IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/h7pue.html
   My bibliography  Save this paper

Receiving vs. believing (mis)information from friends: experimental evidence from India

Author

Listed:
  • Narang, Jimmy

Abstract

Do people believe a news story more if it is shared by a friend? Should they? I investigate this using experiments in India with 800 pairs of friends and a custom social-media platform. I find sharers can distinguish true from false stories but share both equally, making sharing uninformative about a story's truth. Receivers, however, interpret sharing as a sign of truth: they overestimate how well sharers’ beliefs predict veracity; discount how factors besides belief influence sharing decisions; and update the most on stories they least believed initially. Altogether, stories gain (unmerited) credibility from being shared by a friend

Suggested Citation

  • Narang, Jimmy, 2024. "Receiving vs. believing (mis)information from friends: experimental evidence from India," OSF Preprints h7pue, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:h7pue
    DOI: 10.31219/osf.io/h7pue
    as

    Download full text from publisher

    File URL: https://osf.io/download/6690358d5a51c4000fe806a5/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/h7pue?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 95(3), pages 537-557.
    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. Lurås, Hilde, 2009. "A healthy lifestyle: The product of opportunities and preferences," HERO Online Working Paper Series 2001:11, University of Oslo, Health Economics Research Programme.
    2. Niculaescu, Corina E. & Sangiorgi, Ivan & Bell, Adrian R., 2023. "Does personal experience with COVID-19 impact investment decisions? Evidence from a survey of US retail investors," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Dohmen, Thomas & Falk, Armin & Huffman, David & Marklein, Felix & Sunde, Uwe, 2009. "Biased probability judgment: Evidence of incidence and relationship to economic outcomes from a representative sample," Journal of Economic Behavior & Organization, Elsevier, vol. 72(3), pages 903-915, December.
    4. Jason Shachat & Anand Srinivasan, 2022. "Informational Price Cascades and Non-Aggregation of Asymmetric Information in Experimental Asset Markets," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(4), pages 388-407, November.
    5. Salzman, Diego & Trifan, Emanuela, 2006. "Emotions, Bayesian Inference, and Financial Decision Making," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 28163, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Alós-Ferrer, Carlos & Hügelschäfer, Sabine, 2016. "Faith in intuition and cognitive reflection," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 64(C), pages 61-70.
    7. Lex Borghans & Bas ter Weel, 2008. "Understanding the Technology of Computer Technology Diffusion: Explaining Computer Adoption Patterns and Implications for the Wage Structure," Journal of Income Distribution, Ad libros publications inc., vol. 17(3-4), pages 37-70, September.
    8. Ganguly, Ananda R & Kagel, John H & Moser, Donald V, 2000. "Do Asset Market Prices Reflect Traders' Judgment Biases?," Journal of Risk and Uncertainty, Springer, vol. 20(3), pages 219-245, May.
    9. Machina Mark J. & Schmeidler David, 1995. "Bayes without Bernoulli: Simple Conditions for Probabilistically Sophisticated Choice," Journal of Economic Theory, Elsevier, vol. 67(1), pages 106-128, October.
    10. Larry G Epstein & Yoram Halevy, 2024. "Hard-to-Interpret Signals," Journal of the European Economic Association, European Economic Association, vol. 22(1), pages 393-427.
    11. David M. Frankel, 2008. "Adaptive Expectations And Stock Market Crashes," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(2), pages 595-619, May.
    12. Markus Schöbel & Jörg Rieskamp & Rafael Huber, 2016. "Social Influences in Sequential Decision Making," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-23, January.
    13. David L. Dickinson & Eugenio Caleb Garbuio, 2020. "The Influence of Dietary Patterns on Outcomes in a Bayesian Choice Task," Working Papers 21-01, Department of Economics, Appalachian State University.
    14. Castagnetti, Alessandro & Schmacker, Renke, 2022. "Protecting the ego: Motivated information selection and updating," European Economic Review, Elsevier, vol. 142(C).
    15. Dohmen, T.J. & Falk, A. & Huffman, D. & Marklein, F. & Sunde, U., 2009. "The non-use of Bayes rule: representative evidence on bounded rationality," Research Memorandum 038, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    16. Adeline Delavande, 2008. "Measuring revisions to subjective expectations," Journal of Risk and Uncertainty, Springer, vol. 36(1), pages 43-82, February.
    17. Gergely Hajdu & Balázs Krusper, 2023. "Choice-induced Sticky Learning," Department of Economics Working Papers wuwp349, Vienna University of Economics and Business, Department of Economics.
    18. Nicolas Jacquemet & Alexander James & Stéphane Luchini & Jason Shogren, 2011. "Social Psychology and Environmental Economics: A New Look at ex ante Corrections of Biased Preference Evaluation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 413-433, March.
    19. Asanov, Igor, 2021. "Bandit cascade: A test of observational learning in the bandit problem," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 150-171.
    20. Terrance Odean., 1996. "Volume, Volatility, Price and Profit When All Trader Are Above Average," Research Program in Finance Working Papers RPF-266, University of California at Berkeley.

    More about this item

    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:osf:osfxxx:h7pue. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.