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Uncertainty causes rounding: an experimental study


  • Paul Ruud


  • Daniel Schunk


  • Joachim Winter



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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    References listed on IDEAS

    1. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
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    4. Charles Bellemare & Luc Bissonnette & Sabine Kröger, 2010. "Bounding preference parameters under different assumptions about beliefs: a partial identification approach," Experimental Economics, Springer;Economic Science Association, vol. 13(3), pages 334-345, September.
    5. Charles F. Manski, 2004. "Measuring Expectations," Econometrica, Econometric Society, vol. 72(5), pages 1329-1376, September.
    6. Hans-Martin von Gaudecker & Arthur van Soest & Erik Wengstrom, 2011. "Heterogeneity in Risky Choice Behavior in a Broad Population," American Economic Review, American Economic Association, vol. 101(2), pages 664-694, April.
    7. Rustichini, Aldo & Dickhaut, John & Ghirardato, Paolo & Smith, Kip & Pardo, Jose V., 2005. "A brain imaging study of the choice procedure," Games and Economic Behavior, Elsevier, vol. 52(2), pages 257-282, August.
    8. Kristin J. Kleinjans & Arthur Van Soest, 2014. "Rounding, Focal Point Answers And Nonresponse To Subjective Probability Questions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 567-585, June.
    9. John Hey & Gianna Lotito & Anna Maffioletti, 2010. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 81-111, October.
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    Cited by:

    1. Charles F. Manski, 2017. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Chapters,in: NBER Macroeconomics Annual 2017, volume 32 National Bureau of Economic Research, Inc.
    2. Gideon, Michael & Helppie-McFall, Brooke & Hsu, Joanne W., 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.).

    More about this item


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

    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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