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Uncertainty causes rounding: an experimental study

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  • Paul Ruud
  • Daniel Schunk
  • Joachim Winter

Abstract

Rounding is a common phenomenon when subjects provide an answer to an open-ended question, both in experimental tasks and in survey responses. From a statistical perspective, rounding implies that the measured variable is a coarsened version of the underlying continuous target variable. Since the coarsening process is non-random, inference from rounded data is generally biased. Despite the potentially severe consequences of rounding, little is known about its causes. In this paper, we focus on subjects’ uncertainty about the target variable as one potential cause for rounding behavior. We present a novel experimental method that induces uncertainty in a controlled way, thus providing causal evidence for the effect of subjects’ uncertainty on the extent of rounding. Then, we specify and estimate a mixture model that relates uncertainty and rounding. The results suggest that an increase in the exogenous level of uncertainty translates into higher variance of the subjects’ beliefs, which in turn results in more rounding. Copyright Economic Science Association 2014

Suggested Citation

  • Paul Ruud & Daniel Schunk & Joachim Winter, 2014. "Uncertainty causes rounding: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 17(3), pages 391-413, September.
  • Handle: RePEc:kap:expeco:v:17:y:2014:i:3:p:391-413
    DOI: 10.1007/s10683-013-9374-8
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    Cited by:

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    2. 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.
    3. Starkov, Egor, 2023. "Only time will tell: Credible dynamic signaling," Journal of Mathematical Economics, Elsevier, vol. 109(C).
    4. Junichi Kikuchi & Yoshiyuki Nakazono, 2023. "The Formation of Inflation Expectations: Microdata Evidence from Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(6), pages 1609-1632, September.
    5. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    6. Melissa Boyle & Justin Svec, 2022. "The Roundness of Antiquity Valuations from Auction Houses and Sales," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 48(4), pages 602-630, October.
    7. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    8. Michael Gideon & Brooke Helppie-McFall & Joanne W. Hsu, 2017. "Heaping at Round Numbers on Financial Questions : The Role of Satisficing," Finance and Economics Discussion Series 2017-006, Board of Governors of the Federal Reserve System (U.S.).
    9. Gotfredsen, Andreas & Nielsen, Carsten S. & Sebald, Alexander C. & Webb, Edward J.D., 2021. "Manipulating perception: The effect of product similarity on valuations and markets," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 263-286.
    10. Clémence Berson & Raphaël Lardeux & Claire Lelarge, 2021. "The Cognitive Load of Financing Constraints: Evidence from Large-Scale Wage Surveys," Working papers 836, Banque de France.
    11. Germ'an Reyes, 2022. "Coarse Wage-Setting and Behavioral Firms," Papers 2206.01114, arXiv.org, revised Mar 2024.
    12. Junichi Kikuchi, 2022. "Inflation Expectations and Survey Design," ISER Discussion Paper 1198, Institute of Social and Economic Research, Osaka University.

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    More about this item

    Keywords

    Rounding; Experimental methodology; Individual decision-making; Econometric analysis of experimental data; Uncertainty; Survey response; C81; C91;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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