Economics of Course Selection
Luke Johnson | SIPA Class ’18
It is October now and the SIPA community has collectively walked passed the threshold to drop/add courses. With the course shopping season over, and as the door clicked and locked behind us, we look down the hallway of this semester and wonder: “did I choose the right classes”?
Luckily, if you feel overloaded, then you can still w-w-withdraw from a course. The appearance of a “W” on my forthcoming transcript prompted me to try and determine a lesson from all this…
The Economics of Course Credits
There is a linear relationship between the number of credits you take and your workload. Of course, some courses are heavier than others and (some are easier for you than others), but in general the relationship should look something like this:
However, using this to determine how many classes you should take (like what I did ) would be a mistake. The graph above is the supply of workload, which is the wrong thing to look at when determining the optimum number of credits to take. That is, in our model, we do not look at the course load, but rather how we react to the workload.
The problem is that our ability to handle work is not linear, but looks more like a production function — there is a strong law of diminishing returns when getting something done (i.e. my “marginal product of getting-stuff-done”, MPgsd, decreases with time). (Qualitatively speaking, I think we have all been to the point where no number of re-reads will make that paragraph make sense or,wondering why this simple problem is taking so long to solve.) My get-stuff-done (y- axis) to time (x-axis) relationship looks something like this:
Finally, I think we can all agree that as productivity decreases, fun and interest decreases with it. This is a very close relationship (I call it the Fun-Being-Productive Conjecture). The drop in productivity (decrease in MPgsd) is mirrored by the increase in pain. This brings us to the final aspect of our function: the pain-to-work graph.
As the number of credits increase, the pain level increases parabolically (not linearly):
If you overlay the two graphs then you can find an individual’s optimal number of credits. For a semester with free time to work on hobbies or personal projects, then you should be someplace in which Workload > Pain. But there is some point after which pain = workload that I would not recommend venturing.
The biggest takeaway for me was how much besides courses the school has to offer. There are other things we are taking part in, as well, including social events, looking for internships, going to career information sessions, and keeping up with hobbies.
With juggling the other things, although our schedules are just as busy, we are able to return to that productive part of the graph. Some variety in what you do (hobbies, events, job search) might not lower the overall “workload”, but it will certainly feel like it!
(This model adds another factor here)