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.
In standard microeconomics, the long-run demand is unimportant for the market price of a good. Firm competition, entry, and exit causes economic profits to be zero and the price to be equal to firms’ identical minimum average cost. This unreasonably assumes that they have constant technology. That is, they have a constant mix of productive inputs and practices.
Just so we’re clear: time is passing such that firms can enter, exit, and adjust the price – but no productive innovation occurs. For the modeling, we freeze time for technology, but not for other variables. The model ceases to reflect reality on the margin of scale-induced innovation. The standard model assumes an optimal quantity of production for each firm and the only way for total output to change is for there to be more or fewer firms. The model precludes adopting any different technology because firms are already producing at the minimum average cost – if they could produce more cheaply, then they would.
Enter Scale
One of my favorite details about production was taught to me by Robin Hanson.* Namely, that the scale of production isn’t merely with the aid of more raw materials, labor, and capital. There are perfectly well-known existing technologies and methods that reduce the average cost – if the firm could produce a large enough quantity. This helps to illustrate what counts are technology. A firm can achieve lower average costs without inventing anything, and merely by adopting a superficially different production method.
Lot’s of economists use FRED – that’s Federal Reserve Economic Data for the uninitiated. It’s super easy to use for basic queries, data transformations, graphs, and even maps. Downloading a single data series or even the same series for multiple geographic locations is also easy. But downloading distinct data series can be a hassle.
I’ve written previously about how the Excel add-on makes getting data more convenient. One of the problems with the Excel add-on is that locating the appropriate series can be difficult – I recommend using the FRED website to query data and then use the Excel add-on to obtain it. One major flaw is how the data is formatted in excel. A separate column of dates is downloaded for each series and the same dates aren’t aligned with one another. Further, re-downloading the data with small changes is almost impossible.
Only recently have I realized that there is an alternative that is better still! Stata has access to the FRED API and can import data sets directly in to its memory. There are no redundant date variables and the observations are all aligned by date.
Economists overwhelmingly favor looser immigration controls. Allowing people to immigrate would improve the allocation of scarce labor and capital and it is a far cheaper way to aid poorer families than sending direct payments or trying to develop an entire country. Let’s cover some static analysis basics for migrating workers and their dependents.
Workers, Labor Markets, & Output Markets
There are two markets to consider: The new home country and the old home country. If workers leave the old country in search of the higher wages in the new country, then world employment remains unchanged. Employment obviously rises in the new country and falls in the old country. With identical laborers (a terrible assumption that’s the least charitable to immigration), wages in the new country fall and wages in the old country rise. This logic illustrates the cheap aid of which economists are fond.
There have been a lot of popular papers in the past decade or so that make use of textual analysis. A fun one is “The Mainstreaming of Marx” by Magness & Makovi. They use Google Ngram to analyze the popularity of people mentioned in books and determine when Karl Marx became popular. “Measuring Economic Policy Uncertainty” by Baker, Bloom, & Davis is one of my favorites. They use set theory to detect terms in newspapers that denote economic policy uncertainty. In this post, I’m just going to describe practical differences between the two data sources and how the interpretations differ.
Ngram
Ngram measures takes a term and measures how popular that term is in its corpus of book text, which is about 6% of all books ever written (in English, anyway). Because popularity is expressed as a percent, we can make direct popularity level comparisons among words. For example: “Cafe” & “Coffee Shop”. In the figure below, we can see that the word “cafe” was more popular in books until very recently.
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.
Most economists know that the CPI is overestimated and therefore prefer the PCE price index. However, monthly CPI data is consistently released before PCE data for a given month. One would think that they move in the same direction and be highly correlated. Indeed, in the past five years, the correlation is 0.96. Therefore, it stands to reason that the there is less new relevant information on the PCE release dates than on the CPI release dates. Yes, CPI is biased, but it still contains some information about prices and it is known well prior to the more accurate PCE numbers.
Supply and Demand react to new information. Sometimes the new information changes our expectations about the future, and other times we learn that our beliefs about goods and assets were previously not quite right. So, with new relevant information comes new prices as people update their beliefs and expectations.
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.
One of my specializations in graduate school at George Mason University was monetary theory. It included two classes taught by Larry White who specializes in free-banking, Austrian macroeconomics, and monetary regimes. Separately, my dad was a libertarian and I’ve attended multiple Students for Liberty events. Right now, I’m writing from my hotel room at a Catholic/Crypto conference, where I learned that the deepest trench in Dante’s Inferno includes money debasers.
Everything about my pedigree suggests that I should have a disdain for the Federal Reserve and cast a wistful gaze toward the perpetually falling value of the US dollar. But I don’t. I certainly do have opinions about what the Fed should be doing and how our monetary system could work. But I’m not excited by the long-run depreciation of the dollar.
Let me tell you why.
Learning a little bit of theory is a dangerous thing. Monetary theory is especially hard because we examine the non-good side of the transaction: the medium of exchange. In frantic excitement, enthusiasts often point out that the value of the dollar has lost very much of its value in the past 100 years. They describe that loss by describing the lower quantity of something that a dollar can purchase now versus what it could have purchased historically. That information is incapsulated in the price of a good. The price of a good is the number of dollars that one must exchange in order to purchase the good. Similarly, the price of a dollar is the number of goods that one must give up in order to purchase the dollar.
We can consider a variety of goods. Below is a graph that describes the quantity price of the dollar where the quantities are CPI basket units, gold, and housing. In the 35 years following 1986, a single dollar purchases 60% less of the consumer basket, 74% fewer houses (not quality adjusted), and 76% less gold.
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 