D. De Stefano and L. Kronegger and V. Leone Sciabolazza and M. P. Vitale and S. Zaccarin (2021), Social Network tools for the evaluation of individual and group scientific performance, in D.Checchi, T. Jappelli, A.F. Uricchio (eds.), Teaching, Research and Academic Careers, forthcoming

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.

S. Barabuffi, V. Costantini, V. Leone Sciabolazza, E. Paglialunga (2021), Knowledge spillovers through skilled-workers migration network: evidence from OECD countries.

Mission-oriented innovation policies often contributes to the improvement of national production processes. These are ambitious and cross-disciplinary policies tackling clearly defined societal or technological challenges mainly applied through public R&D investment with a market-oriented purpose. The aim of this paper is to analyse the extent to which competitiveness improvement in the technological trajectory determined by such national policies is magnified by knowledge capital spilling from skilled migrants coming from other countries. Technological capabilities developed abroad to design and implement local processes of innovation might provide large benefits via positive externalities, where the capacity to gain from such spillover depends on the relative position of countries in the knowledge network.

In order to conduct our investigation, we propose a simple analytical framework of national innovation system, where the innovation performance of a country –proxied by the number of registered triadic patents–is determined by its investments in mission-oriented innovation policies, weighted by its position in the skilled migration network. The model is then tested using a panel database covering 20 OECD countries for the time span 1987-2016.

A concern with our analysis is that skilled migrants will self-select into destination countries where they will find better opportunities, e.g., where innovation performances are already significantly high. This endogeneity issue might hinder the identification of skilled migration effects, since it might be that higher performances will be mechanically correlated with a higher presence of migrants, but not determined by them. For this reason, we propose an empirical strategy based on a two-step Heckman correction which sorts out such endogeneity concerns and allows a causal interpretation of our results.

Findings from our analysis show that high skilled migration networks magnify the effect of mission-oriented innovation policies in improving national innovation performances, even when controlling for other common drivers of innovation, and time and country fixed effects. On the contrary, being central in middle or low skilled migration networks has no statistically significant effect on innovation production.

At the same time, we find that the role of migrants is heterogeneous across countries. Their contribution to innovation production is highest in host countries where public R&D investments are still relatively low. On the contrary, the extent to which migrants’ origin countries invest in mission-oriented policies does not exert any significant effect on their ability to contribute to innovation processes in the host country. This suggests that skilled migration is valuable to innovation regardless of its national composition, and it is most valuable when host countries are still on a catching-up path.

L. Forastiere, V. Leone Sciabolazza, C. Tortú (2020), Dyadic Treatment Effect on Network Formation using Multi-level Propensity Score Matching: Lobbying Activities and Legislative Collaborations.

Firms and corporate companies often work to sway a legislator agenda. For instance, they financially support the electoral campaign of political candidates running for a seat, trying to influence their political activity once elected. It is thus natural to expect that politicians funded by the same firms will collaborate in Congress to achieve the goals of their funders.

In this work, given the bipartite network of financial support from firms to politicians, we define a network of strong ties between congress members where a strong tie is present if the two politicians have a substantial number of common supporters. We then use this network of support ties and the network of collaborations to evaluate the effect for two elected politicians of being supported by common firms on their legislative collaboration.

To conduct our analysis, we develop an estimator for causal effects of the formation of links on a ‘treatment network’ on the formation of links on an ‘outcome network’, with both networks being directed. The estimator is based on an extension of the propensity score matching approach to handle multi-valued treatments, network data and conditional effects.

Using data from the US House of Representatives (111-113 Congress), our results show that sharing common supporters encourages collaborations among politicians.

M. Battaglini, Leone Sciabolazza V., Patacchini E. (2020), Effectiveness of connected legislators, American Journal of Political Science, 64(4), 739-756 [lead article] [Center for Effective Lawmaking Best Article Award]

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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V. Leone Sciabolazza, R. Vacca, C. McCarty (2020), Connecting the dots: A network intervention to foster scientific collaboration and productivity, Social Networks, 61, 181-195

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.


V. Leone Sciabolazza (2018), A Net of Moving People: Network Analysis of International Migration Flows, In A. Amighini, S. Gorgoni, M. Smith, Networks of International Trade and Investment, Vernon Press

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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V. Leone Sciabolazza, R. Vacca, T. Kennelly Okraku, C. McCarty (2017), Detecting and analyzing research communities in longitudinal scientific networks, Plos One, 12(8), e0182516

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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Abdel-Baki M., V. Leone Sciabolazza (2014), A consensus-based corporate governance paradigm for Islamic banks, Qualitative Research in Financial Markets, 6 (1), pp. 93-108

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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