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

Andrew Clark, Conchita D'Ambrosio, Niccoló Gentile and Alexandre Tkatchenko

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: life satisfaction; well-being; machine learning; British cohort study (search for similar items in EconPapers)
JEL-codes: C63 I31 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2022-06-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hap and nep-ltv
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http://eprints.lse.ac.uk/117887/ Open access version. (application/pdf)

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Working Paper: What makes a satisfying life? Prediction and interpretation with machine-learning algorithms (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:117887

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