Brokering between (not so) overt and (not so) covert networks in conflict zones

Abstract

2020) Brokering between (not so) overt and (not so) covert networks in conflict zones, Global Crime, 21:1, 74-110, ABSTRACTThere is a tendency to consider covert networks as separate from overt networks. Drawing on data from the Democratic Republic of the Congo, we demonstrate that this is not the case and identify how covert and overt networks are mutually constitutive. While most studies of African brokers have relied on network metaphors like 'Big Men' and 'social membranes', we consider the embeddedness of 'covert' networks in 'overt' networks explicitly. We perform two analyses on a large original dataset encompassing 396 partially overlapping ego-nets obtained from a hybrid link-tracing design. An ego-net analysis reveals a large degree of homophily and a deep embeddedness of the different networks. A multilevel exponential random graph model fitted to the reconstructed network of a 110-node subset shows that demobilised combatants are the actors likely to broker between armed groups, state forces, and civilian blocs, suggesting their capacity to broker peace or foment war.

Leveraging the Peacebuilding Potential of Cross-border Trader Networks in Sub-Saharan Africa

“…For example, state and non-state armed groups operating in the border regions of DR Congo, Uganda, and Rwanda, including the recently U.S. designated terrorist organization, the Allied Democratic Forces (ADF) have a long history of establishing deals with cross-border traders and local businesspeople. 22 Quintessential to the resilience of armed groups in the region is their successful integration into the cross-border economy both in terms of taxing schemes and the illicit trafficking of natural resources such as gold, timber, and most recently cocoa across borders. 23 New research finds that there is a strong correlation between the illicit trade of cocoa and spikes in violence by ADF and other armed groups.…”

Multilevel determinants of collaboration between organised criminal groups

“…The multilevel network perspective has brought useful insights to the study of social systems such as legitimate markets and organisations (Brass and Greve, 2004;Rank, 2017, 2016;Glückler and Doreian, 2016;Hollway and Koskinen, 2016;Meredith et al, 2017;Paruchuri et al, 2019;Zappa and Robins, 2016). It can also contribute to the study of illicit markets, but has only recently been applied for covert networks (Stys et al, 2020). Criminal network ties are determined not only by individual attributes and interpersonal factors, but also by group membership; interdependencies among groups; the geographic distribution of criminal activity; and interactions among these factors (Descormiers and Morselli, 2011;Papachristos et al, 2013Papachristos et al, , 2012.…”

“…estimation process. We thus follow the procedure of Stys et al (2020), where change-statistics associated with cross-level effects are coded as dyadic-covariates. Similar to the case in that study, affiliations (there to armed groups) can be said to be antecedents to individuals' ties due to the different time-scales on which these operate.…”

“…Similar to the case in that study, affiliations (there to armed groups) can be said to be antecedents to individuals' ties due to the different time-scales on which these operate. We also apply a Bayesian inference scheme implemented as in Koskinen et al (2019) and Stys et al (2020). This relies on the approximate exchange algorithm also used in Caimo and Friel (2011).…”

“…interpersonal ties. Consequently, as indicated above, multilevel dependencies are treated using dyadic covariates and are fixed in the model estimation (Robins and Daraganova, 2013;Stys et al, 2020). Table 6 shows the local structural effects that are relevant to testing our hypotheses, represented as configurations (Moreno and Jennings, 1938).…”