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This article documents that the Gini index is an insufficient measure of inequality and, according to the traditional logic of interpretation, that it may lead to incorrect deductions. Since, apart from concentration, it cannot grasp other relevant features of inequality like heterogeneity and asymmetry-which, beyond its intensity, allow for considering the direction of inequality too-we suggest using the less known Zanardi index of asymmetry of the Lorenz curve as an appropriate measure of inequality. Our findings are supported with estimates from the Luxembourg Income Study Database.

This study analyzes the link between temperature and COVID-19 incidence in a sample of Italian regions during the period that covers the first epidemic wave of 2020. To that end, Bayesian model averaging techniques are used to analyze the relevance of temperature together with a set of additional climatic, demographic, social, and health policy factors. The robustness of individual predictors is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by temperature given that it appears as one of the most relevant determinants reducing regional coronavirus disease 2019 (COVID-19) severity. The strong negative link observed in our baseline analysis is robust to the specification of priors, the scale of analysis, the correction of measurement errors in the data due to under-reporting, the time window considered, and the inclusion of spatial effects in the model. In a second step, we compute relative importance metrics that decompose the variability explained by the model. We find that cross-regional temperature differentials explain a large share of the observed variation on the number of infections.

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