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Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders


  • Nair, Harikesh S.

    (Stanford U)

  • Manchanda, Puneet

    (U of Chicago)

  • Bhatia, Tulikaa

    (Rutgers U)


We quantify the impact of social interactions and peer effects in the context of prescription choices by physicians. Using detailed individual-level prescription data, along with self-reported social network information, we document that physician prescription behavior is significantly influenced by the behavior of research-active specialists, or "opinion leaders" in the physician's reference group. We leverage a natural experiment in the category, whereby new guidelines released about the therapeutic nature of the focal drug generated conditions where physicians were more likely to be influenced by the behavior of specialist physicians in their network. We find important, statistically significant peer effects that are robust across model specifications. We use the estimates to measure the incremental value to firms of directing targeted sales-force activity to these opinion leaders, and present estimates of the social multiplier of detailing in this category.

Suggested Citation

  • Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:1970

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    References listed on IDEAS

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    Cited by:

    1. Bimpikis, Kostas & Ozdaglar, Asuman & Acemoglu, Daron, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    2. Reto Hoffstetter & Harikesh Nair & Scott Shriver & Klaus Miller, 2009. "Social Ties and User Generated Content: Evidence from an Online Social Network," Working Papers 09-28, NET Institute, revised Nov 2009.
    3. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
    4. Liu, Hong & Sun, Qi & Zhao, Zhong, 2014. "Social learning and health insurance enrollment: Evidence from China's New Cooperative Medical Scheme," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 84-102.
    5. Andrew Ching & Masakazu Ishihara, 2010. "The effects of detailing on prescribing decisions under quality uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 123-165, June.
    6. Anindya Ghose & Sang Pil Han, 2009. "A Dynamic Structural Model of User Learning in Mobile Media Content," Working Papers 09-24, NET Institute, revised Oct 2009.
    7. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    8. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
    9. Dae-Yong Ahn & Jason A. Duan & Carl F. Mela, 2011. "An Equilibrium Model of User Generated Content," Working Papers 11-13, NET Institute, revised Dec 2011.
    10. Epstein, Andrew J. & Nicholson, Sean, 2009. "The formation and evolution of physician treatment styles: An application to cesarean sections," Journal of Health Economics, Elsevier, vol. 28(6), pages 1126-1140, December.
    11. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    12. Itay P. Fainmesser & Andrea Galeotti, 2013. "The Value of Network Information," Working Papers 2013-13, Brown University, Department of Economics.
    13. Michael Haenlein, 2011. "A social network analysis of customer-level revenue distribution," Marketing Letters, Springer, vol. 22(1), pages 15-29, March.
    14. Paola Zappa, 2011. "The network structure of knowledge sharing among physicians," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1109-1126, August.
    15. Zsolt Katona, 2013. "Competing for Influencers in a Social Network," Working Papers 13-06, NET Institute.
    16. Ching, Andrew & Ishihara, Masakazu, 2007. "The Effects of Detailing on Prescribing Decisions under Two-Sided Learning," MPRA Paper 4935, University Library of Munich, Germany.

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