Addressing the School-to-Prison Pipeline: Examining Micro- and Macro-Level Variables that Affect School Disengagement and Subsequent Felonies

Abstract

This study examines the School-to-Prison Pipeline (STPP) by identifying individual legal and extra-legal variables and school-level factors that predict juvenile/criminal justice involvement using 2006–2012 matched data from education and justice systems (n?=?21,457). The role of school disengagement is also assessed, measuring unexcused absences that follow suspensions in the previous academic year. For the court-involved subsample (n?=?7349), after controlling for student behavior, demographic, and school-level factors; extra-legal racial differences remain a significant factor in determining higher counts of felonies with African American and Multi-racial students at increased likelihood (1.65 and 1.86 times, respectively for the higher latent class) of juvenile/criminal justice involvement as compared with White students. And, although White students were found to either be more disengaged or equally disengaged when compared with students of color, sharp differences in criminal justice involvement and outcomes exist by race/ethnicity. These findings suggest that addressing the STPP will require future research and focus on more than individual-level behaviors (school disengagement and school-based offenses) and attention to the impact of extra-legal variables and systemic implicit bias.     

A dynamic multiple-variety choice adaption model

The choice of a product on one purchase occasion by one consumer could be multiple varieties and influenced by past usage experience of this product. To mimic the real situation, this article proposes a new dynamic multiple-variety choice (DMC) model which incorporates quantitative and qualitative dynamics into an additive utility function. This model exhibits three major features of consumer purchase behavior: more than one variety purchased, learning behavior from use experience, and forgetting with the passage of time. All these are achieved by combining a simultaneous demand model with Bayesian learning theory embedded in an exponential function. The model is tested and validated using Hong Kong television viewing data. Empirical results show that including Bayesian learning in a multiple-choice model significantly improves model performance and prediction accuracy, and consideration of the effect of forgetting when studying learning behavior renders the Bayesian learning model much more accurate in practical application.