Depopulation processes are strongly connected with ageing, driven by a lower birth rate compared to the death rate and negative net migration. An alternative to migration is commuting, which may help counteract depopulation. However, travel-to-work or travel-to-school patterns significantly impact subjective well-being and quality of life, especially for residents in inner areas. This paper aims to study the relationships between depopulation processes, mobility choices, daily life activities and individual well-being by taking into account the spatial heterogeneity between those living in small towns and disadvantaged areas versus those living in core areas. Our case study is Italy. To investigate the determinants of positive commuting experiences, we draw on the most recent wave (2013–2014) of the Italian Multipurpose Time Use Survey (IT-TUS). Our results confirm that commuting is not experienced uniformly across individuals and territories but reflects the interplay between spatial opportunities and gendered time constraints.
Sapienza University of Rome, Italy - ORCID: 0000-0002-3483-6936
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0003-2412-0819
Sapienza University of Rome, Italy - ORCID: 0000-0002-2352-3571
Sapienza University of Rome, Italy - ORCID: 0000-0001-7533-1435
Titolo del capitolo
Depopulation of inner areas and subjective wellbeing during commuting: Evidence from the Italian time use survey
Autori
Erica Aloè, Marina Zannella, Roberta Di Stefano, Alessandra De Rose
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.4
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
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