COVID-19 Data: The Logarithmic Scale Misinforms the Public and Affects Policy Preferences

Mass media routinely present data on COVID-19 diffusion using either a log scale or a linear scale. We show that 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 can alter public policy preferences and the level of worry, despite the fact that people are exposed to a lot of COVID-19 related information. Reducing misinformation can help improving the response to COVID-19, thus, mass media and policymakers should always describe the evolution of the pandemic using a graph on a linear scale, or at least they should show both scales. More generally, our results confirm that policymakers should not only care about what information to communicate, but also about how to do it, as even small differences in data framing can have a significant impact.