IDEAS home Printed from https://ideas.repec.org/p/fip/fedbwp/10-5.html
   My bibliography  Save this paper

Public and private values

Author

Listed:
  • Dan Ariely
  • Anat Bracha
  • Jean-Paul L'Huillier

Abstract

This paper experimentally examines whether looking at other people's pricing decisions is a type of heuristic - a decisionmaking rule - that people use even when it is not applicable, as in the case of clearly private value goods. We find evidence that this is indeed the case - an individual's valuation of a purely subjective experience under full information, elicited using an incentive compatible mechanism, is highly influence by valuations made by others. This result can shed light on price behavior, price rigidities, and rents.

Suggested Citation

  • Dan Ariely & Anat Bracha & Jean-Paul L'Huillier, 2010. "Public and private values," Working Papers 10-5, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:10-5
    as

    Download full text from publisher

    File URL: http://www.bostonfed.org/economic/wp/wp2010/wp1005.htm
    Download Restriction: no
    File URL: http://www.bostonfed.org/economic/wp/wp2010/wp1005.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Uri Simonsohn & Dan Ariely, 2008. "When Rational Sellers Face Nonrational Buyers: Evidence from Herding on eBay," Management Science, INFORMS, vol. 54(9), pages 1624-1637, September.
    2. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    3. Dan Ariely & George Loewenstein & Drazen Prelec, 2003. ""Coherent Arbitrariness": Stable Demand Curves Without Stable Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 73-106.
    4. 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.
    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. Silvio Vismara, 2018. "Information Cascades among Investors in Equity Crowdfunding," Entrepreneurship Theory and Practice, , vol. 42(3), pages 467-497, May.
    2. Chan, C.S. Richard & Parhankangas, Annaleena & Sahaym, Arvin & Oo, Pyayt, 2020. "Bellwether and the herd? Unpacking the u-shaped relationship between prior funding and subsequent contributions in reward-based crowdfunding," Journal of Business Venturing, Elsevier, vol. 35(2).
    3. Jacobs Martin, 2016. "Accounting for Changing Tastes: Approaches to Explaining Unstable Individual Preferences," Review of Economics, De Gruyter, vol. 67(2), pages 121-183, August.
    4. Shijie Lu & Dai Yao & Xingyu Chen & Rajdeep Grewal, 2021. "Do Larger Audiences Generate Greater Revenues Under Pay What You Want? Evidence from a Live Streaming Platform," Marketing Science, INFORMS, vol. 40(5), pages 964-984, September.
    5. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2018_1716, CEMFI.
    6. Michael E. Cummings & Hans Rawhouser & Silvio Vismara & Erin L. Hamilton, 2020. "An equity crowdfunding research agenda: evidence from stakeholder participation in the rulemaking process," Small Business Economics, Springer, vol. 54(4), pages 907-932, April.
    7. Amy Wenxuan Ding & Shibo Li, 2019. "Herding in the consumption and purchase of digital goods and moderators of the herding bias," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 460-478, May.
    8. Mohammadi, Ali & Shafi, Kourosh, 2015. "The contribution patterns of equity-crowdfunding investors: Gender, Risk aversion and Observational learning," Working Paper Series in Economics and Institutions of Innovation 419, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    9. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2017_1716, CEMFI.
    10. Liangfei Qiu & Zhan (Michael) Shi & Andrew B. Whinston, 2018. "Learning from Your Friends’ Check-Ins: An Empirical Study of Location-Based Social Networks," Information Systems Research, INFORMS, vol. 29(4), pages 1044-1061, December.
    11. Svatopluk Kapounek & Zuzana Kucerová, 2019. "Overfunding and Signaling Effects of Herding Behavior in Crowdfunding," CESifo Working Paper Series 7973, CESifo.
    12. Greg Fisher & Emily Neubert, 2023. "Evaluating Ventures Fast and Slow: Sensemaking, Intuition, and Deliberation in Entrepreneurial Resource Provision Decisions," Entrepreneurship Theory and Practice, , vol. 47(4), pages 1298-1326, July.
    13. Irene Comeig & Ernesto Mesa-Vázquez & Pau Sendra-Pons & Amparo Urbano, 2020. "Rational Herding in Reward-Based Crowdfunding: An MTurk Experiment," Sustainability, MDPI, vol. 12(23), pages 1-21, November.
    14. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    15. Nilkanth Kumar & Nirmal Kumar Raut & Suchita Srinivasan, 2022. "Herd behavior in the choice of motorcycles: Evidence from Nepal," CER-ETH Economics working paper series 22/366, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    16. Egebark, Johan & Ekström, Mathias, 2011. "Like What You Like or Like What Others Like? Conformity and Peer Effects on Facebook," Working Paper Series 886, Research Institute of Industrial Economics.
    17. Ali Mohammadi & Kourosh Shafi, 2018. "Gender differences in the contribution patterns of equity-crowdfunding investors," Small Business Economics, Springer, vol. 50(2), pages 275-287, February.
    18. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    19. Andrew M. Davis & Vishal Gaur & Dayoung Kim, 2021. "Consumer Learning from Own Experience and Social Information: An Experimental Study," Management Science, INFORMS, vol. 67(5), pages 2924-2943, May.
    20. Panos, Georgios A. & Theodossiou, Ioannis, 2010. "Unionism and Peer-Referencing," SIRE Discussion Papers 2010-122, Scottish Institute for Research in Economics (SIRE).

    More about this item

    Keywords

    Pricing; Human behavior;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedbwp:10-5. 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: Catherine Spozio (email available below). General contact details of provider: https://edirc.repec.org/data/frbbous.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.