Using Deep Learning to Uncover the Relation Between Age and Life Satisfaction

Micha Kaiser*, Steffen Otterbach, Alfonso Sousa-Poza

*Corresponding author for this work

Research output: Working paperResearch


This study applies a machine learning (ML) approach to around half a million 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 55 years of age.
Original languageEnglish
PublisherResearch Square
Number of pages22
Publication statusPublished - 6 Oct 2021

Bibliographical note

Published online: 06 October 2021
Preprint is under consideration at Scientific Reports


  • Learning
  • Relation
  • Age and Life
  • Machine learning

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