Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach

Johnsson, Ida; Moon, Hyungsik Roger

doi:10.2139/ssrn.3043404

Cited by 23 publications

(34 citation statements)

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“…Recent extensions of this approach include van Kippersluis (2018), Hsieh, Lee, and, Auerbach (2019), Arduini, Patacchini, and Rainone (2015), Qu and Lee (2015), and Johnsson and Moon (2019) who develop econometric frameworks based on control functions, following ideas proposed by Blume et al (2015). These studies are naturally connected to the literature on the econometrics of network formation (see Chandrasekhar, 2016;Graham, 2015;de Paula, 2017), and researchers are leveraging progress made in that literature, as in Graham (2017), to help account for the endogeneity of the network in peer effect regression.…”

Section: Structural Endogeneitymentioning

confidence: 99%

Peer Effects in Networks: A Survey

2020

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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.

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Section: Structural Endogeneitymentioning

confidence: 99%

Peer Effects in Networks: A Survey

2020

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“…Recent extensions of this approach include Hsieh and van Kippersluis (2018), Hsieh, Lee, and Boucher (2019), Auerbach (2019), Arduini, Patacchini, and Rainone (2015), Qu and Lee (2015), and Johnsson and Moon (2019) who develop econometric frameworks based on control functions, following ideas proposed by Blume et al (2015). These studies are naturally connected to the literature on the econometrics of network formation (see Chandrasekhar, 2016;Graham, 2015;de Paula, 2017), and researchers are leveraging progress made in that literature, as in Graham (2017), to help account for the endogeneity of the network in peer effect regression.…”

Section: Structural Endogeneitymentioning

confidence: 99%

Peer Effects in Networks: A Survey

2020

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The purpose of the Working Papers is to disseminate the results of research conducted by CIRANO research members in order to solicit exchanges and comments. These reports are written in the style of scientific publications. The ideas and opinions expressed in these documents are solely those of the authors. Les cahiers de la série scientifique visent à rendre accessibles les résultats des recherches effectuées par des chercheurs membres du CIRANO afin de susciter échanges et commentaires. Ces cahiers sont rédigés dans le style des publications scientifiques et n'engagent que leurs auteurs.CIRANO is a private non-profit organization incorporated under the Quebec Companies Act. Its infrastructure and research activities are funded through fees paid by member organizations, an infrastructure grant from the government of Quebec, and grants and research mandates obtained by its research teams.Le CIRANO est un organisme sans but lucratif constitué en vertu de la Loi des compagnies du Québec. Le financement de son infrastructure et de ses activités de recherche provient des cotisations de ses organisations-membres, d'une subvention d'infrastructure du gouvernement du Québec, de même que des subventions et mandats obtenus par ses équipes de recherche. CIRANO Partners -Les partenaires du CIRANOCorporate Partners -Partenaires corporatifs Autorité des marchés financiers Bank of

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“…This latter question was a key driver of the peer effects literature which emerged after Manski (1993). Developing methods for simultaneously modeling the formation and consequences of social and economic networks remains an important open area (Auerbach, 2016;Johnsson & Moon, 2017). Finally, although more and more empirical work with a network dimensions appears each year, application of the methods outlined above in substantive empirical work is a high priority.…”

Section: The Bright and Happy Future Of Network Econometricsmentioning

confidence: 99%

“…Although, Goldsmith-Pinkham & Imbens (2013) provide one recent "proof of possibilities" example of such an integrated approach. Qu & Lee (2015), Auerbach (2016), Badev (2017), and Johnsson & Moon (2017) represent other steps in this direction.…”

Section: Introductionmentioning

confidence: 99%

Network Data

Graham¹

2019

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Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. Input sourcing by firms, interbank lending, scientific research, and job search are four examples, among many, of networked economic activities. Motivated by the premise that networks' structures are consequential, this chapter describes econometric methods for analyzing them. I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network parameters (e.g., the degree distribution or transitivity index); and (iii) empirical models of strategic network formation admitting interdependencies in preferences. Current research challenges and open questions are also discussed.

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Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach

Cited by 23 publications

References 4 publications

Peer Effects in Networks: A Survey

Peer Effects in Networks: A Survey

Peer Effects in Networks: A Survey

Network Data

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