Machine learning in the prediction of human wellbeing

Ekaterina Oparina, Caspar Kaiser, Niccolò Gentile, Alexandre Tkatchenko, Andrew E. Clark (), Jan-Emmanuel de Neve and Conchita D’ambrosio
Additional contact information
Ekaterina Oparina: LSE - London School of Economics and Political Science
Caspar Kaiser: University of Oxford, WBS - Warwick Business School - University of Warwick [Coventry]
Niccolò Gentile: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
Alexandre Tkatchenko: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
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
Jan-Emmanuel de Neve: University of Oxford
Conchita D’ambrosio: uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg

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Abstract: Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.

Keywords: Subjective wellbeing; Prediction methods; Machine learning (search for similar items in EconPapers)
Date: 2025
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Published in Scientific Reports, 2025, 15 (1), pp.1632. ⟨10.1038/s41598-024-84137-1⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04928500

DOI: 10.1038/s41598-024-84137-1

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