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What makes a satisfying life? Prediction and interpretation with machine-learning algorithms

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  • Clark, Andrew E.
  • D'Ambrosio, Conchita
  • Gentile, Niccoló
  • Tkatchenko, Alexandre

Abstract

Machine Learning (ML) methods are increasingly being used across a variety of fields and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non-linear method, Random Forests. We present two key model-agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non-penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non-negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non-linear analysis.

Suggested Citation

  • Clark, Andrew E. & D'Ambrosio, Conchita & Gentile, Niccoló & Tkatchenko, Alexandre, 2022. "What makes a satisfying life? Prediction and interpretation with machine-learning algorithms," LSE Research Online Documents on Economics 117887, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:117887
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    References listed on IDEAS

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    2. Clark, Andrew E. & Lepinteur, Anthony, 2019. "The causes and consequences of early-adult unemployment: Evidence from cohort data," Journal of Economic Behavior & Organization, Elsevier, vol. 166(C), pages 107-124.
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      by maximorossi in NEP-LTV blog on 2023-03-01 10:32:26

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    More about this item

    Keywords

    life satisfaction; well-being; machine learning; British cohort study;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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