INCENTIVIZING HIGH-PRIORITY DEGREES:
THE ROLE OF STATE FUNDING
by Will Carroll, Managing Director of Strategic Finance and Student Success and Tom Allison, Director of State Policy and Finance
JUNE 2025
Key Takeaways
Incentivizing certain degree programs in a state postsecondary funding formula can help reflect higher operating costs, state policy objectives, or workforce needs.States are responding to these calls with a heightened focus on how to incorporate measures of value into funding and policy decisions.
As states build more robust connections between their education and workforce data systems, states should use a data-driven approach to prioritize degrees that are most likely to help graduates attain good jobs and upward mobility.
Currently, though, a number of states prioritize degree programs using approaches that are not as student-centered and have the potential to create unintended consequences or exacerbate historical inequities.
This paper considers the pros and cons of different approaches and discusses data sources that can help states make informed decisions about which degree programs to incentivize and why.
Reasons for Prioritizing Degree Programs
STEM. Nursing. General studies. Philosophy. Even Golf Course Management. Postsecondary education offers a wide variety of fields of study opening different opportunities in the workforce—over 2,800 according to the National Center for Education Statistics. Within the national conversation about the value of a college degree is an understanding that not all degrees are created equal. The payoff to a student varies widely by the degree they acquire. Indeed, the choice of major is likely more or just as important than the choice of institution.
Policymakers, the public, and students see some degrees as providing better value than others. For example, one survey of students at four-year colleges and universities found that 62% of STEM majors were confident their major field of study would lead to a good job, compared to 40% of Liberal Arts majors. Not surprisingly, policymakers are finding ways to encourage institutions to generate more degrees in certain fields. States are launching financial aid or support programs targeted at certain fields or providing grants for colleges to expand particular programs.
Many states also incentivize certain types of degrees in their funding formulas for colleges and universities. The reasons for doing so vary greatly, as do the approaches they take. An ideal funding formula reflects the major cost with drivers of delivering an education and advances a state’s priorities. Incentivizing certain degrees can address both of these components. An institution’s mix of programs influences its costs, as some fields are more expensive to deliver than others. Further, state priorities often include meeting workforce needs and ensuring students’ degrees prepare them for a good career.
A few states have used cost studies to explore the variation in institutional expenditures across disciplines. Engineering, nursing, and fine arts are commonly identified as high-cost across such analyses of bachelors degrees in Florida, Texas, Illinois, and Minnesota. One study of four-year colleges found that the cost to deliver a program ranged from $175 per credit hour (math) to $475 per credit hour (engineering), with an average of $222. That study identified class size as the largest contributor to the difference, faculty pay impacting program cost to a lesser extent. Specialized equipment for lab- or manufacturing-oriented disciplines can contribute to higher instructional costs. Higher-level courses and advanced degrees typically have smaller class sizes, which leads to higher costs. Recognizing these variations, states may want to allocate more funds to ensure colleges and universities have the resources necessary to deliver these programs.
States also create incentives for institutions to produce certain degrees. Postsecondary education is critical to developing a skilled workforce and a certain field may be critical to a state or region’s economy. Common approaches include priorities for STEM, education, high-demand, high-wage, or high-value degrees. Increasingly, policymakers are pushing to ensure degrees provide graduates with a good value. HCM’s Investing in Value brief provides greater detail about how states can define, measure, and apply value in their funding formulas.
Identifying and Applying Priority Degrees
We have identified three ways that states select the high-priority degrees that they incentivize in their funding formulas. These are: cost-based, strategy-based, and market-based. Some states use more than one. We discuss the strengths and weaknesses of each and provide examples of how they are operationalized in funding formulas.
Cost-Based
A cost-based approach identifies fields and degrees based on the cost or expenditures required or typically used to deliver the program. Often, cost studies use historical expenditure data to identify high-cost programs, sometimes bringing in regional, national, and peer benchmarking. Whereas K-12 has a fairly long history of efforts and approaches to defining an adequate level of spending, there is not nearly as much research on how to derive what a college degree program “should” cost, also known as an adequacy study. Despite this lack of research, this is a fairly common approach in state funding formulas.
Advantages: Done well, a cost-based approach will ensure that institutions with high-cost programs have more resources. If engineering programs are 2.5 times more expensive to offer than math programs, a state would understandably want to allocate more funds to an engineering-focused college. This will help pay the higher faculty salaries and support the lower class sizes that are necessary in some fields.
Disadvantages: It is difficult to distinguish what has been spent from what is necessary to spend on a program. The former is usually the only data available, but it can be problematic. Faculty salaries by discipline might reflect gender or racial wage disparities in those fields outside of academia, which would be reinforced if they were used to allocate state funds. Allocating more funds to high-cost programs is also likely to result in fewer resources and opportunities for underrepresented minority students, who enroll less frequently in high-cost programs than white and Asian students. Finally, using historical expenditure data gives no indication as to the efficiency of the spending and whether the same degree could be delivered more efficiently.
Examples:
Ohio allocates its funding to universities and community colleges entirely through an outcomes-based funding model. Ohio has 26 course and 153 degree “models,” representing the cost for a particular combination of subject area and level. The costs are used as weights applied to the total number of completions in that model. As a result, institutions receive more revenue from the formula for a completion in a third-year nursing course than an entry-level arts and humanities course, for example. The costs are derived from actual expenditure data reported by institutions to the Ohio Department of Higher Education.
Nevada distributes a significant portion of its funds based on each institution’s share of Weighted Student Credit Hours. The weights are based on 11 program areas and four course levels (lower level, upper level, masters, doctorate). The weights were developed based on a synthesis of other state’s cost analyses. In 2017, Nevada also added a weight for certain CTE programs based on their presumed higher cost of delivery.
Texas’ General Academic Institutions Expenditure Study collects each public 4-year institutions’ expenditures and credit hours by discipline to produce a relative weight matrix. This matrix is then employed in calculating the Instruction and Operations (I&O) formula funding, which is a primary mechanism for allocating state funds to higher education institutions.
Strategy-Based
In a strategy-based approach, degrees are typically identified through a policy or strategic process. The legislature, Governor’s office, or a state agency might name specific fields in a report, legislation, or budget. Alternatively, they may set broad areas of emphasis for the state, which can then be mapped to degrees. A commission or a state agency might develop a list of priority fields based on stakeholder input and data.
Advantages: This approach offers flexibility for a state to address its strategic needs, such as if it is attempting to build talent for an emerging industry. This can also be a suitable strategy when data is limited, if a state has not conducted an adequacy study, or does not have robust or reliable labor market projections. A strategy-based approach can also prioritize important fields that might be overlooked in a high-cost or supply-and-demand analysis, such as those providing high societal value but low wages.
Disadvantages: While this approach may be informed by labor market or other data, it is not always data-driven. As a result, some prioritized fields may be the result of misjudgement or over-generalization. STEM is the most common priority-based field, but that encompasses many different degrees that have varying levels of cost and need for more degree-holders. There is a general perception of a STEM skill shortage that does not always match reality. States should examine their data closely and with sufficient disaggregation.
Examples:
Oregon’s university funding formula prioritizes three degree areas: STEM, Health, and Bilingual Education. Degree completions in STEM and health fields receive an additional 20% weight in the formula, while bilingual education receives an additional 100% weight. These fields were selected by the state’s Higher Education Coordinating Commission, but were generated through stakeholder input and a general agreement that bilingual educators were a workforce priority in a state where the Hispanic population had doubled since 2000. Oregon’s formula also includes cost-based adjustments as well, applying weights to credentials based on the program and course level.
Virginia’s Tech Talent Investment Program (TTIP) is a performance-based funding program designed to incentivize the production of Bachelor’s degrees and Master’s degrees in priority fields. Institutions receive funding based on hitting degree-production targets in certain fields. The program was originally negotiated during Amazon’s search for a HQ2 location and is set to run through 2039. Statute designates eligible degrees as those in computer science, computer engineering, other closely related fields of study, or that otherwise align with traded-sector, technology-focused growth opportunities identified by the Virginia Economic Development Partnership. Collectively, TTIP programs are exceeding their overall targets. The bachelor’s degree programs have more than doubled their goal, producing 2,520 new degrees through FY25 compared to a target of 1,155.
Market-Based
Most commonly, this approach evaluates workforce data to identify priority occupations, which are then mapped back to degrees aligned with those fields. These occupations may be in high demand currently, be projected to grow at a fast rate, or have a shortage compared to the number of degrees being produced. Many states have existing lists of priority or high-demand occupations, which are often used to guide certain state policy decisions and investments. These lists are most commonly produced by state departments of labor, workforce, or similar entities. Some states also incorporate wage data into their assessment. This can be the typical wage of the occupations the degree leads to or the wage of students who complete the degree, regardless of what field they enter.
Advantages: The use of labor market and post-completion student data aligns education programs with the workforce and removes the possibility of misconceptions or historical inequities influencing the process. Arguably, this approach best addresses value to students. When accounting for both job shortages and wages, this approach prioritizes degrees that increase a graduate’s chance of getting a good-paying job and achieving a positive return on investment.
Disadvantages: A narrow definition of demand could prioritize the wrong degrees. For example, an occupation with many current openings might be met with current degree production, or the field may be projected to shrink in the future. An occupation or field may be projected to grow at a percentage rate, but starts from a relatively low base. A state using this approach should review its data and application in the formula regularly, so that fields are not on the list longer than they are actually in demand. Finally, while supply and demand can incentivize degrees likely to have plentiful job opportunities, there is not always consideration of the quality of the job or career, an issue discussed further below.
Examples:
Louisiana’s Workforce Commission publishes a Star Jobs Rating System that ranks jobs based on current job openings, forecasted growth, wages, and other factors. The Star Jobs ratings bring in input from a diverse group of stakeholders including academic, economic development, workforce development, and industry experts. The state’s postsecondary funding formula includes a measure for degrees earned in fields leading to a 4 or 5 Star Job for both two-year and four-year institutions.
Utah’s outcomes-based funding formula also draws on a state rating system for high-wage and high-demand jobs. Every two years, the Utah Department of Workforce Services designates “targeted jobs,” which rank in the top 20% for outlook in growth and projected openings and the top 20% for median wages. Universities are rewarded in the formula for graduates with degrees that map to these targeted jobs.
Mix of Strategy- and Market-Based
Combinations of these approaches can capture the benefits while mitigating some of the shortcomings. The data in a market-based approach may not identify fields the state strategically wants to invest in to diversify or grow its economy. It may also exclude fields that are of critical importance to society but are not growing as rapidly or pay well. The strategy-based approach offers the flexibility to add such fields to a list identified through market-based.
Examples:
Arkansas prioritizes STEM and High-Demand degrees in its funding formula for two-year and four-year colleges and universities, providing additional points for credentials awarded in those programs. For the STEM definition, Arkansas uses a list of the six digit CIP codes identified as STEM degree programs by US Immigration and Customs Enforcement. The High-Demand weight is assigned to the six digit CIP codes listed in the statewide Projected Employment Opportunities List published by the Arkansas Division of Workforce Services. This list crosswalks CIP codes to the SOC codes of occupations with short-term projected employment needs for each of the state’s 10 local workforce development board areas. Arkansas updates each list every five years.
Texas’s community college funding formula includes a High-Demand premium that combines the strategy-based and market-based approach. Credentials earned in these fields provide a 25%-100% premium. The market-based approach uses the top 10 growing occupations in the state and top five in a college’s region, based on 10-year projections from the Texas Workforce Board and the U.S. Bureau of Labor Statistics. The strategy-based approach allows the Commissioner to designate additional fields that are critical to the state’s “economic needs and legislative priorities.” It also allows institutions to petition for additional fields that are on the local workforce board’s priority list.
Methodologies and Potential Data Sources
Developing a quality definition of high-priority degrees requires quality data. There are some minimum requirements, but states have choices in the data sources they use to define high-demand fields. This section highlights the opportunities and tradeoffs presented by different data sources.
→ Education records: Student-level records on the numbers and demographics of students enrolling and completing at the program level are an important foundation. This student-level collection can be matched to individuals’ employment records. This match is critical to understanding which programs, for which students, lead to positive workforce outcomes.
→ Wages and Employment: Preferably, states have connected their education records to their state unemployment compensation wage record systems, which, at a minimum, collects wages and industry of employees in the state who work for participating firms. Agencies can then match the student and employment records on unique identifiers (e.g., Arkansas, Tennessee) or develop agreements to receive the employment data directly (e.g., Virginia).
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States can enhance their employment records to collect additional fields such as occupation, hours worked, and work location. This allows states to understand the actual occupations graduates fill, not just the industry in which they work. Hours worked adds important context to total wages earned by providing context about full- or part-time employment and illustrating the rate of compensation received. South Carolina, Nebraska, and Washington have enhanced their wage record systems in these ways.
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Unemployment wage record systems come with other blindspots. In addition to the lack of occupations and hours worked, these systems typically do not capture the self-employed. Some states have combined their unemployment insurance wage file with records from their state tax commission (e.g., Oklahoma) in order to capture individuals paying taxes on wages, like the self-employed. To capture former students who have left the state, some states have developed multistate data exchanges. The Western Interstate Commission for Higher Education (WICHE) piloted a data exchange between six states. KY Stats’ Multi-State Post-Secondary Report connects Kentucky, Indiana and Ohio post-graduate records with in-state and out-of-state employment outcomes. Federal data is especially helpful in capturing employment data from students who have left the state. However, federal sources are either limited to federal financial aid recipients (College Scorecard) or omit the self-employed and independent contractors (Post-secondary Employment Outcomes).
→ Connecting Programs to Occupations: Beyond individual-level collections, many states also look at the projected growth or wages of a given occupation, and then crosswalk that to the degrees typically associated with that occupation. Many states use federal projections of occupations, as organized through Standard Occupational Classification system (SOC). States have many options for how to index or rank SOCs when prioritizing degrees. A combination of short-term and long-term projected growth, openings, wages, and changes in wages can be incorporated. SOCs can also be delimited by the education or training typically required to hold that occupation. This can be helpful for states to isolate priority occupations to those to be funded by a higher education funding formula. However, seniority of role, firm, and state regulations will cause variation in the education and training levels for many jobs.
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Originally developed by the U.S. Department of Education and now stewarded by Advance CTE, the Career Clusters Framework provides a shared structure and language for program design and communication to students. While research showed no examples of states employing the Career Clusters Framework into finance structures, it could be a way to identify high-value careers and trace back to the programs that prepare students for them.
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A partnership between the Bureau of Labor Statistics and the National Center for Education Statistics maintains a crosswalk between SOC and CIP codes. This is a many-to-many relationship: a single education program can lead to multiple occupations, and multiple degrees can lead to a single occupation. As a national crosswalk, it may miss on state and regional variation. Some stakes like Oklahoma have amended the crosswalk to fit their state context.
Innovative Sources and Methodologies
There are also innovative ways states can incorporate labor market data into a market-based approach. For instance, using job postings to model workforce demand can create a more accurate and real-time depiction of jobs currently available. This has an advantage over long-term projections that use historical data, sometimes two years old. However, job postings are often duplicative and are not organized by a common classification system. Third-party organizations like Lightcast have aggregated job postings to calculate real-time demand, while using social and employment profiles to calculate supply. These innovative approaches improve the supply and demand calculations to identify and track high-demand occupations. The downside is that these data are proprietary and not publicly available for free.
State entities may also be interested in projecting how emerging technologies and fields will impact the labor market. Models may project how technology may impact the employment levels and productivity of specific sectors and occupations. Predicting the future comes with substantial levels of uncertainty, so public entities may be more hesitant to engage in aggressive predictions.
Qualitative information can also play an important role. For instance, developing and incorporating a business voice can transcend limitations in quantitative calculations. Chambers of Commerce or sector-specific trade groups may target programs for state priority. A state’s business development arm may also identify the need to to produce skills and credentials in order to lure potential employers to relocate. Virginia’s development of the Tech Talent Investment Pipeline, during negotiations to attract Amazon HQ2, is a good example of this.
Cautions and Questions
As states tie higher education funding to workforce outcomes, prioritizing degrees in high-demand or high-cost fields can seem like a straightforward way to align educational investments with state economic needs. But without thoughtful design, these strategies can present some shortcomings or create unintended consequences.
Demand doesn’t guarantee quality. Some growing occupations offer low wages, limited benefits, high turnover, or limited growth potential. Incentivizing these high-demand jobs may fill some workforce needs, but it will not necessarily benefit students. For example, early childhood educators and medical assistants commonly show up on states’ in-demand lists but typically pay less than $40,000 a year. States should not prioritize degrees based on labor market demand alone, but should also include earnings at a minimum. Longer-term earnings could indicate promotion potential and upward mobility, while other measures of quality, like access to benefits, are still unexplored in state finance policy.
Access to high-priority programs. High-priority degree fields often enroll fewer students from historically underserved communities due to systemic barriers in access, preparation, and support. In 2018, Hispanic students earned 15% of all bachelor’s degrees, but only 12% of STEM degrees. Black students earned 10% of all bachelor’s degrees, but only 7% of STEM degrees. The gender gap can be even more extreme; women earn only 19% of computer science bachelor’s degrees. When states allocate funds to these fields, they may inadvertently divert resources from institutions serving more diverse student populations, thereby exacerbating disparities. At a minimum, states should monitor data to understand any unintended consequences or trends. States should also consider building funding incentives into their formulas for institutions to enroll more underserved students in these programs.
Alignment with student outcomes. Without accounting for student outcomes, all three approaches described above can create incentives for institutions to create or expand programs that are not linked to student outcomes. Helpfully, some states utilizing the market-based approach do account for high-wage jobs in their definition. States can also consider other student-centered, post-completion metrics such as licensure and credential exam pass rates, job placement rates, or graduates earning above a certain threshold.
States that use any of these methods must track and disaggregate actual student outcomes. States need to regularly assess whether prioritized degrees are delivering on their promise for students. This assessment should be as disaggregated as possible—by student demographics and by institution—since the benefits of a degree program will likely not be realized equally across students or institutions. A definition that relies on median wage of a field or occupation will obscure important variations in outcomes and may reflect stratifications in the workforce rather than program quality. Identifying where and for whom wages fall short of that median can help states implement targeted interventions to improve outcomes—such as increased financial aid, enhanced student services or accountability measures. With a close eye on these disparities, states can build funding systems that reward not only alignment with workforce demand, but also real progress toward expanding opportunity and mobility for all students.
Acknowledgements
This brief was written by Will Carroll and Tom Allison with HCM Strategists, national experts on postsecondary finance and higher education policy. Their colleague Martha Snyder made significant contributions to this brief.