The drivers of regulatory networking
Policy Learning Between Homophily and Convergence
Dados Bibliográficos
| ID | 6296199 |
|---|---|
| Autores | Francesca Pia Vantaggiato (0000-0003-1154-4540, University of East Anglia, autor correspondente) |
| Ano | 2019 |
| Volume | 39 |
| Fascículo | 3 |
| Páginas | 443-464 |
| Data de publicação | 2019-09-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Journal of Public Policy (JOURNAL) |
| Identificadores do periódico | ISSN: 0143-814X • E-ISSN: 1469-7815 |
| Editora | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0143814x18000156 |
| OpenAlex | W2809302048 |
| Idioma | EN |
| Citações recebidas | 17 |
| Referências citadas | 63 |
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
Agency (philosophy · Convergence (economics · Economic growth · Economics · Exponential random graph models · Graph · Homophily · Political science · Public relations · Random graph · Social science · Sociology · Computer Science · Policy Transfer and Learning · Political Influence and Corporate Strategies · Regulation and Compliance Studies
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| Obras citantes distintas | 17 |
|---|---|
| Citações por ano | 2,83 |
| Intervalo de citações | 2020 - 2026 (7) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 16 |