The Divergence Index: A Decomposable Measure of Segregation and Inequality

Decomposition analysis is a critical tool for understanding the social and spatial dimensions of inequality, segregation, and diversity. In this paper, I propose a new measure – the Divergence Index – to address the need for a decomposable measure of segregation. Although the Information Theory Index has been used to decompose segregation within and between communities, I argue that it measures relative diversity not segregation. I demonstrate the importance of this conceptual distinction with two empirical analyses: I decompose segregation and relative homogeneity in the Detroit metropolitan area, and I analyze the relationship between the indexes in the 100 largest U.S. cities. I show that it is problematic to interpret the Information Theory Index as a measure of segregation, especially when analyzing local-level results or any decomposition of overall results. Segregation and diversity are important aspects of residential differentiation, and it is critical that we study each concept as the structure and stratification of the U.S. population becomes more complex.