Where Does the Money Go? Higher Education Expenditures

Posted on May 28, 2020

Higher education, like death and taxes, is increasingly becoming an inevitability in most American households. 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.


About Author



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...
View all posts by Nathan >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp