Communicating Data Uncertainty: Experimental Evidence for the UK GDP


Communicating Data Uncertainty: Experimental Evidence for the UK GDP

Research Seminar

Tuesday 15 October 2019, 16:00 — 17:00

Presented by Ana Beatriz Galvão (University of Warwick and ESCoE)

Venue: Office for National Statistics, 1 Drummond Gate, London, SW1V 2QQ

Economic statistics, such as GDP growth, are subject to data revisions. But initial releases of GDP data are commonly published without any direct quantitative indication of their uncertainty. With the aim of assessing empirically if and how the public and more experienced users interpret and understand GDP data uncertainty in the UK, we conduct both randomised controlled online experiments and a targeted expert survey. The specially designed surveys are used to assess: (1) perceptions of the uncertainty in single-valued 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.

Ana Beatriz Galvão is Professor of Economic Modelling and Forecasting at Warwick Business School, University of Warwick.  Her areas of expertise are empirical macroeconomics, forecasting and nonlinear time series. Ana’s research has been widely published in leading academic journals such as the Journal of Econometrics and the Journal of Business and Economic Statistics. Ana is currently Associate Editor of the International Journal of Forecasting, the Journal of Applied Econometrics and Economic Modelling.

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