What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms

Niccolò Gentile, Michela Bia, Andrew E. Clark (), Conchita d'Ambrosio and Alexandre Tkatchenko
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Niccolò Gentile: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
Michela Bia: LISER - Luxembourg Institute of Socio-Economic Research
Andrew E. Clark: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
Conchita d'Ambrosio: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
Alexandre Tkatchenko: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg

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Abstract: Machine Learning (ML) methods are increasingly being used across a variety of fields, and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non‐linear method, Random Forests. We present two key model‐agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non‐penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non‐negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most‐important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non‐linear analysis.

Keywords: British Cohort Study; Life satisfaction; Machine learning; Well-being (search for similar items in EconPapers)
Date: 2025-05
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Published in Review of Income and Wealth, 2025, 71 (2), pp.e70003. ⟨10.1111/roiw.70003⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-05148848

DOI: 10.1111/roiw.70003

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