Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/65529
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dc.contributor.authorNatthaphat Kingnetren_US
dc.contributor.authorSupanika Leurcharusmeeen_US
dc.contributor.authorJirakom Sirisrisakulchaien_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2019-08-05T04:35:04Z-
dc.date.available2019-08-05T04:35:04Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85065612974en_US
dc.identifier.other10.1007/978-3-030-04200-4_65en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065612974&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/65529-
dc.description.abstract© Springer Nature Switzerland AG 2019. In this study, we investigate the labour income risk across industries in Thailand using the Labour Force Survey (LFS) data over 2008–2017 consisting of more than a million individuals. Two types of income risk are considered in this study: permanent and transitory. In order to estimate the risk, the LFS data is transformed into the pseudo-panel framework based on multiple labour characteristics. The results suggest that the transitory income risk is nearly twice as large as the permanent. In addition, we found that the top five industries facing strong income risks are transportation, agriculture, professional activities, manufacturing and financial and insurance activities.en_US
dc.subjectComputer Scienceen_US
dc.titleIncome risk across industries in Thailand: A pseudo-panel analysisen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume809en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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