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

Basit Öğe Kaydı
dc.authoridKahraman, Serpil/0000-0003-4570-1604
dc.authoridsariyer, gorkem/0000-0002-8290-2248
dc.authoridAtaman, Mustafa Gökalp/0000-0003-4468-0020
dc.authoridSOZEN, Mert Erkan/0000-0002-7965-6461
dc.authorwosidKahraman, Serpil/B-4175-2016
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.authorwosidAtaman, Mustafa Gökalp/O-4644-2017
dc.contributor.authorSarıyer, Görkem
dc.contributor.authorKahraman, Serpil
dc.contributor.authorSözen, Mert Erkan
dc.contributor.authorAtaman, Mustafa Gökalp
dc.date.accessioned2023-03-22T19:47:28Z
dc.date.available2023-03-22T19:47:28Z
dc.date.issued2023
dc.departmentBelirleneceken_US
dc.description.abstractFiscal 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.en_US
dc.identifier.doi10.1016/j.strueco.2022.12.011
dc.identifier.endpage198en_US
dc.identifier.issn0954-349X
dc.identifier.issn1873-6017
dc.identifier.pmid36590330en_US
dc.identifier.scopus2-s2.0-85145861191en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage191en_US
dc.identifier.urihttps://doi.org/10.1016/j.strueco.2022.12.011
dc.identifier.urihttps://hdl.handle.net/20.500.14034/717
dc.identifier.volume64en_US
dc.identifier.wosWOS:000918980200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.journalStructural Change And Economic Dynamicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFiscal policyen_US
dc.subjectCOVID-19en_US
dc.subjectEconomic development levelen_US
dc.subjectBig data analyticsen_US
dc.subjectRandom foresten_US
dc.titleFiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analyticsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Fiscal response.pdf
Boyut:
3.44 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin / Full text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Dahili Tıp Bilimleri Bölümü Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu