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

Author

Listed:
  • 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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    File URL: http://eprints.lse.ac.uk/117887/
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    References listed on IDEAS

    as
    1. 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.
    2. repec:hal:pseose:halshs-01109062 is not listed on IDEAS
    3. Erzo F. P. Luttmer, 2005. "Neighbors as Negatives: Relative Earnings and Well-Being," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(3), pages 963-1002.
    4. Andrew E. Clark, 2018. "Four Decades of the Economics of Happiness: Where Next?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(2), pages 245-269, June.
    5. Stutzer, Alois & Frey, Bruno S., 2006. "Does marriage make people happy, or do happy people get married?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 35(2), pages 326-347, April.
    6. Richard Layard & Andrew E. Clark & Francesca Cornaglia & Nattavudh Powdthavee & James Vernoit, 2014. "What Predicts a Successful Life? A Life‐course Model of Well‐being," Economic Journal, Royal Economic Society, vol. 124(580), pages 720-738, November.
    7. Andrew E. Clark & Sarah Flèche & Richard Layard & Powdthavee Nattavudh, 2018. "The Origins of Happiness: The Science of Well-Being over the Life Course," Post-Print halshs-01631510, HAL.
    8. Ekaterina Oparina & Sorawoot Srisuma, 2022. "Analyzing Subjective Well-Being Data with Misclassification," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 730-743, April.
    9. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    10. Dolan, Paul & Peasgood, Tessa & White, Mathew, 2008. "Do we really know what makes us happy A review of the economic literature on the factors associated with subjective well-being," Journal of Economic Psychology, Elsevier, vol. 29(1), pages 94-122, February.
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
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    Full references (including those not matched with items on IDEAS)

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    1. No title
      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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