ArticleCriminology & public policy2025
Do foster youth face harsher juvenile justice outcomes? Reinvestigating child welfare bias in juvenile justice processing.
Article in Criminology & public policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Research summary: For decades, child welfare scholars and policy makers have been concerned with the strong association between foster care and juvenile justice involvement. Foster care placement may lead to differences in justice system outcomes if youth in foster care face "processing bias"-differentially harsh treatment by agents of the juvenile court. Previous research found that youth in foster care at the time of juvenile justice contact were treated more harshly by the court, resulting in higher rates of punitive case outcomes. We revisit the question of processing bias using detailed administrative data on more than 10,000 adolescents in Pennsylvania in 2015-2019 and a selection-on-observables design. We find no evidence of processing bias against youth in foster care. Compared to observationally equivalent cases, those that involve youth in foster care do not experience more punitive outcomes. If anything, our estimates suggest the opposite-youth in foster care are less likely to have a charge adjudicated, be placed under court-ordered supervision, or enter into juvenile detention. The precision of our estimates and bounding exercises allow us to rule out even modest evidence of punitive processing bias. Policy implications: This paper highlights the importance of revisiting the evidence of processing bias within juvenile justice and child welfare agencies. Given the decentralized and continuously evolving nature of these systems, local jurisdictions should investigate their own case outcomes and contexts before implementing reforms to address bias. Yet, many lack the resources for such research and federal support is essential to enhance local data analysis capabilities, promoting more tailored and effective policy reforms. Initiatives that aim to integrate data from multiple systems can better understand and address the needs of overlapping populations, ultimately improving the quality of services and outcomes.
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