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DC Field | Value | Language |
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dc.contributor.author | Natthaphat Kingnetr | en_US |
dc.contributor.author | Supanika Leurcharusmee | en_US |
dc.contributor.author | Jirakom Sirisrisakulchai | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.date.accessioned | 2019-08-05T04:35:04Z | - |
dc.date.available | 2019-08-05T04:35:04Z | - |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.issn | 1860949X | en_US |
dc.identifier.other | 2-s2.0-85065612974 | en_US |
dc.identifier.other | 10.1007/978-3-030-04200-4_65 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065612974&origin=inward | en_US |
dc.identifier.uri | http://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.subject | Computer Science | en_US |
dc.title | Income risk across industries in Thailand: A pseudo-panel analysis | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Studies in Computational Intelligence | en_US |
article.volume | 809 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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