Teaching

Writing with ChatGPT Buchanan Seminar on YouTube

Joy Buchanan3 Comments

I was pleased to be a (virtual) guest speaker for Plateau State University in Nigeria. My host was (Emergent Ventures winner) Nnaemeka Emmanuel Nnadi. The talk is up on Youtube with the following timestamp breakdown:

During the first ten minutes of the video, Ashen Ruth Musa gives an overview called “The Bace People: Location, Culture, Tourist Attraction.”

Then I introduce LLMs and my topic.

Minute 19:00 – 29:00 is a presentation of the paper “ChatGPT Hallucinates Nonexistent Citations: Evidence from Economics“

Minute 23:30 – 34 is summary of my paper “Do People Trust Humans More Than ChatGPT?”

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Probability Theory for the Minecraft Generation

Joy Buchanan1 Comment

If you are teaching statistics to 20-year-olds (or maybe even if you are not), you might be interested in ways to make probability theory more engaging. I watched a students eyes light up when I showed this in class, so that makes it feel worth sharing.

The Law of Large Numbers is a standard part of statistics or business analytics classes. Something that goes along with it conceptually is “The Law of Truly Large Numbers,” sometimes also called The Infinite Monkey Theorem. The idea is that if you put monkeys in front of typewriters, perhaps infinite monkeys with infinite typewriters and with infinite time, they will eventually write a Shakespeare play.

To illustrate this feature of probability theory for the video gamers, a fun and well-produced video is
“Can Mobs Beat Minecraft?” by Wifies

There is nothing inappropriate for students. The video is 13 minutes, which is too long to show during a class session. I recommend watching the first minute and a half and then explaining that the middle is a lot of gaming details to prove that it is technically possible that a randomly acting “mob” could eventually beat the entire Minecraft game, given enough time.

At the 10-minute mark, the math begins. You could watch about one more minute and a half to see how he tries to calculate the infinitesimally-small-and-yet-positive probability that this could happen. Given enough time, just about anything that is possible will happen.

Another possibility for a teacher is not to show the video in class but to offer it as an optional or extra credit assignment, so that a student who loves Minecraft could really have fun with it and other students can skip.

For me, this pairs with Chapter 5 on Probability for the textbook Applied Statistics in Business and Economics.

Another teaching tip. If you ever need to print out paper rulers, you might be Googling “printable rulers” and you’ll see a bunch of scams as the top results. THIS link works: https://www.brightonk12.com/cms/lib/MI02209968/Centricity/Domain/517/Ruler_6-inch_by_16.pdf

Paper on Finance and Economics Women Club

Joy Buchanan1 Comment

I am one of several founders of a club with the abbreviation F.E.W. for Finance and Economics Women. This is a student organization that we have at Samford and that Dr. Darwyyn Deyo runs at San Jose State University.

Read our report here: The Finance and Economics Women’s Network (FEW): Encouraging and Engaging Women in Undergraduate Programs published in the Journal of Economics and Finance Education

Our short paper is mostly a how-to guide including a draft of a club charter document. We describe our institutions and how we use this group to engage and encourage students. Please read it for more details on how to start a club.

Like most student groups, the FEW model relies on student leaders who take initiative. Having done this for more than 6 years, we have a growing network of alumni and local business partners who connect to current students through FEW events. Personally, I am lucky that 3 faculty members total support the club at my school.

Women are often minorities in upper-division econ and finance classes. Women also have some unique challenges when it comes to choosing career paths and navigating the workplace. These events (e.g. bringing in a manager from a local bank to talk with student over lunch) allow a space for students to ask questions they might not normally ask in a classroom setting or in a standard networking environment.

We report the results of a small survey in our paper. We can’t infer causality, nor did we run any experiments. However, we did find that women were more likely to report that a role model in their chosen profession influenced their choice of major. Part of the purpose of the FEW model is to expose students to a variety of role models who they might not otherwise connect with.

Here’s a news article with a picture of the founding group at Samford. I have great appreciation and respect for our student leaders who keep it going, and I am grateful to the graduates who stay in contact with us.

Suggested citation: Buchanan, Joy, and Darwyyn Deyo, “Finance and Economics Women’s (FEW) Network: Encouraging and Engaging Women in Undergraduate Programs” (2023) Journal of Economic and Finance Education, 22: 1, 1-14.

Interpreting New DIDs

Zachary BartschLeave a comment

If you didn’t know already, the past five years has been a whirl-wind of new methods in the staggered Differences-in-differences (DID) literature – a popular method to try to tease out causal effects statistically. This post restates practical advice from Jonathan Roth.

The prior standard was to use Two-Way-Fixed-Effects (TWFE). This controlled for a lot of unobserved variation over individuals or groups and time. The fancier TWFE methods were interacted with the time relative to treatment. That allowed event studies and dynamic effects.

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Taxes & Unemployment – Know Your Bias?

Zachary Bartsch2 Comments

Say that there is a labor market and that there is no income tax. If an income tax is introduced, then what should we expect to happen? Specifically, what will happen to employment, the size of the labor force, and the number of people unemployed? Will each rise? Fall? Remain unchanged? Change ambiguously? Take a moment and jot down a note to test yourself.

As it turns out, what your answer is depends on what your model of the labor market is. Graphically, they are all quantities of labor. The size of the labor force is the quantity of labor supplied contingent on some wage that workers receive. It’s the number of people who are willing to work. Employment is the quantity of laborers demanded by firms contingent on to wage that they pay. Finally, the quantity of people unemployed is the difference between the size of the labor force and the quantity of workers employed (Assuming that the labor force is greater than or equal to employment).

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Childhoods of exceptional people

James Bailey2 Comments

Henrik Karlsson read lots of biographies of geniuses and tried to sum up the things their childhoods had in common here. Some highlights:

At least two-thirds of my sample was home-educated (most commonly until about age 12), tutored by parents or governesses and tutors. The rest of my sample had been educated in schools (most commonly Jesuit schools).

As children, they were integrated with exceptional adults—and were taken seriously by them.

They had time to roam about and relied heavily on self-directed learning

A common theme in the biographies is that the area of study which would eventually give them fame came to them almost like a wild hallucination induced by overdosing on boredom. They would be overcome by an obsession arising from within.

They were heavily tutored 1-on-1

An important factor to acknowledge is that these children did not only receive an exceptional education; they were also exceptionally gifted.

There is lots of discussion of John Stuart Mill and John Von Neumann, who each had major contributions to economics:

When they were done, James Mill took his son’s notes and polished them into the book Elements of Political Economy. It was published the year John Stuart turned fifteen….

There is a moving scene in John Stuart Mill’s biography, when John Stuart is about to set out into the world and his father for the first time lets him know that his education had been . . . a bit particular. He would discover that others his age did not know as much as he did. But, his father said, he mustn’t feel proud about that. He’d just been lucky.

Let’s make more people lucky.

Other nice posts along similar lines are Erik Hoel’s “How Geniuses Used to Be Raised” (linked in Karlsson’s piece), and Scott Alexander’s review of Laszlo Polgar’s book “Raise a Genius” (about raising his 3 daughters to be chess grandmasters). Karlsson’s post, worth reading in full, is here.

Social Cost Irregularities

Zachary BartschLeave a comment

If you want an economist to support a government intervention, then there are two major sets of logic that they generally find attractive.

The first concerns rate of return and attracts narrower support. If the government can invest in a project in a way that the private sector couldn’t/wouldn’t and the payoff is bigger than the investment by enough, then the project should be built. 

The second set of logic is more accepted more broadly. If there is an externality, and the administration costs are small relative to the change in the externality, then the project should be pursued in order to increase total welfare.

I’m going to criticize and refine the second argument.  I was inspired by a student who wrote about education creating positive externalities for “all”. They kept using the word “all”. And I notated each time “not *all*”. While we might refer to something called ‘social’ cost and value, the existence of externalities does not imply that everyone is affected by the them identically. That’s a representative agent fallacy. The externalized costs and benefits are often irregularly distributed among 3rd parties. This is important because government intervention can impose its own externalities depending on how the administrative costs funded.

I’ll elaborate with two examples that illustrate when an irregular distribution of externalities is a problem and when it isn’t a problem.

Electric Plant Pollution

The first example illustrates how resolving an irregular distribution of externalities can be resolved without issue. Consider a coal-powered electric plant that serves a metropolitan area and creates pollution. That pollution drifts east and passively harms residents in the form of asthma exacerbation and long-term ill health. The residents to the west are unaffected by the pollution, thanks to favorable weather patterns. Obviously, one would rather live on the west side, all else constant (importantly, all else it not always constant and there is a case to be made that there is no externality here).

To resolve the externality, the government imposes a tax per particle on the power plant at a low administrative cost. That’s nice and efficient – we won’t waste our time with means-oriented regulations. In turn, the cost of electricity increases for all metropolitan residents, both those in the east and in the west. Why is this appropriate? Prior to the intervention, the electricity users in the west were enjoying electricity at a low price, failing to pay for the harm done by their consumption. For that matter, the residents to the east are also paying the higher rates, but now they enjoy better health.

In the end, the externality is resolved by imposing a cost on all consumers of the good – which happens to be everyone. This circumstance is not pareto efficient, but it is Kaldor-Hicks efficient. Everyone now considers the costs that they were previously able to impose on others and ignore.

That’s the best case scenario.

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The Self-Correcting Property

Zachary Bartsch1 Comment

Say that the Federal Reserve Prints a boatload of money. We can use the AS-AD model (aggregate supply & aggregate demand) to evaluate the effect on prices and output.

Printing money results in more total spending in the economy. How much of that initial greater total spending is composed of higher prices versus higher output depends on business marginal costs and whether firms know or expected the greater demand to be due to a broad inflationary event (rather than just greater demand for their particular products).

If there is broad inflation, then the price level that is observed in the economy, including inputs, will deviate from what firms expected. Naturally, firms update their expectations. In so doing, they increase the price that they would require in order to produce every quantity of output. The vertically rising SRAS reflects both of these. The rising itself reflects the higher required prices, and the intersection with the LRAS reflects the expected price level. Notice that updating the expectations places upward pressure on prices, resulting in still higher than anticipated prices. This occurs repeatedly and each time that expectations are updated, the difference between the actual and the expected inflation gets smaller. 

This is what macroeconomists call the “self-correcting property’. The economy will adjust to an AD shock ‘automatically’. Of course, automatic isn’t quite the right word. It’s automatic from the perspective of a policy maker. But the self-correction is the result of an economy’s worth of people bidding for scarce goods and changing their price expectations. It’s automatic in the sense that people don’t need to be told to make the effort. The same results won’t occur if buyers and sellers do nothing, which sounds less automatic.

Since the fundamental productivity of the economy hasn’t changed, we eventually return to the original level of output. If monetary policy doesn’t change in the meantime, then prices will simply rise until the long-run price change composes 100% of the change in total spending. Indeed, given the AS-AD model above, half of the price difference between the current price and the long run price is eliminated each period. Similarly, half of the output gap is eliminated each period. This is why monetary and fiscal stimulus that just focuses on total spending only has short-run output and employment effects. The self-correcting property asserts itself and prices rise in the long run.

*In the figures above, I’ve illustrated an initial sharp price change, though sticky prices and very surprising inflationary stimulus can cause a delay in the initial price adjustment.

**Of course, all of this can be expressed in percent change rather than levels.

Coffee’s Supply & Demand Dance during Prohibition

Zachary BartschLeave a comment

I’ve written about coffee consumption during US alcohol prohibition in the past. I’ve also written about visualizing supply and demand. Many. Times. Today, I want to illustrate how to use supply and demand to reveal clues about the cause of a market’s volume and price changes. I’ll illustrate with an example of coffee consumption during prohibition.

The hypothesis is that alcohol prohibition would have caused consumers to substitute toward more easily accessible goods that were somewhat similar, such as coffee. To help analyze the problem, we have the competitive market model in our theoretical toolkit, which is often used for commodities. Together, the hypothesis and theory tell a story.

Substitution toward coffee would be modeled as greater demand, placing upward pressure on both US coffee imports and coffee prices. However, we know that the price in the long-run competitive market is driven back down to the minimum average cost by firm entry and exit. So, we should observe any changes in demand to be followed by a return to the baseline price. In the current case, increased demand and subsequent expansions of supply should also result in increasing trade volumes rather than decreasing.

Now that we have our hypothesis, theory, and model predictions sorted, we can look at the graph below which compares the price and volume data to the 1918 values. While prohibition’s enforcement by the Volstead act didn’t begin until 1920, “wartime prohibition” and eager congressmen effectively banned most alcohol in 1919. Consequently, the increase in both price and quantity reflects the increased demand for coffee. Suppliers responded by expanding production and bringing more supplies to market such that there were greater volumes by 1921 and the price was almost back down to its 1918 level. Demand again leaps in 1924-1926, increasing the price, until additional supplies put downward pressure on the price and further expanded the quantity transacted.

We see exactly what the hypothesis and theory predicted. There are punctuated jumps in demand, followed by supply-side adjustments that lower the price. Any volume declines are minor, and the overall trend is toward greater output. The supply & demand framework allows us to image the superimposed supply and demand curves that intersect and move along the observed price & quantity data. Increases toward the upper-right reflect demand increases. Changes plotted to the lower-right reflect supply increases. Of course, inflation and deflation account for some of the observed changes, but similar demand patterns aren’t present in the other commodity markets, such as for sugar or wheat. Therefore, we have good reason to believe that the coffee market dynamics were unique in the time period illustrated above.

*BTW, if you’re thinking that the interpretation is thrown off by WWI, then think again. Unlike most industries, US regulation of coffee transport and consumption was relatively light during the war, and US-Brazilian trade routes remained largely intact.

Classroom activities for teaching Monetary Policy

Joy BuchananLeave a comment

I got lucky last week. I saw this tweet go by just in time to learn about some activities the I added to my unit on monetary policy.

It’s called the Monetary Policy Unit Plan

https://learn.mru.org/lesson-plans/monetary-policy-unit

I’m not using all of it, but it’s very helpful to see what other instructors have come up with to make teaching monetary policy more fun and more effective. You have to sign up to access it, using your official instructor email address.

It can feel relatively easy to talk to students about their role in the economy as consumers. It is relatively hard to lecture about central banking, because it is less relatable to everyday life. These exercises help us get into the “mind” of a bank.

Thank you to Econiful and Marginal Revolution University for making these resources available. There will probably be an equivalent for fiscal policy produced in the future.