We develop an empirical approach to analyse, measure and decompose Inequality of Opportunity (IOp) in health, based on a latent class model. This addresses some of the limitations that affect earlier work in this literature concerning the definition of types, such as partial observability, the...

Recommended readings (Machine generated): 1. Richard Auster, Irving Leveson and Deborah Sarachek (1969), 'The Production of Health, an Exploratory Study', Journal of Human Resources, 4 (4), Autumn, 411-36 -- 2. Mark R. Rosenzweig and T. Paul Schultz (1983), 'Estimating a Household Production...

We use data from Wave 9 of UK Household Longitudinal Study (UKHLS) and the April 2020 Wave of the UKHLS COVID-19 survey to compare measures of ex ante inequality of opportunity (IOp) in psychological distress, as measured by the General Health Questionnaire (GHQ), before (Wave 9) and at the...

We designed an experiment to explore the extent of measurement error in body mass index (BMI), when based on self-reported body weight and height. We find that there is a systematic age gradient in the reporting error in BMI, while there is limited evidence of systematic associations with...

We designed an experiment to explore the extent of measurement error in body mass index (BMI), when based on self-reported body weight and height. We find that there is a systematic age gradient in the reporting error in BMI, while there is limited evidence of systematic associations with...

Shocks to health have been shown to reduce labour supply for the individual affected. Less is known about household self-insurance through a partner's response to a health shock. Previous studies have presented inconclusive empirical evidence on the existence of a healthrelated 'added worker...

This paper extends the earlier work of Davillas and Jones (2021) on socioeconomic inequality in mental health, measured by the General Health Questionnaire (GHQ), to include the second national lockdown up to March 2021.

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...