Resource Library

The brief summarizes lessons learned from using machine learning to study the implementation of career pathways programs. First, this brief describes the research questions that guided the study and summarizes the machine learning methods designed for the data collection and analysis activities, including study limitations and challenges encountered. It then provides lessons learned on using machine learning methods for social science research.

The paper presents a new analysis examining gender and racial/ethnic differences in wage growth among workers in the United States who, between the ages of 18 and 34, entered occupations that require some training or experience beyond a high school degree, but do not necessarily require a college degree (referred to as “mid-level” occupations). The paper includes implications for policymakers and practitioners.

The datasets compiled for the Career Trajectories and Occupational Transitions (CTOT) Study can be used to inform career pathways and other employment and training programs by (1) identifying launchpad occupations associated with higher than average wage growth, (2) identifying occupational and worker characteristics associated with wage growth, and (3) identifying specific occupational steps associated with wage growth.

The report of the Career Pathways Descriptive and Analytical Project focuses on “mid-level” occupations—occupations that typically require education or experience beyond a high school diploma or equivalent, but less than a four-year college degree. The report presents study findings on the magnitude of the differences between occupations in the career outcomes that entrants go on to experience within 10 years after entering, which occupations are associated with high wage growth, and what traits of occupations predict higher wage growth.

The brief summarizes findings of the Career Pathways Descriptive and Analytical Project’s meta-analysis study, which analyzes research on the impacts of 46 career pathways programs, based on evaluation findings published between 2008 and 2021. The brief first describes the programs and participants in the evaluations included in the meta-analysis. It then discusses the study’s overall impact findings and the findings about which program characteristics were associated with impacts, as well as the implications of each for policymakers, practitioners, and researchers.

The report focuses on implementation of key changes to governance of the workforce system and how state and local workforce boards engage in planning across the core programs. Discussed here are the successes and challenges, promising practices, and possible areas for further technical assistance related to WIOA for workforce system governance and planning.

The report summarizes 46 impact evaluations that focus on programs that embed elements of the career pathways approach. In the past decade, the career pathways approach to workforce development emerged as a promising strategy to promote long-term earnings advancement and self-sufficiency by helping workers attain in-demand postsecondary credentials (Fein, 2012). The approach involves a combination of rigorous and high-quality education, training, and other services to support participant success (WIOA, 2014).

In 2021, the Chief Evaluation Office (CEO) funded contractor Westat Insight and their partner American Institutes for Research to conduct the Explorations in Data Innovations project under the Administrative Data Research and Analysis portfolio of studies. This use case study was designed to explore options around employing machine learning to create and maintain a public-facing, labor-related data catalog.

The report describes the National Health Emergency (NHE) Demonstration Grants to Address the Opioid Crisis: Implementation Evaluation findings and considers lessons learned and practices that appear potentially promising for future efforts to provide workforce services and system investments to support people directly and indirectly affected by the opioid crisis.

The report focuses on the implementation and short-term impacts of TechHire and Strengthening Working Families Initiative (SWFI) — capturing between 7 and 14 months of follow-up—in the five programs that participated in a randomized control trial. The implementation analysis explored broad research questions about how the programs were implemented and what factors facilitated or inhibited implementation.