Instrumental variables estimates of peer effects in social networks

An, Weihua

doi:10.1016/j.ssresearch.2014.08.011

Cited by 26 publications

(26 citation statements)

References 51 publications

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“…The first is the use of an IV estimator in a 2SLS modelling framework, which helps address a host of issues in estimating peer effects, particularly omitted variable bias (An, 2015). Second, the IV has been employed in a flexible 2SLS setup, which permitted the statistical adjustment for other characteristics that may directly affect educational expectations, or modify susceptibility to peer influence (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Zimmerman, 2003). However, both are often practically unfeasible or potentially unethical, and may lack external validity (An, 2015).…”

Section: Estimating Peer Influencementioning

confidence: 99%

“…The last of these can be especially useful in identifying endogenous peer effects, and their use has been advocated in many social science disciplines including economics (Davidson & MacKinnon, 1993) and only more recently in developmental psychology (Foster, 2010). Despite their advantages, relatively few studies have used IV estimation to estimate peer effects (An, 2015).…”

Section: Estimating Peer Influencementioning

confidence: 99%

“…Picou & Carter, 1976). Moreover, though there are concerns about quality of self-reports (Alwin, 2007), respondents are generally accurate when asked to report on characteristics of those with whom they are close (Kogovsek & Ferligoj, 2005).…”

Section: Main Explanatory Variablementioning

confidence: 99%

“…Why might the difference be significant when the estimates are derived from OLS models, but not the 2SLS models? The reason for is this disagreement is IV estimation identifies the local average treatment effects, as opposed to the average treatment effects (Angrist & Pischke, 2009, 2015.…”

Section: Gender As a Moderatormentioning

confidence: 99%

See 4 more Smart Citations

Friends’ plans for college and adolescents’ educational expectations: an instrumental variable approach

Carolan

2017

International Journal of Adolescence and Youth

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Friends are an important influence on adolescents' educational outcomes. However, because of their similarity on numerous attributes, it is difficult to estimate whether friends influence each other, or whether they select friends who are similar to them. To address this endogeneity, this study uses instrumental variable estimation on panel data from the High School Longitudinal Study of 2009 to estimate the effect of friends' plans for college on adolescents' educational expectations. Results from two-stage least squares models indicate that friends' plans for college has a significant effect on educational expectations. This effect does not significantly vary by gender. Results suggest the need for policies that focus on ways to diversify adolescent peer networks in order to provide greater access to resource-rich peers.

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Section: Resultsmentioning

confidence: 99%

“…Zimmerman, 2003). However, both are often practically unfeasible or potentially unethical, and may lack external validity (An, 2015).…”

Section: Estimating Peer Influencementioning

confidence: 99%

Section: Estimating Peer Influencementioning

confidence: 99%

Section: Main Explanatory Variablementioning

confidence: 99%

Section: Gender As a Moderatormentioning

confidence: 99%

See 3 more Smart Citations

Friends’ plans for college and adolescents’ educational expectations: an instrumental variable approach

Carolan

2017

International Journal of Adolescence and Youth

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The Diffusion of Smoking: Association Between School Tobacco Policies and the Diffusion of Adolescent Smoking in 38 Schools in 6 Countries

et al. 2023

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Statistical methods for the estimation of contagion effects in human disease and health networks

2020

Computational and Structural Biotechnology Journal

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Contagion effects, sometimes referred to as spillover or influence effects, have long been central to the study of human disease and health networks. Accurate estimation and identification of contagion effects are important in terms of understanding the spread of human disease and health behavior, and they also have various implications for designing effective public health interventions. However, many challenges remain in estimating contagion effects and it is often unclear when it is difficult to correctly estimate contagion effects, or why a particular method would need to be applied. In this review I explain the challenges in estimating contagion effects, and how they can be framed as an omitted variable bias problem. I then discuss how such challenges have been addressed in randomized experiments and traditional statistical analyses, as well as several state-of-the-art statistical methods. Finally, I conclude by summarizing recent advancements and noting remaining challenges, as well as appropriate next steps.

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Instrumental variables estimates of peer effects in social networks

Cited by 26 publications

References 51 publications

Friends’ plans for college and adolescents’ educational expectations: an instrumental variable approach

Friends’ plans for college and adolescents’ educational expectations: an instrumental variable approach

The Diffusion of Smoking: Association Between School Tobacco Policies and the Diffusion of Adolescent Smoking in 38 Schools in 6 Countries

Statistical methods for the estimation of contagion effects in human disease and health networks

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