Predicting program outcomes for vocational rehabilitation customers: A machine learning approach

Abstract

BACKGROUND: The Vocational Rehabilitation (VR) program provides support and services to people with disabilities who want to work. OBJECTIVE: Approximately one-third of eligible VR customers are employed when they exit the program. The remainder either exit without ever receiving services or without employment after receiving services. In this study, we explore how customer characteristics and VR services predict these outcomes. METHODS: We examined VR case level data from the RSA-911 files. Machine learning techniques allowed us to explore a large number of potential predictors of VR outcomes while requiring fewer assumptions than traditional regression methods. RESULTS: Consistent with existing literature, customers who are employed at application are more likely to exit with employment, and those with mental health conditions or low socioeconomic status are less likely to exit with employment. Some customers with low or no earnings at application who are not identified in prior studies are more likely than others to have poor program outcomes, including those with developmental disability who are under 18, customers without developmental or learning disabilities, and customers who do not receive employment or restoration services. CONCLUSIONS: VR counselors and administrators should consider implementing early, targeted interventions for newly identified at-risk groups of VR customers.