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Herding in Quality Assessment: An Application to Organ Transplantation

Author

Listed:
  • Stephanie de Mel

    (Institute for Fiscal Studies)

  • Kaivan Munshi

    (Institute for Fiscal Studies and University of Cambridge)

  • Soenje Reiche

    (Institute for Fiscal Studies)

  • Hamid Sabourian

    (Institute for Fiscal Studies)

Abstract

There are many economic environments in which an object is offered sequentially to prospective buyers. It is often observed that once the object for sale is turned down by one or more agents, those that follow do the same. One explanation that has been proposed for this phenomenon, which goes back to Banerjee (1992) and Bikhchandani (1992) is that agents making choices further down the line rationally ignore their own assessment of the object's quality and herd behind their predecessors. We develop novel tests to detect information-based herding, based on heterogeneity in agent ability, together with a methodology to quantify its welfare consequences, that are applied to organ transplantation in the U.K. We find that herding is common and is an important contributor to the high rate at which organs are rejected by transplant centers (and subsequently discarded). However, herding does not raise discard rates much above the level that would be obtained if private assessments were made publicly available. Instead, the (limited) information contained in predecessors' decisions substantially reduces the acceptance of bad organs. This is because in our application (i) high ability centers are often willing to deviate from the herd and follow their own positive signals, and (ii) sequences are exogenously terminated relatively quickly.

Suggested Citation

  • Stephanie de Mel & Kaivan Munshi & Soenje Reiche & Hamid Sabourian, 2020. "Herding in Quality Assessment: An Application to Organ Transplantation," IFS Working Papers W20/22, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:ifsewp:20/22
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    References listed on IDEAS

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    1. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W., 1992. "The impact of institutional trading on stock prices," Journal of Financial Economics, Elsevier, vol. 32(1), pages 23-43, August.
    2. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    3. Vives, Xavier, 1996. "Social learning and rational expectations," European Economic Review, Elsevier, vol. 40(3-5), pages 589-601, April.
    4. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    5. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    6. Andreas Park & Hamid Sabourian, 2011. "Herding and Contrarian Behavior in Financial Markets," Econometrica, Econometric Society, vol. 79(4), pages 973-1026, July.
    7. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    8. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    9. Bogaçhan Çelen & Shachar Kariv, 2004. "Distinguishing Informational Cascades from Herd Behavior in the Laboratory," American Economic Review, American Economic Association, vol. 94(3), pages 484-498, June.
    10. Gale, Douglas, 1996. "What have we learned from social learning?," European Economic Review, Elsevier, vol. 40(3-5), pages 617-628, April.
    11. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    12. Pascaline Dupas, 2014. "Short‐Run Subsidies and Long‐Run Adoption of New Health Products: Evidence From a Field Experiment," Econometrica, Econometric Society, vol. 82(1), pages 197-228, January.
    13. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    14. Avery, Christopher & Zemsky, Peter, 1998. "Multidimensional Uncertainty and Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 88(4), pages 724-748, September.
    15. Bohren, J. Aislinn, 2016. "Informational herding with model misspecification," Journal of Economic Theory, Elsevier, vol. 163(C), pages 222-247.
    16. Russ Wermers, 1999. "Mutual Fund Herding and the Impact on Stock Prices," Journal of Finance, American Finance Association, vol. 54(2), pages 581-622, April.
    17. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    18. Agarwal, Nikhil & Ashlagi, Itai & Rees, Michael & Somaini, Paulo & Waldinger, Daniel, 2019. "An Empirical Framework for Sequential Assignment: The Allocation of Deceased Donor Kidneys," Research Papers 3724, Stanford University, Graduate School of Business.
    19. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
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