People tend to underestimate the speed at which exponential processes unfold. This is especially relevant in the early stages of an infectious disease outbreak. This paper uses an incentivized, survey instrument to document an exponential-growth prediction bias (EGPB) in the context of COVID-19: the “degree of convexity” in the predicted path of the disease is significantly lower than in the actual path, and respondents from countries at a later stage of the disease progression showed higher bias relative to those at an early stage. This is policy relevant because those who exhibit EGPB also show reduced compliance with the WHO-recommended safety measures. A simple behavioral nudge which shows data numerically, as opposed to graphically, causally reduces EGPB.

Researchers:
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Program Area:
Study Type:
Pilot
Timeline:
June 2020
Impact Goals:
Reduce COVID-19 transmission rates
Outcomes of Interest:
Prediction bias
Data Collection Mode:
Web
Data Collection Instruments:
External Website:
Results Status:
Results