Human Wellbeing and Machine Learning

Caspar Kaiser, Ekaterina Oparina, Niccolò Gentile, Alexandre Tkatchenko, Andrew Clark, Jan-Emmanuel De Neve and D’Ambrosio, Conchita
Authors registered in the RePEc Author Service: Conchita D'Ambrosio

INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford

Abstract: There is a vast literature on the determinants of subjective wellbeing. Yet, standard regression models explain little variation in wellbeing. We here use data from Germany, the UK, and the US to assess the potential of Machine Learning (ML) to help us better understand wellbeing. Compared to traditional models, ML approaches provide moderate improvements in predictive performance. Drastically expanding the set of explanatory variables doubles our predictive ability across approaches on unseen data. The variables identified as important by ML – material conditions, health, social relations – are similar to those previously identified. Our data-driven ML results therefore validate previous conventional approaches.

Pages: 36 pages
Date: 2022-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hap, nep-hea and nep-ltv
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.inet.ox.ac.uk/files/HWB_and_ML_210622_v3.pdf (application/pdf)

Related works:
Working Paper: Human Wellbeing and Machine Learning (2022)
Working Paper: Human wellbeing and machine learning (2022)
Working Paper: Human wellbeing and machine learning (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:amz:wpaper:2022-11

Access Statistics for this paper

More papers in INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by INET Oxford admin team ().