Yale Law School, New Haven, Connecticut, USA.
London School of Economics and Political Science, London, UK.
Health Econ. 2020 Nov;29(11):1482-1494. doi: 10.1002/hec.4143. Epub 2020 Aug 25.
Mass media routinely present data on coronavirus disease 2019 (COVID-19) diffusion with graphs that use either a log scale or a linear scale. We show that the choice of the scale adopted on these graphs has important consequences on how people understand and react to the information conveyed. In particular, we find that when we show the number of COVID-19 related deaths on a logarithmic scale, people have a less accurate understanding of how the pandemic has developed, make less accurate predictions on its evolution, and have different policy preferences than when they are exposed to a linear scale. Consequently, merely changing the scale the data is presented on can alter public policy preferences and the level of worry about the pandemic, despite the fact that people are routinely exposed to COVID-19 related information. Providing the public with information in ways they understand better can help improving the response to COVID-19, thus, mass media and policymakers communicating to the general public should always describe the evolution of the pandemic using a graph on a linear scale, at least as a default option. Our results suggest that framing matters when communicating to the public.
大众媒体经常使用对数刻度或线性刻度的图表来呈现有关 2019 年冠状病毒病(COVID-19)传播的数据。我们表明,这些图表采用的刻度选择对人们如何理解和对所传达的信息做出反应有重要影响。特别是,我们发现,当我们以对数刻度显示与 COVID-19 相关的死亡人数时,人们对大流行的发展的理解就不太准确,对其演变的预测也不太准确,并且与他们接触线性刻度时的政策偏好不同。因此,尽管人们经常接触与 COVID-19 相关的信息,但仅更改数据呈现的刻度就可以改变公众的政策偏好和对大流行的担忧程度。因此,向公众提供他们更易于理解的信息可以帮助改善对 COVID-19 的应对措施,因此,向公众传播信息的大众媒体和政策制定者应始终使用线性刻度的图表来描述大流行的演变,至少应作为默认选项。我们的研究结果表明,在向公众传达信息时,框架很重要。