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on Network Economics |
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Issue of 2018‒07‒16
four papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Müller, Karsten (University of Warwick); Schwarz, Carlo (University of Warwick) |
| Abstract: | This paper investigates the link between social media and hate crime using Facebook data. We study the case of Germany, where the recently emerged right-wing party Alternative fur Deutschland (AfD) has developed a major social media presence. We show that right-wing anti-refugee sentiment on Facebook predicts violent crimes against refugees in otherwise similar municipalities with higher social media usage. To further establish causality, we exploit exogenous variation in major internet and Facebook outages, which fully undo the correlation between social media and hate crime. We further find that the effect decreases with distracting news events; increases with user network interactions; and does not hold for posts unrelated to refugees. Our results suggest that social media can act as a propagation mechanism between online hate speech and real-life violent crime.Keywords: social media, hate crime, minorities, Germany, AfD JEL Classification: D74, J15, Z10, D72, O35, N32, N34. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:373&r=net |
| By: | Bezin, Emeline; Verdier, Thierry; Zenou, Yves |
| Abstract: | We develop a two-period overlapping generations model in which both the structure of the family and the decision to commit crime are endogenous and a culture of honesty is transmitted intergenerationally by families and peers. Having a father at home might be crucial to prevent susceptible boys from becoming criminals, as this facilitates the transmission of the honesty trait against criminal behavior. By "destroying" biparental families and putting fathers in prison, we show that more intense crime repression can backfire because it increases the possibility that criminals' sons become criminals themselves. Consistent with sociological disorganization theories of crime, the model also explains the emergence and persistence of urban ghettos characterized by a large proportion of broken families and high crime rates. This is because for children who come from these broken families, negative community experiences (peer effects) further encourage their criminal participation. Finally, we discuss the efficiency of location and family policies on long-term crime rates. |
| Keywords: | crime; neighborhood segregation; Social interactions |
| JEL: | J15 K42 Z13 |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13014&r=net |
| By: | Durand, Rodolphe; Mani, Dalhia |
| Abstract: | In this paper, we investigate family firms’ position in the intercorporate ownership network. Rooting our predictions in the Behavioral Agency Model and a Network analytical framework, we predict and find that family involvement decreases the likelihood of business group affiliation and of cross-group ties leading to a lower embeddedness within the overall network. We predict and find the opposite effect for community involvement. We use the complete longitudinal dataset of publicly listed firms’ corporate ownership ties in India (2001, 2005, and 2009). Theoretical and substantive contributions are to research on family businesses and to research on interorganizational networks. |
| Keywords: | Family Firms; Community; Embeddedness; Network |
| JEL: | M10 |
| Date: | 2018–04–18 |
| URL: | http://d.repec.org/n?u=RePEc:ebg:heccah:1275&r=net |
| By: | Tiziano Squartini; Guido Caldarelli; Giulio Cimini; Andrea Gabrielli; Diego Garlaschelli |
| Abstract: | When studying social, economic and biological systems, one has often access to only limited information about the structure of the underlying networks. An example of paramount importance is provided by financial systems: information on the interconnections between financial institutions is privacy-protected, dramatically reducing the possibility of correctly estimating crucial systemic properties such as the resilience to the propagation of shocks. The need to compensate for the scarcity of data, while optimally employing the available information, has led to the birth of a research field known as network reconstruction. Since the latter has benefited from the contribution of researchers working in disciplines as different as mathematics, physics and economics, the results achieved so far are still scattered across heterogeneous publications. Most importantly, a systematic comparison of the network reconstruction methods proposed up to now is currently missing. This review aims at providing a unifying framework to present all these studies, mainly focusing on their application to economic and financial networks. |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1806.06941&r=net |