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on Network Economics |
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Issue of 2018‒01‒22
three papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Paige Ouimet; Geoffrey Tate |
| Abstract: | We use unique data on employee decisions in the employee stock purchase plans (ESPPs) of U.S. public firms to measure the influence of networks on investment decisions. Comparing only employees within a firm during the same election window and controlling for a metro area fixed effect, we find that the local choices of coworkers to participate in the firm’s ESPP exert a significant influence on employees’ own decisions to participate. Local coworkers’ trading patterns also disseminate to colleagues through the network. In the cross-section, we find that some employees (men, younger workers) are particularly susceptible to peer influence. Generally, we find that more similar employees exert greater influence on each other’s decisions and, particularly, that high (low) information employees are most affected by other high (low) information employees. However, we also find that the presence of high information employees magnifies the effects of peer networks. We trace a value-increasing investment choice through employee networks. Thus, our analysis suggests the potential of networks and targeted investor education to improve financial decision-making. |
| JEL: | D14 G02 G11 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24058&r=net |
| By: | Koen Jochmans (Institute for Fiscal Studies and Sciences Po); Martin Weidner (Institute for Fiscal Studies and cemmap and UCL) |
| Abstract: | This paper studies inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two-way regression model, which is a workhorse method in the analysis of matched data sets. Networks are typically quite sparse and it is difficult to see how the data carry information about certain parameters. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the structure of the network. These bounds depend on the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and on the degree structure of the network. The Laplacian is a matrix that describes the network and its smallest non-zero eigenvalue is a measure of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent estimation and convergence rates, and allow to evaluate the accuracy of first-order approximations to the variance of the fixed-effect estimator. The bounds are also used to assess the bias and variance of estimators of moments of the fixed effects. Supplement for CWP26/17 |
| Keywords: | fixed effects, graph, Laplacian, network data, two-way regression model, |
| JEL: | C23 |
| Date: | 2017–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:26/17&r=net |
| By: | Ramina Sotoudeh; Dalton Conley; Kathleen Mullan Harris |
| Abstract: | We introduce a novel use of genetic data for studying social influences on behavior: Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we deploy the distribution of genotypes in a given grade within a school to instrument the influence of peer smoking on an individual’s own smoking behavior. We argue that this design alleviates many problems inherent to estimating peer effects. Using this approach, we find the relationship between peer smoking and individual smoking to be larger than that estimated by prior studies. Further, we explore the reduced form relationship between peer genotypes and ego smoking and find that the impact of peers’ genetic risk for smoking on ego’s smoking behavior is at least half as large as the effect of individual’s own genotype and sex, and 30% the effect of age. Moreover, peer influence on smoking appears heterogeneous by race: although whites and non-whites are equally susceptible to peer influence with respect to smoking, white egos are more likely to be influenced by white alters. This analysis suggests a promising way that genetic information can be leveraged to identify peer effects that avoids the reflection problem, contextual effects and selection into peer groups. |
| JEL: | D79 I12 I20 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24113&r=net |