Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics

dc.contributor.author Gorkem Sariyer
dc.contributor.author Serpil Kahraman
dc.contributor.author Mert Erkan Sözen
dc.contributor.author Mustafa Gökalp Ataman
dc.date.accessioned 2025-10-06T17:49:33Z
dc.date.issued 2023
dc.description.abstract Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021 this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries which had the largest total and health-related fiscal responses achieved the lowest fatality rates defined as the ratio of COVID-related deaths to cases despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology the random forest algorithm to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level fiscal expenditure (both total and health-related) and initial fatality ratio. Finally the study conducted a counterfactual exercise to show that had less developed economies implemented the same fiscal responses as the rich (as a share of GDP) then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one. © 2023 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.1016/j.strueco.2022.12.011
dc.identifier.issn 0954349X
dc.identifier.issn 0954-349X
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145861191&doi=10.1016%2Fj.strueco.2022.12.011&partnerID=40&md5=511c5c2e56ffa61b478701aef038ae4a
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/8477
dc.language.iso English
dc.publisher Elsevier B.V.
dc.relation.ispartof Structural Change and Economic Dynamics
dc.source Structural Change and Economic Dynamics
dc.subject Big Data Analytics, Covid-19, Economic Development Level, Fiscal Policy, Random Forest, Covid-19, Data Set, Development Level, Fiscal Policy, Machine Learning
dc.subject COVID-19, data set, development level, fiscal policy, machine learning
dc.title Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics
dc.type Article
dspace.entity.type Publication
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gdc.coar.type text::journal::journal article
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gdc.description.endpage 198
gdc.description.startpage 191
gdc.description.volume 64
gdc.identifier.openalex W4313252601
gdc.identifier.pmid 36590330
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gdc.oaire.keywords Article
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gdc.oaire.sciencefields 0502 economics and business
gdc.oaire.sciencefields 05 social sciences
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gdc.opencitations.count 12
gdc.plumx.crossrefcites 9
gdc.plumx.mendeley 25
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gdc.virtual.author Sözen, Mert Erkan
oaire.citation.endPage 198
oaire.citation.startPage 191
person.identifier.scopus-author-id Sariyer- Gorkem (57189867008), Kahraman- Serpil (57210156913), Sözen- Mert Erkan (57430116000), Ataman- Mustafa Gökalp (57192943136)
project.funder.name This research did not receive any specific grant from funding agencies in the public commercial or not-for-profit sectors.
publicationvolume.volumeNumber 64
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