Veröffentlichung des Artikels „Using machine learning to uncover the relation between age and life satisfaction” von Micha Kaiser, Steffen Otterbach, und Alfonso Sousa-Poza.   [06.06.22]

This study applies a machine learning (ML) approach to a 400,000 observations from the German Socio-Economic panel to assess the relation between life satisfaction and age. We shown 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 co-variates- 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 lifespan, with a minimum at around 50 years of age.

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