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.
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.
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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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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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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