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Identification of Peer Effects using Panel Data

Author

Listed:
  • Marisa Miraldo

    (Imperial College Business School,)

  • Carol Propper

    (Imperial College Business School)

  • Christiern Rose

    (School of Economics, University of Queensland, Brisbane, Australia)

Abstract

This paper provides new identification results for panel data models with contextual and endogenous peer effects. Contextual effects operate through individuals’ time-invariant unobserved heterogeneity. Identification hinges on a conditional mean restriction requiring exogenous mobility of individuals between groups over time. Some networks governing peer interactions preclude identification. For these cases we propose additional conditional variance restrictions. We conduct a Monte-Carlo experiment to evaluate the performance of our method and apply it to surgeon-hospital-year data to study take-up of minimally invasive surgery. A standard deviation increase in the average time-invariant unobserved heterogeneity of other surgeons in the same hospital leads to a 0.12 standard deviation increase in take-up. The effect is equally due to endogenous and contextual effects.

Suggested Citation

  • Marisa Miraldo & Carol Propper & Christiern Rose, 2020. "Identification of Peer Effects using Panel Data," Discussion Papers Series 639, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uq2004:639
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    References listed on IDEAS

    as
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    Keywords

    Peer effects; panel data; networks; identification; innovation; healthcare;
    All these keywords.

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