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Boundary Spanning Through Engagement of Policy Actors in Multiple Issues
Author(s) -
Brandenberger Laurence,
Ingold Karin,
Fischer Manuel,
Schläpfer Isabelle,
Leifeld Philip
Publication year - 2022
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/psj.12404
Subject(s) - homophily , popularity , alliance , interdependence , multitude , politics , exponential random graph models , political science , set (abstract data type) , policy learning , public relations , sociology , computer science , graph , social science , random graph , theoretical computer science , machine learning , programming language , law
Prominent current policy problems such as climate change, migration, or the financial crisis embrace a multitude of issues that are tackled within single‐ or multiple‐policy subsystems. However, interdependencies among actors that arise due to their multi‐issue engagement are often discounted when studying policy processes, including learning dynamics and alliance or trust formation among actors engaged in multiple issues. Various issues compete for actors’ attention, and actors need to choose an appropriate set of issues to deal with given their scarce resources. In this, why do actors engage in multiple issues? We present an innovative inductive approach that identifies policy issues related to Swiss water politics and actors involved therein. We use a two‐mode exponential random graph model to estimate actors’ multi‐issue activity. Results show that 39% of actors engage in more than one water‐related issue and that cross‐subsystem and homophily clustering and clustered issue popularity drive this issue engagement.