The spread of fake news and misinformation on social media is blamed as a primary cause of vaccine hesitancy, which is one of the major threats to global health, according to the World Health Organization. This paper studies the effect of the diffusion of misinformation on immunization rates in Italy by exploiting a quasi‐experiment that occurred in 2012, when the Court of Rimini officially recognized a causal link between the measles‐mumps‐rubella vaccine and autism and awarded injury compensation. To this end, we exploit the virality of misinformation following the 2012 Italian court's ruling, along with the intensity of exposure to nontraditional media driven by regional infrastructural differences in Internet broadband coverage. Using a Difference‐in‐Differences regression on regional panel data, we show that the spread of this news resulted in a decrease in child immunization rates for all types of vaccines.

This paper presents new decomposition-based approaches to measure inequality of opportunity in health that capture Roemer's distinction between circumstances and effort and are consistent with both compensation and reward principles. Our approach is fully nonparametric in the way that it handles differences in circumstances and provides decompositions of both a rank-dependent relative (the Gini coefficient) and a rank-independent absolute inequality index (the variance). The decompositions distinguish the contribution of effort from the direct and indirect (through effort) contribution of circumstances to the total inequality. Our approach is illustrated by an empirical application that uses objectively measured biomarkers as health outcomes and as proxies for relevant effort variables. Using data from the Health Survey for England from 2003 to 2012, we find that circumstances are the leading determinant of inequality in cholesterol, glycated haemoglobin, and in a combined ill-health index whereas effort plays a substantial role in explaining inequality in fibrinogen only.

The relationship between income and health is one of the most explored topics in health economics but less is known about this relationship at different points of the health distribution. Analysis based solely on the mean may miss important information in other parts of the distribution. This is especially relevant when clinical concern is focused on the tail of the distribution and when evaluating the income gradient at different points of the distribution and decomposing income-related inequalities in health is of interest. We use the unconditional quantile regression approach to analyse the income gradient across the entire distribution of objectively measured blood-based biomarkers. We apply an Oaxaca-Blinder decomposition at various quantiles of the biomarker distributions to analyse gender differentials in biomarkers and to measure the contribution of income (and other covariates) to these differentials. Using data from the Health Survey for England, we find a non-linear relationship between income and health and a strong gradient with respect to income at the highest quantiles of the biomarker distributions. We find that there is heterogeneity in the association of health to income across genders, which accounts for a substantial percentage of the gender differentials in observed health. Copyright © 2016 John Wiley & Sons, Ltd.

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We adopt 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 ad hoc selection of circumstances, the curse of dimensionality and unobserved type-specific heterogeneity that may lead to biased estimates of IOp. We apply our latent class approach to measure IOp in allostatic load, a composite measure of biomarker data. Using data from Understanding Society: The UK Household Longitudinal Study (UKHLS), we find that a latent class model with three latent types best fits the data, with the corresponding types characterised in terms of differences in their observed circumstances. Decomposition analysis shows that about two thirds of the total inequalities in allostatic load can be attributed to the direct and indirect contribution of circumstances and that the direct contribution of effort is small. Further analysis conditional on age-sex groups reveals that the relative (percentage) contribution of circumstances to the total inequalities remains mostly unaffected and the direct contribution of effort remains small. K E Y W O R D S biomarkers, decomposition analysis, equality of opportunity, finite mixture models, health equity, latent class models J E L C L A S S I F I C A T I O N

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