This paper provides the first systematic analysis of migration to, within, and from Libya. The data used in the analysis are from the Displacement Tracking Matrix data set of the International Organization for Migration. The analysis uses this unique source of data, combining several techniques to analyze various dimensions of migration in Libya. First, the paper provides a detailed description of the demographic characteristics and national composition of the migrant populations in Libya. Next, it discusses the determinants of migration flow within Libya. The findings show that migration in Libya can be characterized as forced migration, because conflict intensity is the main determinant of the decision to relocate across provinces. Finally, the paper describes the direction, composition, and evolution of international migration flows passing through Libya and identifies the mechanisms of location selection by migrants within Libya by identifying hotspots and cluster provinces.
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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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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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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.
This paper presents a methodology to draw causal inference in a non-experimental setting subject to network interference. Specifically, we develop a generalized propensity score-based estimator that allows us to estimate both direct and spillover effects of a continuous treatment, which spreads through a network with weighted and directed edges. 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 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.