Measuring Inequality and Segregation

In this paper, I introduce the Divergence Index, a conceptually intuitive and methodologically rigorous measure of inequality and segregation. The index measures the difference between a distribution of interest and another empirical, theoretical, or normative distribution. The Divergence Index provides flexibility in specifying a theoretically meaningful basis for evaluating inequality. It evaluates how surprising an empirical distribution is given a theoretical distribution that represents equality. I demonstrate the unique features of the new measure, as well as deriving its mathematical equivalence with Theil's Inequality Index and the Information Theory Index. I compare the dynamics of the measures using simulated data, and an empirical analysis of racial residential segregation in the Detroit, MI, metro area. The Information Theory Index has become the gold standard for decomposition analyses of segregation. I show that although the Information Theory Index can be decomposed for subareas, it is misleading to interpret the results as segregation. The Divergence Index addresses the limitations of existing measures and accurately decomposes segregation across contexts and nested levels of geography. By creating an alternative measure, I provide a distinct lens, which enables richer, deeper, more accurate understandings of inequality and segregation.