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?:abstract
  • The paper aimed to investigate the determinants of both one-dimensional and multidimensional affluence and to compare these with the determinants of one-dimensional poverty. The study was conducted based on the Polish data from EU-SILC from 2020. Household income was analyzed in the one-dimensional approach, while the multidimensional analysis considered eight determinants: the ability to make ends meet, capacity to face unexpected financial expenses, total housing cost, repayment of debts, arrears, capacity to afford to pay for the one-week annual holiday, income from rental and interests, and household income. It was assumed, that four and five dimensions are required to be affluent (two versions were included in the analysis). Logistic regression (a type of supervised learning algorithm) was used to identify the determinants of affluence and poverty, incorporating explanatory variables such as the sex, age, and education level of the main respondent, household type, urbanisation level, and region. Full models (with all explanatory variables) and models with subsets of indicators were estimated. The education level is the most important determinant of one- and multidimensional affluence. While the determinants of one-dimensional affluence and poverty are similar, household type emerged as a more critical determinant of poverty, almost as significant as the education level (AUC higher for education level and intersecting ROC curves for these two determinants). The odds of being affluent or poor were significantly higher in the Mazowieckie voivodeship compared to the south region, and lower in households with children compared to single-person households. (xsd:string)
?:author
?:comment
  • (SILC) (xsd:string)
?:dataSource
  • EU-SILC-Bibliography (xsd:string)
?:dateModified
  • 2024 (xsd:gyear)
?:datePublished
  • 2024 (xsd:gyear)
?:doi
  • 10.1016/j.procs.2024.09.360 ()
?:fromPage
  • 3122 (xsd:string)
is ?:hasPart of
?:inLanguage
  • english (xsd:string)
?:isPartOf
?:issn
  • 18770509 ()
?:name
  • One-dimensional and multidimensional affluence: analysis of determinants using a supervised learning algorithm (xsd:string)
?:publicationType
  • article (xsd:string)
?:reference
?:sourceInfo
  • Bibsonomy (xsd:string)
  • In Procedia Computer Science, 246, 3122-3131, 2024 (xsd:string)
?:studyGroup
  • European Union Statistics on Income and Living Conditions (EU-SILC) (xsd:string)
?:tags
  • 2024 (xsd:string)
  • FDZ_GML (xsd:string)
  • SILC (xsd:string)
  • SILC_input2024 (xsd:string)
  • SILC_pro (xsd:string)
  • article (xsd:string)
  • english (xsd:string)
  • indexproved (xsd:string)
  • noindex (xsd:string)
  • qq (xsd:string)
  • review_proved (xsd:string)
  • transfer24 (xsd:string)
?:toPage
  • 3131 (xsd:string)
rdf:type
?:url
?:volumeNumber
  • 246 (xsd:string)