Methods: Using the methods outlined in the first symposium paper, the longitudinal study assesses how foster care admission rates for Black and White children changed between 2000 and 2015. We also considered the extent to which changes in race-differentiated child poverty rates were correlated with Black child admission rates, White child admission rates, and admission disparities over time using dynamic Poisson regression models. For the cross-sectional study, we examined whether race-specific county-level measures of poverty and socio-ecological diversity were correlated with county-level variation in Black/White foster care placement rate disparity. For this analysis, we aggregated placement counts for 2017, 2018, and 2019 into a single placement count. We applied Poisson regression models to explore how the ecological context affects placement rate disparity.
Results: We found that Black children are more likely to enter foster care than White children, regardless of whether one is looking longitudinally or cross-sectionally. With the longitudinal study, we found that in counties with rising poverty rates placement rates also increased. There is also a strong correlation between placement rates historically and the increase in placement rates. Regarding disparity, we found that the relationship between poverty and placement depends on race. Specifically, the correlation between White child poverty and White child placement rates is stronger than the relationship between Black child poverty and Black child placement rates. The cross-sectional study results align with the longitudinal study. In counties with the fewest socio-ecological assets, placement rates are higher for both Black and White children and Black/White child placement rates are more similar.
Conclusions and implications: The magnitude of Black/White placement rate differences is to a very large extent conditional on where and when one looks. It is therefore unlikely that a single explanation accounts for the disparity we observe. This is not meant to suggest that common explanations for disparity – structural bias, racism among those reasons – are not applicable. Rather, the findings here suggest that sweeping, one-size-fits-all generalizations do little to push the search for solutions very far. The nature of disparity is sensitive to the context in which it is observed. If we do not manage to take these nuances into account, we are likely to be disappointed with our efforts to address this problem, yet again.