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on Network Economics |
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Issue of 2018‒01‒29
three papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Julie Beugnot (Université Bourgogne Franche-Comté, CRESE); Bernard Fortin (Université Laval, CRREP, CIRANO); Guy Lacroix (Université Laval, CRREP, CIRANO); Marie-Claire Villeval (Université de Lyon, CNRS, GATE, IZA) |
| Abstract: | We investigate whether peer effects at work differ by gender and whether the gender difference in peer effects –if any- depends on work organization, precisely the structure of social networks. We develop a social network model with gender heterogeneity that we test by means of a real effort laboratory experiment. We compare sequential networks in which information on peers flows exclusively downward (from peers to the worker) and simultaneous networks where it disseminates bi-directionally along an undirected line (from peers to the worker and from the worker to peers). We identify strong gender differences in peer effects, as males’ effort increases with peers’ performance in both types of network, whereas females behave conditionally. While they are influenced by peers in sequential networks, females disregard their peers’ performance when information flows in both directions. We reject that the difference between networks is driven by having one’s performance observed by others or by the presence of peers in the same session in simultaneous networks. We interpret the gender difference in terms of perception of a higher competitiveness of the environment in simultaneous than in sequential networks because of the bi-directional flow of information. |
| Keywords: | Gender, peer effects, social networks, work effort, experiment |
| JEL: | C91 J16 J24 J31 M52 |
| Date: | 2017–03 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2017-03&r=net |
| By: | Timo Gschwind (Johannes Gutenberg-Universität Mainz, Germany); Stefan Irnich (Johannes Gutenberg-University Mainz, Germany); Fabio Furini (LAMSADE Université Paris Dauphine, France); Roberto Wolfler Calvo (LIPN Université Paris, France) |
| Abstract: | In social network analysis (SNA), relationships between members of a network are encoded in an undirected graph where vertices represent the members of the network and edges indicate the existence of a relationship. One important task in SNA is community detection, that is, clustering the members into communities such that relatively few edges are in the cutsets, but relatively many are internal edges. The clustering is intended to reveal hidden or reproduce known features of the network, while the structure of communities is arbitrary. We propose decomposing a graph into the minimum number of relaxed cliques as a new method for community detection especially conceived for cases in which the internal structure of the community is important. Cliques, that is, subsets of vertices inducing complete subgraphs, can model perfectly cohesive communities, but often they are overly restrictive because many real communities form dense, but not complete subgraphs. Therefore, di erent variants of relaxed cliques have been defined in terms of vertex degree and distance, edge density, and connectivity. They allow to impose application-specific constraints a community has to fulfill such as familiarity and reachability among members and robustness of the communities. By discussing the results obtained for some very prominent social networks widely studied in the SNA literature we demonstrate the applicability of our approach. |
| Keywords: | Community detection, graph decomposition, clique relaxations, social network analysis |
| Date: | 2017–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:jgu:wpaper:1722&r=net |
| By: | Bennett, Magdalena (Columbia University); Bergman, Peter (Columbia University) |
| Abstract: | Truancy correlates with many risky behaviors and adverse outcomes. We use detailed administrative data on by-class absences to construct social networks based on students who miss class together. We simulate these networks and use permutation tests to show that certain students systematically coordinate their absences. Leveraging a parent-information intervention on student absences, we find spillover effects from treated students onto peers in their network. We show that an optimal-targeting algorithm that incorporates machine-learning techniques to identify heterogeneous effects, as well as the direct effects and spillover effects, could further improve the efficacy and cost-effectiveness of the intervention subject to a budget constraint. |
| Keywords: | social networks, peer effects, education |
| JEL: | I21 D85 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp11267&r=net |