Performance Based Funding Effects on Academic Success

Before talking about performance based funding (PBF from hereon), one must understand the history and complexities of the two distinct forms. According to the book “Performance Funding for Higher Education”, PBF originated in the lower education systems for elementary and secondary schools. Similar funding systems have been around in that realm since earlier in the twentieth century, unlike higher education where it is relatively new. From a lawmaker’s perspective, they see PBF to be beneficial because the sole focus of the school is to achieve written standards for various academic goals, such as retention and degree completion. In such simple terms this seems to be a fantastic idea, but as the book mentions very early on there are many unforeseen or poorly thought-out impacts of most policy.

The decision makers at the state level see higher education as a business, simple numbers. We, being the state, invest x amount of money, and expect x amount of return on this investment. If the schools do not meet our expectations, then they will not receive funds. In the current climate where educational funding is alreadr being reduced, and costs are ever climbing for students, every penny matters to the institutions. To further complicate PBF, there are two different models that can be employed by the states.

Performance funding model 1.0 , and performance funding model 2.0 (PF 2 from hereon) are the new guys on the block. PF 1 is a kinder version of PBF. Simply put, it uses these performance standards as a means to rewarding a bonus. It does not affect the typical base funds the school receives, it only typically reflects “between 1% and 5% of state funding for higher education”. Here there is an emphasis for schools to perform well to receive even more money than the previous year. In a similar way that someone might work harder in their career for the potential to obtain a raise, but their base salary would not be at jeopardy.

PF 2 places a large percentage, in some cases up to 90%, on these same performance metrics. This is an evolved version of PF 1 that obviously places a much larger emphasis on these targets. This is high stakes poker at the World Series of Poker final table. An immense pressure is applied on the numbers, neglecting the fact that there are people and experiences behind them. If it is not apparent yet, tying upwards of 90% of a schools funding to the performance and degree completion to simple statistics can have many unwarranted after effects. These problems then fall to the institutions to solve, the state simply sets the standard and suffers no consequence, or do they?

Before answering that question, there is still more to PBF. The standards I have been mentioning must be explained further. Referring to table 1.1, courtesy of “Performance Funding for Higher Education”, we can examine these metrics further.

The first column indicates the states that were using PF 1, PF 2, or some combination when this chart was completed in November of 2015. 33 states, or about 64% of all states are participating in PBF. Of that group, 12 are using PF 1, 15 in PF 2, and finally 6 use a combination. The percentages respectively are 37%, 45%, and 18%. The next 2 columns illustrate what institutions are held to these standards, two or four-year colleges and universities. Again some states require only four-year institutions, some only two-year, but here most seem to keep it uniform and hold both to this model. The last four columns present rather interesting information. Firstly the subsection is titled Greater weight for outcomes for certain kinds of students.

This means that the state places an even larger importance on said group. First are the under-represented minorities. As defined by the University of California at San Francisco’s Office of Diversity and Outreach, it is “someone whose racial or ethnic makeup is from one of the following: African American/black, Asian, Hispanic/Latinx, Native American/Alaskan Native, Native Hawaiian/other Pacific Islander, two or more races, when one or more are from the preceding racial and ethnic categories” (diversity.ecsf.edu). This means that anyone who identifies under one or more of those categories would be looked at in a separate category. The states may set a standard that needs to be achieved by just that set of students, which charges schools with the responsibility of ensuring the equal success of all of its students. Only about 21% of all PBF models use these lenses as an emphasis.

The second category is low socio-economic status (SES). This is a more common at almost 50% of states gauging the success and degree completion of this group. Historically the less economically well off students have the lower graduation and retention rates for a variety of reasons. One can assume that this emphasis is to push institutions to focus on these groups so that the nation as a whole can climb the ranks of highly educated populations.

From a personal perspective, over a lifetime this 2 or 4 year investment in education can help low-SES families escape that cycle. On the flip side, going to college and failing leaves a large burden of debt and missed income. Thus the importance to graduate and ensure the success of this population. The next column has one single state that uses the measure of “Less prepared students”. Being that this state would appear to be an outlier, and that it represents only 3% of the sample, there will be no focus here.

Last, and most popular but potentially misleading, is the category of other. Two of the examples are STEM, science, technology, engineering, and math, and then older. STEM has increased in popularity and demand amongst both lawmakers and students as technology has grown ever present in society. The older lenses may be an attempt to help those non-traditional students who took a different route, and can be oft forgotten, but are no less important. Unfortunately there is no breakdown as to what states are using which “Other” category. As a whole, 61% of the states use an “Other”. You might be wondering why the percentages add up to more than 100%, and that is no mistake.

Many states use some combination of the 4 categories, thus increasing the importance to cater to many different groups. These policies, while set forth with good intention, can cause strain on the university and its ability to allocate resources in a manner that serves as many students as possible while maintaining cost and quality at similar, if not improving levels. Now that there is a well-developed knowledge of PBF, its different models, and how states utilize multiple lenses, let’s dig deeper. 

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