Exponential Growth Bias and Knowledge-Intention Gap: Numeracy and Affect during the COVID-19 Pandemic

29 Pages Posted: 11 Apr 2022

See all articles by Xiao Yu

Xiao Yu

VU Amsterdam; Tinbergen Institute

Date Written: February 17, 2022

Abstract

This paper investigates the impact of numeracy interventions to pandemic preventative behaviour, with a focus on reducing the exponential growth bias (EGB; i.e., underestimation of exponential growth). In a pre-registered online experiment, I test the impacts of numeracy interventions aiming to reduce EGB. Apart from eliciting cognitive changes and behaviour intentions during the experiment, I also measure people’s self-reported real-life behaviour one week after. As expected, the numeracy intervention led to cognitive improvements. Paradoxically, the treatments did not improve behaviour - neither intended nor actual. This study shows that the gap between knowledge and behaviour, in the context of COVID-19 pandemic, consists of two components: knowledge-intention gap and intention-behaviour gap. It is important to be aware that “cognitively” communicating public risks, e.g. by providing statistical information, is not enough to nudge behavioural improvement regarding COVID-19. Information about the infection risk will be more effective if provided in ways that arouse relevant emotions - a well-known finding from other domains such as donations for identifiable victims.

Keywords: exponential growth bias, social distancing, risk communication, deliberation, COVID-19

JEL Classification: D8, D03

Suggested Citation

Yu, Xiao, Exponential Growth Bias and Knowledge-Intention Gap: Numeracy and Affect during the COVID-19 Pandemic (February 17, 2022). Available at SSRN: https://ssrn.com/abstract=4079000 or http://dx.doi.org/10.2139/ssrn.4079000

Xiao Yu (Contact Author)

VU Amsterdam ( email )

De Boelelaan 1105
Amsterdam, 1081 HV
Netherlands

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
88
Abstract Views
377
Rank
520,585
PlumX Metrics