IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp16736.html
   My bibliography  Save this paper

Lost in Transmission

Author

Listed:
  • Graeber, Thomas W

    (Harvard Business School)

  • Noy, Shakked

    (Massachusetts Institute of Technology)

  • Roth, Christopher

    (University of Cologne)

Abstract

For many decisions, people rely on information received from others by word of mouth. How does the process of verbal transmission distort economic information? In our experiments, participants listen to audio recordings containing economic forecasts and are paid to accurately transmit the information via voice messages. Other participants listen either to an original recording or a transmitted version and then state incentivized beliefs. Our main finding is that, across a variety of transmitter incentive schemes, information about the reliability of a forecast is lost in transmission more than twice as much as information about the forecast's level. This differential information loss predictably distorts listeners' belief updates: following transmission, reliable and unreliable messages converge in influence and average belief updates from new information are weakened. Mechanism experiments show that the differential loss is not driven by transmitters deliberately trading off the costs and benefits of transmitting different kinds of information. Instead, it results from memory constraints during transmission, which can be overcome through targeted reminders.

Suggested Citation

  • Graeber, Thomas W & Noy, Shakked & Roth, Christopher, 2024. "Lost in Transmission," IZA Discussion Papers 16736, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp16736
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp16736.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Peter Andre & Ingar Haaland & Christopher Roth & Mirko Wiederholt & Johannes Wohlfart, 2021. "Narratives about the Macroeconomy," ECONtribute Discussion Papers Series 127, University of Bonn and University of Cologne, Germany.
    2. Matthew O. Jackson & Leeat Yariv, 2007. "Diffusion of Behavior and Equilibrium Properties in Network Games," American Economic Review, American Economic Association, vol. 97(2), pages 92-98, May.
    3. Benjamin Enke & Thomas Graeber & Ryan Oprea, 2023. "Complexity and Time," CESifo Working Paper Series 10327, CESifo.
    4. Luca Braghieri, 2023. "Biased Decoding and the Foundations of Communication," CESifo Working Paper Series 10432, CESifo.
    5. Benjamin Enke & Thomas Graeber & Ryan Oprea & Thomas W. Graeber, 2023. "Complexity and Hyperbolic Discounting," CESifo Working Paper Series 10861, CESifo.
    6. 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.
    7. Arun G. Chandrasekhar & Esther Duflo & Michael Kremer & João F. Pugliese & Jonathan Robinson & Frank Schilbach, 2022. "Blue Spoons: Sparking Communication About Appropriate Technology Use," NBER Working Papers 30423, National Bureau of Economic Research, Inc.
    8. Cade Massey & George Wu, 2005. "Detecting Regime Shifts: The Causes of Under- and Overreaction," Management Science, INFORMS, vol. 51(6), pages 932-947, June.
    9. Dietmar Fehr & Johanna Mollerstrom & Ricardo Perez-Truglia, 2022. "Listen to Her: Gender Differences in Information Diffusion within the Household," NBER Working Papers 30513, National Bureau of Economic Research, Inc.
    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. Thomas Graeber & Christopher Roth & Constantin Schesch & Thomas W. Graeber, 2024. "Explanations," CESifo Working Paper Series 11131, CESifo.
    2. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," American Economic Review, American Economic Association, vol. 106(7), pages 1663-1704, July.
    3. H Peyton Young & Lucas Merrill Brown, 2016. "The Diffusion of a Social Innovation: Executive Stock Options from 1936," Economics Series Working Papers 777, University of Oxford, Department of Economics.
    4. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
    5. Michel Grabisch & Agnieszka Rusinowska & Xavier Venel, 2019. "Diffusion in countably infinite networks," Documents de travail du Centre d'Economie de la Sorbonne 19017, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    6. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2016. "Networks: An Economic Perspective," Papers 1608.07901, arXiv.org.
    7. Lamberson PJ, 2010. "Social Learning in Social Networks," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-33, August.
    8. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    9. Pongou, Roland & Serrano, Roberto, 2013. "Dynamic Network Formation in Two-Sided Economies," MPRA Paper 46021, University Library of Munich, Germany.
    10. Pongou, Roland & Serrano, Roberto, 2016. "Volume of trade and dynamic network formation in two-sided economies," Journal of Mathematical Economics, Elsevier, vol. 63(C), pages 147-163.
    11. Chang, Eric C. & Cheng, Joseph W. & Khorana, Ajay, 2000. "An examination of herd behavior in equity markets: An international perspective," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1651-1679, October.
    12. Ferdinand Thies & Sören Wallbach & Michael Wessel & Markus Besler & Alexander Benlian, 2022. "Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1691-1705, September.
    13. Ruomeng Cui & Dennis J. Zhang & Achal Bassamboo, 2019. "Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon," Management Science, INFORMS, vol. 65(3), pages 1216-1235, March.
    14. Stéphan Marette, 2017. "Jill E. Hobbs, Stavroula Malla, Eric K. Sogah and May T. Yeung, 2014, Regulating Health Foods. Policy Challenges and Consumer Conundrums," Review of Agricultural, Food and Environmental Studies, Springer, vol. 98(1), pages 93-94, July.
    15. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    16. Cao, Melanie & Shi, Shouyong, 2006. "Signaling in the Internet craze of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 12(4), pages 818-833, September.
    17. Ben Klemens, 2013. "A Peer-based Model of Fat-tailed Outcomes," Papers 1304.0718, arXiv.org.
    18. Kraemer, Carlo & Noth, Markus & Weber, Martin, 2006. "Information aggregation with costly information and random ordering: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 59(3), pages 423-432, March.
    19. Ye Zhang, 2020. "Discrimination in the Venture Capital Industry: Evidence from Field Experiments," Papers 2010.16084, arXiv.org, revised Aug 2022.
    20. 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.

    More about this item

    Keywords

    information transmission; word-of-mouth; narratives; reliability;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:iza:izadps:dp16736. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.