“There’s so much talk about “oh we’re going to drive housing prices down”. I don’t want to drive housing prices down, I want to drive housing prices up, for people who own their homes. And they can be assured that is what’s going to happen.

Which begs the question: do we want housing to be cheaper or more expensive? Two-thirds of households own their own home, and 60% of homeowners carry a mortgage, so it would seem that higher housing prices are better for Americans. But some would argue that there is a paradox here, because most young people do not yet own a home and wish to eventually buy one, and higher housing prices lock them out of the American Dream.

This is a tricky question, so let’s start by asking a simpler question: are we better off with higher oil prices or lower oil prices? Most people would intuitively answer that we are better off when oil prices are lower. High oil prices mean high gas prices, and the less money we spend at the pump, the more money we have to spend on everything else. Another way to think about this is that we are all short oil — that is, we have to buy oil in the future at the market price — and lower oil prices reduce the future liability of our collective short position.

But are we really better off when oil prices are low? Falling oil prices are sometimes associated with severe economic downturns. Oil peaked at $140 per barrel in 2008 and fell to $40 in 2009, after the financial crisis. Oil began 2020 at $50 per barrel but briefly plummeted below zero that April after Covid hit. The converse is true as well: oil prices often rise when the global economic outlook brightens.

This happens because supply and demand are not that responsive to price in the short run — it takes a while to drill new wells or improve the average fuel efficiency of the cars on the road — so when global incomes rise or fall, the demand curve abruptly shifts, and oil prices must rise or fall significantly to maintain equilibrium. It is thus common for us to be economically better off during periods in which oil prices are rising. Another way to think about this is that for most of us, our largest asset is the present value and stability of our future earnings (whether that is in the form of wages or dividends or rents), which is tied to the health of the economy. A major recession will save us a few bucks on gas, sure, but at the much greater cost of higher unemployment and lower income.

The problem is that our intuitive framing of the question is flawed. As economic participants, we are used to simply responding to shifts in price. The great thing about prices is that economic participants don’t need to know why a price changed — the new price transmits all of the information and incentive we need to change our behavior.

But as economic analysts, we actually do need to know what caused a price change, because two events can have the same impact on price while having opposite impacts on consumer welfare. We have to think in terms of supply and demand, which allows us to untangle the confusion.

In this example, lower oil prices are sometimes the result of a shift in the supply curve that increases the aggregate quantity of oil that producers are willing to supply at most prices, like the shale revolution that unlocked oil supply in the U.S. in the 2010s. This kind of shift makes consumers better off. Lower oil prices can also be the result of a shift in the demand curve resulting from a deep recession, which generally makes consumers worse off.

As Scott Sumner likes to put it: Never reason from a price change. If we start by implicitly assuming that all price changes have similar causes, we will see paradoxes and contradictions where none exist. It is necessary to trace the root cause of the price change before drawing conclusions about likely impacts.

With this in mind, we can see that the original question about housing prices is simply poorly framed. We can think of cases where higher housing prices are associated with people generally being worse off, and we can think of cases where higher housing prices are associated with people generally being better off.

Let’s start with an example where housing prices go up and people generally end up worse off: restrictions and taxes on new construction. In the US, this can take the form of land use restrictions, years-long procedural delays, new units set aside to be sold at a discount (“inclusionary zoning”), and excessive permitting fees. Normally, when local housing demand rises, prices rise, which causes developers to build more housing, which causes prices to fall back toward the cost of building new housing (at which point developers are no longer incentivized to build).1 Supply restrictions break the usual feedback loop, causing prices to permanently rise far above the cost of new construction, up to double or triple in the most expensive American cities.

If the majority of households own their own home, what is the harm here? Well, similar to our oil case study, remember that we all have a built-in short position in housing services: that is, we all are locked into procuring a place to live for the rest of our lives. One key is understanding that buying a house mostly only hedges this short position — increases in market rent improve your long position as a homeowner, but harm your short position as a future housing consumer by roughly the same amount.2

No matter how much your house goes up in value on paper, as long as you are locked into living in the same place by your career and/or your family and friends, you won’t be much better off in any real sense (at least not in your lifetime). Also, supply restrictions are not even unambiguously good for home values. The value of a house lies in the combined value of the land and structure; supply restrictions are likely to increase the value of newly scarce structures, but strip the ability for a homeowner to realize the full value of their land by demolishing their single family home and building a 20-unit apartment building in its place, if future conditions warrant.

Then you have to consider the households who don’t own, a group that skews younger. (27% of Gen Zers that head a household are homeowners, vs. 80% of Boomers.) Some might have rent control, which economically is a little similar to ownership in the sense that it confers a hedge, but won’t result in a net benefit. The rest simply have the short position with no offset — they face the choice between paying more for housing, or moving to a new city away from family and friends. Even the ones that stand to eventually inherit part of an appreciated home from their parents are unlikely to do so until they are well into middle age, at which point they will have paid inflated rents for years and will have also likely already overpaid for a home. Supply restrictions are usually at best a net zero, and at worst a huge negative.

We can also use this framework to identify specific winners and losers from local supply restrictions. A retiring Boston homeowner that is planning on moving to Florida doesn’t have a short position in the Boston market, and will be a big winner after selling. Another homeowning couple who derives happiness from knowing that their three kids will be able to live nearby with their grandchildren will be a big loser. An event that leaves people worse off on average will create enough variance to have a few individual winners alongside some major losers.

Now let’s look at something that raises housing prices and leaves people better off: higher wages driven by strong economic growth.

Armed with higher wages, people bid up the price of housing. Developers respond to higher prices by building more housing, but recall that developers stop building when the price falls to the cost of construction. Construction requires employing local labor, so higher wages translate to more expensive construction which results in a higher equilibrium price for all housing, though not so much higher that it fully offsets the benefit of higher wages. (Construction also requires the input of globally tradable commodities like lumber, steel and energy, the prices of which will be less affected.) Housing prices go up, and people are also better off, similar to what we saw in our oil example.

This sounds like a pretty stylized model with lots of arbitrary assumptions, but it actually pretty well matches what we observe over time and across geographies: higher incomes are correlated with higher real housing prices but also higher material living standards and more affordable housing. Within the US, the cost of shelter has outpaced inflation by 50% since 1959, but real disposable income per capita has tripled (see the graph below). Housing prices are higher than they were a generation or two ago, but our incomes today are much higher, so we live in much larger and more modern homes than our grandparents did.

The same pattern emerges when we look across countries. The US is richer than most countries, and so it is no surprise that monthly rents in US cities are among the highest in the world (per a Deutsche Bank survey):

But because the US is so rich, its cities actually rank among the most affordable in the world when considering housing prices relative to income:

Note that Chicago, which has historically been less affected by supply restrictions, actually has the most affordable housing of the 69 global cities that DB surveyed, despite having the 9th highest monthly rents!

We can connect this back to our earlier observation that the most valuable asset that most people have is their future earning power: depending on your age and income, the present value of your future wages is probably millions of dollars, and the value of your future social security benefits is likely at least in the hundreds of thousands (and is fully dependent on future payroll taxes). An event that increases your future earning power in a meaningful way is always likely to more than offset price increases on a limited subset of the goods and services that you buy.

Note that there is something analogous that goes on when people talk about stock prices. People associate higher stock prices with good economic outcomes, because corporate profits are linked to economic growth, and because stock prices rise when people are feeling good about the future. But high stock prices do not have to be linked to good overall economic outcomes: imagine if the government stopped enforcing antitrust law, which would lead to higher corporate profits and higher stock prices but at the expense of higher prices faced by consumers at the checkout counter.

In the US, we channel a lot of our savings into building home equity, so much so that it makes up a disproportionate share of our financial wealth for the median household. This leads many to the natural conclusion that higher home prices necessarily make us better off by increasing our net worth on paper. In reality, higher home prices are only correlated with good economic outcomes if they result from broadly higher incomes, and are a bad thing if they result from supply restrictions. In the latter case, our off-balance sheet (but still economically real) liability of future housing costs rises at least proportionally, more than neutralizing any benefit.

Reasoning about housing from price changes also causes us to mistakenly conflate high housing prices with desirable amenities such as good schools, low crime, and walkability. Neighborhoods that gain these amenities see rising housing prices, and people subconsciously start thinking that desirable amenities and rising housing prices are inextricably linked. Sometimes people even theorize that desirable amenities cannot be allowed to infiltrate poor neighborhoods lest they cause gentrification and price out the locals.

This is the same logical fallacy as before, except applied to different dimensions of existing housing instead of the total quantity of housing. Goods and services are not expensive solely because they are desirable, but because they are desirable and scarce, and the price system is just a way to ration desirable goods and services (and to incentivize their production). If we lower crime in an existing neighborhood, housing prices in that neighborhood will rise but housing prices in other safe neighborhoods will fall because safety is now less scarce.

One good answer is that investing is a fun intellectual puzzle; if you are wired that way, stock picking can provide a form of entertainment that compares favorably to doing the crossword or watching Netflix, both in terms of utility and in terms of cost (negative if you are good!).

Another answer is that investing can be intellectually enriching: it requires you to come up with models of industry economics and company performance and then actually bet your own money on the predictions generated. It is possible to learn the theory of statistics, economics, and psychology from textbooks and professors, but there is something about repeatedly wagering one’s own money that unlocks important practical dimensions of those subjects, bridging the gap from theory to application.

Investing can teach important lessons about separating skill from luck and signal from noise and the role of emotions in decision-making, lessons that are broadly applicable to the rest of our day-to-day lives. Here we will examine a particularly useful investing concept: the idea of thesis drift.

Thesis drift refers to the tendency of investors to invent new rationales to continue owning a security after their original thesis has been falsified, instead of cutting their losses and moving on.

For example, let’s say you come up with a theory that the steel industry is ripe for imminent consolidation, and you buy stock in Anacott Steel because you think it is a likely takeover target. However, months pass with no sign of any deal activity in the space, and Anacott stock is down.

At this point, you should just admit that you were mistaken, and take your losses and move on. But that is rarely what happens.

Instead, you come up with a different intellectual justification to be long Anacott Steel – perhaps now you have come to the realization that Anacott’s new management team is underrated by the market, and Anacott’s stock will rise as a result.

A year later, Anacott has shown no sign of outperformance, and the stock is still stagnant. Undaunted, you have another brainstorm: steel industry margins are going to rise because of greater capital discipline. And if a year later, that thesis doesn’t play out…well, you get the idea. You just move from thesis to thesis in an effort to justify your original position, losing money at every step of the way.

The problem with thesis drift isn’t necessarily any of these new theories themselves; any of them could be correct. The problem is that when your first theory was falsified, you got a clear, unambiguous signal that there was something wrong with your overall model of the steel industry. You don’t know what was wrong with your model, you just know that your model produced a really bad prediction.

At that point, you should simply take your money out of a situation you clearly don’t understand, and move it to a situation you do understand. Why do you need to speculate on steel stocks? You could invest in one of a hundred other industries, or you could just throw your hands up and put the money in an index fund. Also, you are clearly spiraling. You’ve stopped actually thinking, and instead you are just spinning up narratives that justify your original course of action.

Thesis drift is entirely psychological in nature. It is natural to want to avoid admitting to yourself that you were wrong or that you didn’t know what you were doing, as it conflicts with your self-image as someone who is smart or has good instincts. If you have made public pronouncements about your position, changing your mind threatens your external image as well.

If you don’t recognize thesis drift, you can get stuck in hell. Hell, in this context, is when you get stuck doing the same thing over and over and expect a different result. All you have to do to stay out of hell is to respond to the clear, unambiguous feedback you got when your initial thesis was falsified. That feedback is your lifeline! Once you sever that feedback loop, it’s easy to enter a tailspin, because there is nothing left that can pull you out of it. You just keep losing money on the same failed position with no end in sight. And what’s worse, when you are stuck in hell, you look like an insane person, doing the same thing over and over again while expecting different results.

People expect investing to be a game of intellect, where you produce clever theories that you can turn into money. In reality, it is more of a psychological battle to keep your brain from sabotaging your portfolio. The concept of thesis drift is an example of “name it to tame it”: by anticipating this psychological pattern in advance, you can recognize and neutralize it when it surfaces. You know that all investors will be wrong sometimes, you know that there is no shame in admitting that you were wrong, and you know that it is exceptionally dangerous to stick with a position that stemmed from a broken thesis. It’s actually a clever trick: you don’t need to embrace the Anacott bear case, you don’t need to tinker with your model of the steel industry, you just walk away and don’t waste any time or money on it.

The good news is that in the world of investing, it is expected that even the best investors will make mistakes, and it is considered a sign of discipline when a money manager sells out of a losing position. This is why there are sayings like “Losers average losers” and “You don’t have to make back money the same way you lost it”. To a layperson, it can be jarring to see Bill Ackman suddenly blow out of a $1 billion Netflix stake after a bad earnings report or to see Warren Buffett liquidate a $7 billion airline position post-Covid, but an experienced investor recognizes it as rational and healthy behavior.

Thesis drift has clear applications in our personal and professional lives: most of us find it hard to change jobs or careers or get out of bad relationships when confronted with mounting evidence that we made the wrong choice, and we come up with all sorts of rationalizations. We also see it in organizations of all kinds (for-profit, non-profit, and governmental) that stick with failed strategies with no end in sight. But we will not dwell on these applications here.

Instead, let’s look at thesis drift as it applies to public policy. Via Marc Rubenstein, Rusty Guinn of Epsilon Theory has a wonderful visualization of thesis drift as it pertains to tariffs, reproduced below:

The original rationale behind broad tariffs was that they would strengthen and protect domestic manufacturing, but when that thesis was contradicted, the messaging pivoted to “tariffs can replace the income tax”. When it became clear that wasn’t true, the message became “tariffs give us leverage to force neighbors to secure their borders / lower their tariffs / pay more for defense.” From the outside, it’s clear that these different narratives are contradictory (e.g. a crucial source of revenue or pillar of industrial policy can’t be bargained away) and that tariff supporters are engaging in thesis drift to rationalize their original policy.

It would be tempting to write this off as a quirk of Trumpism, but this is actually an issue with all flawed populist policies that manage to gain traction. Populist policies gain support because, to borrow the words of Dan Williams, they feel like they should work. People support tariffs because it feels like tariffs should make us richer, no matter what the experts say.

The problem with populist policies is that they only feel like they should work. In reality, they are based on flawed models of the world, and as a result they produce undesirable outcomes that were predicted by experts but come as a surprise to their supporters. This is where thesis drift comes into play: instead of abandoning the policy and admitting that their model was wrong, populists simply pivot to a new narrative to justify their original policy.

Thesis drift is especially dangerous because of the confluence of two separate modern intellectual trends. The first trend is the shift toward populism. We tell people that they should “think for themselves” and be skeptical of elites and experts, but we never warn them that most of modern society is built on our experts’ understanding of counterintuitive but (relatively) accurate models of reality. For example, pretty much everything we know about the physical sciences runs contrary to the intuition of anyone that lived before the Scientific Revolution, and the only reason that we easily grasp that objects with different weights fall at the same speed or that the earth revolves around the sun is because we are taught these concepts before we try to figure them out on our own.

When people go forth and think for themselves, they expect to uncover original hidden truths, but instead they rediscover centuries-old folk theories like mercantilism – ideas that originally gained popularity because they feel like they should be true before being debunked through careful study and bitter experience. To compound the problem, when people compare notes and find they have independently come up with the same conclusion, they imagine that they have stumbled on an important idea, instead of it being yet another case of two dumb bitches telling each other “exactlyyyy”.

Populist economic ideas have always been a part of public policy, in one form or another. We’ve had tariffs, price controls, supply restrictions, demand subsidies, “supply-side” deficit spending, and austerity, just to name a few. We are getting more populist health policy as well: the anti-vax movement is well known, but now we even have a health secretary that doesn’t believe in germ theory, instead opting for a version of 19th-century miasma theory.

The most dedicated adherents of populist ideas will make it a major part of their identity, at which point there is no turning back. They involve themselves in activism, they make speeches, they take up positions at think tanks and write papers. They see their advocacy as an important part of their life’s work, a contribution to society that gives them purpose and meaning. This makes them especially prone to thesis drift, as they cannot accept the blow to their status and self-image that would result from repudiating their original ideas. This accounts for the stubborn minority that dig in and defend failed populist policies, even after they produce disastrous outcomes.

The second trend is the idea that we should be maximally charitable toward populist ideas as a good form of epistemic humility or intellectual hygiene. The idea is that any popular theory generated through lived experience has merit and should be taken seriously, and we don’t want to risk throwing away a good theory simply because people can’t articulate why they think it works.

One problem with this approach is that it relies on naive realism, the idea that people see the world as it is. In fact, our first-hand experience captures only a narrow, distorted slice of reality, and we use mental shortcuts that lead us to misinterpret causal relationships and evidence. Experts have developed more rigorous statistical techniques to tease out hidden parts of reality and create better predictive models of the world. It is not impossible for an intelligent layperson to find an accurate idea that the experts have overlooked, but it is a remote probability relative to the much higher odds of simply rediscovering bloodletting, flat earth theory, or another similar debunked idea.

The other problem with this approach is that it encourages thesis drift, which as we have seen is a dangerous trap. I hate to pick on anyone in particular, because I know the intent is good, but here is an old Scott Alexander post on “steelmanning the NIMBYs”, speculating about ways that NIMBYs could be correct about housing policy.

Now we have analyzed “NIMBY” housing policy at length here in past editions, because it is such a typical example of populist economics. A large proportion of populist economic ideas are based around a misunderstanding of what prices are: it is commonly thought that prices are set by greedy suppliers rather than emerging from market forces to ration scarce resources and provide important signals and incentives. The idea behind NIMBYism is that new housing construction causes high housing prices (because you see rising rents anywhere you see new construction), and by restricting construction, we can make housing more affordable.

In reality, this is completely backwards. Rising rents are a function of rising demand (which themselves are a function of external economic forces), rising rents are what incentivize new construction, and new construction is what pushes rents back down by taking away leverage from existing landlords. The cities that have maintained NIMBY housing policies have seen rents spiral out of control, as tenants are forced to outbid each other for (artificially) scarce units.

The problem with “steelmanning” NIMBYism or any other populist policy in this manner is that people are already inclined to find excuses to philosophize about different ways that their pet theory could be correct, even though it has clearly failed in practice. That’s what thesis drift is! Indeed, we see that NIMBYs give ever-changing, contradictory reasons for opposing housing: new buildings will lie empty and they will also create more traffic, and they will attract poor people and will also cause gentrification, and they will make housing unaffordable and will also cause home values to tank.

Experts sometimes imagine that they can convert populists by carefully demonstrating how their models fail to meet rigorous standards for logic and evidence. The problem with that approach in practice is that people who are attached to debunked models also fail to recognize the merit of rigorous standards for logic and evidence — otherwise they wouldn’t have gotten so attached to those debunked models in the first place.

We previously looked at how 19th century European doctors rejected Semmelweis’s discovery of handwashing as the way to prevent the spread of deadly childbed fever (a bacterial infection afflicting new mothers spread through dirty hands or sheets), even after he deduced the method of infection through a (pseudo-)randomized controlled trial and implemented his solution again and again with spectacular results.

One of the problems is that other doctors did not understand causal inference and proper statistical methods as well as Semmelweis did, so they attributed his spectacular results to chance and actually claimed that handwashing was an arrogant theory that had been debunked, and that childbed fever was in fact complex and multicausal. It was nearly four decades before a new generation of doctors recognized that Semmelweis was right and handwashing was actually widely adopted.

This is the danger of thesis drift when reasoning within a complex system. If you can’t accurately connect cause and effect, you get trapped in hell, attributing your bad results to spectacularly terrible luck or sabotage from those around you, finding excuse after excuse to maintain the status quo when the cure is sitting right there in plain sight.

The next best solution is to have a sort of intellectual stop-loss in place, a no-fault way to recognize and get out of a bad position, similar to the way an investor can sell out of a stock with their reputations and self-image intact. Ultimately, the outcome is all that matters: you need to avoid frittering away all your money speculating on steel, you need to wash your hands, and you need to allow housing to be built, even if you don’t understand or accept the theory behind why you need to do these things.

One way this happens in practice is that people find ways to back out of the status quo without having to admit that they were mistaken, either publicly or to themselves. They find a way to graft part or all of the policy they opposed onto their debunked framework, giving them a path to generate desirable outcomes without admitting fault.

For example, many of the doctors that ridiculed Semmelweis publicly also quietly adopted handwashing regimens at their own hospitals. They still denounced Semmelweis’s theory as simplistic and arrogant but they allowed that dirty hands might be a minor contributing cause, so why not do it if you feel like it? This frustrated Semmelweis to no end, and was less than ideal in that it prevented handwashing from spreading as widely as it should have, but it succeeded in saving lives.

We can see elements of this already happening in trade and housing policy. Reframing tariffs as a way to extract concessions gives a clear path to eventually eliminating tariffs without admitting fault. Progressive politicians are finding ways to allow more construction without abandoning their overall intellectual framework.

It’s easy to get hung up on the ideal outcome of making people understand an important model at the same deep level that you do, but the reality is that even very intelligent people either won’t have the time to truly learn about a complicated model and all of the evidence that goes into it (Paul Krugman put it well when he said that modern theories of trade, like most scientific models, relies “on a dense web of linked ideas”) or their brains will reject any model that runs contrary to their intuition. (“My intuition can’t be wrong; I went to Yale.”) The messy reality of life is that sometimes we only get the outcomes we want when we realize that we are trapped in hell, and we need to find a way out.

Had the federal minimum wage simply kept up with inflation since 1968, it would be more than $24 an hour.

I don’t know about you, but when I read that sentence, I stopped in my tracks. I thought to myself: “wow, that’s unbelievable”!

Then I thought to myself: “Wow, that’s really unbelievable”. So I plugged it into Google and I got this:

$12 an hour. A much more believable figure, and mathematically, only half of $24. I’d love to say that this slip-up was a one-off, but sadly, most of the interesting “facts” in the book didn’t survive a quick check on Google. Writing good nonfiction: harder than it looks!

I’ve run an occasional series here reviewing books about the demise of GE. At its peak in the early 2000s, GE was the most valuable and most respected company in America, but it imploded over the following decade and has since been broken up. It’s the kind of extreme outcome that exposes important lessons about investing and management, and the books reviewed here were all well researched and insightful.

I first read The Man Who Broke Capitalism when it was released three years ago. I must confess that I was initially unimpressed: I dismissed it as standard issue airport slop, appealing to midwits that read the New York Times, but of no real interest to the refined intellects that read this blog. (I’m kidding, of course: I totally discovered the book when it was excerpted in the New York Times, which is a publication that I, a certified midwit, read on a daily basis.) Also, writing a book is hard (I imagine), and it seemed ungenerous to write a review of a book with the sole intent of tearing it to shreds.

Still, I had the nagging feeling that something was missing from the other books I reviewed about GE. Finally, I had an epiphany. I’ve been reviewing smart books about an all-time act of stupidity. As good as those books are, one would have to agree that there is some dimension of stupidity that a smart book simply cannot access. So here I present my review of The Man Who Broke Capitalism.

The thesis of The Man Who Broke Capitalism is bluntly stated in the subtitle: How Jack Welch Gutted the Heartland and Crushed the Soul of Corporate America — and How to Undo His Legacy. The deindustrialization of the Upper Midwest, widening income inequality, growing corporate malfeasance: every modern economic ailment can be traced back to 1981, when Jack Welch was appointed as CEO of General Electric.

The theory here is that the period from 1945-1980 was a golden era for the American worker simply because American corporate culture was better back then. Executives paid their employees generously, eschewed layoffs, and cared deeply about their duty to their communities and their country — even if it came at the expense of corporate profits and CEO paychecks. Then Jack Welch came along and destroyed all of that. He shut down factories and shipped jobs overseas and used the profits to buy back stock. Other greedy CEOs followed his example, spreading “Welchism” throughout the American economy. In Gelles’s opinion, to save the economy, we must RETVRN to the culture of the 1950s: if CEOs would just stop laying off workers and cease buying back stock, the factories would reopen, income inequality would abate, and we would enter a new economic golden age.

Gelles actually does identify some (real) evidence that seems to support his theory. He observes that total US manufacturing employment peaked at around the time that Welch took power at GE, and has been declining ever since, particularly in the Upper Midwest, where GE had a large presence. Stock buybacks were almost nonexistent before the early 1980s, but are ubiquitous today. Executive compensation is much higher today than it was in the early postwar era. Is it possible that Gelles has picked up on something that legions of experts have missed over the decades?

The answer: of course not. Upon closer inspection, the evidence he cites is badly misinterpreted, and his analysis is hopelessly confused and naive.

Let’s start with the claim that manufacturing employment peaked around the time Welch became CEO of GE. This is technically true: in absolute terms, the BLS says that manufacturing employment in the US peaked in June 1979, shortly before Welch took the top job at GE in 1981.

The problem with this measure is that manufacturing employment had been steadily shrinking as a share of total employment since 1950. If we want to understand how the economy is evolving, we want to look at the share of workers in each sector, not the absolute number, which is also influenced by demographic fluctuations that grow or shrink the overall workforce.

Even though manufacturing employment had been holding steady from 1950-1979, this happened against the backdrop of a rapidly growing total employment, as baby boomers flooded into the workforce and women began working in much greater numbers. Here is a chart showing the share of American employees working in manufacturing1 since 1939: there was not much of an inflection point after 1981 (beyond the recession), just a steady decline since the 1950s.

Unlike the “golden era” thesis, this actually matches contemporaneous accounts. In 1986, David Halberstam published The Reckoning, a detailed account of how the Japanese auto industry caught up to Detroit, culminating in the 1979 energy crisis and subsequent mass layoffs at the major American automakers, who were caught without any competitive fuel efficient cars to sell.

Incidentally, the 1979 energy crisis was the real shock to Midwestern manufacturing, and it pre-dated Jack Welch. According to Cleveland Fed economist Mark Schweitzer, the industrial heartland lost 1.2 million manufacturing jobs between 1979 and 1983, over 20% of the previous manufacturing workforce. He finds that manufacturing employment in that region actually held flat between 1983 and 2000 (the Welch era), before declining again after 2000.

Halberstam interviewed Detroit residents in the grim winter of 1982, when local unemployment reached 16% and droves of laid-off autoworkers were fleeing to take part in the energy boom in Texas. There he discovered a general awareness that manufacturing had been losing ground to services in America for decades. Halberstam found it symbolic that the local Detroit sports teams had all once been owned by manufacturing tycoons, but lately the Tigers (baseball) and the Red Wings (hockey) had been sold to two local pizza entrepreneurs, Tom Monaghan (Domino’s) and Mike Ilitch (Little Caesars). From the vantage point of people living in the early 1980s, American manufacturing peaked before 1950, and had been steadily shrinking in importance ever since.

The Reckoning does not position the 1950s as a golden era; on the contrary, the 1950s are framed as the source of original sin, when Detroit sowed the seeds of greed and complacency that it would reap three decades later. (You might have guessed that much from the title.) At that time, the Big Three automakers functioned as a cozy oligopoly, with too much scale to ever be threatened, churning out giant gas-guzzlers that were forced on captive consumers at inflated prices.

True, the unions ensured that the workers were paid well, but this came at the expense of other American workers who were forced to pay more for their cars. Schoolteachers and clerks were gouged and the unions divided the spoils with wealthy shareholders and private-jet flying executives. At one point, Halberstam describes a scene in 1946 in which Walter Reuther, the principled head of the UAW, demands a wage increase, but with an additional condition: the auto companies would eat the cost of the pay hike via reduced profits, instead of passing it along to consumers. The auto executives respond that they are willing to grant the wage increase but they will not be told how to pay for it, and after some consideration, Reuther reluctantly accepts. It is suggested that from that point forward, the union becomes complicit in the whole doomed arrangement.

None of this is to say that Halberstam’s narrative is entirely correct, and he himself acknowledges that there were other forces at work that caused the decline of the American auto industry. But it is notable that people in the 1980s seemed to have a much clearer view of the timing and causes of deindustrialization, while modern commentators who venerate the immediate postwar era often get the facts and timelines hopelessly garbled.

The theory that the 1950s were a golden era of corporate investment, one that came to an end with the advent of stock buybacks in the 1980s, is similarly without merit. We have talked about stock buybacks at length before: they are nothing more than a substitute for dividends, a more flexible way to return cash to shareholders that doesn’t entail the same implied commitment that a regular dividend does. In substance, the shift from dividends to stock buybacks is simply a case of moving from six of one to half a dozen of another.

Dividends and buybacks are substantially economically equivalent: an investor who uses the dividends that she receives to buy more stock (as we all do in our 401ks, by default) ends up in almost the exact same place if a company chooses to withhold all dividends and uses the cash to buy back stock (on behalf of her and her co-investors).2 In both cases, excess cash ends up in the hands of investors who want out, and the remaining shareholders own a larger stake in future cash distributions.

We previously found that companies had always retained whatever cash they felt they could invest productively, which for mature companies was generally 20%-40% of earnings. The remaining 60%-80% was returned to shareholders in the form of dividends, to reinvest in other productive projects (or to consume). GE followed this pattern closely: the above graph from GE’s 1960 annual report shows that GE had paid out 67% of earnings as dividends throughout its history to that point, dating all the way back to 1899.

In the 1980s and 1990s, companies started cutting dividends and redirecting the extra cash toward stock buybacks, and today, companies pay out about 30% of earnings in dividends and about 40% in buybacks. Here, GE simply followed the broader corporate trend of the time. GE’s 2000 annual report, the last of Welch’s reign, shows that over the previous three years, 44% of earnings went toward dividends, while 28% of earnings went toward net buybacks, for a total payout ratio of 72%, about the same as it had been for the previous century.

The funny thing is, Jack Welch hated stock buybacks. As William Cohan wrote in Power Failure, Welch (wrongly) believed he could create more value by using spare cash for acquisitions. In the late 1980s, he caved to investor pressure and started buying back stock. Part of the idea behind dividends and buybacks is to restrain CEOs like Jack Welch; if Welch had been able to retain (and squander) earnings as he pleased, GE would have been an even bigger disaster.

The idea that the 1950s were a period of especially high corporate investment is also contradicted by the data; in fact, it is the opposite. In aggregate, business investment, defined as spending by private businesses and nonprofits on structures, equipment, and intellectual property, was only 10%-11% of GDP in the 1950s, a figure that has risen to 13%-14% of GDP today.

Again, the anecdotes match the data. Remember, people in the 1980s were bemoaning how the big automakers squandered their mid-century advantage by underinvesting in research and development, not managing to design a single competitive small car between them. At GE, journalist Thomas O’Boyle found that management shut down a lot of promising research projects in the 1950s, which caused many of their best scientists to flee to Motorola.

The corporate culture in modern big tech is almost the exact opposite, to the point where a hugely profitable company like Amazon won’t return a dime to shareholders year after year and is applauded by Wall Street, and the CEO of Microsoft will casually drop that he is “good for $80 billion” in AI investment this year.

As an aside, Gelles seems to be confused by the entire concept of profit and private investment, which in fairness is commonly misunderstood. He bemoans the fact that “[b]efore Welch, corporate profits were largely reinvested in the company or paid out to workers rather than sent back to stock owners…[after] Welch, a greater share of corporate profits was going to investors and management.”

First of all, corporate profits are what’s left over after paying management and workers, so neither statement makes any sense. Putting that aside, corporate profits reinvested in the company still very much belongs to stock owners. That’s what private investment is — you put in money today in the hopes that you can take out more money tomorrow. If Apple invests money today to develop a new iPhone, their stock owners will profit tomorrow when that iPhone is sold to consumers at a profit. If stock owners want to take cash out of the company in the form of buybacks and dividends, it is because they believe (rightly or wrongly) that they can invest it more effectively in outside projects than management can invest it in internal projects. It is not less greedy (or more greedy) to invest retained earnings internally.

Finally, the idea that the GE of the 1950s was a model of corporate citizenship is pretty laughable. In fact, GE in that era was so corrupt that they were caught leading one of the biggest price-fixing rings ever, a scandal that Fortune dubbed “The Incredible Electrical Conspiracy”. That case, in which the co-conspirators rigged the bidding process for electrical equipment, ultimately raising electrical bills for ordinary Americans, resulted in three GE executives being sent to prison and a round of highly publicized congressional hearings.

John Brooks wrote a hilarious article for the New Yorker reporting on those hearings, in which it became apparent that GE’s participation in the fraud was sanctioned by its senior-most executives. However, it also became clear that the top brass at GE had the foresight to create enough plausible deniability so that if the hammer fell, they could plead ignorance and make their underlings take the blame — which is exactly what they did.

This led to a farcical display in which a parade of GE divisional managers, all making over a million dollars a year (in 2025 terms), unanimously insisted to Congress that the criminal conspiracy for which they had recently been convicted of spearheading was actually just a huge misunderstanding, nothing more than a series of innocent miscommunications between a bunch of bumbling but well-intentioned middle managers. The Congressmen running the hearings understandably found this position implausible and insulting, which led to exchanges like this:

SENATOR KEFAUVER: Mr. Vinson, you wouldn’t be a vice-president at $200,000 a year [over $2 million in today’s money] if you were naive.

MR. VINSON: I think I could well get there by being naive in this area. It might help.

Gelles concludes by profiling two companies which he feels best exemplify the book’s thesis and a better path forward: Unilever and PayPal. He argues that the chief executives of these two companies rejected the pressure to lay off workers and limit wage growth and both companies thrived as a result.

This seemed to be true at the time of the book’s publication in early 2022, but since then, both companies have struggled mightily, and both have been forced to undergo GE-style layoffs and restructuring. Under pressure from activist investor Nelson Peltz (who a few years prior also targeted GE), Unilever has engaged in successive rounds of management layoffs to reduce bloat, most recently announcing plans to cut 6% of its workforce and spin-off its ice cream business. For its part, PayPal eliminated 7% of its workforce in 2023 and cut another 9% of its workforce in 2024.3

Why doesn’t Gelles’ thesis align with the evidence? Intuitively, his logic seems very compelling, and almost tautological. If manufacturing employment is falling, and corporate executives determine how many manufacturing workers to hire, then reversing the decline is a simple matter of jawboning:4 pressure CEOs to hire more workers, and domestic manufacturing will rise again. Judging by the popular response, readers seem to agree: the book was an NYT bestseller, and has thousands of glowing reviews on Amazon and Goodreads, describing the book as provocative, insightful, and even profound.

The fundamental problem with the theory is that it’s kind of — how do I put this politely? — completely ludicrous? The implicit model of the economy here is that CEOs decide how many people to hire and what they get paid and their actions determine economy-wide incomes and corporate profits. But that’s not at all the system we live in. In reality, we, as consumers, pay the wages of the workers that provide our goods and services. We choose what products we want to buy, and the businesses that make those products take the money we give them and use it to pay wages and suppliers, with a sliver left over for profit. (In manufacturing, profit margins average around 9%.) Companies pay their workers, but we are the ones who decide which companies get paid.

Corporate managers have some discretion, but they work within very tight constraints, largely determined by consumer tastes, technology and competition. Consider GE’s housewares division, which at the time of Welch’s appointment made small electrical appliances like toasters and curling irons, mostly right here in the USA. In 1982, Welch shut down an uncompetitive steam iron factory in Ontario, Calif. that prompted 60 Minutes to accuse him of putting profits before people, and in 1983, he sold the whole housewares division to Black and Decker.

Housewares aren’t often manufactured in the US anymore. Apparently, 99.5% of our toasters today are made in China, something J.D. Vance made a campaign issue.5 Is this Jack Welch’s fault? Probably not. Welch claims to have simply foreseen that housewares had no barriers to entry and would eventually be overrun by cheaper competition from Asia, and sold the business when Black and Decker showed an interest. Whether or not he saw the future as clearly as he claimed, Welch was correct that the long-term future of housewares would mostly be determined by external shifts in technology and trade that would permanently change intrinsic economics of the business, and not by anything corporate managers could do.

The location of physical manufacturing tends to be a function of the characteristics of the product itself — labor input, transportation costs, tolerance of long turnaround times, supplier networks, and so on. Executives have to make some predictions — will people pay a premium for a Made in the U.S.A. toaster? — but over a longer period of time, consumers vote with their wallets, and companies that make bad predictions will eventually be forced to exit their bad bets.

If all toasters today are made in China, after having previously mostly been made in America, that probably tells us more about the nature of toasters and the demands of American consumers than it does about Jack Welch, in the same way that domestic assembly of foreign cars tells us more about the nature of cars than it does about the pro-U.S. attitudes of foreign auto executives.

Gelles would have done well to review the extensive literature on deindustrialization.6 Economists that seriously study the issue generally find that falling manufacturing employment doesn’t have much to do with corporate management, and surprisingly, it doesn’t even have that much to do with trade (although a few dissent on this latter point). Instead, 80% - 90% of the decline can be traced to higher manufacturing productivity combined with stagnant demand, the same thing that reduced employment in agriculture in a previous generation.

This is why the decline in manufacturing employment dates all the way back to WWII: greater manufacturing efficiency generally translates to lower manufacturing employment, because there is some upper limit on the number of cars and light bulbs we want to consume. We take the dividends from more efficient manufacturing and redeploy it to deliver services, especially in education and health care:

Jack Welch may be guilty of many sins, but he is probably off the hook for destroying manufacturing employment in the Upper Midwest. The timing doesn’t line up, and the American shareholder capitalism thesis hardly explains why almost every major industrialized country has a rust belt. The Man Who Broke Capitalism pushes an appealing narrative, but falls apart under any kind of scrutiny.

The Man Who Broke Capitalism is a prime example of what the late Michael Crichton dubbed Gell-Mann Amnesia:

Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward — reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

The Man Who Broke Capitalism is very much journalism of the “wet streets cause rain” variety, just expanded into a book. In this particular case, the causal relationship isn’t precisely reversed, but it is totally misidentified: Gelles has sectoral employment being dictated by executive fiat, rather than by consumer demand. (Although you could go one step further and hypothesize that the decline of American manufacturing led to executives like Jack Welch, rather than the other way around, in which case his theory actually would be exactly backwards.)

It seems unfair to single out one book or one journalist for bad economic reasoning, when similarly misguided articles and books come out with such regularity. Almost thirty years ago, Paul Krugman wrote an eviscerating review of a widely lauded pop-econ book written by a very serious journalist who committed the same logical error as Gelles did here — the fallacy of composition — a book which also apparently contained a number of incorrect “facts” as well as the mistaken conclusion that we would imminently stop creating new service jobs. (Nope, not yet, not by a long shot.) Thirty years from now, journalists will still be writing pop-econ bestsellers with the same elementary mistakes and those books will still be received with widespread praise.

In the last edition, we talked about the importance of paradigms in facilitating our understanding of the world. A paradigm, as defined by Thomas Kuhn, is a unified predictive framework that explains what we observe in a given field; for example, after Copernicus and Galileo, astronomers used a heliocentric model of the solar system to predict the positions of the planets. Kuhn explained that even smart people tend to get locked into obsolete paradigms (like geocentrism) — paradigms that are completely backwards, and contradicted by the facts — because these paradigms also dictate how we perceive and evaluate new evidence and thus prevent us from updating our beliefs.

If you look at a job posting for a journalist at a top newspaper, you see a lot of reference to ability to tell stories and cultivate sources, but almost never any requirement that the journalist have any education in, or real understanding of, the field that they will be covering. In theory, this gives reporters the advantage of impartiality — as an outsider to the field they are reporting on, they can approach it with a skeptical eye — but in practice, it is hard to accurately describe something you don’t really understand. And there is no reason to believe that a journalist will gain an understanding of a field simply by reporting on it over time. Remember, we tend to get locked into long-held paradigms without even being conscious of it.

This goes some way to explaining the phenomenon of Gell-Mann Amnesia: no matter how much time and effort a journalist puts into reporting a story, the story will end up being shaped by the paradigm in the journalist’s head. This is true even if the journalist consciously avoids analysis — every story has to package the facts one way or another. If the paradigm is backwards, the story will be backwards. Hence the proliferation of books and articles by experienced journalists that confidently assert that wet streets cause rain.

This brings us back to Gelles’ howler at the beginning, the claim that the federal minimum wage used to be $24 per hour, adjusted for inflation. The popular claim that journalists are lying liars who are out to scam the public is greatly mistaken. Journalists, like all of us, see what they expect to see. It is actually easy to trace the source of this particular error: a think tank pushed a widely-disseminated claim that the minimum wage would have been $24 per hour if it had “kept pace with productivity”, a calculation which was itself the product of a spreadsheet error and incorrectly mixing price indexes.

One can understand how a well-intentioned writer might come across a good factoid, misinterpret it, and publish the result because his mental model of the world told him it was almost certainly true — people tend to overestimate the living standards of the past. Once we think we have a good framework for understanding something, our brains are very good at hallucinating “facts” that fill in the gaps.

At this point you might be thinking, hang on, Not All Journalists. Which is true! A few reporters, like John Brooks, manage to get the key frameworks of the field they cover straight in their heads, even if they don’t know the details like an expert. But for every John Brooks there are a dozen other business journalists who regularly mix up basic concepts like revenue and profit. Again, journalists are hired for a different skill set.

It is hard to blame newspapers or journalists for this state of affairs. Exclusively hiring journalists with subject matter expertise would undoubtedly be much more expensive, and it is not clear that enough mainstream newspaper subscribers would pay a significant premium to read such a publication. Nor is it clear that it is even possible to hire very many people who have subject matter expertise and the ability to write clear, readable prose and the ability to source and report relevant stories, at any reasonable price.

Gelles starts to pull at an interesting thread at one point in the book: he observes that even though most of Welch’s proteges crashed and burned after leaving GE to take other CEO jobs, a few rejected the philosophy of their mentor and went on to great success in their new roles. One might ask: what would cause them to reject the teachings of the great Jack Welch?

We have talked before about Welch’s philosophy, which could be summed up as: “find a way to report quarterly earnings that move in a smooth upward trajectory, and success will follow”. One can understand the basic intuition: executives observe that companies like Coke and Microsoft report quarterly earnings that move in a smooth upward trajectory, and investors award them with a premium valuation. They also notice that when they report a bumpy quarter, investors get really mad. Therefore, the solution is to synthetically create earnings that move in a smooth upward trajectory (ideally while beating sandbagged earnings per share estimates by a penny every quarter), which will garner them a high stock valuation and perpetually happy investors.

This is very much a “wet streets cause rain” view of the world. In reality, building and operating a successful business is what leads to sustainably growing quarterly earnings, not the other way around. Look at the most successful businesses in modern Corporate America, companies like Apple, Google, and Berkshire – how much effort do any of them put into managing or smoothing their quarterly earnings?

Even if earnings smoothing were mostly benign, it is difficult to see how it could possibly be in the interest of long-term investors. Over longer holding periods, total investor returns are chiefly determined by the cash that a business generates for its shareholders and how that cash is invested, and have very little to do with the stock valuation in the meantime. In fact, a higher valuation actually works against the interest of long-term investors that plan to reinvest their cash distributions along the way, forcing them to buy stock at inflated prices. GE’s brief period trading at a 50x PE in 2000 did absolutely no good for any long-term shareholder that stuck with the company.

In practice, earnings smoothing is not benign at all, as Welch’s GE tenure demonstrated. Good accounting is part of the feedback loop that managers rely on to identify and solve important problems. To paraphrase Buffett, the problem with bad accounting is not just that management is lying to investors, it is that management is lying to themselves. Cohan’s Power Failure is replete with anecdotes where a narrow focus on hitting quarterly earnings targets resulted in real damage to the business, culminating with one memorable tale where GE sold a power plant on credit in Angola to book earnings for the quarter, with little hope of ever collecting any cash.

GE’s core industrial businesses were cyclical in nature, so to create smooth earnings, Welch bought into businesses that had a lot of short-term accounting discretion — mostly in finance and insurance.7 When the industrial businesses produced a shortfall in a quarter, Welch could book an offsetting gain by selling an appreciated real estate asset or reducing insurance reserves, and keep overall earnings on track.

However, finance and insurance are very competitive, commoditized industries. They are particularly dangerous for managers with a flawed understanding of their business: catastrophic mistakes made today may not be revealed for several years, and during the interim, management will double down on their failed strategies. Finance and insurance would be a major part of GE’s downfall in the 2000s: the economics of the finance businesses would be permanently impaired following the 2008 financial crises, and GE eventually took a $15 billion bath on the insurance business.

For the majority of Welch’s tenure, earnings smoothing didn’t even get Welch the high multiple he desired. Cohan notes that in the late 80s and early 90s, a decade into Welch’s twenty-year reign, GE’s stock struggled in the face of investor skepticism about its foray into finance and aggressive acquisition strategy.

Welch and his successor battled critics all along the way, people who simply pointed out the obvious, that Welch’s approach to business was completely backwards. GE faced attacks from short-sellers in the early 90s, and in 1994 the WSJ ran a front-page story about GE’s approach to smoothing earnings. In 1998, SEC Chairman Arthur Levitt made a speech entitled The Numbers Game, where he decried the GE approach of managing earnings (without naming GE specifically), which Fortune writer Carol Loomis followed up on with a feature on earnings management (naming GE) and a report on the trend of blue chip companies setting impossible long-term EPS growth targets (15% per annum was the standard goal at the time, i.e. a doubling every five years). In 2002, bond king Bill Gross criticized GE for its opaque disclosure and heavy reliance on short term debt and leverage (which would bite GE six years later), and in the same year, Congress finally passed Sarbanes-Oxley, which tightened accounting rules and enforcement.

In hindsight, Welch owed much of his success to a unique confluence of external forces that lifted GE’s businesses in the 1980s and 1990s — limited financial regulation, an unprecedented bull market, and a one-time boom in gas-fired power plants — which was more than enough to offset that damage wrought by his management approach. His successor would not be so lucky.

Taking all of this into account, it might be better to invert our earlier question, and instead ask: what made Welch’s philosophy so irresistible to other executives?

Contrary to popular belief, Jack Welch didn’t invent earnings management, or even popularize it. Managers have been smoothing reported earnings as long as companies have been issuing regular income statements. In More Than a Numbers Game, Thomas King cites a 1912 editorial in the Journal of Accountancy stating that managers had been playing games with depreciation to even out earnings, taking higher charges in fat years and smaller charges in lean years. (This was before modern accounting rules standardized depreciation.)

King then describes the curious history of the LIFO tax break. Beginning in 1939, Congress allowed companies to use the LIFO (last-in, first-out) inventory method to calculate earnings for tax purposes, as long as the same method to report earnings to shareholders. This was a generous tax break for any company that cared to take it — the LIFO method would create lower reported earnings, particularly during periods of high inflation (like the 1960s and 1970s), and lower reported earnings translate directly to lower cash taxes. LIFO adoption should have been almost universal in any industry with inventories and rising input costs — in 1981, GE reported that LIFO had saved it $1 billion in taxes to date — but remarkably, many companies declined to adopt it, instead choosing to report higher accounting earnings to investors and pay extra tax.

This curious behavior with regard to LIFO was no anomaly. King also cites a 1983 article by Arthur Wyatt, a partner at Arthur Andersen, recounting the many contortions his clients had gone through to report higher, smoother earnings — ignoring LIFO, currency hedging, off-balance sheet borrowing, and favorable acquisition accounting treatments — none of which created value for shareholders, and most of which actually cost money. And for what? Wyatt observed that under the (then-)newfangled efficient markets hypothesis, none of this window dressing should make any difference to investors. Sure enough, empirical research at the time was already finding that markets are very good at seeing through these types of accounting games, something Welch would soon discover for himself firsthand.

Managers were receptive to Welch’s philosophy because it aligned with what they already believed. They had long accepted as self-evident that it was important to manipulate reported earnings, and Welch’s approach at GE was simply a new variation on an old theme. Other executives listened to what Welch had to say, and what they heard resonated with them — they found Welch to be insightful and profound.

Here is what Warren Buffett wrote in 1988:

[T]he heads of many companies are not skilled in capital allocation. Their inadequacy is not surprising. Most bosses rise to the top because they have excelled in an area such as marketing, production, engineering, administration or, sometimes, institutional politics.

Once they become CEOs, they face new responsibilities. They now must make capital allocation decisions, a critical job that they may have never tackled and that is not easily mastered. To stretch the point, it's as if the final step for a highly-talented musician was not to perform at Carnegie Hall but, instead, to be named Chairman of the Federal Reserve.

It is not useful to think of CEOs as being “good at business” any more than it is useful to think of ballplayers as being “good at baseball”. Baseball skill consists of many different dimensions — hitting, fielding, pitching, etc. — and different players succeed by combining these skillsets in different ways. We see lumbering sluggers that struggle in the field and spry shortstops that are little threat at the plate. Similarly, business skill has many different dimensions — sales, engineering, capital allocation, etc. — and even the most successful executives excel at no more than a few of them.

Big corporations gain tremendous efficiency from specialization, which allows employees to be highly productive without having to understand or excel in every aspect of the business. A star employee of a packaged food conglomerate might start his career as a junior marketer working on a small brand, and over the years develop his marketing skills and management skills and knowledge of the food industry to the point where he becomes Chief Marketing Officer of the entire company.

As Buffett observed, most companies pick their CEOs from a pool of these super-successful specialized middle managers: perhaps the Chief Marketing Officer, or the Chief Financial Officer, or even the General Counsel. These are invariably high-caliber people, but they have been selected for their expertise and experience in a narrow area (and probably some talent for storytelling and self-promotion). When they are promoted to CEO, they find themselves in charge of a lot of crucial areas they have no experience with, like capital allocation, investor relations, financial reporting, and overall corporate strategy.

Imagine yourself in this situation, deciding what to do about investor relations, a field that you have no experience in whatsoever. Your first inclination is that investor relations looks like a marketing problem. You know all about marketing, even if that is not your specific area of expertise. You got promoted by marketing yourself, telling a good story and putting your accomplishments in the best possible light. Your company probably does the same when marketing their products to consumers. You conclude that your company’s stock is just another product to be marketed, and you just need to figure out what kind of messaging will trick investors into giving you a high stock price. At that point you start devoting brain cycles to sandbagged earnings targets, smoothed and/or inflated earnings, and adjusted EBITDA.

None of this will work at all because the whole theory is based on a false analogy: reporting to investors is nothing like marketing to your boss or to consumers. The stock market may not be perfectly efficient, but it is incredibly naive to imagine that it will be fooled by any of these simple gimmicks. You can sandbag your earnings targets, but investors will still benchmark you against your peers. You can inflate reported earnings through addbacks, but it’s easy for investors to identify them and just subtract them back out.

The thing is, CEOs get trapped in that backwards paradigm, because the evidence and logic that disproves it is so counterintuitive. CEOs will argue that they’ve met their investors and know that they aren’t that smart, and won’t listen to any explanation of why the wisdom of the crowd makes that irrelevant. They will point to companies whose stock prices crash after missing earnings by a penny, but they fail to think about it through the lens of rational expectations: if a company is known to pull out every legal trick in the books to meet quarterly earnings targets, then the market will realize that an earnings miss by even a penny is a crucial signal that something has gone seriously wrong inside that company (as was the case when GE finally missed earnings in early 2008).8

Here we should acknowledge: Not All CEOs. We previously looked at David Cote, who understood all of this stuff correctly, and who after being pushed out of GE went on to great success running Honeywell. But for every Cote there are a dozen Welch disciples who get all of this completely backwards and refuse to consider any arguments to the contrary.

American corporate structure has evolved to compensate for this specialization problem. The Man Who Broke Capitalism implies that Jack Welch and GE was the harbinger of the modern approach to business, but in truth, it was the last gasp of an obsolete approach, the 1960s-style diversified conglomerate. GE failed in the same way all of its peer conglomerates failed, extending into industries it didn’t understand. For example, GE’s longtime rival in the electrical equipment space was Westinghouse, which collapsed in the early 1990s after making billions in bad loans to real-estate developers. Even before GE started to collapse, its antiquated structure was being lampooned on 30 Rock: Alec Baldwin played GE’s Vice President of East Coast Television and Microwave Oven Programming.9

Companies today are more focused, and CEOs today are more constrained. Long gone are the days when a department store could jump into insurance or NBC could start renting out cars. Today it’s capital allocation by formula, no transformative M&A, and one false move will have activist investors at your throat. Even if a CEO is working from a backwards paradigm, it doesn’t matter, because his job isn’t to set a bold new corporate strategy, it is to produce and market widgets and mail the profits back to investors. Perhaps the modern CEO chafes at being treated like a super-middle manager, but at least the pay isn’t half bad.

Pop-econ books like The Man Who Broke Capitalism and Welchian approaches to management and reporting are two examples of the same common phenomenon: smart people who become seduced by an intuitive-but-backwards framework, and are inspired go forth writing books and making speeches confidently asserting that wet streets cause rain. Because the framework is so intuitive, they quickly find an audience, and that feedback loop eventually inspires self-destructive behavior from anyone unfortunate enough to be inspired to put theory into action. In a sense, The Man Who Broke Capitalism ends up the perfect book for understanding Jack Welch and GE — if you can understand why smart people fall for that book, then by analogy, you can figure out why smart people fell for Jack Welch. At that point, you can go one step further, and understand why so many smart people support the self-destructive economic policies being promulgated today.

Perhaps this is just a hazard of selecting our CEOs and journalists and political leaders more for their storytelling ability and self-confidence than for their expertise or correct knowledge of how things actually work. (Not to knock storytelling and self-confidence as assets, but they tend to magnify output rather than supply any intrinsic utility themselves. Storytelling and self-confidence in the service of bad ideas are what produce GE- and tariff-scale disasters.) In any case, we can try to “name it and tame it”; if we learn to identify these popular “wet streets cause rain” frameworks, we can better resist them.

Now, that sounds like obvious nonsense, the kind of aw-shucks claim a billionaire might make to distract from his 180 IQ or his wealthy father-in-law or his once-in-a-generation luck. There is no possible way that there is any meaningful alpha that can come from understanding introductory economics, much less enough alpha to make someone the 317th richest man in the world. Zell must have either been spinning a self-serving narrative, or he was completely mistaken. Right?

Just for fun, let’s humor him and take a look at his story. He started managing apartment buildings while he was an undergrad at the University of Michigan, in the early 1960s. He realized that real estate in second-tier markets like Ann Arbor was undervalued, so he leveraged that into a promising career managing and investing in properties in cities like Toledo and Reno.

By the early 1970s, he thought that US real estate was getting irrationally overbuilt, because of the organizational incentives in place at construction and lending institutions at the time (emphasis mine):

First, there were dedicated real estate lenders. Each bank or insurance company would start the year with a set amount of lending capital, say $5 billion to invest. The branch would allocate a certain amount to bonds, a certain amount to equity, and a certain amount to real estate. The rule was that at the end of the year, if each department hadn’t invested its full allocation, the capital had to be returned to the parent company. Not surprisingly, the departments never gave anything back.

…

Other new money had also flooded the industry from the early real estate investment trusts (REITs)...The construction REIT industry went from $1 billion to $21 billion within three years, fueling massive real estate development.

…

This has always been a fatal flaw in US real estate: the volume of development has been related to the availability of funds, not to demand. The industry has a long history of overbuilding when there’s easy money, without regard for who will occupy those spaces once they’re built.

…

My thesis was that over the next five years, we would have the opportunity to make a fortune by acquiring distressed real estate…Everyone thought I was nuts. After all, occupancies were still over 90 percent. Absorption was high. Companies were hiring. It was one of many times I would hear people tell me that I didn’t understand.

Zell raised a fund to invest in distressed real estate, and bided his time. In 1974, the real estate market did indeed crash, and Zell had his first opportunity.

Zell’s Econ 101 analytical framework was really quite simple. He would look for good buildings trading for a fraction of replacement cost — replacement cost being the estimated cost to build a comparable asset from scratch. He reasoned that these assets would be insulated from new competition for a long time: no one can raise $20 million to build an apartment building that will be worth $10 million at completion.

In the absence of new construction, he could be assured that prices would rise, as long as demand kept growing. This second part of the equation was a sure thing in his era, as the Boomers came of age and population shifted toward cities. A growing number of households chasing a stagnant supply of apartments would inevitably push up rents, which would in turn make the buildings more valuable.

He understood that eventually the value of the apartment buildings would surpass replacement cost, at which point developers would start building new apartments, putting a lid on future appreciation and rent growth. But by then he would have made a large profit, both from the appreciation of the asset and the cash flow he collected in the meantime.

His approach was hardly a secret: he laid out this exact thesis in a famous 1975 article in Real Estate Review entitled The Grave Dancer, which Zell later adopted as his nickname. And yet, it did not seem to attract much competition. In the mid-1970s, Zell’s investment firm bought billions of dollars of real estate of all types – residential, retail, and offices — all at a fraction of replacement value. Sure enough, construction stopped, asset values recovered, and he made a fortune.

Zell realized that his replacement cost framework would apply to all long-lived capital investments, not just real estate. In the mid-1980s, his firm bought a fleet of railcars, which were then trading at half of replacement cost, following a construction boom a few years before. Same model, same bet, same result. In 1992 he flipped his railcar fleet to GE for a huge profit.

This is not to say that Zell’s fortune can be entirely attributed to simply buying quality assets trading at a discount to replacement cost. Zell was a savvy operator and a talented dealmaker who boosted his returns through leverage and clever structuring. But at the same time, throughout his career, this was Zell’s big shtick: “to look for opportunities in places where others were ignoring the rules of supply and demand”.

There is nothing more basic than the rules of supply and demand, so why didn’t more investors see what Zell saw? Here we have to speculate a bit. We should start by observing that the replacement cost model doesn’t feel right, or at least it probably wouldn’t have felt right to most market participants at the time. Consider:

1. It just seems too simple. How could something as complex as the market for (say) commercial office space could be so well described by such a parsimonious model? What about interest rates, and demographic trends, and shifts in sectoral employment, and international money flows, and oil prices? How could any useful model not consider these important inputs?

2. All of the traditional indicators of real estate industry health — occupancy rates, rents, and so on — were flashing red. It would seem reckless to buy into an industry that is struggling so badly.

3. By the same token, you simply shouldn’t buy into an asset class in freefall. Don’t try to catch a falling knife. The trend is your friend.

4. Zell’s model seems to be based on a paradox. It relies on the market being inefficient enough to not fully understand this model — to first overbuild when money is flowing freely without proper consideration of actual future demand, and then later to shy away from competing for distressed assets trading at a deep discount – but also efficient enough to stop building new assets when prices are low.

5. Zell is in his early 30s, and anyone that young just lacks the experience to understand what he is doing.

We tend to learn about investing the same way we learn any other skill: we get a sense for what works and what doesn’t based on the limited feedback we receive. This develops into a set of heuristics and intuitive models that help us navigate the world. People of the same generation tend to have similar experiences and similar approaches and often converge on a collection of generally accepted investing folk theories.

On one hand, you can see how these folk theories probably are a little useful. Simple models are often too good to be true. Some asset classes in freefall actually are in terminal decline. Young people really do lack experience and are more likely to make naive mistakes. In a market with less experienced counterparties, these heuristics will be a good guide to making money.

On the other hand, these heuristics are just mental shortcuts, not ironclad models of reality. These objections are all demonstrably false. Interest rates and economic cycles matter for asset values in the short term, but in the long term, we can expect asset values to converge on replacement cost, as long as demand growth is there. It is actually often very profitable to invest in a fundamentally healthy industry experiencing a temporary setback. Market participants really do stop building when asset prices are below replacement cost — you will have a hard time finding a real world counterexample. And Zell’s relative inexperience was actually an asset here: while experienced investors picked up the first four heuristics and were bound by them, Zell was not similarly constrained and came up with a more accurate model.

Investing folk theories are simple and easy to apply, but they tend to be unreliable and contradictory. They often lead to Econ 101-flavored conclusions, but sometimes they diverge, as we saw here. In a competitive market, they can be exploited by someone applying a more rigorous (but still basic) Econ 101 analytical model, as Sam Zell did.

This still leaves one open question: Zell was out raising money behind his thesis for years, and he even went so far as to publish it in a trade journal. Why didn’t other market participants pick up their Spring 1975 copies of Real Estate Review, realize their mistake, and take their properties off the market? Or better yet, jump in and buy with both hands?

One explanation might have to do with institutional imperatives: under the rules of the time, banks had to find a way to restructure their outstanding bad loans. But on the other hand, Zell was adamant that his view was always distinctly in the minority, and he doesn’t seem to have had much competition in the market.

The more likely answer lies in how we model reality. It is very difficult to navigate a world with noisy feedback and uncertain attribution, so our default approach is to extrapolate some useful heuristics from our personal experience. If we have some success using these heuristics in the real world to make money, they become etched in our brains as The Inviolable Laws of Investing. Anyone who proposes an investment approach that contradicts one of our Laws is subconsciously written off as an idiot.

In Richard Feynman’s introduction to his lectures on physics, he reminds his students that what we think of as the “laws of physics” are really approximations, the best models we have based on what we have been able to verify through experiments so far. The best approximations we have to model the physical world have evolved considerably over time, from Aristotle’s notion that all objects have a natural tendency to be pulled toward the center of the universe (which he held to be the center of the earth), to Galileo and Newton and beyond.

But our brains are not wired to think of the world that way. We can accept that Einstein is subject to future revision, but Bob’s 6th Law of Trading must be accepted as an ironclad axiom, because Bob used it one time to trade pork bellies in 1985 and made enough money to buy a Firebird. As Charlie Munger put it:

…[T]he human mind is a lot like the human egg, and the human egg has a shut-off device. When one sperm gets in, it shuts down so the next one can’t get in. The human mind has a big tendency of the same sort.

The market is made up of a lot of smart people, but a lot of smart people have similar experiences and thus they independently derive similar heuristics with similar blind spots. And what is a market inefficiency, if not a collective blind spot that cannot easily be arbitraged away in a short period of time?

Sam Zell’s story seems especially unrealistic when we hear it today because he came of age almost two generations ago. We are more likely to understand Econ 101, and more importantly, we know the stories of people like Sam Zell who made billions taking this exact approach to distressed investing. But history at least rhymes, and it is almost certain that the biggest money-making opportunities today are sitting in plain sight, in the market’s blind spot.

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We talk here sometimes about the high cost of housing in major coastal American cities, like New York, San Francisco and Los Angeles. The story goes like this: At various times between the 1960s and 1990s, most of these coastal cities instituted barriers to new housing construction. Since then, the population of the country has grown, broader economic shifts have pulled people toward big cities (fewer workers in agriculture and manufacturing and more workers in services and knowledge-based industries), and changes in preferences and lifestyles have raised demand for urban living (people getting married later and wanting to live in dense, amenity-rich cities in the meantime).1

In short, the demand to live in these big cities has steadily grown, but the supply of housing has hardly budged at all. Most housing in the US is rationed through market forces – that is to say, it gets auctioned off to the highest bidder – and as a result housing prices in these big cities have exploded over the last three decades. For example, housing prices in the Bay Area have risen 2.7x since 1987, adjusted for inflation.

High housing prices are a policy choice. We can understand this by using a simple replacement cost model, very similar to the one Sam Zell used. Without barriers to housing construction, we would expect housing prices to cap out at around replacement cost. (Yes, land is still finite, but new apartment buildings economize on land by building upwards, so land only makes up 10% - 20% of total construction costs.)

When housing prices rise above replacement cost, developers capitalize by constructing new buildings, and pocket the difference between the market value of the completed building and the cost of construction. New construction sets off a game of reverse musical chairs: property owners need to fill their apartments to generate income, but the number of people who want housing is fixed. As a result, property owners have to undercut each other on price to attract occupants. This continues until price falls to replacement cost, and it is no longer profitable to build new housing.

(This model excludes a prominent feature from our prior story: the tendency of developers to overbuild and push housing values below replacement cost, at least temporarily. As Sam Zell put it, “I think that to stay in business, most developers must get 50 percent of their returns from real cash flow and the other 50 percent from the intangible benefit of seeing their phallic symbols rise out of the ground. Otherwise I can’t see the reward.” For simplicity, we can ignore this little detail – it’s sufficient to know that absent barriers, developers will build like crazy when the conditions justify it.)

This simple model holds up very well empirically. Ed Glaeser and Joseph Gyourko pulled the data for American cities over the last decade, and produced the chart above, with the ratio of housing prices to replacement cost on the y-axis and the amount of housing construction on the x-axis. The only cities with housing prices meaningfully above replacement cost are those that have growing demand and strictly limit new construction, such as San Francisco and Honolulu, where housing prices are triple what they should be.

Other cities build enough to meet demand, keeping housing prices in line with or below replacement cost. Glaeser and Gyourko note that there are two major groups: Sunbelt cities that are attracting new residents but build enough to absorb demand while keeping prices at replacement cost, and Rust Belt cities that aren’t attracting new residents and don’t need to build to keep prices below replacement cost.

It should be no surprise that this simple model matches reality so well: if it didn’t, there would be cities in which there was instant free money available to any developer who wanted to pop in and start building. The world is not perfectly efficient, but it’s more efficient than that.

If the solution to high housing prices is as simple as legalizing construction, and most cities do it successfully, why are there so many major holdout cities? We’ve talked about one theory relating to incentives: the idea is that since current homeowners actually profit from high housing prices, maybe they intentionally restrict construction for their own selfish economic benefit.

As the main explanation for high housing prices, this theory has a few major holes:

1. It requires an implausible level of economic sophistication from everyday citizens.

2. It doesn’t make sense if homeowners have kids (which most do), since young adults who will need housing are the main victims.

3. By this theory, voters are knowingly voting for an anti-social policy, which is pretty cynical.

Chris Elmendorf, Clayton Nall, and Stan Oklobdzija recently published a study that points to an alternative hypothesis: they did a series of surveys and found that almost all renters and a slight majority of homeowners say they want lower housing prices, but two-thirds of all surveyed believe that housing construction actually increases housing prices, or at the very least, has no effect. Most people place the blame for high prices on landlords and housing developers.

In other words, voters want housing prices to be lower, but in trying to achieve that goal, they unintentionally enact policies that actually push housing prices higher. When housing prices go up, they don’t connect it to the policies they just implemented; instead, they attribute higher prices to outside forces (e.g. gentrifiers, foreigners, and greedy landlords), and stick with the policies that just backfired, which causes prices to go even higher.

Is it really possible that people are that irrational? Well, let’s dig in a bit on how people form and change beliefs.

We usually imagine that science progresses in a very straightforward way: scientists do experiments, compile and analyze the resulting data, and use what they have learned to collectively update their theories, marching human knowledge forward.

In The Structure of Scientific Revolutions, Thomas Kuhn argues that this idealized model of scientific progress obscures the messy reality. He explains that scientific facts and theories are not free-floating, but rather are only understood as part of what he calls a paradigm, an accepted, unified predictive framework that explains observed phenomena within a specific field. For example, early Western astronomers explained the night sky within a paradigm that held that the earth was at the center of the universe and that the heavens were fixed, as explained by Aristotle and Ptolemy, and this was widely accepted for centuries, and actually did a good job of predicting the positions of the heavenly bodies.

According to Kuhn, the prevailing paradigm also dictates what kinds of scientific questions can be asked, and what even counts as legitimate evidence. Scientists don’t just do random experiments; rather, they rely on the existing paradigm to guide their research. Scientists devise experiments to extend and resolve puzzles within the current paradigm, never seeking to overturn it. For example, early medieval astronomers compiled tables based on the Ptolemaic model, and mostly did not try to come up with alternative models of the universe.

How, then, do scientific fields ever advance from one paradigm to the next? How do we get scientific revolutions? Well, over time, scientists think up more clever experiments, often with newly developed equipment (e.g. microscopes and telescopes), and their experiments, undertaken with the intent of refining and extending the existing paradigm, start to turn up more and more anomalies that require more and more exceptions and epicycles to be piled onto the accepted narrative.

At some point, newcomers to the field begin to suspect that there might be a better paradigm that explains all of this anomalous evidence. And then a Copernicus or Newton or Einstein comes along and provides a new paradigm that does just that. Eventually, scientists see that the new paradigm fits much better with the existing evidence (or if the evidence is still unclear, sometimes they just decide that the new paradigm is much cleaner and more promising) and abandon the old paradigm.

The feature of the Kuhnian model that is most pertinent to our discussion is his observation that most scientists strongly resist conversion to the new paradigm, even as the evidence piles up. In fact, many leading scientists never accept the new paradigm at all, going to their graves as loyal adherents of the old model.

Kuhn cites the story of Joseph Priestley, the 18th century English chemist who discovered oxygen. At the time, the paradigm chemists used to explain combustion was something called phlogiston theory. The idea was that any combustible substance contained an element known as phlogiston: burning wood would cause phlogiston to be released into the air, where it would be absorbed, and once the phlogiston was gone, you would be left with ash. This theory sounds backwards to us today, but in fact it did very well to explain most of the empirical results chemists were able to obtain at the time.

In 1774, Priestley was able to extract oxygen gas by shining sunlight onto a sample of mercuric oxide. Only, Priestley didn’t understand it as oxygen. He decided that it was air, but “more pure, more respirable”. After seeing how well it supported combustion, he concluded that it was “dephlogisticated air”.

It was a young French scientist, Antoine Lavoisier, who picked up on the real implications of Priestley’s discovery. At the time, Lavoisier was trying to figure out why substances like phosphorous gained weight when burned, a result that appeared inconsistent with phlogiston theory. A meeting with Priestley in Paris sparked his curiosity, and he set about reproducing Priestley’s dephlogisticated air experiments for himself.

Lavoisier eventually decided that Priestley had actually isolated a component of air, which he dubbed oxygen. This led him to conclude that phlogiston theory was nonsense, and to offer a rival (and correct) theory of combustion: that it was a reaction between a substance and oxygen in the atmosphere.

Here’s the funny thing: despite being immortalized as the man who discovered oxygen, Priestley stuck with phlogiston theory for the rest of his life. Many of his contemporaries resisted conversion to modern chemistry as well, even as Lavoisier demonstrated the power of his new theory by synthesizing water from hydrogen and oxygen, and then breaking it back down again. The phlogistonists always had some objection or pointed to some anomaly that kept them from abandoning the old paradigm. For his part, Lavoisier seemed to accept this:

Lavoisier did not expect his ideas to be adopted at once, because those who believed in phlogiston would "adopt new ideas only with difficulty." Lavoisier put his faith in the younger generation who would be more open to new concepts. Two years later, in 1791, the results were obvious. "All young chemists," he mused, "adopt the theory, and from that I conclude that the revolution in chemistry has come to pass."

The story of Joseph Priestley reflects a common pattern in science. Kuhn notes that it was many decades before the work of Newton or Copernicus was widely accepted. He also quotes Darwin who in On the Origin of Species accurately predicted: “I by no means expect to convince experienced naturalists whose minds are stocked with a multitude of facts all viewed, during a long course of years, from a point of view directly opposite to mine…[B]ut I look with confidence to the future, – to young and rising naturalists, who will be able to view both sides of the question with impartiality.”

Kuhn also quotes Max Planck on the subject, who said: “a new scientific truth that does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” Or, as Planck’s quote is more commonly paraphrased: science moves forward one funeral at a time.

This historical pattern is often misunderstood as relating to the stubbornness of old people, or as describing how human nature generally resists change or new ideas. In fact, age itself has nothing to do with it, except as a proxy for experience: Kuhn explains that older newcomers to a field are just as likely to discover and adopt new paradigms as their younger counterparts. It is only with experience and repetition that we become hemmed in by a paradigm.

And although devotion to old paradigms presents as an irrational resistance to change and new ideas, in fact it is nothing of the sort. Kuhn tells us that it is something inherently psychological, because what we see in the world is mediated by what we expect to see from previous training and experience. For example, he notes that because the Aristotelian paradigm held that the heavens were perfect, early Western astronomers never noted the existence of sunspots, even though they are visible with the naked eye under the right conditions. By contrast, ancient Chinese astronomers were not bound by any such paradigm, and so they regularly commented on the sunspots that they observed.

As an analogy, Kuhn uses the classic sketch of the duck/rabbit – the drawing is ambiguous enough that a viewer can see it as either a duck or a rabbit. In the same manner, we can understand that scientists of one generation can interpret the exact same set of evidence very differently than scientists of the next generation. For example, early astronomers looked at the night sky and saw a duck (the heavens revolving around a fixed earth), but afterward Copernicus and Galileo, astronomers looked at the exact same night sky and saw a rabbit (our perspective from a spinning earth orbiting around the sun) .

Let’s go back to the survey from Elmendorf et al., the one that found that most people think that new construction raises housing prices. Why do people believe this? Well, in fact, new construction is associated with higher rents. People walk around their city and see new construction in neighborhoods with rising rent and think “Ah, gentrification! These new buildings are making the rent too damn high!” Which, to be fair, is the simplest and most intuitive interpretation for what we see.

In reality, higher rents cause new construction, and not the other way around. The cycle goes:

1. People want to move to a certain neighborhood, pushing up rents. The reason is external: maybe it’s close to growing tech employers, or maybe it offers an increasingly popular lifestyle, such as walkability or transit access.

2. Higher rents attract developers.

3. Developers build new apartments, which push prices in that neighborhood back down to some equilibrium where it is no longer profitable to build.

4. The newly-built apartments in that neighborhood also end up lowering rents everywhere else, as it decreases the number of people chasing the existing supply of housing.

When cities enact supply restrictions, we only get step 1, where rents go up in that city, and to a lesser extent, increase rents and construction everywhere else. For most people, this story is a lot less intuitive, but it is the only story that fits the evidence. Believing that new construction causes higher rents because we observe higher rents in neighborhoods with new construction2 is like believing that hospitals are a major detriment to public health because we observe higher death rates in hospitals.

Now, let’s invert the problem: How do people end up believing in the correct model?

In the first section of this essay, we looked at one group of people who generally get it right: real estate investors. Unlike laypeople, they can draw on a much broader and more detailed experience: they routinely compare price trends across different cities, they see at a micro-level what happens to rents when a new apartment building opens, and so on.

They naturally pick up from their everyday experience that high rents only exist in supply-restricted markets, and correctly conclude that local construction restrictions are the key to maintaining high rents. In fact, they readily publicize this, albeit through the use of passive euphemisms like “long-term supply barriers”, as in this recent investor presentation from AVB, a major publicly-traded apartment owner in California and the Northeast, bragging about the lack of supply growth (and implicitly, higher rent growth) in their markets:

The layperson and the real estate professional generally follow the same intuitive reasoning process. (Zell’s fortune is evidence that real estate professionals don’t necessarily understand Econ 101.) The difference is that the layperson sees a much narrower sample, and draws the wrong conclusion, while the professional sees the bigger picture, and gets it right. Extending the rabbit/duck analogy, the layperson sees only the head and automatically interprets the housing market as a duck, while the real estate professional sees the same head, but with paws and a bushy tail, and reflexively sees the housing market as a rabbit.

Who else believes in the correct model? NYT journalist Conor Dougherty traces one of the earliest “YIMBY” (Yes-In-My-Backyard) pro-housing construction movements to Stockholm in 2007. Stockholm has a rent control system where apartments are allocated by waiting list.

A system that rations housing via a waiting list makes it transparent that housing scarcity can be solved through more construction: just build until everyone on the list has an apartment. A market system that rations housing to the highest bidder makes the nature of the problem more abstract, and misleads people into thinking that the root problem has to do with landlords or developers or capitalism. In fact the solution is the same in the market system as it is with a waiting list: just allow construction until the overhang of demand is satisfied. Continuing our earlier analogy, this group sees a bit of fur and that is enough of a cue for them to conclude that they are dealing with a rabbit.

Dougherty has also extensively reported on the YIMBY movement in the Bay Area, founded by Sonja Trauss, a former economics graduate student. Of course, someone who has studied economics at a graduate level will immediately recognize that the root problem is supply restrictions. Economists know enough about rabbit anatomy to be able to get the rabbit from an ambiguous picture of the head — maybe the ears are a giveaway.

The YIMBY movement is disproportionately made up of young, educated urbanites, whereas people that oppose new construction are predominantly older. This is often attributed to economic incentives, but in fact this is the same pattern that Kuhn observed with scientists: young people haven’t yet been conditioned to see the housing market as a duck. They also come into a different world than the last generation did, one where the price difference between supply-restricted cities and the rest of the country has become substantial. Even if a young person initially sees a duck, if you nudge them into noticing that ducks don’t eat carrots, it will click for them that they are actually looking at a rabbit, and they will only see a rabbit thereafter.

We like to imagine that people arrive at their opinions by weighing the objective evidence, and smart people are more likely to arrive at the right answer because they are better at reasoning through the facts. But there seems to be an element of luck: whether we end up with the right answer has lot to do with the path we take through life, and if smart people are better at coming up with the right answer, it is because they are more likely to have learned more of the relevant facts, or because they have been exposed to the most useful paradigm for thinking about those facts at an early enough age.

This is at odds with our usual conception of the world, but completely aligned with Kuhn’s theory. Remember, the people who got stuck on discarded models of the world were actual brilliant scientists. Many of them are still remembered today for their pathbreaking discoveries. It is just really hard for the human mind to move from one paradigm to another.

Phlogiston theory went away completely because once Lavoisier spelled out modern chemistry and it was adopted by young chemists, it would never again be possible to follow the same intellectual path as the phlogistonists. Phlogiston theory makes sense if you start by knowing nothing about chemistry, and start doing experiments to try to work out basic principles, but every child today learns what oxygen is, and knows that water is H2O.

Economics is different. In a sense, most people do go out in the world and start doing economics to work out the basic principles, without learning them in a textbook first. Even if people encounter the concept of supply and demand early on, it is counterintuitive enough that they usually don’t truly grasp it well enough to properly recognize and apply it in real life.

The thing about working out broader principles from personal experience is that personal experience is almost always very narrow and unrepresentative. What you think is a broader principle is often actually just a statistical artifact: it’s a failure to control for a confounder, or a selection effect, or Berkson’s paradox, or one of a dozen other common statistical illusions, or (as in this example) a simple case of getting the causal relationship backwards.

A smart person who tries to deduce fundamental principles from limited experience will usually end up in the same place as every other past smart person who did the same thing: with some version of phlogiston theory. Which is actually very smart! But also not very useful if you do so at a time when modern chemistry is already well understood.

This is one reason why the economics discourse is afflicted with so many “zombie ideas”, ideas like supply restriction that are easily disprovable and thus rejected by experts, but still enjoy wide acceptance among the masses. People independently rediscover the equivalent of phlogiston theory over and over again, and once they do so, no amount of evidence and reasoning can sway them to modern economics.

(When people learn that their best-loved ideas have been rejected by the experts, they are actually encouraged, and congratulate themselves for their independence of thought; reasoning by analogy, they imagine themselves as having progressed beyond the frontier of expert knowledge, rather than considering they have actually managed to regress to the 18th century.)

All of this is to say that housing opponents aren’t dumb, or if they are dumb, it has very little to do with their beliefs about housing. It is perfectly natural for smart people to adopt narratives based on their first-hand experience and then subsequently to be unable to get away from them when confronted with conflicting evidence.3

Ok, but what about that conflicting evidence? Why doesn’t that convert people? There isn’t any real ambiguity in the data here, since the two models make completely opposite predictions, and the effect size is fairly large. You can look at housing prices by city as Glaeser and Gyourko did, you can do a carefully controlled study at the neighborhood level, or you can look at the success of cities that loosened restrictions. You would think that all of this data would be persuasive enough to win converts, but when these studies are presented, housing opponents usually react like the robots in Westworld: “That doesn’t look like anything to me”.

Kuhn explains that part of what makes converting to a new paradigm so hard is that we see what we expect to see. For example, housing opponents argue that developers will never build enough to lower prices, reasoning that it would be against their collective self-interest. According to them, it is just universally known and beyond question. Many of them also assert that prices are set based on the greed of the business owner, not by any market forces. On these fundamental topics, they cannot see any evidence to the contrary, in the same way that ancient Western astronomers could not see the sunspots that were so obvious to the Chinese.

Kuhn also points out that the standards for what qualifies as evidence is itself contained within the paradigm. Some housing opponents will just say that “I know it to be true”, or “I’ve seen it with my own eyes”, others will spell out intelligent-sounding arguments that upon close inspection are built on circular reasoning (“if we assume my model is true, my model turns out to be true”), and still others will even come up with “evidence” that turns out to be fabricated or built on logical error. Economists will insist on certain standards around logical consistency, proper controls, and statistical significance, but why would housing opponents defer to economists on standards for evidence, if they don’t accept the economists’ paradigm?

Once you have been accustomed to using one paradigm to explain a set of facts, it is very difficult to even properly grasp a totally different paradigm. What even is replacement cost, to someone unfamiliar with it? It is hard to correctly state a foreign paradigm in your own words even when it has been correctly explained to you, and it is hard to even isolate a correct explanation with so many wrong versions floating around. It is very easy to dismiss a new paradigm by taking an incorrect version of it, conjuring up a counterexample to the incorrect version, and declaring it disproven.

Finally, a potential convert also has to cope with consistency bias. Once you announce your position on a topic, it gets wrapped up with your ego and identity and your brain doesn’t want to consider a different interpretation of the facts. Even if you start to have your doubts, you will naturally resist conversion.

In a way, the best evidence against the folk paradigm is the kinds of arguments that are made in its favor. They are usually either appeals to heuristics (the Econ 101 model is said to be “trickle-down”, or the proposed solution of repealing supply restrictions is too simple for a problem that is so complex), or else they try to poke holes in the Econ 101 paradigm. But is it not suspicious that in a $50 trillion dollar market with rich, detailed data dating back a century, no one can point to a single rigorous study demonstrating the accuracy of this theory? Supply restrictions have been tried so many times in so many places, so why is there not a single success story where they led to affordability? For that matter, if this model is such a good representation of reality, why has there never been a Sam Zell who has traded on it and made money? One would think that this would be enough to raise red flags, but such is the power of a paradigm, to blind people to its shortcomings.

Finally, we can extend this model to explain one other puzzling feature of the housing debate. Laypeople usually find YIMBYs (that is, pro-housing activists) to be impossibly strident and arrogant. Scott Alexander said that “YIMBYism is one of the most tribal, most emotional, most closed-minded movements I have ever seen this side of a college campus”.

This is just a natural byproduct of paradigm conflict. Most YIMBYs subscribe to some version of the Econ 101 paradigm, and that paradigm holds that in a competitive market like housing, high prices are a symptom of scarcity — prices are what we use to ration scarce goods. It is almost tautological to say that supply restrictions lead to high prices, and that the only way to lower prices (without resorting to another form of rationing) is to remove those restrictions. They see the housing crisis as frustrating and unnecessary because it is so clearly man-made and because there is one (and only one) simple solution. But when they explain this paradigm, they are belittled and condescended to by the very people they see as causing the crisis in the first place!

Naturally, their opponents see the world very differently. They cannot understand how anyone could be so certain about a subject that is so complicated, and they resent that a bunch of children are accusing them of being the source of the problem. Furthermore, they know based on experience that the Econ 101 paradigm is overly simplistic and useless.

As we have already seen, when it comes to paradigm conflict, there is no resolving the two viewpoints. Because they are operating from two different frameworks, and two separate sets of facts, they each think the other side is ignorant, and they are always talking past each other. The YIMBYs can insist all they want that the shortage of duck eggs is because the “ducks” are actually rabbits, and their opponents will counter that they know a duck when they see it, and the duck egg shortage is obviously caused by greedy farmers, and nothing will sway either side.

Ignaz Semmelweis had a problem.

The year was 1846, and the 28-year-old Semmelweis had just been appointed First Assistant in Division I of the obstetrics department of Vienna General Hospital. This was a promising step in his career: Vienna General Hospital was one of the largest and most respected medical institutions in all of Europe, and a third of all births in Vienna occurred there.

However, Semmelweis was joining an obstetrics clinic in crisis. In recent years, it had been ravaged by outbreaks of what was then known as childbed fever, a fatal disease afflicting mothers who had recently given birth. New mothers who fell victim to childbed fever would come down with chills and a fever, and would almost inevitably pass away within a week; their newborns would frequently die as well soon thereafter, with the same symptoms. According to Theodore Obenchain in Genius Belabored (an excellent book, from which much of this section is drawn), childbed fever was the second leading cause of death for women in that era.

Childbed fever had been known since antiquity, but was rare before the 18th century. At that point, more people started living in big cities, where they were more likely to give birth in hospitals rather than at home. For reasons unknown at the time, childbed fever was often (but not always) much more common in hospitals.

At Vienna General Hospital, childbed fever had been very uncommon between 1784 (when the maternity ward was founded) and 1822, affecting less than 1% of mothers admitted. Between 1823 and 1840, childbed fever became much more widespread, with the mortality rate hovering around 5%.

In 1840, the growing maternity ward was split into two clinics: Division I was staffed by the (male) doctors, and Division II was staffed by the (female) midwives. At that point, the mortality rates of the two clinics diverged: the reported mortality rate in Division I averaged 10% (and in reality was higher: Division I would sometimes move terminally ill patients into the main hospital and off their books), while the mortality rate in Division II averaged only 3%.

Division I thus acquired a terrible reputation both within the hospital and in Vienna at large: women would sometimes give birth on the street (or would do so at home and pretend that they had given birth on the street) before checking into hospital for free medical care afterward, simply to avoid the risk of giving birth there. Some of the patients who were admitted would beg to be allowed to give birth in Division II. As Semmelweis described it:

The disrespect displayed by the employees toward the personnel of [Division I] made me so miserable that life seemed worthless. Everything was in question; everything seemed inexplicable; everything was doubtful. Only the large number of deaths was an unquestionable reality.

Semmelweis was absolutely determined to discover the cause of childbed fever. In his quest, he had a couple of unique advantages. First, Vienna General Hospital was very good about keeping detailed records, and Semmelweis had a two year break before assuming his position during which he took a course in logic and statistics and he studied and charted the available data that pertained to childbed fever.

Second, the two-clinic setup was perfect for isolating the cause of childbed fever. Patients were admitted to Division I or Division II solely based on the day they arrived (e.g. Division I for Monday arrivals, Division II for Tuesday arrivals, etc.), making it possible to rule out characteristics of the patients themselves (since they were randomly selected) or temporary epidemics. The two clinics were housed next to each other in identical buildings, making it possible to all but rule out environmental theories, e.g. that childbed fever could be due to bad air or defects in one of the buildings. In fact, a prior investigation had gone so far as to have the two clinics swap buildings, and still the high mortality rate for Division I persisted.

Semmelweis went about systematically testing and eliminating different hypotheses. One theory was that the bells of the priests who came to perform last rites were frightening the patients and making them more susceptible to illness, so he made the priests silence their ringing, with no effect. He noticed that the midwives had patients lie on their sides during delivery, so he tried that in his clinic, but that produced no improvement either.

Semmelweis turned to the data to eliminate other theories. One popular idea was that male doctors induced a sense of shame in patients, but since the disease also afflicted newborns, he concluded that this story must be incorrect. Yet another theory centered on overcrowding, but the data revealed no correlation there. Semmelweis was stumped, and the stress of the situation was building.

In October 1846, only four months into his appointment, Semmelweis was suddenly forced out of his job: his predecessor had returned and wanted his old position back. Semmelweis took the opportunity to spend the next few months studying English, with the intent of going to Dublin, to get a different perspective on fighting childbed fever.

In February 1847, before he could leave, Semmelweis learned that his old position was re-opening: his predecessor had found a more senior role at a different hospital. Instead of going to Dublin, he joined his friends on a short vacation in Venice, to clear his head before re-taking his old job. When he returned to his job eighteen days later, he was saddened to learn that while he was away, a colleague had passed away in a tragic accident: a student had accidentally cut him on the finger during an autopsy, and he fell ill with a fever, rapidly deteriorated, and died.

Out of curiosity, Semmelweis read the autopsy report. While he was reading it, it struck him that the abscesses and other symptoms described were identical to what the symptoms he had observed when performing autopsies of victims of childbed fever. He realized that the disease that killed his colleague and the disease that was killing new mothers and their infants were one and the same – a kind of pyemia.

That’s when it all clicked for Semmelweis. He understood that his colleague had been killed by “cadaverous particles” introduced to his bloodstream by a wound. He also knew that the doctors of Division I sometimes examined patients after performing autopsies, while the midwives of Division II almost never performed autopsies at all. He concluded that the differential in infection rates was because he and his colleagues were killing new mothers by introducing something from corpses into their bloodstream during examinations.

For Semmelweis, this theory explained some well-documented anomalous patterns of infection that no one had ever been able to make sense of before. Patients who underwent more examinations before delivery were at a much higher risk of infection, whereas patients who never underwent any examinations or gave birth on the street or at home were at almost no risk at all — of course this would be true if the examinations themselves were the cause. Cases of childbed fever would be discovered in random rows within the clinic — this must happen because doctors would examine patients in order, and a doctor carrying cadaverous particles would infect an entire row of patients before he got done.

This theory also explained the historical trends of childbed fever at Vienna General Hospital. From 1784 until 1822, the maternity ward was led by a man who preached minimal intervention, and childbed fever was almost unknown. In 1823, the hospital mandated the use of cadavers in teaching and fired the old head of obstetrics, and childbed fever became much more common. Semmelweis even made a note of a marked decline in childbed fever after he lost his job the preceding October: his replacement was much lazier, and rarely did autopsies on his own, nor did he encourage his students to do them either. When Semmelweis returned in March, deaths surged again.

This discovery also pointed to a possible solution. Doctors already washed their hands with soap and water, but that was insufficient to do away with the corpse-y smell that lingered after performing an autopsy (suffice to say this was before doctors used gloves). Semmelweis’s idea was that the smell was evidence that dangerous cadaverous particles remained. He figured that he could kill or neutralize the cadaverous particles by having doctors thoroughly scrub their hands with a chlorine solution, taking care to brush under their fingernails, until the smell was entirely gone.

In May 1847, Semmelweis implemented this new hand-washing treatment in Division I, and it worked almost exactly as theorized. Childbed fever all but vanished from Division I, with mortality plummeting from 11.4% in 1846 to 1.3% in 1848. Division I was now actually safer than Division II. Based on additional observation, Semmelweis refined his theory to state that childbed fever was spread solely by contact with decaying animal-organic matter of any kind, and that hands, instruments, and sheets should be disinfected so that no contaminants could be introduced to the bloodstream of patients.

His peers at the hospital were naturally quite impressed, and insisted on helping him spread the word about his wonderful discovery around all of Europe. Letters were written, speeches were made, and the response was…crickets. Or even worse than crickets: ridicule. Semmelweis’s theory was almost universally rejected as mistaken and totally implausible. Even Semmelweis’s own boss argued that the true cause of this miracle was a new ventilation system that had recently been installed. More commonly, they alleged that Semmelweis had fallen victim to a simple logical fallacy, confusing correlation with causation. Epidemics of childbed fever were known to come and go, and this wave just so happened to go away at the same time he implemented his handwashing regimen, or so the thinking went. Very few obstetricians were moved to implement chlorine handwashing. Semmelweis himself was pushed out of Vienna and picked up an obstetrics position at a hospital in his native Budapest, where, undaunted, he again wiped out childbed fever with his handwashing regimen.

Why did everyone ignore Semmelweis? In popular culture, it is assumed that 19th century doctors were dumb, arrogant, or possibly even misogynistic. There is little evidence for any of these theories: doctors of that era were intelligent and educated and they cared for their patients. Another hypothesis is that doctors refused to consider any theory that implicated doctors, but even back then doctors were well aware that they could spread disease and harm patients, and Semmelweis readily accepted that he personally had caused many deaths. One prominent obstetrician who successfully implemented Semmelweis’s treatment became so depressed thinking about the women he had killed in his career that he committed suicide.

Some modern scholars see the issue as having to do with politics, egos, or professional incentives. Undoubtedly these were contributing factors, but it hardly seems likely that doctors would have opposed Semmelweis if they truly believed in his theory, given the stakes. They were still heavily incentivized to limit patient deaths under their watch.

Others point the finger at Semmelweis himself, who was initially averse to writing, leaving his audience to glean his theory secondhand. Semmelweis was also hardly the most tactful ambassador for his theory, especially after it was publicly rejected, making him bitter and resentful. When he finally published his Etiology in 1861, he included a 200+ page section devoted to individually naming and eviscerating his many haters, logically demolishing their flawed beliefs and hammering them with personal insults. (He derided them as efficient murderers who were training even more murderers, and he told his biggest rival that he would be “memorialized as a medical Nero” and that he “[would] not escape God’s justice”.) That being said, none of Semmelweis’s more diplomatic allies had much success winning new converts either.

The most persuasive theory comes from Donald Gillies, a philosophy professor who in 2005 invoked our old friend Thomas Kuhn. His idea is that we have to put ourselves in the shoes of an early 19th century doctor, and understand what paradigms they used to think about disease. They knew about contagious diseases like smallpox, but childbed fever didn’t seem to work like that. They had an idea that disease was related to foul conditions and bad air, hence the theories about ventilation and building defects. They also had a notion that disease was often caused by epidemics that are subject to random spikes or come and go with the seasons, thus the fatalism in the face of sudden, prolonged outbreaks. Finally, they universally agreed that diseases were too complex to possibly only have only one possible cause – every disease almost certainly had many different causes.

These frameworks seem primitive today, but are understandable in a world with no knowledge of viruses or bacteria. They at least have some explanatory power – bad conditions do spread disease and illnesses like the flu are seasonal. The problem was that doctors of the time would see what they expected to see, and they would keep returning to these theories that Semmelweis had already disproven.

But what about the overwhelming evidence? Why didn’t that sway them? Semmelweis himself initially assumed that the evidence was so conclusive that he would have to exert little effort for his theory to be widely adopted across the Continent.

When we read the story of Semmelweis today, we immediately grasp what he was doing. We know all about causal inference and statistics and deductive logic, and we are primed to approach problems the same way Semmelweis did. We see the resemblance to a randomized control trial, which wasn’t commonly used in medicine until the 1940s. We can examine his 19th century study with 21st century methods and see that it holds up in terms of study design and statistical significance.

19th century doctors had no familiarity with any of this. Remember, other hospitals didn’t even keep clean records — no one outside of Vienna could have constructed a useful study even if they had understood logic and statistics (which they most certainly did not). The only reason Semmelweis knew anything about it was that he had two years off to learn it from teachers who were at the forefront of the subject at that time, and even that was very little by modern standards. Of course, 19th century doctors didn’t recognize their own ignorance — they knew just enough about logic to be dangerous, observing Semmelweis’s theory superficially resembled a common logical fallacy that they learned about in school and dismissing it from there.

They didn’t even regard the pathological evidence in the same way that Semmelweis did. Semmelweis looked at the description of abscesses and tissue and immediately drew a connection between his colleague and the victims of childbed fever. Other doctors thought that the parallel was absurd: Semmelweis’s male colleague obviously didn’t have anything wrong with his uterus, so how could anyone be so dense as to think that there was any similarity?

To a 19th century doctor, childbed fever resembled any ordinary epidemic: it came and went for reasons no one could possibly explain, and even exhibited pronounced seasonality when it was present. Semmelweis correctly demonstrated that his theory easily explained the observed seasonality: in the summer, there were fewer students doing examinations, and due to the heat, autopsies were conducted in the evenings, after they saw patients, rather than in the mornings before rounds, as was done in the winter. But to most 19th century doctors, this sounded like a roundabout justification — if it looked like a seasonal epidemic, and quacked like a seasonal epidemic, then it must surely be a seasonal epidemic.

Semmelweis’s idea that childbed fever only had one cause (decaying animal-organic matter) was so foreign to their conception of disease that even his supporters refused to accept that part of his theory. His detractors thought it was obvious to anyone with medical experience that a disease must have any number of causes, and he was engaging in a nonsensical rhetorical trick by redefining a disease in such a way that it only had one cause. But Semmelweis scholar K. Codell Carter explains that the way disease had been traditionally defined, as fuzzy groups of symptoms, made it very hard to pin down effective treatments, because the effectiveness of a treatment would vary depending on what disease the patient actually had — e.g. we know today that bacteria and viruses can cause similar symptoms but antibiotics will only work on bacteria. Semmelweis’s modern paradigm was a necessary leap for medicine to progress, but no one in the 1840s was ready for that yet.

Semmelweis’s theory was poorly understood in its time because it didn’t fit with any existing paradigm. Some doctors incorrectly believed Semmelweis was saying that cadaveric material was the only cause of childbed fever (rather than any animal-organic matter) and concluded that he was mistaken, because they knew childbed fever occurred in settings with no autopsies.

One school of British doctors, the contagionists, actually already long believed that doctors were responsible for spreading childbed fever, having observed that cases of the disease contracted during home births followed individual doctors from delivery to delivery all over the map, rather than concentrating in one location like ordinary epidemics. The contagionists thought that childbed fever followed doctors in some sort of miasma, and their prescribed treatment was for affected doctors to bathe, burn their clothes, and close their practices for a while. When they heard of Semmelweis’s theory, they were angry and even accused him of plagiarism. Semmelweis had to explain to them that because childbed fever was only spread by decaying animal-organic matter, burning clothing and closing down shop was useless and unnecessary, and that they just needed to disinfect their hands and instruments.

Following Kuhn’s documented pattern with new paradigms, the first doctors to embrace Semmelweis’s theory were the junior doctors and students who saw first-hand the power of his hand-washing regimen in Vienna. They understood the power of the cure in a visceral way that could not be communicated in a speech or a publication, and they were young enough to be open to a new model. Semmelweis’s most ardent supporters were of course the young doctors who had just taught him modern statistics and logic and pathology: they were fully ready for such a discovery, even if they didn’t comprehend the bit about diseases only having one cause.

We also see here once again the pattern of vitriolic debate, where the adherents of the new paradigm become angry and frustrated that the old school cannot see something that to them is so obvious, that the solution is so simple and sitting in plain sight. By contrast, those that were wedded to the old paradigm experienced Semmelweis as arrogant and irritating, lecturing as confidently and condescendingly as he did about something he clearly misunderstood.

The saving grace for the old school was that their paradigm allowed for diseases to be caused by multiple factors, which meant that they would sometimes quietly adopt handwashing in their own clinics, even while denigrating the theory publicly. Semmelweis’s rival and successor in Vienna, Carl Braun, argued that Semmelweis’s theory was “all humbug” and published a list of thirty possible causes of childbed fever, on which “cadaveric infection” was 28th. But in practice, he continued to enforce Semmelweis’s handwashing regimen in Vienna and even expanded it to Division II, and in so doing managed to keep mortality rates almost as low as they had been under Semmelweis. Another opponent, Eduard Lumpe, opined that childbed fever was too seasonal and handwashing too simple for Semmelweis’s theory to be accurate, but hedged by saying that obstetricians should “wait and wash”. This sort of behavior infuriated Semmelweis, but it was better than nothing.

Converts to Semmelweis’s theory were few and far between in his lifetime. He would die in an insane asylum in 1865, after cutting his finger and contracting the same infection that he had devoted his whole life to studying.

In 1857, Louis Pasteur published a paper describing the world of bacteria he had discovered under his microscope. This was germ theory, the theoretical explanation for what Semmelweis had previously deduced empirically. In 1865, Joseph Lister, a surgeon, was introduced to Pasteur’s paper and applied it to his own work, successfully disinfecting wounds with carbolic acid. Lister published his discovery and just like Semmelweis, he was initially ignored and doubted.

In 1869, a pair of French doctors, Victor Feltz and Leon Coze, showed that the deadly streptococcus bacteria was present in a patient with childbed fever. Pasteur confirmed this discovery in 1879. It was in that year that he was present at a speech where a prominent French obstetrician was lecturing about how childbed fever was caused by the miasmatic air present in a hospital. Pasteur got up and drew the streptococcus bacteria on the board and explained to the crowd that this was the true cause of childbed fever. The obstetrician retorted that even though this bacteria might be present in all victims, correlation was not causation, and that he would probably die before he saw the microbe that actually caused childbed fever.

It was only in the 1880s that the work of Lister and Pasteur and their colleagues finally paid off, and the medical community in Europe widely adopted antiseptic procedures to ward off childbed fever, almost forty years after Semmelweis originally successfully implemented and promoted the same solution. At this point, with germ theory widely accepted and childbed fever conquered, a new generation of doctors rediscovered Semmelweis and hailed him as a misunderstood hero — and they wondered how he could have been ignored for so long.

Armed with the paradigm of germ theory, doctors were able to explain and conquer many diseases that had previously been complete mysteries. But when in the late 19th and early 20th centuries, when confronted with deficiency diseases like scurvy (brought on by lack of Vitamin C) and goiter (brought on by lack of iodine), some doctors would make many of the same mistakes again, insisting that the true cause must lie within previously accepted paradigms like germ theory, and campaigning against proven treatments because it just couldn’t be true that miniscule amounts of a simple, obscure chemical could be the cure to such a complicated disease.

It is eye-opening to go back and see how often that simple solutions are ignored because they conflict with an accepted paradigm, and how quickly previously intractable problems disappeared once that simple solution was implemented. In past editions, we have talked about the struggles of Jack Welch, who insisted that massaged metrics would lead to improved results in the underlying business, rather than improving the underlying business to improve metrics. We looked at the deregulation of domestic air travel under the late Jimmy Carter, which made cross-country flights affordable within a few years. (Carter would do the same thing with trucking, rail, and telecom.)

Modern conflicts between paradigms also frequently play out just as Kuhn predicted back in 1961. If space permitted, this is where we would recount the resistance to the adoption of analytics by the old guard of baseball (as portrayed in Moneyball), complete with angry rhetoric and confusion and the eventual hiring of very young general managers to successfully change organizational approaches.

Perhaps the best approach is to view this pattern optimistically. It is almost certain that many of the most stubborn problems in our own organizations or in our own lives must have solutions that are lying in plain sight, just waiting to be adopted. It is up to us to figure out how to adjust our mental framework to be able to reach out and grab them.

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Further reading:

Conspicuous Cognition by Dan Williams – An excellent Substack about psychology that touches on some of the topics discussed here. His essay about Why People Believe True Things is a classic.

Experimental Psychology by Adam Mastroianni – Another relevant psychology Substack, that also discusses the philosophy of science.

There is No Antimemetics Division by qntm and What the Hell Did I Just Read by Jason Pargin – A pair of horror sci-fi books exploring how our brains perceive (or don’t perceive) the world.

There was one particularly interesting talking point the Times Guild returned to and hammered on repeatedly: the NYT’s stock buyback program. Almost every Guild press release and tweet during this period expressed outrage over the NYT’s share repurchase policy. The Guild characterized the NYT’s repurchase program as “egregious” and an “insult”.

One curious thing about the Guild’s anger over the NYT’s supposedly egregious and insulting repurchase policy is that over the preceding three years, the NYT had in fact hardly bought back any of its own stock!

The NYT’s 2022 annual report reveals that the company had made nearly $500 million in net income over the last three years while spending only $105 million on share repurchases. And most of that $105 million simply went to offset the $72 million of stock-based compensation issued over that time. (Stock-based compensation represents the value of newly issued restricted stock or stock options given to employees in lieu of cash, while buybacks represent the reverse, cash given to investors for stock which is then retired. Any buyback program should therefore be measured net of stock issued.) 

The NYT’s net stock buyback over that timeframe would therefore have been only $33 million (ignoring some minor tax and option adjustments), or only 7% of net income earned over the preceding three years. By comparison, the members of the S&P 500 (an index whose constituents make up the majority of corporate profit in America) spent 44% of their net income on buybacks last year.

If the Guild wanted to be mad about the NYT’s choice to give cash to shareholders, they could have focused their ire on the NYT’s generous dividend, which totaled over $140 million over that period. Or they could have protested the NYT’s acquisition of The Athletic in 2022, which saw over $500 million of the NYT’s cash distributed to shareholders of a different company. But no, it was Times’ meager buyback that set off the Guild.

Something specifically about stock buybacks seems to trigger strong emotions: to most, buybacks intuitively seem frivolous, manipulative, and wasteful. Critics blame buybacks for a wide variety of societal ills, from inequality to low productivity. Last year, Congress passed a 1% excise tax on stock buybacks, with the Treasury Department stating that “as the tax code has favored stock buybacks, many companies have failed to reinvest profits in their workers, growth, and innovation.”

But if you ask financial experts, the fuss around buybacks is much ado about nothing. Morgan Stanley analyst Michael Mauboussin says that buybacks are important and misunderstood, and AQR founder Cliff Asness dismisses buyback criticisms as symptoms of “Buyback Derangement Syndrome”.  And here is what Warren Buffett had to say about repurchases in last year’s Berkshire Hathaway annual letter:

When you are told that all repurchases are harmful to shareholders or to the country, or particularly beneficial to CEOs, you are listening to either an economic illiterate or a silver-tongued demagogue (characters that are not mutually exclusive).

So, do buyback critics have a point, or are they a bunch of economic illiterates? 

First, let’s start with a story.

You have a job at the local widget factory, and you are living a happy life. You collect a wage and spend it on cheeseburgers and beer. One day it occurs to you that you will one day want to retire and be able to eat cheeseburgers and drink beer without having to work every day at the widget factory. 

The next logical step here would be to set aside some of your wages in a savings account, but in our story, savings accounts haven’t been invented yet. You have to find some other way to defer consumption.1

You decide that you will enact your savings plan by launching a successful newspaper. Like, a real, old school, print newspaper, just like the original New York Times. Your plan is to cut back on the cheeseburgers and beer, and use the money you save to buy a printing press and ink and hire journalists and pressmen.2 If people like your newspaper, they will exchange their hard earned dollars for your newspaper, and advertisers will supplement this with even more dollars to reach your readers. 

Most of the dollars you receive will go right back out the door to produce the newspaper — to buy paper and ink and to pay the journalists and pressmen — but the leftover dollars, your profit, will be enough for you to buy cheeseburgers and beer until you die.

We can use this little sketch to understand how private investment affects the economy. In the first part of the story, you contribute to the production of goods and services through your job at the widget factory, and in exchange you receive pieces of paper that you trade for goods and services that others produce (cheeseburgers and beer). 

In the second part of the story, you choose to buy fewer cheeseburgers and less beer, and use your money to buy printing presses and journalists instead. You are reallocating society’s scarce resources (labor and land), nudging society to grow fewer hops and raise fewer cows and instead mine metal and craft it into advanced machinery. Fewer farmers, more miners and craftsmen and journalists.

On a societal level, a successful newspaper is a good trade for everyone involved. Consumers like your newspaper enough to vote for it with their pocketbooks. Workers like your newspaper enough to vote for it with their feet. And you make enough profit to fund your retirement.

You can see the accounting behind an unsuccessful newspaper as well. Consumers don’t vote with their pocketbooks, workers don’t vote with their feet, and after your newspaper shuts down, society’s scarce productive resources will be redirected elsewhere to produce something people actually want, perhaps to back to grow more hops and raise more cows.

There are certainly other important impacts on society from your newspaper that are not captured by economic success or failure: perhaps your newspaper’s coverage creates a more well-informed electorate (good!), or maybe your newspaper disseminates misinformation that sows confusion and division among the masses (less good!). But we will set these externalities aside for now, focused as we are on the narrow issue of stock buybacks.

Successful ventures create economic growth by allowing society to produce more and better output with the same scarce inputs. That’s the name of the game: society is working with a (relatively) fixed pool of labor, land, and natural resources, and we have to figure how best to turn that into useful economic outputs.

Now, you are probably wondering what this all has to do with stock buybacks. Well, we can select individual features of a modern economy to layer onto our simple model, like so:

A business can be launched as a sole proprietorship, where there is no separation between the owner and the company. However, in reality, any significant business venture usually is formed as some kind of limited liability partnership or corporation. Effectively, you take your newspaper, and, as SBF might put it, you drop it into a kind of invisible box. 

The box is not real in a physical sense, but it is very real in a legal sense. The box has a lot of useful features: it can enter into contracts, it can sue or be sued, it limits liability to its owner(s), and economic claims on the box can be sliced in a myriad of different ways.

Most importantly, nothing about the box changes the economics of the newspaper inside in any meaningful way. The newspaper is just as it was before, and the box simply adds a useful layer of abstraction. 

Here is how a modern business venture looks with the addition of the box:

1. Some promoter comes along and tells you that if you put your money in a magic box today, your money will multiply, and in the future you will be able to take more money out of that box than you put in originally.

2. The promoter goes and buys ink and printing presses and hires journalists (all inside the magic box), in an effort to make their promises come true.

3. It worked! The magic box is now spitting out money and rewarding the people who put their money in the box in step 1.

4. Or, alternatively, it didn’t work, too bad, maybe next time. You diversified your savings across a bunch of different boxes, right?

We need to examine step 3, where the magic box returns money to the original investors. The buyback skeptics would have us believe that it is very important how the magic box spits money back out to the original investors. Our task is to ascertain whether that is the case.

Claim: Stock buybacks are uniquely damaging to society when compared to dividends.

In the olden days, if you raised equity investment to start a company (that is, if you sold off slices of your magic box), and it was successful, your investors would expect to receive ongoing regular cash dividends, to be paid out of profits. They would treat that as reliable income that they could expect to use for their everyday consumption needs, e.g. cheeseburgers and beer.

The proportion of profits used for dividends (the “dividend payout ratio”) would vary by circumstance, but it was typical for that figure to reach 60% to 80%, with the remaining 20%-40% reinvested in the business for future growth — think of a newspaper periodically buying larger, more efficient printing presses.

Edgar Lawrence Smith, in his landmark 1924 study Common Stocks as Long Term Investments, found that stocks had been historically undervalued because investors failed to properly appreciate the value of the “profitable reinvestment…of their undistributed earnings”. Investors in that era mostly just focused on the value of the regular dividend, comparing it to the coupon on comparable bonds. However, that 20%-40% of retained earnings, redeployed in the business, compounded into higher dividends over the years, leaving fixed-coupon bonds in the dust.3

Returning to our model, imagine our newspaper now has a hundred investors, each of whom own 1% of the company. If they are all looking for a steady income far into the future, then dividends work great to satisfy everyone’s consumption needs.

But imagine one of our hundred investors wants to make a big purchase, perhaps one of those newfangled cars. To fund it, they want to sell their share, exchanging their future stream of income for a big pile of money that they can spend today. 

One way to solve that is to have a marketplace where they can be matched with a saver who wishes to buy a future stream of income, and make a trade. And as long as there have been stocks, there have been stock exchanges where they can do just that.

Another possibility is for the company to step in and buy that investor’s share on behalf of the remaining investors. This is a stock buyback or repurchase. The company will have to part with a chunk of cash, but at the end of the transaction, the remaining investors will each own a larger piece of the company (1.01%, instead of 1%).

There are a few reasons why this might make sense:

1. The newspaper’s management team should be better informed than most about their own company’s value. They are well placed to accept a good offer and pass up a bad one.

2. The group of people in the world that places the highest value on the newspaper is their current investors. Otherwise they wouldn’t be holding the stock! The existing investors are natural buyers for any stock that comes on the market.

3. The newspaper is generating plenty of excess cash, which it is currently using to pay dividends.

The problem with this plan is that we already established that some of our shareholders are relying on the dividend to pay the bills, so our buyback plan is out. Oh well.

And so it was that in the olden days, buybacks were fairly uncommon. But one can start to see how repurchases could make sense, if conditions were to change in the future.

Now, our shareholders can still use their dividend to buy stock from the selling shareholders, and in fact, if our shareholders all chip in equally to do so, they will be in the exact same economic position that they would have been had we cut our dividend to do the buyback. 

Broadly speaking, dividends and buybacks will be the same economically – they are just different ways to get cash from the company into the hands of shareholders. Buybacks initially put all of that cash into the hands of shareholders who want out, while dividends initially distribute the cash equally among all shareholders.

Going back to our history lesson: In the olden days, stock investing was largely the province of a few very wealthy people buying individual stocks for their dividends. In 1900, a single share of stock would typically cost $100 (no fractional shares back then!), at a time when the average annual income was $450. There were only a couple of hundred listed companies of any size, and the largest among them had about 10,000 shareholders.

Gradually, the world of stock investing expanded. Incomes exploded, opening up new demand for long-term saving, and industrial conglomerates like General Electric and U.S. Steel were formed, soaking up that demand. By 1920, the most widely held stock was AT&T, with 130,000 individual shareholders – but that was still only 0.1% of the country’s population.

The Great Depression put a damper on stock investing for a while, but by the 1950s, retail brokerages like Merrill Lynch had pushed down commissions and expanded stock investing to a somewhat wider audience. In 1952, still in the shadow of a brutal post-Depression bear market, an NYSE survey found 6.5 million shareowners in America, representing about 4% of the population at the time. The go-go market of the 1960s would take this figure all the way to 31 million by 1970, which represented 15% of the population, or a quarter of all adults. 

The painful bear market of the 1970s would cause stock ownership to actually decline slightly over the next decade. Open market buybacks were difficult to execute under the rules of the time, but companies such as Teledyne and the Washington Post took advantage of the low valuations to launch tender offers to buy back cheap stock, often with spectacular success. Still, buybacks were uncommon, and dividends ruled the day.

In 1982, the SEC passed rule 10b-18, which provided a safe and legal way for companies to execute stock buybacks on the open market, without resorting to tender offers. This is usually falsely cited as the takeoff point for buybacks, because even a full decade later, companies still strongly favored dividends: in 1992, companies spent $94 billion on dividends and only $22 billion on buybacks.

The advent of 401(k) and IRA retirement plans in the early 1980s fueled a boom in stock investing through mutual funds. Only 6% of households were invested in mutual funds in 1980, but that proportion would soar to 52% by 2001. That share of households invested in mutual funds is still about the same today, with the main difference being that low-cost index funds have caught up to actively managed funds. Meanwhile, direct ownership of stock stagnated: today, still only 22% of households own stock outside of their retirement plans.  

The popularity of index funds and mutual funds caused a shift from direct retail ownership to institutional ownership of stock (which includes pension funds and mutual funds). Institutional ownership grew from about 30% in 1980 to about 80% today.

Now we have better conditions for buybacks. It is no longer the case that most investors directly buy shares in individual companies in expectation of a reliable passive income. We buy stock via index funds in retirement accounts, with no need for current income, just the expectation that we will cash them out at retirement as needed. We are barely even aware of the dividends we receive, because we automatically reinvest them back into more shares; we have no problem if companies reduce dividends to buy shares on our behalf. Also, if we need cash, it’s now trivial to cash out small slices of our existing holdings with minimal transaction costs.

Buybacks do carry some minor tax advantages over dividends, but the chief advantage is flexibility. In practice, regular dividends are viewed as a commitment, and it is quite a commitment to pay out a dividend of 70% of one’s normalized earnings each and every year. A major recession or an event like Covid can leave a company scrambling for cash. 

Most companies find it much better to have a smaller regular dividend, with the balance of excess cash to be returned to shareholders via a flexible buyback policy; many shareholders would have just used the larger dividend to purchase more stock anyway, and so will be completely indifferent.

Today, most companies pay a steady small dividend alongside a larger but fluctuating sum of cash that goes toward buybacks. In lean years, many companies omit buybacks entirely, as the NYT did in 2020 and 2021. Here is the S&P’s chart showing how buybacks and dividends over the last 25 years, where we can clearly see that buybacks now well outstrip dividends, except during recessions:

In 2023, the constituents of the S&P 500 earned $1.8 trillion in aggregate, and only paid out a third of those earnings ($588 billion) in the form of dividends. They then paid out another 44% of earnings ($795 billion) in buybacks, for a total payout ratio of 77%. American companies pay out about the same share of their earnings that they did a century ago, a decade ago, or any time in between.4 It’s just that over the last thirty years, the form of that payout has shifted from dividends to buybacks.

When you invest in an S&P 500 index fund in your 401(k), you are buying a slice of a magic box that spits out $1.8 trillion a year in earnings, and that $1.8 trillion will grow in the future as the constituent companies reinvest a small portion of those earnings ($400 billion) on your behalf and as they ride the growth of the global economy. The other $1.4 trillion is distributed to you and your fellow owners to reallocate as they please.

You can create compounding financial returns for yourself by using your share of that $1.4 trillion to buy more shares of the magic box from owners that want out. Ignoring taxes, you are indifferent as to exactly how that happens: you can use your dividends to buy shares from selling investors, or the company can use that money to buy shares from selling investors on your behalf. You will end up in the same place either way.

Claim: Corporations are acting immorally or dishonestly when they buy back stock or pay dividends.

Not only is this claim illiterate, it is in a sense precisely backwards. Remember that the whole game is to induce people to defer consumption and put their money inside a box with the promise that if all goes well, they (or their future designees) will someday be able to take more money out of the box than they put in. 

If you are running a company that is throwing off cash, and you don’t have many opportunities for reinvestment, then your investors will say “hey why don’t you give us that cash so we can productively invest it elsewhere”. If you say “haha no”, then a) they will try to fire you if they can and b) you are the one acting immorally. You (or your predecessor) raised money from investors with the promise that they would make money if things went well, and now you are pulling the rug on them. 

The ability to return capital is a load-bearing pillar holding up capitalism. People will only put money in a box if they believe they can get it out later. (Insert your favorite joke here about crypto, NFTs, or airlines, but this statement still rounds to being completely true.) If companies can’t or won’t return capital to shareholders, then people won’t invest, and the system falls apart.

One confusing aspect here is that as a society we want capital to flow to the most productive projects, so companies with lots of investment opportunities should be retaining their earnings for investment or even raising new money from investors, while companies with few investment opportunities should be returning every dollar they can. It’s always debatable what any particular company should be doing at any specific point in time. But we always expect some companies to be returning capital to investors and some companies to be raising capital from investors.

Interestingly, people intuitively understand distributions when they start a personal, wholly-owned LLC for some small new consulting venture. No one worries that they are acting immorally by taking a distribution from their LLC; they understand that the LLC’s money is their money, but temporarily held inside a box that they fully own. 

If Apple has $160 billion of cash, and you own one share of Apple representing one-sixteen-billionth of the company, then Apple is holding $10 of cash that belongs to you that they will someday choose to return. People intuitively believe that Apple and their personal LLC are economically completely different, but ignoring taxes, they are economically very similar.

Claim: Stock buybacks and dividends are a “waste of money”.

No! Again, this is illiterate. A company that has $100 million on deposit at Chase has an entry in a ledger that Chase maintains that entitles the company to $100 million of goods or services in the future. 

Let’s say that the company has ten shareholders that each own 10% of the company, and it decides to distribute that $100 million to those shareholders via a dividend.

What happens now? Chase reduces the company’s ledger balance by $100 million and increases the balance in each of the individual shareholder’s ledgers at Chase by $10 million. That’s it. 

The money was inside the box before, and now it is outside the box, but the money always belonged to the shareholders. Money is simply moved from one pocket to another. Nothing is being “wasted”; in fact, economically nothing is really happening at all. No real goods or services are being consumed here, as opposed to our earlier example when scarce labor and capital was being diverted to produce printing presses and journalism. 

There is some indirect practical future impact from the money no longer being trapped inside the box; presumably the individual shareholders will use the money differently than the company would have, for better (if the company is constrained in their investment opportunities) or for worse (if the company is passing up high-return investment opportunities to make the distribution). But in no case can a distribution unequivocally be framed as a “waste”. For mature companies, there is usually some positive optimal level of distributions, which is why shareholders are willing to pay taxes to free their money from the box.

A stock buyback is economically more similar to the choice to move cash from a checking account to a money market fund than it is to the purchase of a new printing press.

Claim: Stock buybacks are a vehicle for executives to extract more compensation by artificially inflating stock prices.

The rough intuition behind this claim is the notion that stock prices are determined by supply and demand. The argument goes that stock buybacks increase demand, and therefore stock buybacks must artificially inflate stock prices. Furthermore, since executive pay is tied to stock prices, whether through share and option grants or bonuses, it is argued that executives undertake stock buybacks for selfish reasons, to inflate their own pay.

We have already looked at one problem with this model: increased buybacks have historically been funded by reducing dividends, and savers are likely to reinvest their dividends right back into stocks, just as you do automatically in your 401(k). If we account for the reduction in demand from lower dividends, it’s not clear that there is any meaningful net increase in demand from increasing buybacks.

Even if we ignore that, aggregate buybacks are simply not large enough to have a meaningful impact on stock prices. We can use the S&P 500 to stand in for the entire market of publicly traded companies in America; company value follows a power law and the S&P 500 (which consists of the 500 most valuable companies in the US, more or less) accounts for 80% of total publicly-traded US market value (and in fact over half of all US corporate profits). In 2023, S&P 500 companies bought back 2% of their stock in aggregate. One would not expect that buying only 2% of the stock of a company over the course of an entire year would have any meaningful direct impact on the stock price – investors often build 2% positions in much shorter time periods without any noticeable effect on the price of a stock.

Note that this is true for companies in aggregate, but not necessarily true for any specific company. One can easily imagine a smaller company with a cult following aggressively repurchasing enough stock so that all rational investors sell out, leaving only cult members trading stock amongst themselves at a high price. However, most corporate profits, dividends, and buybacks are concentrated in a small number of very profitable companies at the very top – Apple and Alphabet combined accounted for 18% of all S&P 500 buybacks in 2023, and the top 20 companies combined accounted for nearly half  – so activity at smaller companies won’t matter, even in the aggregate.

We have also noted here in the past that one would expect buybacks to shrink executive compensation, all things being equal. Executive compensation is proportional to the size of the company being managed (last year, Tim Cook’s target compensation was seven times that of the CEO of the NYT), and buybacks shrink the value of the company, as they move value outside the box. 

The way to grow executive compensation would be to forego buybacks and dividends and instead retain profits to make internal investments or to buy other companies,5 which will cause the company to grow exponentially over time. This indeed was a popular strategy during the conglomerate craze of the 1960s, but this tended to destroy value (or at least transfer value to the shareholders of target companies) so it quickly fell out of favor with investors. Buybacks usually happen because investors like them; most executives feign enthusiasm but merely tolerate them so they don’t get fired.

Finally, some make the more nuanced argument that buybacks can be a trick to inflate the value of stock option packages specifically; the idea is that buybacks shift returns from dividends to capital appreciation, and stock options only gain value with capital appreciation. The problem with this argument is that the effect size would be fairly small, and in any case, options have fallen out of favor over the years, going from 70% of CEO stock comp in 2006 to only 34% in 2022.

Claim: Stock buybacks divert money that would normally go to increase wages, as demonstrated by the fact that wages flatlined at the same time stock buybacks were liberalized, in 1982.

This one is pretty simple. The NYT goes out and sells newspaper subscriptions and ad space for money. The workers at the NYT, who (in conjunction with the NYT’s external suppliers) produce the newspaper, would like to capture as much of that money as possible in the form of wages. The money that is left over after paying suppliers and employees is profit, which goes to the owners. The owners would like to capture as much profit as possible, and they will fight with the workers and suppliers over how the pie is split.

Buybacks and dividends and investments have nothing to do with it. Sure, after they clear their profits, the owners need to figure out how to reinvest their money in the highest return projects possible, and usually this means pulling cash out of the company via buybacks and dividends and investing in new external projects. Sometimes it means leaving profits in the company and investing in internal projects, but it is the nature of successful investment that it simply turns into more money later, so even internal investment must eventually lead to bigger buybacks and dividends, because at some point, internal investment opportunities will get tapped out. (Indeed, the handful of companies that drive a disproportionate share of all buybacks today are always going to be the companies that successfully reinvested retained earnings in the recent past — that list today includes Apple, Alphabet, Amazon, and Meta.)

Complaining about high profits and high buybacks and/or dividends is totally redundant and illiterate. It is like complaining that the CEO gets paid too much and he deposits his paycheck into his bank account. Profits have to be returned to the owners eventually, and buybacks and dividends (and sometimes the sale of the entire company) are simply the vehicles through which this happens. Higher buybacks have chiefly come at the expense of lower dividends, but in any case, both happen after the company has booked their profits, so it is highly unlikely that buybacks lower wages.

It is natural that the Times Guild should publicly negotiate for higher wages at the expense of corporate profits – after all, collective bargaining is the point of a union. And in a way, it’s quite clever for a newsroom to employ economically illiterate talking points in service of wage negotiations. What is management going to say? “Don’t listen to our newsroom, they are economically illiterate, also subscribe to the New York Times for only $1 per week and become better informed about the world!” But we should still call out economic illiteracy when we see it.

It is also worth noting here that high corporate profits and buybacks can’t be the source of broad-based lower wages anyway, simply because the most profitable companies are not the largest employers. Corporate profits are largely a function of high productivity and sustainable competitive advantages, and so are not remotely evenly distributed among companies. 

In aggregate, the S&P 500 only employs 20% of US workers, despite accounting for over half of all corporate profits. A huge share of American workers are at smaller companies that hardly make any profit at all. There is a huge disparity even within the top of S&P 500: Apple made $97 billion last year despite having only 90,000 US employees, while Wal-Mart made only $16 billion last year with a US workforce of 1,600,000! Even if the most profitable companies decided tomorrow to share more of their profits with their employees, it would do nothing to improve the lot of most workers, simply because the most profitable companies employ such a tiny share of the total American workforce. 

As for the final claim about 1982, the claim that real wages have flatlined since the early 1980s has been repeatedly debunked over time, and we already saw that buybacks didn’t gain much traction until well into the 1990s. In any case, corporate profits are unlikely to have been the source of widespread wage pressure, since they have gone up only a couple of percentage points over the last forty years, and even that increase is partially attributable to falling effective corporate tax rates. 

Claim: Stock buybacks funded by debt are a harmful form of “financial engineering”.

In our original model, we only have one way of funding capital investment, the sale of equity. You put your money in a box, the managers of the box use your money in some business venture, and if that venture is successful, you get a fixed percentage of all the money thrown off by that venture from now until eternity.

In the real world, we have another popular way of funding capital investment: the sale of debt. This is a slightly different kind of contract. You still put your money in a box, but instead of getting a fixed percentage of all of the money thrown off by the venture for eternity, you get a fixed dollar amount for a fixed period of time (and you also get your fixed dollar investment back at the end).

This is a minor feature to add to our model. Equity and debt are very similar at a fundamental level: you defer consumption today in the hope of more consumption tomorrow. The main difference is in the payoff schedule. The payout for the equity investor is subject to much more variance; the debt investor gets a fixed payout, and the equity investor gets whatever is left over. An entrepreneur will usually raise money using a mix of both equity and debt, depending on the availability and price of each instrument in the market.

You will often see a pundit work himself into a lather over these kinds of basic capital structure decisions, fulminating about how virtuous old-fashioned physical engineering has been displaced by dishonest modern financial engineering. As evidence, he will point to transactions where a company swaps debt for equity, such as a leveraged buyout (in which a sponsor buys out existing equity owners in part with money raised from debt investors) or a stock repurchase funded in part with newly-issued debt. (Curiously, no one complains when companies engage in the opposite form of financial engineering – using retained equity earnings to pay down debt.)

There is nothing inherently good or bad about swapping debt for equity or vice versa, despite the moral connotations often applied to debt generally. Debt and equity have different advantages and drawbacks, depending on the specific situation. We have been funding companies with different mixes of debt and equity for centuries. When you made (or will make) your biggest financial investment – your home – you likely funded (or will fund) that investment with a huge slug of debt and a sliver of equity. This is all perfectly well and good, because your debt payments align with your likely future income stream.

The 2010s were a period of very low interest rates. Naturally, some corporate executives responded to this by issuing cheap debt and using the proceeds to retire expensive equity. For example, Apple had $121 billion of cash and zero debt at the end of fiscal 2012; this shifted to $148 billion of cash and $113 billion of debt by the end of fiscal 2023. Apple borrowed money and used it to buy back stock.

This is fine. Managers are supposed to respond to price signals by giving investors what they want. If investors are willing to pay a dear price for debt (Apple’s average effective pre-tax interest rate on outstanding term debt is less than 3%) and demand a high price for equity, Apple executives can make everyone happier by issuing cheap debt and using the proceeds to redeem expensive equity. Investors get the debt they crave and dispose of the equity they don’t want. 

Observe that this is simply a recapitalization – investors as a whole still own Apple in its entirety, just chopped up in a slightly different contractual form. This recapitalization might have been conducted through share repurchases, but it could have been done through dividends instead and ended with the same result.

Nothing immoral occurred here, there was no manipulation, and there was certainly no displacement of physical engineering. This was a good trade for everybody. Within certain limitations around complexity, taxes, and risk, it is desirable that companies respond to changing circumstances and price signals by altering their capital structure, and when they do so, it is inevitable that they will use stock buybacks as a tool. 

Claim: Stock buybacks are harmful to society because they discourage investment.

This is the crux of the debate around stock buybacks. The argument here is that stock buybacks displace productive economic investment. The story goes that if businesses would just reinvest their profits rather than give them back to shareholders, society would be much better off.

Let’s think about what would happen if major companies went from retaining 23% of their earnings (as they do today) to retaining 100% of their earnings.

Well, first, big business would get much, much bigger. Companies that reinvest earnings productively will not only grow, they will grow exponentially. 

We have seen this historically not only with conglomerates like Berkshire Hathaway, but also capital-intensive juggernauts like Wal-Mart, McDonalds, and Southwest. They started from a few stores (or planes), and each year, they reinvested almost all of their earnings into more stores, and after a couple of decades, they had thousands of stores. Eventually, they saturated their markets, stopped building more stores, and started returning gobs of cash to shareholders.

Last year, the S&P 500 returned $1.4 trillion to shareholders in dividends and repurchases, or 3.5% of its market value. If those companies instead used that money to invest internally year after year, in only twenty years, they would double in size relative to the rest of the economy (relative to the counterfactual), and in twenty more years, they would double again.

However, remember that our other economic inputs, such as labor, are fixed. For existing big business as a whole to grow much faster than the broader economy, it would have to aggressively displace smaller businesses. If the S&P 500 employs 20% of all workers today, imagine what it would mean for that group to quadruple in size.

In our thought experiment, new business formation would be throttled as well: while big incumbents would be newly awash in capital to redeploy, savers would be starved of the cash they would usually use to fund new enterprises. 

Historically, startups have been the engine of American growth and innovation, and without them, the corporate landscape would look much different. For example, here is a list of the original Dow Jones Industrial Average components from 1896:

The American Cotton Oil Company 

Distilling & Cattle Feeding Co. 

North American Company 

The American Sugar Refining Company 

General Electric Company 

Tennessee Coal, Iron and Railroad Company 

American Tobacco Company 

The Laclede Gas Company 

The United States Leather Company 

Chicago Gas Light and Coke Company

National Lead Company

United States Rubber Company 

In this world, anyone with an innovative business idea would be at the mercy of gatekeepers at existing big businesses, who now would control all the cash. Would we expect a Manager of Corporate Development at The American Cotton Oil Company to spot and oversee the next Google?

We could go on and on with this reductio, but here we should stop and identify some of the errors in the original premise. 

For one, the statement “buybacks discourage investment” carries with it an implication that more investment is always better, when in fact the goal is to find the optimal dose of investment for each organization. 

Investment consumes scarce societal resources, so it is nonsensical to talk about the risk of discouraging investment without first defining the optimal level of investment and then showing that major companies are falling short of that mark, something that no critic ever does. 

This is a general issue with models that appeal to our intuitions. We never have to think of broader economic constraints when we buy a loaf of bread or fill a tank of gas — we just worry about the price and otherwise think of the supply as infinite, which for our purposes it might as well be. But when we model the economy as a whole, we have to think carefully about all of the economic constraints within the system, such as the size of the labor pool – companies can’t just increase investment without disrupting other parts of the system.

Finally, there is a common fallacy at play here, which is the false analogy between a household budget and a corporate budget. In reality, corporate managers do not have the full discretion to reinvest earnings as they please. Investors are worried that managers will squander their cash, and so they expect managers to justify their capital allocation decisions, and they generally expect managers of large, mature companies to return the majority of the cash they generate back to investors. 

This works the other way as well: companies are not limited by their retained earnings either. If they can convince investors that they have a lot of good investment opportunities, they simply raise more money from investors to act on them. Smaller companies do this routinely, through the sale of equity and debt. Companies are constrained, but in a much different way than households are.

Let us propose a simple framework for thinking about earnings reinvestment. From a company’s point of view, investment opportunities come in three buckets:

1. Growing or improving the existing business. In our newspaper example, it was more printing presses. These investments usually carry a high incremental return, but are hard to come by, as they are constrained by demand – the number of printing presses you need is limited by the number of homes you can deliver newspapers to.

2. Expanding into adjacent businesses, where the assets from your existing business give you some sort of edge over competitors. The NYT has recently gotten into the casual gaming business with some success, rolling it into their existing app. Historically, newspaper companies also reinvested part of their earnings into other media assets, such as magazines, television stations, and cable, which might be considered adjacent if you squint hard enough, but probably didn’t rise to the level of driving any real synergies.

3. Expanding into unrelated businesses. This is a bad idea unless you are literally Warren Buffett. We already referenced how there was a conglomerate fad in the 1960s that went very badly. Investors have learned through bitter experience that companies that lose focus do badly — it is not for nothing that Peter Lynch dubbed this process “diworsification”.6 

Within this framework, it’s easier to see why big companies return most of their cash to shareholders. The most profitable companies today will tend to be more mature, and thus will have fewer opportunities to grow or expand their existing business, especially relative to the gushers of cash they generate. (How profitable are the biggest companies? Apple generated nearly $100 billion of profit in fiscal 2023 after spending $30 billion on R&D, while all US early-stage VC investment last year added up to only $40 billion.) Investors wish to allocate cash to the companies with the best investment opportunities, and so we observe investors moving cash away from large mature companies toward smaller growth companies.

Given that the theory behind this claim is incoherent, it should be no surprise that the evidence shows no relationship between buybacks and investment. Corporate capital expenditure and R&D spending are no lower today than they were before buybacks were popular. Also, the largest buyers of their own stock are Apple, Alphabet, Meta and Microsoft (which combined accounted for a quarter of all S&P 500 buybacks over the last 5 years), which are the same companies that also spend mind-boggling sums investing in AI, self-driving cars, and VR.7

Claim: Nuh-uh, look at this case where a company failed to make an important investment at the same time it was buying back stock!

Here is a common narrative:

1. An opinion writer decides to scrutinize a company that has stumbled recently.

2. They discover that the company would be doing much better if it had not passed on some particular investment.

3. They also learn that this company was buying back stock at the same time it was passing on this investment.

4. They triumphantly conclude that the stock buyback caused the company to miss the investment, and was therefore the root cause of the company’s misfortune. They then pen a thinkpiece about how buybacks are ruining Corporate America, with this particular example as Exhibit A.

For a concrete example, we previously looked at an NYT op-ed that made this argument at the time Southwest had an operating meltdown over Christmas: Southwest was buying back stock at the same time it failed to upgrade its scheduling software, therefore stock buybacks were a root cause of Southwest’s woes. 

This line of reasoning is completely fallacious, and you see it everywhere.

First of all, the writer implies that they have uncovered a correlation between major companies that repurchase stock and major companies that pass on good investments, when they have done nothing of the sort. This is because substantially all profitable major companies buy back stock as a matter of policy. 429 members of the S&P 500 (that is, 86%) bought back stock in 2023 alone. Extend the window more than 12 months, and you will likely find that basically every profitable company has engaged in some sort of stock buyback in the recent past. Of course every investigation of a struggling company will turn up a stock buyback in the past; every investigation of a successful company will uncover a past repurchase as well. All major profitable companies buy back stock!

In addition, we know that 100% of all companies regret passing on some past investment. (Who among us doesn’t?) If you ran the investigation the opposite direction, examining companies that bought back stock, you would find that they all have many investment decisions they wish they could have back. This would also show nothing. The Venn diagram of Major Companies That Repurchased Stock and Major Companies That Missed an Important Investment isn’t even a circle, it’s just the rectangular set labeled “Major Companies”.

The problem with the entire premise the false analogy between a household budget and a corporate budget (again). People assume that any missed investment is due to some cash constraint, when that is never going to be the case at a big company that always generates more cash than they know what to do with. 

Bad investment decisions are just bad investment decisions: they usually result from some other less obvious organizational constraint (such as a cultural resistance to change, or limited bandwidth to implement change) or just plain old bad judgment. People tend to assume that most difficult problems can be solved by writing a check, but if that were the case, big companies would be unbeatable, when in reality they come and go.

Of course it is still possible that there is still some causal relationship here; maybe companies that spend more on buybacks make worse investment decisions. If this were the case, however, we would expect to see some anecdotes where a company passed up on an investment specifically so it could make a larger buyback. We actually have anecdotes like this when it comes to dividends; here is a story about GE during the depths of the financial crisis from Power Failure, which we reviewed in the last edition:

On January 24 [2009], [GE CEO Jeff Immelt] announced that GE would keep its dividend at $1.24 for 2009 and would perhaps cut it in 2010, but only if necessary.

…

After forty-five minutes of debate at the board meeting, Ralph Larsen, the lead director and former CEO of Johnson & Johnson, announced the dividend would have to be cut. “That’s just the way it is,” Larsen told Jeff.

…

After Larsen ruled on the dividend, Jeff said he felt like “a cub getting my ears boxed by an elder lion…No matter how dire the extenuating circumstances…I didn’t want to be the CEO who cut GE’s storied dividend for the first time since the Great Depression,” he recalled.

Even though Immelt was eventually overruled, one can see why he wanted to keep paying a large dividend, even when his company desperately needed to preserve cash. He viewed the dividend as a public commitment, and one does not backtrack on public commitments lightly. And by the same token one can see why the board merely wanted to cut the dividend, instead of eliminating it entirely, which would be the common sense action for a company in financial distress. Later that year, GE ended up having to sell NBCU to Comcast at a deep discount in order to shore up the balance sheet, a move they would later deeply regret.

This story is actually a strong argument in favor of buybacks. Many companies pride themselves on an unbroken dividend streak stretching back decades, but no one strives for an extended repurchase streak. A company can just shut off buybacks for a couple of years with no repercussions, as the NYT did in 2020 and 2021. 

When this newsletter last touched on the subject of buybacks, we said:

[W]e should consider banning buybacks because there is no other topic on earth that causes so many otherwise smart people to contort themselves into logical pretzels and embarrass themselves in public…everything was fine back when everyone was mostly just paying out dividends, so maybe we should think about just going back to that and saving everyone the brain damage.

Buybacks probably don’t matter. There exists another very close substitute, dividends, which produces almost the exact same end result with none of the toxic discourse.

Buybacks are only worth examining because they are a perfect example of how dumb economic ideas take hold in the minds of intelligent people and refuse to let go. Even though the buyback debate has almost zero real stakes, the same cannot be said of housing, trade, price controls, or any of the numerous other issues where popular economic myths lead to destructive public policy.

Stock buybacks would seem to be a strangely arcane topic for the average person to get worked up about. Who cares about the specific financial pipes that your savings flow through to end up backing a promising project? 

We get angry about stock buybacks because our brains work from a series of false analogies to assemble a narrative that triggers strong emotions: we imagine that stock buybacks pit big corporations against everyday workers, demonstrate executive greed, and accelerate the decline of great organizations. The other major financial topic that gets such a reaction is high-speed trading, another case where normal people get very angry about a topic they do not remotely understand because they mistakenly believe it represents some great injustice.

Adam Mastroianni has a great recent post that relates to this topic. He observes that a lot of complicated math was figured out by the ancients, thousands of years ago, but a lot of simpler facts about nature were a mystery until very recently: for example no one knew that objects of different masses fall at the same speed until someone thought to do an experiment in the 1600s, and no one thought to figure out the rules of heredity until Mendel did some experiments in the 1800s. 

He connects this to what psychologists call the illusion of explanatory depth: the idea that people believe they have a deep understanding of a given topic, when in fact they are only working from a folk theory, an incomplete and incorrect intuitive model of a subject. When you are familiar with some subject, and you have some mental model of it that works well enough for your purposes, you incorrectly conclude that you have a real understanding of it, and explore no further. The illusion is only shattered when you are forced to write out a coherent explanation of the topic in your own words. (The original experiment asks people to explain everyday objects like toilets and car batteries.) Mastroianni states:

This, I think, explains the curious course of our scientific discovery. You might think that we discover things in order from most intuitive to least intuitive. No, thanks to the illusion of explanatory depth, it often goes the opposite way: we discover the least obvious things first, because those are things that we realize we don't understand.

He concludes that the key to progress is to break the illusion of explanatory depth; people didn’t explore heredity and physics for many centuries because they were working from folk theories that mostly worked well enough, but once they realized how little they actually knew, they made quick progress by formulating theories and then rigorously verifying them (or discarding them) through careful experiments.

Folk theories are as prevalent in economics as they are in science. People use intuitive models and heuristics that actually manage to work well enough in day-to-day life, but backfire badly when applied to create economic policy because they contain weaknesses that emerge when they are used to model the system as a whole. 

For example, we talk here sometimes about common examples of counterproductive housing policy, where for example a government might propose subsidies for new homebuyers while placing strict limits on the production of new homes. According to folk theory, the subsidy will obviously make new homes more affordable, but a more careful model will show that housing will in fact not become more affordable at all: houses will be no more abundant than they were before, and the extra subsidy will simply cause homebuyers to bid up the price of existing homes, fully negating the value of the subsidy to the homebuyer and transferring the subsidy to selling homeowners. 

It is easy to see here how folk theory works well in day-to-day life but breaks down when considering the economy as a whole: we navigate the world through the abstraction of money, and individually we can procure more goods and services by obtaining more money and outbidding our neighbor for scarce resources, but the economy as a whole is constrained by the total amount goods and services produced, and there is no neighbor that can be outbid. 

The illusion of explanatory depth explains how the Times Guild could be so confident and yet so mistaken about stock buybacks: even the brightest minds are easily fooled into thinking they have a deep understanding of a topic that they only have a superficial familiarity with. Often, the biggest obstacle to learning is having the awareness to realize that most of one’s beliefs derive from unsupported folk theories, and the key to knowledge is to have the discipline to dispose of that folk theory when a more rigorous and evidence-based model comes along to contradict it.8 

It would be wonderful if proven new discoveries were immediately widely accepted, but in reality, smart people tend to cling to folk theories long after being presented with conclusive evidence that contradicts them. We are usually unaware that we are operating from folk theories, and over time, these folk theories become ingrained in our minds as core truths, and as we use them and teach them, they become wrapped up with our egos and identities, and we form communities around them. When these folk theories are challenged, we instinctively look for any way to defend them, as a way to protect our own status and internal narratives. 

One popular story about this process plays out is, of course, Moneyball.9 But as it specifically pertains to economics, Paul Krugman wrote a classic essay exploring this subject in 1996, back when free trade was a hot topic. In it, he expressed shock that so many intellectuals of the day failed to comprehend and publicly opposed the basic concept of comparative advantage, an idea that was originally formulated and fully explained in the 1800s. His thesis was as follows:

(i) At the shallowest level, some intellectuals reject comparative advantage simply out of a desire to be intellectually fashionable. Free trade, they are aware, has some sort of iconic status among economists; so, in a culture that always prizes the avant-garde, attacking that icon is seen as a way to seem daring and unconventional.

(ii) At a deeper level, comparative advantage is a harder concept than it seems, because like any scientific concept it is actually part of a dense web of linked ideas. A trained economist looks at the simple Ricardian model and sees a story that can be told in a few minutes; but in fact to tell that story so quickly one must presume that one's audience understands a number of other stories involving how competitive markets work, what determines wages, how the balance of payments adds up, and so on.

(iii) At the deepest level, opposition to comparative advantage -- like opposition to the theory of evolution -- reflects the aversion of many intellectuals to an essentially mathematical way of understanding the world.

Three decades later, we can see very little has changed. Debunked economic folk theories are still sold as avant-garde, and their inherent complexities and contradictions are actually highlighted to appeal to insecure midwits. The key to breaking free of folk theories is to have to write out how the theory maps to the evidence, but we exempt folk theories from the normal standards of logic and evidence: for example, unrepresentative anecdotes are recast as “lived experience”, and mishandled or outright fabricated data is presented as unassailable proof. 

There is a full set of widely accepted but incoherent talking points that defenders of economic folk theories revert to when challenged: for example, it is often claimed that academic economic models are useless because they cannot describe the world with precision, but it is never explained why folk economic models would be any more reliable by that standard? 

Public policy crafted around folk theories is rarely subject to any accountability; when those policies predictably backfire, instead of engaging in introspection, we simply double down on the same failed policies. 

Finally, there is a huge incentive for politicians and members of the media to pander to the sensibilities of the public and bestow credibility on debunked folk theories, thus the unsupported claim from the Treasury Department that stock repurchases harm innovation. This creates a cycle where bad ideas manage to retain some legitimacy and never quite go away.

In such an environment, we should always expect to be walking around with some web of debunked ideas in our brains, especially as we get further and further away from our area of expertise. The best solution, to constantly evaluate every theory against the available evidence in a consistent epistemological framework, is perhaps unrealistic. Maybe the next best solution comes from the late Charlie Munger:  “Any year that passes in which you don’t destroy one of your best-loved ideas is a wasted year.”

Winning Now, Winning Later by David M. Cote (2020)

The most popular business books are those that chronicle spectacular failure: witness the recent best-sellers about Theranos, Uber and WeWork, all of which have been adapted into successful mini-series. Byrne Hobart has theorized that this is because tragedies make for a compelling narrative, and because the people involved are motivated to talk, in the hope of absolving themselves of blame.

Then again, maybe business failure stories are compelling for the same reason that true crime stories are so popular: They make us feel more prepared to handle and avoid similar situations, and they make us feel better about our own lives by comparison.

Publication-worthy business failure stories frequently fit one of two patterns: straightforward fraud (CNBC’s American Greed recently wrapped its 15th season) or the meteoric rise and fall of an overly ambitious startup (LTCM, WeWork). In both cases, the sums of money involved are usually small — at least by the standards of Corporate America — and the warning signs were clearly visible all along. There is something almost comforting about the patterns these stories follow: both the perpetrators and the victims are greedy, arrogant, and delusional, and the reader gets to feel superior to everyone involved.

The collapse of General Electric stands apart. GE was the bluest of the blue-chips: descended from Thomas Edison and J.P. Morgan, it was one of the original twelve components of the Dow in 1896, and grew to become one of the leading technology giants of the early 20th century. After WWII, GE evolved into an industrial behemoth with dominant positions in a dizzying array of electricity-adjacent markets, from jet engines and turbines to light bulbs and home appliances.

In the 1980s, GE ascended to new heights. Jack Welch took the reins as CEO in 1981, and he established GE a major player in media and financial services while reinforcing GE’s position in select attractive industrial markets. For most of the 1990s and 2000s, GE was the most valuable company in America, with a valuation topping out at over $1 trillion in 2023 dollars. While GE had some skeptics and critics at the time, it was typically seen as a corporate paragon, regularly named by Fortune as the most admired company in the world. Welch was regarded as a management guru, and his underlings were routinely poached to become CEOs at other Fortune 500 companies. 

And then, a few years ago, it all unraveled in spectacular fashion. Much of the supposed success from the Welch era of the 1980s and 1990s proved to be illusory, the product of temporary tailwinds and aggressive accounting. GE’s fortunes worsened under the reign of Welch’s handpicked successor, Jeff Immelt, who took over in 2001. Immelt struggled to cope with the problems he inherited, which were compounded by the 2008 financial crisis and major missteps of his own. In 2017, when the extent of GE’s problems became clear, GE’s stock nose-dived, and Immelt was pushed out. 

GE has been one of the worst performing mega-cap stocks of the modern era. A $1,000 investment in the S&P 500 in 2000 would be worth over $2,700 today (excluding dividends), while a $1,000 investment in GE in 2000 would have dwindled to only $210. Even going all the way back to Welch’s appointment in 1981, the S&P has outperformed GE by a three-to-one margin.

There is something unsettling about the fall of GE. The villains of most failure stories are obviously flawed: most of them are fraudsters, and many of them are comically inept to boot. By contrast, Jack Welch could have been the hero of a Michael Lewis book, the unconventional maverick that defied skeptics to turn a bureaucratic plodder into a nimble world-beater.1 We discussed his successes in a recent edition:

[Welch] decided that GE should get out of businesses where they were not #1 or #2, an idea so successful that it has become almost conventional today. He aggressively deployed capital, making big internal investments in airplane engines, gas turbines, and cable television that all paid off handsomely.  He demanded that managers hit their numbers, and his rigorous operational reviews were legendary.

Jack Welch had most of the traits we typically associate with a great executive. He was incredibly smart (earning his PhD in chemical engineering in only three years), he was demanding of his subordinates, and he worked tirelessly. He had deep operating experience, he was willing to buck convention, and he produced quantifiable results. He was charismatic, ambitious, and a world-class marketer and publicist. And yet, he will forever be remembered as the father of the biggest corporate disaster in American history. 

The story of the fall of GE is worthy of an authoritative book, and we looked at a pair of early entries a couple of years ago – Lights Out, written by the WSJ journalists that covered its fall, and Hot Seat, Jeff Immelt’s memoir.

Power Failure, weighing in at nearly 800 pages, is the most ambitious yet. The author, William Cohan, did an early-career stint as a junior analyst at GE Capital in the 1980s, before becoming an investment banker and then a business writer, putting him in a unique position to tell the GE story.

What sets Cohan’s effort apart is that he got almost everybody to talk to him for his book. He managed to interview both Jack Welch (before he passed away in 2020) and Jeff Immelt, and many former and current senior GE executives as well. Dozens of GE critics, counterparties, and journalists also weigh in throughout. (Not for nothing does Cohan begin with the parable of the blind men and the elephant.) As a result, the book is packed with interesting original anecdotes and pieces of gossip, which Cohan skillfully interweaves around contemporaneous news coverage of GE to give a comprehensive picture of the events that transpired. The result is almost more of an oral history, giving the reader a variety of perspectives on how GE failed.

Still, as comprehensive as Power Failure is, it still doesn’t offer a clear answer to one simple, important question: How did GE lose so much money? 

In fairness, this might be an impossible question to answer because of GE’s intentionally obfuscatory accounting, but it’s worth exploring here. It is no small feat for a diversified conglomerate to be one of the worst performing stocks over a long period of time despite some major commercial successes along the way. GE built a leader in aircraft engines, going from $1 billion of annual profit in the mid-1990s to $7 billion in 2019. GE did the same in power turbines: GE’s power systems business was making nearly $6 billion a year during the peak of the power plant cycle in the late 90s and early 00s ($10 billion in current dollars), nearly a third of GE’s earnings at the time, and continues to be the market leader today. On the other hand, there is no single division or bad decision that can be singled out for GE’s collapse.

While we don’t have clear math showing us where GE lost all that money, Power Failure suggests some leading culprits to examine:

1. Poor operating discipline, particularly after Jack Welch left. It is repeatedly suggested that GE’s operating performance slipped under Immelt.

2. GE Capital. Before the 2008 financial crisis, GE Capital contributed over half of the company’s earnings, and after the crisis, its earnings power disappeared and it was sold off for parts.

3. Poor capital allocation. GE actively bought and sold hundreds of businesses, and they frequently bought high and sold low. 

Even though GE mostly delivered solid operating results in their core industrial businesses over this time period, performance was mixed elsewhere. Here is Comcast’s Steve Burke, describing the situation at NBCUniversal (NBCU) when he took over in 2011:

Burke quickly concluded that NBC had been terribly managed under GE and the culture was abysmal…[Burke] quickly ascertained…that the incentive at NBCU wasn’t to make money. “The game here was to keep GE, to keep Connecticut, happy,” he said. “So the game then became telling a story, as opposed to running businesses, and the incentives were to do that. And then GE–and I fault Immelt on this–they never had the intellectual curiosity or the drive to really understand the businesses.” As one example, Burke cited the way GE managed the local NBC news affiliates. Under Comcast’s management, the local stations made $650 million, up from $150 million under GE. “We invested in local news and we put guys in place who knew what they were doing,” he said, and “and GE never took the time to do that.”

NBCU historically only contributed about 10% of GE’s annual earnings, but after a few years under Comcast’s control, NBCU’s profits more than doubled. In the meantime, GE’s profit machine sputtered. By 2017, only six years after it was taken over by Comcast, NBCU’s annual operating profit surpassed that of its former GE parent.

In time, GE’s industrial businesses would run into operating issues of their own. The biggest culprit was the Power Systems division, which was dependent on the global demand for new gas-fired power plants. In the 1990s and 2000s, gas-fired plant construction was running at all-time highs, but by the 2010s, demand was falling off a cliff. In an effort to hit unrealistic earnings targets demanded by Immelt, divisional management made some ill-advised decisions. Here is the John Flannery, Immelt’s successor as CEO, learning about what happened: 

At one point, the Power team was discussing with Flannery and [GE CFO] Bornstein a $1.1 billion unsecured exposure that GE had in Angola from selling the country railway and power equipment. “We sold a bunch of power equipment in Angola, and we just took an account receivable,” one person in the meeting remembered. “No cash. No security. No nothing. They’re not paying it. Of course, we book the income and the sales.”

GE’s problems in media and industrials were minor compared to what happened to their finance subsidiary, GE Capital. Under Welch, GE transformed its tiny financing arm into a behemoth involved in real estate, aircraft leasing, commercial lending, and insurance. By the 2000s, GE Capital accounted for more than half of GE’s earnings.

GE Capital’s large profits were dependent on the spreads enabled by GE’s AAA rating, which enabled it to borrow at rock-bottom rates. Also, GE Capital was unusually willing to fund its balance sheet with short-term commercial paper, with no backup lines of credit. This allowed it to capture even larger spreads when short-term interest rates were low, but exposed them to a potential liquidity crisis in the event that investors got cold feet and refused to roll over their debt. When that liquidity crisis hit in 2008, GE Capital teetered on the edge of bankruptcy.

Warren Buffett and the US government helped GE Capital avoid disaster, but the days of cheap borrowing and low regulatory oversight were over for good. According to Trian, the activist investment firm that got involved with GE in the mid-2010s, return on tangible equity at GE Capital was 40% in 2007, but that collapsed to only 8% in 2014. GE Capital went from a profit machine to a candidate for liquidation, and indeed, GE would choose to exit the business entirely in 2015. In hindsight, GE Capital’s pre-crisis profitability was completely unsustainable.

While GE’s operating issues in media, finance, and power account for a share of its struggles under Immelt, they can hardly have been major contributing factors to GE’s underperformance dating back to Jack Welch. NBCU may have underperformed, but it should still have been a successful investment considering how little GE paid for it. The same can be said for GE Capital and Power Systems: they may have over-earned for many years, but they should still have delivered a very high return on investment overall.

That leaves capital allocation as GE’s biggest problem. Welch was able to establish an image as a shrewd dealmaker in the 1980s, when he bought RCA for a song, and was able to pay for it by selling the parts he didn’t want (like TV manufacturing) while retaining the part he did want for free (NBC). Welch certainly deserves credit for being far-sighted about getting out of low-quality commodity manufacturing businesses, but his overall track record on acquisitions was actually fairly mixed. 

He missed a chance to close a home run acquisition of Cox immediately prior to getting the CEO job, and he became enamored with finance and insurance, which led to a string of troubled acquisitions, including Kidder Peabody and Employers Reinsurance Corporation (ERC). Finally, at the end of his tenure, he agreed to a deal to acquire Honeywell for a small amount of GE stock, but got cold feet and didn't push to get it through EU approval, allowing the deal to fall through. Today, Honeywell is almost twice as valuable as GE.

GE’s capital allocation problems really took off under Immelt. Immelt did some smaller deals in water, security, and oil and gas that proved to be ill-timed. To Immelt’s credit, he almost managed to mostly get out of the struggling insurance businesses he inherited without too much damage, but he ultimately retained a small piece of ERC that led to a $15 billion loss later on. Finally, Immelt ramped up GE’s exposure to consumer finance in the years leading up to the 2008 financial crisis, another small bet that blew up. 

In 2009, at the bottom of the market, he sold NBCUniversal to Comcast for only $30 billion. By 2017, NBCUniversal was making over $8 billion a year before tax, and was worth over $100 billion. (GE is worth only $85 billion as of this writing.) Finally, there was the notorious 2015 acquisition of Alstom’s power business, which was so troubled that it resulted in a $22 billion writeoff just three years later.

Jack Welch was public in his distaste for stock buybacks, preferring to use cash for acquisitions instead. GE’s track record demonstrates why investors so often prefer buybacks, and are particularly loathe to allow companies to invest outside their core business: managers are high on ego and low on investment expertise. Management teams can vaporize years of earnings with just a few bad deals; GE was making less than $10 billion a year after tax (and declining) while they were losing $15 billion or $20 billion at a time on bad investments.

Power Failure also doesn’t really offer an overarching theory of why GE failed. Power Failure lists many different things that went wrong at GE — bad management, bad acquisitions, bad incentives, bad accounting, bad luck — but almost all companies suffer from some of these issues without running into a GE-scale disaster. Maybe the failure of GE was the result of an unlucky confluence of individual problems, but it feels like for a group of smart, hard-working people to produce such an exceptionally catastrophic result, there must be a larger lesson to be drawn.

One possible clue comes from the story of David Cote, a star GE finance executive who rose to become the head of the Appliances division in the 1990s, and was one of five early candidates to succeed Jack Welch as the CEO of GE. However, he was eliminated before the three finalists were chosen, and he was asked to leave GE. It is suggested that Cote was doomed by the divisional assignment he drew; the finalists were the ones who had been assigned to oversee GE’s crown jewels, while he was stuck trying to fix a basket case.

Cote eventually landed a position in 2002 as the CEO of Honeywell, a much smaller industrial conglomerate – Cohan at one point refers to it as a “mini-GE”. Honeywell had been run since 1991 by Larry Bossidy, who before then had spent his career as a top executive at GE, a close associate of Jack Welch. 

In 2000, GE agreed to acquire Honeywell, in a stock deal where Honeywell shareholders would have received 8% of the combined company. However, the EU moved to block the deal on antitrust grounds, and while the case was pending, GE got cold feet after getting a look at Honeywell’s aggressive accounting. The deal fell through, and Cote was hired to forge a path forward for Honeywell as an independent company.

Cote had an incredibly successful run at Honeywell, leading it until his retirement in 2017. While GE foundered, Honeywell soared. A $1,000 investment in Honeywell in 2003 would be worth over $9,000 today, while the same investment in GE would now be worth only $450. Remarkably, Honeywell managed to surpass GE in overall value as well: Honeywell’s current market capitalization is $140 billion, while GE is now worth less than $90 billion. GE is slated to be broken up, but as it stands today, is nothing more than a mini-Honeywell.

This would seem to be the perfect natural experiment. A GE cast-off takes over a small company run by Jack Welch’s former right-hand man, and turns it around and surpasses GE. What did Cote do so differently from Welch, Immelt, and Bossidy, to get such a spectacular result?

Fortunately, Cote wrote a book about his experience, entitled Winning Now, Winning Later. It is fairly standard for the business advice genre, a short work with stories from his time at Honeywell, with the corresponding lessons he learned. He is scathing when describing the culture he inherited at Honeywell, but diplomatic enough to not mention any of the perpetrators by name, not even Larry Bossidy. Nonetheless, the dysfunctional culture he describes encountering at Honeywell is almost identical to the flawed culture Cohan describes at GE, from the scattershot approach to M&A to the practice of using accounting gimmicks and shortcuts to “hit the numbers” at the end of each quarter. 

What is Cote’s diagnosis of the root problems at Honeywell? Cote opens the book by telling the story of an internal meeting at the beginning of his tenure, a business review of Honeywell’s Aerospace division. The head of Aerospace was steeped in the old culture, and had even been a candidate for the CEO job that Cote won. The meeting does not start well:

We sat down in a conference room so that team members could present their strategic plan to me. A copy of the plan had been placed on the table facing each seat. Flipping through mine, I saw that it was thick–maybe 150 pages long, full of charts and tables. Uh oh, I thought, not good. I had found so far at Honeywell that executives and managers often made presentations far longer than necessary, overwhelming audience members with facts, figures, and commentary to preempt sharp, critical questioning.

Nevertheless, Cote interrupts them with sharp, critical questions. The Aerospace team responds with annoyance — they had planned to put on a show and receive a pat on the back — but Cote interrogates them about the root cause of the $800 million in cost overruns on their biggest project. The team eventually relents and agrees to probe the root causes of their biggest issues, and they turn the ship around. Cote concludes (emphasis mine):

What I learned, to my chagrin, was that Aerospace had become adept at lying to itself, shoehorning costs here and there into a budget without acknowledging them openly. This put enormous strain on the organization, which then had to patch together aggressive bookkeeping and special deals with customers and others, to make its goals. A dysfunctional approach if I’d ever seen one.

Cote says that this approach was pervasive at Honeywell:

Lacking any drive to think deeply about their businesses, and unchallenged by leadership to do so, teams held meetings that were essentially useless, their presentations clogged up with feel-good jargon, meaningless numbers, and analytic frameworks whose chief purpose was to hide faulty logic and make the business look good. When you did a bit of digging, you found that most executives didn’t understand their businesses very well, or even at all.

Cote defines this as intellectual laziness. It is the tendency of organizations to “juke the stats” and lie to themselves instead of diagnosing and solving root problems. This kind of anecdote is everywhere in Power Failure; recall Steve Burke’s appraisal that GE “never had the intellectual curiosity or the drive” to understand and manage NBCU. 

Here we should observe what intellectual laziness is not:

1. It is not actual sloth. People are working hard, just not directed in a way that creates value. Cote acknowledges that the Aerospace division put in a lot of work to prepare a 150 page presentation. It is not like Honeywell had evolved into some kind of adult daycare; they were just in denial about the nature and depth of their problems.

2. It is not necessarily fraudulent or illegal in any way. Yes, the organization is being dishonest with itself, but it is fundamentally a form of mismanagement rather than willful deception. It is true that intellectual laziness might eventually evolve into fraud, as business performance declines and leadership is tempted to shift from making the numbers to making up the numbers (to steal Buffett’s turn of phrase), but that is not inevitable.

3. It is not stupidity or incompetence, either. In this example, the Aerospace team had the capability to identify and solve root problems, but they had just never been forced to do so by senior management. They were doing stupid things, but not because they were stupid; they were just responding to the culture and incentives in place.

Cote preaches that managers should instead strive for intellectual rigor, to probe deeply to identify and confront root problems and think creatively and rigorously to find solutions. He argues that “leadership [is], at its core, an intellectual activity.” In his framework, leadership is almost entirely about picking the right direction for the organization and getting the team to move in that direction. Needless to say, there is no way to pick the right direction without having a deep understanding of the business and approaching problems with rigor.

He argues that unless leadership enforces intellectual rigor, middle managers will manipulate their reported metrics (which he says that “any ninny” can do) while underlying business performance suffers. Cote shares numerous examples of this kind of short-term manipulation from the previous regime at Honeywell, such as the tale of a manager who made his quarterly numbers by chopping down the trees around his factory and selling the lumber for a one-time gain. He then reinforces his point with similar stories from his time at GE.

Cote’s formula for success is not some kind of complex 4-D chess, which GE so often seemed to be engaged in, but simplicity itself. The focus on avoiding denial and properly diagnosing root causes is reminiscent of the “five whys” technique pioneered by Toyota. (This is not the only approach Cote cribbed from Toyota: he also successfully implemented what he called the “Honeywell Operating System”, a customized version of the Toyota operating system.) There are no bonus points for complexity or originality in business: Cote took a couple of simple ideas from a successful company and applied them rigorously.

With the framework of intellectual rigor in mind, let’s revisit what went wrong at GE. At one point in Power Failure, Cohan is quizzing the former leadership of GE Capital about the practice of “earnings smoothing”, the art of making reported earnings grow consistently each quarter, even as the underlying business produces volatile results. 

GE Capital was central to GE’s ability to manipulate reported earnings. Accounting rules allow a company to book a profit whenever they sell an asset for more than they paid for it. In the course of their normal business, GE Capital owned hundreds of billions of dollars of assets, like bonds and office buildings and parking lots (which they funded with short-term and long-term borrowings). Over time, real assets tend to appreciate, at least in nominal terms. Whenever GE was having a bad quarter, they would sell some of these appreciated assets–say, an office building that was bought decades ago for $10 million that was now worth $20 million–and report the $10 million accounting profit as a part of regular earnings, to compensate for the earnings shortfall from the core business. As for GE Capital CEO Gary Wendt put it in Power Failure:

I always had a lot of [asset sales] available for the quarter. I had to because I knew [Jack Welch] was going to call up and say, “I need another $1 million or another $2 million or whatever,” and so I’d go over to [GE Capital CFO James] Parke and I’d say, “Okay, let’s do this one and this one.” Making your earnings was just life to us.

This kind of one-time accounting gain from asset sales is fundamentally different in nature from operating profits from selling jet engines and power turbines. The $20 million office building was already worth $20 million before GE sold it, despite being on the books for $10 million; selling it converts it to cash but does not make shareholders any wealthier (in fact, by triggering a tax bill, it can make them worse off), despite the accounting profit that gets booked. Bundling these kinds of accounting gains with normal operating results only serves to obscure the true performance of the business from investors.

Regardless, the most of the senior GE executives who talked to Cohan continued to stand behind the practice of earnings smoothing:

Over lunch at a Connecticut pub, Denis Nayden, who was one of Wendt’s successors at GE Capital, also defended the practice of harvesting GE Capital assets as needed. “What’s the job of a public company?” he asked rhetorically. “Produce earnings for shareholders.”

“The job of a public company is to produce earnings for shareholders” is a hell of a thing for the former chairman of GE Capital to be saying after the collapse of GE. If you ask GE’s investors, they would say the job of a public company is to make money for shareholders. GE was among the best at consistently “producing earnings” for shareholders; they did so for decades. They were just abysmal at making money. 

There is a plethora of ways to produce short-term earnings without making money, and GE somehow seemed to engage in all of them. You can sell appreciated assets to record an accounting profit. You can overpay for assets with high current earnings and poor long-term prospects. You can sell power equipment to Angola on credit, with little hope of ever getting paid in cash. You can book immediate paper profits from the long-tail insurance policies you sell today, and then find out two decades later that your assumptions were too optimistic and you have to come up with $15 billion of cash to plug the gap. There are no magic metrics, and GAAP earnings are as subject to Goodhart’s Law as any other measure.

According to Power Failure, almost every time GE made a major decision that destroyed shareholder value, the obsession with manipulating earnings was front and center in the thought process. GE lost a lot of money in insurance, but why was a manufacturing company in the insurance business in the first place? Well, insurance companies offer a lot of accounting leeway, in terms of the way reserves are taken and assets are sold for profit, and could act as “shock absorbers” that let Jack Welch report smooth earnings when other divisions stumbled. 

Why did GE Capital recklessly allow itself to become dependent on funding from short-term commercial paper, a practice that would almost bankrupt it in 2008? Well, short-term borrowing lowers interest expense, which boosts short-term earnings. 

Why did GE buy a subprime mortgage broker in 2004? They had just spun off their insurance business, and Immelt felt they needed to replace the earnings that the insurance business had previously generated. 

Why did GE keep expanding GE Capital? Well, it was a good way to increase earnings. Why didn’t GE sell out of noncore businesses like real estate and media when valuations were sky-high in the mid-00s? GE didn’t want to lose the earnings those divisions produced. The catastrophic 2015 acquisition of Alstom? Immelt thought the synergies would definitely increase earnings. The mistimed $40 billion stock buyback in 2015? Jeff Immelt decided on a $2 per share earnings target, and wanted to do anything he could to hit that goal.  Never in Power Failure does it seem like GE management gave any thought to shareholder value when making major decisions: it was always earnings, earnings, earnings. 

Even putting aside the obsession with reported earnings, GE’s culture seems to have been generally lacking in intellectual rigor. GE’s strategies were supported by narratives that sounded compelling at a superficial level, but fell apart under any kind of scrutiny. 

A classic example: Jack Welch liked to tell everyone that his brilliant insight about expanding into finance was that it had higher revenue per employee than industrial manufacturing, thus it must be a better business to be in. Of course, that is nonsense: there is no reason to expect there to be any relationship between revenue per employee and return on invested capital. 

Welch told this story even after GE learned this lesson the hard way in the 1980s, overpaying to acquire Kidder Peabody, a venerable investment banking firm (investment banking being perhaps the highest revenue per employee business that exists), a deal that was an endless source of trouble, and ultimately led to a $2 billion loss when GE finally got rid of it in 1995. (Cohan discovers when talking to a former director that Welch managed to prevent this massive loss from affecting reported earnings by raiding the reserves of the insurance business.)

Return on invested capital is mostly determined by factors like barriers to entry and sustainable competitive advantage, which GE’s industrial businesses had in spades but which GE Capital completely lacked — after all, money is a commodity. After the financial crisis, GE Capital’s return on invested capital collapsed not because revenue per employee declined, but because GE Capital’s lenders and regulators came to understand the true risk inherent in the business, and demanded higher rates, lower leverage, and closer oversight.

As GE placed no value on intellectual rigor, it is no surprise that they ended up promoting executives on the basis of polish and storytelling ability. So it was that when it came time to pick a new CEO, Welch elevated Jeff Immelt, a slick-talking salesman with little understanding of GE’s businesses and little patience for details, and dismissed David Cote, who would go on to have so much success at Honeywell. 

It is not clear that GE’s decision-making process was any worse under Immelt than it was under Welch. Immelt would be skewered by accusations that he encouraged “success theater”, a culture where executives never confronted root problems and pretended everything was going well, but the culture of extreme intellectual laziness certainly dated back to his predecessor. In fact, Welch’s best-selling autobiography was subtitled “Straight from the Gut”. 

It would be technically accurate to state that the dramatic collapse of GE resulted from a perfect storm of mistakes — wrong CEO, bad investments, strategic missteps, operational snafus. But underlying all of those seemingly unrelated mistakes was one thing: this culture of intellectual laziness, the willingness to juke the stats and tell comforting stories rather than diagnose and solve root problems. GE failed to create shareholder value because they didn’t really try to create shareholder value; they were content to be able to report some shiny meaningless numbers and a pleasant narrative.

Lest this seem like pure hindsight bias, it should be noted that these criticisms were leveled at GE from outside observers dating back to the 1990s and 2000s, initially from skeptical short sellers, but eventually also from respected investors like then-PIMCO chief Bill Gross.

None of this is to say that GE didn’t also have other issues. Certainly, there are examples of GE executives being dumb, lazy, or dishonest. There was certainly hubris, arrogance, and a delusion of exceptionalism. But those factors exist at most big, successful companies, and big, successful companies rarely collapse so spectacularly. 

It might seem counterintuitive that the root problem at GE could have been something as simple as intellectual laziness, so it might help to consider an analogy. 

Imagine you have several boats running a race from New York to Liverpool. The participants will try to maximize their speed, maybe by optimizing the design of the boat, or by adding sails, or by adding the largest possible engine. But one determining factor is whether the boat actually stays on course; the fastest boat will still lose badly if it starts drifting toward, say, Lisbon, or even Cape Town. 

Initially, the fastest boat might seem to be on course, maybe because it’s early in the race, or maybe because the winds are keeping it generally on track. But the fastest ship will lose the race very badly if it isn’t pointed in the right direction. GE was always focused on optimizing for speed, but with little thought given to navigation.

If GE’s culture of intellectual laziness under Welch and Immelt were really to blame for its spectacular collapse, why didn’t the cracks show much sooner? Honeywell was already teetering after only a decade under Cote’s predecessor, but GE grew and grew for over thirty years under Welch and Immelt before everything fell apart.

Here it is helpful to think about what GE had going for it:

1. GE had some good industrial businesses – strong franchises in good markets – and good businesses can handle some level of mismanagement and still produce good returns. 

2. GE legitimately did some things well. Cote is complimentary of many aspects of the way GE was run, and Welch made a few good big bets, getting GE deeper into good businesses like jet engines and cable television. 

3. GE management was great at telling a compelling story, so much so that Welch became a best-selling author after his retirement. A lesser storyteller might have been forced to confront his issues much earlier, but GE management was good at seducing investors and drawing their attention away from unaddressed problems.

4. GE benefited from major, major tailwinds for a very long period of time. In hindsight, GE in the 1990s and 2000s rode two bubbles: a one-time burst of construction in gas-fired power plants following electricity market deregulation, which led to huge profits for the power turbine business; and a concurrent boom in the financial services, which made it possible for GE Capital to grow and borrow cheaply and recklessly with no regulatory oversight. Temporary windfalls from those businesses covered up GE’s issues and allowed them to grow. In the 2010s, those bubbles burst, turning tailwinds into headwinds, and revealing the underlying rot in the organization.

5. GE expanded rapidly in finance and long-tail insurance, where the feedback loop is particularly long and noisy. GE’s bad decisions didn’t catch up to it for years or decades: remember that the bill for bad long-term care policies didn’t come due for two decades after they were written.

In a world with perfect short-term feedback, intellectual laziness would never be an issue. Managers that refuse to address root problems or fail to understand core truths about their businesses would be quickly punished by competitive markets and replaced by more competent leaders.

The real world is a messier place, full of noise, randomness, and lags. Subpar, intellectually lazy managers persist for years by taking over strong franchises built by their predecessors, riding big secular waves, and seducing investors with their charisma and storytelling ability. This works only until the tide goes out, at which point people realize that they have been swimming naked the entire time.

GE is the most extreme example of this common phenomenon. GE managed to get as big as they did by riding an extraordinary confluence of tailwinds that covered up their sins for decades, instead of mere years as in the case of Honeywell.

Intellectual laziness is in fact at the root of many sudden corporate disasters. At the time of this writing, the collapse of Silicon Valley Bank (SVB) has captured headlines. SVB was one of the top performing publicly-traded banks over the last decade: riding the tech boom, it grew to become the 16th largest bank in America, and at its peak, its stock was up almost 50 times from its 2009 lows. 

SVB was done in by a massive wrong-way bet on low interest rates, initiated in early 2021. How could a team of experienced bankers at one of the country’s largest banks possibly have erred so badly? As bank analyst John Maxfield has documented, while SVB was betting the farm on low rates, other leading bankers were publicly discussing the risk of higher rates and the steps they were taking to mitigate that risk, and came through this episode unscathed.

The Washington Post reported that mid-level bankers at SVB were indeed aware of the risk at the time:

 In buying longer-term investments that paid more interest, SVB had fallen out of compliance with a key risk metric. An internal model showed that higher interest rates could have a devastating impact on the bank’s future earnings, according to two former employees familiar with the modeling who spoke on the condition of anonymity to describe confidential deliberations.

Instead of heeding that warning — and over the concerns of some staffers — SVB executives simply changed the model’s assumptions, according to the former employees and securities filings.

…

The episode shows that executives knew early on that higher interest rates could jeopardize the bank’s future earnings. Instead of shifting course to mitigate that risk, they doubled down on a strategy to deliver near-term profits, displaying an appetite for risk that set the stage for SVB’s stunning meltdown.

“Management always wanted to tell a growth story,” one former employee involved in the bank’s risk management said. “Every quarter, there was always this pressure to deliver earnings.”

Instead of proactively managing the critical, root problems of running a commercial bank, SVB’s management team took the intellectually lazy path, propping up near-term reported earnings while destroying shareholder value.

The psychologist Adam Mastroianni has an excellent Substack where he frequently writes about strong-link problems and weak-link problems. We frequently evaluate managers as if their output will be determined by their best strengths — we commonly look for executives with exceptional intelligence, motivational ability, and salesmanship.

On the other hand, we know that a manager’s output is also subject to certain weak-link traits. Case studies like GE and SVB demonstrate that intellectually lazy managers are eventually doomed to produce bad results, no matter how hard they work or how exceptionally smart they are. 

If intellectual rigor is predictive of future success in a way that does not show up in traditional short-term metrics, then it must be important to identify underrated managers like David Cote, and avoid overrated managers like Jeff Immelt.

At this point, we have to ask: how does one identify management teams that demand intellectual rigor, and avoid management teams that are intellectually lazy? 

The answer is simple, but not easy. In each example we presented here, the intellectually lazy managers are actually initially exposed when they present their story to a knowledgeable audience. To be sure, they are able to assemble a narrative that sounds convincing to a layman, peppered with vanity metrics and impenetrable business-speak. 

However, the narrative is usually all form and no substance, pure business theater. It leans heavily on rhetorical tricks: accounting chicanery employed to meet previously announced financial targets might be rationalized as “exceptional dedication to meeting our public commitments”. (The implication being that if you don’t fudge the numbers, maybe you’re just the type of person that doesn’t take their commitments seriously.)

Nonsense axioms are invented out of thin air – recall the continued insistence of former GE executives that companies must consistently announce growing earnings, in the face of the evidence that most successful companies did no such thing.

Then there is the midwit appeal to complexity: anyone who argues that the narrative is a convoluted, illogical mess is accused of being an ignorant simpleton who is incapable of grasping such sophistication and brilliance.

The intellectually lazy narrative always contains these sorts logical gaps. When confronted about these inconsistencies, managers respond with plausible-sounding non sequiturs, answers that might be accepted by a novice but would never pass muster with anyone with real expertise.

In the case of GE, experienced analysts knew that an inherently cyclical business could not produce perfectly smooth metrics, and they also realized that GE Capital’s reliance on cheap short-term funding was not sustainable — points they raised at the time. At Honeywell, David Cote immediately identified the flaws in the stories that his underlings were telling, and called them out. At SVB, management would not even publicly acknowledge the interest-rate risk they were taking on to keep earnings moving up and to the right, even as their peers were explaining the steps they were taking to proactively manage their exposure to rising rates.

The goal of an analyst is to become knowledgeable enough to “separate the bluffers from the doers”, to borrow a phrase from Shelby Davis, another legendary investor we studied previously. The intellectually lazy bluffers talk in circles, while the intellectually rigorous doers are able to explain their logic impeccably and concisely. For examples of the latter, read any of Jeff Bezos’s or Jamie Dimon’s annual letters, or better yet, watch an excerpt of any of Warren Buffett’s annual meetings. 

History teaches us that bull markets and secular booms create lots of GEs and SVBs, mismanaged companies that put up great numbers through a combination of luck and recklessness; in fact, the combination of recklessness and strong tailwinds will often make the worst-managed companies appear to be top performers for long stretches of time. There are no doubt more GEs out there waiting to be uncovered, and books like Power Failure will always be useful case studies for those who wish to avoid them.

Further reading:

Lessons from the Titans (2020) — A fantastic book written by a trio of former industrials analysts, looking at what worked and what didn’t in the sector, including GE and Honeywell as case studies.

The announcement Sunday immediately set off a debate over whether these actions amount to a federal “bailout.”

On a call with reporters, a Treasury official emphasized that the federal intervention would not bring SVB or Signature back to life, as the enormously controversial bank bailouts during the 2008 financial crisis had done for banks that were close to failing. Their executives would not retain their jobs. These new safeguards were aimed at protecting people and businesses who had made a reasonable decision to put their money into an accredited and regulated bank — not investors who bought risky securities.

Crucially, the Treasury official also emphasized, the money used to reimburse the depositors would come from a fund paid into by U.S. banks.

This is a big debate now – the New York Times reports that many are expressing suspicion that this is just a bailout by another name. So, is this just a stealth giveaway of government money to wealthy special interests, or is it something else?

Keeping money on deposit at a bank is a necessary evil. You can’t earn any meaningful return: to make real money, you need to tie it up in a riskier investment. On the other hand, money in the bank is convenient, preserves optionality, and represents a safe store of value.

Life as a depositor is not entirely without risk. Deposits of over $250,000 at a bank leave you at least theoretically exposed to losing a small slice of your investment if the bank’s assets go bad, unless you buy insurance separately. In the case of SVB and Signature, some estimates had uninsured depositors losing 10%-20% of their money, even after shareholders and bondholders assumed the first tranche of losses. 

The other major risk to depositors is one that threatens (almost) all fixed income investors: the risk of rising inflation, which erodes the value of the dollars you are owed over time. Your bank will give you all the dollars you deposited a year ago, but by now, those dollars might each only have 93 cents of buying power left.

Owners of real assets are somewhat insulated from the effects of inflation, as they can pass along price increases to their customers, and benefit from the falling real value of their debts. For example, imagine the owner of an apartment building who can raise rents at the rate of inflation, but whose liabilities consist of a mortgage denominated in the local currency. The value of the building will maintain its real value, while the mortgage will become cheaper in real terms.

By contrast, bank depositors are fully exposed to inflation. In modern times in the US, we have come to expect 2% annual inflation, so if you keep $10,000 in a checking account, you can expect to lose $200 of purchasing power each year.

Sometimes, inflation comes in a bit hotter than expected. Inflation was about 7% in the US in each of the last two calendar years, meaning depositors lost 14% of the value of their investment during that period. Even excluding expected inflation of 2% per year, that’s a 10% surprise haircut.

There are about $20 trillion of bank deposits in the US, so that means depositors collectively ate a $2 trillion surprise loss over the last two years. Given the circumstances, bank depositors as a group hardly seem like a fair target for our collective ire for requiring a bailout of less than $100 billion.

If depositors lost $2 trillion, who was on the other side of the trade? One might think that it was the banks that took the deposits, but banks simply turn around and invest those deposits in loans and bonds, which similarly suffer when inflation rises. The real value of a bank’s liabilities and assets will decline in unison, and in fact since banks have to keep more assets than liabilities, banks should be net losers from inflation as well.

The ultimate winners in this scenario are the entities who borrow from the bank, who get to pay the bank back in (unexpectedly) devalued dollars.

These days, much of the borrowing that goes on in the economy happens away from bank balance sheets, through bond issuances and securitizations. Still, the Fed tracks total borrowing and lending in the economy, and we can get a sense of who the major borrowers are.

One major group is consumer borrowers: at the end of 2022, households had $12.5 trillion of residential mortgages outstanding, meaning homeowners collectively received a $1.2 trillion windfall from the devaluation of their future payments. (This is just from inflation, and separate from any benefit from taking out new mortgages or refinancing at low interest rates during that time.) Consumer borrowers also have nearly $5 trillion in other debt outstanding, such as credit card debt and auto loans, which benefit similarly.

Households are also indirect borrowers through the businesses they own equity in, as in our apartment building example. Nonfinancial businesses have $7.5 trillion in bonds outstanding, and $6.3 trillion in mortgages.

Most households are both borrowers and lenders; they have bank accounts and credit cards and mortgages and equity in companies that lend (like banks) and borrow (like real estate companies). The extent to which inflation affects any given household depends on their net position. If you had a small bank account and a huge mortgage, you were a big winner, and if you lived on interest income from your bond portfolio, you were a major loser.

There is one special entity that is a major net borrower: the U.S. government. The total national debt held by the public currently stands at over $24 trillion. Some of this is owed to foreign governments who need to hold dollars, but most if it is held by American entities: banks (who got it from depositors) and corporations and even directly by private citizens. 

About $2 trillion of the national debt is indexed to inflation, and thus does not benefit from rising prices, but the government still had about $20 trillion of non-indexed debt outstanding over the last two years, and thus ended up with a $2 trillion profit from higher than expected inflation.

Inflation is only part of the story, of course. The government also benefited from falling long-term interest rates during that period, locking in low yields on the borrowing it did to pay for Covid relief. The government effectively put on the inverse of the bad interest rate bet that doomed SVB. In fact, even though SVB invested in mortgage backed securities, its investment activity would have pushed down interest rates on similar securities, like Treasury notes, indirectly benefiting all fixed rate borrowers, including the Federal government.

From Q2 2020 through Q1 2022, when longer-term rates were at all time lows, the Treasury issued an additional $4.5 trillion in fixed-rate notes and bonds, and refinanced everything that rolled over during that time as well.

As the economist David Beckworth has pointed out, we can see the combined impact of rising interest rates and high inflation when we look at the massive decline of the public debt burden. The measure Beckworth picks is the ratio of the market value of public debt to nominal GDP, which is a typical public debt measure (public debt/GDP) adjusted for mark-to-market changes in the value of outstanding public debt.

The extra debt the government took on to pay for Covid relief has almost entirely disappeared, due to the combination of higher inflation and lower rates. By this particular measure, public debt is back where it was at the end of Q1 2020, before the real impact of Covid. 

High inflation eroded the real value of outstanding debt (which shows up above through inflated nominal GDP in the denominator), while the period of low rates followed by high rates reduced the mark-to-market value of the government’s longer term fixed rate borrowing (notes and bonds are three-quarters of publicly held debt).

Back-of-the-envelope, the government picked up a $2 trillion profit from the spike in inflation, and another $1 trillion mark-to-market profit by locking in fixed low rates.

Treasury officials think that the rescue package for SVB and its peers (not a bailout!) most likely will end up costing less than the $100 billion in the FDIC fund. The market gyrations that wrong-footed the rescued banks almost certainly ended up making the government a profit several times that. Over the past two years, the Treasury is up over $3 trillion on the backs of its lenders, which include, indirectly, bank depositors.

Maybe the government’s plan really is a bailout – it does, after all, potentially involve taking public money and giving it to specific affected parties, even if the money comes from a tax on banks. Or maybe a bailout is justified because regulators seem to have been asleep at the wheel, or because of the risk of financial contagion. But we should also consider that depositors have been bailing out the Treasury for some time now – maybe the sporting thing to do is to write this little episode off as the cost of doing business and move on.

Inside the airline industry, however, everyone knew Southwest and only too well. It had never lost money, from the time it was fully established in business. And it had flourished while defying almost every success maxim of the post-deregulation world: it had no computer reservations system, offered no frequent-flier program, did not conduct yield management, and had never organized its flight schedules around anything remotely approaching a hub.

Southwest would belatedly add a computer reservations system, a frequent-flier program, yield management, and even something approaching a hub. But to this day, Southwest is still the most unconventional airline in America. They only fly 737s, they don’t charge for bags, and they don’t assign seats.

It makes sense that the most successful companies would be the most unconventional. An organization that does everything conventionally can only be above-average at best; even if they have best-in-class execution, it will by definition be coupled with a conventional, average strategy. You would expect that the most successful companies to have the best execution and also pick some good unconventional strategies, avoiding the mistakes of their more conventional peers. Indeed, this is the premise of William Thorndike’s The Outsiders, which profiles a set of unconventional CEOs who achieved outstanding success.

Southwest had an operational meltdown over Christmas, stranding thousands of passengers over the holiday. This has been catnip for pundits, each pushing their pet theory explaining how the most successful airline in America could have failed so badly. Was it the route network? Stock buybacks? Incompetent management?

To answer this, let us revisit the collapse of another unconventional company, General Electric.1 GE was one of the most successful and admired companies in the country, and from the mid-1990s to the mid-2000s, it was the most valuable company in America. Jack Welch, their iconic CEO, was one of the most unconventional business leaders of his generation, and his maverick approach frequently paid off. He decided that GE should get out of businesses where they were not #1 or #2, an idea so successful that it has become almost conventional today. He aggressively deployed capital, making big internal investments in airplane engines, gas turbines, and cable television that all paid off handsomely.  He demanded that managers hit their numbers, and his rigorous operational reviews were legendary.

The downfall of GE was in Welch’s unconventional strategies that didn’t work. He made GE into a sprawling conglomerate with major interests in media and finance, instead of focusing on industrials as his peers did; the resulting company was unfocused and difficult to manage, and after 2008, the finance side of the business became a millstone for the company. GE had always been unusually acquisitive, which worked well early on when Welch was making good deals, but backfired later on under Welch’s successor, a less talented dealmaker who faced a more competitive deal environment. Finally, Welch insisted on finding a way to report smooth earnings to investors at all costs (often known as “earnings management”), an approach that was going out of style by the 1990s. These accounting shenanigans were a major distraction, and caused GE to make major strategic decisions based on whether they produced results that could be manipulated to smooth GE’s reported earnings, instead of being based on whether they provided value to GE shareholders.

A few years ago, when it became clear that GE was in deep trouble, everyone lined up with their takes about how GE’s failure told us something important about the broader failings of Corporate America. GE was an example of corporate greed, incompetence, or hubris. One journalist even wrote a book about Jack Welch somehow supposedly destroyed capitalism, because apparently no one ever thought about doing layoffs before he came along. (I wonder what he makes of the problems at Southwest, a company that on principle has never done a layoff.) Whatever you thought was wrong with American business, you could spin a story that GE was proof of it. 

The problem with all of these theories was that GE was an unconventional company that failed in unconventional ways. GE was admired more than it was truly imitated. No other industrial company was building up lending, real estate, and aircraft leasing subsidiaries funded with commercial paper, nor were they expanding into entertainment. After Enron, most companies improved transparency and stopped trying to report earnings smoothed to the penny, but not GE. 

When you read about companies that failed during the financial crisis, you find that they all got into trouble in similar ways, namely by blindly following each other into the similar bad bets on real-estate backed loans. By contrast, when you read about GE, you find a story of a company that got into trouble in mostly novel and unique ways.

Which brings us back to Southwest, which appears to have melted down due to the failure of their antiquated scheduling software. Airlines have been known to have high-profile IT problems from time to time, but no one seems to be able to recall a similar situation involving scheduling software. Paul Krugman cites Southwest’s point-to-point network structure as a possible culprit, but while point-to-point networks are less common, there are plenty of other point-to-point airlines in the world that have operated for decades without problems. Michael Hiltzik of the LA Times says that the problem might be Southwest’s tight scheduling, which leaves less room for error, but again, other airlines in the world operate in the same way just fine.

Zeynep Tufekci writes in the New York Times that this is a case of perverse incentives, that corporate executives will inevitably be tempted to postpone investments in software because the cost will reduce reported earnings today, whereas the payoff will be in the future, when someone else might be in charge. This is absolutely something that happens sometimes at companies, but this is unlikely in a company like Southwest, where executives tend to stay in their roles for decades. Gary Kelly was CEO of Southwest for eighteen years before retiring in February 2022, and he was their CFO for many years before that. Even today, he is still Southwest’s Executive Chairman, and very much in the crosshairs for what happened. This is not an organization where the CEO serves for five years before hitting retirement age and passing the baton; executives absolutely expect to be around to suffer the longer term consequences of their decisions.

Tufekci also advances the popular theory that stock buybacks are to blame; Southwest has spent $8.5 billion on share repurchases in recent years, supposedly enriching management while leaving the company without enough money to upgrade their software. The buyback theory makes no sense. Before returning cash to shareholders in the form of buybacks and dividends, executives choose how much cash to reinvest into the business. For an airline, this might involve investing in planes and software. They figure out how much to invest in planes and software based on the returns they think those investments will generate, and only invest in projects that meet their hurdle rate; sometimes they err in their judgment. The important thing to remember is that executives are absolutely incentivized to reinvest the maximum amount that can be justified, because their compensation (and status) is linked not just to the stock price, but also to the size of the company. As a rule, big company CEOs get paid much, much more than small company CEOs. 

No one understands this reinvestment game more than Southwest, which has reinvested most of their profits into new planes for decades. This is how they went from three planes flying in Texas in the 1970s to flying nearly 800 planes all over America today. Their stock is up something like 2,000x since they became publicly traded, and their executive salaries have soared. In the early 1990s, Southwest was a $1 billion a year company, and their top executives were making around $400,000 per year; by the late 2010s, Southwest was doing over $20 billion of revenue annually, and the top executives were making closer to $4 million per year. 

Eventually they achieved a size where they could no longer easily grow, and at this point, they started returning excess cash to shareholders. This is what companies have always done with excess cash at that stage; it’s the only thing they can do with excess cash, without making big acquisitions or lighting it on fire. Every sufficiently big company returns most of the cash they generate to shareholders, because they have to. Now, in hindsight, Southwest probably wishes they had invested more in scheduling software, but that’s an error of judgment, rather than an expected consequence of the incentives at play.

(As an aside, this is not to say that open market buybacks shouldn’t be banned. Not because buybacks are somehow detrimental to corporate value or to the economy: they are not, because they are basically functionally equivalent to dividends, which is the main way companies returned capital before buybacks became easier to execute in the 1980s. A billion dollars “spent” on buybacks and dividends both leave investors as a whole with a billion dollars of cash and full ownership of a company that is a billion dollars lighter. No, we should consider banning buybacks because there is no other topic on earth that causes so many otherwise smart people to contort themselves into logical pretzels and embarrass themselves in public. People have convinced themselves that buybacks somehow constitute “waste”, when a buyback is just a company sending investors the cash that already belongs to them in the first place, and that buybacks disincentivize investment, when we have seen that nothing could be further from the truth. Cliff Asness of AQR once accurately termed this phenomenon as “Buyback Derangement Syndrome”. From a societal standpoint, we want companies to return capital to investors when they run out of internal investment opportunities, rather than hoarding it or wasting it, so investors can turn around and redeploy it into more promising productive opportunities (e.g. venture capital or real estate development) if they like. Anyway, everything was fine back when everyone was mostly just paying out dividends, so maybe we should think about just going back to that and saving everyone the brain damage.)

Southwest’s pilots advance the bean counter theory, that Gary Kelly is an accountant who hired other accountants to fill out senior management and none of them understand how to run an airline. People love simplistic narratives about what professional background makes for a good executive, and none of them are supported by evidence. The legendary founder of Southwest, Herb Kelleher, was a practicing lawyer with no operating experience when he took over as CEO in 1981, a decade after Southwest started flying, and he stayed in the role for two decades, leading the company through its glory years.

Jack Welch was a PhD chemical engineer who spent his entire career as an operating manager. His successor, Jeff Immelt, was also a longtime GE operating exec, albeit with more of a background in sales. At the same time that GE was flailing, another industrial conglomerate, Honeywell, was on the rise. Back in the early 2000s, Honeywell was set to be a small bolt-on acquisition for GE, but the deal was blocked by the EU on antitrust grounds. After that deal fell through, Honeywell became one of the top performing industrial companies in the world, growing to eventually become more valuable than GE. The CEO who turned around Honeywell and led it during its ascension was David Cote, an accountant and finance executive who actually spent most of his career at GE, before being pushed out. Some executives are more effective than others, and it seems to have little to do with education or early professional background.

What happened at Southwest? Usually we would like to examine the evidence, but since the post-mortem is still ongoing, let’s indulge in a bit of speculation, based on what happened at GE. Imagine you’re a senior executive at Southwest. You and your team built Southwest from nothing into a colossus, while your competitors have done little more than parade through bankruptcy court. A big part of your success has been your willingness to be unconventional, to zig when everyone else zags. You’ve looked at upgrading your scheduling software, but you studied it and determined it wasn’t yet worth the investment. Your company has always been comfortable with being slow to upgrade technology, and it always worked out in the past. It doesn’t bother you that other airlines have been more aggressive here; why would the industry leader care about benchmarking themselves against the also-rans?

We admire unconventional companies, but an unconventional approach can cut both ways. Unconventional strategies are great when they improve on convention, but detract from performance when they do not. Even the smartest managers, the ones who employ a lot of unconventional strategies that work, will also (unintentionally) employ some unconventional strategies that hold them back, and initially there will be no easy way to tell the two apart. They observe that their strategies are working as a bundle, they conclude that everything they are doing is brilliant, and arrogance and complacency set in. To make matters more complicated, a lot of bad strategies appear to be good strategies as long as the tide is rising. The critics and skeptics are always there, but they are easy to dismiss as long as the results keep rolling in. Until, of course, one day the tide goes out, and the truth is revealed.

The airlines are making a great case for public regulation of the airlines. American Airlines is clear that a government agency should be setting prices and routes, because without that American Airlines cannot control its pricing and capacity.

…

Do I think the government should set prices and routes? I don't know how such a system would be administrable. It was how things worked until 1978 and it was a terrific system which saw dropping ticket prices and expanding capacity. What I do know is right now isn't workable.

He is referring to the high last minute fares that were still being charged by American, despite the desperate travelers stranded by the Southwest mess; in fairness, by the time he tweeted this, American and other major carriers had already capped fares, as they usually do in case of an emergency.

Stoller is actually echoing a standard airline talking point from the mid-1970s.1 At the time, the government set airfares and determined capacity and routes, and deregulation was being pushed by Senator Ted Kennedy as a measure to introduce competition into the airline market and thus save consumers from being gouged. Airlines loved regulation, which protected them from new entrants (like Southwest) and price competition and pretty much guaranteed their profits and fat salaries. The airlines argued that regulation had given America the world’s best air transportation system and falling airfares, and that tinkering with it would jeopardize the quality of the system without providing any benefits.

Airfares have dropped significantly in the years since deregulation was signed into law by President Carter in 1978, but Stoller and the airlines are correct that airfares had been falling rapidly even before then. In fact, if you look at a chart, it’s hard to discern any difference in the trend in airfares before and after deregulation:

(Source: Airlines for America, Bureau of Transportation Statistics. I had to do some adjusting to compensate for methodological changes over time.) 

If airfares had been falling just as fast before deregulation, what was the point? Is there a way we can know if deregulation did anything to lower airfares at all? 

In the early days of post-war air travel, flying was expensive and slow. In the 1950s, airlines operated propeller planes like the Constellation or the DC-6, which carried around 60 passengers at a speed of 300 mph. A round trip transatlantic flight might cost $5,000 in current dollars; Pan Am famously offered installment loans for people who wished to travel overseas.

At the end of 1958, Pan Am began operating the Boeing 707, which ushered in the age of jet travel. A 707 carried about 140 passengers at close to 600 mph; in other words, it had twice the capacity and twice the speed of its predecessors. Jet travel was not only faster and more comfortable, it was much more cost efficient. Airlines overhauled their fleets, and by 1965, jets accounted for three-quarters of domestic travel, and the airline industry was booming.

The graph above indicates that airline operating costs per unit of capacity fell rapidly as jets took over in the early 1960s. It seems pretty clear that productivity gains in that era were mostly due to the ingenuity of the fine folks at Boeing, rather than anything the airlines or regulators did. Between 1958 and 1968, operating costs per available ton-mile fell 45% in real terms, despite rising union wages. 

Where did these efficiency gains go? Did they translate into lower airfares at the time? Well, yes and no. Government-regulated airfares were mostly steady, but revenue per passenger mile fell because of a mix shift toward cheaper coach and promotional seats. 

While revenue per passenger (i.e., airfare) was declining modestly, the graph above shows that revenue per unit of capacity was declining more significantly, usually not a good sign. One reason was more netural: airlines started to carry more freight, which carries a lower yield. The more worrying reason was that flights were taking off with more empty seats: load factor, or the percentage of seats that were filled with paying passengers, fell from 66% in 1951 to 53% in 1968. With regulated airfares, it was impossible to slash prices to fill seats, and emptier planes raised per-passenger costs. Low load factors would plague the industry into the 1970s, as airlines bought new planes they couldn’t fill.

Finally, as the graph suggests, efficiency gains were also translating into fatter airline profits. In 1965, airlines reported a collective operating profit margin of 14%. In 2022 dollars, they were turning a profit of 9 cents per passenger mile; a remarkable figure, considering airlines today only collect 16 cents of revenue per passenger mile, and are happy to make a penny or two per passenger mile, if they make anything at all.

The 1960s were an era of rapid advancement in aircraft design. The 727 entered service in 1964, the 737 launched in 1968, and the 747 came out in 1970. True, there have been some meaningful advances in fuel efficiency and labor efficiency since then, but the modern aircraft we fly today are still mostly descendants of these models. One would have expected that the 1960s and 1970s should have had the largest drops in airfares, since that’s when the biggest technological advances were implemented. 

This was the backdrop for the hearings on deregulation in 1975. Yes, airfares were falling a bit in real terms, but not nearly as much as might be expected given the abrupt shift from propeller planes to modern jet aircraft. The Civil Aviation Board (CAB) was responsible for overseeing the industry, but they were content to keep airfares high, and to allow the airlines to operate inefficiently. At the same time, airlines, not allowed to compete on price, competed on amenities instead. As Thomas Petzinger Jr. tells it in Hard Landing:

American installed a 64-key Wurlitzer piano in its jumbo jet lounges, with Frank Sinatra, Jr. at the keyboard on the inaugural flight. United staged “happenings” in its 747 lounges, featuring caricaturists, guitarists, and wine tastings.

…

Northeast Air proclaimed itself “the all-steak airline to Florida”. Delta introduced steak with a champagne accompaniment. National Airlines came back with free drinks of any kind…American wound up pouring so many Bloody Marys that it made millionaires of the people who bottled Mr & Mrs T Bloody Mary mix.

Ok, let’s take a look at the American in-flight 747 piano bar:

Now, there is nothing intrinsically bad about steak and a piano bar, but space at 30,000 feet is precious and one imagines that passengers might have liked the option to ditch the amenities and save a few hundred dollars. Alas, that was not an option with regulated airfares. This was the problem: price controls distort incentives, and so many of the efficiencies from new high-tech planes ended up going to empty seats and piano bars and free steaks instead of into the pockets of consumers.

In 1975, the Senate Judiciary Committee held hearings on airline deregulation, and produced a comprehensive report with data and predictions on how deregulation might play out. This gives us a set of hypotheses we can check against the actual evolution of the industry.

The general predictions were as follows:

1. In a competitive industry, airlines will choose to fly fuller planes and charge lower fares. Remember that fuller planes dramatically lower the realized cost per passenger, by spreading the fixed cost of operating the flight over more passengers. An airline that spends 16 cents per passenger mile at a load factor of 50% (that is, if it only fills half of its seats), will only be spending 10 cents per passenger mile with a load factor of 80%; in a competitive market, the savings should flow to the consumer. The same math applies if an airline crams more seats on each flight by reducing legroom. This was supposed to be the main channel through which deregulation would benefit consumers.

2. Airlines will cut back on amenities and compete on price. No more free steak, no more piano bars.

3. New entrants will drive the least efficient operators out of business, which will bring down costs overall.

4. Deregulation will have the biggest impact on airfares on longer flights. This is an artifact of the way regulated fares were calculated, which was strictly proportional to the length of the flight rather than the cost of the flight.

5. If deregulation ends up permitting selective discounting (as it did), then the lowest available fares will be much lower, compared to the existing system which permitted very little discounting.2

Most of these are pretty easy to confirm at a high level. The main prediction held true: even though the airline industry evolved in sometimes surprising ways (hub and spoke networks, loyalty programs), but load factor was indeed one of the biggest factors in lowering costs, rising from as low as 49% in 1971 to 84% today. All things being equal, one might expect such a sharp rise in load factor to lower per-passenger costs by 44% by itself (albeit with the unrealistic assumption that there are zero costs associated with additional passengers).

The other predictions mostly held up too. Airlines don’t offer free steak anymore. New entrants like Southwest and jetBlue have replaced failed incumbents like TWA and Eastern. Last minute airfares are still expensive, but leisure travel is now very cheap. Overall, there seems to be strong evidence that deregulation was the main factor in lowering airfares over the last half century.

We can drill down even further on the impact of deregulation by looking at some of the specific routes that the Senate studied back in 1975. First, they were very interested in routes that took off and landed in the same state, which happened to already be exempt from federal regulation. On routes like San Francisco to Los Angeles and Houston to Dallas, airlines were free to enter and set prices as they wished. 

They noticed that these unregulated intrastate routes carried a much higher load factor and had much lower fares (by about 40%) than comparable regulated routes that crossed state borders, just as theory would predict. As expected, the airlines had all kinds of excuses for why these routes might not be comparable, and the committee considered and dismissed them one by one.

We can revisit these routes today to see whether airfares on routes that had always been deregulated behaved differently from airfares on routes that were deregulated in 1978. We would expect that newly deregulated routes would see much bigger fare declines than routes that had always been deregulated.

(Source: 1975 Congressional Subcommittee Report, Department of Transportation. Note that the 1975 fare is the listed coach fare, while the 2019 fare is the average realized fare for all passengers, including first class, so it’s not precisely comparable.)

We can see that this is indeed the case. Intrastate routes (in italics) have actually gotten more expensive, adjusted for inflation, while interstate routes have gotten only a little bit cheaper. Note that we expect shorter routes like these to actually be the weakest test for deregulation, since they had the least inflated fares under the old system, and they skew more toward price-insensitive business travelers. There are a lot more substitutes for short routes (like driving), and short trips still face high fixed per-segment costs (e.g. airport costs).

With the same dataset, we can see that airfares fell much more sharply on longer flights. The coach airfare in 1974 for the most popular route in America, Los Angeles to New York, was nearly $2,000. The average realized airfare in 2019 of $722 on that route includes people who travel at peak times or at the last minute or opt for premium seating; a typical leisure traveler will pay $500 or less, and sometimes as little as $200. Almost everyone in 1974 had to pay the standard coach fare, while leisure travelers today can expect to pay 75%-90% less. Traffic on these routes is much higher today as well; we take it for granted today that we can fly whenever we want during the day, but high frequency was only made possible by the traffic stimulated by deregulated airfares.

For those that can’t live without the terrific amenities of the regulated era of air travel, take comfort that for the price of a transcontinental coach ticket in 1974, you can buy a lie-flat suite on the same route today, fine dining and open bar included. It’s enough to make the modern system seem workable.

Further reading: We looked at the Great Texas Airline War of the 1970s a few weeks ago, a more relevant story today.

We took a look at the ride-hailing model last year and came to a different conclusion – that even though Uber and Lyft are losing money now, ride-hailing is more efficient than the traditional taxi model and will probably displace it permanently. This is a good opportunity to dust off that analysis and see if it can be reconciled with Lee’s findings, and if there are any broader lessons we can learn.

Claim: Lyft took a 48% cut of Lee’s earnings from driving around for a week, therefore Lyft’s overall take rate is probably very high, much higher than the 20% take rate that prevailed in the early days.

To be fair, Lee doesn’t explicitly state this, but it is the premise of the article, so I think it’s a fair reading. Here is what he says:

After that Saturday night trip, I started asking my passengers how much they’d paid and comparing it to what I’d earned. The results were all over the map. I had two trips where I got more than 80 percent of the total fare, including tips. I had a couple of others where I earned less than 30 percent. 

I wrapped up my Lyft driving experiment after a week, having completed 100 rides in 46 hours and earning $1,111. Lyft eventually sent me a report showing that passengers had paid a total of $2,139.73 for these rides, so on average I got just 52 percent of what my passengers paid.

Ever since then, I’ve been trying to figure out how Lyft could take such a big cut of passenger revenue and still fail to turn a profit—Lyft says that it lost almost a billion dollars in the first nine months of 2022. The more I’ve looked into it, the more I became convinced that it’s going to take major cost-cutting for Lyft to avoid eventual bankruptcy.

Lee is not alone: his experience aligns with stories from other recent articles, where drivers report only receiving only half of the gross revenue paid by riders.

On the other hand, sources that utilize comprehensive data still pin the average take rate of ride hailing networks like Uber and Lyft at closer to 20%, just like it was a decade ago. While Lyft does not report on take rate, Uber reported a take rate of only 20.2%1 in the most recent quarter. Uber’s ride share business is around three times bigger than Lyft’s, so it should be sufficiently representative of the experience of ride-hailing drivers as a whole. Also, a comprehensive study of all rides on all ride-sharing platforms in New York City (the biggest ride-hailing market) in 2017 finds an average take rate of 16.7%. The authors of that study were pushing for a minimum wage for drivers, so if there was to be any bias, you would expect it to go the other way.

Is it possible that some drivers could experience a take rate of 50%, while the overall average is closer to 20%? Or is the comprehensive data fraudulent?

If the comprehensive data is accurate, we should look to see if there are drivers on the other side of the distribution experiencing a very low take rate. 

The Rideshare Guy is a major blog covering the industry from the driver’s perspective, and they had a recent segment on their podcast going over this very topic. One of the hosts shares their personal experience, which includes a lot of rides where Uber actually lost money, and finds that Uber had an overall take rate of only 9% over the 788 rides they gave. (Remember that Lee only gave 100 rides.)

If you think about it, it makes sense that there is a huge variance in driver take rates. Most businesses show significant variance in their take rate and their margins across different segments. Airlines charge much more to fly at peak days and times, or at peak season, or on specific routes where they have little competition; meanwhile, their per-hour operating costs (fuel and labor) should be mostly consistent from flight to flight. Their take rate must be very high on some flights and very low on others.

The ride-hailing networks should be setting their driver payouts and ride prices separately, based on competitive conditions and the responsiveness of supply and demand to price shifts. Like in our airline example, they should have higher margins when there is higher demand. It’s rational for them to continue to offer rides as long as they are making some money, and it’s probably even rational for them to lose money on some rides where it sets them up to make money later (e.g. by repositioning drivers to areas of higher demand, or keeping wait times low).

One of the issues here is publication bias. If there is a lot of variance in experience, the people with the most extreme experiences will be the most likely to shout about it, while people with normal experiences will just shrug and ignore it (unless they happen to run an industry podcast).

Everyone’s personal experience is a small, possibly biased sample, but Lee also accidentally introduced a very particular known bias. A couple of years ago, there was a flurry of hotly contested studies using comprehensive data to look at effective driver wages. One of the studies, done by a team at Cornell, looked at all of the rides given over on Uber and Lyft in Seattle over the course of a week. One of their findings was the existence of a significant cohort of what they termed “casual drivers” (25% of drivers, but only 3% of rides) that drove only a few hours during that week, some of whom had very low effective wages, probably because they were not as strategic about maximizing income. (They were less likely to use both Uber and Lyft simultaneously, for example.)

Lee reports only grossing $24 per hour, while the Cornell study found that the median rideshare driver in their full sample achieved gross earnings of $37 per hour. Granted, these are different cities (Lee is in Washington, DC) and different time periods (the Seattle study is from October 2019), but the divergence is still suggestive. One of the points from the Rideshare Guy segment is that driver income and take rate is highly dependent on driving strategically and taking advantage of bonuses and quests. It’s believable that a journalist driving to collect stories for an article would behave differently than a typical driver focused on making money, and would end up with atypically low earnings and high network take rates as a result.

It’s unlikely that Uber or New York City are somehow publishing fraudulent or incorrect revenue and driver earnings data. What would be the point? Uber is a public company – why would they underreport revenue? The simplest explanation is the most likely explanation: there is a wide variance in driver experience, and the most extreme experiences are the ones that get written up. 

This is a good illustration of the idea that “the plural of anecdote is not data”. Also, while Lee is very reasonable in his article, people are sometimes very insistent in extrapolating their personal experience to a broader conclusion about the world. The Rideshare Guy podcasters remark that they got a lot of hate mail just for sharing their data showing a low Uber take rate. People often take any evidence that contradicts their “lived experience” as a personal attack, rather than just as an expected consequence of the variance that exists in the world. If variance exists, then a lot of people will have a “lived experience” that is wildly out of the norm, and what are the odds that you are the only person in the world that exclusively has normal experiences?

This is something we have also seen when looking at housing issues: people that live in booming coastal metros won’t recognize that they are extreme outliers compared to the rest of the country, and they think housing prices are out of control everywhere, rather than in a small minority of cities.

Properly framing personal experience is an everyday problem in business. Whether you are a journalist, an investment analyst, or a product manager, it is essential to get out into the field and try to experience what is going on first-hand. Doing so sheds light on key details that are invisible in the data. One just has to keep in mind that personal experience is only a sample size of one.

Claim: Lyft has higher overhead than a traditional taxi service.

Here is Lee:

Back in 2016, a transportation economist named Hubert Horan crunched the numbers and found that in the pre-Lyft world, about 15 percent of passenger fares (including tips) went to dispatching and other “back office” functions. The other 85 percent went to driver compensation, fuel, and the costs of vehicle ownership and maintenance.

So if Lyft were as operationally lean as a traditional taxi company, the driver would get 85 percent of passenger fares (Lyft drivers pay to maintain and fuel their own vehicles, after all), while Lyft would be able to cover its costs with the other 15 percent. But if my rides are any indication, Lyft is taking way more than 15 percent and still losing a ton of money. It seems that the smartphone revolution didn’t make dispatching cheaper and more efficient, as you might expect. It made it way more expensive.

Since Lyft doesn’t report gross bookings, let’s look at Uber’s latest quarterly numbers (across all segments, including Eats and Freight), presented on an annualized basis:

One misunderstanding here is that the 20% take rate doesn’t encompass all of the direct costs of providing a ride. Credit card fees (which should be about 2% of gross bookings) and insurance (probably a few percentage points more) get absorbed by Uber after the 20% take. All of the billions of dollars of personnel and advertising and legal expenses only come to about 12% of gross bookings: significant, but still less than the 15% of a traditional taxi service. 

Now, it is possible that Uber’s expenses are still too high, but for now, that is being absorbed by Uber shareholders, not riders and drivers. Either way, this is a good illustration of the importance of digging into the numbers and the fine print.

Claim: Ride-hailing dispatch is only a small improvement on traditional taxis.

Here is Lee again:

I think it’s overstating things to say that Uber and Lyft haven’t improved on traditional taxis. At a minimum, Uber and Lyft have dramatically improved the reliability of pickups. I’m old enough to remember calling a taxi in the pre-Lyft days and not knowing if it would show up. I think many passengers are willing to pay at least a small premium for that.

…

But with that said, I think Horan is right that these are fairly incremental improvements to the basic taxi business model. Which leaves Uber and Lyft with the same challenging economic model as a traditional taxi company.

There is a common pattern where one wants to compare two products that are mostly similar, but differ greatly across a few dimensions. The most common approach is just to eyeball the problem, and use gut feel to guess how important those dimensions are.

Another approach is to figure out how much consumers value each dimension using data. The goal is to find a test that helps isolate each dimension.

We did this before with dispatch. We know that dispatch does not matter very for a certain subset of traditional taxi rides: rides from the airport, where there is consistent demand, and rides downtown, which can be hailed on the street. On the other hand, dispatch is very important for rides in less dense areas.

If dispatch is important, then less dense areas should display different demand patterns from than denser areas when ride-hailing entered the market. Via Todd W. Schneider, here are is the evolution of rides in Chicago originating in less dense areas, 5+ miles from downtown, as ride-hailing entered the market (note that while taxi data dates back to 2014, ride-hailing data in Chicago was not available until 2018):

There were barely 8,000 taxi pickups per day in this area before ride-hailing, and now there are over 60,000 per day. This is what we would expect if dispatching was cripplingly bad prior to ride-hailing. (Note that per-mile and per-minute pricing are now about the same between ride-hailing and traditional taxis; the main difference now should be the reliability of dispatching and rider experience.) Compare this to the pattern at Chicago airports, where dispatch is unnecessary:

As expected, taxis held up much better here. 

We can also estimate the value of ride-hailing by looking at the premium riders are willing to pay to use ride-hailing instead of taxis. We looked at a major subset of routes where ride-hailing has mostly displaced taxis, despite now being 60% more expensive:

There is a temptation to flatten a problem by making a major simplifying assumption, and then when coming to a conclusion, forget that the conclusion was part of the original simplifying assumption. This is a form of “begging the question”: in this case, start by assuming taxis are probably just about as efficient and valuable as ride-hailing, and after many steps, you will conclude that taxis and ride-hailing are about the same.

This is something we came across with housing as well. One common approach to analyze supply is to look at the overall number of housing units in America compared to the number households. The problem is that household formation is dependent on housing construction: young people will only move out to their own place if there are homes available. Nevertheless, people write housing “analysis” that consists solely of this circular logic; as long as housing formation tracks housing available (as it must by definition), everything must be fine. The correct way to look at the supply/demand balance in a market economy is to focus on the change in price: we use price to ration scarce goods, so high prices are the only reliable indicator as to whether supply is too tight.2

Before we move on, there is an additional interesting detail to note here. Before, we looked at the ratio of dispatch costs and overhead to overall revenue as a measure of efficiency. Here, we can see that this is an invalid approach, since many (if not most) traditional taxi rides don’t involve dispatch at all, while all ride-hailing is electronically dispatched and tracked. Taxis apparently had higher dispatch costs even though they didn’t really serve riders in lower density areas that required dispatch.

This is a common hazard with ratio analysis; for ratios to be comparable, the businesses have to be closely comparable. Once we see that taxis and ride-hailing provide two different bundles that are valued very differently by consumers, then we can no longer directly compare line items. It would actually be fine if ride-hailing was more expensive to operate than traditional taxis once you know that consumers are willing to pay a big premium for it; all that matters is that consumers come out ahead in the end.3 It just happens that ride-hailing is more efficient and a better overall product, as is often the case with new technologies.

In general, one should be very wary of programs that seek to analyze or improve a single variable or a single dimension, to the exclusion of everything else. The goal is to improve overall welfare, not to target a vanity metric. Goodhart’s Law suggests that people will take the easiest path to improving a metric, and the easiest path is often one that worsens overall welfare. Again, we saw this with housing: people propose banning new public amenities in poorer areas to prevent gentrification and keep rents down, something that might keep rents down but will not make residents of those neighborhoods better off overall, much less society at large.

Claim: Lyft might be screwed.

Ok, this one has a better chance of possibly being true. Here is where I remind everyone that this is not investment advice, I don’t have any investment position in any ride-hailing company or financial relationship to the industry whatsoever. This is just a fun intellectual exercise.

Here are Lyft and Uber side-by-side in the most recent quarter (ignoring gross bookings, which Lyft doesn’t report):

Part of the original investment thesis for Uber and Lyft in the early days is that ride-hailing might be a winner-take-all business, with monopoly margins for the winner. One idea was that local scale would be essential to keep down wait times, something that doesn’t seem to be the case. But another part of the equation is the high fixed costs involved: Uber is able to spread their fixed product and engineering costs and overhead costs over a much larger user base.

Keeping in mind that Uber and Lyft are not directly comparable – Uber is global, and also runs a food delivery business – overall, Uber spends triple what Lyft does on R&D and G&A, but that as a percentage of revenue, they only spend at half the level of Lyft.

So far, Lyft’s losses have been manageable. They were able to compensate employees with highly valued stock, which kept a lid on cash outflows. However, in the last year, the stock has fallen 76%, and the market cap of the company is now below $4 billion. (Lyft has raised about $5 billion to date, and they reportedly turned down a $6 billion offer from GM in 2016.) A lower stock price makes stock-based compensation much more dilutive, if they continue to issue the same dollar value of stock to employees.

Lyft has announced layoffs last month, and will no doubt keep cutting to try to get to profitability. But this highlights a common danger for industries with major economies of scale; unless there are countervailing forces that benefit the smaller competitors, or significant points of differentiation, the best case scenario for subscale players is survival. As Warren Buffett once said of the newspaper business, it can turn into “survival of the fattest”.

The goal with this series is to highlight some classic essays and articles, with some additional commentary to put them into context. 

It’s Time to Make a Deal – Joe Nocera, Texas Monthly, October 1982.

Joe Nocera gives an award-winning fly-on-the-wall account of an early effort by T. Boone Pickens to “drill for oil on the floor of the New York Stock Exchange” (as he liked to put it), in the early days of the 1980s M&A boom. He tags along as Pickens spends three weeks in New York to attempt an audacious hostile takeover attempt of Cities Service, a major oil company three times larger than Pickens’ Mesa Petroleum. As improbable as such a deal would seem, Pickens is convinced that Cities Service is massively undervalued, and that their shareholders are fed up with management's poor performance. He figures that with the right partners and enough financing, he could pull off the deal of a lifetime.

At the time, Wall Street was still a somewhat obscure place: Nocera has to explain that Morgan Stanley is a “New York investment bank house”. It was also a much more freewheeling environment, where the rules of the hostile takeover game were evolving; there were no poison pill defenses, and the players were formulating new strategies on the fly. 

This is in full evidence at the outset. In response to Pickens, one of Cities Service’s first counter-moves is to launch a competing hostile takeover of Mesa Petroleum, a novel maneuver that would later become known as the “Pac-Man Defense”. For good measure, Cities Service then calls up Pickens’ financing partner, Southland Corporation (the parent of 7-Eleven), and threatens them with a hostile takeover as well if they don’t pull out. (Southland meekly complies, and when the dust clears, they later pick up Cities Service’s refining and retail arm, Citgo, which they eventually sell to Petróleos de Venezuela.)

What follows is an engrossing back and forth between Pickens and Cities Service, with tender offers and bear hugs and boardroom drama and spats played out in the media. We meet the major leading characters from Wall Street and the oil industry, as Pickens recruits financing partners while simultaneously fending off Cities Service’s attacks. As the deal unfolds, we learn more about T. Boone Pickens: where he came from, who he is, and why he’s doing this.

Pickens is ultimately unsuccessful; Cities Service sells at a higher price to a “white knight”, Gulf Oil (a deal that later falls through when Gulf Oil gets cold feet), and Pickens is left with his consolation prize, a $45 million profit on the shares of Cities Service that he purchased before the battle (before taxes and expenses). “Better than a punch in the eye with a sharp stick,” remarks the always-quotable Pickens.

For Pickens, Cities Service was just an appetizer for the big deal that would seal his reputation. The following year, he would accumulate a big stake in Gulf Oil itself, launching a proxy battle that would eventually culminate in the sale of the Gulf Oil to Chevron, netting Pickens and his partners a profit of $760 million, and making T. Boone Pickens a household name.

The Great Airline War – James Fallows, Texas Monthly, December 1975. 

The “Great Airline War” turns out to be the “Great Texas Airline War”, fought entirely within the borders of the Lone Star State during the early 1970s between three small carriers. Also, it turns to be not so much a war as a prolonged campaign by two local incumbent airlines, Braniff and Texas International (“TI”), to murder a tiny startup, Southwest Airlines. This skirmish would foreshadow the battles to come in aviation after deregulation, and TI and Southwest would grow to play starring roles on the national stage.

Besides simply being an entertaining yarn, this piece is an outstanding example of explanatory journalism. He describes the major levers of airline economics clearly and accurately. Here he is describing the structure of the industry at the time: 

In the airline industry, you make money by being in business. Like broadcasting, though to a less grotesque extent, the airlines are government franchisees, whose product is almost guaranteed to sell. Management and intelligence can make a difference…[b]ut the rules of the game are fixed far more than for most businesses. The most vicious competition—the efforts which will make the big difference in profit and loss—take place not before the customer but before the government: before the Civil Aeronautics Board, to be precise, which distributes routes to the various supplicant airlines.

The reader quickly grasps why Southwest, which circumvents this cozy arrangement through a loophole, is such a threat.

The credit does not all belong to Fallows; it helps that the protagonists supply him with clear examples to illustrate the tradeoffs that lead them (for example) to use smaller planes to increase frequency or to coordinate flights through a hub.

More impressively, they predict exactly how the airline industry will evolve if deregulation comes to pass (as it does three years later, in 1978):

Sadly, it seems at least as probable that three or four large [major carriers] would squeeze everybody else out of business…“What you’ll see under President Ford’s proposals,” says [TI President] Francisco Lorenzo, “is two types of airlines. You’ll see United Airlines, and you’ll see some lines like Southwest.” Braniff, TI, and all other inhabitants of the middle tier would be squeezed out of the market.

As predicted, fares would tumble after deregulation: in 1974, a round-trip coach ticket between New York and Los Angeles was fixed at $2,215 (in 2022 dollars), a ticket that can be found as cheaply as $218 today. Airline profitability would be destroyed, employee salaries would be slashed, and smaller players would be forced to merge or liquidate entirely.

Today, of course, Southwest is the largest domestic airline, when measured by total passengers carried. It has also been the most lucrative airline for investors; Southwest stock is up 2,000x since 1980, while other domestic carriers cycled in and out of bankruptcy. The Great Texas Airline War has been enshrined as part of Southwest’s corporate mythology, the triumph of the plucky upstart over the evil incumbents.

Braniff was one of the most profitable airlines in the country in 1975, but it was only the eighth largest, a third the size of giants like United and TWA. Braniff tried to survive by expanding rapidly as soon as the industry deregulated in 1978. This backfired, as they were forced to take delivery of new planes during an oil crisis and a period of surging interest rates; in any case, their expansion was too rapid and haphazard to have any hope of succeeding. Braniff also made the mistake of moving flights out of its profitable Dallas Fort-Worth hub, allowing American Airlines, under Bob Crandall, to swoop in and establish dominance. In 1982, Braniff was the first carrier to be forced into bankruptcy.

The most improbable story belongs to Frank Lorenzo’s tiny Texas International, portrayed in the article as a loveable but doomed bit player, reliant on federal subsidies, and only a tenth the size of Braniff. Frank Lorenzo would learn quickly from competition with Southwest, successfully lobbying authorities for permission to implement half-price “peanuts fares” to boost volume and profits the very next year. Instead of expanding organically like Braniff, he goes the T. Boone Pickens route, making hostile bids for larger players. He builds a stake in National, and pockets $35 million when it gets acquired by Pan Am. He gains control of Continental in 1981, and moves it from Los Angeles to Houston, combining it with TI. He adds People Express and Eastern Airlines and by 1987, his holding company, Texas Air, is the largest airline holding company in the country, with a 20% share of the market.  

By this time, his battles with the unions have made him far less lovable in the eyes of the public, and he has trouble keeping the company profitable enough to service his heavy debt load. In 1990, Texas Air files for bankruptcy.

Further reading:

Books:

The Prize by Daniel Yergin (1990): A thorough history of the oil business. This is a much more entertaining book than one would expect, packed with fun anecdotes and colorful characters (like T. Boone Pickens). Probably one of the best business books ever written.

Hard Landing by Thomas Petzinger, Jr. (1996): The best resource to understand the airline industry as it went through deregulation, written by the Wall Street Journal reporter who covered it. Lots of great stories here as well.

Articles:

Bad Times at Braniff – D Magazine, February 1981

The Man Who Killed Braniff — Byron Harris, Texas Monthly, July 1982: The rise and fall of Harding Lawrence and Braniff. In 1978, Braniff was one of the largest and most profitable airlines; by 1980, Braniff was dying and Lawrence was out of a job; and by 1982, Braniff was bankrupt and gone forever. Life comes at you fast.

Top Gun – Wlliam P. Barrett, Texas Monthly, March 1987: A profile of Frank Lorenzo and Texas Air at its peak. This one is less flattering than the 1975 article.

Bob Crandall Flies off the Handle – Dana Rubin, Texas Monthly, August 1993: A look at Bob Crandall, the legendary CEO of American Airlines, which ended up besting both TI and Braniff.

While that sentiment is shared by many Americans, Californians would seem to actually have a valid gripe. California has some of the highest tax rates in the country. Of the three primary taxes that fund state and local government in the US—sales tax, income tax, and property tax—California has the highest rate in the nation for two of them (sales tax and income tax) and is in the middle of the pack for the third (property tax).

High tax rates in California do not necessarily translate into better funded public services. For state and local governments, K-12 education is the largest single budget item, and here, California is a perennial laggard. In 2011, California’s per pupil education spending was dead last in the nation, although by 2017, it had managed to climb to 41st.1

The more you think about California’s high tax rates, the more the mystery deepens:

1. California is a high-income state, ranking 5th out of 50 in median household income. All things being equal, one might expect richer states to have lower tax rates, since they have larger tax bases. For example, nearby Washington has similar per-capita income but no income tax at all, getting by with a high sales tax and a middling property tax.

2. California has a lot of extremely rich people and a high income tax. In 2019, the 0.55% of California households making over $1 million per year paid 39% of all California state income taxes, and income taxes are by far the largest source of California state and local revenue.2 These few households provide a lot of revenue without consuming proportional public services. One would imagine that California’s reserve of rich people should act like Alaska’s reserve of oil; it should serve as an external source of revenue, allowing the state to have lower taxes on everyone else. Most states have fewer rich people and/or no income tax (examples of the latter include Washington, Texas and Florida), and they still manage to fund their schools while maintaining semi-reasonable tax rates.

3. California has very high property values relative to income, and property tax is simply a wealth tax on property. Again, one would expect this to translate to lower tax rates, since revenues are tied to property values and expenses are tied to incomes. Zillow puts the median California home at $770,000, 9.4 times the California median household income of $81,575. In Ohio, the median home is only $217,000, 3.5 times the Ohio median income of $62,689. High housing prices do inflate the cost of services, but not by that much; starting teacher salaries are only 30% higher in California than they are in Ohio, while California home values are 255% higher than Ohio home values.

There are two common explanations for this puzzle: 

1. California’s sales tax applies to fewer goods than most states’ sales taxes. The theory is that even though the sales tax rate is very high, more goods in California are exempt, so it balances out. However, even factoring this in, California is still in the top third of sales tax revenue per capita, not far behind the leaders, so this isn’t a satisfying explanation. 

2. California’s income tax is more progressive than income taxes in other states; therefore looking at the top rates is a little misleading. This also has some truth, but low to middle income households in California have little income to be taxed anyway (households making less than $50,000 make up 55% of tax returns but only 12% of adjusted gross income), so this doesn’t make much of a difference. Also, the regressivity of California’s high sales tax more than offsets the progressivity of the income tax, as this chart from the Institute on Taxation and Economic Policy shows:

So, what else could explain this puzzle?

In most states, families in need of public assistance face an uphill battle. Annie Lowrey has regularly chronicled the obstacles ordinary Americans must confront when applying for welfare, food stamps, or unemployment: if they even find out about the program in the first place, the path to benefits is littered with “pointless roadblocks” designed to get the applicant to give up, from lengthy forms to unreasonable demands for evidence. 

If the applicant perseveres, the submitted application is then likely to disappear into a bureaucratic black hole, never to be seen again. In the rare case that the application is eventually approved, the state often later accidentally or intentionally kicks the eligible recipient out of the program. In Louisiana, over one in four eligible food stamp recipients incorrectly lose their benefits at some point in a given year, and must apply to get them reinstated.

The numbers are grim. In Mississippi, less than 10% of poor families that apply for welfare are successful. There, only 4% of families with children in poverty receive cash assistance from the government. Of the $86.5 million per year allocated to Mississippi by the federal government, only $4 million reaches needy families, with the balance left unused or misappropriated by the likes of Brett Favre.

By contrast, California is a shining beacon. California operates the largest, most ambitious social welfare program in the entire country, funded entirely out of its own tax revenue. It costs California $68 billion per year, or $5,200 per household.3 Furthermore, it is unquestionably the most efficient welfare program in existence—the state automatically figures out your eligibility and sends you the exact amount you are entitled to, with 100% accuracy. There are no complicated applications to fill out, no bureaucratic overhead, no waste. The money just automatically flows to deserving Californians that need public financial support.

Here is how the program works:

1. California automatically calculates how much your real estate holdings have appreciated in value since you acquired them, after adjusting your original purchase price for inflation.4

2. Every year, California will automatically send you a check for ~1.2% of the total cumulative appreciation in excess of inflation. For example, if you purchased a home for $1 million (adjusted for inflation), and it is now worth $2 million, you will get a check for ~$12,000 each year.

That’s it. That’s the whole program. 

If this sounds too good to be true, you can go online and check for yourself—you can easily look this up for any home on Zillow, by scrolling to the tax records section. 

Consider the example of this ordinary San Francisco home. Zillow says it was purchased for $880,000 in 1999 ($1,300,008 adjusted for inflation), and is worth $2,775,990 as of this writing. (That is not unusual; the typical San Francisco home goes for $1.5 million.)

At the current San Francisco property tax rate of 1.18%, this homeowner should have had a 2022 tax bill of $32,513 (or thereabouts). But Zillow says that the actual tax bill paid in 2022 was $15,987, based on the inflation-adjusted purchase price. This year, California effectively sent this homeowner a check for the difference of $16,526, which simply represents cumulative appreciation to date multiplied by the property tax rate of 1.18%.

Good accounting helps us frame and understand economic reality, and bad accounting obscures it. California has this incredibly successful welfare program, but it is hidden and misunderstood because it is buried and mischaracterized as some kind of technical tax adjustment. 

This type of program is commonly known as a “tax expenditure”. Getting a check in the mail and getting a discount on one’s tax bill are economically identical for the recipient, and proper accounting should reflect that. Consider the Covid-19 stimulus checks—those were technically refundable tax credits, but everyone treated them as if they received a check in the mail. Unlike California, the Federal government actually does a good job correctly reporting on and accounting for tax expenditures.

Once you adjust for this program, California’s fiscal picture makes much more sense. The $68 billion price tag is a huge sum, approximately equivalent to what California collects each year in sales tax, or comparable to what the state spends each year on health and human services. Properly calculated, property tax should be considered the largest source of revenue, as might be expected for the state with the highest property values, and this unique program would be the second biggest item on the expenditure side, trailing only K-12 education ($120 billion). It turns out that California just suffers from murky accounting.

At this point, you probably still have objections, which I will address below.

You are wrong to assert that getting a check in the mail is the same thing as getting a discount on your tax bill. Taxes are impossibly high in California, and this program just brings them down to normal.

As consumers, we have been conditioned to be skeptical of inflated list prices, which exist solely to trick us into thinking we are getting a bargain when in fact we are paying full price. Consider a sweater at J.C. Penney, with a supposed list price of $100, but advertised at 50% off, or $50. 

There are actually rules that require that the sweater must have been listed at $100 in the past, but there is no guarantee that anyone ever paid that price, or that J.C. Penney even expects anyone to buy it unless it is listed at “50% off”. List prices can be arbitrary and misleading. No one believes they are making $50 when they buy a sweater at J.C. Penney. 

Property taxes are not like sweaters at J.C. Penney. If you squint, you can see a baseline in modern America where almost all income is expected to be subject to taxation, regardless of source—whether it be from labor (wages), capital (corporate profits, dividends and capital gains), or real estate (rents).

Labor income is subject to personal income tax and payroll tax, and profits from capital are first subject to corporate income tax and then subject to (reduced) personal income tax and capital gains tax upon distribution.

Owner occupied real estate is unique because the returns are almost entirely exempt from federal taxation. You get tax-free income from your home by renting it to yourself (“imputed rent”), and capital gains realized from selling your home later are usually also tax-exempt.

Instead, you pay annual property tax to your state and/or local government. Property tax is merely an annual wealth tax applied to the value of your property. A wealth tax may or may not be more fair or more efficient than an income tax, but comparing the two suggests that this particular wealth tax is not that burdensome. 

A 1.2% property tax applied to a home purchased at a typical 6% cap rate (that is, a home that each year is returning 6% of purchase price in income or saved rent) is effectively the same as applying an income tax of 20%. This compares favorably to the combined state and federal marginal income tax applied to labor in California, which is upwards of 30% even at lower brackets and can go above 50% at the top levels.

In the past, there have been J.C. Penney sweater-type situations where there was a “sticker” tax rate that no one paid. In the 1950s and 1960s, the top income tax bracket was raised to over 90%, but it was coupled with so many loopholes that virtually all high income taxpayers were actually paying lower rates than they had before.5 At the time, the journalist John Brooks observed that “the evolution of our income tax has been from a low-rate tax relying for revenue on the high income group to a high-rate tax relying on the middle and lower-middle income groups”. In that case, the supposed baseline rate was clearly for show; almost no one paid it.

This is decidedly not the case for property taxes in California. Everyone who has bought a house recently is absolutely paying the full property tax rate, and the full property tax rate in California is perfectly average compared to the property tax rate in other states. Furthermore, the full property tax on owner-occupied homes in California is actually still very low when compared to the effective tax rates on all other sources of income.

You don’t understand this program at all. The primary purpose of this program is to keep seniors from getting kicked out of their homes due to rising property taxes.

Even though California is the only state to have this kind of program, there is no epidemic of senior citizens getting kicked out of their homes elsewhere over rising property taxes, even in other states with higher property tax rates and expensive homes.

First, many states, including California, have a program where seniors can apply to defer payment of property taxes (with interest) until the property is sold. There is no need to exempt property owners from property taxes to allow them to stay in their homes; the state can easily finance tax deferral as needed.

Second, if California’s program was designed to help senior citizens with moderate incomes, then you would expect it to be limited to senior citizens with moderate incomes, like New Jersey’s “Senior Freeze” program. (New Jersey has the highest property tax rates in the nation, at an average of 2.5%, more than double the standard rate in California.) California’s program is much more broadly targeted: It is open to rich people, young people, corporations, anyone that owns a property that has gone up in value. It is clearly designed to redistribute wealth to entities who have made a lot of money by investing in California property. This is clear when comparing the cost of the programs: New Jersey’s Senior Freeze costs $67 per household, while California’s program costs $5,200 per household.

Well, in that case, this is a really bad welfare program. This just redistributes money from renters to landlords, from workers to real estate speculators, from working families who rent apartments in Bakersfield to wealthy executives that own mansions in Los Angeles, etc. Since benefits are mostly tied to historical land appreciation, it doesn’t even incentivize economic investment.

Now we are getting into the realm of value judgments. This program, although underappreciated, is still very popular in California, and therefore must reflect the values and priorities of Californians. Those of us who live in other states should try to respect the culture and values of California residents, even if we do not share them.

That said, it is undeniable that California has developed a very efficient welfare program. Everyone that is eligible receives benefits automatically, and there is almost no bureaucratic overhead at all. That is something other states can learn from.

Even taking value judgments into account, we should remember that welfare programs don’t all have to be about needy families. Landlords have needs, too. 

Also, needy families are unlikely to be long term residents of California, because rents are rising faster than incomes. It only makes sense that California would take money away from transient renters who will eventually have to move to more affordable states like Nevada or Arizona, and give it to wealthier homeowners that will be able to stay in California for the long haul.

Finally, California should be lauded running a uniquely fair program; the largest individual beneficiaries don’t even live in California. The landmark 555 California building in San Francisco is 30% owned by the Trump Organization, and 48 Hills estimated in 2020 that the Trump Organization is receiving around $3.5 million a year from this program. What other state is so generous toward non-residents? 

Wherever you drive in America, when you pass a construction project, you usually see a sign like this:

These signs are effective marketing. They connect the tax dollars you pay to a tangible benefit you can see with your own eyes, something you will remember when you go to the polls to vote on the next highway bill.

And therein lies the solution to California’s problem. California has this very successful program that is misunderstood because it isn’t tangible enough—people can’t connect the taxes they are paying to the benefit they are receiving. They just need some help connecting the dots, something to do what this sign does. This is a simple marketing problem.

Here is a modest proposal:

1. Earmark specific taxes to pay for this program. The cost of this program happens to match total revenue from California’s state and local sales taxes, so let’s use that in this example. From now on, whenever people buy something at the store, they know that the sales tax they pay goes to fund this program.

2. At the beginning of each year, calculate how much each household paid into this program in the previous year. Since we are using sales taxes, this will have to be inferred based on income. For example, research shows that a median California household making $82,000 will typically end up paying $3,700 in sales taxes.6

3. Write a software application that matches program beneficiaries with a household (or more likely, a combination of households) that paid an equivalent amount in the same year. For example, our San Francisco homeowner from earlier that received $16,500 might get matched with five middle class families that rent in Fresno who pay $3,300 each in sales taxes each.7 Now we have made it into an “adopt-a-property owner” program. This makes the benefit much more tangible for taxpayers.

4. Create a state holiday, one day each year where program beneficiaries host an open house for the specific family or families that sponsored them. (There would have to be extra days for the people that own multiple properties.) This is the most important part, as it would allow taxpayers to actually see where their money is going; the private school tuition subsidized, the Tesla payments covered, the kitchen remodel underwritten. This would also allow them to speak to program recipients, and gain an understanding that they are making a real difference in the lives of real families. The state could even issue physical vouchers to taxpayers representing the amount they paid into the program in the past year, so that they could have the pleasure of handing them over to the people they are sponsoring.

A program like this would have the added benefit of increasing empathy and understanding statewide, by bringing together Californians whose lives don’t usually cross. It would pair renters and their landlords, young new homeowners and 40-year residents, rural farm workers and big city lawyers.

This would be a particularly fun program for taxpayers that draw commercial and industrial recipients. One of the largest single recipients is Disneyland, which supposedly benefits to the tune of $20 million or more per year. A thousand or so lucky California families would get a private audience with Donald Trump. Lots of families will get to visit shopping malls and oil wells.

If this scheme works, Californians may even push to expand the program. Either way, once Californians understand exactly where their taxes are going, they will no doubt have fewer complaints about their government.

You are aware that there is an Alternative to the Thing that you believe is measurably superior across most important dimensions. It baffles you that most people consume the Thing instead of the Alternative. 

Your intuition tells you that if people are consuming the Thing instead of the Alternative, there must be some kind of deception or distortion at work; no rational person would consume the Thing when the more attractive Alternative exists.

You do some digging, and you make a discovery: the Thing actually benefits from subsidies! Of course, it all makes sense now! You knew that no one would consume the Thing unless the playing field was rigged. If we stopped subsidizing the Thing, we would all be living in a utopia where everyone happily consumes the superior Alternative, and society would be free of the wasteful subsidies being directed at the Thing.

You assemble your narrative, explaining to the world how we have all been deceived, and how nefarious forces have conspired to foist the inferior Thing upon us, preventing us from enjoying the irrefutable benefits of the Alternative.

The logic of your narrative goes like this:

1. The Alternative is measurably superior to the Thing across some dimensions. You can go into deep, deep detail on this, because you are passionate about the Alternative.

2. The Thing receives subsidies. You can usually easily prove this too, and you can go on at length on the subject.

3. You therefore conclude that if the Thing did not receive subsidies, we would all be happily consuming the Alternative.

This narrative often sounds compelling, usually because points (1) and (2) are fleshed out with considerable detail that was not previously known to the reader. (You know A LOT about both the Alternative and the Thing.) This leads the reader to assume that the overall logic of your narrative is sound. 

Also, some readers will have had the same intuition you had, and so they will not question your logic; they always suspected they understood the true nature of the Thing, and you are supplying evidence that confirms their priors.

However, the logic of the narrative is clearly incorrect. (1) and (2) do not imply (3). 

1. No one cares if the Alternative is better than the Thing across some dimensions, if it is worse than the Thing across other, more important dimensions. For example, the Alternative might be high quality, but unaffordable.

2. It is all fine and well to show that the Thing is receiving some subsidies, but it is crucial to show the magnitude of the subsidies is big enough to plausibly swing people from the Alternative. For example, a 5% tax or a 5% subsidy probably won’t move consumption much either way.

This is all a bit abstract, so let’s look at a couple of concrete examples. Let’s start by revisiting one we looked at last year:

The thesis behind ride-hailing (e.g., Uber or Lyft) is that it is a straightforward application of mobile computing that provides a superior and more efficient alternative to traditional taxis. Software is cheaper and more accurate than a human dispatcher when matching riders and drivers, so ride-hailing drivers will spend less of their time driving an empty car looking for a ride, while riders will spend less time waiting for a driver. Also, software provides an additional layer of transparency, enhancing the rider and driver experience: riders can see where the driver is as they wait, and report bad drivers, while drivers can do the same for riders.

Since software is almost costless to replicate, it does all of the above at a negligible per-ride cost, given enough scale. Since software provides a much better outcome at a much lower cost, we can expect ride-hailing to mostly replace traditional taxis and human dispatchers, in much the same way that software has mostly replaced bank tellers and switchboard operators.

This thesis has its detractors; some people see ride-hailing as unsustainable. To them, ride-hailing is the Thing and taxis are the Alternative. Their thesis goes like this:

1. Taxis basically provide the same service as ride-hailing, except they don’t have to incur all of the corporate overhead and technology expense of an Uber or Lyft.

2. Ride-hailing only exists today because dumb investors subsidize their losses, in the belief that they will drive taxis out of business and reap monopoly profits afterwards.1

3. Ride-hailing is therefore unsustainable and will disappear when investors come to their senses.

Let’s sanity check this thesis. First, how much market share has ride-hailing claimed from taxis? Well, the excellent Todd W. Schneider has created public dashboards based data released by the New York and Chicago, so that’s an easy question to answer. Here is the data for New York:

Ride-hailing apps have a dominant 84% market share in New York, up from zero a decade ago. He has ride-hailing at over 90% in Chicago. These are two of the biggest markets for ride-hailing, so they should be fairly representative.

Next, we want to see how much the ride-hailing companies are spending to subsidize rides. Uber is by far the biggest player, so it should be sufficient just to look at them. If we go to their latest quarterly investor report, they lost $713 million on total bookings of $29 billion – so they are currently subsidizing riders (and food delivery customers) to the tune of 2 cents on every dollar.

Now we have to ask ourselves, what is more likely:

1. Ride-hailing went from 0% to ~90% market share because it provides a better service at a lower cost by replacing a manual approach with software, or;

2. Ride-hailing went from 0% to ~90% market share because ride-hailing companies are subsidizing 2% of the total cost of rides.

This quick sanity check reveals the logical flaws with the ride-hailing-skeptical narrative. It’s true that taxis are superior on some dimensions, and it’s true that Uber and Lyft subsidize rides. But the subsidies are far too small to possibly explain how ride-hailing captured 90% of the market in a short period of time, and so we have to conclude the ride-hailing dominates the market because it has a superior model.

Via Byrne Hobart, Casey Handmer brings us a lengthy essay entitled Why high speed rail hasn’t caught on. The context is the recent NYT article on the woes of the California high speed rail (HSR) project. Handmer argues that high speed rail is fundamentally uncompetitive with air travel, mostly due to physics.

He concludes:

HSR is not a compelling option for generic high speed intercity transport.

His claim is surprising. To be sure, high speed rail is only competitive on shorter trips, where the cost and hassle of getting to and from the airport at each end outweighs the direct speed and price advantage of air travel. But high speed rail has been built extensively where cities are located close to each other, and wherever high speed rail is built, it mostly displaces air travel:2

In fact, the evidence suggests that it is air travel that hasn’t caught on on these shorter routes, limited to capturing passengers that are already near or at the airport (e.g., connecting from another flight). Short plane trips are mostly used between city pairs with no rail option, such as between Los Angeles and San Francisco. This chart from France suggests that rail is dominant for all rail trips under four hours: 

Ah, but what about the subsidies? The subsidies, the subsidies! Here is Handmer again:

Japan’s ostensibly private rail companies have gone bankrupt and been bailed out so many times I’ve lost count, racking up billion dollar yearly deficits year after year. Indeed, as far as I know there isn’t a single HSR route anywhere on Earth that operates profitably on ticketing revenue, and so operation always requires substantial subsidies.

Ok, so, let’s check the subsidies. The high speed rail operators publish their numbers, so this should be easy.

Let’s start with Japan. JR Central is a $20 billion public company whose main business is operating the bullet train from Tokyo to Nagoya to Osaka; they estimate that their operating area covers 60% of the population of Japan.

Their 2019 annual report shows $6.4 billion of operating income on $17.1 billion of revenue, almost entirely from high speed rail. There is no mention of operating subsidies, and in fact it has consistently paid increasing dividends over the last two decades. Their 2019 ROIC clocks in at a respectable 10%.

Let’s move on to Europe. France’s high speed rail operator, Voyages SNCF, reported a € 483 million 2019 profit on revenue of € 8.0 billion. Again, no mention of subsidies or grants. Further north, Eurostar rung up £92 million of profit on £987 million in revenue in 2019, no subsidies there either.

How about the US? Amtrak claims that the Acela brought in $662 million in revenue in 2019, against only $328 million in expenses, for a profit margin of over 50%. (Unlike the Japan and Europe examples, the Acela is not a separate operating segment, so this figure probably does not include track depreciation and thus is less comparable.)

I am not sure what subsidies Handmer is referring to. He is correct that Japan subsidized their rail operator decades ago, but that appears to have gone entirely toward artificially low fares and unnecessary employees, rather than having anything to do with the fundamental economics of high speed rail. 

The transportation researcher Alon Levy also says that high speed rail is profitable in Europe and Japan, so I do not think I am missing anything. The most charitable reading is that high speed rail indirectly benefits from some subsidies on local routes, which provides some feeder traffic, and from government coordination to get the right of way to get it built in the first place, but once built it tends to be profitable given sufficient demand.

Handmer is probably correct that high speed rail is more expensive than air on a per mile basis. Here are the figures I got from 2019 annual reports:3

It appears that high speed rail is more expensive on a per-mile basis, but not by enough to be meaningful on a typical 200 mile trip. Even going from 13 cents to 33 cents per mile only tacks on an extra $40 to the cost of the trip, not enough to compensate for the additional cost and hassle of making an additional airport trip at each end. The Acela is popular despite charging nearly $1 per mile.

The evidence suggests that it is a mistake to focus on speed and cost when evaluating rail. Rail is dominant on most shorter routes despite the higher cost and slower speed vs. air. The evidence indicates that people pick rail because of convenience and comfort, and are willing to pay a premium for a slower trip to get it.

Again, the simple explanation is the correct one. High speed rail is politically popular because people far prefer it over air travel, and we know this because it almost completely displaces air travel wherever it is built.

The subsidy narrative is a good example of the Midwit Fallacy we discussed previously. The Midwit Fallacy describes our tendency to reject simple answers and accept complex answers simply because complex explanations sound smarter. We do so even when the complex answer is contradicted by the available evidence and contains logical holes.

Here is a sampling of other related areas where you sometimes see people make a contrarian midwit case that Thing X is only popular because of subsidies:

• Home ownership. Yes, the US showers homeowners with subsidies, but our home ownership rate of 65% is very much in line with other countries. People want to own their homes regardless of subsidies.

• Car ownership. This has a bit more truth; we subsidize car owners in a multitude of ways, while other countries tax them heavily, which leads us to drive a lot more. However, cars are unmatched for their convenience and flexibility, and so car ownership in other countries is not far below what it is here in the US: in 2022, Americans owned 868 cars per 1000 residents, while Japan and Western Europe are in the 600-750 range.

• Single family homes. People also like living in suburbs and in single family homes. Yes, single family zoning does limit the construction of multifamily housing in the US, which massively inflates rents and condo prices in popular cities, but single family homes are popular everywhere. In the EU, 53% of the population lives in single family homes (not all detached), while almost 70% of housing is single-family in the US.

Here is why the subsidy narrative is generally likely to be wrong:

1. Politicians like to subsidize popular things. That gets them more votes than subsidizing unpopular things. The subsidy narrative usually gets the causation reversed – the Thing is not popular because it is subsidized, the Thing is subsidized because it is popular.

2. In most cases, to get the majority of people to switch from a vastly superior Alternative to an vastly inferior Thing, the magnitude of the subsidy required would be tremendous. Like, implausibly tremendous. A small subsidy will nudge a few fence-sitters, but that’s usually all.4

3. Most importantly, handing out tremendous subsidies for something that is widely popular is impossibly expensive, simply because it is widely popular. 

If the country only consumes a million widgets a year, a $100 per widget subsidy is manageable – $100 times a million is only $100 million. If the country consumes ten billion widgets a year, that’s  a less manageable $1 trillion per year. 

You can hand out small subsidies for popular things (like mortgages), and large subsidies for less popular things (like electric cars), but it is almost impossible to afford large subsidies for popular things. Incidentally, this is the exact impossible dilemma many countries are having to confront with energy prices this winter.

…[A] spongy swelling of the gums, which are apt to bleed, with a loosening of the teeth; an eruption of leaden coloured, purple or livid spots on the legs…

As the disease develops the patients lose the use of their legs and are subject to a difficulty of breathing.

Scurvy frequently led to death, and the disease could decimate a ship’s crew; one particularly disastrous example was Commodore George Anson’s 1740 circumnavigation of the globe:

Anson’s six warships and two supply ships sailed with 1,854 men. 

One ship and 188 men returned; 997 of the 1,415 deaths were from scurvy.

In this manner, scurvy would claim the lives of millions of sailors of that era. It is sometimes theorized that the British lost the Revolutionary War in large part because of scurvy’s impact on the British navy. Shortly thereafter, the British figured out how to prevent scurvy on their ships, and they used this knowledge to triumph over the French in the Napoleonic Wars.

Scurvy is intriguing because the knowledge of how to prevent it and treat it was seemingly found and lost and found again and lost again, over and over, throughout the centuries. Many sailors knew how to treat scurvy hundreds of years before the British tamed it throughout their navy in the early 19th century. Remarkably, scurvy would rise again to plague polar explorers as late as the early 20th century, long after it had supposedly been conquered.

Scurvy may seem like an unusual topic for a blog about economics and investing, but it is a fantastic case study about the different ways people throughout the ages approached a difficult problem involving a very complex system beyond their understanding (the human body) with lots of confounding variables to lead them into blind alleys. We can see now what strategic approaches worked best to solve the problem, and why. The problem of scurvy is worth studying today because it is analogous to the problems we confront every day in economics, investing, and even at the office.

We now know that scurvy is caused by a deficiency in vitamin C, which the human body needs to produce new tissue. (Hence the gruesome symptoms.) Vitamin C is found in various concentrations in a wide variety of fresh foods, particularly in vegetables and fruits, but also in fresh meat. It makes sense to us today that sailors with no access to fresh foods would be susceptible to the disease, while land-dwellers would only encounter it under rare circumstances.

No one knew that scurvy was a deficiency disease until a pair of Norwegian researchers stumbled into the finding while experimenting on guinea pigs, publishing their studies in 1907. No one was aware of the concept of vitamins until 1912. Albert Szent-Györgyi isolated vitamin C in 1931, and won the Nobel Prize for his efforts in 1937. By WWII, vitamin C was being distributed in tablet form, and scurvy was a thing of the past.

Before 1907, no one knew what caused scurvy — many believed it was an infectious disease, or it was caused by tainted food. Regardless, as far back as recorded history goes, people found ways to deal with scurvy when they did not have access to fresh food, whether it was because they were under siege from invaders or because they had to deal with a long winter. 

Anton Howes documents that the residents of northern climates had, over the ages, developed methods of making it through the winter without getting scurvy:

The Iroquois ate the bark, needles or sap of evergreen trees — most likely white cedar, or some other kind of spruce, fir, juniper or pine, all rich in vitamin C…

Cloudberries became something of an obsession for the common people of sixteenth-century Bergen, in northern Norway…and [they] otherwise subsisted almost entirely each winter on honey-sweetened cloudberry jams, which they covered with a butter to preserve it from the air.

Howes tells us that the sailors on the earliest long range voyages quickly discovered the value of oranges and lemons in treating scurvy:

The voyage of Vasco da Gama, having been the first to round the Cape and reach the eastern coast of Africa, was then stricken with scurvy. They were only inadvertently saved when they traded with some Arabian ships laden with oranges, before landing at Mombasa…

[The] status of oranges as a scurvy wonder-cure had entered sailors’ lore. When Pedro Alvares Cabral repeated da Gama’s feat of rounding the Cape of Good Hope in 1500, his crew purposefully treated their scurvy using oranges.

This knowledge spread to other countries during the 1500s:

The first Dutch East Indies Fleet sailed in 1595 with 249 men and returned in 1597 with only 88. The 1598 fleet took lemon juice, grew horseradish and scurvy-grass (a corruption of “scurvy-cress”) on board, and lost only 15 men. 

Sir Richard Hawkins in 1590 bought hundreds of oranges and lemons in Brazil for his men, perhaps based on his experience as a prisoner-of-war in Spain: “that which I have seene most fruitful is sower oranges and lemons . . . I wish that some learned man would write of it, for it is the plague of the sea, and the spoyle of mariners.

The efficacy of lemons, oranges, and other similar foods in preventing and treating scurvy was documented in textbooks of the era (together, these foods were dubbed “antiscorbutics”), but adoption of these proven treatments was still spotty. Part of the problem was misinformation spread by medical professionals:

[O]rthodox physicians, such as Thomas Willis, Gideon Harvey, Boerhaave, Meade, and Cullen, wrote copiously and learnedly about scurvy, but without any maritime experience, simply listing the many standard theories of the cause of scurvy, and the many standard medical treatments such as bleeding, purging, and polypharmacy.

Remember Commodore Anson’s fateful 1740 journey, where he lost a thousand men to scurvy? Well, here is how it happened:

The admiralty ignored the centuries of experience of seamen and instead took advice from the College of Physicians and supplied Anson’s ships with elixir of vitriol (sulphuric acid, alcohol, sugar, and spices) as a substitute for acid fruits on the concept that the efficacy of citrus fruits was related to their acidity.

The traditional story of the triumph over scurvy takes place in the 1750s. Here is how the (US) Military Health System tells it on their website today:

[The] Royal Navy cracked the mystery of the disease in the 18th century thanks to surgeon James Lind. The British began storing citrus fruits on board all of its ships. The British Navy gave its sailors limes or lemon juice rations to ward off scurvy – earning them the nickname of "Limeys" among the American sailors who didn't know about or believe in the preventative treatment.

In this simple telling, a heroic Scottish doctor named James Lind, when confronted with cases of scurvy on a ship, conducts the first clinical trial in history, correctly deducing that oranges and lemons cure scurvy. The Royal Navy is very slow to react to this new information, but a half century later, they get around to giving lemon juice to their sailors, before later switching to their famous limes. The British conquer scurvy, and then conquer the world.

This story is almost completely false.

There is no evidence that Lind’s clinical trial ever happened; on the journey where he supposedly conducted his study, the ship’s logs contain no record of sick passengers. Lind’s Treatise of the Scurvy does not give a clear recommendation that oranges and lemons should be the main treatment for scurvy. Moreover, Lind’s actual proposed treatment was useless:

Lind was well aware that it was impractical to carry citrus fruits on long sea voyages because, as Woodall noted, “oranges and lemons are liable to spoil”, as indeed would lemon juice. Lind therefore devised a system of almost boiling purified citrus juice, so that 24 oranges or lemons were reduced to a few ounces…

Hughes (correctly) joined others in asserting that Lind’s rob [fruit syrup] was later shown to be ineffective in preventing scurvy (now known because he had boiled the heat-labile ascorbic acid), and (correctly) that Lind was unscientific in presenting no evidence for his rob, merely “by my own experience”,“the most incontestable experience”,“remains good for several years”, and “preserving their virtues for years”.

The late Jeremy Hugh Baron argues that the credit should actually go to a subsequent pair of Lind-inspired Scottish doctors, Thomas Trotter and Gilbert Blane. Trotter was assigned to a slave ship in 1779, and calculated that he could improve the slavers’ financial returns by convincing them to invest in a bit of citrus. More importantly, he figured out that lemon juice can be preserved for a long journey by straining it, bottling it, and covering it with olive oil.

Meanwhile, Blane joined the Royal Navy in 1779, and he made similar calculations for his own bosses, who had been suffering great losses from scurvy during the Revolutionary War. Blane calculated that a mere 50 oranges and lemons could effectively “buy” them an extra sailor by reducing mortality from scurvy. 

His method of preserving lemon juice involved mixing it with spirits, but it was equally effective, and it began to gain adoption throughout the British fleets. Thanks to the efforts of Blane and Trotter, by 1796, all British naval ships on foreign service were issued lemon juice.

Here, finally, the British defeat scurvy, and they defeat the French in the Napoleonic Wars:

Between 1795 and 1814 the admiralty issued 1.6 million gallons of lemon juice. Sweet lemons were imported, especially from the Mediterranean region. Nelson turned Sicily into a vast lemon juice factory. Scurvy then more or less disappeared from ships of the navy.

Or did it?

Somehow, scurvy would make a comeback. The British would switch from imported Mediterranean lemons to limes from the West Indies in 1860. This was an unfortunate choice, as we now know that limes contain only half the vitamin C of lemons. Moreover, tests conducted in 1918 demonstrated that the lime juice issued by the British was almost totally ineffective, probably because it came into contact with copper (which oxidizes vitamin C) when it was manufactured.

There were reports of increased cases of scurvy in the Royal Navy in the 1860s, but nothing approaching an epidemic. This is because at the same time, the advent of the steam engine made voyages much shorter, meaning that sailors usually no longer spent enough time between ports to develop scurvy. (It turned out that under the right circumstances, the steam engine prevented scurvy just as effectively as lemon juice did.)

This all changed in 1875, when the British sent George Nares to lead an Arctic expedition, where they would be away from fresh food for months. Maciej Cegłowski picks up the story:

The expedition was a fiasco. Two men in the sledging party developed scurvy within days of leaving the ship. Within five weeks, half the men were sick, and despite having laid depots with plentiful supplies for their return journey, they were barely able to make it back. A rescue party sent to intercept them found that lime juice failed to have its usual dramatic effect. Most damning of all, some of the men who stayed on the ship, never failing to take their daily dose, also got scurvy.

The failure of the Nares expedition provoked an uproar in Britain. The Royal Navy believed itself capable of sustaining any crew for two years without signs of scurvy, yet here was an able and adequately supplied crew crippled by the disease within weeks. For the first time since the eighteenth century, the effectiveness of citrus juice as an absolute preventative was in doubt.

Scurvy would plague polar expeditions for the next forty years, which Ceglowski describes in detail. Three wrinkles made late 19th century / early 20th century polar scurvy particularly difficult for scientists to solve:

1. Many explorers managed to avoid scurvy by eating fresh seal or polar bear meat. We now know that this is because fresh meat also contains vitamin C, but again, they had no way of knowing this.

2. By now, scientists are aware of bacteria and germs, and are more inclined to attribute diseases to those causes.

3. Explorers are regularly consuming lime juice and still coming down with scurvy. 

They used these observations to come up with a dramatically incorrect theory of scurvy, which was that antiscorbutic substances such as lemon juice are actually useless placebos, and that scurvy is really caused by tainted meat or other tainted foods. This theory actually mostly fit the observed data from the time, but on the other hand, it could not have been more wrong.

Ceglowski summarizes the problem well:

This pattern of fresh meat preventing scurvy would be a consistent one in Arctic exploration. It defied the common understanding of scurvy as a deficiency in vegetable matter. Somehow men could live for years on a meat-only diet and remain healthy, provided that the meat was fresh.

This is a good example of how the very ubiquity of vitamin C made it hard to identify. Though scurvy was always associated with a lack of greens, fresh meat contains adequate amounts of vitamin C, with particularly high concentrations in the organ meats that explorers considered a delicacy. Eat a bear liver every few weeks and scurvy will be the least of your problems.

But unless you already understand and believe in the vitamin model of nutrition, the notion of a trace substance that exists both in fresh limes and bear kidneys, but is absent from a cask of lime juice because you happened to prepare it in a copper vessel, begins to sound pretty contrived.

In 1904, the Norwegian bacteriologist Axel Holst set out to find the microbe that causes the disease known as beriberi; an impossible task, because as we now know, beriberi is actually caused by a vitamin B1 deficiency. After a couple of fruitless years, he began to do studies on guinea pigs with his colleague Theodor Frolich, a pediatrician who had been studying infantile scurvy. This was a fortunate choice, as guinea pigs are one of the very few animals besides humans that need to consume vitamin C.

In 1907, the pair published a study showing that guinea pigs fed a diet of pure grain would exhibit all of the symptoms of human scurvy and die, but when antiscorbutics like cabbage and apples were added to their diet, they showed no scurvy symptoms. (They even managed to find that fresh cabbage and fresh potatoes work against scurvy, but boiled cabbage and dried potatoes do not.) 

Based on their guinea pig experiments, they concluded, accurately, that scurvy was a deficiency disease. Holst and Frolich had cracked the code, and within a little over a decade, their findings would be universally accepted, and scurvy would be a thing of the past.

It is worth highlighting that their study was not universally embraced at first. One of the loudest skeptics was a fellow Norwegian scientist, Fridjtof Nansen. Nansen was not just any scientist; he was actually the most famous scientist in all of Norway, a successful polar explorer and national hero who would later win the Nobel Peace Prize. His Fram expedition in 1893-96 reached a record northern latitude (although it fell short of the North Pole), and he accomplished it without suffering issues with scurvy.

He believed wholeheartedly in the theory that scurvy was caused by tainted foods, and that his expeditions were proof that scurvy could be avoided by proper precautions and sanitation. In his mind, he was a real life explorer who had prevented scurvy in humans in the field, while Holst and Frolich were merely doing studies on rodents in a lab. (It didn’t help that Nansen had a personal beef with Holst dating back many years, when Professor Holst attacked then-grad student Nansen’s dissertation.)

Holst replied that despite Nansen’s extensive field experience, he had never done anything approaching a controlled experiment, and logically, he had no way of knowing how he actually avoided scurvy on his trips. Holst was of course completely correct. As it turns out, Nansen had inadvertently avoided scurvy on his Fram expedition in a very traditional Norwegian way – by bringing along a half ton of cloudberry preserve!

What can we learn from this whole episode?

Vitamin C Knowledge vs. Cloudberry Knowledge

Anton Howes has the main conclusion nailed:

It is possible to discover that a thing works, and even to use it for hundreds upon hundreds of years. But without knowing why it works, its potential will never be realised. In fact, the lack of understanding can make a perfectly useful method of solving a problem extremely vulnerable to being discredited and lost.

Scurvy was only truly defeated once we had an accurate theoretical framework for what caused it. Once you know exactly what causes it, you can go on multi-year voyages, you can go to the South Pole, you can go to space, all without worrying about scurvy. You just need to bring along some stable source of vitamin C.

It is true that the ancients had, through trial and error, found some ways of preventing and treating scurvy, but knowledge they acquired was in practice a bit more limited in utility than one might imagine. 

For example, we might think, “the Norwegians knew centuries ago that cloudberry preserves prevented scurvy”, but that statement is far too broad to describe what was actually known at the time. 

It might be more accurate to say “the Norwegians knew that Norwegian cloudberries, preserved and stored and the Norwegian way, when consumed in Norway during the Norwegian winter, prevented scurvy.” They had no way of knowing if their methods would work at all outside of Norway, or if they would work with any substitutions in the preparation process, or even if conditions might change somehow in Norway in the future to make their methods useless.

If this seems excessively nitpicky, consider what happened to the British. They figured out that lemon juice sourced from the Mediterranean and prepared and stored in a certain way works to prevent scurvy on long warm sea voyages. This was useful, but had some limitations: 

1. First, you need a way of reliably sourcing incredible quantities of lemons every year, lemons being a fruit that only grows in warm Mediterranean climates. 

2. Also, you need a way of consistently preparing and storing that much lemon juice, because you don’t know what parts of the preparation and storage process are crucial to its effectiveness.

3. Finally, if it stops working, you have no way of figuring out a potential alternative solution, or even of knowing if lemon juice was ever a key part of the solution in the first place. 

In practice, their lemon juice solution eventually failed on all of these points: they had to switch to limes, the preparation and storage process broke down undetected, and they never managed to figure out an alternative to lime juice when polar expeditions revealed the gaps in their knowledge.

There is a massive gap between the utility of what we might call “vitamin C knowledge”, where we know precisely why and through what channels a solution works, and what we might call “cloudberry knowledge”, where we observe compelling evidence that a solution works in a specific environment, but without any idea why it works, or if it will work in any other environment, or even if the solution will be available in the future.

Even the most basic pieces of vitamin C knowledge are powerful when utilized correctly. We talk here sometimes about value investing, which as taught by Ben Graham in the 1930s, is simply based around the idea that stock represents ownership in a business, and that if you are able to estimate the cash that will be distributed to its owners in the future with any accuracy, you can use that knowledge to buy stock when it is cheap relative to projected future cash distributions.

There are a lot of ifs in that statement, but the statement itself is known to be correct. It must be true over time that the owners of a business will receive all of the cash distributed by the business, no more and no less. 

Everyone else can trade in and out of their shares as they like, pursuing Keynes’ “beauty contest” of guessing the next meme stock or any other macro or trading strategy that strikes their fancy. If you can reliably buy undervalued stocks and hold them as long as necessary to realize their cash flows, you will make money at everyone else’s expense.

This particular bit of knowledge happens to be much more useful if fewer people believe it – after all, you need people to sell you the stock at attractive prices – but investors have used this simple insight to make money in the past, including Ben Graham himself.

Investors have to rely on cloudberry knowledge as well, approaches and relationships that they have found to work in practice, even if they don’t necessarily understand why. Some of these are statistical in nature, like the tendency of momentum stocks to outperform, while others are more qualitative, tendencies they have noticed from their extensive experience that they might talk about on Invest Like the Best.

We treasure the cloudberry knowledge we possess, which is often proprietary and learned from hard experience. But because we lack the “why”, the knowledge is evanescent. Specific investment strategies from the 1990s do not necessarily work at all today, while The Intelligent Investor is timeless.

The more quickly an environment changes, the more quickly that knowledge based only on specific experience will become obsolete. (We have all had the experience of receiving well-meaning but obsolete advice from our parents, and we can all look forward to dispensing the same to our own children.) Cloudberry knowledge is fantastic, but only if we understand its limits.

Vitamin C knowledge describes Elon Musk’s favored method of working through problems from “first principles”, stripping things down to the basic building blocks of theoretical knowledge we fully understand and know to be true, and working to solve problems from there. As the story goes, first principles thinking was key in building Tesla and SpaceX.

First principles thinking is an especially powerful tool for a new venture in a fast-changing market, which lacks the experience and know-how that incumbents possess, but will be unencumbered by increasingly obsolete processes and ideas.

False Theories 

Here is Ceglowski’s top observation about scurvy, which is equally on the mark:

[F]rom the fifteenth century on, it was the rare doctor who acknowledged ignorance about the cause and treatment of the disease. The sickness could be fitted to so many theories of disease—imbalance in vital humors, bad air, acidification of the blood, bacterial infection—that despite the existence of an unambiguous cure, there was always a raft of alternative, ineffective treatments. At no point did physicians express doubt about their theories, however ineffective.

Before 1907, doctors never had an accurate theory of what caused scurvy. It would have been almost impossible given their knowledge of biology. Naturally, this did not stop them from coming up with inaccurate theories, and prescribing treatments based on those theories instead of the actual evidence.

This is a theme we have covered before, with the famous story of Ignaz Semmelweiss:

As a recent medical school graduate in Vienna in the 1840s, Semmelweis learned that the one of the two maternity clinics at his hospital had an extremely high mortality rate from childbed fever. 

One day, his friend was accidentally cut with a scalpel during an autopsy and died of something similar to childbed fever. He realized that the maternity clinic with the high mortality rate was staffed with medical students who performed autopsies, and he worked out that childbed fever could be eliminated if doctors were forced to wash their hands before attending to patients.

He introduced hand washing to the maternity clinic, and childbed fever rates immediately plummeted. Armed with this evidence, he and his colleagues spread the word. To their surprise, they were met with ridicule and rejection from the leading doctors of the time.

The medical community objected that his explanation of how childbed fever is transmitted did not match leading theories of disease at the time (this was before germ theory was developed)...

Semmelweis eventually died in an insane asylum, and it would be decades before hand-washing was widely adopted in the medical community.

Humans are emotional creatures, and we get wrapped up in the stories we tell ourselves and the theories we embrace. They become part of our identity, central to our ego and our status in society. We are suckers for an attractive story, and we find it very comforting to believe that we understand something.

When confronted with incontrovertible evidence that our theories are wrong, do we just abandon them? Of course not. We dismiss the offending evidence, or we seek to find evidence that confirms our original theories. This is easier than one would think, especially if we are willing to use incorrect logic, or if we are intentionally careless about checking the veracity of any new “evidence” that supports our original, disproven theories.

In hindsight, it would have been best if everyone just admitted they had no verifiable theory for what caused scurvy, and simply proceeded based on the known effective treatments, which were plentiful going back for centuries: again, the word “antiscorbutics” was coined in the 1600s to describe foods that cured scurvy. Theories are valuable if they are true, but backfire if they are inaccurate. 

It is very common to learn with a high level of certainty that something works, before knowing exactly why. That’s fine! Cloudberry knowledge might have its limits, but it is still effective. Solutions don’t stop working just because you don’t understand how they work. Especially when the stakes are life or death, it makes sense to go purely on the evidence collected. 

No one needed to know about vitamin C to be able to treat scurvy with oranges, and no one should have needed to wait for germ theory before deciding to wash their hands before they delivered babies.

Even outside of medicine, false theories have always dictated human behavior long after they were disproven by real world evidence. As Keynes famously put it:

The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back.

Economics and investing are particularly fertile territory for disproven but seductive theories to gain popular traction. Economics also features complicated systems and confounding factors, which make it easier for people to “prove” any lie they wish with distorted statistics and incorrect logic. Thus people are able to justify their support of policies guided by their mistaken beliefs, policies which so often coincidentally happen to fulfill some selfish interest of theirs.

This newsletter has often looked at NIMBYism, which is based around a central myth that the quantity of housing we construct does not affect the price of housing or the quantity of housing we can consume, something that has been disproven by every careful study, as well as by basic logic. But NIMBYism is hardly unique – economic policymaking is dominated by incorrect populist theories that never seem to go away, from the lump of labor fallacy to greedflation.

Facts and Analysis

One painful recurring theme from the story of scurvy is that even with no knowledge of vitamins, people with authority often still had enough evidence about effective treatments to be able to control the disease, but they usually failed to successfully implement them.

The problem was not the failure to listen to experienced and credentialed practitioners. Reputable doctors published all sorts of misinformation for centuries, without a hint of self-doubt. James Lind had years of experience as a doctor at sea, and for most of his life, he propounded a completely useless treatment, “based on his own experience”. 

As late as 1900, scientists were publishing papers that said things like: “Neither lime juice nor fresh vegetables will either prevent scurvy or cure it; it is a disease produced through the eating of tainted food.” Even after the solution was discovered, Fridjtof Nansen, a scientist and successful explorer, was still pushing a false theory based on incorrect logic.

How did all of these experienced and credentialed practitioners end up so far off base? We speculated previously that a likely cause was confirmation bias, and that is certainly part of it. However, an equally important likely cause is that they were not skilled or disciplined when it came to proper logical reasoning and analysis. There is nothing about first-hand experience or success that confers knowledge and wisdom by osmosis – it still has to be gained by disciplined study and correct analysis.

What worked to treat scurvy were careful studies and sound reasoning. Even if Lind’s supposed first clinical trial in history never happened, his immediate successors took a more rigorous approach. Here is what Baron tells us about Sir Gilbert Blane, the man who convinced the Royal Navy to adopt lemon juice in the 1790s:

Blane persuaded his admiral to instruct all ships’ surgeons to keep precise daily records of the numbers, diagnoses, and fates of sick sailors for Blane to tabulate…

[H]is colleagues admitted he was “a painstaking physician . . . he was so cold in temperament, that we called him “Chilblaine”...

Blane had a reputation in the navy of being unflappable, and his statistical analyses were unanswerable.

And here are Blane’s own words in 1799:

Lemon juice may be deemed a medicine . . . better as a dietetic modifying the alimentary matter. I am glad thus to escape from the slippery paths of theory, having no great confidence in the accuracy, nor the utility of such speculations.

That was all it took. Careful data gathering, careful reasoning, and the discipline to admit that he didn’t know what he didn’t know.

In a much different context, Ben Graham once said:

The stock investor is neither right or wrong because others agreed or disagreed with him; he is right because his facts and analysis are right.

What is true in finance is true in medicine, and is true everywhere else. Scurvy does not care about credentials, experience, past successes, or seductive stories. It only responds to effective treatments. If we truly desire to achieve a successful outcome, what matters most is that we get our facts and analysis right.

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Further reading:

This essay is inspired by Plague of the Sea, a post written by Anton Howes last year. Even if you’re not sure if you’re interested in the history of innovation, you should consider subscribing to his Substack, which is consistently interesting and thought provoking. 

Much of the history of scurvy in this essay comes from this excellent journal article published in 2009 by the late Jeremy Hugh Baron, a respected physician and writer.

The history of polar scurvy comes from Maciej Ceglowski’s excellent 2010 essay, which is very much worth reading in its entirety as well.

“They don’t seem like they want to work,” Mackey told Reason about younger generations in the workplace. 

“Younger people aren’t quick to work because they want meaningful work,” was Mackey’s diagnosis of the problem, referring to the well-known importance to Gen Z of finding work with some kind of social significance. This is a mistake, he said. “You can’t expect to start with meaningful work. You’re going to have to earn it over time.”

This is a very dunkable statement – what law of the universe makes it impossible to start with meaningful work? – but it is worth examining in the context of Mackey’s career.

At the time of this June 2017 Texas Monthly profile of Mackey, Whole Foods was struggling badly. The stock price was down by over 50% despite the ongoing bull market, same-store sales had been negative for a year and a half, and Jana Partners had recently launched an activist campaign that would shortly end in a sale of the company to Amazon.

Whole Foods had long been the dominant player in what the FTC would later define as the “premium and organic supermarket” segment of the retail market. Their stores were known for peddling gourmet foods at eye-watering prices, which earned them their well-deserved “Whole Paycheck” moniker.

By Mackey’s own reckoning, Whole Foods’ growth was long fueled by a weak competitive environment:

In the late nineties and early aughts, Walmart got into the grocery business in a big way, and mainstream grocers were “scared to death,” Mackey says. “Walmart was coming in and just taking away all their share, so they all tried to become more like Walmart. They cut their costs, they cut their labor at their megastores, didn’t invest as much into them, and the stores got to be kind of crummy. That created an opening for Whole Foods Market, which wasn’t trying to compete with Walmart on having the lowest prices. We transitioned over to a middle-class customer that we never thought we were going to get.”

By the late-00s, competitors like Safeway and Kroger were showing signs of catching up, improving their organic and prepared foods offerings. Still, when Whole Foods moved to buy smaller organic rival Wild Oats in 2007, the FTC blocked the deal, on the grounds that they both participated in a defensible retail submarket that they would then monopolize. (Hindsight is 20/20.)

Whole Foods would expand through the early 2010s, but by 2016, competition was taking its toll:

The regular grocery, in time, became good enough on quality—and usually better on price.

“Some of those middle-class customers, who weren’t really our customers from the philosophical standpoint but came to us just because our stores were nice, began to drift back to H-E-B and Safeway and whatnot,” Mackey says. Meanwhile, Trader Joe’s began to blanket America with its combination of smiling Hawaiian-shirt-clad salesclerks, low prices, small and convenient (and thus cheaper) store layouts, and clever, only-at-Trader-Joe’s packaged products.

This is the conundrum that has dogged Whole Foods for much of the past ten years. It continued to grow handsomely as it added more stores and ever more in-house dining areas and special services, but eventually the competition caught up to it. “They didn’t evolve,” says Phil Lempert, a longtime food-industry analyst and the editor of supermarketguru.com. “I think the chain really had blinders on and thought they were so far ahead of everyone else that they didn’t have to pay attention to competitors. The reality is, I can go to Kroger and buy the same or similar goods at a lower price—it’s that simple.”

A March 2017 Barclays research note by Karen Short (as summarized by Reuters) underscored the depth of the challenge:

Whole Foods is losing millions of customers to what was once an unthinkable threat: Kroger.

The organic-food chain has lost as many as 14 million customers in the past six quarters, according to Barclays analyst Karen Short.

Most of those customers are instead going to Kroger and probably won't ever go back to Whole Foods, Short said in a recent research note.

"The magnitude of the traffic declines ... is staggering," Short said. "As most retailers know — once traffic has been lost, those patterns rarely reverse."

Kroger — a conventional grocer not known for organic offerings — has not historically been regarded as a significant threat to Whole Foods.

…

Kroger's sales of organic and natural food totaled $16 billion in the past year, compared to $15.8 billion at Whole Foods, according to Barclays.

…

As Kroger's share of the organic-food market grows, Whole Foods' is shrinking.

Whole Foods' same-store sales fell 2.4% in 2016. That metric is expected to fall another 2.5% this year. Meanwhile, Kroger's same-store sales grew 1% in 2016.

The Jana 13D from April 2017 was very blunt about Whole Foods’ operational shortcomings:

JANA…intends to have discussions with the Issuer's board of directors and management regarding topics including:

…

[(4)] pursuing opportunities [for Whole Foods] to improve performance by advancing its brand development and by addressing core operating deficiencies in areas including customer loyalty and analytics, category management and analytics, technology and digital capabilities, procurement and buying practices, pricing strategies and value proposition communication, and online offerings, 

(5) improving in-store execution, including labor scheduling and management, management of inventory and shrink levels, stocking practices, product layout, in-store signage, private label program strategy and management, and assessing broader cost structure and operating opportunities, 

(6) evaluating opportunities to re-engineer the Issuer's suboptimal and cost-disadvantaged grocery procurement and distribution strategy, such as by internalizing distribution or pursuing other hybrid strategies…

In plain English, they had fallen behind in technology, they didn’t have a loyalty card program, and their distribution and in-store operations were inefficient and uncompetitive.

It’s hard not to feel a little bit of sympathy for Mackey and Whole Foods here. For years, they had been focused on innovation and growth, and understandably they never developed the scale or operational efficiency of a Kroger or a Safeway. Then, one day, Kroger and Safeway started copying their innovations, and on the other side, Trader Joe’s and Amazon came in with innovative models of their own.

This is the fundamental challenge for an innovative company: how to turn an early competitive lead into an enduring moat before another startup or an entrenched incumbent copies your innovations and executes on them better. As Netflix’s Ted Sarandos memorably put it in 2013 before they released House of Cards, “The goal is to become HBO faster than HBO can become us.”

There is no guarantee that this will turn out well for the innovator. Ford dominated the early automotive market, only to be crushed by General Motors. Apple had a near-death experience after losing their initial lead in PCs to Microsoft and Intel. Diners Club has long since given way to Visa and MasterCard.

How did Mackey react to the Jana activist campaign?

The timing was intentional,” Mackey says curtly … “They hijacked my book tour. It’s not that I think that they were trying to harm the book tour. It’s just like, ‘Okay, the CEO is going to be distracted. He’s not going to be able to give full attention to this.’ ”

We humans are self-centered creatures. The shareholders were thinking of the billions of dollars they were losing, the employees were worried about their jobs, and Mackey, the guy who was supposed to be accountable for the direction of this struggling company, was thinking about missing his book tour!

Which brings us back to his comment about the younger generation being entitled. Of course each successive generation is going to be more entitled. As long as the economy keeps growing, more productive companies will have to bid up wages to snatch workers away from less productive enterprises. This means that each new generation will have the ability to make increasing demands on employers, including demands for non-pecuniary benefits, like (gasp!) meaningful work.

The CEO of a less productive company (like Whole Foods), will always feel like the Red Queen, forced to run faster and faster just to stay in place. From their perspective, they are offering the same wages they did before, but suddenly workers are turning them down. They are charging the same prices they did before, but somehow customers are defecting to competitors. Therefore the problem must be external, not internal. 

The logic is absurd, of course – even if you had never watched a game of basketball, you could be a successful NBA GM today if you were somehow able to pay players 1980s-level salaries – but considered as a product of motivated reasoning, the mistake is understandable.

Being human, they invent convoluted narratives to explain what they see – a mysterious sudden societal tidal wave of wokeness, entitlement, or laziness – to avoid the simple explanation that is obvious to all, which is that they are simply no longer able to effectively compete in the marketplace. Notice that the executives of successful companies somehow never report experiencing these supposed problems! John Mackey’s comments illustrate an example of what I believe Gen Z now calls “copium”.

The truth is, we are all John Mackey. We make excuses, we vent, we contort ourselves into pretzels to avoid accepting reality. The difference is, we do not run a $15 billion company, so we do not have a platform to offer ourselves as thought leaders on “conscientious capitalism”.1 (Riddle me this: what the hell is conscientious about charging $8 for a dozen eggs?) When we say dumb or offensive stuff in public, no one notices. (Usually. Don’t try that at home.)

Of course the younger generation is not “entitled” for having things better than the previous generation. That's just economic progress. “Entitled” might be a word you apply to a CEO that is publicly fretting about his personal brand and book tour while his company is falling apart.2

The hardest thing to do in the world is to truly consider something from the perspective of someone else. We subconsciously invent narratives and belief systems that justify our behavior. The trick is to build the self-awareness necessary to consciously know that we subconsciously invent narratives and belief systems that justify our behavior, and moderate our beliefs and public comments accordingly. 

According to Know Your Meme, “midwit” is a derogatory term used to describe someone of average intelligence who mistakenly believes they are among the intellectual elite.

The Midwit Meme is usually used to defend a position that seems too simple and unsophisticated to be correct. 

The idea behind the Midwit Meme is that sometimes the correct solution to a complicated problem turns out to be very simple. In this scenario, the genius and the simpleton will both end up with the right answer for very different reasons: the genius will fully understand and correctly solve the problem, while the simpleton will blunder into the right answer for the wrong reasons. (The simpleton always picks the simple answer whether it is right or wrong, an approach that will sometimes lead to the right answer by chance). 

It is only the midwit who manages to get the answer wrong, by coming up with a complicated solution that appears smart and sophisticated but ultimately proves to be incorrect.

In the example above, the genius works out that simple index funds are the best solution for most people because the management fees and trading costs associated with most active investing strategies are very high, and end up being the dominant factor in determining returns for most people. The passive investor will generate average returns without incurring any meaningful costs, while active investors considered as a whole must still generate average returns and after that still must contend with high management and trading costs. 

The simpleton doesn’t understand any of this but just goes with the default 401(k) index funds  and ends up in the same place. 

It is only the midwit that believes that all smart people should pursue high-fee active investing strategies, and as a result the midwits (as a whole) are the only ones that underperform.

To be sure, the midwit meme is not remotely true as a rule. Some problems have simple solutions, and other problems have complex solutions. For example, what is the ratio of the circumference of a circle to its diameter?

See, it doesn’t work. You can’t just default to the simple answer as a rule.

However, the Midwit Meme does seem to pop up a lot in finance and economics, because there, complicated problems do often have simple solutions (or at a minimum, a simple optimal course of action). This runs counter to our intuition, which is that a complicated problem must have an equally complicated optimal course of action. People are often reflexively dismissive of simple approaches, like index funds.

The Midwit Meme has actually been an unintentional theme here to date; in fact, one could say that this newsletter has been an exercise in writing 5,000 word essays that could have been simple memes.

For example, we looked at solutions to the housing shortage:

We examined the sustainability of ride-hailing:

And we studied the fall of General Electric:

You get the idea. The formula for an interesting post is to examine a problem where conventional midwit wisdom would suggest a very complicated solution, and show that in fact the best approach is relatively straightforward and simple. This begs the question: why do smart people fall into the Midwit Trap?

Complex != Smart

There is a famous scene from The Simpsons:

Immigration Examiner: All right, here's your last question. What was the cause of the Civil War?

Apu: Actually, there were numerous causes. Aside from the obvious schism between the abolitionists and the anti-abolitionists, there were economic factors, both domestic and inter...

Immigration Examiner: Wait, wait... just say slavery.

Apu: Slavery it is, sir.

The way we learn about a subject like the Civil War in school is to receive the sanitized, simple outline when we are very young, only to have the true detail and complexity revealed to us as we revisit the subject in later years. 

There is nothing wrong with this approach, but it incorrectly implies that every simple explanation in life is a lie taught to us in elementary school, and that we will only understand the truth when we study the subject in detail in graduate school and can write a 60 page dissertation covering every facet.

Since we assume that simple answers are for children, we mock those who offer a simple explanation for a very complicated topic. We laugh at the scene with Apu, but the consensus among those who have studied it in depth seems to be that the Civil War indeed was caused by slavery.

Or, if you prefer:

If one argues that the high rents are mostly the result of laws that prohibit housing construction, they will be met with condescension and mockery. “Oh, dear, you don’t understand, housing is much more complex than that.” “Oh, that’s just what you learned in Econ 101.” 

This applies equally to all of our other examples as well. GE pitched their accounting shenanigans as 4-D chess necessary to appease investors and build confidence. Wall Street has always sold complex (high-fee) products as being tailored for “sophisticated” wealthy people and institutions. The case against ride-hailing apps is hundreds of pages of circular “analysis” that deploys fancy financial terminology to obfuscate the lack of substance.

We assume that a complex solution is likely to be the product of more sophisticated and nuanced reasoning than a simple solution, and thus more likely to be correct. This is far from true. In fact, in all of our examples, the complex solution is the result of less sophisticated and nuanced reasoning than the simple solution.

In our housing example, the proposed Rube Goldberg solutions are ineffective because they ignore the economic forces that pull people toward big cities in a modern economy; agglomeration effects literally make people who move to big cities richer and more productive, and therefore only by building enough housing to allow people to move can we hope to achieve progressive goals. The GE strategy of fudging the numbers ignored the simple truth that “you make what you measure”. And so on.

If we accept that a “midwit” is a real type of person, then it is the midwit that will be most vulnerable to this particular logical fallacy, because it targets their insecurity about their intelligence and because they will fail to recognize it. An intelligent person will know that there is no correlation between the simplicity of a solution and the sophistication of the reasoning that led to it (or if anything, there is an inverse correlation).

The Pareto Principle

The Pareto principle is better known as the 80/20 rule: in many cases, a good rule of thumb is to assume that 80% of consequences come from 20% of causes. The classic example is to look at economic aggregates: 20% of the population of a country usually accounts for about 80% of taxes paid, or 80% of wealth.

Some version of the Pareto principle pops up everywhere there is a power law distribution, and power law distributions are far more common than anyone intuitively thinks, at least in business and economics. 

In venture capital, a tiny handful of investments account for most of the return, or in gaming, a few “whales” account for most of the profits. Wikipedia has a more thorough list of examples of the Pareto principle; notice that most of them come from business and economics.

The corollary to the Pareto principle is that you can address most of the consequences of a problem by focusing on a tiny number of causes, or maybe even a single cause. Consider our investment management example: management fees are such a dominant factor in determining total return that it is sufficient for most to just focus on minimizing fees while maintaining diversification and to ignore everything else.

In business, one usually gathers data to identify which causes are creating most of the consequences, taking advantage of the Pareto principle to focus one’s limited resources on addressing the main sources of the problem while ignoring the rest for now. Outside the office, people have difficulty maintaining this habit.

The other challenge with the Pareto principle is that the less significant causes continue to exist, even if the consequences are too small to be meaningful. This, in turn, gets people hung up on the logic – how can we acknowledge that a cause exists, and then proceed to just ignore it? 

We can illustrate the problem with some napkin math on housing construction. (Note that this is purely illustrative and not meant to be fully logically coherent.)

Housing skeptics will often accuse supporters of housing construction of ignoring other solutions, like prohibiting Airbnbs and taxing vacant homes. So, let’s look at the data. Housing construction in the 2010s was 6.5 million units lower than it was in the 2000s. Meanwhile, there are 660,000 Airbnbs in the US. Vacancy rates have actually been declining, but let’s be generous and say that vacancy taxes can increase national supply by 0.3%, or 500,000 units, about what other cities have achieved. 

In this crude analysis, the causes break down 85 / 8 / 7. (Also, this breakdown is very generous, because the underlying logic is incorrect; construction can always be high enough to entirely offset other factors, so you could argue the causes are really 100 / 0 / 0.) 

The typical midwit rhetorical strategy is to present all of the causes without also presenting which causes account for what share of the consequences, falsely implying that all causes are equally important. Consider a statement like: “Airbnbs and speculators are among the causes of high rents.” This is a technically true statement, but presented in a way that is intended to lead the reader to a false conclusion, such as “Shark attacks are a growing cause of death in Maine.”

The fallacy here is easy to identify if we transfer our housing example to an office setting. If you were tasked with analyzing customer service issues for a client, and you presented the issues without also showing how common each issue was, you would be fired. If you showed that the issues broke down to an 85 / 8 / 7 ratio, and you still insisted they were all equally important, again, you would be looking for a new job. 

It is not clear what the main culprit is here. It could be motivated reasoning, or it could be that we do not encounter the Pareto principle much in our everyday lives and fail to look for it. Nevertheless, failure to recognize the pervasiveness of the Pareto principle is a common factor in incorrectly rejecting simple solutions to complicated problems.

Theory of Constraints

In business, where specialization is critical to achieving efficiency, you see a lot of assembly lines and supply chains. One critical feature of a chain is that it is only as good as its weakest link. This was illustrated during the pandemic, when automotive production was temporarily shut down for a lack of inexpensive computer chips.

For such a system, total output is always dictated by the biggest bottleneck at any given time. You can only increase output by addressing the specific bottleneck that is constraining the whole system at any given time. In our car example, you cannot increase production by increasing the supply of steering wheels or tires; the only thing that will help is more computer chips. Once you have enough computer chips, then something else will be the limiting reagent.

In manufacturing, this is known as the Theory of Constraints. Popular books such as The Goal (for manufacturing) and The Unicorn Project (for software) have been written to show how common it is in the business world, and how to solve the problems it poses. We don’t encounter this issue as much in our everyday lives because we usually have access to lots of substitutes; if the road we usually take to work is closed, we can take an alternate route.

This is not a factor in any of our chosen examples, but this is an issue that confounded people during the pandemic, when shortages abounded. It is not intuitive that a small percentage of the workforce calling out sick will cause major production disruptions, but it is a predictable result of the world being designed around supply chains with limited redundancy. 

The Theory of Constraints is a good example of how we are surrounded by complicated systems where the optimal strategy, by definition, is always a simple one: in this case, attack the biggest constraint.

The Divide by Zero Problem

Here is a version of a classic math puzzle:

a = b

ab = a²

ab - b² = a² - b²

b(a - b) = (a + b)(a - b)

b = a + b

b = 2b

1 = 2

Every step of this seems correct at first glance. And yet, it doesn’t seem right that 1 = 2. 

The trick is that in the fourth step, you divide both sides by (a - b), and we established in the beginning that a - b = 0. Since you can’t divide by zero, the whole statement is invalid.

Some solutions rely on convoluted chains of logic that are strictly dependent on every single statement being true. They are more likely to have hidden “divide by zero” problems that may be easily noticeable to the experienced practitioner but are invisible to the layman. Simple solutions might have errors too, but they will be much more obvious. Also, complicated chains of logic “feel” correct because a lot of the steps will be verifiably true; people sometimes forget that all of the steps have to be true for the entire argument to have any truth.

In our GE example, management believed that relying on accounting tricks to impress investors would lower their cost of debt and raise the stock price which in turn would improve their ability to attract good employees and make acquisitions which would then lead to a positive feedback loop that would eventually increase real world profits. This is an impressive solution on paper but ignored the simple fact that managers rely on accurate accounting to get the feedback they need to successfully run a business! It did not end well for GE.

Complicated stories seem more likely to be logically sound to a midwit, but simple strategies are actually far less likely to have hidden land mines.

We Share 99% of our DNA with Chimpanzees

In business, as in biology, sometimes it is just the 1% that is different that matters, and you can ignore the 99% that is the same. It doesn’t matter if we share 99% of our DNA with chimpanzees, because the difference is all in the 1%.

Successful entrants often only change a tiny percentage of the business model. Netflix produces video entertainment in much the same way as cable networks did for decades, it’s just that

they deliver it on-demand over the internet. Still, an on-demand model is better than scheduled linear programming across every dimension, so we have mostly shifted our consumption of scripted entertainment from linear cable networks to on-demand streaming providers.

This phenomenon is everywhere. New airplane models and engines might differ from the old ones by slightly improving capacity and fuel efficiency, and are otherwise identical; still, that is often enough to render the old models fully obsolete.

Note that this only holds if the 99% is actually the same. Many (if not most) new technologies are worse across some important dimensions. Planes are fast and fuel efficient but are only suitable for longer trips because they require going to an airport, enduring a long boarding and takeoff process, and then doing the reverse on the other end. Despite being slower and more expensive on a per-mile basis, trains are better for shorter trips because they are quick to board and leave and you start and end at stations in the city.

Nevertheless, people get hung up on the idea that two products need to be drastically different to generate meaningfully different outcomes. If a new product is 99% similar to an existing product X, people immediately exclaim: “X. You invented X.”

In our ride-hailing example, skeptics make much of the fact that ride-hailing is mostly the same as taxis, and devote much attention to proving this; they both use drivers, cars, fuel, etc. The difference is in using software to manage dispatch and improve the user experience. No one denies the other 99% is the same; in fact, that is a key selling point!

Again, the incorrect heuristic here is to assume that making a small change in one part of a complicated system cannot have a huge impact on the output. How can restricting construction in a few cities drive up rents nationwide? How can reducing management and trading costs have such a big impact on total return? Once you understand that a small change can have an outsized impact, it is easy to see that a simple solution will often be more effective than a complex one.

In Fooled by Randomness, Nassim Nicholas Taleb introduced the idea of Mediocristan and Extremistan. In Mediocristan, everything is normally distributed, and extreme events rarely happen. In Extremistan, there is an exponential distribution and extreme events happen much more often.

The Midwit Trap is to some extent just an application of Extremistan. The effectiveness of a simple solution depends on having a problem where one cause is responsible for most or all of the negative consequences. This is likely to be the case where there is a power law distribution or strict dependencies, but not so much elsewhere. We are used to solving problems in Mediocristan, where we usually cannot achieve a significant outcome with “one simple trick!” and so we are dismissive of simple solutions or simple explanations.

The Midwit Meme turns out to have a useful purpose, to remind us in a concise, memorable way that we cannot accept complicated solutions or explanations only because they feel smarter than a simple solution.

You probably intuitively understand why, but just for fun, let’s break down the economic model more explicitly.

In 1970, George Akerlof, the Nobel prize winning economist (and husband to Treasury Secretary Janet Yellen), wrote a famous paper entitled The Market for Lemons: Quality Uncertainty and the Market Mechanism. 

The model describes how a market works in the presence of what economists call “asymmetric information”, where the seller knows more than the buyer or vice versa. He describes a model of the used car market, where there are two types of cars: good used cars and lemons. (In America, “lemon” is slang for a defective car.) Sellers know which is which, but buyers have no idea.

Buyers demand to pay a discount to compensate for the risk of taking on a lemon. Sellers with good cars refuse to sell at a discount and prefer to keep driving them instead of trading them in, which increases the proportion of lemons in the used car market, and so on until one is left with a very small used car market with a lot of lemons.

(The paper is very short and is worth reading in its entirety; he goes into other examples, such as health insurance for the elderly, employment discrimination against minorities, and credit markets in developing countries, and discusses institutions that seek to resolve information asymmetry, such as chain stores and licensing bodies.) 

Employers also seek to avoid lemons, and they too must contend with imperfect information.

Here we can add a twist to Akerlof’s model, which is that the seller sometimes also has imperfect information. One formulation comes from the television show Justified:

“If you run into an asshole in the morning, you ran into an asshole. If you run into assholes all day, you're the asshole.”

Another formulation goes to the effect of, “If it smells like shit everywhere you go, check the bottom of your shoe.” 

Either way, the point is that human nature dictates that lemons do not believe that they are the lemon. From the point of view of a lemon, it is actually everyone else that is a lemon.1

Therefore when a prospective employee says “My former boss was an asshole”, the prospective employer only hears, “My last workplace smelled like lemons”. 

The prospective employer knows that many bosses are lemons, but they also know that most lemons will report that their former boss was a lemon. If you badmouth your former boss to a prospective employer, the prospective employer has learned that you are now more likely to be a lemon. After all, you just said the exact thing a lemon would say.

Maybe you know for sure that your boss was a lemon; maybe he collected severed human fingers and rarely showed up for work. That does not matter in this model. All that matters in this model is that the prospective employer has no way to verify whether your boss was a lemon, and that lemons frequently report being surrounded by lemons. 

Admittedly, this simple model is an incomplete explanation of why you should not badmouth a former employer. As the initial article explains, complaining about your last employer in an interview demonstrates a lack of familiarity with basic social norms, which for most roles also signals that you might be a lemon!

Last week, the Wall Street Journal published an article about the apparent phenomenon of “ghosting” among new hires, which included quotes like this:

Home-cleaning business Duster & Daisy Green Clean Service in Corpus Christi, Texas, has been trying to hire another five cleaners. But getting new recruits to show up even for a few paid training sessions has been a struggle, said manager Sunny Zhang.

Sometimes job seekers sign on and almost immediately stop answering text messages about where to go for training. Others show up for one or two shifts, then disappear without picking up their paychecks. About 80% of new hires eventually disappear without notice, Ms. Zhang said.

About two months ago, after it happened again, she reached a breaking point. “I was so mad,” she said. She updated the company’s online job listings to say: “Please apply if you are a serious JOB SEEKER. No job ghosting.” Even that, she said, hasn’t helped.

Before, we only considered the desire of employers to avoid lemon employees in the face of imperfect information, but it is equally true that employees want to avoid lemon employers. Furthermore, potential customers, suppliers, and investors will all want to avoid getting involved with lemon companies. It is not only employees who need to watch what they say.

We know from our prior example that saying “lots of employees are ghosting me” translates to the reader as “it smells like lemon everywhere I go”. Why are so many employees ghosting them? If it is the employees that are the problem, why isn’t every employer struggling with this? Is this employer really bad at hiring? Is the company poorly managed? Is the pay terrible?

There are two additional differences to consider in this role reversal:

1. Employers usually have more choice than employees in selecting whom to work with; hiring a bunch of lemons reflects poorly on the employer’s judgment, whereas the employee probably had less choice when accepting a job.

2. Our employee only smelled a lemon once, whereas this employer is smelling lemons repeatedly. One lemon is probably bad luck, but what are the odds someone independently runs into a dozen lemons?  

If it is poor judgment for an employee to privately tell one potential employer about one bad experience with a former employer, then it is borderline insane for an employer to talk about their many bad experiences with former employees IN THE WALL STREET JOURNAL.

This is a particularly egregious example, but it underlines an increasing epidemic of people obliviously badmouthing the people they work with in a public forum.

It is human nature to want to vent publicly about those that they feel have wronged them or failed them, but now you don’t even need to find a journalist; Twitter has made the experience frictionless.

This model generalizes beyond employer/employee relationships to anyone speaking publicly about anyone they work with, even in a general way. Let’s say you are a successful venture capitalist, and you choose to tweet: “Founders these days suck”. Here are the possible reader interpretations:

1. Founders these days really do suck, and we are all drowning in a sea of lemon founders. If that is really the case, though, almost everyone would have already noticed this, and there is no new information here - so what was the point of tweeting this? Also, if most founders these days don’t suck, then many readers will know that from experience and instantly dismiss this interpretation.

2. Founders these days are as good as they ever were, but the founders you meet with and work with now suck. You are smelling lemons everywhere because you can no longer get meetings with good founders. That can happen in life, because the world gets more competitive over time, but is this something you want to publicize on social media?

3. The founders you meet with are fine, but you suck. You are having lemon experiences all the time because you are the lemon and are being treated accordingly (e.g. you are losing deals to hungrier competitors). Again, this is maybe normal, because the world is always getting more competitive, but again, how is it to your advantage to publicize this?

It seems like a good idea to complain that you are surrounded by lemons, but logic dictates that in a world of imperfect information, you should never confess that you smell lemons. People will conclude that you can only attract lemons, or you tend to pick lemons, or that you yourself are the lemon! At the very minimum, you are sending the signal that you do not understand the model, or social norms in general. Complaining about lemons is 🚩🚩🚩.

The problem is with your intuition. You think that you are a reliable narrator and observer and that your personal experiences constitute an unbiased sample that you can confidently extrapolate from, while the observations of others are inherently unreliable and biased.

People think that they are making uniquely insightful observations about society when they complain about their lazy Gen Z employees, or their rude customers, or their short-sighted investors, when really they are revealing much more about themselves and their current situation than they ever intended.

Here we will leave an exercise for the reader. In light of the model we discussed here, why did some people react negatively to this tweet last week from Shopify CEO Tobi Lutke?

Health care: The U.S. has fewer physicians per capita than almost every other developed country, in part because our medical-residency system has for 40 years constricted the supply of U.S. physicians by forcing them to go through scarce and poorly funded residency-training programs. Meanwhile, the American Medical Association, the country’s top trade group for doctors, has in the past few decades blocked nurses from delivering care and impeded foreign-trained doctors from practicing here. America has tried very diligently to create medical scarcity, and in typically plucky American fashion, we’ve succeeded.

Housing: Homes have become famously unaffordable in many coastal cities. Since 1980, average house prices in the New York City metro area have risen about 700 percent; in San Francisco they have increased by more than 900 percent. Simply redistributing cash or slashing taxes alone won’t do much to fix this problem. The culprits are largely regulations that prevent the construction of taller apartment buildings that can hold more units.

College: Elite colleges are failing every abundance-agenda test imaginable. They’re hardly expanding the total number of admissions; their share of total enrollment has actually been shrinking; and they’re admitting fewer of the low-income students who gain the most by attending elite colleges in the first place.

Everybody loves abundance, and everybody hates scarcity. Having access to an abundance of material goods is more commonly thought of as simply “being rich”, and saying someone is plagued with material scarcity is just creating a euphemism for “being poor”.

There are harmful goods that probably should be more scarce, such as dangerous drugs or weapons. However, Thompson is talking about essential goods, goods that absolutely should be as abundant as possible.

A progressive might be concerned that a focus on abundance will come at the cost of equity; what if increased material abundance were to be captured entirely by the rich? This might be an issue if Thompson was calling for an abundance of expensive yachts, but again, we are looking exclusively at essential goods, which are consumed much more evenly by rich and poor. An abundance of essential goods is going to make society more equal, not less.

Abundance is the name of the game. We go to our jobs every day to create abundant material goods that we then trade with each other. We even invent new technologies to create even more abundance in the future, raising living standards for everyone. Policies that create artificial scarcity would seem to be self-defeating.

Yet, Thompson is correct that as a society we have somehow embraced policies that make essential goods artificially scarce. This is doubly mysterious when we consider that artificial scarcity policies are often championed by self-described “progressives”, a label usually reserved for those concerned with improving the lot of the less fortunate. While the American Medical Association might be traditionally conservative, elite colleges are famously liberal, and restrictive housing policy is a hallmark of our bluest cities. 

Many influential voices have recently spoken in favor of some version of the abundance agenda, from U.S. Treasury Secretary Janet Yellen to New York Times columnist Ezra Klein.1 The idea of an abundance agenda is good framing and perhaps also good marketing. But if an abundance agenda for essential goods is such a good idea, we must first ask: How did we even end up here? How did the artificial scarcity agenda become so popular?

Let us start with a partial answer: Everybody hates scarcity, except for the people that control access to scarce goods.

Any time that scarcity is reduced, the pie gets bigger for society. But while most members of society gain, the people that controlled access to scarce goods lose. 

This is most clear when we get advances in technology that render scarce goods obsolete, what Joseph Schumpeter called “creative destruction”. For example, here is Charlie Munger last month reminiscing about the World Book encyclopedia:

One of the investments that nobody ever talks about at Berkshire is the World Book Encyclopedia. I grew up on it. You know, they used to sell it door to door. They had every word of the English language graded for comprehension and a vast amount of editorial input, so it was easy for a child who wasn’t necessarily a brilliant student to understand that encyclopedia. It was more understandable. Berkshire made $50 million a year pre-tax out of that business for years and years. I was always so proud of it because I grew up with it. It helped me and so forth. And, of course, I liked the 50 million a year.

Then a man named Bill Gates came along and he decided that they were going to give away a free encyclopedia with every damn bit of his personal computer software. Away went our $50 million a year. Now, we still sell the encyclopedias in libraries, making a few million per year doing that. But most of the wealth just went away and all that wonderful constructive product.

…

That’s just the way capitalism works. It has destruction.

The ideal way to make money in business is to make something new that is better than anything that exists today. The new thing will be scarce by virtue of being unique and hard to replicate; for example, consider the iPhone. This creates abundance for society because the new thing is a better deal than the scarce thing came before it.

This is not the only way to make a lot of money in business; we have also discussed the scenario where artificial scarcity is created by monopolies or cartels.

Imagine you live on an island, and you and your friends grow oranges. You and your friends trade some of your excess oranges to other islanders for rice and pearls and canoes.

If you and your friends get together and conspire to fix the price of oranges, you will all be richer. You have locked down all of the oranges you need for yourself, and you can tell everyone else that from now on you will be demanding more rice and pearls and canoes for the oranges you are trading away, and they will have to comply if they don’t want to die of scurvy. 

Since oranges are now more expensive, the other islanders will buy less oranges from you, but you will get richer overall, because you are gouging them for more rice and pearls and canoes in trade for the oranges you do sell. This arrangement is great for you as long as you can keep the other islanders from finding alternate sources of Vitamin C.

The problem with this arrangement for society as a whole is that the pie has shrunk; in total nothing has changed on the island except there are fewer oranges to go around. You and your friends are richer but the other islanders are much poorer. 

The policy of allowing monopolies and cartels will make some members of society better off, but does not universalize. Orange scarcity works for you because there are other islanders that are producing abundant rice and pearls and canoes that you can extract. If the other islanders also make rice, pearls, and canoes scarce, what remains will simply be a poor island where everything is scarce.

We saw that self-interested cartel behavior is certainly at least part of what drives the scarcity agenda. The American Medical Association lobbies to restrict the number of doctors that can practice and homeowners lobby to prevent new construction, and they have both done so quite effectively. 

It would be tempting to simply conclude that the scarcity agenda is the product of selfish special interests, but we still have to answer one nagging question from before: Why are progressives so often the leading advocates for artificial scarcity?

From what we saw before, artificial scarcity is bound to be very regressive, especially when applied to essential goods and services. If there are fewer slices of the pie to go around, it is inevitable that the poorest and least powerful members of society will be the ones left without a bite.

If goods are rationed by the market, clearly the poor will be left behind; someone who makes $200,000 a year will be able to afford high prices, but someone who makes $20,000 will have to make some difficult choices.

But this is not a problem specific to free markets. Even if we instead ration scarce goods by a lottery or waiting list, it is still all but inevitable that the poor and middle class will be the only ones impacted by artificial scarcity. 

The rich and powerful always maintain their slice by using a parallel system—they own their own homes, they hire private doctors, and they send their kids to private school—so everyone else still ends up fighting it out over the shrunken pie that remains. The only way to help the less fortunate is by minimizing scarcity. 

Even though America is the richest major country in the world, essential goods often remain out of the reach of low income Americans because we have passed laws mandating that they be scarce. It is illegal to build new homes, and so the rent is too damn high and so some people live with their parents well into adulthood. The supply of doctors is limited, and so it is too expensive for some to get insurance or treatment. 

The cynical explanation is that progressives are dishonest hypocrites, but there is plenty of evidence to the contrary. They regularly support and pass redistributive policies, raising taxes on the rich and expanding services for the poor. Their detractors would be the first to tell you that they are actually a bunch of dangerous socialists.

The problem with redistribution is that it cannot overcome an artificial cap on supply. Within the constraints of a supply cap, there is not that much to redistribute; it is not like very many rich people have three apartments or spend time in the hospital for fun or take nine years to finish undergrad. The only way to provide for the less fortunate is to lift the cap.

If the abundance agenda is clearly the progressive solution, then why is everyone so eager to create artificial scarcity?

Let us zoom in on housing scarcity, a topic we have covered several times here before:

The general story is that from the 1960s through the 1980s, many of our largest cities—places like New York, Los Angeles, and San Francisco—all enacted policies to strictly limit new development. These “NIMBY” (Not In My Back Yard) policies aimed to preserve cities just the way they were at the time the new policies were passed, and to prevent more crowding and congestion. Note that these large cities have actually always been extremely progressive places; for example, San Francisco votes about 90% Democratic in national elections.

As manufacturing declined and the knowledge economy grew, those large cities became more attractive places to live and work. 

Normally, when demand increases, new construction would meet that demand, and housing prices would not go far beyond the cost of new construction. This is what happens for other goods: for example, the demand for air travel has gone way up, but air travel has actually gotten cheaper because Boeing and Airbus produce enough planes to satisfy that demand.

This should generally hold for housing as well. Even though land is finite, we can build upward, so each individual new unit uses a negligible amount of land.

These artificial scarcity policies eventually caused housing prices to decouple from construction costs. As demand grew, housing prices had to go way, way up, to discourage potential newcomers from moving in and to encourage existing residents to move away. Home prices in San Francisco and Los Angeles rose by around 600% since 1987, a period in which consumer prices only grew by 150%.

This made artificial scarcity even more popular; not only could existing residents preserve their communities, but existing property owners could now generate extraordinary levels of passive income and wealth by gouging desperate future would-be residents. The median home price today in San Francisco is an astounding $1.5 million.

Housing scarcity in major cities inevitably spilled over into the rest of the country; everyone has to live somewhere. 

Most people have the intuition that housing is totally fungible, and that blocking housing construction in one city will be offset by additional equivalent housing being constructed in another city, so that the net effect of NIMBYism is minimal. This is contradicted by the evidence. The median monthly rent for a 1-bedroom apartment is $1,200 in Houston, while it is $3,300 in San Francisco. We might construct housing in other places instead, but it is hardly equivalent.

The disconnect, as we have discussed in the past, is that in a knowledge economy, our ability to be productive is increasingly dependent on our ability to collaborate in person. We want to live where the jobs are, and companies want to locate where the skilled workers are, and so we get industry-specific agglomerations in places like Silicon Valley and New York. This then drives up wages for the local residents that service them, like teachers, doctors, and lawyers. Under a regime where local housing is artificially scarce, local property owners end up capturing most of the higher wages for themselves through higher rents and housing prices.

A century ago, manufacturing was on the rise, and we constructed dense cities at logistical hubs on major navigable rivers and lakes, to be able to ship raw materials and finished goods but also to able to efficiently exchange ideas. People moved from farms to our biggest cities at the time, places like Buffalo, St. Louis, and New York. 

Today, our most innovative organizations cluster in cities like San Francisco and Los Angeles, for much the same general reason that manufacturers clustered in Buffalo and St. Louis a century ago; to take advantage of natural amenities (like sunshine!), but also to be able to swap ideas and trade with similar organizations. 

When we restrict these cities from growing, we lose out on the natural economic advantages from size and density, we transfer wealth from workers to property owners, and we put a heavy strain on low-income households and households who do not own property and miss out on the bounty.

We can verify this by looking at the data:

Here we can see that for a long time, nationwide housing construction never fell too much, as fast growing cities like Dallas and Phoenix picked up the slack; however, other cities are starting to get tapped out, and since 2005, housing construction has been very low:

This led to a situation where rents have been growing faster than inflation. Nationwide, rents have cumulatively risen more than 20% faster than inflation since 2000, with most of the growth occurring in the past decade:

Keep in mind that rising rents disproportionately affect low-income households because they spend a much higher share of their income on rent. In many cases, the lowest income households spend at least half their income on rent:

Beyond low income households, the biggest losers from housing scarcity are members of the younger generation. In the US, over 70% of adults over the age 45 own their own home, which fully insulates them from housing scarcity and higher rents. In fact, housing scarcity benefits homeowners by giving them the option of selling their home at an inflated price later and downsizing or moving to a cheaper locale for retirement. It is not only older homeowners that are shielded from housing scarcity; some older renters are also insulated from housing scarcity through local rent control.

By contrast, the younger generation does not yet own a home, nor have they been living anywhere long enough to benefit from rent control. In finance terms, they are born “short housing”; they will have to consume housing in the future, and figure out a way to acquire it. Since the burden of housing scarcity is entirely borne by non-homeowners, we expect young people to be particularly hard hit.

The Urban Institute compiled Census data showing that the share of 25-34 year olds living at home with their parents shot up since 2005, which marked the end of the last housing boom, and the beginning of the period where nationwide rent growth outstripped income growth and inflation. They note that the shift from 11.6% in 2005 to 22.0% in 2017 represented an additional 5.6 million adults moving in with their parents.

The share of young adults living with their parents increased at every income level, but as one might expect, was especially concentrated among low-income young adults:

They find that the growth in young adults living at home cannot really be explained by other social factors, such as delayed marriage. But it makes total sense that the artificially constrained housing supply had to be rationed, and the only way for that to happen was by driving up rents enough to force millions of young adults to move in with their parents.

This divide between young and old goes some way to explain why housing is such an emotional topic. NIMBYs, largely made up of older property owners, see themselves as the defenders and preservers of the neighborhoods that they moved into, against the developers that would have destroyed them, and high housing prices are a just reward for their years of activism. 

In response, some younger adults organized as “YIMBYs” (Yes In My Back Yard), who advocate for more construction and who see NIMBYism as price gouging and generational theft. They see NIMBYs as members of a generation that moved into the homes and cities prior generations built for them, and blocked new homes from being built for the next generation. They are also upset that NIMBYs passed special tax exemptions for themselves (like Prop 13 in California) so that they would be spared the burden of funding social services.

They see NIMBYs less as preservationists and more as the private-equity buyers that took over cities and slashed investment and raised prices to milk the asset dry.

Naturally, NIMBYs do not agree with this characterization, seeing themselves as “real” progressives. So how do big city NIMBYs reconcile their progressive ideals with their support for regressive housing policies? 

The answer is a surprising one: NIMBYs insist that NIMBYism actually isn’t regressive, because housing scarcity isn’t real.

To be clear, housing scarcity is real. Skyrocketing rents in big cities and around the nation are a clear signal that housing is scarce.

Still, that does not stop people from claiming otherwise. Sometimes they will say so in so many words, as Kevin Drum did last week:2

The real issue here is that America doesn't have a housing crisis…There's just a disconnect from reality that seems to motivate so much of what [urbanists] say and do.

Usually, the NIMBY claim that housing scarcity isn’t real is less explicit. Here is an example of a mainstream NIMBY argument, as made recently by Nathan J. Robinson in Current Affairs:

Even though [YIMBYs] talk a lot about the need for affordable housing, they tend to be opposed to requiring developers to make housing affordable, assuming that the Invisible Hand of the free market will take care of that.

    …

Here’s an excerpt from the [YIMBY] article linked in the FAQ on how it’s a “fallacy” and a “myth” that we need more affordable housing rather than market-rate housing:

What really matters is not whether new housing is created at a price point that low- and moderate-income households can afford, but rather, whether the overall housing supply increases enough that the existing housing stock can “filter down” to low and moderate income households.”… that process depends on wealthier people moving into newer, more desirable homes. Where the construction of those homes is highly constrained, those wealthier households end up bidding up the price of older housing—preventing it from filtering down to lower income households and providing for more affordability.

Funnily enough, while conservatives often insist that there is no such thing as “trickle-down economics,” here we have a very clear example of it in the wild—but with the word “filter” used instead of “trickle.”

“Trickle-down economics” is usually a term used to justify tax cuts for the rich. Tax cuts for the rich give them a larger portion of the economic pie, assuming they come at the cost of government services for society as a whole. (The only way this might work is if tax cuts incentivize rich people to work harder and expand the economic pie by enough to offset their larger slice - a dubious assumption in all but the most extreme cases.)

“Filtering” just describes the ordinary reuse of durable goods, which is about as mundane and uncontroversial as it gets. Everyone now knows that if you choke off the supply of new cars, new cars and used cars will become more scarce and more expensive. This is because people don’t usually throw away their used cars after they buy a new one. It is equally true that if you choke off the supply of new homes, used homes will also become more scarce and more expensive.3 

What is the alternative theory here - that homes vanish into thin air once the current tenants move out? Or that rich people vanish into thin air if we do not let them build new homes?

It is unclear the reuse of homes can possibly have to do with trickle-down economics. No one is advocating that we use public money to buy new homes for rich people. No one is giving rich people a bigger piece of the pie. Rich people want to allocate their own consumption toward building new homes, and we are just observing that this has the side effect of reducing scarcity for everybody else, since the homes they move out of do not disappear from the available supply.

With the current disruption in automotive manufacturing, this should be common sense for everybody by now, but just in case there was any doubt, there is a plethora of studies showing that this holds for the housing market. Note that Robinson does not present any evidence supporting his argument, because there is no evidence - just say “trickle-down economics” and hope everyone nods along.

This whole line of reasoning is an example of Scarcity Trutherism. Every time someone argues that we must reduce scarcity by increasing supply, the response by artifical scarcity advocates is some convoluted conspiracy theory, that explains that scarcity isn’t real and increased supply won’t help and you’re dumb for even suggesting it.

Here is a second argument he gives, where he tells a hypothetical story of a 30 unit building being replaced by a 100 unit ultra-luxury building in which:

They are swiftly bought up, 20 by rich people who live in the city, 30 by rich people lured to the city by its new pencil tower, and 50 by rich people who have no intention of living in the city but think pencil tower condos are an asset worth owning in a swiftly-gentrifying city.

He then concludes that the Econ 101 story is too simple because:

In the elaborate perfect game of musical chairs envisioned by the Econ 101 story that causes housing to “filter down,” there are still more people for fewer units in our city. The overall housing supply has increased but the per-capita housing supply has not, because we’re luring rich people from elsewhere to our city.

If we accept the terms of his hypothetical, he is of course correct, assuming that he is talking about luring people from outside the country. We have added only 20 inhabitable units (because 50 of the 100 units will never be inhabited, and we destroyed 30 units) but we have brought 30 new residents into the country. Rents will go up and ten families will be displaced to make room for the foreigners and speculators.

We actually want to focus on the assumptions in his hypothetical, because they make explicit what is at the heart of the NIMBY narrative. The NIMBY narrative, as he makes clear, is that high rents are exclusively caused by foreigners and speculators, both of which are attracted by newly constructed housing. If we don’t build new housing, they won’t come, and rents will stay low for native residents, and everyone will have an affordable place to live. Ergo, housing scarcity isn’t real.

Proof of the popularity of this narrative is the excitement among NIMBYs around vacancy taxes, billed as an important tool for making housing more affordable. This enthusiasm is based entirely around the NIMBY narrative: Expensive cities are expensive because foreign speculators have bought up all of the condos and are keeping them empty, and a vacancy tax will encourage them to lease them out or sell them. 

Vacancy taxes are politically popular because they fit this popular narrative. Vancouver passed a vacancy tax in 2017, Melbourne and Oakland passed vacancy taxes in 2018, and Los Angeles and San Francisco are scheduled to vote on vacancy taxes this year. 

The problem is that the popular NIMBY narrative is nonsense, and vacancy taxes have been ineffective as a result. Bloomberg reports that at the time the vacancy tax passed in Vancouver, only 2,538 of the city’s 200,000 homes were vacant or under-utilized, and that number fell by only 645 units after the tax passed (representing 0.3% of the total housing stock). Meanwhile, rents continued to rise. Melbourne ended up taxing only 587 vacant units, a fraction of expectations. 4

The problem isn’t foreigners and speculators, it’s the lack of new housing.

We could continue going through different popular NIMBY arguments, from insufficient rent controls to greedy landlords to corrupt developers, but they all hinge on the same point: the myth that housing scarcity somehow isn’t real.

Why do NIMBYs persist in claiming that housing scarcity isn’t real, despite all clear evidence to the contrary? 

It turns out that above all else, we have deep psychological needs, and our beliefs are shaped by our emotions and our identities far more than by any facts or evidence.

Progressive NIMBYs want two contradictory things: They want their communities to be frozen in time, and they want to support the less fortunate. They have been successfully doing the former for decades, but that creates artificial scarcity, which hurts the less fortunate.

If the rich NIMBYs stay in their homes, and few new homes get built, and we have continued population growth and urbanization, then it follows that there will be too many people chasing too few empty homes. Since the rich NIMBYs have their housing needs locked down, the group subjected to the Housing Hunger Games will be, on average, a lower-income cohort.

The NIMBYs resolve this contradiction by latching on to any narrative that suggests that housing scarcity isn’t real, which in turn would exonerate their policies and show that they have been good progressives all along.

Here is a possible story to consider: Most NIMBYs are intelligent, generous members of society. They are true progressives that support redistribution and racial equality, and they vote and act this way almost all of the time. This is in fact what motivates their NIMBYism - their desire to protect and improve their communities.

The problem with NIMBYism is that it rests on an elementary logical fallacy, something you might have learned in school as the “fallacy of composition”, that what is true for one must be true for the whole. 

NIMBYism is fundamentally based around exclusion, and around hoarding access to vital and finite existing infrastructure. NIMBYism works great for existing generations and existing communities, but in a growing country, NIMBYism creates a growing underclass of The Excluded, people forced by housing scarcity to live in overcrowded conditions or to pay backbreaking rents or to pay extortionate prices to purchase a home from a NIMBY.

Like everybody else, NIMBYs think of themselves as “the good guys”. In their free time, they engaged in NIMBY activism as a way to give back to their communities and protect them from greedy developers and speculators who would despoil their communities for personal profit. 

There were always voices warning of the corrosive impact of artificial scarcity, but for many decades this could be dismissed as developer propaganda. Housing prices were rising, but not at an alarming rate, as growing suburbs in the development-friendly Sunbelt handled much of the overflow from NIMBY cities.

Sometime after the last housing boom, development slowed even in the Sunbelt, as the available development sites became more and more marginal, and the housing crisis began to bite in earnest. Housing scarcity became a fact of life, and under the rules of the current system, the burden was entirely pushed onto the younger generation. This caused a backlash against NIMBYism, correctly identified as the cause of housing scarcity.

For NIMBYs, this backlash was a threat to their identities and their egos, and the framework with which they view the world. YIMBYs cast NIMBYs as “the bad guys”, the group whose blundering and selfishness created the housing crisis that we must solve today.

Faced with this, NIMBYs desperately looked for any narrative or any evidence that would confirm that they were right all along, and that their original framework was actually the correct one for viewing the world. 

This is simply human nature - when faced with evidence that contradicts our core beliefs about ourselves, our tribe, or our society, our first reaction is to find any reason to reject it, and in that pursuit we become open to very dubious theories that promise to confirm our prior beliefs, and blind to the most obvious evidence that contradicts them.5

The classic example you might be aware of is the famous story of Ignaz Semmelweis. As a recent medical school graduate in Vienna in the 1840s, Semmelweis learned that the one of the two maternity clinics at his hospital had an extremely high mortality rate from childbed fever. 

One day, his friend was accidentally cut with a scalpel during an autopsy and died of something similar to childbed fever. He realized that the maternity clinic with the high mortality rate was staffed with medical students who performed autopsies, and he worked out that childbed fever could be eliminated if doctors were forced to wash their hands before attending to patients.

He introduced hand washing to the maternity clinic, and childbed fever rates immediately plummeted. Armed with this evidence, he and his colleagues spread the word. To their surprise, they were met with ridicule and rejection from the leading doctors of the time.

The medical community objected that his explanation of how childbed fever is transmitted did not match leading theories of disease at the time (this was before germ theory was developed). Moreover, they felt disrespected:

The established physicians of high reputation and not accustomed to new ideas could not accept that a young assistant (and a foreigner to boot) declared their hands to be unsanitary. Instead of respecting the convincing results and scientific progress, they exhibited a symptom called “Semmelweis reflex” (“Semmelweis effect”), a reaction that denotes denying new results out of principle.

Semmelweis, frustrated by the resistance from the medical establishment, lashed out, calling them “efficient murderers” (not without justification), which caused the establishment to dig in even harder.6 Semmelweis eventually died in an insane asylum, and it would be decades before hand-washing was widely adopted in the medical community.7

It is tempting to hear this story and conclude that the Semmelweis reflex is somehow unique to ignorant 19th century doctors, just as when we observe modern-day examples of popular conspiracy theories and delusions, we try to take comfort that they seem to be confined to the Other Tribe.

The conclusion we should take is that you and I are no different from anyone else. To think otherwise is to all but guarantee that we will consistently fall victim to confirmation bias and motivated reasoning.

This is just the way we are wired. When confronted with irrefutable evidence that our deepest held and most loved beliefs are inherently contradictory, we reflexively resolve the cognitive dissonance by rejecting the evidence instead of changing our beliefs. We all have dozens of dumb narratives stuck in our heads that we preserve in this manner.8

The best we can hope to do is to be conscious of the Semmelweis reflex and try to examine our best loved ideas more closely against available evidence. As Charlie Munger put it, “Any year you don’t destroy one of your best loved ideas is probably a wasted year.”

Derek Thompson wrote a follow-up article last month about artificial scarcity in medicine:

Imagine you were planning a conspiracy to limit the number of doctors in America. Certainly, you’d make sure to have a costly, lengthy credentialing system. You would also tell politicians that America has too many doctors already. That way, you could purposefully constrain the number of medical-school students. You might freeze or slash funding for residencies and medical scholarships. You’d fight proposals to allow nurses to do the work of physicians. And because none of this would stop foreign-trained doctors from slipping into the country and committing the crime of helping sick people get better, you’d throw in some rules that made it onerous for immigrant doctors, especially from neighboring countries Mexico and Canada, to do their job.

Okay, I think you’ve caught on by this point. America has already done all of this. Starting in the late 20th century, medical groups asserted that America had an oversupply of physicians. In response, medical schools restricted class sizes. From 1980 to 2005, the U.S. added 60 million people, but the number of medical-school matriculants basically flatlined. Seventeen years later, we are still digging out from under that moratorium.

He goes on to note that Europe has a shorter path to becoming a doctor, far more doctors per capita, lower medical expenditures, and similar health outcomes. And yet, no one seems to be in a hurry to reform the American health care system. No doubt if you asked the AMA, they would have a million reasons why there is in fact no doctor shortage and their policies were right all along. Sound familiar?

As he goes through his other examples, from higher education to nuclear energy, he will find the same story, over and over. A special interest group gets a policy passed that creates artificial scarcity for their own benefit, and then they create narratives that deny that scarcity is real.

Policies designed to create artificial scarcity are great to study because they always eventually do exactly what they say on the tin. Since everyone hates scarcity, the victims of the policy get very upset when that comes to pass. Artificial scarcity strikes an especially heavy blow against the least powerful and most vulnerable in society, who are stuck with a disproportionate share of the burden.

The people who created the artificial scarcity policy in the first place, who were themselves exempted from the effects and are almost certainly currently profiting from it, respond to the inevitable criticism by insisting, improbably enough, that in fact no scarcity exists! 

They express dismay over the disrespect shown by those who have the temerity to question the policy, and cast aspersions on the intelligence and motives of the critics.

When confronted with incontrovertible evidence that other states or countries have an abundance of the good or service in question, they argue that local conditions are different, and no honest comparisons can be drawn. If they finally admit that scarcity is real, they explain that the simultaneous existence of scarcity and an active policy designed to create scarcity is mere coincidence.

They claim that repealing the policy will be deleterious to public safety. They theorize that repeal will somehow set a dangerous precedent. They blame scarcity on capitalism or communism or nefarious and greedy actors. They find a disadvantaged group that actually benefits from artificial scarcity, and ask why the critics hate them.

When considered in aggregate, these arguments resemble the ravings of a conspiracy theorist. Of course, their proponents believe them sincerely. The psychological biases that cause them to persevere in supporting artificial scarcity operate entirely subconsciously.

Then, finally, once the policy is repealed, everything is fine and dandy, and the whole experience is memory-holed. The hard-liners continue to insist that it was all better in the good old days, but no one ever dares to propose reinstating artificial scarcity after it has been repealed. 

Consider all of the Depression-era laws that created artificial scarcity which were repealed in the 1970s under the banner of “deregulation”: “fair trade” laws that permitted retail price fixing, and regulations that kept supply low and prices high in airlines, trucking, and banking.

Can you imagine a politician today proposing to triple airfares to support the profits of United Airlines? The next artificial scarcity policy always surfaces in a different sector, just as it did in the 1970s with housing.

Artificial scarcity policies are by no means unique to America. If anything, they might be worse overseas. Witness labor laws that discourage business formation and hiring, which benefit existing companies and older employees but result in high youth unemployment and brain drain. Or policies that keep inefficient companies alive, weighing down the productive capacity of the entire economy. 

It is remarkable to consider that there are all of these artificial scarcity switches scattered everywhere, and if we could just find a way to flip them to the off position, it would have a far bigger immediate impact on our material well-being than developing affordable solar power or cheap electric cars or any of the technologies people work so hard on to provide abundance for society. Such is the power of human nature that those switches are tenaciously guarded by people who sincerely believe they have society’s best interest at heart.

Perhaps the larger lesson is how much we underrate the power and pervasiveness of self-deception in our professional and personal lives.

We fret about how hard we work and what tools we use when our organizations routinely refuse to adopt simple best practices due to not-invented-here syndrome or other similar biases. This is the reason so many underperforming companies have a miraculous turnaround after a change in management, even though the new management team is much less knowledgeable than the old management team – they will improve the situation simply by making the obvious changes that the previous management team refused to accept. This is also why skilled salespeople are so important even for products that seemingly should sell themselves; there is a natural resistance to anything new in an organization.

And for all of the benefits that a small dose of self-deception can have for our mental health, it should go without saying that many of the problems we have in our personal lives are because we live in denial of certain important truths, and we can experience a major turnaround the moment we manage to accept them.

We can be optimistic that the abundance agenda will eventually make some headway, if only because society cannot long tolerate too much artificial scarcity. However, it is human psychology that is at the root of our problems with artificial scarcity, and history suggests that that is a problem we can never fully solve.

Thorsen speaks for many in the local coin-operated economy, a diverse, somewhat old-school community of businesses and consumers that has been in a state of agitation since COVID-19 interrupted the normal cycle of coins.

“It’s something I have to think about all the time,” says Queen Anne resident Dan White, whose apartment has a coin-operated laundry. Early in the pandemic, White had to frantically group-text friends to secure enough quarters for a weekend’s wash.

Another time, after White scored a precious stack from the change machine in a downtown bar, he was followed out and lectured by an employee. “He was like, ‘I’ll let it slide this time but you can never do that again,'” White says.

White is more systematic these days, periodically hitting a handful of businesses to get his rolls for the month. But the routine is time-consuming, with a furtiveness that often feels “weirdly like I was doing like a drug deal or something,” he says. “People that aren’t using quarters for a laundry machine have no idea that this is even happening.”

Since the COVID-19 outbreak, we have experienced periodic global shortages in products as varied as toilet paper, semiconductors, and cream cheese. These can usually be traced back to abrupt shifts in demand combined with disruptions in the supply chain, both of which are linked to the global pandemic. The coin shortage, however, seems to be especially unusual.

First, the coin shortage seems to be an exclusively American phenomenon; although the alleged cause is disruption to coin circulation due to COVID-19, every other country apparently experienced the same COVID-19 disruption without a coin shortage. 

Furthermore, most other developed countries are far more coin-dependent than the United States; Europe has 1-euro and 2-euro coins in lieu of paper bills of those denominations, and Canada has their loonies and their toonies. If a coin shortage was going to strike any country, why would it be the US?

Second, one would think that a disruption to normal business activity should create a coin surplus, not a coin shortage. Consumers and businesses hold currency and coin so they have enough on hand for day-to-day transactions; if they are engaging in fewer transactions, they should prefer to hold less currency and coin, all things being equal. Also, if Covid is causing people to shift from cash to electronic payments, they should be getting rid of their coins, not accumulating more. 

Data from Europe backs up this intuition; the Eurosystem reports an annual net issuance of nearly 1 billion euro worth of coins in 2018 and 2019, which collapsed to a net issuance of only 400 million euro in 2020 in the wake of Covid. Net coin issuance in Europe actually turned negative in the first quarter of 2021, meaning Europeans were, in aggregate, disposing of coins they no longer needed.

Finally, why has the shortage gone on so long? Many of the Covid-related shortages have been quite short in duration, disappearing as producers and consumers adjusted to new conditions. The coin shortage started at the beginning of the pandemic, and two years would seem to be more than enough time for everyone to adjust. At the outset, government officials expressed confidence that the shortage would be resolved quite soon, but there still seems to be no end in sight. 

Our investigation will start with the Global Toilet Paper Shortage of 2020, to put together a potential model.

No one can ever be absolutely certain what caused the toilet paper shortage, but as the Washington Post reported at the time, there were two leading demand-side theories:

 1. We’re buying too much toilet paper because we’re panicked there won’t be any when we need it.

2. We’re actually using way more than usual at home because most people are sheltering in place rather than using the facilities while at work, school, restaurants or other public places.

In this simple model, there are only two things that influence consumer demand for toilet paper: how much toilet paper we consume every day, and how much toilet paper we wish to hold as inventory, in our closets at home. 

At the time, 4-week toilet paper sales were up 102% year-over-year, implying that either we suddenly doubled our rate of toilet paper consumption, or we built up an extra four weeks of at-home toilet paper inventory, or some combination of both.

Almost exactly a year later, the Wall Street Journal reported that toilet paper sales rose from $9 billion in a normal year to $11 billion in 2020, but 2021 sales were on pace to fall below $9 billion, as consumers worked through the excess inventory they built up in 2020. Clearly, most of that 102% spike was from our sudden desire to hold larger inventories, as a buffer against disaster, although undoubtedly the extra time at home has also elevated toilet paper consumption a bit.

It should be noted that most Covid-related shortages seem to be linked to higher actual end consumption, rather than stockpiling. For example, the most recent product to be in short supply, cream cheese, is actually perishable, and indeed the evidence seems to be that we are simply eating more of it than we used to. 

Demand spikes do not typically result in shortages, as producers are usually eager to meet demand by producing more. However, it turns out that toilet paper production capacity is relatively fixed, and cannot easily respond to demand. As the WSJ recently reported:

[W]hereas companies were able to more quickly increase capacity for cleaning products, hand sanitizer and other in-demand items, doing so for toilet paper was less feasible given that making toilet paper in bulk requires four-story-tall machinery that costs billions of dollars and takes months to build.

Toilet paper companies were able to produce a bit more by running extra shifts on existing machinery, but they could not hope to meet a sudden 102% increase in demand.

However, demand spikes and a relatively fixed supply do not usually result in a true shortage complete with empty store shelves, as we saw with toilet paper. Usually prices adjust enough to encourage consumers to reduce or defer consumption and to encourage suppliers to increase production, until supply and demand are balanced. 

To take just one example, housing prices since the pandemic adjusted sharply upwards (up 25% nationally), so while the inventory of homes for sale has been low, the market still clears, and homes are available for those able to pay.

Retailers like Costco could have raised prices on toilet paper, but they had little incentive to do so. Retailers rely on repeat business and reputation, which they do not want to jeopardize by appearing to profit from a crisis (and toilet paper is such a miniscule business for them, the extra profit from higher prices would have been very low). Most retailers were content to set purchase limits per customer and allow their shelves to empty temporarily.1

In summary, we had a spike in toilet paper demand from the sudden desire of consumers to build inventory, coupled with inability of toilet paper manufacturers to meaningfully increase production (at least in the short-term). That might have simply resulted in high prices, but retailers prefer to keep prices steady, so instead we ended up with empty shelves.

Before we move on to coins, let us first analyze money more broadly defined, which includes coins, paper money, and bank accounts.

In a way, money is like toilet paper. Demand for money fluctuates depending on how much inventory people want to hold. In most models, consumers try to hold enough money so that they can conveniently buy what they need to, but they also may choose to hold money as a store of wealth, instead of stocks or bonds. 

Usually you will not want to hold too much money, since you are forgoing interest you could be earning if you put the money into bonds (for example). Lower interest rates lower that opportunity cost, so people are more willing to hold money today, with interest rates near zero.

Here is a graph of the historical “velocity” of money, which is the ratio of GDP to money supply. (It is called velocity because it measures how many times each dollar is spent per year on a final good or service; hence, it measures how fast money passes through the economy.) If people are holding more money, it will be reflected in lower velocity.

This graph is upside down; a lower ratio means that people are holding more money, and thus money is circulating more slowly.2 We can see that during the financial crisis of 2008 and the Covid outbreak of 2020, there were sudden spikes in demand for money, as people simultaneously decided they would feel more comfortable with more money in the bank, just as they also suddenly felt that they would be more comfortable with more toilet paper at home in early 2020.

In 2003, the late economist Milton Friedman observed that inflation had been quite volatile through the mid 1980s, even though velocity had been very steady. He notes that this is quite odd when you consider the quantity equation of money: PY = MV. 

In this equation, P is the price level (which, when it goes up, is called inflation) and Y is the quantity of goods and services produced in the economy, and on the other side, M is the money supply and V is money velocity.

Simple algebra suggests that a stable velocity (V) should make it easy to produce a steady price level (P). This is the exact opposite of what actually occurred, which was a volatile inflation rate that reached double digits at times.

Perhaps more perplexing is that velocity has apparently become much more volatile since then, but inflation suddenly became very steady by historical standards, as we see below.

Friedman’s explanation is that central banking policy, properly executed, is like a thermostat. A thermostat does not need to know how cold it is outside, it merely targets an indoor temperature and turns the heat up or down if it is missing its target. A thermostat doesn’t care if it is 72 degrees in January or if there is a May snowstorm, it just needs to know the current indoor temperature and turn the heat up or down as necessary. 

Likewise, a central bank does not need to know anything directly about the demand for money or the money supply. It recieves signals about inflation and economic conditions and tighten or loosen monetary policy accordingly. For example, the 2008 financial crisis might be analogous to a May snowstorm, and unconventional monetary policy like QE would be analogous to running the heater at record levels in response.

His theory is that central banks learned to target inflation like a thermostat, and ended up with much better results than when they had other theories about how to control inflation. 

Central banking is a hot button topic, so we will consider it only to observe that the Federal Reserve tries to print just enough money to keep inflation constant and consumers adequately supplied, just as Kimberly-Clark and Procter and Gamble try to produce enough toilet paper to keep the price of toilet paper constant and consumers adequately supplied.3 

The difference between the two is that the Fed can “print” money out of thin air with the push of a button, while Kimberly-Clark is constrained by the scarcity of heavy machinery required to print toilet paper. It should be no surprise that we have generally been able to get our hands on money when we need it, but the same cannot be said for toilet paper.4

In July 2020, in response to the outcry over the coin shortage, the government convened the U.S. Coin Task Force. The U.S. Coin Task Force studied the matter and pronounced that there are actually plenty of coins available, but they are just stuck, somehow. The official government statement on the coin shortage begins:

There is currently an adequate overall amount of coins in the economy. But business and bank closures associated with the COVID-19 pandemic significantly disrupted normal circulation patterns for U.S. coins.

The U.S. Coin Task Force created a website complete with video PSAs and hashtags (#getcoinmoving), urging consumers to get out and return their coins. Their statement goes into a bit more detail: 

Coin circulation has emerged as a new disruption caused by the COVID-19 pandemic. Many have referred to this as a shortage; however it is not. There is more than $40 billion in coin already in circulation, most of which is sitting dormant inside America’s 128 million households. As people have changed their spending habits, and coin-intensive businesses and financial institution lobbies have been less accessible, the nation’s coin is pooling in change jars, in car cup holders and in shuttered businesses, making it difficult for the businesses of this country to get the coin that they need to support cash transactions.

This statement should leave one a bit skeptical. $40 billion of coin would be over $300 of coins per household; there is no way the average household has $300 in change lying around, “dormant”. Also, business has mostly returned to normal, but news outlets are clearly still reporting a general shortage, even while the U.S. Coin Task Force insists that no shortage has ever existed.

One clue as to the true nature of the coin shortage lies in the reporting on the crisis, which like our original Seattle Times story, consistently and inevitably comes back to coin-operated laundries. The Wall Street Journal wrote an article on the shortage back in August:

The night before she was scheduled to serve jury duty, Kuromi Hendricks realized the clothes she planned to wear were dirty. She couldn’t find the 12 quarters required for a wash and dry at her Boston apartment building.

By 11 p.m., desperation took hold. She hopped in a $10 Uber to the only place she knew was open and might have spare change—a 24-hour cafe with a couple of arcade games. Not wanting to use the business just for their coins, she ordered a lemonade, asked for about $5 in quarters, played a few rounds of pinball, and headed home to wash.

…

Aeryn Emmerich-Wise and her husband piled four loads of dirty laundry into their car for the nine-hour drive from the borough of Queens in New York City to visit relatives in North Carolina, after both sets of in-laws encouraged them to do so. Before that, Mrs. Emmerich-Wise didn’t wash her favorite pair of jean shorts for about three months. She finally gave up and ordered a new pair because she was having such a hard time finding quarters.

And here is a Philadelphia Inquirer story from last year:

Roughly once a week, Marta Rusek’s studio apartment is adorned with damp clothes laid out to dry.

With coins scarce all over the country, the Center City resident has favored air drying rather than the coin-operated laundry machines in her Spruce Street building.

“Basically,” she said, “my quarters are very near and dear to me.”

Rusek, 35, washes much of her wardrobe in her bathtub, much as she did in 2011 while serving with the Peace Corps in Gambia.

Most Americans rarely think about coins, but a subset of Americans are wholly reliant on quarters to be able to have clean clothes. The demand for coins seems to be less about making change at the grocery store and more about demand from coin-operated laundromats, the majority of which currently have no way of taking cash or any kind of electronic payment.

At the beginning of Covid, the U.S. Mint temporarily slowed down coin production, as they implemented social distancing. This would likely have caused some people to have trouble getting their hands on coins. If you really need coins, you might respond to a temporary disruption by hoarding coins, as a buffer against future disruption. Reported Bankrate:

When SAFE Credit Union heard the Federal Reserve would reduce the number of coins [a] financial institution could order, Baldi remembers thinking “whaaat?” While she was surprised, she knew the credit union didn’t want to experience the financial equivalent of a toilet paper shortage. Then, she had a quick aha moment. The Fed restrictions were per location and the credit union had only been ordering for one.  “Suddenly, we discovered we had 20 other locations that could be used,” Baldi says.

So SAFE ordered coins for all their locations and stored them in a third-party coin vault to avoid overwhelming the individual branches. “We had almost no impact from it from a member standpoint,” Baldi says.

The only way for the SAFE Credit Union to fill their vaults with coins is by removing coins from circulation; in this case, they are taking new coins that would have ordinarily entered circulation and instead placing them in their vaults. It seems perfectly reasonable to assume that every other coin-dependent business and consumer had the exact same idea at the exact same time. If everyone tries to hoard coins simultaneously, then the result will be a shortage of coins in circulation, just as toilet paper hoarding resulted in a shortage of toilet paper on store shelves.

This is also a good illustration of the bullwhip effect, whereby small fluctuations in demand get amplified through the supply chain. It might not be so bad if it was just consumers and laundromats ordering directly from the U.S. Mint and building inventories for themselves, but distribution is intermediated by banks, who take it upon themselves to distort their own orders and build their own additional coin stockpiles, worsening the problem. This is no different from what retailers and distributors of ordinary goods like shoes are reportedly doing today. 

To the credit of the U.S. Mint, they have been hard at work trying to produce enough quarters to satisfy the spike in demand. According to the Philadelphia Inquirer:

At the Philadelphia Mint — the nation’s largest producer of coin currency — 14 presses, each producing 750 coins a minute, are running seven days a week to compensate for the pandemic-caused coin supply problems that turned quarters, nickels, and dimes into rare commodities.

“I’ve never seen anything like this in the past,” said a veteran Mint worker who maintains the coin presses and asked not to be named because he was not authorized to speak publicly.

During each two-week pay period, he said, “I’m putting in about 30 hours' overtime.”

We can presume from this article that coins are as difficult to produce as toilet paper, with the only flexibility available being the ability to run extra shifts. The U.S. Mint reported shipments of 2.9 billion quarters in 2020, up from 1.8 billion the year before. They managed to increase annual production of quarters by 60%, despite falling behind in the first half of the year.5

And yet, 1.1 billion extra quarters is a mere $275 million worth of coins. The coin-operated laundromat industry does nearly $5 billion of revenue per year, so $275 million is only 3 weeks worth of quarters, split between everyone in the supply chain, including consumers, laundromat owners, and banks.6 It is easy to understand why the coin shortage lingers on, nearly two years later.

At this point we must consider potential solutions, and we will start with Patrick McKenzie’s original tweet; he believes that we should let the quarter trade above face value, to balance supply and demand. He is of course correct that this would solve the issue, as surely the SAFE Credit Union and other hoarders would reconsider if they had to pay a large premium to get their hands on extra coins. 

Also, if everyone felt that they would always be able to get their hands on coins when they needed them, just by paying a premium over face value, they would feel less need to stockpile quarters.7

This solution has the same issue we identified with retailers of toilet paper; as with retailers, the U.S. Mint is incentivized to avoid bad publicity, hence the press releases telling us that the coin shortage is not really a coin shortage. Charging banks fifty cents for a quarter likely will not give them the publicity they are looking for.8

Furthermore, moving down the supply chain, it is not clear that most businesses even have the ability to charge consumers a premium for a quarter. They rely on change machines that are probably only capable of giving four quarters for a dollar.

Finally, there is an online market for quarters ($79 + shipping for $50 of quarters on eBay as of this writing), and banks and businesses have offered bonuses to get consumers to turn in coins. The existence of an active black market for quarters does not seem to have solved the issue.

The libertarian Cato Institute, in typical fashion, suggests that the true problem is that the government is preventing the private sector from executing a supply-side solution:

Although history is rife with examples of the private sector stepping in to provide alternative money when official currencies have been in short supply (e.g., Selgin 2008; Champ 2007), that didn’t happen here. Why not? The most likely answer is that the U.S. government is not one to tolerate competition.

Perhaps the government usually does not tolerate competition, but anyone who thinks that private businesses cannot create their own coins and tokens has never been inside a casino, or even a Chuck E. Cheese. 

The problem with a private sector supply side solution is that laundromats use quarters precisely to avoid having to manufacture their own tokens at great expense. Laundromats already have the ability to solve their quarter dependency by installing machines that take cash and credit card, no coin manufacturing needed. The Seattle Times article suggests that in many cases they already are, but it is a process that takes time and costs money. 

There are other potential supply side solutions. We could perhaps build extra manufacturing capacity at the Mint, to handle future spikes in demand, or we could mint enough coins to establish a Strategic Quarter Reserve, to be tapped whenever businesses and consumers want to hoard quarters.

The problem with supply side solutions is that they are bound to be very expensive, relative to the coin-operated laundromat industry they would primarily be supporting. We might be fine with spending hundreds of millions of dollars a year to support a $5 billion a year industry, but we would like to find a cheaper or simpler solution if we can.

If the supply side is out, perhaps we can find a solution on the demand side.

The challenge is that the demand for coins for use in transactions has been declining, as we switch to electronic payment; as we see below, the increase in coin production in 2020 is barely visible against the longer secular decline in coins (at least when considered relative to population and economic growth). In the meantime, the coin-operated laundry industry has continued to grow at the same pace as the broader economy.

Laundromats and other coin-operated machines piggybacked off of the coins we were already using every day,9 but as electronic payment gains share and inflation renders coins obsolete, we make fewer coins for everyday use. Machines that are exclusively coin-operated are hostage to the general availability of coins.

One possible solution is to subsidize any laundromat that wishes to eliminate their dependency on coins and install an electronic payment system. It costs hundreds of millions of dollars a year to manufacture extra coins; presumably it would be cheaper to just pay once to install electronic payment systems at a few thousand laundromats. In this case, it is probably easier to reduce demand than to increase supply.

If we are feeling less generous, we could simply mandate that laundromats of a certain size accept cash or electronic payment by a certain date, and perhaps couple it with mandates regarding parking meters and other coin-operated machines. If most vending machines today accept cash, surely it is possible for most other machines to accept cash or card with a small investment as well. 

There is one other solution we must consider. I must caveat this by saying that this is my least favorite idea, but we must examine it for the sake of completeness.

Most other developed countries have high value coins in common use. Japan has a 500 yen coin (about $4.40), and Europe has a 2 Euro coin ($2.25). Canada and the UK both have coins worth about $1.50. The US is something of an outlier, with a maximum coin value of $0.25.

It was not always this way. The last time the US had a coin shortage was the early 1960s, and that came about in the traditional way, because the value of the silver contained in a quarter approached face value, causing people to hoard the coins to be melted down later.10 Adjusted for inflation, 25 cents back then is equivalent to nearly $2.25 today, so the quarter was a high value coin by modern standards.

If you have a high value coin, then curing any shortage becomes much simpler, because you get more value per coin minted, by an order of magnitude. We mint billions of extra quarters and barely make a dent in our shortage, because we are all running around with rolls of coins, heaping them by the dozen into washing machines and dryers. If you go to a laundromat in Europe, you can do a load of laundry for about three coins.

This may also explain why the coin shortage is unique to the United States. High value coins mean that coins are more frequently used in everyday transactions, which makes it easier to absorb fluctuations in demand from the users of machines that require coins. There are many ways to substitute away from coins in regular transactions, from nudging customers toward electronic payment to adjusting prices, which frees up coins for everyone else. Also, the machines that use coins become much more coin-efficient.

The problem is that coins are not user friendly. Coins are inconvenient to store and to use. High value coins have been phased out over time in favor of paper money, mostly through inflation. Efforts to introduce a dollar coin alongside the paper dollar have consistently fallen flat. High value coins may save the government money by replacing short-lived paper money with long-lived coins, but at the cost of inconveniencing the user.

From the point of view of coin-operated laundromats and their customers, however, phasing out dollar bills in favor of dollar coins should solve the problem of future coin shortages. Laundromats would have to install new coin machines that accept dollar coins, but once that was complete, they would be more resistant to future coin shortages. 

The question of the dollar coin comes up from time to time, and the last effort in 2012 was led by the Dollar Coin Alliance, an organization made up of:

an eclectic coalition of metal companies, mines, merchandisers, vending interests, the United Steelworkers union, a government watchdog group and even an association for car washes.

This is exactly what we would expect; the organizations that favor high value coins are those that supply the materials for those coins and those that run coin-operated machines.

And who is to blame for defeating the dollar coin? As Bloomberg reports:

This led some laundromats to join the armored-car industry and paper money–printing industry in Americans for George, which lobbied (successfully) to preserve the print dollar in 2012.

It's a divisive issue on Planet Laundry, the world's leading forum for laundromat owners. On the one hand, some laundromat owners fear that the quarter is going to be destroyed by inflation: Soon enough, people will treat it like the penny and simply stop carrying it, or laundry will require so many quarters that the costs associated with capturing them will rise. On the other hand, it could be expensive for owners of older laundromats or laundry machines to convert to machines that accept dollar coins.

[In November 2016], Hubert Horan, a little-known expert on the airline industry, wrote a blog post for an equally little-known website, called Naked Capitalism, that went in a different direction. Horan’s thesis was that Uber was a tremendously unprofitable, inefficient, and not particularly innovative company that would make money only if it bought its way to an unregulated monopoly.

Compared to the Forbes piece and many others like it, Horan’s article was a fiduciary root canal. (“There is no simple relationship between EBITAR contribution and GAAP profitability,” reads part of one sentence.) The goal was to show that Uber was hugely unprofitable and how, in the event that it did succeed, profit would come from hurting consumers and overall economic welfare by cornering the taxi market.

Horan, who started consulting in the airline industry after getting his MBA at Yale in 1980, has now written 26 more installments of his Uber series. When combined as PDFs, the posts run 193 pages. They reveal him to be much more a Cassandra than a crank. Uber has lost in the neighborhood of $28 billion since it launched in 2009. Its rides now cost far more than cabs in many major cities, its workers are as exploited as ever, and taxi drivers face massive debts, partly as a result of its business practices. Consumers, meanwhile, are shocked to learn what the rides investors have been subsidizing actually cost.

The market currently values Uber at $78 billion, so Horan’s thesis that it is actually worthless certainly qualifies as contrarian. And yet, the idea is not so easy to dismiss out of hand.  

If I told you that Google was worthless, a scam that will inevitably collapse, you would tell me that everyone uses Google and Google makes a kajillion dollars a year and that should be adequate evidence that it is a real, innovative, sustainable business. Even if I knew nothing about the internet or search, I could conclude from the revealed preference of consumers and Google’s immense profits that there is something there.

Now if I told you Uber was worthless, you would tell me that everyone uses Uber and I might say “true, but what about the billions of dollars of cash Uber burns every year”? And you might say, “well, it’s complicated, for starters, have you heard of adjusted EBITDA?”

A business that loses money is not inherently unsustainable or damaging to consumer welfare. For example, the airline industry has been notorious for losing money throughout its existence; aside from some periods of government subsidy and protection, the US airline industry has never turned a consistent profit in its entire century-long existence.2 In the opening decade of the 2000s, the US airline industry somehow lost a combined $56 billion.3 

This does not imply that the airlines will all eventually collapse and we will soon all be traveling cross-country by covered wagon. The profits (or losses) of an industry are more a function of competitive dynamics than of the underlying utility of the product. A commercial airliner still travels at ten times the speed of a car for a fifth of the per-mile price, but the individual airlines themselves will only make money if they can rationalize pricing and capacity. 

Airlines that collapse merely transfer their planes to other airlines (both new and existing), and air travel as a whole continues to grow. Airline losses simply represent a transfer of wealth from airline investors to passengers and employees.

On the other hand, an industry that consistently loses money raises questions about the underlying economic model. Horan has been vocal about Uber for years, publishing his bearish thesis in the Transportation Law Journal, American Affairs, and Promarket (a publication affiliated with the University of Chicago Booth School of Business). He has the rare distinction of recently having received positive coverage in The American Conservative as well as this article in the left-wing Mother Jones. Clearly, his message has struck a nerve with a diverse audience.

The temptation when analyzing a stock is to jump straight to the fun stuff - in the case of Uber, the future of self-driving cars, or perhaps the theoretically vast total addressable market. In fact, the first step in analyzing a business should always be to figure out how exactly the business is supposed to sustainably make money, and in so doing one should get some insight into pricing power and long-term profitability. Horan’s model of Uber is useful as a sort of null hypothesis - he believes Uber can never make money. So let’s tear it down and find out: is Horan a crank or a Cassandra?

Uber is a software company stapled to a taxi service. Uber the software company creates taxi service software and then hands it to Uber the taxi service. Uber the taxi service then deploys the software in order to run a more effective transportation company. If the cost of developing the software is lower than the benefit the taxi service gets from using it, Uber should eventually be able to make money.

Horan’s thesis boils down to the idea that the software is not very valuable. In his opinion, a taxi service is a taxi service. No matter how you dress it up, you have to pay the driver, you have to pay for fuel, and you have to pay for the vehicle. Having a moving dot on your iPhone looks nice, but the overall package does not improve efficiency and only marginally improves the passenger experience. Worse yet, Uber is no good at running an efficient taxi service, which is only to be expected from a company that outsources scheduling and maintenance to their drivers. In his opinion, Uber’s persistent cash burn is supposedly evidence of this. 

The bull case for ride-hailing is that the software is very valuable; it changes everything about the experience for both riders and drivers, and it improves overall efficiency. In the old days, you had to call a human dispatcher, describe where you were, hope that they knew of a cab that might be close to you, and hope it eventually showed up. Now you press a button on your phone, and the software matches you with a driver, and you have full transparency as to where your ride is.

You are undoubtedly familiar with the most popular features of a modern ride-hailing app: As a rider, you get your pricing upfront, payment is automatic, your driver is being monitored, and you can see where your ride is at all times. As a driver, you can generally start and stop working whenever you want (although you will be paid more if you drive where and when there is high demand), and you also have some transparency into who your rider is. Also, the software is supposed to be good at predicting where demand will be, nudging you toward those areas, and generally ensuring that you will spend more time carrying passengers than looking for a new fare.

The true utility of the software is really supposed to show up in the second order impact: higher liquidity. The direct utility of the software attracts more riders and drivers, which lowers wait times and increases utilization, which is what riders and drivers really want. A car service that forces you to wait up to 30 minutes for a 10 minute ride is not very useful, no matter how cheap it is. Meanwhile, drivers only get paid for the fraction of time they are actually carrying a passenger - if they can keep the car utilized for 80% of the time instead of 40%, they will double their income if fares are the same.

Higher utilization and lower wait times attracts more riders and drivers, which results in higher utilization and lower wait times, which attracts more riders and drivers, and so on in a flywheel. This is all online marketplaces 101, and in fact Uber investor and board member Bill Gurley basically laid this case out for Uber back in 2014, just two years after UberX was launched.

The potential economics of online marketplaces have been widely understood since the days of eBay, a quarter-century ago. Scores of entrepreneurs since have tried to launch online marketplaces, building and polishing software and subsidizing early users to kickstart the flywheel. Some, like Etsy, succeeded and are worth billions, while many others failed to provide enough utility to make the business self-sustaining and folded. For all we know, Uber and Lyft are still subsidizing early users and have no path to make the business viable.

To see which side is correct, we need to observe how much riders and drivers value the software, and compare that to the cost of the software. To summarize, if the software is truly valuable, we should usually observe most of the following:

• Ubers and Lyfts will generally have higher utilization rates than taxis; that is, taxis will spend a larger share of time driving around empty, looking for a ride, than Ubers or Lyfts do;

• Ubers and Lyfts will have much higher geographic coverage and lower wait times; 

• All things being equal, riders will pay more for an Uber or Lyft than they would for a taxi;

• At the same price, riders will usually opt for an Uber or Lyft over a taxi, and they will take many more rides with Uber and Lyft than they previously took with taxis.

Conversely, if Horan is correct, we will observe that users are mostly indifferent between Ubers and taxis at the same price, and that there was be little change in the total number of total car service rides taken since Uber and Lyft became available (holding price constant), and Ubers and taxis will have similar geographic coverage and utilization rates.

To get our bearings, let’s start with the basic numbers we have about Uber, as of the most recent quarter. “Gross Bookings” is all the money people spend on Uber (rides) and Uber Eats, excluding tips. Last quarter, that was $23.1 billion, meaning that Uber is pulling in over $92 billion a year. 

“Loss from operations” represents the GAAP operating loss, which totaled $572 million. (For simplicity, we will stick with conservative GAAP measures here, except for gross bookings.) That translates to a bit over $2 billion a year.

So, Uber is bringing in $92 billion a year, but spending $94 billion a year. For every $1 you spend with Uber, they are spending a bit over $1.02 to provide you with your ride or your meal.4 That is not ideal, but perhaps not insurmountable. It is not Google, but it is also not MoviePass. It is at least plausible that they might have enough pricing power to raise their take rate enough to cover that.

The income statement above shows that they are recording $22 billion per year in operating expenses, which are the outflows outside of the ordinary share of Gross Bookings handed over to the driver or the restaurant. It is important to note that some portion of that $22 billion represents investment that will grow the business in the future, not expenditure required to keep the business running now. 

Uber might spend to open up an operation in a new city, or they might spend on upfront incentives to recruit new drivers who they expect will be on the platform for some time. They might even spend on incentives to get new users to make their first purchase, in the hopes that they will order again in the future, preferably without checking Doordash or Lyft for competitive prices. All of that gets clumped together with ordinary expenses like credit card fees, salaries for customer support agents, and stock options for the executive team.

In that sense, it is perhaps not precisely accurate to say that they are losing two cents for every dollar they take in. We do not have a good way to estimate “growth” vs. “maintenance” expenses, and perhaps at this stage the difference is immaterial for Uber, but it is an important concept to keep in mind.

Another line to highlight is “Research and development”, which is all the money that Uber spends directly on product managers, designers, and software developers: $2 billion a year. The great thing about software is that it scales well - once you’ve built it, it costs about the same whether a hundred people or a hundred million people are using it simultaneously.  (Emphasis on “about” - clearly, software that works at that scale is bound to be more complicated and expensive, and Uber must have a heck of an AWS bill, but the total cost per user generally goes way down as you get millions of users.)

Not all expenses related to software are in the research and development line - hosting bills end up in cost of revenue, the salaries of the HR people that support the developers might be in G&A, and so on. Even if we are generous and say that the total software bill is $4 billion a year, that means that software only represents 4% of the cost of your ride or delivery order. Is their software good enough to make your user experience 4% better or cheaper than that of a traditional taxi? It seems at least plausible, but we have to check the data to be sure.

A final note: For now, the Eats business has surpassed the traditional Rides business in volume - Eats currently accounts for $50 billion in annual gross bookings, vs. $40 billion for Rides. If you believe Uber’s non-GAAP accounting, Eats accounts for all of the losses and Rides is breakeven or profitable, depending on how you allocate shared costs, and losses in Eats are understandable since they are spending on acquiring new users and restaurants and drivers and doing so quite rapidly. 

If we believe the ride-hailing business is already profitable, this whole exercise becomes redundant, so we will ignore all of the non-GAAP accounting for now. The economic logic of the meals and rides businesses should be similar anyway, so strictly for this exercise we will ignore the distinction - but keep in mind that to properly analyze the business, one would want to separate the two.

The wonderful thing about analyzing a transportation business is that it is highly regulated, which frequently means useful public data sets a mere Google search away. Chicago and New York both release some of their ride-hailing and taxi data, which data scientist Todd W. Schneider has used to create some wonderful public dashboards, accessible here. He also links to the original datasets, which you would want to use to do any serious diligence, but what he has created is more than enough for this exercise.

Ride-hailing is mostly concentrated in large, dense cities; in 2017, 70% of all ride-hailing trips in the US were in the nine largest cities. Looking at two of the largest of these cities should give us a useful picture of the industry. According to his data, the taxi and ride-hailing market was about $2 billion a year within the Chicago city limits before Covid, and about $6 billion a year in New York. 

We will mostly focus on Chicago, since Chicago has fare data for ride-hailing, but Schenider’s dashboard shows that New York mostly exhibits the same trends, even though it is an unusual market.

Let’s start with an overview of the ride-hailing market in Chicago. Note that the taxi dataset in Chicago goes back to the beginning of 2014, while the ride-hailing dataset doesn’t start until the end of 2018, but this won’t be a problem for our purposes. UberX did not start until the middle of 2012, and we can see from the New York dataset that ride-hailing was fairly small even at the beginning of 2015, so we will get a full sense of how ride-hailing affected the taxi market by starting in 2014.

There is a popular conception that ride-hailing grew by undercutting and taking market share from taxis, but the data shows that not really to be the case. Immediately prior to Covid, ride-hailing was a $5 million a day market in Chicago, while taxis went from $1.1 million a day in February 2015 to $600,000 per day in February 2020. Roughly speaking, 90% of the ride-hailing market is new, while 10% came from taxis. This lends a lot of credence to the theory that the ride-hailing software created a valuable new market, and undermines Horan’s theory that ride-hailing is a predatory pricing scheme targeted at taxis.

We also see that ride-hailing is on average about a third cheaper than taxis on a per-mile basis. (This understates the price difference, since tipping is much higher for taxis.) That said, lower prices alone are unlikely to account for the magnitude of ride-hailing demand in Chicago. The demand for transportation can be elastic, but almost nothing is elastic enough to trigger a 400% increase in demand from a 30% price cut. 

Also, we observe that taxis have managed to maintain a significant portion of their original volume in spite of much higher average prices, which implies that there is some hidden heterogeneity in the market we should try to uncover.

Keep in mind that the fare difference does not tell us anything directly about driver compensation per hour, since compensation is a function of fare per mile, speed and utilization (as well as the take rate of the dispatch company). A driver can make up a lower fare per mile by driving faster (increasing compensation per hour) or through higher utilization (as we discussed earlier). We will look at utilization later, but above we see that speed appears to be a meaningful factor - the fare per minute gap was only 17% before Covid, perhaps because ride-hailing is more likely to be used when and where there is less traffic. Also note that the fare per hour has been significantly higher for ride-hail than taxis since the pandemic, due to the flexible pricing of the ride-hail model. We can exploit this natural experiment later to estimate how much users prefer ride-hail over taxis, all things being equal.

Here we can see that taxi pickups have always been very geographically concentrated. Chicago is a huge city (234 square miles), but taxi pickups have always mostly been within a very small slice of the city, either within two miles of the Loop (downtown) or at the airport - probably 10 square miles combined at the most. By contrast, ride-hailing is much more evenly spread across the city; over half of rides start more than 2 miles from downtown, in semi-dense residential areas.

These charts make it clear that taxi service was close to non-existent in neighborhoods outside of downtown prior to ride-hailing. Ride-hailing took a little bit of the market from taxis downtown, but it looks a lot less like ride-hailing undercut taxis and a lot more like ride-hailing created a new market. 

Way back in 2014, right after UberX got started, UC Berkeley did a survey to understand ride-hailing in San Francisco (emphasis mine):

When calling a taxi to their home, only 35% of San Francisco residents said they usually waited less than ten minutes on a weekday during the day; on nights and weekends, this figure dropped to 16%. By comparison, close to 90% of ridesourcing respondents said they waited ten minutes or less, at all times, and 67% waited five minutes or less. Ridesourcing wait times are also much more consistent than those of taxis: whereas taxi waits are more variable by time and day, ridesourcing customers could expect a wait of ten minutes or less regardless of day or time. 

And here is what the Chicago Tribune reported in 2019 (emphasis mine):

In March 2019, for example, ride-share drivers made more than 47,000 pickups in the low-income, majority African-American neighborhood of Englewood, compared with 85 cab pickups in March 2017, the most recent comparable information available.

In most Chicago neighborhoods, ride-hailing has 95%+ market share, and is at least an order of magnitude bigger than taxis ever were. For most of the city, taxis were too unreliable to be useful even before Uber and Lyft entered the scene, so ride-hailing was the basically only game in town by default. This seems to definitively contradict Horan’s thesis, which is that ride-hailing is about predatory pricing and undercutting taxis, and that improving liquidity to this extent should be impossible.

This is not a uniquely Chicago phenomenon; here is the same graph Schneider produced for the outer boroughs of New York, up to 2018. A major part of the ride-hailing story is coverage of markets where it has always effectively been the only option, short of taking public transportation, which is often much slower, and sometimes completely impractical.

Airports provide an interesting potential natural experiment. Airports should take liquidity out of the picture to some extent, as airports have always supplied large and consistent demand for car service. Both taxis and ride-hailing should be equally able to offer short wait times, so the only difference is price and the overall user experience.

Airports are where taxis have held up the best - remarkably, taxis kept the number of pickups consistent even as ride-hailing surged past it, implying the total airport market has grown considerably. This might be because ride-hailing has made it more realistic for travelers to get around a city without a rental car for the duration of their trip; it has been well-documented that Uber and Lyft have cut into demand for rental cars at airports. At the same time, Uber and Lyft have also created a new source of car rental demand, for drivers renting on a weekly basis. Ride-hailing, taxis, and car rental are all substitutes, but they are all complements to some extent as well.

Ultimately, this means that there are too many moving parts to arrive at any useful conclusions from looking at airport demand at a high level. Taxis have retained some market share, but anecdotally this seems to be because people opt for fixed rate taxis when there is surge pricing and/or there are no ride-hailing cars available.

Schneider also includes an intriguing case study of a single popular route during rush hour, a 15-20 minute trip from downtown to Lake View, which is a wealthy region a couple of miles to the north that includes neighborhoods like Wrigleyville. This route is very well served by inexpensive public transportation, so it may not be representative, but it does begin downtown (in this dataset, the Near North Side includes the part of downtown north of the Chicago River), where taxis should generally be more available and convenient.

Ride-hail was about the same price as a taxi ride before Covid, probably due to rush hour surge pricing, but ride hail peaked at 600 rides per day, triple the 200 rides per day taxis served before ride-hail came along. Post-Covid, ride-hail is almost double the price of a taxi, and still manages 200 rides a day, which is what taxis managed before ride-hail. Again, this particular route is bound to have a very wealthy ridership, and perhaps the pandemic is shifting some users away from public transportation, but it gives some possible sense of the price gap that it takes to make some users indifferent between taxis and ride-hail, even when wait times are not as large of a factor.

It is also interesting to look at the ride-hail market for the Chicago as a whole; returning to the charts from the beginning, the average cost per mile of ride-hail is for now equal to the current and historical average cost per mile of taxis, but ride-hail is currently running at $3.5 million per day, compared to $1.5 million per day for taxis at the same time in 2014, before ride-hail really entered the picture. Prices are the same (on average), but consumption of for-hire vehicle service has more than doubled, all thanks to ride-hail.

The immediate post-pandemic (or late-pandemic) environment has created a very interesting natural experiment, showing what would happen to ride-hail with much higher prices; as it turns out, people love it and stick with it.

There has been a recent proliferation of bad takes on how Uber and Lyft are now forcing us to pay the “true” cost of ride-hailing. In reality, the data seems to be clear that riders came back faster than drivers, who are reluctant to pick up strangers during an ongoing pandemic (especially if they can make nearly as much delivering food). Ride-hail companies are trying to balance supply and demand by raising prices, effectively creating hazard pay for drivers while keeping rides available for those who are willing to pay. Taxis are stuck to fixed rates, and as a result they have not returned in great numbers, even though they are making a bit more because of higher utilization and/or speed.

We might expect higher prices to be a temporary phenomenon, but either way, it is important to remember that higher prices that get passed on to drivers at best only marginally improve the economics of ride-hailing apps, as long take rates stay constant, as is the case now. Higher prices result in fewer trips, so higher prices may not translate into significantly more revenue. Ride-hailing companies may see a bit of margin improvement from higher prices if they have some operating costs that are actually tied to the number of rides or miles driven, but it is not clear they have any such costs that are significant.

To grow profitability significantly, the ride-hailing companies will have to increase their take rate, reduce spending on incentives to passengers and drivers (which is effectively just another way of raising their take rate), and/or find other cost efficiencies that they decline to pass on to riders and drivers. 

It seems clear from the data that competition from taxis does not force ride-hailing companies to lose money any more than competition from trains force airlines to lose money. If Uber is truly losing money on ride-hailing, it is because they have chosen to keep take rates low, either to keep pressure on Lyft or to grow usage. From the perspective of the rider, ride-hailing is massively superior to taxis on almost every dimension and in almost every use case; sometimes, software really works!

So far we have seen that users strongly prefer ride-hailing over taxis. Lower prices alone cannot explain it, since expensive ride-hailing now is far more popular than cheaper taxis ever were, at a level that cannot be explained by a pandemic-related temporary aversion to public transportation. 

Lower wait times and higher reliability seem to be the biggest factors, particularly in less dense areas that never had much taxi service, but people also seem to significantly prefer the ride-hailing user experience, as we can see in downtown and airport pickups, where wait times have not changed much but people have demonstrated a willingness to pay a premium for ride-hailing.

We must consider the issue from the perspective of the driver as well. Horan and other critics have raised concerns that ride-hailing companies might be reducing wait times by flooding the streets with empty cars. The general story is that ride-hailing companies are either directly subsidizing drivers, or worse yet, they trap drivers by persuading them to borrow money to buy cars that they must drive for several years to pay off, even if the market is oversaturated and they do not end up earning minimum wage.

This is a serious issue; if this is the case, then the strong preference for ride-hailing we observed from riders is not a story about technology, but actually a story about powerful organizations tricking and exploiting vulnerable workers.

We have already documented that city governments have exploited taxi drivers in the past, convincing them to borrow to buy expensive taxi medallions with the promise that the medallions would magically rise in value in the future. Medallions are not real productive assets, but rather represent a share of a cartel. The only way for medallions to rise in value is by allowing the cartel to extract more wealth from future riders and/or future medallion buyers. 

Ride-hailing may have been the proximate cause of the collapse in medallion values, but ponzi schemes such as the medallion racket are inherently unstable. There is no reason to believe that future riders or future medallion buyers would necessarily have consented to participate in this arrangement, even if ride-hailing had never come along. 

High current “returns” to medallion owners proportionally raise the necessary level of future wealth extraction from future consumers, which in turn raises the incentive for consumers to vote to terminate the entire arrangement and leave the last generation of medallion owners holding the bag. We observed that this is perhaps beginning to happen in big city housing markets, which are massively inflated by the artificial scarcity of building permits; younger voters are slowly realizing that they are being asked to prop up a pyramid scheme.

The taxi medallion scheme took place in many major cities, most tragically in New York, which saw a spate of driver suicides, and where drivers recently concluded a hunger strike to extract debt relief. 

The story the ride-hailing companies tell is that software is much better than humans at matching drivers and riders, paying drivers to go to where demand will be and charging riders extra for rides to areas where there will be few pickups. They also argue that their system lowers barriers to entry for new drivers, bringing on lots of part-time drivers who already own a car and pay for insurance, which makes it easier to balance supply and demand in busy periods.

We should be able to partially test this model by examining the utilization rate of taxis and ride-hailing vehicles. The utilization rate is the percentage of time a driver spends with a paying passenger, as opposed to driving around in an empty car. If ride-hailing apps are truly effective, we should see high utilization rates. If they are lowering wait times by flooding the streets with empty cars, we will see low utilization rates.

The most cited study of utilization, by Princeton’s Alan Krueger and Judd Cramer, was published with data from 2015, very early in Uber’s history. As we see below, it showed much higher utilization rates for Uber in most cities, except in New York, which as we have seen is uniquely dense enough to support high utilization for taxis.

More recent studies, conducted in Seattle and New York to analyze driver compensation, continue to find high ride-hailing utilization rates. This intuitively makes sense; computers should be much better than human dispatchers at organizing drivers to maximize efficiency, and they are widely used elsewhere in logistics for this purpose.

We also want to test whether ride-hailing apps are achieving high service levels and low prices by tricking and exploiting drivers. There is a massive debate on the proper way to regulate compensation and benefits for ride-hail drivers, with both sides marshaling volumes of economic research purporting to show driver compensation. 

It turns out that measuring effective driver compensation is difficult because it is sensitive to the method used to estimate driver expenses. Furthermore, many studies rely on survey data, which is likely to be heavily skewed by the incentives of the organization that produced it. Even the ride-hailing companies themselves lack good data, since many drivers drive for multiple platforms simultaneously and each company cannot see what a driver is doing on another app.

James Parrott and Michael Reich did a study in 2018 for New York to justify the minimum wage law for ride-hailing enacted by New York in 2019, and a Cornell team did a study of ride-hailing in Seattle in 2020, commissioned by the ride-hailing companies to oppose a proposed minimum wage rule there (which was passed anyway). Both were done with actual trip data from all of the different ride-hailing apps, so they avoid the issue of unreliable survey data, and they were done for opposing reasons, which might neturalize bias issues.

The general conclusion seems to be that ride-hailing pays comparably to taxi driving and other minimum wage-type jobs. Parrott and Reich followed up in 2020 to show that the New York minimum wage generally had little impact on the price and volume of rides, and perhaps modestly increased total driver compensation. This is consistent with the idea that ride-hailing drivers were already usually getting paid minimum wage or higher. (The policy is still controversial; Uber and Lyft had to ration driver access to the app to ensure every trip complied with the policy, upsetting drivers who were locked out.)

The majority of New York households do not already own a car, which makes New York an outlier; most drivers in New York have to purchase or rent a car to drive for the ride-hailing apps, resulting in a mostly full-time workforce. Most Seattle households already own a car, and the Cornell team found that only 5% of ride-hailing drivers in Seattle worked for over 40 hours a week, accounting for only 17% of all hours worked. This is somewhat consistent with the argument that the ride-hailing apps are largely tapping into a new part-time workforce that is earning extra side cash and squeezing more use out of an asset they already own. 

The Cornell team found that the median ride-hailing driver in Seattle earned $23/hour after expenses, a finding that was challenged by Parrott and Reich, which had been commissioned by Seattle to do their own study to support the new minimum wage. Even if the true average effective wage is a bit lower than $23/hour, it seems unlikely that it is much different than what taxi drivers earn, similar to New York.

The debate over the labor policies of the ride-hailing apps and the proper role of regulation will go on, and is far beyond the scope of this essay. We certainly cannot rule out the possibility that the ride-hailing companies sometimes take advantage of drivers. We want to understand the value of the software, and the evidence seems to indicate that the software is effective enough so that ride-hailing companies, riders and drivers can all benefit. 

It appears that ride-hailing is a sustainable business. This is good, as I personally have become quite reliant on Uber and Lyft over the years. Yay!

Horan’s narrative aligns with what a certain audience wants to believe, and as a result probably gets more coverage than it would otherwise. There is a certain type of person that wants to believe that every purported new innovation is actually useless, and a cover for a techbro conspiracy to exploit the masses, and that the facade will collapse at any minute. (“Taxis. You invented taxis.”, etc.)

In reality, even though some technological advances fizzle or turn out to have massive drawbacks (like leaded gasoline), most new technology improves the standard of living for society, and is in fact almost the only way we can raise living standards for everyone. Replacing taxis with ride-hailing apps does not offer the same leap as replacing trains with jet airplanes, but even small innovations add up. 

In Horan’s defense, his thesis was fairly coherent and he offered testable predictions. Back when he wrote his first piece in 2016, available data was limited, but now it is easier to test.5 If you are not wrong sometimes, you are not generating interesting ideas. You only need to be a successful Cassandra once to hit it big and offset a dozen incorrect crank theories.

This does not mean Uber and Lyft are good stocks. With the caveat that this essay is not investing advice, it does indicate you might be able to scour public datasets and conclude that Uber and Lyft have a lot of untapped pricing power, and there is a path to high margins, perhaps even high enough to justify their current stock prices.

Even if they do have untapped pricing power, you have to assess the competitive dynamics to ascertain whether they will ever realize it. Are consumer habits sticky enough to dissuade both companies from waging price wars, or is it the airline industry all over again? 

Then you have to ask whether regulators will actually allow them to raise profits; local governments have already shown an interest in regulating food delivery take rates, and they are also interested in regulating driver earnings. Then you have to ask about the threat of self-driving cars - if self-driving cars become a reality, perhaps the first company to successfully develop a self-driving car will build their own app and undercut Uber and Lyft. (What’s the value of an app that matches drivers and passengers if you don’t need drivers?)

There are a lot of write-ups of Uber and Lyft on the internet - here is a good recent one from MBI, but there are many more in the usual places. Ben Thompson has several older articles on ride-hailing that dive into his view of the economics and have held up well. Undoubtedly there are some useful sell-side reports as well. 

It is useful to build the habit of working out the levers that go into determining the long-term economics of a business from scratch, whether you are an entrepreneur building your first slide deck for investors, or if you are an experienced investor trying to get a handle on a complicated business. Then, later on, you can see if the data matches the prediction of your model, and adjust your model as needed. Hopefully this case study is useful as a guide to start thinking about that for your own investments or your own business.

(Disclosure: I don’t own any Uber or Lyft stock, nor do I know anyone that works at those companies, nor do I have any particular expertise in ride-hailing or taxis. This is merely intended to be an exercise with public data to show how one might go about thinking about a new industry. If there is anything important about ride-hailing that I am overlooking (which is quite likely), please note it in the comments and I will edit this as appropriate.)

(Edit: A couple of topics that came up from feedback:

There are two very different groups of ride-hailing critics. They both think that the apps are exploiting workers. However, one group (the Horan cohort discussed in the article) thinks that the ride-hailing apps are inefficient. The other group, the Parrott and Reich cohort, thinks that the apps are very efficient.

If you think the ride-hailing apps are inefficient, then your implicit belief is that if we tax and regulate them, that will be the straw that breaks the camel’s back and they will collapse. (Horan tries to develop an out whereby maybe the apps are engaging in predatory pricing to try to drive the taxis out of business before that happens, but as we can see in the data (pre Covid at least), they’ve barely made a dent in the number of taxis in NY or Chicago after spending tens of billions of dollars - they have huge market share but it’s all new, people substituting away from rental cars or public trans or taking new trips. The idea that they would easily totally drive taxis out of business never made sense if you’ve actually taken a taxi or Uber at the airport or in Manhattan - street hail is often faster than any app and when you are at the airport, you just have to take whatever you can find.) This is a totally coherent opinion to have! Regulators spot businesses that are mere regulatory arbitrages all the time, and regulate them out of existence without a second thought. This happens in finance for example.

Most *serious* ride-hailing critics are like Parrott and Reich in that they think ride-hailing apps are *very* efficient and we should regulate them to make sure they share their oligopoly profits. If you read Parrott and Reich, they are very explicit in laying out their case that Uber and Lyft are in fact *already* very profitable and that they are hiding their profitability with their investments in self driving cars and in overseas affiliates and scooters and so on. (Their NY study was written in 2018.) Their whole case is that it is fine to tax ride-hailing and impose a minimum wage *because* the apps are so efficient - the cost will end up coming out of the apps’ allegedly huge hidden profit margin - and as noted in the article, they ended up being correct that it would not really affect riders, although that is not to say there is not a lot of controversy about the policy still. The key point is that careful regulation makes the most sense if the apps are really efficient and useful because you want the companies to share the surplus, and if they aren’t really efficient and sustainable, you can write crude regulation because anything will drive them out of business anyway and we should all be fine with that.

There was once a time when people used to argue that Amazon would shrivel up as soon as they were forced to pay sales tax. It’s important to remember that a company with an advantaged business model might also be benefiting from regulation and tax preferences, but if you take away the preferences, they will still have an advantaged model and they won’t go away.

This essay is really just supposed to be a case study in how to use revealed preference to get a sense of pricing power - I don’t want to wade to much into the regulation debate - but this is an important distinction that sometimes get lost. I focus here on the “is it sustainable” because I am trying to create a case study that can be applied to other businesses but if you want to take ride-hailing or food delivery regulation seriously, you have to engage with the stronger case which is that we have to assume the apps are not only sustainable but already very profitable (or going to be very soon) and we should regulate on that basis.

A second point that got cut from the essay but maybe shouldn’t have - taxis really get a disproportionate share of their revenue from airports at this point, and the graph does not capture that well because it shows *number* of rides. Airport pickups are far more expensive than normal rides, more than triple the cost usually, so if the graph shows that 17% of taxi pickups are coming from the airport, that means 40%+ of revenue is coming from the airport (and that is only from the airport - that does not even include rides *to* the airport). If you read the reporting now in some cities, it seems like taxis are mostly just camping out at the airport and they can usually make enough to scrape by if they just wait to pick up people who are stranded when ride-hailing cars run out or become super expensive due to surge. That might not be sustainable if we ever get to a post-pandemic world and drivers come back, but that seems to be the case now.