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.


Oparina, E., Kaiser, C., Gentile, N., Tkatchenko, A., Clark, A.E., De Neve, J-E. & D'Ambrosio, C. (2022). 'Human Wellbeing and Machine Learning'. INET Oxford Working Paper No. 2022-11.
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