Methods: Using state administrative records, we harmonized the placement histories of 138,000 young people. From these records, we looked to see whether the young person ran away from care either temporarily and/or permanently. We included information about the child and the removal county. With these data, we built a series of multilevel logit models that relate the risk of running away to the race/ethnicity of the young person after controlling for other factors. We then added county random effects to the model to determine whether there is significant between-county variation in the disparity rate. To the county-random effects model, we also added attributes of the county to understand the between-county differences in disparity rates.
Results: Black and Hispanic youth are more likely to run away than White youth. At the population level, the disparity rates are 1.89 and 1.54, respectively. Rates of running away were also higher for older youth, girls, and young people with a history of congregate care placement. We also found substantial between county variation. In counties that rely on congregate care as a placement resource, the disparity ratios are much larger than in counties were congregate care placement is less important. Because Whites tend to live in places where the reliance on congregate care is less profound, the population-level disparity ratios reflect important geographic differences in congregate care utilization.
Conclusions and Implications: Overall, about 1 in 5 in the highest risk groups runaway from foster care at least once. For the most part, as a matter of policy, child welfare agencies pay little attention to running away because the federal government does not track running away as part of its state evaluations. This situation is unlikely to change, especially as it relates to disparity, without further research. The study also makes significant methodological advances. Because Blacks tend to live in urban areas and rates of running away are higher in urban areas, our understanding of population-level disparities is confounded by where people live. Random effects models make this geographic reality apparent. From a policy perspective, these insights suggest where, when addressing disparity, we should start.