{
  "abstract": "Poverty reduction remains one of the socio-economic concerns of the international agenda. This research aims to examine the multidimensional nature of poverty across EU Member States through a multivariate analysis of selected socio-economic indicators. Ten indicators were initially selected to capture the multidimensional nature of poverty and the broader socio-economic situation encompassing income poverty and deprivation within the sustainable development goal 1, housing conditions, economic performance and digital factors. Based on the Kaiser-Meyer-Olkin test and subsequent statistical analyses, six indicators were retained for the final multivariate analysis. Data were drawn from the Eurostat Database. Univariate and multivariate statistical techniques were applied during the analyses. Pearson’s correlation coefficient was used to find linear relationships between pairs of indicators, while a principal component analysis and a cluster analysis were implemented to examine multidimensional structures and patterns. The multivariate analysis was conducted across three time spans: 2015–2019, 2020 (analysed separately as the Covid-19 pandemic disrupted economies) and 2021–2023. The results reveal a general decline in poverty; nevertheless, they also emphasise persistent regional disparities across EU Member States. Bulgaria, Greece, Romania, and Latvia form the most vulnerable cluster in the final period, whereas several countries in Northern and Western Europe show more stable trends. The results further indicate that multidimensional poverty profiles cannot be explained solely by economic performance or geographical location. In conclusion, the cluster-based approach provides a dynamic perspective on the diverse experiences of poverty in the EU. The results show the need for tailored policy responses that reflect the specificities of individual EU countries and clusters to promote sustainable and inclusive development and to strengthen resilience to potential future crises.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Business Economics with seat in Košice, Bratislava University of Economics and Business, Košice, Slovak Republic"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Silvia Megyesiova"
    },
    {
      "name": "Cecilia Olexova"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3kj7yq4cdmk13.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0343050",
  "fulltext_url": null,
  "id": "3kj7yq4cdmk13",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0343050",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Income, Poverty, and Inequality"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Human geography/Housing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Pandemics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Socioeconomic aspects of health"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Socioeconomic aspects of health"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Europe"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Europe/European Union"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Clustering algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Clustering algorithms"
    ],
    [
      "plos",
      "/Social sciences/Economics/Human capital/Economics of poverty"
    ],
    [
      "plos",
      "/Social sciences/Human geography/Housing"
    ]
  ],
  "openalex": "W7214973895",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0343050&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/3kj7yq4cdmk13.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0343050",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0343050"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Multivariate analysis of poverty and social exclusion across EU countries in the context of Sustainable Development Goal 1",
  "title_text": "Multivariate analysis of poverty and social exclusion across EU countries in the context of Sustainable Development Goal 1",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/3kj7yq4cdmk13",
  "venue": "PLOS One",
  "versions": []
}