 Overreaction and the value of information in a pandemicKeyvan Eslami and Hyunju Lee European Economic Review, 2024, vol. 161, issue C Abstract: This paper studies optimal mitigation and testing during a pandemic in the presence of partial information. We develop a stylized dynamic epidemiological model where the true number of infected individuals can only be partially inferred from two noisy signals: hospitalization and positivity rate. An egalitarian planner chooses the level of mitigation and testing, which respectively affect the infection rate and signal noise, at a certain economic cost. We first show that the planner is willing to pay a significant “information premium” to eliminate the uncertainty by extensive testing. However, if testing is prohibitively costly, then a stringent mitigation becomes optimal, as it partially substitutes for testing as an information acquisition device. Such policies were often criticized as excessive at the onset of the COVID-19 pandemic. We argue that this “optimal overreaction” is a result of the extreme costs of policy mistakes – such as high future casualties – and not due to an aversion to risk. Keywords: Mitigation; Testing; Partial information; Bayesian updating; Particle filtering; COVID-19 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eecrev:v:161:y:2024:i:c:s0014292123002520 DOI: 10.1016/j.euroecorev.2023.104624 Access Statistics for this article European Economic Review is currently edited by T.S. Eicher, A. Imrohoroglu, E. Leeper, J. Oechssler and M. Pesendorfer More articles in European Economic Review from Elsevier |
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