[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/nsr/escoed/escoe-dp-2021-06.html
   My bibliography  Save this paper

Communicating Data Uncertainty: Multi-Wave Experimental Evidence for U.K. GDP

Author

Listed:
  • Ana Galvao
  • James Mitchell
Abstract
Economic statistics are commonly published without any explicit indication of their uncertainty. To assess if and how the UK public interpret and understand data uncertainty, we conduct two waves of a randomized controlled online experiment. A control group is presented with the headline point estimate of GDP, as emphasized by the statistical office. Treatment groups are then presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understand there is uncertainty inherent in official GDP numbers. But communicating uncertainty information improves understanding. It encourages the public not to take estimates at face-value, but does not decrease trust in the data. Quantitative tools to communicate data uncertainty – notably intervals, density strips and bell curves – are especially beneficial. They reduce dispersion of the public’s subjective probabilistic expectations of data uncertainty, improving alignment with objective estimates.

Suggested Citation

  • Ana Galvao & James Mitchell, 2021. "Communicating Data Uncertainty: Multi-Wave Experimental Evidence for U.K. GDP," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-06, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2021-06
    as

    Download full text from publisher

    File URL: https://escoe-website.s3.amazonaws.com/wp-content/uploads/2021/06/23113609/ESCoE-DP-2021-06.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    5. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. Manski, Charles F., 2016. "Credible interval estimates for official statistics with survey nonresponse," Journal of Econometrics, Elsevier, vol. 191(2), pages 293-301.
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. repec:wrk:wrkemf:30 is not listed on IDEAS
    3. 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.
    4. repec:wrk:wrkemf:35 is not listed on IDEAS
    5. Felici, Marco & Kenny, Geoff & Friz, Roberta, 2023. "Consumer savings behaviour at low and negative interest rates," European Economic Review, Elsevier, vol. 157(C).
    6. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    7. Bahety, Girija & Bauhoff, Sebastian & Patel, Dev & Potter, James, 2021. "Texts don’t nudge: An adaptive trial to prevent the spread of COVID-19 in India," Journal of Development Economics, Elsevier, vol. 153(C).
    8. Hermes, Henning & Krauß, Marina & Lergetporer, Philipp & Peter, Frauke & Wiederhold, Simon, 2022. "Early Child Care and Labor Supply of Lower-SES Mothers: A Randomized Controlled Trial," IZA Discussion Papers 15814, Institute of Labor Economics (IZA).
    9. Henning Hermes & Philipp Lergetporer & Frauke Peter & Simon Wiederhold, 2021. "Application Barriers and the Socioeconomic Gap in Child Care Enrollment," CESifo Working Paper Series 9282, CESifo.
    10. Hermes, Henning & Lergetporer, Philipp & Peter, Frauke & Wiederhold, Simon, 2021. "Behavioral Barriers and the Socioeconomic Gap in Child Care Enrollment," IZA Discussion Papers 14698, Institute of Labor Economics (IZA).
    11. Amrou Awaysheh & Rocio Bonet & Jaime Ortega, 2023. "Performance feedback and productivity: Evidence from a field experiment," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 98-115, January.
    12. Rafkin, Charlie & Shreekumar, Advik & Vautrey, Pierre-Luc, 2021. "When guidance changes: Government stances and public beliefs," Journal of Public Economics, Elsevier, vol. 196(C).
    13. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    14. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    15. repec:wrk:wrkemf:22 is not listed on IDEAS
    16. 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.
    17. Ritwik Banerjee & Priyama Majumdar, 2023. "Exponential growth bias in the prediction of COVID‐19 spread and economic expectation," Economica, London School of Economics and Political Science, vol. 90(358), pages 653-689, April.
    18. Eszter Czibor & Danny Hsu & David Jimenez-Gomez & Susanne Neckermann & Burcu Subasi, 2022. "Loss-Framed Incentives and Employee (Mis-)Behavior," Management Science, INFORMS, vol. 68(10), pages 7518-7537, October.
    19. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    20. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
    21. Alexandra de Gendre & Nicolás Salamanca, 2020. "On the Mechanisms of Ability Peer Effects," Melbourne Institute Working Paper Series wp2020n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    22. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    23. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).

    More about this item

    Keywords

    data revisions; data uncertainty; uncertainty communication;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nsr:escoed:escoe-dp-2021-06. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ESCoE Centre Manager (email available below). General contact details of provider: https://edirc.repec.org/data/escoeuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.