Exponential Growth Bias in the Prediction of COVID-19 Spread and Economic Expectation

Exponential growth bias (EGB) is the pervasive tendency of people to perceive a growth process as linear when, in fact, it is exponential. In this paper, we document that people exhibit EGB when asked to predict the number of COVID-19 positive cases in the future. The bias is positively correlated with optimistic expectations about the future macroeconomic conditions and personal economic circumstances, and investment in a risky asset. We design four interventions to correct EGB and evaluate them through a randomized experiment. In the first treatment (Step), participants make predictions in several short steps; in the second and third treatments (Feedback-N and Feedback-G) participants are given feedback about their prediction errors either in the form of numbers or graphs; and in the fourth treatment (Forecast), participants are offered a forecast range of the future number of cases, based on a statistical model. Our results show that a) Step helps mitigate EGB relative to Baseline, b) Feedback-N, Feedback-G, and Forecast significantly reduce bias relative to both Baseline and Step, c) the interventions decrease risky investment and help moderate future economic expectations through the reduction in EGB. The results suggest that nudges, such as behaviorally informed communication strategies, which correct EGB can also help rationalize economic expectations.