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Policy brokers and policy entrepreneurs are assumed to have a decisive impact on policy outcomes. Their access to social and political resources is contingent on their influence on other agents. In social network analysis (SNA), entrepreneurs are often closely associated with brokers, because both are agents presumed to benefit from bridging structural holes; for example, gaining advantage through occupying a strategic position in relational space. Our aim here is twofold. First, to conceptually and operationally differentiate policy brokers from policy entrepreneurs premised on assumptions in the policy-process literature; and second, via SNA, to use the output of core algorithms in a cross-sectional analysis of political brokerage and political entrepreneurship. We attempt to simplify the use of graph algebra in answering questions relevant to policy analysis by placing each algorithm within its theoretical context. In the methodology employed, we first identify actors and graph their relations of influence within a specific policy event; then we select the most central actors; and compare their rank in a series of statistics that capture different aspects of their network advantage. We examine betweenness centrality, positive and negative Bonacich power, Burt's effective size and constraint and honest brokerage as paradigmatic. We employ two case studies to demonstrate the advantages and limitations of each algorithm for differentiating between brokers and entrepreneurs: one on Swiss climate policy and one on EU competition and transport policy. Adapted from the source document.

Policy brokers and policy entrepreneurs are assumed to have a decisive impact on policy outcomes. Their access to social and political resources is contingent on their influence on other agents. In social network analysis (SNA), entrepreneurs are often closely associated with brokers, because both are agents presumed to benefit from bridging structural holes; for example, gaining advantage through occupying a strategic position in relational space. Our aim here is twofold. First, to conceptually and operationally differentiate policy brokers from policy entrepreneurs premised on assumptions in the policy-process literature; and second, via SNA, to use the output of core algorithms in a cross-sectional analysis of political brokerage and political entrepreneurship. We attempt to simplify the use of graph algebra in answering questions relevant to policy analysis by placing each algorithm within its theoretical context. In the methodology employed, we first identify actors and graph their relations of influence within a specific policy event; then we select the most central actors; and compare their rank in a series of statistics that capture different aspects of their network advantage. We examine betweenness centrality, positive and negative Bonacich power, Burt's effective size and constraint and honest brokerage as paradigmatic. We employ two case studies to demonstrate the advantages and limitations of each algorithm for differentiating between brokers and entrepreneurs: one on Swiss climate policy and one on EU competition and transport policy.

AbstractMost trade and macroeconomic models imply positive impacts on the economy from greater openness. And a key factor linking openness and progress is the efficiency with which resources are used. Empirically, however, the efficiency impacts of trade have been ambiguous. Using a stochastic frontier analysis, we examine the impact of openness on technical (in)efficiency for a sample of OECD economies. Unlike the bulk of related studies, we work at the industry level. Given recent debates, we additionally examine whether Information and Communication Technologies (ICT) expenditures impact openness and efficiency. We establish the elasticity of openness with respect to (in)efficiency; TFP and Scale Economies; and Technical Inefficiency across countries and sectors. Both openness and ICT usage have robustly positive impacts on efficiency. Our results shed light on the impact of, spillovers between, and heterogeneity across countries and industries from, increasing openness interacted with the use of advanced technologies.

This article deviates from the current practice of regional convergence by allowing output convergence to follow a non-linear process. In this scenario all standard linear unit root tests have low power, thus frequently leading to misguided conclusions. In light of this we adopt a unit root test based on a non-linear model which tests the null hypothesis of a unit root against a non-linear alternative. Our findings overwhelmingly support the tendency of US regions to converge over time.

AbstractThis paper provides an examination of inter-organizational collaboration in the UK research system. Data are collected on organizational collaboration on projects funded by four key UK research councils: Arts and Humanities Research Council, Economic and Social Research Council, Engineering and Physical Sciences Research Council, and Biotechnology and Biological Sciences Research Council. The organizational partnerships include both academic and nonacademic institutions. A collaboration network is created for each research council, and an exponential random graph model is applied to inform on the mechanisms underpinning collaborative tie formation on research council-funded projects. We find that in the sciences, collaborative patterns are much more hierarchical and concentrated in a small handful of actors compared to the social sciences and humanities projects. Institutions that are members of the elite Russell Group (a set of 24 high-ranking UK universities) are much more likely to be involved in collaborations across research councils.

AbstractCentralities are a widely studied phenomenon in network science. In policy networks, central actors are of interest because they are assumed to control information flows, to link opposing coalitions and to directly impact decision-making. First, we study what type of actor (e.g., state authorities or interest groups) is able to occupy central positions in the highly institutionalized context of policy networks. Second, we then ask whether bonding or bridging centralities prove to be more stable over time. Third, we investigate how these types of centrality influence actors' positions in a network over time. We therefore adopt a longitudinal perspective and run exponential random graph models, including lagged central network positions at t1 as the main independent variable for actors' activity and popularity at t2. Results confirm that very few actors are able to maintain central positions over time.