Methods: Using the multi-level Poisson framework described earlier in the symposium, we analyze a county’s overall placement rate. The overall placement rate is adjusted for poverty rates and an index of socio-ecological diversity. Although we expect a positive correlation between poverty and placement rates, we are interested in counties with similar socio-ecological features but vastly different placement rates. The Poisson model with random effects is used to obtain a county’s Empirical Bayes residual (EBR). The EBR measures the difference between the expected placement rate given the socio-ecological context and the observed placement rate. We repeat the analysis, first for both Black children and White children overall and then for children divided into sub-populations based on race and age.
Results: The results locate each county within a taxonomy that characterizes placement rate deviation. For White and Black children, we organize counties into categories defined as average, above average, or below-average placement rates. The grid we create has nine possibilities. Out of 63 counties, we found 21 counties with an above-average placement rate for White children and a below-average placement rate for Black children. At the other end of the continuum, we found 24 counties with a below-average placement rate for White children and an above-average placement rate for Black children. The remaining counties are dispersed throughout the grid. When the results are stratified by age groups, we find a similar pattern: counties cluster in the grid’s corners where the White placement rate is above average and the Black placement rate is below average and vice versa.
Conclusion and Implications: Measures of the average disparity rate foster one-size-fits-all strategies. We know, however, that local child welfare systems vary substantially in the resource base and other features. Those resource differences give rise to disparities that require a local response cognizant of system strengths and weaknesses. With this study, we show how precision targeting around the intersection of race and age brings greater nuance to the problem-solving local communities must take on if we hope to advance equity.