M. Di Maio, V. Leone Sciabolazza (2021), Conflict exposure and health: Evidence from the Gaza Strip, Health Economics.

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.


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

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.

D. F. De Souza, M. Fontana, M. Iori, V. Leone Sciabolazza, Specialization vs Interdisciplinarity.

Using data on 26,926 focal articles published by 6,105 researchers affiliated to the University of Florida in the period 2008-2013, we evaluate the extent to which a variation in a paper interdisciplinarity affects the accruing of citations and the extramural influence of these researchers. We find that scholars’ performance and reach depends on the dimension of interdisciplinarity being explored. Consistent with this, we identify a trade-off between the accumulation of citations and the dispersion of citations across fields of study. We conclude that the costs of interdisciplinary research are important enough to negatively impact researchers academic careers, but the public benefits arising from knowledge diffusion across domains are substantive and cannot be dismissed.

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.

V. Leone Sciabolazza, L. Riccetti (2021), Diffusion Delay Centrality: decelerating diffusion processes across networks [R&R Journal Industrial and Corporate Change]

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 represent a useful tool to reduce diffusion processes both for policy makers and for corporate managers in the organization of production.

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

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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