The EARN (Early Assessment and Retention Network) Model for Effectively Targeting WIA and TANF Resources to Participants

Abstract

his technical assistance project was developed by the Employment and Training Institute for the Milwaukee Area Workforce Investment Board to demonstrate the advantages of implementing a large-scale comprehensive data-driven IT capacity for administering WIA and TANF (Temporary Assistance for Needy Families) training programs. Longitudinal state wage match data, WIA data bases, welfare files, corrections data, and driver’s license records provide essential program planning and evaluation tools to assess the effectiveness of program interventions by client characteristics and can offer an early warning system for agencies on their progress on measures of post-program employment outcomes. The key lesson of the project was that urban WIA boards and TANF agencies – and the state agencies supervising them -- need to take steps to combine large institutional databases to identify which employment outcomes work and which don’t. The EARN Model shows how valuable these data bases can be in identifying costs of various approaches and in monitoring short-term and long-term employment and earnings for those served. A key element is the state employer wage match data which should be used as the primary post-program outcome measure along with statistics on reduced need for welfare, food stamps and medical assistance.