  We conduct a unique, Amazon MTurk-based global experiment to investigate the
importance of an exponential-growth prediction bias (EGPB) in understanding why
the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the
well-established fact that disease spread, especially in the initial stages,
follows an exponential function meaning few positive cases can explode into a
widespread pandemic if the disease is sufficiently transmittable. We define
prediction bias as the systematic error arising from faulty prediction of the
number of cases x-weeks hence when presented with y-weeks of prior, actual data
on the same. Our design permits us to identify the root of this
under-prediction as an EGPB arising from the general tendency to underestimate
the speed at which exponential processes unfold. Our data reveals that the
"degree of convexity" reflected in the predicted path of the disease is
significantly and substantially lower than the actual path. The bias is
significantly higher for respondents from countries at a later stage relative
to those at an early stage of disease progression. We find that individuals who
exhibit EGPB are also more likely to reveal markedly reduced compliance with
the WHO-recommended safety measures, find general violations of safety
protocols less alarming, and show greater faith in their government's actions.
A simple behavioral nudge which shows prior data in terms of raw numbers, as
opposed to a graph, causally reduces EGPB. Clear communication of risk via raw
numbers could increase accuracy of risk perception, in turn facilitating
compliance with suggested protective behaviors.

General Economics;Economics