I recently learned about an interesting statistic for social scientists. It’s called the “Dissimilarity Index”. It allows you to compare the categorical distribution of two sets.
Many of us already know how to compare two distributions that have only 2 possible values. It’s easy because if you know the proportion of a group who are in category 1, then you know that 1-p will be in category 2. We can conveniently denote these with values of zero and one, and then conduct standard t-tests or z-tests to discover whether they are statistically different. But what about distributions across more than two possible categories?
By now, everyone should consider using ChatGPT and be familiar with how it works. I’m going to highlight resources for that.
My paper about how ChatGPT generates academic citations should be useful to academics as a way to quickly grasp the strengths and weakness of ChatGPT. ChatGPT often works well, but sometimes fails. It’s important to anticipate how it fails. Our paper is so short and simple that your undergraduates could read it before using ChatGPT for their writing assignments.
A paper that does this in a different domain is “GPT4GEO: How a Language Model Sees the World’s Geography” (Again, consider showing it to your undergrads because of the neat pictures, but probably walk through it together in class instead of assigning it as reading.) They describe their project: “To characterise what GPT-4 knows about the world, we devise a set of progressively more challenging experiments… “
For example, they asked ChatGPT about the populations of countries and found that: “For populations, GPT-4 performs relatively well with a mean relative error (MRE) of 3.61%. However, significantly higher errors [occur] … for less populated countries.”
ChatGPT will often say SOMETHING, if prompted correctly. It is often, at least slightly, wrong. This graph shows that most estimates of national populations were not correct and the performance was worse on countries that are less well-known. That’s exactly what we found in our paper on citations. We found that very famous books are often cited correctly, because ChatGPT is mimicking other documents that correctly cite those books. However, if there are not many documents to train on, then ChatGPT will make things up.
I love this figure from the geography paper showing how ChatGPT estimates the elevations of mountains. This visual should be all over Twitter.
There are 3 lines because they did the prompt three times. ChatGPT threw out three different wrong mountains. Is that kind of work good enough for your tasks? Often it is. The shaded area in the graph is the actual topography of the earth in those places. ChatGPT “knows” that this area of the world is a mountain. But it will just put out incorrect estimates of the exact elevation, instead of stating that it does not know the exact elevation of those areas of the world.
The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.
If you feel like you already are proficient with using ChatGPT, then I would recommend Wolfram’s blog because you will learn a lot about math and computers.
Scott wrote “Generative AI Nano-Tutorial” here, which has the advantage of being much shorter than Wolfram’s blog.
The government is unique among economic institutions insofar as it can use coercion legally. But not all activities are coercive. Clearly, taxation is overwhelmingly coercive. Some people say that they are happy to pay taxes, but the voluntary gifts to the US Treasury are itsy-bitsy (just over $1m for FY 2023). Most regulations also include the threat of fines or jail time for non-compliance.
But once the government has the money in their coffers, there is plenty that they can do consensually. Once they have the resources, they are often just another potential transactor in the markets for goods and services. While the government can transact as well as anyone else, there is a fundamental theoretical difference for how we should interpret those transactions. Specifically, there is a principal-agent problem such that we can’t quite identify the welfare that is enjoyed by consumers when the government makes purchases. We really have very little idea.
Garett Jones uses the analogy of the government confiscating potatoes. The worst use would be for the government to throw the valuable resources into the river. Those resources help no one. Improved welfare would be yielded if the government just transferred those potatoes back to people. Sure, there’s the transaction cost of administration, but people get their potatoes back. Finally, the great hope is that the government takes the potatoes and makes tasty potato fritas such that they return to the public something more valuable than they took. These might be things that fall into the public goods category or solving collective action problems generally.
The above examples illustrates that how the government spends matters a lot for the welfare implications of the newly purchased government resources. But, we need to recall that there is an entire private segment of the market that is affected by the government transactions.
Short-Run Analysis
In a competitive market, firms face increasing marginal costs and make decisions about their levels of output. When the government makes purchases, it’s simply acting as another demander. How does the entry of a larger demander affect everyone else in the market? See the below GIF.
Last time the gifs were simply about price & quantity and welfare. I’m sharing some more GIFs, this time in regard to welfare and taxes.
First, see the below gif. It shows us that both consumer surplus (blue area) and producer surplus (red area) always rise if there is a demand increase (assuming the law of supply and law of demand).
Next, let’s consider a basic tax. We can represent it as the difference between what the demander pays and what the supplier receives. The bigger the tax, the bigger the difference between the two.
Now let’s combine the tow ideas: If taxes rise, then the quantity transacted falls, price paid rises, price received falls, and both consumer and producer surplus fall. Not only that, since there is an inverse relationship between the tax rate and the quantity transacted, it may be that increasing the tax rate more *reduces* revenue. The idea that there is a tax revenue maximizing tax rate is illustrated below right and is known as the Laffer curve.
In addition to all the usual items for a principles of macroeconomics class, I’m asking my students to listen to one podcast episode this semester. They have to write a short summary on a discussion board for credit.
It took me a bit of time to collect this list of links. I also give them some discretion to find their own episode, but I’m not posting my rules on that point here. This list is something you can copy, paste, and modify. The point is to have all the web links in one place so that students can just click around. There have been many great podcasts over past 2 decades, but I list relatively new content so that we get a bit of “current events” thrown in. So, even if you’ve assigned podcasts before, this new list might be helpful.
I’ve discussed the ways to teach supply and demand in the past. Regardless, almost all principles of economics classes require a book. But even digital books are often just intangible versions of the hard copy. Supply and demand are illustrated as static pictures, using arrows and labels to do the leg-work of introducing exogenous changes. There’s often a text block with further explanation, but it lacks the kind of multi-sensory explanation that one gets while in a class.
In a class, the instructor can gesticulate and vary their speech explain the model, all while drawing a graph. That’s fundamentally different from reading a book. Studying a book requires the student to repeatedly glance between the words and the graph and to identify the appropriate part of the graph that is relevant to the explanation. For new or confused students, connected the words to one of many parts of a graph is the point of failure.
This is part of why the Marginal Revolution University videos do well. They’re well produced, with context and audio-overlaid video of graphs. It’s pretty close to the in-person experience sans the ability to ask questions, but includes the additional ability to rewind, repeat, adjust the speed, display captions, and share.
Have you ever tried to do something objectively. It’s impossible. We might try, but how do we know when we’ve failed to compensate for a bias or when we’ve over compensated. Russ Roberts taught me 1) all people have biases, 2) all analysis is by people, & 3) analysis should be interpreted conditional on the bias – not discarded because of it.
The only people who don’t have biases are persons without values – which is no one. We all have apriori beliefs that color the way that we understand the world. Recognizing that is the first step. The second step is to evaluate your own possible biases or the bias of someone’s work. They may have blind spots or points of overemphasis. And that’s OK. One of the best ways to detect and correct these is to expose your ideas and work to a variety of people. It’s great to talk to new people and to have friends who are different from you. They help you see what you can’t.
Finally, because biases are something that everyone has, they are not a good cause to dismiss a claim or evidence. Unless you’re engaged in political combat, your role is usually not to defeat an opponent. Rather, we like to believe true things about the world. Let’s get truer beliefs by peering through the veil of bias to see what’s on the other side. For example, everyone who’s ever read Robert Higgs can tell that he’s biased. He wants the government to do much less and he’s proud of it. That doesn’t mesh well with many readers. But it’d be intellectually lazy to dismiss Higgs’ claims on these grounds. Higgs’ math and statistics work no differently than his ideological opponents. It’s important for us to filter which claims are a reflection of an author’s values, and the claims that are a reflection of the author’s work. If we focus on the latter, then you’ll learn more true things.
Know Multiple Models
In economics, we love our models. A model is just a fancy word which means ‘argument’. That’s what a mathematical model is. It’s just an argument that asserts which variables matter and how. Models help us to make sense of the world. However, different models are applicable in different contexts. The reason that we have multiple models rather than just one big one is because they act as short-cuts when we encounter different circumstances. Understanding the world with these models requires recognizing context clues so that you apply the correct model.
Models often conflict with one another or imply different things for their variables. This helps us to 1) understand the world more clearly, and 2) helps us to discriminate between which model is applicable to the circumstances. David Andolfatto likes to be clear about his models and wants other people to do the same. It helps different people cut past the baggage that they bring to the table and communicate more effectively.
For example, power dynamics are a real thing and matter a lot in personal relationships. I definitely have some power over my children, my spouse, and my students. They are different kinds of power with different means and bounds, but it’s pretty clear that I have some power and that we’re not equal in deed. Another model is the competitive market model that is governed by property rights and consensual transactions. If I try to exert some power in this latter circumstance, then I may end up not trading with anyone and forgoing gains from trade. It’s not that the two models are at odds. It’s that they are theories for different circumstances. It’s our job to discriminate between the circumstances and between the models. Doing so helps us to understand both the world one another better.
I’ve taught GT a total of 5 time. Below are my average student course evaluations for “I would recommend this class to others” and “I would consider this instructor excellent”. Although the general trend has been improvement, improving ratings and the course along the way, some more context would be helpful. In 2019, my expectations for math were too high. Shame on me. It was also my first time teaching GT, so I had a shaky start. In 2020, I smoothed out a lot of the wrinkles, but I hadn’t yet made it a great class.
In 2021, I had a stellar crop of students. There was not a single student who failed to learn. The class dynamic was perfect and I administered the course even more smoothly. They were comfortable with one another, and we applied the ideas openly. In 2022, things went south. There were too many students enrolled in the section, too many students who weren’t prepared for the course, and too many students who skated by without learning the content. Finally, in 2023, the year of my changes, I had a small class with a nice symmetrical set of student abilities.
Historically, I would often advertise this class, but after the disappointing 2022 performance, and given that I knew that I would be making changes, I didn’t advertise for the 2023 section. That part worked out perfectly. Clearly, there is a lot of random stuff that happens that I can’t control. But, my job is to get students to learn, help the capable students to excel, and to not make students *too* miserable in the process – no matter who is sitting in front of me.
Joy: This post was written by my friend and fellow econ professor Cameron Hardwick.
One of my biggest ongoing teaching challenges is keeping students engaged during lectures.
Sure, there are ways to add interactivity here and there, but sometimes there’s just no way around an old-fashioned lecture.
There are a few ways of dealing with this, and I haven’t been satisfied with any.
It’s their grade, if they zone out that’s on them. In terms of the incentives, sure, the externalities are all internalized. But as a macroeconomist, I also know: if time-inconsistency problems are hard for policymakers, how much more for students! We shouldn’t be surprised when students do poorly if the main feedback they get from paying attention or not comes a week later with the homework grade.
Posing questions and waiting for answers. Either you get a minute of awkward silence, or you get the same two engaged students answering everything while everyone else keeps zoning out.
Cold calling. I started doing this a few years into teaching. The advantage is that it keeps students on their toes and paying attention. But a few problems left me unsatisfied:
“How about you in the red shirt”. Hard to catch a student’s attention that way, and in a class of 40 or more, learning names takes a good chunk of the semester.
I had no systematic way of keeping track of participation. Every semester I’d look at the roster and still have a few names I couldn’t put a face to.
Humans are really bad at making random choices! Much as I tried, I couldn’t guarantee I wasn’t biased toward or against (say) the corners of the room, or students whose names I knew.
LMS software. These can offer a lot of great student participation tools. But students have to pay for them – which isn’t worth it if you’re just looking for one feature. On top of that, then you’re locked into an ecosystem.
So, I made an app myself. It does one thing and does it well.
Pick.al (pronounced Pickle) picks students at random from a roster and keeps track of participation points. I can now pose a question in class, ask “what do you think…”, pull out my phone and hit a button, and have a name.
I can also record the quality of their answers:
✓: 1 point, good attempt! (Since this is for participation points, I record ✓ whether right or wrong, as long as they give it a good shot)
?: 0.5 points, if they ask “wait, what was the question?”
×: 0 points, if they’re not there or don’t respond at all.
There’s also a 1-5 scale option, for those who want a more fine-grained evaluation.
This has a lot of benefits in the classroom:
Since I can call on students by name, I learn names more quickly.
Pick.al chooses randomly from the pool of students who have been called on the least so far. So, I know my participation points are as fair as possible.
Students know they can get called on at any time, so they pay attention more in class, and then do better on the homeworks and tests.
Students appreciate being brought in more frequently. One noted on the evaluations the first semester I piloted it: “something specific I like is he got the class involved by calling people out which forced them to test their knowledge which is something teachers need to do more of.”
Using Pick.al is as simple as registering (with an email address or an OrcID), uploading a roster, and then hitting a button during class. You can also swipe through the history and edit or undo participation events, and go back in the admin interface and add, edit, and remove participation events after the fact if necessary.
Pick.al is secure and password-protected, and has a number of handy features:
You can set excused absences if a student lets you know beforehand, so their name doesn’t come up until a certain date.
You can select specific students from the roster in a sidebar, if you want to give credit to – say – a student who raises his hand unbidden.
If you’d like to use the classroom computer instead of pulling out a phone, you can use it with full keyboard navigation.
Scores can be downloaded as a CSV to be put in your own gradebook.
Private notes can be added to students to show up when their names are selected, e.g. “sits in the back corner”
If you use it and find a bug or have an idea that would make it more useful to you, feel free to let me know. It’s been a great tool in my own classes, and I hope it’ll be useful for other teachers to keep students engaged too.
It’s been almost a decade since I taught principles of economics classes. One major allocation of my time this semester is course prep, since I am teaching 3 different classes.
For my Principles of Microeconomics course, I chose Modern Principles of Economics, because I figured Tyler and Alex had done a good job and I have heard good reviews from others.
I’m writing a short review of their instructor resources, and then I’ll have to get back to course prep.
Like most textbooks, they provide you with slides that you can modify. Not having to start from scratch on lecture slides is great.
They also have teacher guides for each chapter. I find these helpful, because I have not taught this class in many years. Even though “I’m an economist,” there is still a technique to presenting these ideas for the first time to undergraduates. No need to re-invent that wheel completely.
They have suggestions for in-class activities. For example, to illustrate demand shifts, ask the students about a recent celebrity scandal and how that created a fall in the demand for concert tickets. It works. Everyone loves talking about celebrity scandals. It will be an evergreen idea. There are always new scandals for each semester – the students know more about it than the professors. My students (Fall 2023) informed me that Lizzo got in hot water for fat-shaming.
Their online learning platform called Achieve within MacMillan works well. It integrates really well with Canvas, our LMS. One warning I would give you is to make sure that students buy Achieve through an account on their .edu email address. I have headaches over students signing up with a personal email address and then not having their data integrate with Canvas.
You can sign up for EconInbox, which will email you topical relevant news stories right before you would want to present them in class. You’ll have to tell them ahead of time what your schedule of topics is, but that is something you ought to have worked out in your syllabus at the beginning of the semester. Obviously, you can’t cover every chapter in one semester. There are far more resources, generally, than you can use. But picking and choosing from a great library is easier than trying to build something from scratch yourself.
Lastly, the Cowen Tabarrok textbook integrates nicely with the free Marginal Revolution University video library. MRU is free to all. So, as an instructor you could still use it heavily even if you are not assigning their textbook or even if you are not doing Achieve. Still, I think that making use of the MRU resource is easiest if you are using their textbook. A fun video that might even be worth using class time for is Avengers: The Story of Globalization
Economist Writing Every Day 