Network centrality, social loops, and utility maximization
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This study examines the effect of social loops on individual effort. Under a situation wherein agents’ effort induces additional efforts to others through a network, an optimum effort corresponds to the subgraph centrality that captures the number of social loops in a network. In contrast to Bonacich centrality, this centrality avoids a problem of divergence and it is based on bounded rational behaviors. We tested this theoretical fact using data for 122 Japanese university sophomores and juniors from 2013 to 2018. Bonacich cenrality had a divergence problem in all cases. On the other hand, students who had a higher subgraph centrality index tended to put forth more efforts in a class after controlling for the individual characteristics. Subgraph centrality had a robust effect even if we added an effect of other centralities, i.e., out-degree, in-degree, betweenness, and closeness centralities. This fact shows that the social loop of communication was critical in the class of university students.
KeywordsSubgraph centrality Micro-foundation of centralities Peer influence University student network
JEL ClassificationD85 I20 D01
The author thanks Ryuhei Tsuji for advice about a questionnaire, Kayo Fujimoto for advice about estimation methods and Naoko Matsuda for her helpful comments. I also thank the editor and two anonymous referees for helpful comments.
Compliance with ethical standards
Conflict of interest
As for Conflict of Interest, the author declares that I have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of my institution.
Informed consent was obtained from all individual participants included in the study.
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