Many regular Americans and policymakers say they want the US to manufacture more things domestically. But when they ask economists how to accomplish this, I find that our most common response is to question their premise- to say the US already manufactures plenty, or that there is nothing special about manufacturing. It’s easy for people to round off this answer to ‘your question is dumb and you are dumb’, then go ask someone else who will give them a real answer, even if that real answer is wrong.
Economists tell our students in intro classes that we focus on positive economics, not normative- that we won’t tell you what your goals should be, just how best to accomplish them. But then we seem to forget all that when it comes to manufacturing. Normally we would take even unreasonable questions seriously; but I think wondering how to increase manufacturing output is reasonable given the national defense externalities.
So if you had to increase the value of total US manufacturing output- if you were going to be paid based on a fraction of real US manufacturing output 10 years from now- how would you do it?
I haven’t made a deep study of this, but here are my thoughts. Better ideas at the top, ‘costly but would increase manufacturing output’ ideas at the bottom:
Claims that the middle class or working class has been “hollowed out” in the US have been made for years, or decades really. The latest claim is an essay in the Free Press by Joe Nocera. But these claims are usually lacking in data, while strong in anecdotes. Let’s look at the data.
Notice that the latest data point is for 2024, which is the highest they have ever been in this data series, and likely higher than any point in the past. While many point to about the year 2000 as when troubles for the working class started (this is when manufacturing employment really fell off a cliff, and China joined the WTO in 2001), inflation-adjusted earnings have risen 11% for this group of workers since then. You might say that’s not a lot of growth — and you would be correct! But this group is better off economically than in the year 2000, which is a point that gets lost in so many discussions about this issue.
But that’s just a national number. Might some states that were especially hit by manufacturing job losses be worse off? Nocera mentions North Carolina and the Midwest. To answer this, we can use BLS OEWS data, which has not only median wages by state, but also the 10th percentile wage — the lowest of the working class. Here’s what median real wage growth (again inflation-adjusted with the PCEPI) since 2001 (the earliest year in this series with comparable data):
The VIX is a calculated measure of stock market volatility, based on the prices of stock options. It spikes up when there is a market upset, then seemingly always settles back down again after a few days or weeks. So, it seems simple to make a quick profit from this behavior: short the VIX when it spikes, and then close your trade when it comes back down. What could possibly go wrong?
VIX Index, May 2024-April 2025. From Seeking Alpha.
It’s a bit more nuanced than that, since you can’t directly buy or sell the VIX. It is just a calculated number, not a “thing.” However, there is a market for VIX futures. The value of these futures is based on expectations for what VIX will be on some specific date. The values of these futures go up and down as the VIX goes up and down, though there is not an exact 1:1 relationship. There are funds that short VIX futures, which are a proxy for shorting the VIX futures yourself. So, the individual investor could buy them after the VIX spikes (which would drive down the short VIX fund price), then sell them when VIX declines (and the short VIX fund goes back up).
The chart below shows the VIX (% change, orange curve) in the past twelve months prior to May 1. There were three episodes (Aug 2024, Dec 2024, Apr 2025) where VIX spiked up. These episodes are marked with green arrows. As expected, when VIX spikes up, the short volatility fund SVIX (purple line) drops down. In August and December, if you were clever enough to buy SVIX at its low, you could turn around and sell in a week later for a good profit. The movements of SVIX are dwarfed this plot by the gyrations of VIX in this chart, but a couple of short red horizontal lines are drawn at the bottoming values for SVIX, to show the subsequent rise. A 3x leveraged S&P 500 fund, UPRO, is shown in blue.
There are important nuances with these funds. One is that a long or short VIX futures fund, at the end of the trading day, must buy and sell some futures shares to meet their performance mandate. As of say May 1, the -1X VIX fund SVIX was short 14,311 May VIX futures contracts (expiring 5/20/2025), and short 10,222 June futures (exp. 6/17/2025). To keep its exposure centered at on one month out from the present date, the fund must buy back some near month (here, May) contracts each day, and short some additional next month (June), at the close of every trading day. If the market value of the near month VIX futures contract is lower than the next month contract (being in “contango”), as it generally is during periods of low volatility, this rolling process makes money every day, to the tune of maybe 5% per month. That compounds big time over time, to over a 60% gain in twelve months. That’s the good side. The VIXcentral site shows current and historical VIX futures prices for the next several months out.
A bad side of these short funds is that the day-to-day inverse movements can rachet the fund value down and down, as VIX goes up and down. So even if the VIX ends up in six months at the same value as it is today, it is possible for a short VIX fund to be lower or higher. This can lead to a more or less permanent step down in fund value. Also, in volatile times, the near futures price is higher than the next month out, and so the daily roll works against you.
There is a term that trading pros use for amateurs who jump into volatility funds without really knowing what they are doing: “volatility tourists”. These hapless investors sometimes hear of big profits that have been made recently in vol, and then buy in, often at what turns out to be the wrong time. Then market storms arise, things don’t go the way they expected, and they get shipwrecked.
Such was the case in 2018. SVXY at that time was a fund that moved inversely to volatility futures, on a -1X daily basis. This short vol trade made insane profits in 2H 2016 and in 2017, far outpacing stocks. Someone who bought into SVXY at the start of 2017 would have quintupled their money by the end of the year. (See chart below, orange line).
However, February 5, 2018 is a day that will live in volatility infamy. Because of the roaring success of short VIX in the previous two years, investors had piled into short VIX ETFs. The VIX suddenly doubled that day, and the short vol funds could not do the daily futures trades they needed, and so their value was decimated. This event is known as Volmageddon. The chart below shows the rise (and fall) of the -1X VIX fund SVXY in orange, compared to a stodgy S&P 500 fund SPY (in green).
Folks who bought SVXY looked like geniuses, until Feb 5. Then they lost it all, more or less. The tourists licked their wounds and moved on, and short vol went clean out of fashion for a while. One short VIX fund, XIV, actually an exchange traded note (ETN), went to zero and closed. SVXY itself lost over 90% of its value. After this near-death experience in 2018, SVXY contritely modified its charter from being -1X VIX futures to being -0.5X. That reduces its exposure to vol shocks. That modification served it well in March, 2020 when the world shut down and VIX shot to the moon and stayed there for some time. SVXY lost something like 70% of its value then, but it lived to trade another day, and slowly clawed its way back.
However, short vol has made a comeback in recent years. The -0.5X SVXY was joined in mid-2022 with a new -1X VIX fund, SVIX (for investors who don’t remember what happened to -1X funds in 2018! ). Short vol actually had a very good run in 2022, 2023, and first half of 2024:
The chart above shows SVIX ( -1X, purple) and SVXY (-0.5X, blue), along with the S&P500 (stodgy orange line) over the past three years. The two inverse vol funds totally smoked the S&P through July, 2024. Investors in SVIX were up over 300%, compared to 35% in stocks. Even the more conservative vol fund SVXY was up 165%. Yee-haw!
The volatility tourists poured in, and then came August 5, 2024, with a short, sharp, unexpected spike in volatility. As we noted earlier, it was not so much that stocks cratered, but there was a hiccup in the global financial system, mainly around unwinding of the yen carry trade. The values of the short vol funds got decimated. Then the recent brouhaha over tariffs in April 2025 whacked them again. This drove the value of SVIX below the three-year rise in stocks, although SVXY still outpaces stocks (57% vs 35% rise).
There were dips in SVIX and SVXY in March 2023 (Silicon Valley Bank blowup), October 2023 (Yom Kippur attacks on Israel by Hamas), and April, 2024, corresponding to spikes in VIX. In those cases, it worked great to buy the dip, since within a few months SVIX and SVXY churned to new highs. Many were the articles in the investing world on the wonderful virtues of the daily VIX futures roll. But then August 2024 and April 2025 hit, where there was no complete, rapid recovery from the huge price drops.
What to take away from all this? What comes to my mind are well-worn truisms like:
If it looks too good to be true, it’s probably not true; There is no free lunch on Wall Street; It’s not different this time.
The reason I know this much about these trading products is that I got sucked in a bit by the lure of monster returns. Fortunately, I kept my positions small, and backstopped some trades by using options, so all in all I have probably roughly broken even. That is not great, considering how much attention and nail-biting I have put into short vol trading in the past twelve months.
In an upcoming post, I will report on an alternative way to trade volatility spikes, which has worked out much better.
Disclaimer: Nothing here should be considered advice to buy or sell any security.
As of 10:30am this morning Berkeshire Hathaway is down 5.6% on the news that Warren Buffet is retiring at the end of the year. At first blush, this makes sense. Buffet is an irreplaceable input into their production function. However, the man is 94 years old, a full 24 years after nearly everyone retires, so this was not exactly an unforseeable event. Why wasn’t more of this already baked into the price? Further, this would appear a far better outcome – announcing retirement more than 6 months in advance- than a more sudden and unfortunate event, such as the passing of a man in his mid 90s. It’s not unreasonable to suggest that both event possibilities would be baked into the price and, with his retirement beingthe better outcome, thus the price could have even gone up.
To me, this is a reminder that there limits to how much Knightian uncertainty can be baked into a price. Put another way, it is a reminder of the costs that uncertainty (nearly?) always imposes on markets. We would all, voters and legislaters, be wise to remember that as the current Presidential administration continues to inject seeming daily boluses of constitutional, existential, and economic uncertainty into our lives.
If you like to read and discuss with smart people, then you can make a free account in the Liberty Fund Portal. If you listen to this podcast over the weekend: Eliezer Yudkowsky on the Dangers of AI (2023) you will be up to speed for our asynchronous virtual debate room on Monday May 5.
Russ Roberts sums up the doomer argument using the following metaphor:
The metaphor is primitive. Zinjanthropus man or some primitive form of pre-Homo sapiens sitting around a campfire and human being shows up and says, ‘Hey, I got a lot of stuff I can teach you.’ ‘Oh, yeah. Come on in,’ and pointing out that it’s probable that we are either destroyed directly by murder or maybe just by out-competing all the previous hominids that came before us, and that in general, you wouldn’t want to invite something smarter than you into the campfire.
What do you think of this metaphor? By incorporating AI agents into society, are we inviting a smarter being to our campfire? Is it likely to eventually kill us out of contempt or neglect? That will be what we are discussing over in the Portal this week.
Is your P(Doom) < 0.05? Great – that means you believe that the probability of AI turning us into paperclips is less than 5%. Come one come all. You can argue against doomers during the May 5-9 week of Doom and then you will love Week Two. On May 12-16, we will make the optimistic case for AI!
See more details on all readings and the final Zoom meeting in my previous post.
There was a recent Planet Money Podcast episode that includes a fun exercise. An NPR employee produces a dozen chicken eggs and wants to sell them at cost to another employee for $5. That’s the setup. How does the employee decide who should receive the eggs? Clearly, the price mechanism won’t work since the price is fixed. A lottery is also not allowed. The egg recipient could engage in arbitrage, reselling the eggs for a higher price. But that’s not very likely and would be socially awkward. The egg producer wants to make someone happy. Who would he make the happiest?
That’s the challenge that the Planet Money team tries to solve.
First, they started with a survey. Rather than asking coworkers to rank a long list of things that includes eggs, the survey adopts a more robust method of pairwise comparisons. Do you prefer toast vs eggs? Eggs vs oatmeal? Toast vs oatmeal? and so on. One problem that they encounter, however, is that there is a lot of diversity among preparations methods. My oatmeal is better than my eggs. But my brother’s oatmeal is not. As it turns out, there is not a standard quality of prepared oatmeal and prepared eggs. So the survey is a flop.
Then they consult an economist. They decide to try to measure “willingness to pay”, which is an economic concept that identifies the maximum that a person could pay for something without becoming worse off. They couldn’t really ask the coworkers what their WTP is. People are social creatures and have many reasons to lie, mislead, signal, and to simply not know. Since someone’s WTP reflects preferences and values, we need a way to solicit the true preference while avoiding lies and most mistakes. Here’s how the economist suggested that they reveal the coworker preferences.
Step 1: Tell the coworker these rules.
Step 2: Coworker reports their WTP for a single egg in dollars
Step 3: A random price will be chosen by a machine. If the price is above the self-reported WTP, the coworker is not allowed to buy the egg. If the price is below the WTP, then the coworker must buy the egg at the random price.
I like to take existing datasets, clean them up, and share them in easier to use formats. When I started doing this back in 2022, my strategy was to host the datasets with the Open Science Foundation and share the links here and on my personal website.
OSF is great for allowing large uploads and complex projects, but not great for discovery. I saw several of my students struggle to navigate their pages to find the appropriate data files, and they seem to have poor SEO. Their analytics show that my data files there get few views, and most of the ones they get come from people who were already on the OSF site.
This year I decided to upload my new projects like County Demographics data to Kaggle.com in addition to OSF, and so far Kaggle is the clear winner. My datasets are getting more downloads on Kaggle than views on OSF. I’ve noticed that Kaggle pages tend to rank highly on Google and especially on Google Dataset Search. I think Kaggle also gets more internal referrals, since they host popular machine learning competitions.
Kaggle has its own problems of course, like one of its prominent download buttons only downloading the first 10 columns for CSV or XLSX files by default. But it is the best tool I have found so far for getting datasets in the hands of people who will find them useful. Let me know if you’ve found a better one.
The 2025 first quarter GDP data came in slightly bad: negative 0.3%. I think the number is a bit hard to interpret right now, but it’s hard to spin away a negative number. A big factor pulling down the accounting identify that we call GDP was a massive increase in imports, specifically imports of goods. It’s likely this is businesses trying to front-run the potential tariffs (and keep in mind this was pre-“Liberation Day,” so probably even more front running in April), so the long-run effect is harder to judge.
But aside from the interpretation of the GDP estimate, we can ask a related question: did anyone predict it correctly? I have written previously about two GDP forecasts from two different regional Federal Reserve banks. They were showing very different estimates for GDP!
Both the Fed estimates ending up being pretty wrong: -1.5% and +2.6%. But there are two other kinds of forecasts we can look at.
The first is from a survey of economists done by the Wall Street Journal. The median forecast in that survey was positive 0.4%. This survey got the direction wrong, but it was much closer than the Fed models.
Finally, we can look at prediction markets. There are many such markets, but I’ll use Kalshi, because it’s now legal to use in the US, and it’s pretty easy to access their historical data. The average Kalshi forecast for Q1 (a weighted average of sorts across several different predictions) was -0.6%. Pretty close! They got the direction right, and the absolute error was smaller than WSJ survey. And obviously, much better this quarter than the Fed models.
But this was just one quarter, and perhaps a particularly weird quarter to predict (Atlanta Fed even had to update their model mid-quarter, because large gold inflows were throwing of the model). You may say that weird quarters are exactly when we want these models to perform well! But it’s also useful to look at past predictions. The table below summarizes predictions for the past 9 quarters (as far back as the current NY Fed model goes):
Chickens were apparently domesticated from the red jungle fowl (Gallus gallus), a native of southeast Asia, thousands of years ago. Humans have been selectively breeding them ever since. Traditionally, chickens were valued mainly for their eggs. Surplus roosters would get eaten, of course, and tough overage laying hens would end up in the stewpot. But your typical chicken was a stringy, hardy bird whose job was to stay alive and to lay eggs.
Raising chickens en masse just for eating started in 1923 with Celia Steele of southern Delaware, somewhat by accident. She wanted to set up a small flock of egg-laying chickens to supplement her husband Wilmer’s Coast Guard salary. She placed an order for 50 chicks, but it was mistakenly heard as 500. When she got this huge shipment, she thought fast and decided to raise them to eating size (“broilers”) and then immediately sell them. She built a coop designed for grow-out, rather than for egg-laying. This enterprise was profitable, so she expanded operations. She doubled production the next year, and by 1926 she had 10,000 chickens. Her neighbors saw her success, and also went into the broiler biz. Thus was spawned the modern broiler industry. All this was aided by the general prosperity in the 1920s, together with technical progress in refrigeration and transportation. Her first broiler house is now on the U.S. Registry of Historic Places.
However, chickens themselves were still scrawny by today’s standards. As of 1948, chicken meat was still an expensive luxury. With the broiler (meat chicken) market established, breeders naturally tried to develop strains that would grow big and fast. That not only allows more meat to be grown in a given flock, but fast growth means less feed is consumed to get to market weight.
For several years around 1950, A&P Supermarkets sponsored a “Chicken of Tomorrow” program, overseen by the USDA, to promote improved broiler breeding. As examples of chickendom as of 1948, here are plucked carcasses of contestants for the Chicken of Tomorrow contest of that year. Note how stringy they are, compared to the plump, meaty bird you buy at the grocery store today:
Without going into much detail, the ultimate product was a cross (hybrid) between the Cornish chicken and other breeds. Cornish cross chickens were initially bred for size and growth rate. By say the 1990s, that led to birds that were so heavy that they sometimes could not support their own weight. More recent breeding programs promote leg strength and other health factors, as well as sheer growth.
To produce today’s optimized broiler is a complex process. Breeders must maintain something like four purebred strains, and then carefully cross-breed them, and then cross-breed some more, to get the final hybrid chick to send out for farmers to raise. Only these hybrids have the optimized characteristics; you can’t just take a bunch of these crossed chickens and breed a good flock from them:
Only a few large outfits can afford to do this, so most hatcheries are supplied by a handful of big breeders. However, there seems to be enough competition to keep the prices down for the consumer. Some folks will always find something to complain about (reduced genetic diversity or hardiness, etc.), but they are welcome to breed and grow less efficient chickens, if it pleases them.
In terms of dollars: “The inflation-adjusted cost of producing a pound of live chicken dropped from US$2.32 in 1934 to US$1.08 in 1960. In 2004, the per-pound cost had dropped to 45 cents, according to the USDA Poultry Yearbook (2006).”
According to the National Chicken Council, in 1925 it took a broiler chicken an average of 112 days to reach a market weight of 2.5 pounds. As of 2024, the market weight has soared to 6.5 pounds, and chickens reach that weight much faster, in 47 days (about the time it takes leafy green vegetables). The net result is that now it only takes about 1.7 pounds of feed to grow one pound of chicken, compared to 4.7 lb/lb in 1925. This nearly three-fold reduction in resource consumption translates into lower consumer costs, lower load on the environment and agricultural resources, and even lower CO2 generation. The largest jump feed conversion efficiency (from 4 to 2.5 lb/lb) occurred between 1945 and 1960, thanks to the development of the Cornish cross.
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