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Title: Model-based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study
Authors: Brunori, Paolo
Davillas, Apostolos
Jones, Andrew M.
Scarchilli, Giovanna
Type: Article
Subjects: FRASCATI::Social sciences::Economics and Business
FRASCATI::Social sciences
Keywords: Inequality of opportunity
Health equity
Machine learning
Unhealthy lifestyle behaviours
Issue Date: Dec-2022
Publisher: ELSEVIER
Source: Journal of Economic Behavior & Organization
Volume: 204
First Page: 543
Last Page: 565
Abstract: 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.
ISSN: 0167-2681
Other Identifiers: 10.1016/j.jebo.2022.10.011
Appears in Collections:Department of Economics

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