Using machine learning to uncover the relation between age and life satisfaction

This study applies a machine learning (ML) approach to around 400,000 observations from the German Socio-Economic Panel to assess the relation between life satisfaction and age. We show that with our ML-based approach it is possible to isolate the effect of age on life satisfaction across the lifecycle without explicitly parameterizing the complex relationship between age and other covariates-this complex relation is taken into account by a feedforward neural network. Our results show a clear U-shape relation between age and life satisfaction across the lifespan, with a minimum at around 50 years of age.

Automated summary

This study applies a machine learning approach to analyze around 400,000 observations from the German Socio-Economic Panel and assess the relationship between life satisfaction and age. The results show a clear U-shape relation between age and life satisfaction, with a minimum at around 50 years of age.