Motivation for Applying to the State IMPACT Collaborative

The Ohio team applied to the State IMPACT Collaborative, a joint project of MDRC and Coleridge, to strengthen its culture of evidence and use data more systematically to guide program and policy decisions. The Ohio Department of Job and Family Services (ODJFS) is conducting a statewide Reemployment Services and Eligibility Assessment (RESEA) impact and implementation evaluation with The Ohio State University. Through this partnership, the research team has been exploring whether Supplemental Nutrition Assistance Program (SNAP) participants should be included or are better served within the RESEA program. When the team learned about the State IMPACT Collaborative, they recognized it as an opportunity to gain access to expert coaches, training, and support in refining research questions and applying advanced analytic methods, as well as additional grant funding. 

The Ohio team includes staff from ODJFS’s Office of Workforce Development and other internal offices who support data extraction and researchers from The Ohio State University.

Applied Data Analytics Project: Design and Findings

State IMPACT Collaborative participants complete an applied research project using Arkansas state data. For the Applied Data Analytics (ADA) project, the Ohio team examined whether RESEA services increase unemployment insurance (UI) claimants’ employment rate and wages at two, four, and six quarters after program exit compared with similar UI claimants who do not receive RESEA services.

The Ohio team linked RESEA, reportable individual, UI claims, workforce program, and UI wage data. They used several statistical techniques to compare similar groups of people and make sure the groups were as alike as possible. They then estimated the program impact on participants’ employment and earnings.    

The team found modest positive employment impacts and little evidence of meaningful wage gains, which aligned with the RESEA intention of reducing UI duration by helping UI claimants return to work faster. Earnings effects were small and not statistically significant. They also highlighted a recurring training lesson, namely that cohort construction and data linkage are often more difficult than the final modeling stage. Sample size limitations, post COVID data complications, and missing claims history constrained the team’s ability to draw any inferences.

Lessons Learned from Cross-State Collaboration

The State IMPACT Collaborative has created a forum for the Ohio team to discuss shared challenges and approaches with other states. Hearing that others face similar hurdles—particularly the time and effort required to navigate data-sharing agreements and protect sensitive participant information—has helped reframe these delays as an  expected feature of responsible bureaucracy rather than a uniquely Ohio problem. The team has become more aware of the safeguards needed to prevent data breaches and maintain public trust, even when that means involving many reviewers and moving more slowly than they might like.

At the same time, conversations with peer states have expanded Ohio’s thinking about research questions and analytic priorities. Other teams have surfaced lines of inquiry that Ohio had not yet considered, prompting the team to ask whether those questions might also yield valuable insights in their own context. This cross-state exchange has not only validated Ohio’s existing efforts but also sparked new directions for evidence-building that the team can bring back home.

ADA Training Benefits

The ADA training brought together program, analytics, and academic partners and equipped the Ohio team, particularly non‑coders, to participate more effectively in data‑driven analysis and decision making.

●      The ADA training broadened the Ohio team’s comfort with data, strengthened collaboration among program, research, and academic partners, and created a shared foundation for working with more complex analyses.

●      For staff who do not expect to code, the ADA course demystified the technical side of data work, replacing the sense that analysis happens in a “black box” with a clearer understanding of what coders are doing and how to interpret results.

●      The training helped non‑data staff learn how to frame more precise data requests, anticipate analysts’ follow‑up questions, and provide the detailed context needed upfront, improving collaboration between program and analytics staff. For data‑focused team members, it offered a structured way to connect their technical skills to real program and policy decisions, making it easier to align methods, measures, and research questions with on‑the‑ground needs.

●      The course reinforced broader habits of evidence use, encouraging the team to continually ask questions, seek out available data even when it cannot fully answer an issue, and use those insights to inform program improvements, performance discussions, and communication with stakeholders.

●      Beyond technical skills, the cohort model and cross‑state interactions helped build a community of practice in which states regularly compare challenges, share analytic approaches, and learn from one another’s experiences.

Future Goals for Analytical Work

Looking ahead, the Ohio team plans to use the State IMPACT project as a foundation for more systematic, data‑driven inquiry into whether SNAP recipients benefit from RESEA services and whether the program might be extended to include more people in that group. They also hope to keep building a community of practice with other states to continue comparing research questions, sharing data challenges and solutions, and adapting promising ideas from peers to the Ohio context.