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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| 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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