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[journal article]

dc.contributor.authorGreselin, Francescade
dc.contributor.authorJȩdrzejczak, Alinade
dc.contributor.authorTrzcińska, Kamilade
dc.date.accessioned2024-11-14T10:06:22Z
dc.date.available2024-11-14T10:06:22Z
dc.date.issued2023de
dc.identifier.issn1932-1872de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/97850
dc.description.abstractReal income distribution comparisons are of interest to policy makers across European countries. Nowadays, a crucial component of income inequality remains the discrepancy between men and women, often called the gender gap. Since the gender gap is related to the whole distribution of incomes in a population, popular single metrics are not adequate, and previous studies applied the relative distribution method, a non-parametric approach to the comparison of distributions. Here, we propose a parametric approach for estimating the relative distribution. Then we extend it to assess the impact of selected covariates - related to the personal characteristics of the samples - on the existing gender gap in both countries. In more detail, models for income were fitted to empirical data from Poland and Italy, from the European Survey of Income and Living Conditions (wave 2018). Afterwards, their parameters were employed to obtain the estimates of relative distribution characteristics. The methods applied in the study turned out to be relevant to describe the gender gap over the entire income range. Finally, the results of the empirical analysis are discussed to reveal similarities and substantial differences between the countries.de
dc.languageende
dc.subject.ddcSozialwissenschaften, Soziologiede
dc.subject.ddcSocial sciences, sociology, anthropologyen
dc.subject.otherDagum; gendergap; parametric inference; relative distribution method; EU-SILC 2018de
dc.titleA new parametric approach to gender gap with application to EUSILC data in Poland and Italyde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalStatistical Analysis and Data Mining: The ASA Data Science Journal
dc.source.volume16de
dc.publisher.countryUSAde
dc.source.issue4de
dc.subject.classozErhebungstechniken und Analysetechniken der Sozialwissenschaftende
dc.subject.classozMethods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methodsen
dc.subject.thesozEinkommensunterschiedde
dc.subject.thesozdifference in incomeen
dc.subject.thesozUngleichheitde
dc.subject.thesozinequalityen
dc.subject.thesozEinkommensverteilungde
dc.subject.thesozincome distributionen
dc.subject.thesozgeschlechtsspezifische Faktorende
dc.subject.thesozgender-specific factorsen
dc.subject.thesozItaliende
dc.subject.thesozItalyen
dc.subject.thesozPolende
dc.subject.thesozPolanden
dc.subject.thesozDatengewinnungde
dc.subject.thesozdata captureen
dc.subject.thesozSchätzungde
dc.subject.thesozestimationen
dc.subject.thesozParameterde
dc.subject.thesozparameteren
dc.identifier.urnurn:nbn:de:0168-ssoar-97850-6
dc.rights.licenceCreative Commons - Namensnennung, Nicht-kommerz. 4.0de
dc.rights.licenceCreative Commons - Attribution-NonCommercial 4.0en
ssoar.contributor.institutionFDBde
internal.statusformal und inhaltlich fertig erschlossende
internal.identifier.thesoz10041654
internal.identifier.thesoz10041153
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internal.identifier.thesoz10048114
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dc.type.stockarticlede
dc.type.documentZeitschriftenartikelde
dc.type.documentjournal articleen
dc.source.pageinfo319-335de
internal.identifier.classoz10105
internal.identifier.journal3163
internal.identifier.document32
internal.identifier.ddc300
dc.source.issuetopicCLADAG 2021 special issue: Selected papers on classification and data analysisde
dc.identifier.doihttps://doi.org/10.1002/sam.11623de
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence32
internal.identifier.pubstatus1
internal.identifier.review1
internal.pdf.validfalse
internal.pdf.wellformedfalse
internal.pdf.encryptedfalse


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