The scale of COVID‐19 graphs affects understanding, attitudes, and policy preferences

The scale of COVID‐19 graphs affects understanding, attitudes, and policy preferences

by Alessandro Romano in collaboration with Yale University, London School of Economics and University City Dublin


Mass media routinely present data on coronavirus disease 2019 (COVID19) 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 COVID19 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 COVID19 related information. Providing the public with information in ways they understand better can help improving the response to COVID19, 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.


The Paper: "The scale of COVID‐19 graphs affects understanding, attitudes, and policy preferences"