Michigan Department of Technology, Management and Budget

Project Overview

High-quality data is critical to improving the economic well-being of vulnerable populations, but data alone does not lead to action. To bridge the gap between data and labor market success, the State of Michigan will create an interconnected set of products and tools to provide data-driven, personalized support to assist underrepresented and low-income individuals in making the transition to sustainable employment.

The goals of the project are threefold. First, to enhance available employment outcome data and provide a more comprehensive listing of education and training options available to the target population.

Second, to provide the target population with personalized recommendations for optimal career pathways. This will be achieved through better data sharing mechanisms to foster policy innovations, as well as the development of two web-based tools that provide information tailored to each individual. One tool will be for job seekers and will provide information on which career pathways present the greatest opportunities based on an individual’s education and work history. The other tool, for case managers, will use administrative data on job seeker characteristics and machine learning algorithms, to provide critical information regarding which support services are most likely to help each job seeker make a successful transition to employment.

Third and finally, the project will deliver personalized career pathway recommendations using intuitive, user-friendly designs to maximize understanding and limit impediments to action.

Combined, these products will present a powerful resource to help underrepresented populations bridge the gap between employment information and successful employment outcomes.

Lead Organization

Michigan Department of Technology, Management and Budget

Partner Organizations

Michigan Department of Labor and Economic Opportunity

Upjohn Institute for Employment Research

Michigan Works! Southwest

Coleridge Initiative at NYU