Multiplex networks and interest group influence reputation: An exponential random graph model

Abstract

Interest groups struggle to build reputations as influential actors in the policy process and to discern the influence exercised by others. This study conceptualizes influence reputation as a relational variable that varies locally throughout a network. Drawing upon interviews with 168 interest group representatives in the United States health policy domain, this research examines the effects of multiplex networks of communication, coalitions, and issues on influence reputation. Using an exponential random graph model (ERGM), the analysis demonstrates that multiple roles of confidant, collaborator, and issue advocate affect how group representatives understand the influence of those with whom they are tied, after accounting for homophily among interest groups.

studying policy advocacy through social network analysis

“…Advocacy success can then represent the dependent variable of a study on IGs advocacy activities in policy-making processes. This advocacy success variable is compatible (even if not identical) with dependent variables studied previously by Box-Steffensmeier et al (2013) and Heaney (2014), who measured and explained (perceived) policy influence. Furthermore, this 'preferred outcome' variable goes one step further than the dependent variable used by Beyers and Braun (2013), who basically capture 'venue access' of IGs as precondition for any policy influence (Eising, 2007).…”

“…In contrast to Heaney (2014) who is predominantly interested in tie collaborate with other well-connected IGs and, who frequently co-sign amicus curiae briefs have a greater effect on the probability that a justice vote in their favor. In other words, the network position of an IG is more important, for influencing judicial decisions, than the number of briefs it signs and the number of IGs filling these briefs.…”

“…A study by Heaney (2014) argues that the embeddedness of IGs in multiplex networks pays off in terms of their influence reputation. On the basis of document analysis and personal interviews, the author operationalises influence reputation and various network relations of IGs within the US health policy domain.…”

“…For instance, Box-Steffensmeier et al (2013) focus on one institutional venue only. By contrast, Heaney (2014) compares different venues involved during US health policy formulation. However, he neither systematically compares the relative influence of IGs across venues, nor does he aggregate the policy impacts of IGs through their network position within the three networks (communication, venue and issue overlap).…”

“…And transitivity is the tendency of two actors to be connected if they share a mutual neighbor or partner in the network (see Wasserman and Faust, 1994). 6 One may additionally note that IGs coalition participation can also be asked through interviews such as presented by Heaney (2014) or Heaney and Lorenz (2013). configurations and relationships.…”