Economics is everywhere, but I find endless enjoyment in watching others learn about economics in unexpected places. In this case, it’s in the largest Dungeons and Dragons subreddit, where a particularly long-playing and supremely powerful group of adventurers get the bright idea to institute a tax…on magic. What could possibly go wrong?
Well, that is precisely the discussion that emerges. All the things that will and should go wrong if you decide to place a tax on what is essentially the wellspring of all technology and welfare in their world. All healthcare, production, sanitation, agriculture, public safety, etc etc. It all comes back to magic. And these hearty and hale adventurers think it would nice to rake in a few extra million gold pieces by placing, and presumably enforcing, a tax on it.
What’s fascinating is to walk through the comments and watch the crowd collectively puzzle out all the ways this is going to backfire, only to then march through ways the world is going to collectively respond and eventually rebel. There’s a reason why most tax discussions eventually become, at the very least, targeted and, at their very best, highly nuanced. Because it is no small decision how a society should best leverage taxation as a means to solve a problem. Do we want to tax the rich? Ok, what’s the line at rich? Do we want to eliminate regressive taxes targeting the poor? Ok, but is this only a tax or is it doing something else as well?
In this world of magic and mayhem, we see our heroic players stumble into literally the worst possible thing you could ever tax: the entire body of technology. Not labor, not capital, not income, not capital gains…no, they target the single most important input into all of economic growth and human welfare. From a pedagogical point of view, this is <chef kisses fingers> perfection. Absolutely no notes. I’m not kidding when I say that, if I was still teaching Principles of Economics, I would build a class discussion around this exact thread.
And special shout out the commenter to worked through the economic, political, and ecumenical consequences in real time:
It’s economics all the way down, apparently even into the depths of The Nine Hells.
I wrote a post about debt delinquency way back in 2023. At the time, people were concerned about an impending recession. I argued that, if there were to be a recession, then debt defaults would not be the cause. The delinquency numbers were low and stable. Though delinquencies did rise some, no recession materialized. I’ll say a little more about how to interpret the numbers and give an update.
There exists a stock of loan balances. Most loans are in good standing with scheduled payments being made. This is good debt. Some debt is delinquent, meaning that payments are not being made. This is bad debt. What happens to bad debt? Sometimes those borrowers catch up on their payments and their loan balances switch to being good debt. Borrowers can also transform their bad debt into good debt by restructuring it with new terms. Temporary administrative adjustments can also change the classification from bad to good debt. At any moment, the total stock of debt is composed of good and delinquent debt. We can express these as proportions of all debt.
But the lenders also recognize that not all bad debt will be made good. For one reason or another, sometimes borrowers just don’t repay. It doesn’t make sense to list delinquent debt as a balance sheet asset if it will never be paid. Rather than accumulating more bad debt every year that will never be paid, banks ‘charge off’ some of that bad debt. Charging off bad debt lets banks realize losses and makes for a more realistic balance sheet. The flow of charge offs is deducted from the stock of delinquent debt.
If banks charge off some delinquent debt, then the proportion of delinquent debt should be lower in the next period, all else constant. But all else isn’t constant. Some good debt will become delinquent and some delinquent debt will become good. Though, after a charge off it’s true that delinquent debt is less than it would have been otherwise. Below, I denote the net flow of good & bad debt transitions as ‘r’ and solve for it.
The variable ‘r’ is the net transition to good or to bad debt after charge offs. If r>0, then net new delinquencies occurred faster than banks realized their losses with charge offs. Is that good or bad? A higher rate of net new delinquencies can be bad because it reflects that people aren’t paying their contractually obligated debts. But it can also be good if the new delinquencies are a result of experimental entrepreneurship and an innovative economy. The bad interpretation is probably relevant cyclically as a short or medium run variable. The innovation interpretation probably changes in the medium or long run as a structural variable.
Let’s look at the numbers. There are several categories of loans, but let’s start with just consumer loans.
The delinquency rate is higher than it was after the pandemic stimulus checks, but is still lower than historical rates. The charge off rate is also near the historical average. Below right graphs ‘r’ and it’s always greater than zero, meaning that there’s always more people transitioning from good debt to delinquency than the reverse. There was more debt becoming delinquent as post-pandemic interest rates rose, but net delinquency transitions have been falling since 2024q1 until 2026q1 when they mildly up-ticked. In other words, the aggregate consumer debt picture looks pretty average except for the secular decline in rates of delinquency. I don’t know why that is. Maybe banks have gotten better are identifying risk? Or maybe newer forbearance rules are friendlier to borrowers who need to pause payments?
Below are the same two graphs for single-family residential mortgages. These delinquencies are close to historical lows and charge offs are average. However, the ‘r’ graph below has been rising for a decade and is currently at a twelve-year high. Since the data only goes back so far, it’s hard to say whether the low numbers of the late twenty-teens were an aberration of the post GFC, low interest rate environment or whether we should be concerned. It is worth noting that the ‘r’ values are often below zero, which means that people do often come back from delinquency. We know it’s not simply charge offs doing the work there since the charge off rate has been steady and very low.
The Washington Post recently ran a fun, data-filled article on berry consumption and parenting. Lots of good tidbits in the article, including that Americans eat a lot more berries than in the recent past, and that a lot of the availability is thanks to foreign trade and imports. But despite being somewhat light-hearted, the article does seem very negative, especially in the title and introduction, about how parents are spending a lot of money on berries.
First things first, are berries breaking the budget for parents? Probably not. While the Consumer Expenditure Survey doesn’t give us data on specific types of berry spending, the broader category of Fresh Fruits is a very small share of consumer spending. It has pretty consistently consumed between 0.30% and 0.45% of income for families with children over the past 4 decades. That’s less than $1 out of every $200 of income. True, there has been a slight rise since over the past 20 years or so, but this is still a small share of the budget.
On average, families with children are spending around $600 per year on Fresh Fruit. And that’s all fruit, not just berries! Just a little over $10 per week. But even for an item that families spend a small share of their income on, such as eggs, perhaps the fact that prices have increased so much recently makes families stand up and notice. Berry spending might seem out of control, even if it’s a small share of income.
What does the price data on berries show? My usual source on this the BLS average price data that forms the basis for the CPI, but they only publicly publishes a series for strawberries, not the other famous berries (blueberries, raspberries, etc.). There is one chart on prices in the WaPo article, but it only compares strawberries to bananas over time (they got both of these from BLS). Because banana prices have been very stable in nominal prices over time, it looks like strawberry prices are exploding! But it’s really more notable that banana prices haven’t rise.
USDA does have some fruit and vegetable specific retail price data, but it only goes from 2013 to 2023. That’s shorter than I would normally like, but it can give us a clue about whether there has been some recent explosion in berry prices. And ending in 2023 isn’t ideal either, but overall inflation has been moderate since 2023, so it’s probably an OK source to use. Here’s what the data shows (prices are for fresh berries, except cranberries which are for dried):
Relative to median wages, berries of all kinds are now more affordable than a decade ago. Parents may still feel squeezed by all the berries their kids are eating, but in terms of affordability and share of the family budget, there is probably no need for a Berry Panic.
“Boomers- live it up now at the expense of your kids, the government, charities, and your future selves.” That’s what I worried the popular book “Die With Zero” by Bill Perkins* might advocate based on its title and the brief descriptions I heard. After reading it, I’d say it’s at most 20% the book I worried about. A more accurate summary would be “planning ahead is great but it doesn’t always mean saving more” or even “here’s how to plan out your optimal consumption path like an economist”.
The core argument is that you’ll be happiest if you spend or dispose of all your money while you’re alive, then die right as you run out of money. He acknowledges that “dying with exactly zero is an impossible goal” because you don’t know when you’ll die, but he thinks most people could get much closer to zero than they do and would be better off for trying.
He then considers a variety of obvious objections.
Q: Isn’t the risk of running out of money early worse than the risk of not spending everything?
A: It’s a real risk, but one that can easily be eliminated with financial products like annuities and long-term care insurance.
R (My reaction): This is basically right. In fact, the best argument for his thesis he seems to miss is that there’s also always Social Security and Medicaid, so in America you’d never really hit zero; still less so in a country with a stronger welfare state.
Q: What about kids? Or charity?
A: Figure out how much you want them to have, then give it to them before they die. They’d rather have it sooner- right now the modal recipient of an inheritance is 60 years old, but money is more useful to people when they are younger, closer to 30.
R: True as far as it goes, but my guess is that most people would end up giving much less this way. Especially if they also listen to Perkins’ advice about working less. He mentions giving money away early but his heart doesn’t seem in it compared to planning out the optimal consumption path.
Highlights: Your ability to enjoy your wealth depends on your health, since many fun activities can’t be done when you are frail or sick. It seems obvious when you hear it, but the idea of measuring the marginal utility of wealth with respect to health is underrated even in health economics. The book does lots of good work with data on Americans’ finances; maybe the best argument for Perkins’ idea that many people over-save is that 1/3 of Americans end up increasing their wealth after retirement.
Lowlights: Graph of optimal net worth by age (page 166) contradicts graph of optimal spending by age (page 172). Arguing that John Arnold should have retired earlier than he did (age 38) because he already had more than enough money for himself, without considering how this would have made one of the world’s most innovative and effective charities much less effective. Arguing that Warren Buffett should have given his money away sooner because the charities would rather have it sooner- arguably this is true for most people, but definitely not for the one guy who really can beat the market and give much more later!
Do I recommend Die With Zero? It’s a quick and easy read that I enjoyed, but I don’t think it changes any of my financial plans. If we over-simplify its message to be “consume more now”, it’s a bad message for the typical American (who saves only 2.6% of their income), but perhaps a good message for the typical reader of personal finance books. As always it’s good to ask yourself “who is this for” and “should you reverse any advice you hear”.
“the people I’m writing for- people who are saving too much for their own good” -Die With Zero
“Objectivism might be a vicious cycle. The people who are already too selfish see an opportunity to be selfish with a halo. They join Objectivism, egg each other on, and become even more selfish still. Meanwhile, the people who could really have benefitted from Objectivism, the people who feel guilted into living for others all the time while ignoring their own needs, are off in some kind of effective charity group, egging each other on to be even more self-destructively altruistic….. Every piece of social commentary is most likely to go to the people who need it least.” – Scott Alexander
*Bill Perkins is the only name on the cover, but the Acknowledgements and the ending note that the book was co-written by Marina Krakovsky with some work done by economist Kay-Yut Chen.
Two years ago I wrote about post about how long it took consumer prices to double in the US. The most recent time period looked pretty good compared to most of the 20th century. But lately I’ve seen a lot of social media posts talking about prices doubling (e.g., “you need twice as much income as the 1990s to match the standard of living back then”), so it’s worth looking at again.
The results aren’t that different:
Using the CPI-U, consumer prices in the US doubled in the most recent 321 months. Not only is that a longer period of time to double than most of the 20th century, in the prior 321 months (November 1972 to August 1999) consumer prices doubled twice: nominal prices were almost 4 times higher in August 1999 than in November 1972!
While the CPI-U does slightly overstate inflation, we don’t get much different results if we used chained indexes. For example, using the PCEPI, it took 390 months for prices to double between October 1993 and April 2026. Either way, prices roughly doubling from some time in the 1990s to today is accurate. But wages have more than doubled since then: you only have to go back to July 2005 for average wages to double (they are up 139% since August 1999 and 190% since October 1993). Or if we use a median wage series (such as EPI’s using CPI data), nominal wages doubled from 2002 to 2025 (I have readjusted that series back to nominal wages). In real terms, median wages are 22 percent since 1999 and 29 percent since 1993.
Of course, it would be better if prices weren’t doubling over any time frame! But the most recent doubling of prices that we lived through is the longest period to double in the lifetime of almost everyone alive in the US today.
Last week I discussed absolute measures of portfolio performance and management, specifically between two portfolios that are composed of different assets (utilities and tech). I began with comparing the basics of return, standard deviation, and Sharpe ratio to some other possible portfolio in the Markowitz cloud. But, simply comparing the difference between these possible portfolios can be sensitive to the spread of stats within a specific Markowitz cloud. In other words, it’s not scale independent. A larger spread of possible stats can make a portfolio look bad due to the spread return/standard deviation/Sharpe ratio alone.
In this post I introduce quasi-relative measures. Again, I lean on the Markowitz cloud. They’re pasted below (Utilities on the left, tech on the right).
If we can somehow express the returns, volatilities, and Sharpe ratios on a common scale that is independent of the level values, then we can make the realized portfolios more comparable. One thing that we can do is to express a stat as a weighted linear average between the maximum and minimum possible values. Conditional on the realized standard deviation, there exists a maximum and minimum of possible return. Something like the below. Rho is the weight on the maximum return. It’s also the proportion of possible conditional returns that are lower than the realized return.
The unconditional version is the same, but would be relative to the global maximum and minimum stats. We can represent the weigh on the maximum return and the percentile among possible returns as gamma.
A final quasi-relative measure of performance is the dissimilarity index between the realized portfolio weights and some reference portfolio weights. This provides a measure of how much the asset weights would need to change in order to adjust the portfolio. If changing portfolio weights is costly, then it’s also a measure of the transaction cost of reallocation. It’s quasi-relative because it is independent of the spread of possible performance stats.
Below are the quasi-relative measures for each the utility and tech company portfolios.
It’s feeling like the late ’90s, with an impressive new technology pushing tech stocks and the broader US market to all-time highs. Retail investors are using new platforms to get in on the action, tech companies are doing more IPOs to take advantage of the higher stock prices, and other companies are trying to boost their stocks by saying they are pivoting to the new technology (though often they aren’t really changing).
The excitement drives valuations to record levels:
Shiller CAPE Ratio
In the ’90s, the internet really was a transformational new technology that would enable lots of profitable new companies. But the market got ahead of itself, a bubble that led to a crash- the S&P fell by almost half, while the tech-heavy NASDAQ fell by over 3/4 and took 15 years to recover.
History rhymes, but it doesn’t repeat exactly. I don’t currently expect a big crash driven by AI stocks; it helps that unlike in the ’90s, many of the big players are currently profitable. But I also don’t expect the NASDAQ to keep posting 20+% returns every year.
If the AI bull market doesn’t end in a dramatic crash, how will it end? It’s already shrugged off a war. A US recession is unlikely this year, though plausible next year.
The end I see slowly approaching comes from crowding out. What Robert Solow said about computers in 1987 is true about AI today: you see the AI age everywhere except the productivity statistics. There’s only so much money to go around in markets when productivity growth is unexceptional and savings rates are falling.
We’re already seeing the war hit certain markets (if not US stocks). Iran’s gulf neighbors are now putting lots of money into missile defense, money they now won’t be spending on data centers or gold (down 16% from pre-war), and everyone else has to spend more on oil.
Interest rates have been rising- partly due to central bank attempts to fight inflation, partly due to ongoing high rates of government borrowing, and partly due to financing the AI buildout itself. Higher rates make it more expensive for companies to invest in the physical AI buildout, and make investors discount future AI revenues more while making bonds a more attractive substitute for stocks today. 10-year TIPS now yield 2% over the inflation rate, a sharp contrast to the 2021 stock boom when they yielded less than inflation. If I were older I’d be loading up on TIPS, and even at 38 I’m starting to get tempted.
Trying to call the top exactly is a fool’s errand, but if I were feeling foolish, I’d point to the big upcoming IPOs. SpaceX just filed for an IPO that would be the biggest ever both for the amount of money raised ($75 billion) and the total company valuation ($1.77 trillion). This shatters the previous records for the biggest overall raise ($29 billion raised by Saudi Aramco when it went public in 2019) and the biggest raise by an American company ($18 billion raised by Visa in 2008). OpenAI and Anthropic are likely to follow with IPOs that would also break the previous records- making 3 companies each trying to raise more than the $45 billion raised by the entire US IPO market in 2025. Even if the process of going public doesn’t reveal any flaws in the companies, that money has to come from somewhere- and it takes up a substantial proportion of all net inflows to US stocks in a typical year (IPOs plus new money into existing stocks).
In short- where will the money come from? What are investors going to sell in order to buy into these IPOs? Technically they could do it all with cash, but I think it’s at least plausible that they start selling other stocks. The selling pressure will continue after the IPOs as employees of the newly-public companies see their stocks vest and other early investors become able to sell off.
I’m not trying to time the market. Even if this is a ’90s re-run, we could easily still be in the 1998 buildup, not the 2000 peak and crash. But I am diversifying. US stocks are currently the world’s most expensive. Investors value US stocks that highly because there’s a real chance that US companies are profitably building the technologies that will drive the future. But there’s also a real chance they aren’t– and if that state of the world comes to pass, I’d prefer to own a significant chunk of bonds, foreign stocks, and real assets.
Over the years, many people have tried to create alternatives to the CPI for measuring inflation. Probably the most famous is “Shadow Stats,” which Tim Lee has convincingly shown isn’t actually measuring price inflation (it’s just adding a fixed factor to the CPI).
But the CPI critics keep coming. One that was recently released is called the “Reality Index.” This index tries to improve on the CPI-U in two ways. First, it uses fixed weights for the items in the basket, and importantly it uses the 2024 weights and applies them to past years (this is called a Paasche index). Second, it takes out some BLS prices to avoid using hedonically adjusted prices, and other price calculations that the Reality Index author thinks are weird.
Both of these changes are problematic. I will explain why.
1. Fixed Basket of Goods/Services Doesn’t Make Sense
Many critics of the CPI complain about the shifting weights in the CPI. “We just want to measure the cost of a fixed basket over time.” But measuring a fixed basket over time isn’t actually that useful. I will explain why in a moment. But that’s not even what the Reality Index does! Instead, it takes the 2024 CPI weights (which come from the Consumer Expenditure Survey), and then consistently applies those weights to past years. The Index isn’t measuring the cost of a fixed basket of goods from some past year — it is using the 2024 basket, and assuming that’s what people consumed in the past.
The author of the Reality Index, Tom Elliott, is either confused about this or is being deliberately misleading, for example in a recent WSJ essay promoting the Index, he says “That same basket, the one the government says rose 1.87 times since 2000, has actually risen about 2.4 times.” But that’s false. To do that calculation, you would need to use the 2000 CPI weights and follow them forward to 2024 (this is called a Laspeyres index). Instead, he uses the 2024 weights and follows them backwards. He could do the calculation that he references in the WSJ essay, but he does not.
To see why this is a bad approach, let’s compare the weights in the Reality Index with a few past years. I have done my best to translate the weights for the 10 categories listed on this page to actual BLS categories, though I will admit that none of their category weights matched exactly to what I found at BLS. But I’m pretty confident it is correct.
I am also pretty confident that the “discretionary” category is just a residual for everything that wasn’t in the other 9 categories, though I can’t find them explicitly saying this. Yellow highlighting indicates the category in past years was smaller than the 2024 weights. Green highlighting indicates past years were larger weights.
The first thing you might notice is that the CPI weights have changed significantly over time. Relative to 1970, housing/shelter gets almost twice as much weight today. Conversely, groceries/food at home gets about half the weight today as it had in 1970. The “discretionary” category (the residual to make it add to 100%) used to be 30 percent of a household budget, using this approach! That should really give you pause: do we really think a typical household in 1970 considered 30% of their budget to be “discretionary”? I highly doubt it. That discretionary category includes clothing, which was over 10% of household spending in 1970 (it’s around 2% today).
Related to that, you may also notice that categories which have had above average inflation over this time frame — such as housing, healthcare, and education — all have bigger weights today than in the past. Meanwhile, food and clothing have seen less price inflation, but they are weighted much less. This process will tend to overstate inflation of the past, as the CPI in 1970 placed less weight on, say, housing, so when you put more weight on it, of course the inflation rate will go up. And indeed, as the Reality Index’s historical analysis shows, the biggest gaps in inflation between the RI and CPI were in the 1970s (4.9% gap in 1979 and 4.7% gap in 1978). But this is ahistorical: people were not spending 37% of their budget on shelter in the 1970s! In fact, they were spending almost as much on groceries in 1970 as they did on shelter.
The Reality Index is essentially projecting backwards to a fake reality of the past, because it uses the 2024 weights in all past years. But this isn’t capturing anything real about the world, and it is at best an interesting thought experiment. Of course, part of the reason people now spend more of their budget on housing and healthcare is because they have gotten more expensive and to some extent crowded out other spending. But they are also categories we might expect demand to increase as incomes increase (normal goods). And notice this is the opposite of the standard critique of the CPI: as things get more expensive, critics claim the CPI assumes people spend less on those items. Instead, the CPI-U weights are updated each year based on the latest Consumer Expenditure Survey data, and goods/services with higher rates of inflation now consumer more of the weight of the CPI than in the past.
(*Note: the “pet” category is listed as 0% in 1970 because BLS didn’t itemize it separately due to it being so small. That’s of little consequence, since it is such a small share in every year — I’m surprised they didn’t just stuff pets in the discretionary category.)
2. Swapping Quality-Adjusted Measures for Nominal Prices is Often a Bad Idea
Using the 2024 weights for past years is reason enough to not find the Reality Index useful. But let me just say a few words about the substitute prices that the Reality Index uses. The changes are either trying to use something that isn’t hedonically adjusted for quality, or to overcome some of the strange calculations, especially for housing and health care.
The basic idea is that we want to compare the performance of different portfolios or their managers. This is relatively easy as long as the portfolios contain the same assets. Then, the portfolios are simply characterized by the different weights among the different assets. But how do we compare the performance of portfolios whose assets are different? In finance, we usually assume that everyone can invest in everything. But there are plenty of cases in which that’s a bad assumption: when clients want exposure to particular industries, when there are statutory limitations on holding certain assets, or when an individual company is considering specific projects within the same company under conditions of scarce financing.
The most primitive step is to compare the return and standard deviation of two different portfolios. However, higher risk investments tend to have higher returns in dynamic equilibrium. So, if we were to compare the returns of a tech company to a utility company, then we’d often see the tech companies performing better. But, if we compare the volatilities, then the utility companies would tend to perform better. Sharpe stepped in with a ratio to express the excess return (benefit) per standard deviation (the cost). This way, we can compare the price of volatilities between two portfolios. We’ll stick with just these basic 3 measures: return, standard deviation, and Sharpe ratio. (Others do exist)
Let’s put some meat on this with an example. Say that we have two portfolios, each composed of different assets. There’s a utility portfolio that’s composed of NEE, DUK, and SO. There’s also a tech portfolio that’s composed of AMD, MSFT, and NVDA. Both portfolios have weights of (0.33, 0.33, 0.34). The results of the utility versus the tech portfolio are:
Returns: 14.2% vs 136.3%
Standard Deviation: 14.9% vs 32%
Sharpe: 0.684 vs 4.134
Goodness me! The tech portfolio returns much more in absolute terms and much more per unit of risk. It’s twice as volatile as the utility portfolio, but the returns are almost ten times as high. If you could, then many of us would choose the tech portfolio over the utility portfolio. But, what if, for one reason or another, you can only invest in one of the two industries? Or, what if you want to invest your money with a skilled manager, rather than a risky one?
One way to tackle this problem is to introduce the Markowitz cloud. Specifically, we can essentially list out all of the possible portfolios along with their return and standard deviations. Then, we can compare the actual performance to the entire menu of possible performances within each set of assets. Below are the possible performances for the utility (left) versus the tech (right) portfolio. The actual portfolios are marked with an X.
One way to evaluate the two portfolios is to compare their return, standard deviation, and Sharpe ratio to the other candidates that were achievable with the same assets. As we can see, conditional on the assets, neither portfolio minimized the volatility, maximized return, nor maximized the Sharpe ratio. Furthermore, assuming that the realized rate of return was the goal, neither portfolio minimized the conditional volatility. Assuming that the realized volatility was the goal, neither portfolio maximized the conditional return. Below are two tables that describe some candidate alternatives and how they differ from the realized portfolio.
Data centers seem to be popping up everywhere. And based on the value of current construction, the US is indeed building a lot more data centers than we were in 2020 or 2021, about four times as much data center construction (inflation adjusted).
But… did you know that we build a lot more good-old manufacturing than data centers? Almost four times as much in recent months. And that’s even after a decline in manufacturing construction over the past year and a half.
The US also builds about the same amount of warehouses and chemical plants as we do data centers. Data centers may exceed those two categories in a few years, but for now they are pretty similar.
Keep in mind that manufacturing and chemical facilities also use a lot of electricity and water, and have plenty of local negative externalities! Warehouses probably have a lot less resource consumption and external effects, but it’s not zero either.
Are data centers popping up everywhere? Well, people are certainly noticing them. But so are lots of other types of buildings, which rarely register more than a peep from concerned citizens and local media, unless there is some clear and obvious external effect.
Economist Writing Every Day 