Measuring and Decomposing Inequality among the Multidimensionally Poor Using Ordinal Data: A Counting Approach

Poverty has many dimensions, which, in practice, are often binary or ordinal in nature. A number of multidimensional measures of poverty have recently been proposed that respect this ordinal nature. These measures agree that the consideration of inequality across the poor is important, which is typically captured by adjusting the poverty measure to be sensitive to inequality. This, however, comes at the cost of sacrificing certain policy-relevant properties, such as not being able to break down the measure across dimensions to understand their contributions to overall poverty. In addition, compounding inequality into a poverty measure does not necessarily create an appropriate framework for capturing disparity in poverty across population subgroups, which is crucial for effective policy. In this paper, we propose using a separate decomposable inequality measure - a positive multiple of variance - to capture inequality in deprivation counts among the poor and decompose across population subgroups. We provide two illustrations using Demographic Health Survey datasets to demonstrate how this inequality measure adds important information to the adjusted headcount ratio poverty measure in the Alkire-Foster class of measures.