widely seen today as a crucialinstrument for alleviating poverty due to income earnings and employment opportunities for poorhouseholds and the ability of trade networks to allocate supplies during food crises (CILSS, 2014).This revival is supported by numerous initiatives that aim at improving both soft and hard infra-structures (African Development Bank, 2012b; McLinden, Fanta, Widdowson, & Doyle, 2010;OECD, 2009; World Bank, 2013). Instead of capturing mobile investment and redirecting it towardscertain regions, growth-enhancing policies aim at improving the qualifications of the local workforce,supporting existing entrepreneurs, and creating the conditions for the development of new economicactivities. A crucial aspect of such policies is the improvement of the body of rules and regulationsgoverning regional trade, which are currently seen as an obstacle to regional integration. The objectiveof ECOWAS’s Regulatory Informal Trade Programme, for example, is to incorporate current business

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practices that circumvent the state into the formal economy, by simplifying administrative, tax andcustom procedures, and encouraging traders to register their businesses. Other programmes developedby UEMOA, USAID and ECOWAS aim at eliminating corruption and illegal payments at border postsand along trade routes (USAID, 2014), or establish joint border posts, such as the one inaugurated in2014 between Gaya and Malanville, to accelerate border clearance. Complementary to growth policies,network-enhancing policies currently implemented in West Africa aim at improving both the internalconnectivity of economic actors at the local level, and their external connectivity with the rest of theworld. The extension and rehabilitation of road, rail and maritime infrastructure is a crucial aspect ofsuch policies. The Trans-African Highway and the African Development Bank’s Aid for Trade TrustFund, for example, plan to develop the roads connecting the major urban centres of the continent and,hence, reduce transport costs.

A formal approach to trade networks could contribute to both growth-enhancing and network-enhancing policies. It could first help identify the strong ties between economic actors within theregion and the relatively weaker ties between these local actors and the rest of the world. As our studyshows, the most successful traders are those who can achieve a high level of internal and external ties,whereas those deprived of both dimensions of social capital are disadvantaged. Development initia-tives would have a higher impact on trade if they enhance the positive aspects of intracommunity tiesin business communities, which are synonymous with solidarity and protection against uncertainty,while simultaneously supporting the creation of external ties between traders and governments, non-governmental organisations, and aid agencies.

Combined with qualitative approaches that provide information on the historical and social natureof the ties, formal approaches to trade could also contribute to the development of trade policies thattake into account the local economic, social, political and institutional conditions in which eco-nomic actors are embedded. As our article has shown, despite the proximity to the border, regionalintegration occurs either through a multitude of cross-border ties, or through a limited number ofkey brokers depending on the historical roots of the networks. Because of the local variety in thestructure of trade networks, development policies that aim at facilitating cross-border trade wouldbe more successful if they are place-specific rather than spatially blind. Unlike spatially blindpolicies that are applied without explicit consideration to space and encourage migration to largeand densely populated regions, place-based policies acknowledge that the local actors and institu-tions shape the development potential of cross-border trade. They appear crucial for the localdevelopment of traders and border markets, for which the existence of national borders constitutesa resource.

6. Conclusion

So far, studies devoted to cross-border trade have rarely – if ever – used formal approaches torepresent and analyse social networks in West Africa, relying instead on econometric methods thatconsider the price of commodities as a proxy for evaluating regional integration, or on qualitativeapproaches that aim at understanding the professional history of traders as well as their sociability. Ourarticle argues that the models describing the organisational structure of social networks developed bysocial network analysis over the last decades also apply to West African trade networks.

Our first contribution is to study how trade networks vary in terms of centralisation. We find thatboth networks are rather decentralised, with few business relations between the actors. In a businessenvironment where transaction costs are extremely high, decentralised networks are well adapted tothe various uncertainties induced by long-distance trade. As in other regions of the world, combiningembeddedness and brokerage, the former referring to the inclusion of social actors within their groupand the latter referring to the ties that actors build beyond their group, is crucial for the success ofcross-border trade.

Our second contribution is to show that the effect of national borders varies greatly across regions.Between the markets of Birni N’Konni and Illela, large traders have developed numerous cross-border

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links. This contrasts with the situation between the markets of Gaya, Malanville and Kamba, where,despite the proximity to national borders, trade mainly relies on few cross-border ties that provideopportunity for a limited number of brokers. The article shows that the spatial form of trade networksis constrained by the historical origin of the traders engaged in cross-border activities. In those marketswhere trade has followed the recent liberalisation of commerce and where most of the traders are fromoutside the region, borders exert a greater influence than where trade has developed since pre-colonialtimes.

Our third contribution is to show that future research would benefit from going beyond the currentgap between ethnographic and network-based approaches to trade in West Africa. Qualitativeapproaches are well adapted to capturing the complexity of social ties, the meanings that social actorsgive to their relations, the historical formation of networks, and their institutional context, but theywould be ideally complemented with a more formal approach. This appears especially productivewhen a great number of actors is involved or when many of them are hidden, as is often the case inborder regions. Cutting across sectors and professional status, social network analysis can complementthe ethnographic studies and provide a comprehensive picture of cross-border trade by addressing thestructure and agency simultaneously.

Acknowledgements

This research was supported by the National Research Fund of Luxembourg (CROSSTRADE ProjectC10/LM/783313 and WANETS Project MOBILITY/12/4753257). An earlier version of this articlewas presented at the African Studies Association Annual Meeting in Baltimore in November 2013 andat the World Bank Workshop on Cross-Border Trade in Washington, DC in March 2014. The authorthanks Emmanuel Grégoire, Leena Hoffmann, Al Howard, Moustapha Koné, and Bill Miles for theircomments. Data can be obtained from the author on request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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