A Computational Social Science Approach to Understanding Predictors of Chafee Service Receipt

TL;DR

This study uses computational social science methods to analyze large-scale data, identifying key factors influencing service receipt among foster youth and linking these findings to existing theories to improve understanding of service allocation.

Contribution

It introduces a forensic social science approach to analyze administrative data, revealing major factors associated with service receipt and connecting them to theoretical frameworks.

Findings

Youth age, time in care, and state are key predictors of service receipt.

The analysis links data-driven findings to existing social science theories.

Provides a foundation for future research on service allocation mechanisms.

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. However, surprisingly little is known about which youth receive which 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. 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 most important factors associated with service receipt. Doing so helps us to identify three major factors-youth age, youth time in care, and the state in which a youth is in care-that are most heavily associated with service receive. We then conduct an analysis that links existing theory to these factors, expanding our understanding of how services are allocated and paving the way to future work to understand why such associations exist.