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Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP

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  • Ana B. Galvão
  • James Mitchell

Abstract

Economic statistics are commonly published without estimates of their uncertainty. We conduct two waves of a randomized controlled online experiment to assess if and how the UK public understands data uncertainty. A control group observes only the point estimate of GDP. Treatment groups are presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that GDP numbers are uncertain. Quantitative communications of data uncertainty help align the public’s subjective probabilistic expectations of data uncertainty with objective estimates, but do not decrease trust in the statistical office.

Suggested Citation

  • Ana B. Galvão & James Mitchell, 2021. "Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP," Working Papers 21-28R, Federal Reserve Bank of Cleveland, revised 13 Jul 2022.
    • Handle: RePEc:fip:fedcwq:93544
    DOI: 10.26509/frbc-wp-202128r
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    1. Carola Binder, 2020. "Coronavirus Fears and Macroeconomic Expectations," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 721-730, October.
    2. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    3. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    4. Charles F. Manski, 2015. "Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern," Journal of Economic Literature, American Economic Association, vol. 53(3), pages 631-653, September.
    5. Nathan F. Dieckmann & Robin Gregory & Ellen Peters & Robert Hartman, 2017. "Seeing What You Want to See: How Imprecise Uncertainty Ranges Enhance Motivated Reasoning," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 471-486, March.
    6. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    7. Damian Clarke & Joseph P. Romano & Michael Wolf, 2020. "The Romano–Wolf multiple-hypothesis correction in Stata," Stata Journal, StataCorp LP, vol. 20(4), pages 812-843, December.
    8. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    9. Young, Alwyn, 2019. "Channeling Fisher: randomization tests and the statistical insignificance of seemingly significant experimental results," LSE Research Online Documents on Economics 101401, London School of Economics and Political Science, LSE Library.
    10. Bholat, David & Broughton, Nida & Ter Meer, Janna & Walczak, Eryk, 2019. "Enhancing central bank communications using simple and relatable information," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 1-15.
    11. 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.
    12. Alwyn Young, 2019. "Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(2), pages 557-598.
    13. Manski, Charles F., 2016. "Credible interval estimates for official statistics with survey nonresponse," Journal of Econometrics, Elsevier, vol. 191(2), pages 293-301.
    14. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    15. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
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    Cited by:

    1. Johnny Runge, 2021. "Communicating Data Uncertainty on GDP and Unemployment: Interviews with the UK Public," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-07, Economic Statistics Centre of Excellence (ESCoE).

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    More about this item

    Keywords

    Experiments; Data Uncertainty; Uncertainty Communication; Data Revisions;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

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