Human Wellbeing and Machine Learning

TL;DR

This study explores how machine learning can improve the prediction of subjective wellbeing using large-scale survey data, showing modest but meaningful improvements over traditional models and validating known wellbeing determinants.

Contribution

It demonstrates that machine learning enhances wellbeing prediction accuracy and confirms key factors like health and social relations as important, using extensive survey data.

Findings

ML outperforms traditional regression models in predicting wellbeing.

Expanding explanatory variables doubles predictive power.

Key wellbeing factors identified align with existing literature.

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 do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be 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.