Exponential growth bias in the prediction of COVID-19 spread and economic expectation

Abstract

Exponential growth bias (EGB) is the pervasive tendency of people to perceive a growth process as linear when in fact it is exponential. We document that people exhibit EGB when asked to predict the number of COVID-19 positive cases in the future. Using four experimental interventions, we examine the effect of EGB on expectations about future macroeconomic conditions, and investment choices in risky assets. In the first intervention (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 in the form of either 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. We find that Feedback-N, Feedback-G and Forecast significantly reduce EGB relative to Step. A reduction in the bias, through the interventions, also decreases risky investment and helps to moderate future economic expectations. The results suggest that nudges, such as behaviourally informed communication strategies, that correct EGB can also help to rationalize economic expectations.