Arbeitspapier
Exponential-Growth Prediction Bias and Compliance with Safety Measures in the Times of COVID-19
We conduct a unique, Amazon MTurk-based global experiment to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. We define prediction bias as the systematic error arising from faulty prediction of the number of cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our design permits us to identify the root of this under-prediction as an EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Our data reveals that the "degree of convexity" reflected in the predicted path of the disease is significantly and substantially lower than the actual path. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. We find that individuals who exhibit EGPB are also more likely to reveal markedly reduced compliance with the WHO-recommended safety measures, find general violations of safety protocols less alarming, and show greater faith in their government's actions. A simple behavioral nudge which shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Clear communication of risk via raw numbers could increase accuracy of risk perception, in turn facilitating compliance with suggested protective behaviors.
- Language
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Englisch
- Bibliographic citation
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Series: IZA Discussion Papers ; No. 13257
- Classification
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Wirtschaft
Health Behavior
Health: Government Policy; Regulation; Public Health
Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡
- Subject
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COVID-19
exponential growth bias
WHO safety measures
- Event
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Geistige Schöpfung
- (who)
-
Banerjee, Ritwik
Bhattacharya, Joydeep
Majumdar, Priyama
- Event
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Veröffentlichung
- (who)
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Institute of Labor Economics (IZA)
- (where)
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Bonn
- (when)
-
2020
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Banerjee, Ritwik
- Bhattacharya, Joydeep
- Majumdar, Priyama
- Institute of Labor Economics (IZA)
Time of origin
- 2020
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