Publication type
Journal Article
Authors
Publication date
December 15, 2022
Summary:
We measure unfair health inequality in the UK using a novel data- driven empirical approach. We explain health variability as the result of circumstances beyond individual control and health-related behaviours. We do this using model-based recursive partitioning, a supervised machine learning algorithm. Unlike usual tree-based algorithms, model-based recursive partitioning does identify social groups with different expected levels of health but also unveils the heterogeneity of the relationship linking behaviors and health outcomes across groups. The empirical application is conducted using the UK Household Longitudinal Study. We show that unfair inequality is a substantial fraction of the total explained health variability. This finding holds no matter which exact definition of fairness is adopted: using both the fairness gap and direct unfairness measures, each evaluated at different reference values for circumstances or effort.
Published in
Journal of Economic Behavior & Organization
Volume and page numbers
Volume: 204 , p.543 -565
DOI
https://doi.org/10.1016/j.jebo.2022.10.011
ISSN
1672681
Subjects
Notes
Open Access
Under a Creative Commons license
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