Many of my classes consist of a large writing component. I’ve designed the courses so that most students write the best paper that they’ll ever write in their life. Recently, I had reason to believe that a student was using AI or a paid service to write their paper. I couldn’t find conclusive evidence that they didn’t write it, but it ended up not mattering much in the end.
Continue readingTeaching
Message To My Students: Don’t Use AI to Cheat (at least not yet)
If you have spent any time on social media in the past week, you’ve probably noticed a lot of people using the new AI program called ChatGPT. Joy blogged about it recently too. It’s a fun thing to play with and often gives you very good (or at least interesting) responses to questions you ask. And it’s blown up on social media, probably because it’s free, responds instantly, and is easy to screenshot.
But as with all things AI, there are numerous concerns that come up, both theoretical and immediately real. One immediately real concern among academics is the possibility of cheating by students on homework, short writing assignments, or take-home exams. I don’t want to diminish these concerns, but I think for now they are overblown. Let me demonstrate by example.
This semester I am teaching an undergraduate course in Economic History. Two of the big topics we cover are the Industrial Revolution and the Great Depression. Specifically, we spend a lot of time discussing the various theories of the causes of these two events. On the exams, students are asked to, more or less, summarize these potential causes and discuss them.
How does ChatGPT do?
On the Industrial Revolution:
And on the Great Depression:
Now, it’s not that these answers are flat out wrong. The answers certainly list theories that have been discussed by at various times, including in the academic literature. But these answers just wouldn’t be very good for my class, primarily because they miss almost all of the theories that we have discussed in class as being likely causes. Moreover, the answers also list theories that we have discussed in class as probably not being correct.
These kinds of errors are especially true of the answer about the Great Depression, which reads like it was taken straight from a high school history textbook, ignoring almost everything economists have said about the topic. The answer for the Industrial Revolution doesn’t make this mistake as much as it misses most of the theories discussed by Koyama and Rubin, which was the main book we used to work through the literature. If a student gave an answer like the AI, it suggests to me that they didn’t even look at the chapter titles in K&R, which provide a roadmap of the main theories.
So, my message to students: don’t try to use this to answer questions in class, at least not right now. The program will certainly improve in the future, and perhaps it will eventually get very good at answering these kinds of academic questions.
But I also have a message to fellow academics: make sure that you are writing questions that aren’t easily answered by an AI. This can be hard to do, especially if you haven’t thought about it deeply, but ultimately thinking in this way should help you to write better exam and homework questions. This approach seems far superior to the one that the AI suggests.
The Imperfection of Subgame Perfection
I’ve written previously about Pure Strategy Nash Equilibria (PSNE). They are the set of strategies that players can adopt in equilibrium – with no incentive to change their strategy. Students have an intuition that PSNE aren’t great because some outcomes that they identify depend on players making silly decisions in the past. In jargon, we can say that some PSNE depend on players choosing irrationally in a subgame while still reaching a PSNE.
See the extensive form game (below right). There are two players, each with two strategies per information set, and player two has two information sets. All PSNE will include a strategy for each information set. We can present the same game in normal form in order to make it easier to identify the PSNE (below left).
Player 1 (P1) can choose the row (B or C) and Player 2 (P2) can choose the column. Importantly, whether P1 might want to change his mind depends on P2’s strategy at the decision node in the alternative information set. Therefore, P2 must have two strategies, one per information set.
The four PSNE strategies and payoffs are underlined in the above table and they are noted in red on the below extensive form games. Again, the logic of PSNE states that no player can improve their payoff by changing only their own strategy, given the opposing player’s strategy. After all, a player can control their own strategy, but not that of their opponent. For example, note PSNE II. In the left subgame, P2 chooses M. His payoff would be unchanged if he changed his strategy, given the strategy of P1.
Continue readingCheers to Sumproduct!
I teach macroeconomics, finance, and other things.
Often, I use Excel to complete repetitive calculations for my students. The version that I show them is different from the version that I use. They see a lot more mathematical steps displayed in different cells, usually with a label describing what it is. But when I create an answer calculator or work on my own, I usually try to be as concise as possible, squeezing what I can into a single cell or many fewer cells. That’s what brings me to to the sumproduct excel function that I recently learned. It’s super useful I’ll illustrate it with two examples.
Example 1) NGDP
One way to calculate NGDP is to sum all of the expenditures on the different products during a time period. The expenditures on a good is simply the price of the good times the quantity that was purchased during the time period. The below image illustrates an example with the values on the left, and the equations that I used on the right. That’s the student version. There is an equation for each good which calculates the total expenditure on the individual goods. Then, there is a final equation which sums the spending to get total expenditures, or NGDP.
6 Tips for Taming Your Inner Spock
The younger, high school and undergrad version of me was not the best person. My sense of humor was too dark and I didn’t much care about the experience of other people. When I went to grad school, I was so excited. I would finally be around other economists and I would be able to drop all of the niceties, empty social signals, and fuzziness that I thought non-economists employed. And I was oh so very wrong.
It turned out that economists are also human beings and that no amount of self-congratulatory Spock-praising would stop that from being the case. Indeed, with some candid feedback, I became convinced that I was in desperate need of the kind of prosocial norms that could help me to better produce social capital. In other words, I needed to figure out how to get along. Below is some advice that I’ve found pivotal. Maybe you can share it with another person who might be well-served by reading it too.
Below are six norms that are good to employ in order to improve social cohesion, agreeableness, and, frankly, better mental health. And these aren’t just for economists. I suspect that there are plenty of people (maybe young men) who can benefit from what took me too long to learn. So here we go!
Continue readingAre Teacher Salaries Held Back by “Bloat” in K-12 Schools?
In the past 20 years in the US, per pupil spending in K-12 schools has increased by about 20%. That’s in CPI-U inflation-adjusted dollars. What’s the cause of this increase? Higher teacher salaries? Administrative bloat? Something else?
Here’s a chart you may have seen floating around the internet. It shows the growth in the number of employees at K-12 public schools.
This looks like a lot of administrative bloat! The source of the data is the National Center for Education Statistic’s Digest of Education Statistics, Table 213.10.
But hold on, here’s another chart, showing the percent of employees in each of these same categories.
The numbers don’t add up to 100% because I’ve left off a few categories (the biggest one is “support staff,” which was 30-31% of the total throughout the time period). But overall, this chart appears to show much less bloat. Instructional staff (including aides) were by far the biggest category of employees in both categories in both time periods. Administrative staff at the district level did grow, but only by 1 percentage point of the total.
What’s the source of this data? Well, it’s a little trick I played. The source is the National Center for Education Statistic’s Digest of Education Statistics, Table 213.10. It’s the exact same data.
How is this possible?
Continue readingBusiness Analytics Textbook plus Discussion Book
Many undergraduates take at least one business analytics course at the 200 course level. A book that I and other professors at our business school have selected to teach business statistics is by Albright and Winston
Business Analytics: Data Analytics and Decision Making (Amazon link)
This book provides three essential ingredients to a successful course:
- Covering core concepts like descriptive statistics and optimization
- Providing relevant examples in a business context (e.g. how much inventory should a retail store order)
- Showing step-by-step instructions for how to do applications in a specific software which in this case is Excel
Microsoft Excel is essential for business school graduates (arguably all college graduates). No one is born knowing how to select cells or enter formulas. The book does not assume anything, so the professor does not have to require supplementary material on how to use Excel. There are lots of exercise and examples that teach proficiency in the tool while demonstrating the concepts. Analytics courses should be hands-on.
Sometimes statistics courses do not feel like they allow for critical thinking or discussions. There is only one correct formula for an average, and it is merely and exactly what the formula determines it to be. Therefore, an interesting addition to a technical class is the book by Muller
The Tyranny of Metrics (Amazon link)
Muller spends most of the book pointing out cases where measuring results backfired. He is not so much against “analytics” as he is skeptical of pay-for-performance management schemes. Many of these schemes were sold to the public as incredible technocratic improvements, such as No Child Left Behind. I do not always agree with Muller, but he gives students something to debate. Note that only select chapters should be assigned so that it does not take up too much time from the other course material.
Data Analytics with R Textbook
For an advanced undergraduate analytics class for business school students, I use a textbook by Saltz and Stanton called
Data Science for Business with R (Amazon link)
This textbook teaches R and analytics at the same time. The professor does not have to provide a separate R curriculum or require students to buy a second book.
The running example in the textbook is an airline business scenario that is interesting and builds with the complexity of the subject matter. The authors provide the dataset that students can work with for the airline case study. Many examples in the textbook use data that is available online and therefor can be imported to R with just a few lines of code.
One semester is not enough time to cover every chapter in the book. I emphasize predictive analytics, so I skip the chapters on maps and shiny apps.
I do some supplemental lectures on concepts in predictive analytics before students reach the chapters on regression and decision trees. For example, overfitting is a new concept to undergraduates. I want them to have a more intuitive grasp of that subject before learning the R code to separate data into training and validation sets.
Note that these students have already taken what has traditionally been called Business Statistics, so they already understand basic descriptive statistics and graphing. The book is no substitute for that primary class.
There are free supplementary materials online for learning R. Students find message boards especially helpful in pinpointing answers for questions that come up while coding.
A Gauche Gift
We’ve written about gifts before.
- We’ve written posts recommending Christmas gifts (here and here and here and here).
- I wrote that gifts might be good for the macroeconomy.
- Joy Wrote about birthday presents at school parties.
- James wrote about considering supply chain status when ordering a gift.
Michael Maynard and I wrote about giving a good gift. A good gift is one in which the giver has an information advantage. Gifting an object or a service can provide a consumption bundle to the recipient that they didn’t know was even possible or that they didn’t know that they would prefer. They would have chosen the items themselves, if only they had known about them. Giving a gift card can be similar if the recipient did not know about the vendor previously. Cash is a good gift when the giver does not have an information advantage over the recipient.
In our previous post, we showed diagrammatically that ‘better off’ was indicated by the higher utility. But this spurs an important question:
Can good gifts cost the giver zero dollars?
Continue readingMarket Concentration & Inflation
We are living in volatile times. With covid-19, big federal legislation packages, and the Ruso-Ukrainian conflict disruptions to grain, seed oils, and crude oil, relative prices are reflecting sudden drastic ebbs of supply and demand. I want to make a small but enlightening point that I’ve made in my classes, though I’m not sure that I’ve made it here.
Economists often get a bad rap for being heartless or unempathetic. Sometimes, they are painted as ideologues who just disguise their pre-existing opinions in painfully specific terminology and statistics. Let’s do a litmus test.
Consider two alternative markets. One is a perfect monopoly, the other has perfect competition. All details concerning marginal costs to firms and marginal benefits to consumers are the same. In an erratic world, which market structure will result in greater price volatility for consumers? Try to answer for yourself before you read below. More importantly, what’s your reasoning?
Extreme Market Power
A distinguishing difference between a competitive market and a monopoly concerns prices. While firms maximize profits in both cases, the price that consumers face in a competitive market is equal to the marginal cost that the firms face. There is no profit earned on that last unit produced. In the case of monopoly, the price is above the marginal cost. Profits can be positive or negative, but the consumer will pay a price that is greater than the cost of producing the last unit.
Below are two graphs. Given identical marginal costs of production and benefits that the consumers enjoy, we can see that:
- The monopoly price is higher.
- The monopoly quantity produced is lower.
But static models only go so far. What about when there is volatility in the world?
Volatile Costs
Oil and gasoline are important inputs for producing many (most?) physical goods. Not only that, they are short-lived, meaning that they disappear once they are used, making them intermediate goods. Therefore, changes in the price of oil constitutes a change in the marginal cost for many firms. If the price of oil rises, or is volatile otherwise, then which type of market will experience greater price and quantity volatility?
Below are two figures that illustrate the same change in the marginal cost. We can see that:
- Monopoly price volatility is lower (in absolute terms and percent).
- Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).
The take-away: While monopoly does constrict supply and elevate prices, Monopoly also reduces price and output volatility when there are changes in the marginal cost.
Volatile Demand
That covers the costs. But what about volatile demand? A large part of the Covid-19 recession was the huge reallocation of demand away from in-person services and to remote services and goods. What is the effect of market power when people suddenly increase or decrease their demand for goods?
Below are two figures that illustrate the same change in demand. We can see that:
- Monopoly price volatility is higher (in absolute terms, though no different as a percent).
- Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).
Monopolies Don’t Cause Inflation
Economists know that inflation can’t very well be blamed on greed (does less greed beget deflation?). Another problematic story is that market concentration contributes to inflation. But the above illustrations demonstrate that this narrative is also a bit silly. Monopolistic markets cause the price level to be higher, it’s true. But inflation is the change in prices. Changing market concentration might be a long term phenomenon, but can’t explain acute price growth. If demand suddenly rises, monopolies result in no more price growth than perfectly competitive markets. If the marginal cost of production suddenly rises, monopolies result in less price growth.
All of this analysis entirely ignores welfare. Also, no market is perfectly competitive or perfectly monopolistic. They are the extreme cases and particular markets lie somewhere in between.
Did you guess or reason correctly? Many econ students have a bias that monopolies are bad. So, in any side-by-side comparison, students think that “monopolies-bad, competition-good” is a safe mantra. But the above illustrations (which can be demonstrated mathematically) reveal that economic reasoning helps to reveal truths about the world. Economists are not simply a hearty band of kool-aid drinking academics.
Economist Writing Every Day 