We study the extent to which personal connections among legislators influence abstentions in the U.S. Congress. Our analysis is conducted by observing representatives’ abstention for the universe of roll call votes held on bills in the 109th-113th Congresses. Our results show that a legislator’s propensity to abstain increases when the majority of his or her alumni connections abstains, even after controlling for other well-known predictors of abstention choices and a vast set of fixed effects. We further reveal that a legislator is more prone to abstain than to take sides when the demands from personal connections conflict with those of the legislator’s party.
Over the last decades, scientific collaboration has been widely considered an important driver of research innovation. By collaborating, scientists can benefit by both methodological and technological complementarities and synergy, improving the quality and quantity of their research output. As evidence, collaboration among scientists is increasing in all disciplines and government policies in international exchange programs aimed at promoting the collaborative network among researchers. Collaboration among scientists can be represented as a network, usually adopting co-authorship as linkages. In this view, Social Network Analysis (SNA) provides a useful theoretical and methodological approach to the study of collaboration among scientists since collaboration features can be related to the topological characteristics of the network. Recently, several empirical studies found positive correlations between researchers’ position in the co-authorship network and their productivity although results can be different by the disciplines, scientific performance measures, and data sources retrieved to construct the co-authorship networks. In this contribution, we propose the use of SNA tools for scientific evaluation purposes. Network indices at individual and subgroup level will be introduced to analyze relation with both the individual research productivity and scientific output quality measured on bibliographic information provided by the individual academic researchers involved in the evaluation exercise VQR from the period 2011-2014.
This paper presents a new measure (the Diffusion Delay Centrality – DDC) to identify agents who should be put into isolation to decelerate a diffusion process spreading throughout a network. We show that DDC assigns a high rank to agents acting as the gatekeepers of the fringe of the network. We also show that the ranking of nodes obtained from the DDC is predicted by the difference in the values of betweenness and eigenvector centrality of network agents. The findings presented might represent a useful tool to reduce diffusion processes both for policy makers and for corporate managers in the organization of production.
Little is known about the bargaining process of the Council of the European Union (EU), because negotiations of member countries occur behind closed doors. Using a brand-new dataset, we analyze the factors leading a country to a successful negotiation over one of the most important decisions taken by the Council every year, that for the allocation of the European budget. Important predictors of a country’s bargaining success, proxied by the quota of EU budget received, are the extent to which its votes are pivotal to form a winning coalition in the Council, its seniority, the control over the Council presidency office, and the political orientation of its government on the EU integration process. We also provide new evidence that countries advancing a similar policy agenda may benefit from each other’s effort. Finally, we demonstrate that the reforms of the Council introduced after 2004 had no significant impact on the bargaining process and the balance of power among member countries.
In this paper, we propose a model of national innovation production that formalizes the role of trade partnerships as a channel of knowledge spillovers across countries. The model is used to investigate the energy efficiency technological domain in the European Union (EU) using a panel database covering 19 EU countries for the time span 1990-2015. The model is estimated by using a new empirical strategy which allow to assess the knowledge spillover effects benefiting a country depending on its relative position in the trade network, and correct for common endogeneity concerns. We show that being central in the trade network is a significant determinant of a country’s innovative performance, and that learning-by-exporting is responsible for positive knowledge spillovers across countries. We further reveal that neglecting network effects may significantly reduce our understanding of domestic innovation patterns. Finally, we find that the benefits obtained from knowledge diffusion varies with the domestic absorptive capacity and policy mix composition. Our main implication is that policy mix design informed by network-based case studies could help maximizing the exploitation of positive knowledge spillovers.
This paper generalizes the original Schelling (1969, 1971a,b, 2006) model of racial and residential segregation to a context of variable externalities due to social linkages. In a setting in which individuals’ utility function is a convex combination of a heuristic function à la Schelling, of the distance to friends, and of the cost of moving, the prediction of the original model gets attenuated: the segregation equilibria are not the unique solutions. While the cost of distance has a monotonic pro-status-quo effect, equivalent to that of models of migration and gravity models, if friends and neighbours are formed following independent processes the location of friends in space generates an externality that reinforces the initial configuration if the distance to friends is minimal, and if the degree of each agent is high. The effect on segregation equilibria crucially depends on the role played by network externalities.
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