A multi-level modeling approach to understanding residential segregation in the United States

A well-known limitation of commonly used segregation measures is their inability to describe patterns at multiple scales. Multi-level modeling approaches can describe how different levels of geography contribute to segregation, but may be difficult to interpret for non-technical audiences and have rarely been applied in the US context. This paper provides a readily interpretable description of multi-scale Black–non-Black segregation in the United States using a multi-level modeling approach and the most recent Census data available. We fit a three-level random intercept multi-level logistic regression model predicting the proportion of the population that is Black (Hispanic and non-Hispanic) at the block group level, with block groups nested in tracts and tracts nested in Metropolitan Statistical Areas (MSAs). For the 102 largest MSAs in the United States, we then estimated the extent to which micro- versus meso-level variability drives overall racial residential patterning within the MSA. Finally, we created a typology of racial residential patterning within MSAs based on the total proportion of the MSA population that is Black and the relative contribution of block groups (micro) versus tracts (meso) in driving variation. We find that nearly 80% of the national variation in the geographic concentration of Black residents is driven by within-MSA, tract-level processes. However, the relative contribution of small versus larger scales to within-MSA segregation varies substantially across metropolitan areas. We detect five meaningfully different types of metropolitan segregation across the largest MSAs. Multi-level descriptions of segregation may help planners and policymakers understand how and why segregated residential patterns are evolving in different places and could provide important insights into interventions that could improve integration at multiple scales.