Day 1: Introduction to the Economics of Higher Education
A series following my new class: The Economics of Higher Education
It’s Day 1 of Block 8 here at Colorado College and I’m teaching a brand new prep: The Economics of Higher Education. Two weeks ago I posted my preliminary syllabus (in Substack form) and I received a few very useful comments and additions from you all. Thank you LAL readers!
This is Day 1 of the chronicle of the class. I plan to post about each topic as we go, so you can read along with us about the history of higher education in the US, why college is so expensive, whether college is worth the money, and whether the value of college is about actually building skills or instead just identifying who was skilled to begin with. And that’s just Week 1!
Three Big Themes
We’ll return to three big themes repeatedly in this course, and our first activity hit on all three.
What is the point of college?
How do we know what we know?
When you don’t know how to do something, Figure It Out.
Today’s activity: Analyze text data using Excel/Sheets, Stata, or GenAI
It just so happens that in Blocks 4 and 5 this year I surveyed my Principles of Macroeconomics classes about why they were there. After introducing myself on the first day and detailing the requirements of the class, I asked them:
What is the point of college?
I asked them to enter their answers in a Google Form, and read their anonymous answers back to the group.
Then I showed them this video of Elon Musk talking about why he thinks college is an unnecessary and expensive waste of time and asked:
What is Elon Musk right about?
They entered their answers in my form and I read them back. And then:
What is Elon Musk wrong about?
And finally:
What are you hoping to get out of this class?
My Principles classes were full of first- and second-year students, some of whom actually wanted to know something about macroeconomics and others of whom were there because the class fulfilled some requirement or another. I really liked how this exercise got them thinking about what they really wanted, out of my class and out of college in general.
It also turned out that the data they provided was super useful for the class I’m teaching now!
This morning, after answering these same four questions themselves, the class split into three groups to analyze the text data. One group used Google Sheets, another used Stata, and a third used GenAI. I gave them 30 minutes and then we shared what we got done.
The Sheets and Stata teams went about the task as I would have. They read a bunch of answers, identified some keywords and phrases that seemed distinguishing of different motives, and then set about counting how many responses had each keyword/phrase. By the end of 30 minutes, the Stata team could tell us how many responses mentioned something about career. The Sheets team was stuck on figuring out what to do with responses that mentioned multiple keywords/phrases. So, progress, but slow progress.
The GenAI team got much further. That team uploaded the three sheets of responses to ChatGPT, Claude, and Gemini and asked GenAI what to make of their data. I knew GenAI was going to be powerful, but honestly I’m floored by what all they got done in 30 minutes. Check it out.
This is just one of several learnings they pulled out of the text data. In less than 30 minutes!
This chart really nicely illustrates some ideas in Big Theme #1: What is the point of college? Is it career preparation and making money? Or is it friends? Friends in the here and now, or friends who can help you find a job later? Maybe it’s about maturity and personal growth. Or then again, maybe it’s just about learning interesting things. Great fodder for discussion, both on Day 1 and throughout the course.
The GenAI team was also able to tell me that lexical sophistication (measured by TTR, the Type-to-Token Ratio) was highest for this class (a 300-level Econ election) and lowest for one of the Principles sections. This led to a great conversation about whether they had greater sophistication because they had actually learned something since they took Principles, or whether the kinds of students who select into upper-level econ classes are different from the get-go. Look at that. We even got to touch on human capital building v. the signaling value of college. On Day 1.
This exercise was also a good way to introduce Big Theme #2: How do we know what we know? We talked about turning qualitative information into quantitative statistics, how much we could trust GenAI to do an accurate analysis, and how we would have redesigned my little survey if we could. We also talked about the kind of information we’ll get from our guest speakers this term: experts for certain, but also just anecdotes from one single individual.
Lastly, we talked about Big Theme #3: When you don’t know how to do something, Figure It Out. In the pre-AI world, I had a decent sense of what kinds of skills and knowledge would be useful to my students in their careers. Post-AI, I don’t. I think the most valuable skill they can practice now is adaptability. So, we’ll try to do a lot of that.
Tomorrow’s readings
Goldin, C., & Katz, L. F. (1999). The shaping of higher education: The formative years in the United States, 1890 to 1940. Journal of Economic Perspectives, 13(1), 37-62.
Hoxby, C. M. (1997). How the changing market structure of US higher education explains college tuition. (No. w6323). National Bureau of Economic Research Working Paper.
(Optional) The "Dear Harvard" Letter and Institutional Autonomy
(Optional) How Universities Became So Dependent on the Federal Government
Tomorrow we’ll discuss the History of Higher Education. Please leave a comment with a question for discussion! I’d love to hear what you all think of these readings, and your thoughts will help me be better prepped for class next time.
100% feel we have to come alongside students and gAI to improve our collective query skills. The benefits exceed the potential costs in my opinion! Love this content.