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Peer Effects in Networks: A Survey

Author

Listed:
  • Yann Bramoullé

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Habiba Djebbari

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics)

  • Bernard Fortin

    (IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics, Département d’économique [Université Laval, Québec] = Department of Economics - ULaval - Université Laval [Québec], CRREP - Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques - ULaval - Université Laval [Québec], CIRANO - Centre interuniversitaire de recherche en analyse des organisations [Montréal, Canada] = Center for Interuniversity Research and Analysis on Organizations [Montréal, Canada])

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.

Suggested Citation

  • Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Post-Print hal-03072119, HAL.
    • Handle: RePEc:hal:journl:hal-03072119
    DOI: 10.1146/annurev-economics-020320-033926
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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