Human wellbeing and machine learning

Oparina, Ekaterina ORCID: 0000-0002-1544-8751, Kaiser, Caspar, Gentile, Niccoló, Tkatchenko, Alexandre, Clark, Andrew E., De Neve, Jan-Emmanuel and D'Ambrosio, Conchita (2022) Human wellbeing and machine learning. CEP Discussion Papers (1863). London School of Economics and Political Science. Centre for Economic Performance, London, UK.

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Abstract

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches perform better than traditional models. Although the size of the improvement is small in absolute terms, it is substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - i.e. material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.

Item Type:	Monograph (Discussion Paper)
Official URL:	https://cep.lse.ac.uk/_new/publications/discussion...
Additional Information:	© 2022 The Authors
Divisions:	Centre for Economic Performance
Subjects:	H Social Sciences > HD Industries. Land use. Labor B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
JEL classification:	C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications I - Health, Education, and Welfare > I3 - Welfare and Poverty > I31 - General Welfare; Basic Needs; Living Standards; Quality of Life; Happiness
Date Deposited:	19 Jan 2023 11:33
Last Modified:	15 Sep 2023 23:58
URI:	http://eprints.lse.ac.uk/id/eprint/117955

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