The Divergence Index: A Decomposable Measure of Segregation and Inequality

TL;DR

This paper introduces the Divergence Index, a new decomposable measure that distinctly captures segregation, complementing diversity measures like the Information Theory Index, and demonstrates its application in analyzing U.S. metropolitan areas over two decades.

Contribution

The paper presents the Divergence Index as a novel, decomposable measure of segregation that maintains conceptual clarity and can be used alongside diversity measures for comprehensive analysis.

Findings

Divergence Index increased in U.S. suburbs from 1990 to 2010.

Information Theory Index decreased within cities over the same period.

Divergence Index provides a new tool for analyzing segregation separately from diversity.

Abstract

Decomposition analysis is a critical tool for understanding the social and spatial dimensions of segregation and diversity. In this paper, I highlight the conceptual, mathematical, and empirical distinctions between segregation and diversity and introduce the Divergence Index as a decomposable measure of segregation. Scholars have turned to the Information Theory Index as the best alternative to the Dissimilarity Index in decomposition studies, however it measures diversity rather than segregation. I demonstrate the importance of preserving this conceptual distinction with a decomposition analysis of segregation and diversity in U.S. metropolitan areas from 1990 to 2010, which shows that the Information Theory Index has tended to decrease, particularly within cities, while the Divergence Index has tended to increase, particularly within suburbs. Rather than being a substitute for measures of diversity, the Divergence Index complements existing measures by enabling the analysis and decomposition of segregation alongside diversity.