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Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data

Abstract:
The implementation of large-scale containment measures by governments to contain the spread of the COVID-19 virus has resulted in large impacts to the global economy. Here, we derive a new high-frequency indicator of economic activity using empirical vessel tracking data, and use it to estimate the global maritime trade losses during the first eight months of the pandemic. We go on to use this high-frequency dataset to infer the effect of individual non-pharmaceutical interventions on maritime exports, which we use as a proxy of economic activity. Our results show widespread port-level trade losses, with the largest absolute losses found for ports in China, the Middle-East and Western Europe, associated with the collapse of specific supply-chains (e.g. oil, vehicle manufacturing). In total, we estimate that global maritime trade reduced by -7.0% to -9.6% during the first eight months of 2020, which is equal to around 206–286 million tonnes in volume losses and up to 225–412 billion USD in value losses. We find large sectoral and geographical disparities in impacts. Manufacturing sectors are hit hardest, with losses up to 11.8%, whilst some small islands developing states and low-income economies suffered the largest relative trade losses. Moreover, we find a clear negative impact of COVID-19 related school and public transport closures on country-wide exports. Overall, we show how real-time indicators of economic activity can inform policy-makers about the impacts of individual policies on the economy, and can support economic recovery efforts by allocating funds to the hardest hit economies and sectors.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pone.0248818

Authors

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Role:
Author
ORCID:
0000-0002-5277-4353
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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Role:
Author


Publisher:
Public Library of Science
Journal:
PLOS ONE More from this journal
Volume:
16
Issue:
4
Article number:
e0248818
Publication date:
2021-04-14
Acceptance date:
2021-03-06
DOI:
EISSN:
1932-6203
Pmid:
33852593


Language:
English
Keywords:
Pubs id:
1172837
Local pid:
pubs:1172837
Deposit date:
2021-08-10
ARK identifier:

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