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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions
Organization Unit
  • Emmanuel Mamatzakis
  • Steven Ongena
  • Pankaj C Patel
  • Mike Tsionas
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Applied Economics
Publisher Taylor & Francis
Geographical Reach international
ISSN 0003-6846
Volume 56
Number 25
Page Range 2990 - 3010
Date 2024
Abstract Text This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular, we show that economic policy variables are of importance for the main epidemiological parameters derived from the policy learning model. In an empirical second-stage application, we further investigate the underlying dynamics between the epidemiological parameters and household debt repayments, a key economic variable, in the UK. Results show no Bayesian learning, although a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact. Therefore, suboptimal learning is taking place.
Free access at Official URL
Digital Object Identifier 10.1080/00036846.2023.2203462
Other Identification Number merlin-id:23690
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