2301 | Machine Learning in the Prediction of Human Wellbeing

Ekaterina Oparina, Caspar Kaiser, Niccolò Gentile, Alexandre Tkatchenko, Andrew E. Clark, Jan-Emmanuel De Neve, Conchita D’Ambrosio

Subjective wellbeing data are increasingly used across the social sciences. Yet, our ability to model wellbeing is severely 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 the data between 2010 and 2018. In terms of predictive power, our ML approaches perform better than traditional ordinary least squares (OLS) regressions. 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, personality traits, 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.