Network Economics
http://lists.repec.orgmailman/listinfo/nep-net
Network Economics
2017-04-09
Gender and Peer Effects in Social Networks
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01481999&r=net
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 realeffort 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.
Julie Beugnot
Bernard Fortin
Guy Lacroix
Marie-Claire Villeval
Gender, peer effects, social networks, work effort, experiment
2017-03-01
An empirical model of dyadic link formation in a network with unobserved heterogeneity
http://d.repec.org/n?u=RePEc:hhs:gunwpe:0698&r=net
In this paper I study a fixed effects model of dyadic link formation for directed networks. I discuss inference on structural parameters as well as a test of model specification. In the model, an agent's linking decisions depend on perceived similarity to potential linking partners (homophily). Agents are endowed with potentially unobserved characteristics that govern their ability to establish links (productivity) and to receive links (popularity). Heterogeneity in productivity and popularity is a structural driver of degree heterogeneity. The unobserved heterogeneity is captured by a fixed effects approach. This allows for arbitrary correlation between an observed homophily component and latent sources of degree heterogeneity.The linking model accounts for link reciprocity by allowing linking decisions within each pair of agents to be correlated. Estimates of structural parameters related to homophily preferences and reciprocity can be obtained by ML but inference is non-standard due to the incidental parameter problem (Neyman and Scott 1948). I study t-statistics constructed from ML estimates via a naive plug-in approach. For these statistics it is not appropriate to compute critical values from a standard normal distribution because of the incidental parameter problem. I suggest modified t-statistics that are justified by an asymptotic approximation that sends the number of agents to infinity. For a t-test based on the modified statistics, critical values can be computed from a standard normal distribution. My model specification test compares observed transitivity to the transitivity predicted by the dyadic linking model. The test statistic corrects for incidental parameter bias that is due to ML estimation of the null model. The implementation of my procedures is illustrated by an application to favor networks in Indian villages.
Dzemski, Andreas
Network formation; fixed effects; incidental parameter problem; transitive structure; favor networks
2017-03
Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity
http://d.repec.org/n?u=RePEc:ehl:lserod:70769&r=net
Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently being undertaken on the feasibility of identifying and measuring systemic risk. In principle, there are two main schemes to measure interlinkages between financial institutions. One might wish to construct a mathematical model of financial market participant relations as a network/graph by using a combination of information extracted from financial statements like the market value of liabilities of counterparties, or an econometric model to estimate those relations based on financial series. In this paper, we develop a data-driven econometric framework that promotes an understanding of the relationship between financial institutions using a nonlinearly modified Granger-causality network. Unlike existing literature, it is not focused on a linear pairwise estimation. The method allows for nonlinearity and has predictive power over future economic activity through a time-varying network of relationships. Moreover, it can quantify the interlinkages between financial institutions. We also show how the model improve the measurement of systemic risk and explain the link between Granger-causality network and generalized variance decompositions network. We apply the method to the monthly returns of U.S. financial Institutions including banks, broker and insurance companies to identify the level of systemic risk in the financial sector and the contribution of each financial institution.
Jalal Etesami
Ali Habibnia
Negar Kiyavash
Systemic risk; Risk Measurement; Financial Linkages and Contagion; Nonlinear Granger Causality; Directed Information Graphs
2017-03