To kick off 2024, I’m just going to give you a chart to think about:
Notice that in 1990, Poland had about half the average income of Portugal, as did South Korea compared to the UK. By about 2021, those gaps had been completely closed. And while the 2021 data is a bit uncertain given the pandemic, IMF estimates for 2024 suggest that both Poland and South Korea have now pulled slightly ahead of Portugal and the UK.
You can find many other examples like this. Why have some countries grown rapidly while others have slowed or stagnated? In some sense, this is an age-old question in economics, and at least as far back as Adam Smith economists have been trying to answer that question.
But it’s actually a bit different now. In Smith’s day, the big question was why some countries had started on their path of economic growth, while others hadn’t started at all. Today, nearly all countries have started economic growth, but some of the early leaders in growth seem to have slowed down. But there isn’t some global reason for this that affects all countries: Poland and South Korea will likely keep growing for a while, and eventually there will be a big gap between them and Portugal and the UK.
The answer to this question is not, of course, just One Big Thing. But for countries like Portugal and the UK (and Japan and Spain and Italy and etc. etc.), the key to their economic future is figuring out what Many Little Things these economic miracles are doing right so that they can return to a path of high economic growth. And this isn’t just a race to see who wins: all countries can be winners! But without continued growth, solving economic, political, and social problems will be a huge challenge.
Maybe 2024 is when they will start to figure it out.
This continues our occasional series on stock options for amateurs.
I find options to be a nice tool in my investing arsenal. The previous post in this series was Stock Options Tutorial 1. Options Fundamentals. That post dealt with buying options, to provide simple examples. For reasons to explained in a future post, I usually prefer to sell options. Anyway, here we will look briefly at how options are priced. It is important to get an intuitive understanding of this, in order to be comfortable actually using options in your account.
The current price of an option, if you wanted to buy or sell it, is called the premium. There are two components that go into the premium, the intrinsic value and the extrinsic (or “time”) value:
The intrinsic value is easy to figure out, once you understand it. It is simply how much you would profit if you owned the option, and decided to exercise it right now. For instance, if you owned a call with a strike price of $50, but the stock price is $55, you could exercise the call and force whoever sold you the call to sell you the stock at a price of $50/share; you could turn around and immediately sell that share for $55, pocketing $5/share. We say that the option in this case is $5 in the money, and the intrinsic value is $5.
If the stock price were $60, it would be $10 in the money; you could pocket $10/share for exercising it. If the stock price were say $90, the option would be $40 in the money, and so on.
However, if the stock price were $50 (the $50 option is “at the money”) or lower (option is “out of the money”), you would get no benefit from being able to purchase this stock for $50, and so the intrinsic value of the option would be zero.
With a put (which is an option to sell a stock at a particular price), this is all reversed. If the stock is $5 lower than the option strike price, the option is $5 in the money and has a $5 intrinsic value, since if you own it, you could say buy the stock at $45, and force the put option seller to buy it from you at $50/share:
Suppose the current price of a stock is $50. And suppose you suspect its price may be above $50, say $60 sometime in the next month, so you would like to have the option of buying it at $50 sometime in the future, and then selling it into the market at (say) $60, for a quick, guaranteed profit of $10. Sounds great, yes?
Since a $50 call is right at the money (since the stock price is also $50), the intrinsic value of a $50 call is zero. Does this mean you could go out and buy a $50 call option for nothing? No, because the seller of the option is taking a risk by providing you that option. If the stock really does go to $60, he could be out the $10. Therefore, he will demand a higher price than the intrinsic price, to make it worth his while. This extra premium over the intrinsic premium is the extrinsic premium, which varies greatly with the time till expiration of the option.
If you wanted the option of buying the stock at $50 sometime in the next week, the option seller would charge only a small amount; after all, what are the odds that the stock will rise a lot in one week? However, if you wanted to extend that option period out to one year, he will charge you a high extrinsic premium, since there is a bigger chance that the stock could soar will over $50 sometime in that long timeframe.
Another way of framing this is, if you buy a $50 call option today with an expiration date a year from now, you will pay a high extrinsic value. But as the months roll by, and it gets closer to the expiration date, this extrinsic value or time premium will shrink down ever more quickly towards zero:
Now, computing the actual amount of the extrinsic value is really gnarly. The Black-Scholes model provides a theoretical value under idealized conditions, but for us amateurs, we pretty much have to just take what the market gives us. In deciding whether to buy or sell an option, I look at what the current market pricing is for it.
It turns out that an option which is priced at the money has the highest extrinsic value. As you get further into or out of the money, the extrinsic component of the total premium for the option diminishes. Below is one final graphic which pulls all this together:
The call option strike price is $25. The blue line shows the intrinsic value (labeled as “payoff at expiration”) at each stock price – this is zero at or below $25, and increases 1:1 as the stock price climbs above $25. The red curve shows the full market price of the option, including the extrinsic (time) premium. The spacing between the red and the blue lines shows the amount of the extrinsic premium. That spacing is greatest when the stock price is equal to the $25 strike price. The shaded areas specify the intrinsic and extrinsic values at a stock price of $27.
And (not shown here) as time passed and the option got closer to expiry, the extrinsic value would shrink (decay), and the red curve would creep closer and closer to the blue curve.
Some of us are relishing what by all accounts appears to be a successful recession-resistant soft landing that was enabled, at least in part, by the management of interest rates by the Federal Reserve bank. But some of us also might be a little bummed. Pandemic stimulus led to non-trivial inflation for the first time in 30 years that had real consequences for the economy. Those issues were confronted by policy makers at the Federal Reserve bank who did their part to raise interest rates that ease us out of this inflationary window without triggering a recession. Those consecutive events, stylized facts even, appear to have left “Modern Monetary Theory” in shambles. I only interject the “appear to” qualification because MMT is a theoretic vacuum that better serves as a quasi-economic Rorschach test than falsifiable model. What are we to do without our favorite economic punching bag? What could ever unite us all, Keynesians, New Keynesians, Neoliberals/New Liberals, Monetarists, Austrians, like defending the shared empirical reality that money is real, printing money isn’t a policy free lunch, and hyperinflation is an economic tragedy to be taken deadly serious?
Well, don’t fret. There’s one more gift not everyone has opened yet. The gift that is “degrowth”. Not unlike MMT, degrowth is a little tricky to pin down. The central tenet, if there is one, is that economic growth needs to be both reversed and re-defined. That we all need to learn to live with less. As best I can tell. I guess I could link to Jason Hinkel’s book…but I don’t want to. If wikipedia is to be trusted: “The main argument of degrowth theory is that an infinite expansion of the economy is fundamentally contradictory to the finiteness of material resources on Earth”. I’m not going to spend an entire blog post dismantling this school of thought that is somehow both amusingly silly and darkly bleak in what it speaks of it’s advocates. Though spare me this one shot at an obviously wrong idea: the entire point of economic growth is that the economy can, in fact, expand forever, because new ideas (i.e. technology) and exchange both add value to the world without requiring any additional material resources (i.e. they are “non-rival”). There will never be an end to new ideas and, given those new ideas, there will never be an end to the prospective gains from exchange. Are we done here?
Of course not, don’t be silly. There are careers to be had. Keynotes to be given. Books to sell. Conferences to host. I took a shot at this on twitter, but I’m actually far more sanguine on the subject than I come across in my grim little tweet.
The most frustrating thing about the social media age is that a) people can get a lot of attention for exceptionally bad ideas b) those people revel in the attention and the opportunity that it brings c) even when their bad ideas are discredited, they will still personally gain https://t.co/IVWK2aRIQz
— Mike Makowsky is here to violently equivocate (@mikemakowsky) December 29, 2023
I’m emotionally unburdened by the attention paid to degrowth for the same reason I slept fine knowing MMT advocates were out their peddling their terrible policies. I take it as a sign of good health within the broader discipline of economics that for all of our squabbles, most of us are speaking the same language and engaging with an objective reality. Which is not to say that there aren’t knock-down, drag-out arguments about what we are observing empirically and what it means, but everyone knows what it is we are arguing about. There’s no Sokal hoax on the horizon for economics. The data is real. The policies are real. The consequences of bad decisions are very, very real.
What that means is that when a tribe forms around bad ideas and pushes them into the broader public, they have to defend those ideas. And their defense can’t elude criticism with nothing but rhetorical sleight of hand or pandering to fortified political identities for shelter from the scholarly storm. At least not for long. Whether they like it or not, their ideas will have to come into contact with reality, with formal rigor, with the data. There’s no postmodern escape hatch - to be exposed as unfasifiable is to fail at first contact. *
Yes, bad ideas can get you tenure somehwere. Or a letter published in Nature. Or a nice circuit of hosts willing to prop you up as the academic scribbler to provide the intellectual scaffolding their political movement is desperate for. But you’re not going to matter to the discipline. Your terrible, vacuous ideas will be confronted, considered, and then dismissed. No harm, no foul.
That these ideas can enter the arena at all is a sign of excellent health within the discipline. You can posit some truly wild ideas and still get them in front of the global jury of economists. You don’t have to be a Harvard economist. You don’t even have to be an economist. No position of power, no union card. The doors are open. That doesn’t mean, however, that you’re going to get a show. There’s no minimum stage time owed. Your ideas are terrible, get off the stage, next. You expected to come home from the battle either with your shield or on it, a grand warrior exposing the soft underbelly of the dismal dragon, but turned out to be just another 5 seconds of empty calories. You didn’t get what you wanted from this belch of a conflict, but the economists sitting together in the jury box did get something: a reminder that we’re all doing the best we can. We’re hissing and fighting, but only because we care. We’re trying to do it right, which is hard, and but that’s what matters the most at the end of the day. The trying.
*Sometimes what looked, to some or most, to be bad ideas turn out to, in fact, be good ideas. Great ideas, even, the kind that move the discipline forward. That’s actually the most beautiful part, that small minority of ideas that look too far afield to be taken seriously only to survive these trials by fire and become internalized in the broader mainstream of economics. Ironically, this often proves a harder test for many members of the revolutionary factions. From my own interactions, I would note that the internalization of “public choice” into the broader mainstream of economics as “political economy” proved hard for some scholars to adapt to.
Back in August I listed the most-read posts of 2023. Here I will finish out the year by listing a few more highlights. This has been another big year for our website.
Zachary has been giving out good advice for economics teachers, backed up by his data. All professors can read: 5 Easy Steps to Improve Your Course Evals. Econ professors check: Update on Game Theory Teaching. It’s about how to teach Game Theory, but I also see it as a testament to how much a course can improve if you allow a teacher to iterate multiple times at the same school. Administrators, take note.
What We Are Learning about Paper Books is jointly my reflection on AI generative books and a review of Tyler Cowen’s new book GOAT. I’m a techno-optimist, but I think there is value in an old-fashioned paper book, mostly from a behavioral or neuro perspective.
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.
Information on the internet was born free, but now lives everywhere in walled gardens. Blogging sometimes feels like a throwback to an earlier era. So many newer platforms have eclipsed blogs in popularity, almost all of which are harder to search and discover. Facebook was walled off from the beginning, Twitter is becoming more so. Podcasts and video tend to be open in theory, but hard to search as most lack transcripts. Longer-form writing is increasingly hidden behind paywalls on news sites and Substack. People have complained for years that Google search is getting worse; there are many reasons for this, like a complacent company culture and the cat-and-mouse game with SEO companies, but one is this rising tide of content that is harder to search and link.
To me part of the value of blogging is precisely that it remains open in an increasingly closed world. Its influence relative to the rest of the internet has waned since its heydey in ~2009, but most of this is due to how the rest of the internet has grown explosively at the expense of the real world; in absolute terms the influence of blogging remains high, and perhaps rising.
The closing internet of late 2023 will not last forever. Like so much else, AI is transforming it, for better and worse. AI is making it cheap and easy to produce transcripts of podcasts and videos, making them more searchable. Because AI needs large amounts of text to train models, text becomes more valuable. Open blogs become more influential because they become part of the training data for AI; because of what we have written here, AI will think and sound a little bit more like us. I think this is great, but others have the opposite reaction. The New York Times is suing to exclude their data from training AIs, and to delete any models trained with it. Twitter is becoming more closed partly in an attempt to limit scraping by AIs.
So AI leads to human material being easier for search engines to index, and some harder; it also means there will be a flood of AI-produced material, mostly low-quality, clogging up search results. The perpetual challenge of search engines putting relevant, high-quality results first will become much harder, a challenge which AI will of course be set to solve. Search engines already have surprisingly big problems with not indexing writing at all; searching for a post on my old blog with exact quotes and not finding it made me realize Google was missing some posts there, and Bing and DuckDuckGo were missing all of them. While we’re waiting for AI to solve and/or worsen this problem, Gwern has a great page of tips on searching for hard-to-find documents and information, both the kind that is buried deep down in Google and the kind that is not there at all.
Today I’ll go into more detail on several measures of the labor force, but I won’t only compare it to 2019. I’ll compare it to all available data. And the sum total of the data suggests the 2023 was one of the best years for the US labor market on record. Note: December 2023 data isn’t available until January 5th, so I’m jumping the gun a little bit. I’m going to assume December looks much like November. We can revisit in 2 weeks if that was wrong.
The Unemployment Rate has been under 4% for the entire year. The last time this happened (date goes back to 1948) was 1969, though 2022 and 2019 were both very close (just one month at 4%). In fact, the entire period from 1965-1969 was 4% or less, though following January 1970 there wasn’t single month under 4% under the year 2000!
Like GDP, the Unemployment Rate is one of the broadest and most widely used macro measures we have, but they are also often criticized for their shortcomings, as I wrote in an April 2023 post.
With that in mind, let’s look to some other measures of the labor market.
Boaz Weinstein is a really smart guy. At age 16 the US Chess Federation conferred on him the second highest (“Life Master”) of the eight master ratings. As a junior in high school, he won a stock-picking contest sponsored by Newsday, beating out a field of about 5000 students. He started interning with Merrill Lynch at age 15, during summer breaks. He has the honor of being blacklisted at casinos for his ability to count cards.
He entered into heavy duty financial trading right out of college, and quickly became a rock star. He joined international investment bank Deutsche Bank in 1998, and led their trading of then-esoteric credit default swaps (securities that payout when borrowers default). Within a few years his group was managing some $30 billion in positions, and typically netting hundreds of millions in profits per year. In 2001, Weinstein was named a managing director of the company, at the tender age of 27.
Weinstein left Deutsche Bank in 2009 and started his own credit-focused hedge fund, Saba Capital Management. One of its many coups was to identify some massive, seemingly irrational trades in 2012 that were skewing the credit default markets. Weinstein pounced early, and made bank by taking the opposite sides of these trades. He let other traders in on the secret, and they also took opposing positions.
(It turned out these huge trades were made by a trader in J. P. Morgan’s London trading office, Bruno Iksil, who was nick-named the London Whale. Morgan’s losses from Iksil’s trades mounted to some $6.2 billion.)
For what it’s worth, Weinstein is by all accounts a really nice guy. This is not necessarily typical for many high-powered Wall Street traders who have been as successful as he.
Weinstein and the Sprawling World of Closed End Funds
If you have a brokerage account, you can buy individual securities, like Microsoft common stock shares, or bonds issued by General Motors. Many investors would prefer not to have to do the work of screening and buying and holding hundreds of stocks or bonds. No problem, there exist many funds, which do all the work for you. For instance, the SPY fund holds shares of all 500 large-cap American companies that are in the S&P 500 index, so you can simply buy shares of the one fund, SPY.
Without going too deeply into all this, there are three main types of funds held by retail investors. These are traditional open-end mutual funds, the more common exchange-traded funds (ETFs), and closed end funds (CEFs). CEFs come in many flavors, with some holding plain stocks, and others holding high-yield bonds or loans, or less-common assets like spicy CLO securities. A distinctive feature of CEFs is that the market price per share often differs from the net asset value (NAV) per share. A CEF may trade at a premium or a discount to NAV, and that premium or discount can vary widely with time and among otherwise-similar funds. This makes optimal investing in CEFs very complex, but potentially-rewarding: if you can keep rotating among CEF’s, buying ones that are heavily discounted, then selling them when the discount closes, you can in theory do much better than a simple buy and hold investor.
I played around in this area, but did not want to devote the time and attention to doing it well, considering I only wanted to devote 3-4% of my personal portfolio to CEFs. There are over 400 closed end funds out there. So, I looked into funds whose managers would (for a small fee) do that optimized buying and selling of CEFs for me.
It turns out that there are several such funds-of-CEF-funds. These include ETFs with the symbols YYY and PCEF, CEFS, and also the closed end funds FOF and RIV. YYY and PCEF tend to operate passively, using fairly mechanical rules. PCEF aims to simply replicate a broad-based index of the CEF universe, while YYY rebalances periodically to replicate an “intelligent” index which ranks CEFs by yield, discount to net asset value and liquidity. FOF holds and adjusts a basket of undervalued CEFs chosen by active managers, while RIV holds a diverse pot of high-yield securities, including CEFs. The consensus among most advisers I follow is that FOF is a decent buy when it is trading at a significant discount, but it makes no sense to buy it now, when it is at a relatively high premium; you would be better off just buying a basket of CEFs yourself.
I settled on using CEFS (Saba Closed-End Funds ETF) for my closed end fund exposure. It is very actively co-managed by Saba Capital Management, which is headed by none other than Boaz Weinstein. I trust whatever team he puts together. Among other things, Saba will buy shares in a CEF that trades at a discount, then pressure that fund’s management to take actions to close the discount.
The results speak for themselves. Here is a plot of CEFS (orange line) versus SP500 index (blue), and two passively-managed ETFs that hold CEFs, PCEF (purple) and YYY (green) over the past three years:
The Y axis is total return (price action plus reinvestment of dividends). CEFS smoked the other two funds-of-funds, and even edged out the S&P in this time period. It currently pays out a juicy 9% annualized distribution. Thank you, Mr. Weinstein, and Merry Christmas to all my fellow investors.
Boilerplate disclaimer: Nothing in this article should be regarded as advice to buy or sell any security.
Whether it’s a high holiday or just a nice day to get Chinese food with your family and watch old movies, I hope you all have the very best Christmas possible, no matter what that means for you. But do take the day off if at all possible and, if the opportunity arises, make it a little easier for those who can’t.
I have a paper that emphasizes ChatGPT errors. It is important to recognize that LLMs can make mistakes. However, someone could look at our data and emphasize the opposite potential interpretation. On many points, and even when coming up with citations, the LLM generated correct sentences. More than half of the content was good.
Apparently, LLMs just solved an unsolvable math problem. Is there anything they can’t do? Considering how much of human expression and culture revolves around religion, we can expect AI’s to get involved in that aspect of life.
Alex thinks it will be a short hop from Personal Jesus Chatbot to a whole new AI religion. We’ll see. People have had “LLMs” in the form of human pastors, shaman, or rabbis for a long time, and yet sticking to one sacred text for reference has been stable. I think people might feel the same way in the AI era – stick to the canon for a common point of reference. Text written before the AI era will be considered special for a long time, I predict. Even AI’s ought to be suspicious of AI-generated content, just in the way that humans are now (or are they?).
Many religious traditions have lots of training literature. (In our ChatGPT errors paper, we expect LLMs to produce reliable content on topics for which there is plentiful training literature.)
I gave ChatGPT this prompt:
Can you write a Bible study? I’d like this to be appropriate for the season of Advent, but I’d like most of the Bible readings to be from the book of Job. I’d like to consider what Job was going through, because he was trying to understand the human condition and our relationship to God before the idea of Jesus. Job had a conception of the goodness of God, but he didn’t have the hope of the Gospel. Can you work with that?
Economist Writing Every Day 