Transnationalism in West

Africa: A network approach

Olivier Walther, Ph.D.

Associate Professor, University of Southern Denmark

Visiting Assistant Professor, Rutgers University

olivier.walther@rutgers.edu

World Bank Trade Seminar, March 5, 2014

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Social network analysis in Africa?

• Social networks are frequently seen as a metaphor

for informal relations

• A marginal field of research compared with those of

look at trade through markets and prices

A credible alternative in a context of informal and

unrecorded circuits, hidden actors, and clientelist

ties

2

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Cross-Border Trade

and Border Markets

3

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Research questions and hypotheses

Social structure

• Are trade networks rather centralized or decentralized?

– Decentralized networks more adapted to the uncertainties of trade

• Are traders rather embedded in their group or brokers?

– Combine strong embeddedness within the group with brokerage ties

Spatial structure

• What is the influence of national borders on trade networks?

– The spatial form is constrained by the history of the trade diaspora

4

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Social structure

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Trade-off 1

Network level

Hierarchy

Heterarchy

Single central

actor (star)

Authority

flows from

the top

Fully connected

network

Nodes

determine their

own path

Trade-off 2

Actor level

Embeddedness Brokerage

High level of

clustering

Low level of

clustering

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The entrepreneur’s dilemma

6

• How to combine strong embeddedness within the group

with brokerage ties beyond the group?

Source: Walther 2012, based on Uzzi 1996

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Two case study between Nigeria, Niger and Benin

7 Source and cartography: Walther 2014

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A social network approach

8

• Data collected between Jan-Dec 2012 on five markets

• Identification of main products (cement, cereals, used clothes,

textiles) based on longitudinal data from customs authorities, pre-

existing surveys, and list of goods banned

• Identification of 43 large traders (> $200,000) with freight agents

• Snowballing technique 3 waves of interviews 114 nominees

• Response rates: 88% in GaMaKA and 89% in BNI

• Who is doing business with whom? At which frequency?

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Key metrics: two decentralized networks

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Birni N’Konni-Illela

(BNI) network

Gaya-Malanville-Kamba

(GaMaKa) network

Number of nodes 53 83

Number of links 64 104

Number of dyadic isolates 0 2

Density 0.046 0.031

Average number of ties 2.385 2.357

Clustering coefficient 0.094 0.062

Average path length distance 3.712 11.092

Average degree 0.046 0.026

Average betweenness 0.053 0.036

Average closeness 0.279 0.042

Degree centralization 0.172 0.185

Betweenness centralization 0.403 0.320

Closeness centralization 0.268 0.020

Source: Walther 2014. CROSSTRADE Project. Produced with *ORA (Carley 2012)

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Central actors: degree centrality in GaMaKa

10 Source: Walther 2014. CROSSTRADE Project. Produced with *ORA (Carley 2012)

Decentralized

network, with

few business

ties

resilience to

threats

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Homophily

11

• Main difference: the proportion of ties exchanged within

the GaMaKa group (87%) is larger than in BNI (67%)

• E/I Index is high and negative (-.727**) in GaMaKa

indicating a preference for homophilous ties

• E/I Index is neutral for BNI (.108) signaling that country

membership is not a relevant attribute

E/I index: difference between external ties and internal ties / total number of ties

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Brokers: betweenness centrality in BNI

12 Source: Walther 2014. CROSSTRADE Project. Produced with *ORA (Carley 2012)

Key brokers

bridge

nationally-

organized

markets in

border

regions

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Spatial structure of trade networks

13

• Networks that developed since pre-colonial times (BNI) have

more cross-border ties than recent networks, which rely on key

brokers (GaMaKa)

Source: Walther 2014

Source: Walther 2014

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Discussion

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(1) Redefine the space of action targeted by policies by

identifying relevant functional regions that potentially cut

across national boundaries

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Discussion

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Potential cross-border functional regions

Source: Walther 2014. Atlas of the Sahel-Sahara. OECD

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Discussion

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(2) Within functional areas, identify market places for support,

based on their roles in promoting exchange

Investment in border market facilities could promote both

trading and productive activities simultaneously

(3) Provide an alternative to the chain approach, which does

not adequately capture the nature of social networks

Traders can interact between hierarchical levels in order

to access information or resources

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Terrorism

and

Border Conflicts

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Terrorism in West Africa

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Source: Armed Conflict Location and Event Dataset (ACLED). Cartography: Retaillé, Walther, Pissoat. 2013.

Two spatial

strategies:

• AQIM:

Kabylia

Mali

neighboring

countries

• Boko

Haram:

diffusion from

Maiduguri

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Two flags

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National Movement for the Liberation of

Azawad (MNLA) Ansar al-Dine (“Defenders of the Faith”)

“We want unity for the sons of Azawad”,

MNLA Political Bureau, 2012

“All we want is the implementation of Sharia.

We are against independence”, July 2012

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Research questions

• Studies have focused on the historical development

of terrorist groups, geopolitical challenges and

counterterrorism measures

• SNA has not been applied to the terrorist networks

operating in this region

What are the relationships between Islamist and

rebels?

What are the internal relationships within each of the

subgroups?

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Methodology

• Selection of 82 articles published between July 2010 and

September 2012 by various French newspapers

• Keywords: ‘Al-Qaeda’, ‘AQIM’, ‘Mujao’, ‘MNLA’, ‘Ansar al-

Dine’, ‘Malian rebellion’, ‘Islamism’ and/or ‘terrorism’

• Names and surnames contained in this corpus: 42 actors

including 28 Islamists and 14 rebels

• Joint participation in a political meeting, training, combat,

hostage releases, or involvement with a killing, an abduction

or a bombing Who is connected with whom?

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Network degree centrality

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Source: Walther and Christopoulos 2014. Note: Terrorists in green, rebels in blue

Small number of

highly connected

actors who play a

coordination role

in a decentralized

structure

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Iyad Ag Ghaly, the broker

• Fighter in the Islamic Legion in the 1980s

• Rebel in the 1990-1996 Tuareg rebellion

• Negotiator, release of European hostages (2003, 2010)

• Consular councilor in Saudi Arabia (2010)

• Tried to take the lead of the MNLA (2011)

• Founded the Islamist group Ansar al-Dine (2012)

• Specially Designated Global Terrorist (2013)

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Iyad Ag Ghaly, the broker

• The key position of Ag Ghaly can be explained by the

fact that the network is highly homophilous - E/I Index: -0.735***

- Only 17% of the actors have ties to another group

- Only 9% of the ties are exchanged between sub-groups and half of

them go through Ag Ghaly

• This gives a prominent advantage to brokers who can

bridge the two sub-groups (information, resources,

knowledge)

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Understanding West African conflicts

(1) Social actors

– Circumstantial alliances between groups

– Group restructuring: GSPC AQMI MUJAO Signataires

Almoravides, Ansar Dine MIA HCUA

– Actors changing sides: Malian army MNLA AQMI/Ansar

Dine rebellion army

(2) Spatial strategies

– Highly mobile

– Cross-border movements

– Control of places rather than territories

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Understanding West African conflicts

(3) Networks vs. state territorial response

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Source: Retaillé, Pissoat, Drevet and Pierson. 2014. Atlas of the Sahara-Sahel

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Related papers…

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• Walther O. 2014. Trade networks in West Africa: A social network

approach. Journal of Modern African Studies 52(2).

• Walther O. 2014. Business, brokers and borders: The structure of West

African trade networks. Univ. of Texas Africa Conf. Paper, April 3-6.

• Walther O. 2012. Traders, agricultural entrepreneurs and the

development of cross-border regions in West Africa. Entrepreneurship

and Regional Development 25(3-4): 123-141.

• Walther O. 2009. Traders, patrons and the cross-border economy in

Sahelian Africa. Journal of Borderlands Studies 24(1): 34−46.

• Walther O, Christopoulos D. 2014. Islamic terrorism and the Malian

rebellion. Terrorism and Political Violence 26(2).

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Acknowledgments

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This research is supported by the National Research Fund

of Luxembourg (FNR CROSSTRADE Project

C10/LM/783313 and WANETS Project

MOBILITY/12/4753257)