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Model-Based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study

Paolo Brunori (), Apostolos Davillas, Andrew Jones () and Giovanna Scarchilli ()
Additional contact information
Paolo Brunori: London School of Economics
Giovanna Scarchilli: University of Trento

No 14925, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: machine learning; health equity; inequality of opportunity; unhealthy lifestyle behaviours (search for similar items in EconPapers)
JEL-codes: D63 I14 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur and nep-hea
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Working Paper: Model-based recursive partitioning to estimate unfair health inequalities in the United Kingdom Household Longitudinal Study (2022) Downloads
Working Paper: Model-based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study (2021) Downloads
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