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on Network Economics |
| By: | Ushchev, Philip (National Research University); Zenou, Yves (Monash Universitiy) |
| Abstract: | Although the linear-in-means model is the workhorse model in empirical work on peer effects, its theoretical properties are understudied. In this study, we develop a social-norm model that provides a micro foundation of the linear-in-means model and investigate its properties. We show that individual outcomes may increase, decrease, or vary non-monotonically with the taste for conformity. Equilibria are usually inefficient and, to restore the first best, the planner needs to subsidize (tax) agents whose neighbors make efforts above (below) the social norms. Thus, giving more subsidies to more central agents is not necessarily efficient. We also discuss the policy implications of our model in terms of education and crime. |
| Keywords: | Social norms; Conformism; Local-average model; Welfare; Anti-conformism; Network formation |
| JEL: | D85 J15 Z13 |
| Date: | 2019–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:1302&r=all |
| By: | Michael P. Leung |
| Abstract: | This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the use of spatial and network HAC variance estimators in applied work, the latter constructed by using network path distance in place of spatial distance. Toward this end, we prove new central limit theorems for network moments in a large class of social interactions models. The results are applicable to discrete games on networks and dynamic models where social interactions enter through lagged dependent variables. We illustrate our results in an empirical application and simulation study. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.07106&r=all |
| By: | Krishna Dasaratha |
| Abstract: | We study a model of innovation with a large number of firms that create new technologies by combining several discrete ideas. These ideas can be acquired by private investment or via social learning. Firms face a choice between secrecy, which protects existing intellectual property, and openness, which facilitates social learning. These decisions determine interaction rates between firms, and these interaction rates enter our model as link probabilities in a resulting learning network. Higher interaction rates impose both positive and negative externalities on other firms, as there is more learning but also more competition. We show that the equilibrium learning network is at a critical threshold between sparse and dense networks. A corollary is that at equilibrium, the positive externality from interaction dominates: the innovation rate and even average firm profits would be dramatically higher if the network were denser. So there are large returns to increasing interaction rates above the critical threshold---but equilibrium remains critical even after natural interventions. One policy solution is to introduce informational intermediaries, such as public innovators who do not have incentives to be secretive. These intermediaries can facilitate a high-innovation equilibrium by transmitting ideas from one private firm to another. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.06872&r=all |