The aim of this paper is to contribute to the understanding of the structural evolution of scientific collaboration networks. A large body of literature has focused on the structure and evolution of co-authorship networks, typically examining networks within a specific discipline, but spanning different academic organizations. By contrast, this paper narrows its focus to a single academic organization (the University of Florida), but expands the network boundary in two ways: including collaborations among scientists in many different disciplines; and examining three dimensions or layers of scientific collaboration, namely, co-authorship on peer-reviewed scientific articles, co-participation in awarded grants, and co-membership in PhD/Master committees. As a result, collecting data from a five-year time window (2011-2015), we obtain a multiplex longitudinal network including three layers (publications, grants, committees). The geometric intricacies of this network are analyzed by looking at the evolution of its global and local properties, in order to shed light on its stochastic formation process, and on the role played by single investigators. First, we study the network community structure of each layer, and the extent to which community membership is explained by factors such as disciplinary affiliation and workplace location. Results show that intra-department relations are as important as inter-department relations for community formation in the three layers, with department affiliations predicting approximately 50% of the community structure over time. However, we find a high rate of heterogeneity in network communities: publication communities predict respectively 45% and 30% of community memberships in the grant and committee layer. This finding suggests that each dimension of collaboration only partially influences the other, and different mechanisms may drive connectivity in different layers. Second, we test the topological weaknesses of the layers to assess the role of single scholars in connecting different areas of the network. We find that co-authorship and committee network structures are somewhat similar: they appear to gradually converge toward a power-law degree distribution, with a network architecture sustained by interlinked “stars”, which for the co-authorship network is consistent with a small-world model. On the contrary, the grant network shows a core-periphery structure. By testing different breakdown scenarios, we conclude that only the committee layer presents a highly resilient architecture, while network connectivity in the other two layers is strongly dependent on the presence of few hub investigators. This finding has significant implications for academic research policy, suggesting that academic research networks would benefit from a system of incentives for highly-connected scholars to i) remain in the university maintaining an efficient network of collaborations; and ii) increase the involvement of their collaborators in research projects, in order to reduce the dependency of the overall network from their own work. A number of inferential tests and heuristic methodologies are implemented to assess the robustness of our findings
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In the 1970s Thomas C. Schelling presented a series of models where the heuristic choice of individuals about staying or moving out from a neighborhood would generate a segregated city notwithstanding individual agents’ preferences over residential integration. The mild micro preference about majority of its own group generates macro segregation.
We propose an extension of the Schelling model that allows individuals to be connected by social links and we highlight the contrast between Schelling original heuristic and the role played by social connections in characterizing the long-term dynamics of the model.
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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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.
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Purpose – Islamic banking is a viable sustainable banking model that has shown resilience to financial crises. The aim of this research is to design a consensus-based ethical and market-driven corporate governance index (CGI) to boost financial performance and ensure compliance with Islamic rulings.
Design/methodology/approach – The design of the CGI is the outcome of the feedback obtained from a cross-country survey to measure bank efforts in enhancing corporate governance (CG) throughout the ten-year period of 2001-2011. The CGI is divided into six core CG themes and 40 sub-themes.
Findings – First, the results of the multiple regression analysis show a consistent positive relationship between CG and financial performance metrics. Second, the authors detect misaligned compensation structures for directors. Third, poor governance leads to higher risk exposures.
Research limitations/implications – CG in Islamic banks is yet an evolving discipline and infant practice. This research aims to introduce a CGI that should be updated and improved as the discipline evolves.
Practical implications – The research concludes by proposing a CG paradigm. The outcome of the research could also be of use to both Islamic banks and to the rapidly growing sustainable banking sector in designing a similar CGI and CG model incorporating the ethical features of sustainable finance.
Social implications – The core ethos of Islam are: avoiding the exploitation of the needy, avoiding excessively risky transactions, avoiding unethical transactions and justice, equity and income redistribution. If properly applied, Islamic banking will display all features of sustainable finance as well as enhance social welfare.
Originality/value – To the best of the authors’ knowledge, this is the first CGI that is based on an ethical and all-inclusive input of all stakeholders.
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Access to knowledge is crucial for the development of new and innovative ideas. In the last 20 years, an increasing number of scholars have explored the role of scientific collaborations in connecting a diverse pool of knowledge and in reducing the cost of access to information, as well as their effect on scientific productivity. Along this line of research, in this paper we explore the importance of intellectual interactions among the scientists at the University of Florida, the state’s flagship university, as channeled by their network of publication co-authorship between 2008 and 2014 (approximately 5,000 nodes and 20,000 edges). Assessing the significance and the magnitude of the effect of interactions on academic productivity contributes to the literature on social interactions and scientific networks, and provides policy implications encouraging academic collaborations. An inherent selection effect arises in the formation of co-authorships. We use data on spatial and social proximity of investigators to account for this endogeneity problem. After controlling for endogenous co-authorship formation, unobservable heterogeneity, and time varying factors, we find a positive impact of intellectual collaboration on individual performance, measured in terms of success rate for grant funding.
Extensive work has been done to assess the role played by externalities in policy interventions. Studies dealing with this issue in experimental settings abound in the literature. Much less attention, however, has been devoted to the case when data comes from observational studies. In this paper, we address this gap by presenting a methodology to draw causal inference in a non-experimental setting. Specifically, we develop a generalized propensity score-based estimator that allows estimating both direct and spillover effects under continuous treatment interference. In order to showcase this methodology, we investigate whether and how spillover effects shape the optimal level of producers’ support in agricultural markets. Our results show that, in this context, neglecting interference may lead to a downward bias when assessing policy effectiveness.
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The international fragmentation of production processes is dramatically deepening the structural interdependence of the world economy. Recent literature has shown that global value chains are modifying countries’ incentives to impose import protection. However the complex structure of their connections entails the existence of specific direct and indirect effects that affect the price domestic suppliers receive. The aim of this paper is to show that final goods tariffs tend to decrease in the domestic content of foreign-produced final goods but at a different pace when distinguishing the direct partner country from third countries. To get the two separate contributions, we decompose the Leontief inverse matrix into its direct and indirect connections and recompute the domestic and foreign valued added content embodied in final goods. Our results show that both direct and indirect flows play a crucial role in shaping trade policy.
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This paper contributes to the debate on the design of policies that are effective in promoting interdisciplinary research and adequate for evaluating their impact. To this purpose, we present a novel approach to the problem of facilitating team science, by using an experimental framework based on the notion of social network intervention. Specifically, by using social network analysis of publication and grant data at the University of Florida, we designed and implemented an innovative type of pilot funding program conceived as a form of intervention on the University’s collaboration network. The intervention, conceived as a randomized experiment, broadly consisted in identifying research communities in the University’s collaboration network, and maximally increasing the cohesion of selected communities by adding collaborative links with specific structural properties in their subnetwork. This article describes the intervention design, reports findings from the program implementation, and presents a framework for rigorous future evaluation of the intervention.
A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.
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