Measuring Within and Between Group Inequality in Early-Life Mortality Over Time: A Bayesian Approach with Application to India
Most studies on inequality in early-life mortality (ELM) compare average mortality rates between large groups of births, for example, between births from different countries, income groups, ethnicities, or different times. These studies do not measure within-group disparities. The few studies that have measured within-group variability in ELM have used tools from the income inequality literature. We show that measures from the income inequality literature, such as Gini indices, are inappropriate for ELM. Instead we develop novel tools that are appropriate for analyzing ELM inequality. We illustrate our methodology using a large data set from India, where we estimate ELM risk for over 400,000 births using a Bayesian hierarchical model. We show that most of the variance in mortality risk exists within groups of births, not between them, and thus that within-group mortality needs to be taken into account when assessing inequality in ELM. Our approach has broad applicability to many health indicators.