- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
The regional gender equality index (R-GEI) through PLS-PM models
- Sara Preti
- Enrico di Bella
- Leonardo Salvatore Alaimo
- Annalisa Bellino
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.58
Gender equality is a pressing and globally relevant issue that has gained increasing attention in recent years. While various indicators have been proposed to measure gender equality, studies that analyze its regional variability remain limited and underexplored. In this study, we develop a composite gender equality index for each Italian region using Partial Least Squares Path Modeling (PLS-PM). This methodological approach not only enables us to synthesize the multiple dimensions of the complex phenomenon of gender equality but also allows us to examine the relationships and relative influence of each individual indicator. To construct our Regional Gender Equality Index (R-GEI), we draw on widely recognized indicators used in the literature, particularly those employed by the European Institute for Gender Equality (EIGE) in developing its Gender Equality Index (GEI). The analysis focuses on two specific years - 2013 and 2022 - allowing us to assess potential changes that may have occurred after the COVID-19 pandemic. Our findings highlight the importance of analyzing gender gaps at the regional level to identify benchmark models, set strategic priorities, and guide targeted regional policy actions.
- Keywords:
- Single Digital Market,
- Data governance,
- NSIs,
- Schema.gov.it,
- Semantic interoperability,
University of Genoa, Italy - ORCID: 0000-0002-7424-9998
University of Genoa, Italy - ORCID: 0000-0002-2792-3476
Sapienza University of Rome, Italy - ORCID: 0000-0002-9785-0436
Regione Puglia, Italy
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Informazioni sul capitolo
Titolo del capitolo
The regional gender equality index (R-GEI) through PLS-PM models
Autori
Sara Preti, Enrico di Bella, Leonardo Salvatore Alaimo, Annalisa Bellino
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.58
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series