Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/27113
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dc.contributor.authorKantachote K.
dc.contributor.authorWiroonsri N.
dc.date.accessioned2022-12-14T03:16:54Z-
dc.date.available2022-12-14T03:16:54Z-
dc.date.issued2022
dc.identifier.issn335177
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127308404&doi=10.1007%2fs11135-022-01366-0&partnerID=40&md5=6e7c854661bdecd91cefcf2f0d9cc332
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/27113-
dc.description.abstractThailand has entered into an aging society since the year 2000. Using the 2017 Survey of the Older Persons in Thailand collected by National Statistical Office of Thailand, this study uses cross tabulation, random forest with variable importance measure and lasso logistic regression to examine factors that have effects on the elderly’s decision to remain in the labor market after retirement. This study reveals that these following variables: age, education level, healthcare eligibility, marital status, health condition, total assets, gender, residential type, percent of elderly in the household, and number of children have strong influences on an elderly’s desire to continue work. By knowing which factors contribute to the elderly wish to continue work in the market, this research allows for future prediction of the labor market that can accommodate elderly in Thailand. Our final models of random forest and lasso logistic regression provide prediction accuracy of 68.19 and 69.58 percent on the elderly’s desire to work, respectively. This study has a significant impact as policymakers can utilize our models in predicting elderly’s desire to work after retirement age and design a labor market that can accommodate elderly in Thailand in the future. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
dc.languageen
dc.publisherSpringer Science and Business Media B.V.
dc.subjectAging society
dc.subjectElderly retirement
dc.subjectLasso logistic regression
dc.subjectMachine learning
dc.subjectRandom forest
dc.subjectThailand
dc.titleDo elderly want to work? Modeling elderly’s decision to fight aging Thailand
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationACS Omega. Vol 7, No.21 (2022), p.17881-17893
dc.identifier.doi10.1007/s11135-022-01366-0
Appears in Collections:Scopus 2022

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