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Information Experiments

Author

Listed:
  • Ingar Haaland

    (NHH Norwegian School of Economics)

  • Julian König

    (University of Bonn)

  • Christopher Roth

    (University of Cologne and ECONtribute)

  • Johannes Wohlfart

    (University of Cologne and ECONtribute)

Abstract
Information provision experiments have become pivotal in understanding how beliefs influence human behavior in various social science contexts. These experiments, which manipulate the information sets available to respondents, enable the exogenous alteration of beliefs and perceived constraints, providing valuable insights. This review article explores methodologies for measuring and updating beliefs, designing effective information treatments, and addressing experimenter demand effects. The paper also discusses the challenges of belief measurement, such as overcoming numerical anchoring and understanding the persistence of belief changes. Additionally, it highlights the growing importance of studying the impact of qualitative information and the attentional foundations of expectation formation.

Suggested Citation

  • Ingar Haaland & Julian König & Christopher Roth & Johannes Wohlfart, 2024. "Information Experiments," ECONtribute Discussion Papers Series 271, University of Bonn and University of Cologne, Germany.
  • Handle: RePEc:ajk:ajkdps:271
    as

    Download full text from publisher

    File URL: https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_271_2024.pdf
    File Function: First version, 2024
    Download Restriction: no
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    References listed on IDEAS

    as
    1. Olivier Armantier & Scott Nelson & Giorgio Topa & Wilbert van der Klaauw & Basit Zafar, 2016. "The Price Is Right: Updating Inflation Expectations in a Randomized Price Information Experiment," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 503-523, July.
    2. Ansolabehere, Stephen & Meredith, Marc & Snowberg, Erik, 2013. "Asking About Numbers: Why and How," Political Analysis, Cambridge University Press, vol. 21(1), pages 48-69, January.
    3. Roth, Christopher & Settele, Sonja & Wohlfart, Johannes, 2022. "Beliefs about public debt and the demand for government spending," Journal of Econometrics, Elsevier, vol. 231(1), pages 165-187.
    4. Ilyana Kuziemko & Michael I. Norton & Emmanuel Saez & Stefanie Stantcheva, 2015. "How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments," American Economic Review, American Economic Association, vol. 105(4), pages 1478-1508, April.
    5. Amy Finkelstein & Matthew J Notowidigdo, 2019. "Take-Up and Targeting: Experimental Evidence from SNAP," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1505-1556.
    6. Allcott, Hunt & Boxell, Levi & Conway, Jacob & Gentzkow, Matthew & Thaler, Michael & Yang, David, 2020. "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    7. Clifford, Scott & Sheagley, Geoffrey & Piston, Spencer, 2021. "Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments," American Political Science Review, Cambridge University Press, vol. 115(3), pages 1048-1065, August.
    8. Coibion, Olivier & Gorodnichenko, Yuriy & Kumar, Saten & Pedemonte, Mathieu, 2020. "Inflation expectations as a policy tool?," Journal of International Economics, Elsevier, vol. 124(C).
    9. Facchini, Giovanni & Margalit, Yotam & Nakata, Hiroyuki, 2022. "Countering public opposition to immigration: The impact of information campaigns," European Economic Review, Elsevier, vol. 141(C).
    10. Mummolo, Jonathan & Peterson, Erik, 2019. "Demand Effects in Survey Experiments: An Empirical Assessment," American Political Science Review, Cambridge University Press, vol. 113(2), pages 517-529, May.
    11. Haaland, Ingar & Roth, Christopher, 2020. "Labor market concerns and support for immigration," Journal of Public Economics, Elsevier, vol. 191(C).
    12. Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1826-1863.
    13. Felix Chopras & Ingar Haaland & Christopher Roth, 2024. "The Demand for News: Accuracy Concerns Versus Belief Confirmation Motives," The Economic Journal, Royal Economic Society, vol. 134(661), pages 1806-1834.
    14. Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2023. "Designing Information Provision Experiments," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 3-40, March.
    15. Cruces, Guillermo & Perez-Truglia, Ricardo & Tetaz, Martin, 2013. "Biased perceptions of income distribution and preferences for redistribution: Evidence from a survey experiment," Journal of Public Economics, Elsevier, vol. 98(C), pages 100-112.
    16. Leonardo Bursztyn & Georgy Egorov & Stefano Fiorin, 2020. "From Extreme to Mainstream: The Erosion of Social Norms," American Economic Review, American Economic Association, vol. 110(11), pages 3522-3548, November.
    17. Matthew Gentzkow & Jesse M. Shapiro, 2006. "Media Bias and Reputation," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 280-316, April.
    18. Simon Jäger & Christopher Roth & Nina Roussille & Benjamin Schoefer, 2024. "Worker Beliefs About Outside Options," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1505-1556.
    19. Prior, Markus & Sood, Gaurav & Khanna, Kabir, 2015. "You Cannot be Serious: The Impact of Accuracy Incentives on Partisan Bias in Reports of Economic Perceptions," Quarterly Journal of Political Science, now publishers, vol. 10(4), pages 489-518, December.
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    More about this item

    Keywords

    Information;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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