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 constitute a useful tool to reduce diffusion processes both for policy makers and for corporate managers in the organization of production.
Using individual-level longitudinal data and geo-localized information on conflict-related violent events, we study the impact of conflict on health in the Gaza Strip. Results show that individuals living in localities exposed to more conflict events have a higher probability of suffering from a physical impairment and a chronic disease. The effect is larger for men and older individuals. Two mechanisms contribute to explain why living in conflict-affected area increases the incidence of physical impairment: conflict increases the difficulty to reach health facilities and it decreases individual income. The conflict-induced increase in the probability of having high blood pressure is instead consistent with the development of Post-Traumatic Stress Disorder (PTSD) due to the exposure to conflict-related violent events.
The R package econet provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and Patacchini (2018), which examine the determinants of US campaign contributions when legislators care about the behavior of other legislators to whom they are socially connected.
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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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.
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.
In this paper, we study the extent to which social connections influence the legislative effectiveness of members of the U.S. Congress. We propose a simple model of legislative effectiveness that formalizes the role of social connections and generates simple testable predictions. The model predicts that a legislator’s equilibrium effectiveness is proportional to a specific weighted Katz-Bonacich centrality in the network of social connections, where the weights depend on the legislators’ characteristics. We then propose a new empirical strategy to test the theoretical predictions using the network of cosponsorship links in the 109th-113th Congresses. The strategy addresses network endogeneity by implementing a two-step Heckman correction based on an original instrument: the legislators’ alumni connections. We find that, in the absence of a correction, all measures of centrality in the cosponsorship network are significant. When we control for network endogeneity, however, only the measure suggested by the model remains significant, and the fit of the estimation is improved. We also study the influence of legislators’ characteristics on the size of network effects. In doing so, we provide new insights into how social connectedness interacts with factors such as seniority, partisanship and legislative leadership in determining legislators’ effectiveness.
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This article presents the design and implementation of a network intervention to foster scientific collaboration at a research university, and describes an experimental framework for rigorous evaluation of the intervention’s impact. Based on social network analysis of publication and grant data, an innovative type of research funding program was developed as a form of alteration of the university’s collaboration network. The intervention consisted in identifying research communities in the network and creating a new collaborative relation between pairs of unconnected researchers in selected communities. The new collaboration was created to maximally increase the overall cohesion of the target research community. In order to evaluate the impact of the program, we designed a randomized experiment with treatment and control communities based on the Rubin Causal Model approach. The paper describes the intervention design, reports findings from the program implementation, and discusses the statistical framework for future evaluation of the intervention.
This paper delves into the determinants of global migration flows by applying a social network methodology. In order to explicitly address the multidimensional aspects of this phenomenon, I employ data on bilateral flows between 169 countries from 1990 through 2010 in combination with information about language and colonial history of each population, and bilateral distance between countries. With respect to traditional approaches, the underlying mechanism characterizing the international migration network (IMN) is described using a data driven approach which takes into account both bilateral and multilateral resistances to migration. To enhance the understanding of the relationships occurring between countries, I analyze contextual effects at different granularity levels and use graph theory to explore the topological properties of the IMN. Overall, complementing existing literature, the results suggest that the IMN is characterized by a strongly persistent hierarchical architecture regardless the spatial granularity of the data. Few countries, both at the local and at the global level, control the whole connectivity of the network, with a prevalence of star-like structures. In terms of network analysis, this means that human international migration patterns can be described by a modular structure displaying high geographical clustering, where the preferential attachment mechanism is driven by cultural homophily (i.e. same language or colonial past) and economic disassortativity (e.g. migration, conditional on distance, take place between countries which differ in terms of GDP). The persistence of this topological structure over time is consistent with a well-established finding in literature, that is the presence of a self-reinforcing process on the intensive margin of the IMN. In conclusion, this paper demonstrates that the level of embeddedness of one country in the IMN is a powerful source of information to improve and further unfold the dynamics underlying migratory processes and its multilateral resistances. With this respect, SNA provides an appropriate theoretical framework of analysis that can be used to extend this study in many innovative ways.
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