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Exponential-growth prediction bias and compliance with safety measures related to COVID-19

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  • Banerjee, Ritwik
  • Bhattacharya, Joydeep
  • Majumdar, Priyama

Abstract

We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB.

Suggested Citation

  • Banerjee, Ritwik & Bhattacharya, Joydeep & Majumdar, Priyama, 2021. "Exponential-growth prediction bias and compliance with safety measures related to COVID-19," Social Science & Medicine, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:socmed:v:268:y:2021:i:c:s0277953620306924
    DOI: 10.1016/j.socscimed.2020.113473
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    Cited by:

    1. Sebastian Jäckle & Thomas Waldvogel, 2022. "Attitudes toward Coronavirus Protection Measures among German School Students: The Effects of Education and Knowledge about the Pandemic," Social Sciences, MDPI, vol. 11(7), pages 1-13, June.
    2. Ritwik Banerjee & Priyama Majumdar, 2023. "Exponential growth bias in the prediction of COVID‐19 spread and economic expectation," Economica, London School of Economics and Political Science, vol. 90(358), pages 653-689, April.

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