The drivers of regulatory networking: policy learning between homophily and convergence

Abstract

The literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word "network" only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU.

Who Interacts with Whom? Drivers of Networked Welfare Governance in Europe

“…However, this 'technocratic' understanding of transgovernmental networks may run into limits in the case of politicized policy areas. Our thesis is that institutional differences and political cleavages will be important drivers of networked governance, in line with recent scholarly work by Vantaggiato (2018) and Efrat and Newman (2018), among others. Interactions are found to be influenced by domestic factors (Bach and Newman 2014), strategic action (Danielsen and Yesilkagit 2014;Ruffing 2015), political cleavages (Beyers and Kerremans 2004), and capacity (Beyers and Donas 2014).…”

“…We develop exponential random graph models (ERGMs) to test our hypotheses regarding the driving forces of network interaction (see Handcock et al 2008). The underlying assumption of ERGMs is that networks self-organize through continuing processes of forming ties over time, influenced by both attributes of the actors involved as well as network dependency structures (Robins et al 2012;Schrama 2018;Vantaggiato 2018). Simply put, network ties depend on one another by definition, as one tie influences the likelihood of the existence of another tie.…”

“…Interactions are found to be influenced by domestic factors (Bach and Newman 2014), strategic action (Danielsen and Yesilkagit 2014;Ruffing 2015), political cleavages (Beyers and Kerremans 2004), and capacity (Beyers and Donas 2014). Furthermore, interactions are more likely among members that are perceived as similar or more influential (Efrat and Newman 2018;Vantaggiato 2018). Therefore, we assume that network members are strategic actors that choose with whom to interact based on domestic considerations.…”

“…Yet given the highly politicized nature of the policy area, this expectation can be questioned. This insight first flows from the work by Vantaggiato (2018) and Efrat and Newman (2018), who argue that domestic political and institutional factors may condition the interactions within a transgovernmental network. Similarly, Beyers and Kerremans (2004) have pointed out that political cleavages help structure interactions within networks, and Bach and Newman (2014) note that political factors count when explaining who interacts with whom in network governance.…”

“…It investigates the extent to which domestic factors related to social security drive member state interactions within the AC. To this end, we present three hypotheses on what explains network interactions, which follow the recent call for more fine-grained analysis of institutional and political factors as structuring premises for network interaction (Efrat and Newman 2018;Ruhs and Palme 2018;Vantaggiato 2018). Our first hypothesis uses welfare state types to explain who interacts with whom.…”