2023-09-28 14:46:10 +00:00
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abstract: 'Purpose This paper aims to identify the disproportionate impacts of the
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COVID-19 pandemic on labor markets. Design/methodology/approach The
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authors conduct a large-scale survey on 16,000 firms from 82 industries
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in Ho Chi Minh City, Vietnam, and analyze the data set by using
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different machine-learning methods. Findings First, job loss and
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reduction in state-owned enterprises have been significantly larger than
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in other types of organizations. Second, employees of foreign direct
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investment enterprises suffer a significantly lower labor income than
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those of other groups. Third, the adverse effects of the COVID-19
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pandemic on the labor market are heterogeneous across industries and
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geographies. Finally, firms with high revenue in 2019 are more likely to
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adopt preventive measures, including the reduction of labor forces. The
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authors also find a significant correlation between firms'' revenue and
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labor reduction as traditional econometrics and machine-learning
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techniques suggest. Originality/value This study has two main policy
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implications. First, although government support through taxes has been
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provided, the authors highlight evidence that there may be some
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additional benefit from targeting firms that have characteristics
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associated with layoffs or other negative labor responses. Second, the
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authors provide information that shows which firm characteristics are
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associated with particular labor market responses such as layoffs, which
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may help target stimulus packages. Although the COVID-19 pandemic
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affects most industries and occupations, heterogeneous firm responses
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suggest that there could be several varieties of targeted
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policies-targeting firms that are likely to reduce labor forces or firms
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likely to face reduced revenue. In this paper, the authors outline
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several industries and firm characteristics which appear to more
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directly be reducing employee counts or having negative labor responses
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which may lead to more cost-effect stimulus.'
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affiliation: 'Huynh, TLD (Corresponding Author), Univ Econ Ho Chi Minh City, Sch Banking,
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Ho Chi Minh City, Vietnam.
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Lam Hoang Viet Le, Univ Peoples Secur, Ho Chi Minh City, Vietnam.
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Toan Luu Duc Huynh, Univ Econ Ho Chi Minh City, Sch Banking, Ho Chi Minh City, Vietnam.
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Toan Luu Duc Huynh, WHU Otto Beisheim Sch Management, Chair Behav Finance, Vallendar,
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Germany.
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Weber, Bryan S., CUNY Coll Staten Isl, New York, NY USA.
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Bao Khac Quoc Nguyen, Univ Econ Ho Chi Minh City, Sch Finance, Ho Chi Minh City,
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Vietnam.'
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author: Le, Lam Hoang Viet and Huynh, Toan Luu Duc and Weber, Bryan S. and Nguyen,
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Bao Khac Quoc
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author-email: toanhld@ueh.edu.vn
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author_list:
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- family: Le
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given: Lam Hoang Viet
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- family: Huynh
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given: Toan Luu Duc
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- family: Weber
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given: Bryan S.
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- family: Nguyen
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given: Bao Khac Quoc
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da: '2023-09-28'
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doi: 10.1108/IJOEM-02-2021-0292
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earlyaccessdate: JUL 2021
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eissn: 1746-8817
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files: []
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issn: 1746-8809
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journal: INTERNATIONAL JOURNAL OF EMERGING MARKETS
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keywords: 'COVID-19; Employment; Labor forces; Organizational behavior;
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Disparities; Vietnam; J22; J23; J21; J62; J63; J64; E24'
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keywords-plus: CRISIS
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language: English
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month: 2021 JUL 27
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number-of-cited-references: '56'
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orcid-numbers: 'Weber, Bryan/0000-0003-1806-4451
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Nguyen, Khac Quoc Bao/0000-0001-7735-2096
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Huynh, Toan Luu Duc/0000-0002-1486-127X'
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papis_id: 50f865b105a872f98498ad3d3bc305ae
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ref: Le2021differentfirm
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times-cited: '5'
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title: 'Different firm responses to the COVID-19 pandemic shocks: machine-learning
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evidence on the Vietnamese labor market'
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2023-10-01 08:15:07 +00:00
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type: article
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2023-09-28 14:46:10 +00:00
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unique-id: WOS:000678046000001
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usage-count-last-180-days: '1'
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usage-count-since-2013: '21'
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web-of-science-categories: Business; Economics; Management
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year: '2021'
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