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

Micha Kaiser; Steffen  Otterbach; Alfonso Sousa-Poza

doi:10.21203/rs.3.rs-943521/v1

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

Micha Kaiser^*, Steffen Otterbach, Alfonso Sousa-Poza

^*Corresponding author for this work

Department of Management, Society and Communication

University of Hohenheim

Research output: Working paper › Research

Abstract

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 language	English
Publisher	Research Square
Number of pages	22
DOIs	https://doi.org/10.21203/rs.3.rs-943521/v1
Publication status	Published - 6 Oct 2021

Bibliographical note

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

Keywords

Learning
Relation
Age and Life
Machine learning

Access to Document

10.21203/rs.3.rs-943521/v1Licence: CC BY

Handle.net

Persistent link

Cite this

@techreport{813b77f655694ee5adc5eafaea80647f,

title = "Using Deep Learning to Uncover the Relation Between Age and Life Satisfaction",

abstract = "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.",

keywords = "Learning, Relation, Age and Life, Machine learning, Learning, Relation, Age and Life, Machine learning",

author = "Micha Kaiser and Steffen Otterbach and Alfonso Sousa-Poza",

note = "Published online: 06 October 2021 Preprint is under consideration at Scientific Reports",

year = "2021",

month = oct,

day = "6",

doi = "10.21203/rs.3.rs-943521/v1",

language = "English",

publisher = "Research Square",

address = "United States",

type = "WorkingPaper",

institution = "Research Square",

}

TY - UNPB

T1 - Using Deep Learning to Uncover the Relation Between Age and Life Satisfaction

AU - Kaiser, Micha

AU - Otterbach, Steffen

AU - Sousa-Poza, Alfonso

N1 - Published online: 06 October 2021 Preprint is under consideration at Scientific Reports

PY - 2021/10/6

Y1 - 2021/10/6

N2 - 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.

AB - 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.

KW - Learning

KW - Relation

KW - Age and Life

KW - Machine learning

KW - Learning

KW - Relation

KW - Age and Life

KW - Machine learning

U2 - 10.21203/rs.3.rs-943521/v1

DO - 10.21203/rs.3.rs-943521/v1

M3 - Working paper

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

PB - Research Square

ER -