Evidence map›Paper›PMID 42814725›Full record

ArticlePloS one2026

Multivariate analysis of poverty and social exclusion across EU countries in the context of Sustainable Development Goal 1.

Silvia Megyesiova, Cecilia Olexova

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Silvia MegyesiovaFaculty of Business Economics with seat in Košice, Bratislava University of Economics and Business, Košice, Slovak Republic.ORCID https://orcid.org/0000-0002-6815-5629
Cecilia OlexovaFaculty of Business Economics with seat in Košice, Bratislava University of Economics and Business, Košice, Slovak Republic.ORCID https://orcid.org/0000-0003-2154-9564

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

PovertySustainable DevelopmentCluster AnalysisEuropean UnionHumansMultivariate AnalysisSocioeconomic Factors

Identifiers

PMID42814725
PMCPMC13626335

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