Data Analysis of Higher Education Expenditures

Posted on May 28, 2020

Higher education, like death and taxes, is increasingly becoming an inevitability in most American households based on data. While it's value is well-established (even among those of us who opt for the much-maligned liberal arts degree), its rising price has made an undergraduate degree one of the largest purchases faced by most Americans during the course of their adult lives.

Explanations for the faster-than-inflation growth of university tuition ranges from bloated organizational structures (see here, here and here), to post-2008 cuts in state education budgets, to an explosion of services and amenities offered by institutions seeking to attract prospective students, to increasing demand for university mental health services, to increases in costs associated with litigation and litigation insurance, to name a few.  Notably absent from the suspects is faculty pay and other instructional expenditures, with successive reports finding professorial salaries barely keeping pace with inflation in recent years. 

For my project I evaluated institutional spending and revenue data for 2016-2017 published in a Chronicle of Higher Education analysis of Department of Education data. I focused primarily on aggregated and per-student instructional expenditures and their relationship with revenue from tuition and fees as well as overall institutional structure with an eye towards developing a general view of how institutions spend their money and the relationship between non-tuition revenue (state funding, endowment income, etc) and instructional spending in order to inform more specific investigations of expenditure.  A few of my findings are below:

  1. Institutions in the dataset received a median of $2,892.9 more per student in tuition and fees than they spent on per student instruction. The median ratio of spending to instructional expense was .75, meaning that for every dollar institutions brought in in tuition and fees, they spent about 75 cents on instruction.
  2. Public institutions spent noticeably more on instructional expense per tuition dollar than private institutions did, with the median public institution spending $1.33 on instruction for every dollar in tuition and fees, while the median private institution spent only $.59 for the same dollar.

This is notable because, in general, it is private institutions that are noted for owning the largest endowments and therefore possessing the greatest resources to supplement tuition income for their students. As a group, only 8% of private institutions spent more than they received in tuition and fees, compared to 79% of public institutions. It is important to note that this is not necessarily a consequence of increased non-instructional spending on the part of private institutions, since state institutions tend to draw substantial resources from state governments to supplement their budgets which is unavailable to private institutions.

Proportion of private (Above) and public (Below) schools that spent more on instruction than they received in tuition (Left) and distribution of spending to revenue ratios among private institutions (Right), respectively. 

3.  In general, institutions that derived a lower proportion of revenue from tuition and fees spent more on instruction relative to tuition and fee revenue. Among institutions that derived less than 25% of their total revenue from tuition and fees, the median spending on instruction was $3,214 more than tuition revenue per student.  

However, in institutions that derived more than 75% of revenue from tuition and fees, median per student tuition and fee revenue exceeded revenue by $8,323. Whereas 93% institutions that derived less than 25% of their total revenue from tuition and fees spent more on instruction than they received in tuition revenue, no institutions that derived more than 75% of their revenue from tuition and fees spent more on instruction than they received in revenue.

Institutions graphed by the difference between revenue from tuition and fees and instructional expenditures against proportion of revenue derived from tuition and fees (Public Above, Private Below).

Want to learn more? View the project here.

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

 

About Author

Nathan

Data scientist in training with a background in education and a passion for solving challenges using data-driven decision-making. Methodological, tenacious self-starter. Building skills with Python, R, SQL, statistical analysis, and machine learning models. Ask me where I am...
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