De facto regulatory decision-making processes in telecommunications regulation: explaining influence relationships with exponential random graph models

Abstract

Research on regulation has focussed on explaining the independence of sector regulators and assessing the effects of regulations on markets. This article broadens the scope of such research by studying and explaining how regulatory actors interact at the de facto level in a multi-actor regulatory arrangement when making regulatory decisions in the telecommunications sector of Colombia. We propose that regulatory decisions depend on the manner in which actors influence each other. In this article, we are not only focussing on the policy outcome itself but also on the regulatory decision-making process. We performed a social network analysis and used an exponential random graph model to analyse the data. Our findings suggest that actors’ level of influence is affected by the access they have to other organisations, the divergence of positions they have with these other organisations and the power resources of an organisation. In addition, there are structural network characteristics that affect regulatory decisionmaking.

Transboundary crisis management under the volcano: The case of the Icelandic ash cloud

“…Firstly, its analytical focus is more concerned with relational ties than with characteristics of individual actors. Secondly, SNA captures interdependencies between decision‐making players at the formal and de facto levels (González & Verhoest, 2018). 7 These features are not trivial for the purpose of this paper, since crisis responses demand quick coordination and frequent overriding of formal regulations for the sake of rapid and effective reactions.…”

“…However, the high salience of this episode in an environment where these kinds of incidents are otherwise infrequent might have allowed the respondents to keep fresh memories of general features, such as the actors with whom they interacted throughout the crisis and their importance for the resolution of this incident. Self‐assessments may in turn introduce biases related to the subjectivity of the answers (Newman, 2010), such as an over‐ or underestimation of influence (González & Verhoest, 2018). Additionally, perceptions might vary according to the role of each respondent within their employing organization.…”

Inferential Analysis of Road Infrastructure PPP Sponsor Networks

“…In this way, from this perspective, relationship formation processes are driven by the firms' efforts towards establishing linkages with organizations capable of offering analogous or complementary skills. In this context, prior relationships between two actors have a clear influence on tie formation processes because the presence of repeated relationships strongly indicates high levels of trust and shared values (Goerzen 2007;González and Verhoest 2018).…”

“…These models have emerged as inferential statistical tools through which graph theory can be applied in order to examine interdependencies between individual observations and network behaviors (Robins et al 2007;Snijders and Borgatti 1999). In contrast to traditional regression models (e.g., logistical regressions), ERGMs can use relational data to analyze how relationships between any two actors may be explained by reasons associated with network characteristics beyond individual node attributes (González and Verhoest 2018). This allows ERGM scholars to study endogenous (i.e., network-specific properties) and exogenous (i.e., actor-and dyad-configurations) network features by considering interdependent relationships (Pattison and Robins 2002;Wasserman and Pattison 1996).…”

“…This creates a computational problem because, for any undirected network with N nodes, there are 2 ( 𝑁 2 ) possible arrangements (Geyer and Thompson 1992). Accordingly, researchers usually employ the Markov Chain Monte Carlo Maximum Likelihood (MCMCML) procedure as a method to calculate ERGM parameters (Cranmer et al 2012;González and Verhoest 2018;Khalilzadeh 2018;Williams and Hristov 2018).…”