[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ifs/ifsewp/20-22.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.ifs.org.uk/uploads/WP2020220-Herding-in-Quality-Assessment.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephanie De Mel & Kaivan Munshi & Soenje Reiche & Hamid Sabourian, 2021. "Herding with Heterogeneous Ability: An Application to Organ Transplantation," Cowles Foundation Discussion Papers 2308, Cowles Foundation for Research in Economics, Yale University.
    2. Puput Tri Komalasari & Marwan Asri & Bernardinus M. Purwanto & Bowo Setiyono, 2022. "Herding behaviour in the capital market: What do we know and what is next?," Management Review Quarterly, Springer, vol. 72(3), pages 745-787, September.
    3. Yang, Xiaolan & Gao, Mei & Wu, Yun & Jin, Xuejun, 2018. "Performance evaluation and herd behavior in a laboratory financial market," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 75(C), pages 45-54.
    4. Hirshleifer, David & Teoh, Siew Hong, 2008. "Thought and Behavior Contagion in Capital Markets," MPRA Paper 9164, University Library of Munich, Germany.
    5. Marco Cipriani & Antonio Guarino, 2009. "Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 206-233, March.
    6. Jonathan E. Alevy & Michael S. Haigh & John List, 2006. "Information Cascades: Evidence from An Experiment with Financial Market Professionals," NBER Working Papers 12767, National Bureau of Economic Research, Inc.
    7. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66, March.
    8. Fishman, Arthur & Fishman, Ram & Gneezy, Uri, 2019. "A tale of two food stands: Observational learning in the field," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 101-108.
    9. Boortz, Christopher & Kremer, Stephanie & Jurkatis, Simon & Nautz, Dieter, 2014. "Information risk, market stress and institutional herding in financial markets: New evidence through the lens of a simulated model," SFB 649 Discussion Papers 2014-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
    11. Marco Cipriani & Antonio Guarino, 2014. "Estimating a Structural Model of Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 104(1), pages 224-251, January.
    12. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    13. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
    14. repec:hum:wpaper:sfb649dp2014-029 is not listed on IDEAS
    15. B Kelsey Jack, "undated". "Market Inefficiencies and the Adoption of Agricultural Technologies in Developing Countries," CID Working Papers 50, Center for International Development at Harvard University.
    16. Stone, Daniel F. & Miller, Steven J., 2013. "Leading, learning and herding," Mathematical Social Sciences, Elsevier, vol. 65(3), pages 222-231.
    17. Christophe Bisière & Jean-Paul Décamps & Stefano Lovo, 2015. "Risk Attitude, Beliefs Updating, and the Information Content of Trades: An Experiment," Management Science, INFORMS, vol. 61(6), pages 1378-1397, June.
    18. Thomas Stebro & Manuel Fernnndez Sierra & Stefano Lovo & Nir Vulkan, 2017. "Herding in Equity Crowdfunding," Working Papers hal-01970724, HAL.
    19. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    20. Penczynski, Stefan P., 2017. "The nature of social learning: Experimental evidence," European Economic Review, Elsevier, vol. 94(C), pages 148-165.
    21. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 733-762.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ifs:ifsewp:20/22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emma Hyman (email available below). General contact details of provider: https://edirc.repec.org/data/ifsssuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.