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Clustering techniques applied on cross-sectional unemployment data

2015Conference object Open access
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dc.contributor.authorBalsa, Carlos
dc.contributor.authorNunes, Alcina
dc.contributor.authorBarros, Elisa
dc.date.accessioned2018-03-26T11:22:56Z
dc.date.available2018-03-26T11:22:56Z
dc.date.issued2015
dc.description.abstractUsing a cross-section database that observes the Portuguese labour market in two different phases of the business cycle, the present paper aims to address the issue of the segmentation of the Portuguese labour market taking into account the heterogeneity resulting from different unemployment characteristics observed along the Portuguese geographical space and applying two optimization clustering methods: the k-means and the spectral methods. The k-means is a traditional optimisation clustering method applied to cluster data observations. Spectral clustering is an alternative method based on the computation of the dominant eigenvectors of a matrix related with the distance among data points. The results obtained by the two methods are not identical but are very close and show that, apart the economic phase of the cycle, Portugal presents two very different profiles of registered unemployment. One of them can be considered problematic because it presents a higher percentage of unemployed women, long duration unemployed and unemployed with low levels of formal education—these are the groups that present more difficulties in the labour market and for which is more difficult to find a job after losing one. The segmentation of the labour market is a reality and the labour market is not adjusting to the business cycle.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBalsa, Carlos; Nunes, Alcina; Barros, Elisa (2015). Clustering techniques applied on cross-sectional unemployment data. In Bourguignon, Jean-Pierre [et al.] (Eds.) Dynamics, Games and Science. springer. 1, p. 71-88. ISBN 978-3-319-16117-4pt_PT
dc.identifier.doi10.1007/978-3-319-16118-1_5pt_PT
dc.identifier.isbn978-3-319-16117-4
dc.identifier.urihttp://hdl.handle.net/10198/16549
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.ispartofseriesCIM Series in Mathematical Sciences;
dc.relation.publisherversionwww.springer.compt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.titleClustering techniques applied on cross-sectional unemployment datapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlacePortugalpt_PT
oaire.citation.endPage88pt_PT
oaire.citation.startPage71pt_PT
oaire.citation.titlenternational Conference and Advanced School Planet Earth, Dynamics, Games and Science II (DGS II)pt_PT
oaire.citation.volume1pt_PT
person.familyNameBalsa
person.familyNameNunes
person.familyNameBarros
person.givenNameCarlos
person.givenNameAlcina
person.givenNameElisa
person.identifier1721518
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.ciencia-id1111-680F-0CAF
person.identifier.orcid0000-0003-2431-8665
person.identifier.orcid0000-0003-4056-9747
person.identifier.orcid0000-0001-8515-695X
person.identifier.ridM-8735-2013
person.identifier.ridM-8259-2013
person.identifier.scopus-author-id23391719100
person.identifier.scopus-author-id55907654000
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublicationf96c3560-c1d3-432c-aa84-49982ea86106
relation.isAuthorOfPublication29601d32-5c12-4b5f-84ec-55d83617d04e
relation.isAuthorOfPublication.latestForDiscovery29601d32-5c12-4b5f-84ec-55d83617d04e

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