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dc.creatorLuna Romera, José Maríaes
dc.creatorNúñez Hernández, Fernandoes
dc.creatorMartínez Ballesteros, María del Mares
dc.creatorRiquelme Santos, José Cristóbales
dc.creatorIbáñez, Carlos Usabiagaes
dc.date.accessioned2020-03-11T17:08:59Z
dc.date.available2020-03-11T17:08:59Z
dc.date.issued2019
dc.identifier.citationLuna Romera, J.M., Núñez Hernández, F., Martínez Ballesteros, M.d.M., Riquelme Santos, J.C. y Ibáñez, C.U. (2019). Analysis of the evolution of the Spanish labour market through unsupervised learning. IEEE Access 7 (Article number 802202005), 121695-121708.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/94124
dc.description.abstractUnemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in the European Union, and in the second quarter of 2018 there is a 15.2% unemployment rate, some 3.4 million unemployed. Construction is one of the activity sectors that have suffered the most from the economic crisis. In addition, the economic crisis affected in different ways to the labour market in terms of occupation level or location. The aim of this paper is to discover how the labour market is organised taking into account the jobs that workers get during two periods: 2011-2013, which corresponds to the economic crisis period, and 2014-2016, which was a period of economic recovery. The data used are official records of the Spanish administration corresponding to 1.9 and 2.4 million job placements, respectively. The labour market was analysed by applying unsupervised machine learning techniques to obtain a clear and structured information on the employment generation process and the underlying labour mobility. We have applied two clustering methods with two different technologies, and the results indicate that there were some movements in the Spanish labour market which have changed the physiognomy of some of the jobs. The analysis reveals the changes in the labour market: the crisis forces greater geographical mobility and favours the subsequent emergence of new job sources. Nevertheless, there still exist some clusters that remain stable despite the crisis. We may conclude that we have achieved a characterisation of some important groups of workers in Spain. The methodology used, being supported by Big Data techniques, would serve to analyse any alternative job market.es
dc.description.sponsorshipMinisterio de Economía y Competitividad TIN2014-55894-C2-R y TIN2017-88209-C2-2-R, CO2017-86780es
dc.formatapplication/pdfes
dc.format.extent14 p.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)es
dc.relation.ispartofIEEE Access 7, 2019, 7 (Article number 802202005), 121695-121708.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLabour marketes
dc.subjectCluster analysises
dc.subjectLabour mobilityes
dc.subjectBig dataes
dc.titleAnalysis of the evolution of the Spanish labour market through unsupervised learninges
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas Ies
dc.relation.projectIDTIN2014-55894-C2-Res
dc.relation.projectIDTIN2017-88209-C2-2-Res
dc.relation.projectIDCO2017-86780es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8798630es
dc.identifier.doi10.1109/ACCESS.2019.2935386es
dc.journaltitleIEEE Accesses
dc.publication.volumen7es
dc.publication.issueArticle number 802202005es
dc.publication.initialPage121695es
dc.publication.endPage121708es

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