A computational social science approach to understanding predictors of Chafee service receipt

Abstract

The John H. Chafee Foster Care Program for Successful Transition to Adulthood (CFCIP) allocates funding to provide services to youth who are likely to age out of foster care. These services, covering everything from mentoring to financial aid, are expected to be distributed in ways that prepare youth for life after care. One natural question to ask is, which youth receive Chafee services? The present work makes use of the National Youth in Transition Database (NYTD), a large-scale administrative dataset that tracks services allocated to youth that use CFCIP funds to answer this question. Specifically, we conduct a forensic social science analysis of the NYTD data. To do so, we first use computational methods to help us uncover the factors that best predict which youth will receive services associated with service receipt. We find that the majority of variables in the Adoption and Foster Care Analysis and Reporting System (AFCARS) and NYTD have limited or no utility in predicting Chafee service receipt, and that a subset of three variables—youth age, youth time in care, and the state in which a youth is in care—explain almost all variability in service receipt. We conclude with a discussion of the implications of these and other findings on future research on Chafee service allocation, and the utility of predictive modeling in child welfare, with a particular focus on the utility of the NYTD in this context.