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Communicating Data Uncertainty: Experimental Evidence for U.K. GDP

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  • Ana Beatriz Galvão
  • James Mitchell
  • Johnny Runge

Abstract

Many economic statistics are subject to data revisions. But initial estimates of GDP growth are commonly published without any direct quantitative indication of their uncertainty. To assess if and how the public and experts interpret and understand UK GDP data uncertainty, we conduct both a randomised controlled experiment and a targeted expert survey. The surveys are designed to assess: (1) perceptions of the uncertainty in singlevalued GDP growth numbers; (2) the public's interpretation and understanding of uncertainty information communicated in different formats; and (3) how communicating uncertainty affects trust in the data and the producer of these data. We find that the majority of the public understand that there is uncertainty inherent in GDP numbers, but communicating uncertainty information improves the public’s understanding of why data revisions happen. It encourages them not to take GDP point estimates at face-value but does not decrease trust in the data. We find that it is especially helpful to communicate uncertainty information quantitatively using intervals, density strips and bell curves.

Suggested Citation

  • Ana Beatriz Galvão & James Mitchell & Johnny Runge, 2019. "Communicating Data Uncertainty: Experimental Evidence for U.K. GDP," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-20, Economic Statistics Centre of Excellence (ESCoE).
  • Handle: RePEc:nsr:escoed:escoe-dp-2019-20
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    Cited by:

    1. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    2. Mazzi Gian Luigi & Mitchell James & Carausu Florabela, 2021. "Measuring and Communicating the Uncertainty in Official Economic Statistics," Journal of Official Statistics, Sciendo, vol. 37(2), pages 289-316, June.
    3. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
    4. Johnny Runge & Nathan Hudson-Sharp, 2020. "Public Understanding of Economics and Economic Statistics," Economic Statistics Centre of Excellence (ESCoE) Occasional Papers ESCOE-OP-03, Economic Statistics Centre of Excellence (ESCoE).

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    More about this item

    Keywords

    Data Uncertainty; Uncertainty Communication; Data Revisions; Fan Chart;
    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
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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