Peer Effects in Networks: A Survey

Abstract

We survey the recent, fast-growing literature on peer effects in networks. An important recurring theme is that the causal identification of peer effects depends on the structure of the network itself. In the absence of correlated effects, the reflection problem is generally solved by network interactions even in nonlinear, heterogeneous models. By contrast, microfoundations are generally not identified. We discuss and assess the various approaches developed by economists to account for correlated effects and network endogeneity in particular. We classify these approaches in four broad categories: random peers, random shocks, structural endogeneity, and panel data. We review an emerging literature relaxing the assumption that the network is perfectly known. Throughout, we provide a critical reading of the existing literature and identify important gaps and directions for future research. Expected final online publication date for the Annual Review of Economics, Volume 12 is August 3, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Externality of Negative Emotion

“…Despite widespread public interest in understanding emotional contagion, the existing empirical knowledge is limited. Identifying the causal effect of emotional contagion has proved to be di cult because of identi cation issues such as self-selection, common shocks, and re ection 15,69 . Additionally, obtaining information on every individual in a speci c social network is usually not feasible because of data limitations.…”

“…If there is spillover, the bene ts of helping an individual with negative emotions could largely outweigh the cost, and undertaking interdependent interventions could be much more effective than independent individual-intervention strategies 5 .Despite widespread public interest in understanding emotional contagion, the existing empirical knowledge is limited. Identifying the causal effect of emotional contagion has proved to be di cult because of identi cation issues such as self-selection, common shocks, and re ection 15,69 . Additionally, obtaining information on every individual in a speci c social network is usually not feasible because of data limitations.…”

Peer effects in consumption: a spatial demand system estimation using data from Brazil

“…Many studies have demonstrated how network interactions can be used to overcome the correlated effects and reflection problems. The identification of peer effects has been greatly benefited from the literature on network interactions that is fast-growing with a number of recent surveys on the topic (Bramoullé et al, 2020;Advani and Malde, 2018;Boucher and Fortin, 2016;De Paula, 2017;Blume et al, 2015). Bramoullé et al (2020) points out that the following four studies independently understood that the reflection problem is naturally solved by network interactions: Bramoullé et al (2009); De Giorgi et al (2010); Lin (2010); Laschever (2005).…”

“…The identification of peer effects has been greatly benefited from the literature on network interactions that is fast-growing with a number of recent surveys on the topic (Bramoullé et al, 2020;Advani and Malde, 2018;Boucher and Fortin, 2016;De Paula, 2017;Blume et al, 2015). Bramoullé et al (2020) points out that the following four studies independently understood that the reflection problem is naturally solved by network interactions: Bramoullé et al (2009); De Giorgi et al (2010); Lin (2010); Laschever (2005). Bramoullé et al (2009) demonstrates that endogenous and contextual effects are identified through intransitive triads, that is, when peers of peers are not peers.…”

“…To address the correlated problem and identify peer effects, the strategies developed by the literature can be classified in the broad categories (Bramoullé et al, 2020): random peers, random shocks, and panel data. Using econometric terminology, the strategies may use experimental data, instrumental variable estimation methods, and fixed effects control.…”