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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
    as

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    File URL: http://www.uq.edu.au/economics/abstract/639.pdf
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    References listed on IDEAS

    as
    1. Thomas Cornelissen & Christian Dustmann & Uta Schönberg, 2017. "Peer Effects in the Workplace," American Economic Review, American Economic Association, vol. 107(2), pages 425-456, February.
    2. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2015. "Linear Social Interactions Models," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 444-496.
    3. Laurent Davezies & Xavier D'Haultfoeuille & Denis Fougère, 2009. "Identification of peer effects using group size variation," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 397-413, November.
    4. Paul Goldsmith-Pinkham & Guido W. Imbens, 2013. "Social Networks and the Identification of Peer Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 253-264, July.
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    8. Alexandre Mas & Enrico Moretti, 2009. "Peers at Work," American Economic Review, American Economic Association, vol. 99(1), pages 112-145, March.
    9. Leila Agha & David Molitor, 2018. "The Local Influence of Pioneer Investigators on Technology Adoption: Evidence from New Cancer Drugs," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 29-44, March.
    10. Christiern D. Rose, 2017. "Identification of peer effects through social networks using variance restrictions," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 47-60, October.
    11. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    12. Eliana Barrenho & Eric Gautier & Marisa Miraldo & Carol Propper & Christiern Rose, 2020. "Innovation Diffusion and Physician Networks: Keyhole Surgery for Cancer in the English NHS," Discussion Papers Series 638, School of Economics, University of Queensland, Australia.
    13. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
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    15. Antoni Calvó-Armengol & Eleonora Patacchini & Yves Zenou, 2009. "Peer Effects and Social Networks in Education," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1239-1267.
    16. Giacomo De Giorgi & Michele Pellizzari & Silvia Redaelli, 2010. "Identification of Social Interactions through Partially Overlapping Peer Groups," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 241-275, April.
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    Keywords

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

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