In this paper I propose a class of measures of rank-order segregation, each of which may be used to measure segregation by a continuous (but not necessarily interval-scaled) variable, such as income. These rank-order segregation indices have several appealing features that remedy flaws in existing measures of income segregation. First, the measures are insensitive to rank-preserving changes in the income distribution. As a result, the measures are independent of the extent of income inequality and allow comparisons across place and time regardless of the units of income or differences in the cost of living. Second, the measures can be easily computed from either exact or categorical income data, and are largely insensitive to variation in how income is tabulated in Census data. Third, the measures satisfy a number of mathematical properties necessary or desirable in such indices. Fourth, the measures are easily adapted to account for spatial proximity. Finally, the indices can be interpreted in a variety of equivalent ways that illustrate their correspondence with standard notions of segregation. I illustrate the computation and interpretation of these measures using Census data from two U.S. cities: San Francisco, CA and Detroit, MI.
