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The Process of Crafting Equity Metrics and Selecting Priority Populations in Ohio’s Community College Outcomes-Based Funding Formula Introduction

An increasing number of states are using Outcomes-Based Funding (OBF) to address persistent lags in college attainment among poor and minoritized students. But state context matters, and the policy development process matters too. ​

How do states craft policies that address the specific equity challenges they face? RFA’s OBF Equity Toolkit is designed to answer that question by providing concrete, practical information on how six states developed and implemented OBF policies that factored in equity. The Toolkit’s Series 2:  Policy Development and Redesign shows why the process of policy development is so important.

In the interview transcript below, Jack Hershey and Laura Rittner from the Ohio Association of Community Colleges, which led the development of the state’s two-year sector OBF policies, describe their process.

Ohio’s two-year OBF policy focuses on reducing attainment gaps among four groups: minoritized students, those over age 25, low-income students, and academically underprepared students. How did you decide to prioritize these particular groups? 

We had a process where we engaged all community colleges in brainstorming what potential at-risk factors might be for students. We had a long list when we started–12 to 15 factors. Then, each college’s representative took back a list to their campus, examined their data to see which risk factors were salient, and prioritized the factors. This allowed them to identify student populations they were seeing on their campus that they were concerned about.

We then worked with the Department of Higher Education, completing research and data analysis to look at which student groups were most at-risk, in terms of completing courses, degrees, certificates, and/ or transferring. That helped us further narrow down the at-risk factors to the four categories that ended up being included in the model.

We also changed the name of our categories from “at-risk” to “access” because we felt those were the factors that were connected closely to our “access mission” at community colleges.

States vary widely in how, specifically, they include equity metrics in their OBF formulas.  How did you craft Ohio’s metrics? 

Equity metrics were based on data. The equity gap between different student groups becomes much larger when you examine degree completion, or certificate completion, or transfer. In addition, when looking at degree completion, the equity gaps became even bigger for students who were some combination of low-income, minority, or academically under-prepared.

Because of that, our equity metrics hold a lower weight in the formula for course completions, a higher weight for credential completions, and students with more access factors receive a greater weight in the formula than students with just one access factor.

Through this data-driven process, as we developed the formula, we were also raising awareness of the attainment gaps that existed in our state. Raising this awareness helped justify the rationale for fully funding the formula. Certainly, everyone knew equity gaps existed, but really digging down into the data and highlighting how big they were elevated the conversation about the formula and its broader purpose.

OACC commissioned an intermediary to assist in the technical elements of formula creation.  Why did you make this decision, and what happened because of it? 

OACC is a member organization whose budget comes from the institutions we represent statewide. Because of that role as an organization, as we lead the formula process we needed a third-party intermediary who could be unbiased throughout the formula development process. Our intermediary (HCM Strategists) also came with a research-based approach. That lens and their expertise provided a neutral party that was invaluable in helping our 23 colleges work through the myriad of issues that came up throughout the process to develop a completely need funding formula.

Ohio’s process for identifying priority populations involved significant input from each community college.  At-risk student populations vary across these colleges, but at the end of the day the state included only four groups in its OBF formula.

What have been the biggest challenges in determining the priority populations in equity metrics in Ohio? And how did involving the colleges mitigate these challenges?

After college leaders had developed their lists of at-risk factors specific to their institutions and we had a statewide list of around 15 factors, we had to cut down that list to the eventual four. Navigating through that was a challenge.

There are questions that still arise about giving weight in the formula to certain populations.  We try to remind individuals about the thoroughness of our process, which was designed to consistently capture populations across all 23 colleges.

Why is it important for other states to consider priority populations and equity metrics when developing OBF formulas?

Legislators and state policy makers are interested in OBF formulas because they want to drive colleges to close attainment gaps and to bring more people into the workforce. That’s at the core of why these policies and these formulas are taking hold across the nation.

To really do OBF well, you must dig down into where your actual attainment gaps are–and that’s where priority populations and equity metrics in the formula are so important. We’ve seen certain models that have put a small amount of money into a very broad OBF formula—which we don’t believe is an effective way to drive a lot of change. Our formula is driving change because 100% of our funding is outcomes-based, but it also has targeted funds for equity populations.

When reviewing the OBF Equity Toolkit we were struck by a module discussing priority populations in New Mexico, where a Hispanic population who would be a minority population in Ohio wasn’t a minority population there. It shows you’ve got to go through the data in your own state and figure out where the attainment gaps are.

What would you recommend to other states seeking to reduce equity gaps by including priority populations in their outcomes-based funding formulas?

OACC conducts data analysis for our community colleges.  We regularly share with each of our colleges a report that shows how their institution is performing under the formula and where they might need to focus their efforts for improvement.

Making people pay attention to the drivers in the formula is, in our opinion, more important than the actual allocation for each metric. While you should put your money where your mouth is and fund equity metrics, you’ve got to have the formula running and working well so that people on campus know the changes will result in additional funds.

That said, states have an ongoing responsibility to fund an OBF formula once it is adopted. State leaders put the formula in place so that colleges and universities make changes that lead to increased production, and increased graduation rates.  Funding the mandate behind OBF is important.

Why is having the right data so important to successful OBF policies?   

The process that the community college sector went through in Ohio to have colleges think through their important access populations was meaningful. It also allowed us to do an audit of the state data system to see which populations of students we can even track and identify. Because of that process, we’ve identified issues that we can continue to work on.

Is there anything that Ohio could have done better in the formula development process?

In a perfect world, we would have set the state’s attainment goal first and then derived the formula with equity goals after that. In Ohio, the OBF formula was set first, which was such a herculean task.  But the attainment goal came after the funding formula. Sometimes it feels that our outcomes-based funding work is on one track, and our state attainment goal is running on another track. They may not be as integrated as they could be. An attainment goal may have been a good first step to supporting and driving the development of outcomes-based funding in Ohio.

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