Has it gotten easier or harder for Americans to afford the basic necessities of life? Part of the answer to this question depends on how you define “basic necessities,” but using the common triad of food, clothing, and housing seems like a reasonable definition since these composed over 80% of household spending in 1901 in the United States.
If we use that definition of necessities, here is what the progress has looked like in the US since 1901:
The data comes from various surveys that the Bureau Labor Statistics has collected over the years, collectively known as the Consumer Expenditure Surveys. The surveys were conducted about once every 1-2 decades from 1901 up until the 1980s, and then annually starting in 1984. Some of these are multi-year averages, but to simplify the chart I’ll just state one year (e.g., “1919” is for 1918 and 1919). The categories are fairly comprehensive: “food” includes both groceries and spending at restaurants; “housing” includes either mortgage or rent, plus things like utilities and maintenance; and “clothing” includes not only the cost of the clothes themselves, but services associated with them such as repairs or alterations (much more important in the past).
We can see in the chart that over time the share spent on these three areas of spending has declined dramatically, taken as a group. Housing is different, but it has been fairly stable over time, mostly staying between 22% and 29% of income (the Great Depression being an exception). There are two time periods when these costs rose: the Great Depression and the late 1970s/early 1980s. Both are widely recognized as bad economic times, but they are aberrations. The jump from 1973 to 1985 in spending on necessities was fully offset by 2003, and today spending on necessities is well below 1973 — even though for housing, it is a few percentage points greater.
A chart like this shows great progress over time, but it will inevitably raise many questions. Let me try to answer a few of them in advance.
In last week’s post, I described how short volatility funds work. They are short (as opposed to long) near-term VIX futures. This means that when a market panic hits and VIX (as measure of volatility) spikes, the prices of these short vol funds plunge, along with stock prices. But as optimism returns to the markets, prices of short vol funds start to recover, as do stocks.
Thus, both short vol funds and general stock funds are reasonable ways to play a market panic. If (!!!) you manage to call the bottom and buy there, you can hold for maybe a couple of weeks until prices recover, and then sell at a profit. I tried to do just that with the market meltdown last month in the wake of the president’s tariff ultimatums: I bought some short vol funds (SVXY, which is a moderate -0.5X VIX fund, and the more aggressive -1X fund SVIX), and also some leveraged stock funds. I discussed leveraged funds here.
I chose to buy into SSO, a 2X leveraged S&P 500 stock fund, whose daily price moves up (or down) by twice the percentage as does the S&P. Obviously, if you think stocks will go up say 10% in the next month, you will make more money by buying a fund that will go up 20% instead, which is why I bought a 2X fund rather than a plain vanilla (1X) stock fund. A related fund, which I did not buy this time, is UPRO, which is a 3X stock fund.
Things are always clear in hindsight. After the smoke of battle clears, you can see right where the bottom was. But it is not clear when you are in the thick of it. I erred by committing much of my dry powder trading funds too early, maybe halfway through the big drop. C’est la vie. It’s hard to improve on that for next time. But a significant learning, that I will act on during the next panic, was how differently short vol versus leveraged stocks recovered from the crash. They both plunged and recovered, but leveraged stocks recovered much better.
It turns out that much of the time, the price movements over say a six-month period of SVXY and SSO largely match each other, so these are useful for comparisons for trading short vol versus leveraged stocks. For instance, below is a chart of SVXY (orange line) and SSO (green line) over the past six months or so. The blue arrow notes the April crash, which bottomed roughly April 8. For November through early April, the price movements of the two funds roughly matched. By April 8, both had plunged to a level some 35% lower than their starting prices. However, by May 12, SSO had recovered to -10% (relative to starting), which is about where it was in late March (green level line drawn in). SVXY, however, remained 21% below its start.
Chart of SVXY ( -0.5X VIX ETF, Orange line) and SSO (2X Stock fund, green line), Nov 2024-May 2024. Blue arrow marks April 2025 volatility spike/stock crash. Chart from Seeking Alpha.
Thus, from its nadir (-35%) to its recovery as of Tuesday, May 12, SSO gained by 38% (i.e., ratioing 0.90/0.65), whereas SVXY gained only 21% (from ratioing 0.79/0.65). Also, it looks like SVXY will not regain its earlier price levels any time soon. So SSO looks like the winner here.
We can do a similar comparison between the -1X VIX fund SVIX and the 3X stock fund UPRO. These two funds are plotted below, along with a plain (1X) S&P 500 stock fund, SPY (in blue). SVIX (orange) and UPRO (green) trend pretty closely for October through March. When the April crash came, SVIX dropped much harder, down to a heart-stopping -59%, compared to -44% for UPRO. SPY dropped only to -15%. SPY comes to a full recovery (0%) by May 12, while UPRO recovers only to -13% [1]. SVIX has recovered only to -21%. If you managed to buy each of these funds on April 8, and sold them today, you would have made the following gains:
SPY 17% ; UPRO 55%; SVIX 43%. Clearly the winner here in short term trading of the April crash is the 3X stock fund UPRO.
Chart of SVIX ( -1X VIX ETF, Orange line), UPRO ( 3X Stock fund, green line), and SPY (1X Stock fund, blue line), Oct 2024-May 2024. Chart from Seeking Alpha.
As a cross check, below is a plot of SVXY (orange) and SSO (green) covering the August, 2024 volatility spike. This was a peculiar event, discussed here, where volatility went crazy for a couple of days, while stock prices experienced only a moderate drop. If (!!!) you timed it just right, and bought at the bottom and sold a week or so later, you could have made good money on SVXY. But zooming out to the larger picture, SVXY never came close to recovering its old highs, whereas SSO just kept going up and up (green arrow). So SSO seems like a safer trading vehicle: it is a reasonable buy-and-hold, whereas SVXY may be hazardous to your portfolio’s health if you don’t get the timing perfect.
Chart of SVXY ( -0.5X VIX ETF, Orange line) and SSO ( 2X Stock fund, green line), Oct 2023-Oct 2024. Blue arrow marks early August 2024 volatility spike. Chart from Seeking Alpha.
Over certain longer (say one-year) periods, there are regimes where short vol could out-perform leveraged stocks (discussed earlier), but that is the exception, rather than the rule.
Disclaimer: Nothing here should be considered advice to buy or sell any security.
ENDNOTE
[1] While UPRO changes X3 the change of SPY on a daily basis, for reasons discussed earlier, the longer-term performance of UPRO diverges from a simple X3 relationship with SPY. In volatile times, UPRO tends to fall well below a 3X performance over say a six-month period.
I’m not just doing this to plug my own event. It’s also about the only thing on my mind after spending the week leading and moderating this timely discussion.
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: Marc Andreessen on Why AI Will Save the World (2023) you will be up to speed for our asynchronous virtual debate room on Monday May 12.
Keeping in mind the stark contrast between this and the doomers we discussed in the past week, here is Marc’s argument in a nutshell:
“The reason I’m so optimistic is because we know for a fact–as sort of one of the most subtle conclusions in all of science–we know for a fact that in human affairs, intelligence makes everything better. And, by “everything,” I mean basically every outcome of human welfare and life quality that essentially we can measure.”
When it’s put that way, it’s hard to disagree. Who would want less intelligence?
See more details on all readings and the final Zoom meeting in my previous post.
Another interesting bit by Marc:
“By the way, look: there’s lots of work happening that’s not being published in papers. And so, the other part of what we do is to actually talk to the practitioners.”
Even though it might seem strange to look to podcasts instead of published books and papers for cutting edge information, it really does seem like the story was told in human voices for the past 3 years. Dwarkesh was probably the best, but Tyler and Russ deserve credit as well for bringing these conversations out of the closed rooms and into the public domain.
I just learned about the Bayesian Dawid-Skene method. This is a summary.
Some things are confidently measurable. Other things are harder to perceive or interpret. An expert researcher might think that they know an answer. But there are two big challenges: 1) The researcher is human and can err & 2) the researcher is finite with limited time and resources. Even artificial intelligence has imperfect perception and reason. What do we do?
A perfectly sensible answer is to ask someone else what they think. They might make a mistake too. But if their answer is formed independently, then we can hopefully get closer to the truth with enough iterations. Of course, nothing is perfectly independent. We all share the same globe, and often the same culture or language. So, we might end up with biased answer. We can try to correct for bias once we have an answer, so accepting the bias in the first place is a good place to start.
The Bayesian Dawid-Skene (henceforth DS) method helps to aggregate opinions and find the truth of a matter given very weak assumptions ex ante. Here I’ll provide an example of how the method works.
Let’s start with a very simple question, one that requires very little thought and logic. It may require some context and social awareness, but that’s hard to avoid. Say that we have a list of n=100 images. Each image has one of two words written on it, “pass” and “fail”. If typed, then there is little room for ambiguity. Typed language is relatively clear even when the image is substantially corrupted. But these words are written, maybe with a variety of pens, by a variety of hands, and were stored under a variety of conditions. Therefore, we might be a little less trusting of what a computer would spit out by using optical character recognition (OCR). Given our own potential for errors and limited time, we might lean on some other people to help interpret the scripts.
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.
Economist Writing Every Day 