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Spatial Determinants of Life Satisfaction on the Aquitaine Coast: A Geographically-Weighted Regression Approach

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Listed:
  • Jean-Christophe Dissart

    (Univ. Grenoble Alpes)

  • Vanessa Kuentz-Simonet

    (INRAE Nouvelle-Aquitaine Bordeaux)

Abstract

This article explores the determinants of life satisfaction in Nouvelle-Aquitaine, France. We use the capability approach as the theoretical framework of the research, which enables us to select variables that reflect freedom of access to leisure activities, everyday life services and mobility. We also focus on place by taking account of amenities. Using Geographically Weighted Regression (GWR) on data from 1,039 questionnaires completed in 2021, measures of life satisfaction are regressed locally on synthetic variables that reflect place-based capabilities and amenities, and control variables that reflect more general socio-economic conditions. Empirical results show that though life satisfaction levels are generally high across the study area, space does play a role in explaining those levels as the most contributing variables differ from one area to another. In particular, all synthetic variables of place-based capabilities and amenities contribute to well-being variation, as well as household economic situation and housing condition, which reflect broader capabilities. Therefore, capability- and amenity-related variables largely explain life satisfaction, and GWR captures spatial differentiation.

Suggested Citation

  • Jean-Christophe Dissart & Vanessa Kuentz-Simonet, 2025. "Spatial Determinants of Life Satisfaction on the Aquitaine Coast: A Geographically-Weighted Regression Approach," Journal of Happiness Studies, Springer, vol. 26(2), pages 1-25, February.
    • Handle: RePEc:spr:jhappi:v:26:y:2025:i:2:d:10.1007_s10902-024-00842-8
    DOI: 10.1007/s10902-024-00842-8
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    References listed on IDEAS

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    1. Kubiszewski, Ida & Jarvis, Diane & Zakariyya, Nabeeh, 2019. "Spatial variations in contributors to life satisfaction: An Australian case study," Ecological Economics, Elsevier, vol. 164(C), pages 1-1.
    2. Schaeffer, Y. & Dissart, J.-C., 2018. "Natural and Environmental Amenities: A Review of Definitions, Measures and Issues," Ecological Economics, Elsevier, vol. 146(C), pages 475-496.
    3. Fleurbaey, Marc & Blanchet, Didier, 2013. "Beyond GDP: Measuring Welfare and Assessing Sustainability," OUP Catalogue, Oxford University Press, number 9780199767199, Decembrie.
    4. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    5. Mohsen Joshanloo & Veljko Jovanović & Tim Taylor, 2019. "A multidimensional understanding of prosperity and well-being at country level: Data-driven explorations," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-31, October.
    6. Chavent, Marie & Kuentz-Simonet, Vanessa & Liquet, Benoît & Saracco, Jérôme, 2012. "ClustOfVar: An R Package for the Clustering of Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i13).
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