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

Research output: Contribution to journalArticle

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The drivers of regulatory networking : Policy learning between homophily and convergence. / Vantaggiato, Francesa Pia.

In: Journal Of Public Policy, Vol. 39, No. 3, 09.2019, p. 443-464.

Research output: Contribution to journalArticle

Harvard

Vantaggiato, FP 2019, 'The drivers of regulatory networking: Policy learning between homophily and convergence', Journal Of Public Policy, vol. 39, no. 3, pp. 443-464. https://doi.org/10.1017/S0143814X18000156

APA

Vantaggiato, F. P. (2019). The drivers of regulatory networking: Policy learning between homophily and convergence. Journal Of Public Policy, 39(3), 443-464. https://doi.org/10.1017/S0143814X18000156

Vancouver

Vantaggiato FP. The drivers of regulatory networking: Policy learning between homophily and convergence. Journal Of Public Policy. 2019 Sep;39(3):443-464. https://doi.org/10.1017/S0143814X18000156

Author

Vantaggiato, Francesa Pia. / The drivers of regulatory networking : Policy learning between homophily and convergence. In: Journal Of Public Policy. 2019 ; Vol. 39, No. 3. pp. 443-464.

Bibtex Download

@article{43d3290b48134186b9af00a0cf071b98,
title = "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.",
keywords = "learning, regulatory networks, Energy, SNA",
author = "Vantaggiato, {Francesa Pia}",
year = "2019",
month = sep,
doi = "10.1017/S0143814X18000156",
language = "English",
volume = "39",
pages = "443--464",
journal = "Journal Of Public Policy",
issn = "0143-814X",
publisher = "Cambridge University Press",
number = "3",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - The drivers of regulatory networking

T2 - Policy learning between homophily and convergence

AU - Vantaggiato, Francesa Pia

PY - 2019/9

Y1 - 2019/9

N2 - 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.

AB - 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.

KW - learning

KW - regulatory networks

KW - Energy

KW - SNA

UR - http://www.scopus.com/inward/record.url?scp=85048769086&partnerID=8YFLogxK

U2 - 10.1017/S0143814X18000156

DO - 10.1017/S0143814X18000156

M3 - Article

AN - SCOPUS:85048769086

VL - 39

SP - 443

EP - 464

JO - Journal Of Public Policy

JF - Journal Of Public Policy

SN - 0143-814X

IS - 3

ER -

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