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dc.contributor.authorColgan, Briande
dc.date.accessioned2024-03-08T11:15:18Z
dc.date.available2024-03-08T11:15:18Z
dc.date.issued2023de
dc.identifier.issn1435-8921de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/92865
dc.description.abstractIn the absence of panel data, researchers have devised alternative methods for estimating synthetic poverty dynamics using repeated cross section surveys. These methods are not only salient in the absence of panel data, but also in contexts where there are concerns over the quality of panel data and/or the panel data are of insufficient length to analyse medium- to long-term mobility trends. Both of these issues afflict the longitudinal element of the European Survey on Income and Living Conditions (EU-SILC) (Hérault and Jenkins, J Econ Inequ 17(1):51–76, 2019). Using the longitudinal element of EU-SILC, this paper assesses the accuracy of the synthetic panel approach put forth by Dang and Lanjouw (2021). For most conventional poverty lines, the DL approach is found to be highly accurate when the true \(\rho \) is known. Similar to Hérault and Jenkins (J Econ Inequ 17(1):51-76, 2019) the pseudo-panel approach for estimating \(\rho \) is found to be highly sensitive to cohort definition. The longitudinal element of EU-SILC, however, offers a unique route for overcoming this shortcoming.de
dc.languageende
dc.subject.ddcSozialwissenschaften, Soziologiede
dc.subject.ddcSocial sciences, sociology, anthropologyen
dc.subject.othersynthetic panel; pseudo-panel; poverty dynamics; EU-SILCde
dc.titleEU-SILC and the potential for synthetic panel estimatesde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalEmpirical Economics
dc.source.volume64de
dc.publisher.countryDEUde
dc.source.issue3de
dc.subject.classozForschungsarten der Sozialforschungde
dc.subject.classozResearch Designen
dc.subject.classozErhebungstechniken und Analysetechniken der Sozialwissenschaftende
dc.subject.classozMethods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methodsen
dc.subject.thesozPanelde
dc.subject.thesozpanelen
dc.subject.thesozArmutde
dc.subject.thesozpovertyen
dc.subject.thesozSchätzungde
dc.subject.thesozestimationen
dc.subject.thesozQuerschnittuntersuchungde
dc.subject.thesozcross-sectional studyen
dc.identifier.urnurn:nbn:de:0168-ssoar-92865-2
dc.rights.licenceCreative Commons - Namensnennung 4.0de
dc.rights.licenceCreative Commons - Attribution 4.0en
ssoar.contributor.institutionFDBde
internal.statusformal und inhaltlich fertig erschlossende
internal.identifier.thesoz10054018
internal.identifier.thesoz10036765
internal.identifier.thesoz10057146
internal.identifier.thesoz10055842
dc.type.stockarticlede
dc.type.documentZeitschriftenartikelde
dc.type.documentjournal articleen
dc.source.pageinfo1247-1280de
internal.identifier.classoz10104
internal.identifier.classoz10105
internal.identifier.journal2137
internal.identifier.document32
internal.identifier.ddc300
dc.identifier.doihttps://doi.org/10.1007/s00181-022-02277-7de
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence16
internal.identifier.pubstatus1
internal.identifier.review1
internal.pdf.validtrue
internal.pdf.wellformedtrue
internal.pdf.encryptedfalse


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