Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19

Global Health & Nutrition

Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19

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Researchers

Ritwik Banerjee, Joydeep Bhattacharya, Priyama Majumdar

Abstract

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.

Project Outcomes of Interest

Prediction bias

Link to Results

Link to working paper

Impact Goals

  • Reduce COVID-19 transmission rates

Project Data Collection Mode

  • Web

Link to Data Collection Instruments

https://www.dropbox.com/s/qnvco22dgpd1t11/draft_Covid-WP.pdf?dl=0

Results Status

Results