A lot of the favorite casual criticisms of economics relate to GDP. They typically go something like this. GDP does a bad job of measuring X and X is a thing that really matters, therefore GDP is a bad measure. Often times, these criticisms aren’t necessarily wrong. GDP doesn’t measure X because that is not what it is designed to measure. GDP is designed to measure the total value of newly produced final goods and services in the economy (meaning within the borders of that economy) over a given period of time. Unless that is your definition of X, GDP isn’t going to measure it.

Ironically, however, GDP often tends to be a good proxy for X. For example, some people say that GDP isn’t good because what we really care about is happiness or life expectancy or consumption. It turns out that GDP is actually strongly correlated with all of these things.

Nevertheless, I don’t think that these correlations are something that we as economists should hang our hats on. It is fun to point out that “GDP is fine, actually,” but I also think that sometimes we risk taking this too far. After all, GDP is designed to measure a particular thing. Let’s keep it to that thing. When we want to measure something else, we should try to measure it precisely using the tools that we have.

One thing that we frequently use GDP for is to measure living standards. This seems to make sense. We calculate economic growth by finding the growth rate of the trend in real GDP per capita. As a matter of measuring how much more stuff the economy is producing over time, this seems like a good measure. However, while the phenomenon of economic growth is associated with a corresponding rise in living standards, it isn’t obvious that we can measure improvements in living standards using the growth of real GDP per capita. A recent paper by Philip Trammell and Chad Jones makes this point in an easy to understand way. Allow me to explain.

Preferences, GDP, and Living Standards

Back in the 1970s, Paul Samuelson and Subramanian Swamy wrote a paper that showed that real GDP provides a consistent measure of living standards if preferences are homothetic (a frequent assumption made by economists). I don’t really want to spend any time explaining what homothetic preferences are because I value you, the reader, too much to make you read through that. However, it suffices to say that with homothetic preferences, the income elasticity of all goods is unit elastic and the expenditure shares on each good is constant over time.

To see why that might be important, consider how we measure economic growth when we use a chain-weighted measure. The growth rate of real GDP is just the weighted average of the growth rate of the production of each good. With homothetic preferences, the weights are fixed. Thus, if there are two goods and the production of one good is growing at 4% and the other good is growing at 2% and each make up half of the expenditures, then the growth rate will be 3%.

However, consider the case of non-homothetic preferences. One example of this would be to consider a utility function in which the utility of consuming each good is bounded from above. In other words, consider a world in which there is (at least eventually) diminishing marginal utility for each of the two hypothetical goods already mentioned such that total utility from consuming that good reaches some maximum. Given that the production (and consumption; markets clear) of the first good is growing at 4%, consumers will hit the point of diminishing marginal utility earlier than for the other good. As a result, consumers will start to substitute away from this good and consume more of the other good because the marginal utility of the latter is higher. In fact, in the extreme case in which the consumer ultimately hits the upper bound for utility associated with the first good, all of consumption will be of the second good and thus the growth rate converges to 2%.

Note that the only thing that differs in each of these examples is consumer preferences. The measured growth rate is different just because the expenditure shares are changing over time under one set of preferences, but not the other.

Why does this matter?

As Trammell and Jones note in their paper, when we think about the way in which economic growth improves living standards, we tend not to think about it in terms of just more stuff. Instead, we think about economic growth improving living standards because of a wider variety of goods that are available. We don’t just get more of the same stuff. We get new stuff that is better than the old stuff. An example that they give in their paper is of Nathan Rothschild. Back in the 1830s, this was the richest man in the world. However, he died from something that would have been cured today by cheap antibiotics. The availability of antibiotics would have greatly improved his living standards.

With that in mind, let’s return to our previous example, but instead let’s imagine that the second good doesn’t initially exist. The introduction of the second good makes people better off. It raises total lifetime utility. However, with different assumptions about preferences, we get two different growth trajectories. More importantly, for non-homothetic preferences, the growth trajectory gives us a really perverse result if we think about living standards in terms of real GDP.

The reason for this perverse result is as follows. If only the first good exists, the economy will grow at 4% per year. If the second good is introduced, lifetime utility (and thus living standards) rise for everyone in the economy. However, the growth rate converges to 2%. In other words, projecting this out, real GDP will be lower in the world with the second good even though living standards are higher.

How to Measure Changes in Living Standards

Trammell and Jones suggest that to properly measure living standards, what we really need is lifetime utility. This is a problem since we cannot measure utility. Nonetheless, price theory can help here. Remember that I had a previous post on the statistical value of human life (or the value of a statistical life, VSL, for people who want to butcher the English language). We can estimate the VSL using data from labor markets. People are compensated for taking on risks in particular jobs. The VSL can be measured as the compensation for this risk per change in probability. This gives us a dollar value for human life. Conceivably, this could tell us something about living standards. As living standards improve, life is worth more to people and so they are less likely to take on risk without additional compensation. VSL will capture this change in living standards.

Of course, this is a measure in terms of dollars. To properly measure things, we need to have things in terms of total utility. Fortunately, if one solves an infinite horizon, representative agent problem, it follows that

Or

Where U is total utility from aggregate consumption and MU is the marginal utility of aggregate consumption. In other words, there is a way to convert lifetime utility to dollars to get VSL and correspondingly a way to convert VSL to lifetime utility. Unfortunately, doing so requires having some ability to measure the marginal utility of total consumption. We don’t have a way to do that. Thus, we are back to square one.

Or are we?

Trammell and Jones point out that although we cannot get the level of living standards, we can estimate the growth rate. For example, it follows from our equilibrium condition for VSL that:

where g_u measures the growth rate of lifetime utility, g_VSL measures the growth rate of VSL, and g_mu measures the growth rate of the marginal utility of consumption.

But we know from standard models of consumption that

In words, this means that the growth rate of the marginal utility of consumption is equal to the rate of time preference minus the risk-free interest rate. It therefore follows that

The right-hand side of this expression is all stuff we can measure. Thus, if we can measure the growth rate, we are always able to measure the magnitude of the difference in living standards over different periods of time.

Does This Matter Empirically?

One might wonder whether this matters empirically. Well, using Trammell and Jones’s baseline calculation, they find that the average annual growth rate of total utility from 1940 to 2024 was about 2.3%. This implies that living standards were 6.9 times higher in 2024 than in 1940. As a method of comparison, the average annual growth rate in real GDP per capita from 1940 to 2024 is about 2%. That doesn’t seem like much of a difference, but compound growth makes a difference here. Real GDP per capita would suggest that living standards were 5.4 times higher in 2024 than in 1940.

It is important to note that the estimates depend critically on how we measure VSL. We lack good time series data on VSL. Trammell and Jones splice together estimates from Costa and Khan (which I discussed in my post on VSL) and from the U.S. Bureau of Labor Statistics.

An alternative would be to start with estimates of VSL and then project them forward based on the income elasticity of VSL. Trammell and Jones also do this. They find that if you use the standard BLS assumption that the income elasticity is 1, then this implies that living standards are 5.1 times better in 2024 than 1940. This is pretty close to the estimates implied by the growth in real GDP per capita.

However, Costa and Khan found much higher income elasticity of VSL. When Trammell and Jones use an income elasticity of 1.3, this implies that living standards are 21.1 times higher in 2024 than in 1940!

What this demonstrates is that it is possible that the growth in real GDP per capita might actually understate the rise in living standards associated with economic growth. This shouldn’t necessarily be surprising since its ability to measure living standards requires specific assumptions about preferences that might not hold.

Nonetheless, whether the real GDP per capita is a good proxy ultimately depends on measures of VSL. We need much more frequent and consistent estimates of VSL to make this determination.

Implications for Our AI Future

Why does all of this matter?

Well, one of the things that both Brian and I have been writing about recently is AI and the likely economic effects thereof. To some extent, Brian and I have spent a lot of time throwing cold water on the more provocative claims made by people with respect to AI.

In fact, one argument that I made based on the literature on economic growth is as follows. Some types of production might be easy to automate. AI might be expected to have a huge influence on those types of production. However, other types of production might be hard to automate. If production is a function of both easy-to-automate inputs and hard-to-automate inputs and the elasticity of substitution between those inputs is less than one, then the growth rate of the economy will be determined by the growth in the production of those hard-to-automate inputs.

Nonetheless, in a world of more advanced AI, living standards might increase substantially, in part due to all of that automation, but also due to inventions and innovations that humans might not have discovered in the absence of these AI tools. However, that might imply that real GDP per capita becomes a bad measure of living standards since total utility might be growing substantially faster than measured production.

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On its face, it seems like the answer to both of these questions is easy. The corporate income tax is a tax on the income made by corporations. Okay, easy enough. But that answer is superficial. Let’s dig into the details.

In a standard optimizing model of the firm, a corporate income tax increases the user cost of capital and therefore reduces the demand for capital. Thus, if you want a simple answer, you might say that the corporate income tax is a tax on capital – although that is also not a complete answer because it ignores the issue of tax incidence. However, let’s stick with calling it a tax on capital and see where that takes us.

Suppose that all firms are corporations and capital is homogeneous. Under that scenario, it seems permissible to refer to the corporate income tax as a tax on capital. Alternatively, suppose that there are two types of capital and two industries. One type of capital is used by one industry and the other type of capital is used by the other. If the corporate income tax is levied on one industry, then this is a tax on the capital of that industry.

However, if we assume that some firms are corporations and some are not and that both types of firms produce the same product, then calling this a tax on capital is not correct. It is not correct to call it a tax on capital because the capital is only taxed if the firm calls itself a corporation. It seems reasonable to me to think that both corporations and non-corporations produce the same goods and therefore use the same types of capital. Only one firm is subject to the tax.

What this framing reveals is that when both corporate and non-corporate firms produce the same good, a tax on corporate income is not really a tax on capital, but rather a tax on incorporation. Okay, now we are getting somewhere.

We know that if you tax something, you get less of it. If we tax corporate income, we should see fewer firms that call themselves corporations relative to the counterfactual of no corporate income tax.

To understand what this means, we first have to know what constitutes a corporation. According to the IRS, a corporation is a firm that has filed articles of incorporation with the state. (Thanks IRS!)

In general, corporations have some specific characteristics, such as shareholders with easily transferable shares and limited liability. Thus, if we think of a corporation as a firm that issues marketable ownership shares, then a tax on corporate income is actually a tax on the decision to issue marketable ownership shares.

This raises the question as to why a firm might sell marketable ownership shares and the corresponding implications of making it more expensive to do so. On this point, Laurence Kotlikoff and Jianjun Miao develop a framework for thinking about this. Their crucial point is this. The decision for the marginal entrepreneur about whether or not to incorporate depends on risk, the fixed cost of issuing ownership shares, and the cost of the corporate income tax. On the one hand, an entrepreneur would like to incorporate because adding shareholders allows for risk-sharing. However, to get the benefit of risk-sharing, the firm will have to pay a tax that it would not have to pay if the entrepreneur operated the firm on his/her own. Ultimately, whether an entrepreneur will decide to incorporate depends on whether the marginal benefits of incorporation are greater than or equal to the marginal costs. What this tells us is that there is some threshold for the skill or productivity of entrepreneurs. Those above the threshold will find it beneficial to incorporate. Those below it will not. Thus, when the government increases the corporate income tax, they are increasing the marginal cost of incorporating. As a result, the marginal benefit of incorporation must be higher than it was before the tax increase. This means that the skill or productivity threshold that the entrepreneur must cross to want to incorporate increases. In other words, fewer entrepreneurs will choose to incorporate.

In comparison to a world with no corporate income tax, the world with a corporate income tax will have fewer entrepreneurs who decide to incorporate. Since the benefit of incorporating is that it brings about risk-sharing, this also means there is less risk-sharing. Recall that when you tax something, you get less of it. What their model shows is that when you tax corporate income, you get less risk-sharing. Thus, it seems like the corporate income tax is a tax on risk-sharing.

To this point, I have focused on determining what is being taxed by the corporate income tax. We also need to know who is paying the tax. Politicians often complain when companies like Amazon do not “pay their fair share.” Of course, fairness is subjective. Nonetheless, what this highlights is that politicians often frame the tax as a tax on corporations, but corporations don’t pay taxes. Capital doesn’t pay taxes. People pay taxes. Do the owners of capital pay the tax through lower profits? Do firms pass along the cost of the tax to workers through lower wages and/or less employment? Who pays the tax depends on tax incidence. Of course, who pays the tax is related to what is being taxed. As it turns out, the assumptions made about what distinguishes corporate and non-corporate entities as well as some ancillary assumptions about the supply of capital have important implications for determining who pays the tax.

Maybe what politicians, like those I described, have in mind is that the owners of the firm (i.e., the shareholders) pay most of the tax. Arnold Harberger’s pioneering paper suggests that capital owners pay the tax — even those in the non-corporate sector. However, Harberger assumes that corporate firms produce a different good than non-corporate firms. In addition, Harberger assumes that factor inputs, like capital, are in fixed supply. The assumption that corporate and non-corporate firms produce different goods seems to violate casual empirical observation. In addition, since it assumes that the factor supplies are fixed, the model is unable to examine the effects of the tax on capital accumulation.

If we drop the fixed factor supply assumption, we get a much different result. Suppose that all firms are corporations and use the same capital. In this case, the corporate income tax is just a tax on capital. Here, the neoclassical model will suffice. The neoclassical model has a horizontal supply curve for capital. As a result, the equilibrium allocation of capital is entirely demand-determined. In the neoclassical model, since the supply curve is horizontal, a tax on capital is entirely paid by workers.

Of course, as I said before, there might be characteristics of corporations that make them different from non-corporations. For example, we could assume that corporations and non-corporations produce different goods and use different types of capital. We can now modify the neoclassical model to have multiple types of capital and assume that the corporate income tax is a tax on a particular type of capital. In the new textbook, Chicago Price Theory, the authors have a nice example of a corporate income tax as a tax on a particular type of capital. The conclusion that workers pay the entirety of the tax holds, but the cost of the tax to workers is higher than a tax on capital itself because of the additional distortion created by having different taxes on different types of capital. (Kevin Murphy provides an excellent discussion of this here.)

If we drop the assumption that corporate and non-corporate firms produce different goods, we can turn to Jane Gravell and Laurence Kotlikoff. As they show, this assumption results in a much larger deadweight loss than what Harberger finds because it distorts production within a sector that consists of both corporate and non-corporate firms producing the same good. In addition, capital owners are worse off from the tax at the expense of non-corporate entrepreneurs. Whether workers are better off or worse off depends on how the labor demand of entrepreneurs adjusts.

Finally, we could just abstract from capital completely like Kotlikoff and Miao do in the paper referenced above and examine the corporate income tax from the perspective of choices about being a worker, an entrepreneur, and incorporation. They show that the corporate income tax is a tax on high-skilled entrepreneurs who incorporate and workers who see lower wages due to the decline in the demand for labor as a result of the tax.

Are you following all of this?

If not, that is okay. Maybe the fundamental lesson here is that what and who is paying the corporate income tax and the size of the deadweight loss are issues of considerable disagreement because it depends on assumptions that we make about the differences between corporate and non-corporate entities as well as other ancillary assumptions. This disagreement is something to remember the next time your favorite politician (or economist) speaks confidently about the precise corporate income tax rate we should choose.

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We are beyond the point where we are asking if AI will be useful. It obviously will be. But the economic question is who will capture the returns, the benefits, the rewards?

The valuations suggest the big labs will, and the AI boomers and doomers (not the AI-will-kill-us-all doomers, but the AI-will-destroy-the-economy people) are in agreement on this. The boomers see a fortune waiting to be won by the companies that build the most powerful models. The doomers see the same fortune being taken away from workers, pushing labor’s share of income down. Both sides imagine a large pool of economic value moving toward AI. They disagree mostly about whether that is exciting or frightening.

In this newsletter, I want to think through whether that is true, and who else we can expect to capture the gains.

There is a distinction between creating value and capturing value. A technology, more generally, any good can generate enormous value while earning almost nothing for the property right holders of that good.

Water is essential to life, yet it is nearly free. Yes, the value generated, or the demand for what, does matter for prices,but only on the margin. Supply matters just as much. Supply and demand, after all. Water is plentiful, so water is cheap, no matter how much life depends on it. The holders of the property rights for water are not becoming billionaires, and those who are make money not from the actual essential part, but from bottling and marketing it in a specific way.

Returns go to goods with the right combinations of value generated and scarcity. We’ve known that basic idea since Ricardo, but the marginal revolution really tied it into a self-contained theory of value and price. Price does not measure the total usefulness of a thing. It measures the value of one additional unit, given how hard that unit is to obtain.

When we start thinking about prices and AI, we already see a water-like aspect going on. For example, the thing these companies are racing to build is collapsing in price. That’s not uncommon. Chips get cheaper. Computers get cheaper. Televisions get cheaper.

What is not ordinary is the speed. When GPT-3 became available in 2021, it was the only model that could score 42 on the MMLU benchmark, and running it cost about $60 per million tokens. Three years later, the cheapest model capable of clearing the same bar cost six cents. I’m not great with numbers, but that seems like a big drop. Epoch AI tracks inference prices and has prices falling to 1/9th the price each year up to 1/900th per year. I’m sure if I looked in the past week, these numbers would have fallen more for some models. Just today, Meta released a model (Spark 1.1) that further competes hard on price.

So we have two competing forces. A fixed level of intelligence is becoming abundant, and abundant things are cheap at the margin no matter how useful they may be in total. So you have a horse race, whether the moving out of the intelligence frontier will outstrip the plummeting price for a fixed amount of intelligence.

Nobody is paying nearly a trillion dollars because today’s frontier intelligence will remain scarce. Within a year or two, much of it will be cheap. Investors are betting that the leading labs can keep producing (and controlling) something that does remain scarce. Maybe that’s just the moving frontier, or other things like proprietary data, locked-up compute, privileged distribution, or the workflows that make their products habitual.

But what exactly remains scarce? Alex Imas asks a related question—that I’ll say more about—but focuses on the demand side more. I will focus more on the supply side and who owns the property rights to that which remains scarce.

AI makes intelligence easier to copy

Let’s think of intelligence as a kind of recipe. It is a way of combining ordinary things into something worth more than the ingredients. It is non-rivalrous. My knowing a recipe doesn’t hinder you from knowing it. A baker turns a few dollars of flour, water, butter, and heat into a croissant that people line up to buy. The ingredients are not especially scarce, so the baker’s intelligence, knowledge, his know-how, whatever, allows him to earn a profit.

But there are two different things hiding inside the word recipe.

One is the card. There’s a list of ingredients and steps, the kind of thing you could write down and hand to a stranger. But there’s also stuff that is missing from the recipe card, that the baker forgot to write down or is implicit, like the judgment about what matters, the sequence in which things should happen, the adjustments when conditions change, the tacit sense of quality that separates a good croissant from a mediocre one. I’ll think of the first one as tasks, just like task-based models, and then the second that lumps it all together to generate value, the intelligence.

Pre-AI, even the card was easy to copy. The know-how is not, and that is what makes it scarce. Anyone can buy flour and butter, and anyone can read a recipe card. They cannot easily reproduce the timing and the touch that make the croissant good. The baker earns a premium because the valuable intelligence cannot be separated from the person who has it.

For most of history, much of that recipe was trapped inside the person who knew it. They could teach others. They could write down the ratios and temperatures, but, still, there remained some unique ability (let’s focus on the knowledge side) around maybe timing and judgment. The true recipe remained scarce because it could not be separated completely from its owner, even if you could write down a word-filled recipe. Some important part of the know-how stayed embodied in people, teams, and firms. Knowledge was sticky.

That’s obviously not completely gone yet, but AI seems to loosen that connection between knowledge and where it is embodied. Because intelligence is nonrivalrous, it can replicate and disseminate much faster. That’s definitely true of the written layer, although it is starting to imitate the tacit layer too. It can pull out subtle parts of the craft that were never written down, maybe never articulated at all.

If you give the baker’s recipe to every shop in town, supply increases, and more people may eat them than ever. But no baker earns a premium merely for possessing the recipe, because every baker now has it. Just like on other dimensions, competition pushes the price of the croissant closer to the cost of its ingredients and production. The return to the knowledge drops.

Some of the old premium goes to customers in the form of lower prices. The remainder settles on whichever inputs are still scarce. Maybe there is still something to being on the corner with the foot traffic. Or maybe the oven (not written down) is the key. We could go down the list: the trusted brand, the financing, or the baker whose hands really are faster. Output rises, but the return to knowing the recipe falls. To stay ahead of this process, the baker has to continually come up with new recipes that aren’t copied and ahead of the market.

James Bessen called it the paradox of technical knowledge. Replicable ideas allow us to produce more with the same resources, but the resulting wealth does not automatically accrue to the people who once held the knowledge. When intelligence becomes abundant, bottlenecks matter more

So we get to a world where we have 100 million Einsteins in a datacenter. A huge amount of know-how has become copyable. But the model still has to run somewhere. It requires chips, electricity, cooling, buildings, land, and time. Unlike knowledge, those inputs are rivalrous. Two workloads cannot consume the same GPU-hour or the same megawatt at once.

So the cheapening of intelligence can raise the returns to the complements needed to use it. As intelligence gets easier to copy, users bid more aggressively for the scarce inputs required to run it at scale.

We see this already with physical complements. For example. OpenAI’s gross margin reportedly fell from 40 percent to about 33 percent in 2025. This is while its inference bill quadrupled. Upstream, Nvidia reported a 71 percent gross margin and $193.7 billion in data-center revenue in its latest fiscal year. ASML offers a physical version of this strategy. It is the only company capable of producing EUV lithography machines. The newest cost roughly $400 million each, and the company earned a 53 percent gross margin in 2025. ASML stays rich because competitors cannot simply copy what it makes.

Chips embody a lot of intelligence, but once produced they are rival physical goods. And those are not doing too shabby in a world flooded with intelligence. And those aren’t doing too shabby in a world flooded with intelligence.

Energy has the same structure. It remains scarce for the foreseeable future. The International Energy Agency estimates that data centers consumed about 415 terawatt-hours of electricity in 2024, roughly 1.5 percent of global electricity use. It projects a “demand” of approximately 945 terawatt-hours by 2030. (I can’t seem to tell what price this is assuming for the quantity demanded, which must depend on price, but let’s not fight that battle.)

Again, this is Ricardo in a nutshell. The rent goes to the scarce complement. Yes, it’s not conclusive but it suggests the value is spread around, not just to the frontier models. It seems related inputs are doing well.

Bottlenecks have clocks

That does not mean chips and electricity will remain scarce forever. High returns attract new fabrication plants, generators, transmission lines, and data centers. It means they are among the clearest bottlenecks today. Markets respond. I think we’ve mentioned that in this newsletter.

So any scarcity has a clock on it. It depends on what is scarce now and how fast it can respond. It depends on elasticities, as always. But those scarcities are responding. We can see that in the data center build out, which requires lots of these inputs into compute which is then an input into intelligence.

No all digital is replicable

Under open and competitive supply, the pure return to a copyable model is pushed toward zero. The service still has a cost because inference consumes scarce compute.

But the recipe itself earns no rent. So we have a distinction between the recipes return and the return to everyone in the supply chain.

Every company wants to avoid that, so they look to ways to exclude. If you prevent the recipe from being copied, you turn a nonrival capability into something rivalrous, which can become a priced asset. All of the physical production has this, ASML or chips, for example. offers a physical version of this strategy. The labs are trying to construct an informational equivalent. Can they have unique intelligence that no one else has? One possible way to get that is to input unique data. So you get that Reddit licensed its archive under contracts worth $203 million. And law may respond. In Thomson Reuters v. Ross, a federal judge rejected an AI startup’s fair-use defense for training on Westlaw material. Copyright and contract can manufacture scarcity around information that would otherwise be cheap to reproduce. So that changes relative scarcity.

This is basically the model in Cockburn, Henderson, and Stern. As algorithms diffuse, the return shifts toward the complementary assets that remain protected. OpenAI understands the distinction. As models improve, it argues, “the limiting factor shifts from intelligence to usability.”

What happens to workers?

So far, we haven’t really talked about workers. But I think we can easily extend the model. What parts of the job become replicable? What is exclusive?

We have to be careful about what the worker is selling. A worker sells an output, whether it be a diagnosis, a legal brief, a repaired machine, a lesson, a design, a relationship, a judgment someone trusts enough to act on. She does not sell “tasks” in isolation.

Let’s think about this in production function terms. To produce that output, she combines inputs. Some are personal, like her knowledge, judgment, taste, reputation, relationships, attention, and time. Some are external and bought over the market, like software, equipment, institutional authority, licenses, assistants, referrals, customers, and the firm or platform that connects her to demand.

Garicano, Li, and Wu work through this in more detail. Given markets price jobs, not tasks. A task only comes loose from the worker/job when it can be cleanly separated from everything bundled around it.

AI enters different places. It becomes an input into the worker’s own production. It makes research cheaper, drafting faster, scheduling easier, or diagnosis more accurate. In that case, AI feeds her work. Inputs become cheaper. If the rest of what she supplies remains scarce, her return can rise. She didn’t do anything more, but her productivity increased, and she captured (at least some of ) the gains.

At the same time, all of her competitors (and new competitors) are also using AI. Maybe some of that is much more “pure AI” and we can think of the AI as the direct competitors. The knowledge that you had is no longer scarce.

So we have a horse race between AI as an input and AI as a competitor. It’s not obvious which of those forces wins.

A decade after Geoffrey Hinton said in 2016 that we should stop training radiologists, the number of radiologists has risen by about 17 percent. AI became better at reading scans, but reading scans was only one input into the radiologist’s output. So far, a scan cannot be cleanly separated from the clinical judgment, the responsibility for the call, or the conversation with the surgeon. So AI did not reproduce the entire bundle of work performed by a radiologist.

Evidence from Upwork already points in this direction. Tracking 49,610 freelancers across more than two million contracts, Auyon Siddiq and Niuniu Zhang find that after ChatGPT, buyers in the most AI-exposed categories placed less weight on credentials and past performance and more weight on price. Demand shifted toward cheaper workers. As the work became easier to standardize, workers began to look more interchangeable. Interchangeable suppliers compete on price, not reputation. You could imagine that same thing happening even for relational goods.

Who owns the human premium?

Alex Imas approaches the same question from the demand side in his essay “What will be scarce?”

As standardized goods become cheaper, richer consumers may spend more on provenance, exclusivity, authenticity, and human connection. In one experiment, Imas and Graelin Mandel found that exclusivity raised the value of human-made art by 44 percent, compared with 21 percent for AI-made art. I think this demand-side argument makes sense.

There are two ways to extend it. First, “human” is too broad a category. A human premium can rise in some markets while falling for particular workers in others. The supply side still matters. If AI lets many more people provide an adequate human service, the premium attached to any one provider can fall, even though the service remains human.

Consider a relationship advisor. Her warmth and personal manner may be genuinely unique. But much of the service around that relationship, such as advice, scripts, interpretation, suggested replies, and emotional framing, can be separated from her and reproduced by AI. If AI cheapens her research and preparation, it feeds her work. She can spend more time on the relationship itself. If AI lets a thousand adequate advisors offer similar scripts, similar advice, and similar reassurance to the same clients, it floods her market. Her humanity remains real. The market is just as “human” as before, but the specific service she sells today will become less scarce.

This cannot be the aggregate story, since the supply of humans is basically fixed. The distributional story can still be true. If AI lets many more people provide an adequate human service in a particular market, the premium attached to any one provider can fall even though the service remains human.

This is the same ownership question we have been talking about all along. For workers, ownership often shows up as portability. Can the scarce human input walk away? Can it reach customers without the firm, platform, brand, license, or copyright wrapped around it? If yes, the worker has a claim on the premium. If no, the premium can show up somewhere else.

This isn’t a new problem. Things people think are separable aren’t always.

In the 1920s, a young Walt Disney built a studio around Oswald the Lucky Rabbit, only to discover that the character’s copyright belonged to his distributor. The distributor made the obvious move: keep the character, hire away Disney’s animators, and remove the man who appeared to supply only ideas. The distributor seemed to own every copyable part of the production process: the character and the hands that drew it.

But he did not own Disney’s sense of what was funny. That scarce input could walk out the door. It did, and as far as I can tell, had some success.

The rule

So what’s gonna happen? No idea. But I think we have a framework to think through it.

AI will create enormous value by making intelligence, and the diffusion happens by making it easier to copy. The returns go to whatever remains scarce once the intelligence is cheap. None of this is new. Right now, we see that the scarce thing is physical, like chips, megawatts, and land. Sometimes it is generated or manufactured, like data, distribution, a copyright, a brand. And sometimes it is a person whose judgment or relationships cannot be separated from the service she sells.

When the scarce thing is a person, there is a further twist worth thinking about. Does the intelligence AI is copying feeds her work or floods her market? Copied intelligence that becomes her input raises her return. Copied intelligence that creates her competitors lowers it, even though her own intelligence was never touched.

It is not enough to say intelligence goes brrr, so money goes brrr. Scarcity depends on ownership. Scarcity depends on property rights. What is AI making abundant, what remains scarce, and who owns it? That is where the returns to AI go.

This doesn’t always make me many friends. I occasionally make arguments that defend particular policies that my friends and colleagues dislike. They roll their eyes. They suggest I give too much credit to politicians or the political process or voters or … you get the point. In fairness to my friends and colleagues, their skepticism is often rooted in a basic fact of life. Because most people agree that national defense is an important function of the state, politicians love to invoke national defense in arguments that support their favorite pet project. Often, these claims are dubious.

Nonetheless, that doesn’t mean that all claims are dubious. In fact, one benefit of studying defense-based policies is that there can be clear evidence of failure. If a policy is particularly important for defense and one country chooses not to enact that policy, one would expect to observe military defeats and other defense-related setbacks.

Because of my interest in defense-related policies, I’ve been asked quite a bit recently about what I think of defense-related AI policies. To be honest, I haven’t fully formed an opinion on policies related to AI and defense. These discussions often make it difficult to interpret whether there is a legitimate issue or whether politicians are up to their old tricks of using dubious justifications to do what they want.

However, a recent AI-related policy has given me the opportunity to think through some of these issues. In this week’s post, I’m going to discuss this recent policy action and how to think about it in terms of policy and national defense.

Anthropic Can’t Let People Use Their Model

In June, Anthropic (the company behind Claude) issued a statement saying that the U.S. government had issued an export control on its Fable 5 and Mythos 5 models. This export control restricted access to all foreign users, including foreign nationals, from using these models. It is difficult to think about an export control for an AI model. Partly for that reason, Anthropic announced that it had decided to restrict access to these models for everyone because it seemed like the only way to comply with the export control. This week, they announced that the controls have been lifted and people can use the Fable 5 model. The Mythos 5 model is still somewhat limited.

The decision of the U.S. government and Anthropic sparked my interest. The reason is that not too long ago, Anthropic and the Trump administration had a very public spat about the technology and the government’s use thereof. This time around, there was no public spat, only an announcement.

It took a few days to get a sense of what was going on. However, according to reports, the head of the National Security Agency (NSA) informed the Senate Intelligence Committee that the Mythos model had broken classified systems within a matter of hours.

I have no idea how I, your humble newsletter writer, can independently verify the validity of these claims. In fact, I have some reason to doubt the specific claims. Suppose they were true. Would the NSA want to broadcast this to the world? That seems doubtful. Nonetheless, even if the specific claims isn’t true, that doesn’t mean that wasn’t some type of serious concern or issue.

With that in mind, let’s just assume that these claims are correct. This type of issue, or something like it, certainly sounds like a serious (not dubious) problem for national defense. A cybersecurity risk to the government’s intelligence operations (and who knows what else) certainly seems like something that warrants some type of action on the part of the government. The action taken by the government is a de facto ban on the use of a particular model (even if they are temporary, like this one).

How should we think about this?

The Economic Incentives, and Relating this Back to Other Policies

When we think about AI models in the context of current and recent events, AI models present both benefits and potential costs pertaining to national defense. AI is useful for drones. Unmanned sea-crafts are in development, if not already in use. AI can help military leaders assess a great deal of information and use it to analyze their options. It also aids with both the collection and use of intelligence. At the same time, advancements in AI that can help with things like cybersecurity can also potentially create threats to cybersecurity. A model capable of identifying your weaknesses might also reveal the key to exploiting those weaknesses.

If we accept the reports of what happened and we assume that this will be the way in which the government handles such problems in the future, we can start to think a bit about policy.

Suppose that any time a new AI model is introduced, there is a chance that the government shuts it down and restricts access to the public. This creates relatively lumpy returns to the development of new models and potentially costs firms a lot of money.

The training of new models requires the use of tens of thousands of GPUs. Those GPUs have to be produced by someone. The GPUs have to be located somewhere, which requires overhead and the access to and expenditures on the power necessary to run the machines.

Periodic shutdowns of models make the rate of return on each model lumpy. Ex ante, such restrictions lower the expected rate of return on a new model. Lower expected returns tend to result in reductions in investment. A decline in investment means fewer GPUs, fewer data centers, a decline in the production of computer memory, and less development of power infrastructure.

The restriction on output (in this case, the export control) makes it less likely than an existing model can harm national security, but also leads to a reduction in capital investment and therefore a slowdown the development of new models. Even accepting that the export control is the only appropriate policy, this still leads to an underinvestment in capital related to AI that is suboptimal. The reason for that is that the slowdown in the development of future AI models means a slowdown not only in AI-related threats to national security, but also to a slowdown in all of the benefits that would accrue to the national security apparatus from continued development of new models. In some sense, a policy that simply limits access to models creates an immediate benefit for the government, but a longer term cost.

The appropriate thing for the government to do (again, if it is intent on using export control-induced bans) is to couple the de facto ban with subsidies to AI infrastructure. Note that it is not appropriate to subsidize AI output, but rather to subsidize capital used in the production of new AI models. This would require subsidies for power-related infrastructure, data centers, and the production of memory and GPUs used in the training of models. The optimal policy resembles a bargain. The government offers subsidies to build up the infrastructure necessary to develop frontier AI models in exchange for limiting access to the models when national security threats arise. The policy thus mitigates the longer term costs of underinvestment that result from short-term bans on new models.

This sort of systematic policy would not be unprecedented. Earl Thompson once applied similar logic to rationalize other defense-related subsidies. In particular, Thompson pointed out that, during times of war, states usually adopt some form of price controls and that peacetime subsidies to particular industries could be rationalized by the cost of those wartime controls. He showed that wartime price controls result in intertemporal distortions in prices similar to a tax on capital and therefore result in an underinvestment in capital. Like the AI example, this theory argues that although regulation occurs at the level of output, the subsidies should be for capital investment.

When he looked at the data, that is precisely what he found in the postwar era. Wartime price controls on beef and dairy products did not result in peacetime subsidies for things like beef or milk, but rather for cows. The G.I. Bill can be rationalized as a subsidy for investments in human capital due to the tax on human capital created by conscription. In addition, he even found that where wartime controls were present and peacetime subsidies were absent, this could easily be explained by other characteristics of the market. For example, coffee was subject to price controls during WWII, but there was no peacetime subsidy. Thompson showed that there was no need. International coffee production was produced by a cartel. Thus, the high return to coffee during peacetime was enough to offset the low return during wartime and thus didn’t necessitate a subsidy. Overall, this general pattern was quite extensive.

There is already evidence of the U.S. government doing something similar for AI. The U.S. has taken steps to subsidize things like memory and GPUs through legislation like the CHIPS Act. However, despite executive orders from both the Biden and Trump administrations, there hasn’t been as much done to directly subsidize the build out the broader AI infrastructure.

Concluding Thoughts

For some time, there have been vague references to national defense and AI models. Recently, we got what I would consider the most concrete case of a threat to defense resulting from the development of new AI models. If the claims that have been reported are true (or even if they are strategic diversions designed to hide the true threats), then one can understand why the U.S. government might step in to try to prevent the use of a particular model. Nonetheless, there are broader implications associated with de facto bans (even temporary ones) on the use of particular AI models. Those implications include costs to the government in the form of foregone improvements in national defense on other margins.

What I’ve argued here is that de facto bans are likely to lead to underinvestment in AI-related capital infrastructure if such bans are standalone policies. Thus, if the government is going to use these de facto bans, they will need to couple the option for de facto bans with subsidies for AI-related capital. Otherwise, such bans are likely to produce short-term gains and long-term costs.

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If you ever come across an economic argument that seems to make sense but goes completely against conventional economic wisdom, it’s probably “reasoning from an accounting identity.” Run in the other direction. Accounting does not explain anything. That’s why we have price theory.

Take the following example. We all know that “Growth in real GDP depends on only four factors: consumption, government spending, business investment and net exports (the difference between exports and imports).” That comes from the definition of GDP as GDP = C + I + G + NX, so any growth in GDP comes from growth in one of those factors.

From there, one may mistakenly believe that, as Peter Navarro suggested, “Reducing a trade deficit through tough, smart negotiations is a way to increase net exports — and boost the rate of economic growth.” After all, there’s an equal sign there! Y EQUALS C + I + G + NX. It’s just math losers. QED.

No. Wrong.

Just as we never reason from a price change, we need to never reason from an accounting identity. My income equals my savings plus my consumption: I = S + C. But we would never say that if I spend more money, that will cause my income to rise.

It all depends on what caused the change. In my income example, more spending could cause me to work more to cover those expenses. Or I could save less. It depends. In the GDP example, we need to know what caused the change in net exports (or any other part of the equation). We need to be explicit about what actually caused the change in net exports. Exports don’t simply fall from the sky, and move up and down based on our wishes. We need to know the causes. There is an added complication in this example since the reason we subtract imports from GDP is that they are already part of consumption (C) and shouldn't have been since imports are not domestic production.

Did we discover a new fracking technology? Net exports will likely go up, as will GDP. Did we impose tariffs on imports from China? Those are likely inputs into other production processes and, therefore, lower “US gross domestic production,” aka GDP. Therefore, the tariffs cause an increase in net exports, but they cause a decrease in GDP. The reason is that the first mover— the cause of tariffs— pushed up NX, but it also at the same time pushed down something else, such as the C in C+I+G+NX. To say GDP moves up if net exports move up requires an assumption the C, I, and G are not changing.

For basically any macroeconomic example we are interested in, holding C, I, and G fixed is a ridiculous assumption. We need to think about how all of them are affected by a certain cause. Luckily, as I’ve explained before, economics helps us establish causation.

Economics—whether theoretical or empirical—helps us establish causal claims about the world. If X changes, then Y changes. If you prefer the framing of counterfactuals: If X had been different, then Y would have been different.

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3 years ago · 34 likes · 15 comments · Brian Albrecht

Yes, the accounting identity must hold, and we need to keep track of that, but it tells us nothing about causation.

Luckily, price theory allows us to trace out the myriad of effects that are caused by a single policy change. We must constantly ask, and then what? Suppose China (for political reasons) decides it doesn’t want its companies to trade with US companies and stops them from doing so. Now from here, we can try to trace out different implications. Chinese firms stop producing goods for exports. And then what? US consumers have fewer goods to consume (C goes down). And then what? They substitute partially to US manufacturing (C partially goes up). And then what? Workers produce fewer services (C goes down). And so on.

That’s just following the goods being traded. The policy change also means that Chinese firms have less demand for dollars. And then what happens? They weren't using those for purchasing US goods, so they must have invested in US debt or equities. And so on.

Every semester I make sure to include “Never Reason from an Accounting Identity” in my courses. The reason I’m mentioning trade policy today is that there was a recent “handbook for conservative policymakers” released by American Compass. One of their policy proposals was to eliminate the trade deficit. To accomplish this, we should:

Establish a uniform Global Tariff on all imports, set initially at 10% and adjusted automatically each year based on the trade deficit. After any year when the trade deficit has persisted, the tariff would increase by five percentage points for the following year. After any year when trade is in balance or surplus, the tariff would decline by five points the following year.

The essays defending this general position (although not necessarily the exact policy proposal) are just a series of reasoning from accounting. There is no economic content. As one essay put it,

For decades, the implicit and explicit subsidies to manufacturers that have driven surpluses in countries like China and Germany have caused global manufacturing to migrate from deficit countries to surplus countries, and from none more so than the largest deficit country by far, the United States.

The argument above supposes that there exist countries out there in the ether called surplus countries, and manufacturing moves to them. In reality, when you’re not just playing with definitions but looking at causation, Chinese subsidies increase manufacturing which increases the trade surplus (in some cases). The causal arrows point from subsidy to manufacturing and trade surplus, not from surplus to manufacturing.

The essay goes on to say that “American trade deficits force Americans to choose between higher unemployment, more household debt, or greater fiscal deficits.” Ignore the unemployment red-herring. The trade deficit doesn’t force (or cause) household debt or fiscal deficits. Something else causes both trade deficits and fiscal deficits to go up together.

For example, if people really want US treasury debt because there is a global financial crisis and US treasuries are the safest asset available, they will buy up more debt, driving down interest rates, and increasing fiscal deficits. They will want dollars to buy that debt, so they will need to sell goods to get dollars. The unique position of US treasuries in the global financial market causes both fiscal and trade deficits. They don’t cause each other. It’s a bit more complicated since we need economics to trace out causation and can’t just look at an equation, but it is vital.

The purpose of this newsletter is not to trace out all the implications of a trade war. The purpose is to point out that we cannot simply reason from the accounting identity. We need to take seriously all of the feedback.

Profits Do Not Cause Inflation

Before we mock the trade deficit people too much (who, in this case, are “conservatives”), there has been a more prominent example in recent years that has generally come from people on the left. It is the idea that profits cause inflation.

After all, any revenue PQ = Costs + Profits. So P = Costs/Q + Profits/Q. If inflation means that P goes up, it must be “caused” by costs or profits.

No, again. Stop it. This is like saying consumption causes income.

Instead, we need to ask, what changed? What actually caused things to be different? Did the Fed print a bunch of money? Then consumer spending will go up, which will drive up prices, costs, and profits. It may be that profits rose more than costs but that’s not a causal relationship. Or did prices rise because of an oil shock? Costs will probably rise, along with prices, but it could be that profits fall enough to offset that. We can do the decomposition, and that’s fun, but let’s not mistake that for economic analysis.

Instead, we need to use economic reasoning to think through what is causing profits and prices to move, sometimes in the same direction and sometimes in different directions.

Economic ignorance is omnipresent. Price theory is the answer. Economic Forces is the answer.

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The way that externalities are typically taught at the introductory level is as follows. A negative externality is a cost paid by a third party to normal market activity that is not reflected in the price of the good being traded. As a result, the social cost is greater than the private cost. Because this cost is not paid by market participants, no one internalizes the cost and the resulting equilibrium quantity is inefficiently high. The standard textbook argument is that the government should levy a tax on this activity equal to the marginal external cost (the difference between the marginal social cost and the marginal private cost). This tax is referred to as a Pigouvian tax. By doing so, this forces market participants to internalize the cost. The resulting equilibrium quantity in the market is the socially efficient quantity.

But is this policy really optimal? If so, in what sense? If not, why not?

Setting the Stage

Let’s imagine that there are 3 distinct groups. There are consumers, producers, and those who are not in the market at all but who suffer the external costs. Let’s assume that all costs are variable and the cost of production and the external cost are both linear in quantity. This implies that both the marginal cost of production and the marginal external cost are constant and equal to the average cost of production and the average external cost, respectively. The market is competitive and the supply curve is horizontal.

As any student of introductory economics knows, a tax drives a wedge between the price that buyers pay and the price that sellers receive. Since the supply curve is horizontal, the entire area to the left of the demand curve due to this tax wedge is a reduction in consumer surplus. Some of this area goes to the government in the form of tax revenue. The remainder is the excess burden, or deadweight loss, of the tax.

If the size of the tax is equal to the marginal external cost, this does in fact reduce the quantity traded to the amount that the market would choose if all the costs were internalized. This is often referred to as the efficient quantity.

Nonetheless, it is important to note that the consumer is worse off. This is true even if the tax revenue is used to make a lump sum transfer to the consumers, or otherwise paid to them in a way that doesn’t affect their choice in the relevant market we are discussing. The reason for this is that the total reduction in the consumer surplus is greater than the tax revenue.

It follows that the Pigouvian tax is not Pareto improving since consumers are worse off.

An Alternative Framework

Pigou wasn’t the only economist famously known for thinking about externalities. Ronald Coase is also famous for thinking about the problems of social cost. Coase emphasized property rights. These external costs we’ve been referencing often follow from an incomplete definition of property rights. For example, a frequently used example of a negative externality is a factory that pollutes a river upstream from a fishery. If either the fishery or the factory are assigned the property rights to the river, then the two parties could negotiate. For example, if the fishery is assigned property rights to the river, the fishery could prevent the factory from polluting the river unless the factory offers sufficient compensation to the fishery. That compensation forces the factory to internalize the cost of its pollution. Alternatively, if the property rights to the river are assigned to the factory, the fishery could pay the factory to limit its production in order reduce pollution.

Ideally, it would be great to be able to compare Pigouvian taxes to Coaseian logic within one coherent framework. Fortunately, the new edition of Chicago Price Theory contains an example that does just that. It requires thinking about demand a bit differently.

Consider the following example. Suppose that we have a demand curve as depicted in the figure below. The demand curve shows the typical negative relationship between price and quantity demanded. A tool that Kevin Murphy likes to use is to draw indifference curves in the price and quantity space. But what would an indifference curve look like in this context? As depicted in the figure below, for the price equal to 1, one can draw and indifference curve that is tangent to a horizontal line consistent with the price. Near this price, the indifference curve is relatively flat. This is because consumers are pretty indifferent about the quantity around the given price. They’ll take a little higher or a little lower quantity at a slightly lower price and be just as content as they are with the quantity demanded associated with a price of 1. The indifference curve is pretty steep to the left of the demand curve. This is because as the quantity the consumer gets to consumer declines, the consumer has to be compensated with a lower and lower price to keep them just as well off. To the right of the demand curve, the indifference curve is relatively flat because the consumer doesn’t need a much lower price to consume a little more.

Note that this also implies that lower indifference curves are preferred by consumers on this figure. This is because for a given quantity, the consumer prefers a lower price.

Now, let’s suppose that marginal private cost is 1 and the marginal social cost is

The figure below shows that quantity that would be chosen in the absence of a Pigouvian tax would indeed be q_u. A Pigouvian tax equal to the marginal external cost would result in the equilibrium quantity, q_e. This is the quantity demanded when consumers must pay the marginal social cost and is therefore the efficient quantity of output in the market.

Given what we know about the indifference curves plotted in price and quantity space, it is straightforward to see from this second graph that a Pigouvian tax makes the consumer worse off. This is a different way of illustrating the point I discussed above. A Pigouvian tax can make sure that the equilibrium quantity of output is efficient, but the tax isn’t Pareto optimal. In order for the tax to be Pareto improving, it would need to make some people better off without making others worse off. However, it clearly makes consumers worse off.

This begs the question: Is there a way to make sure that the efficient quantity is the equilibrium outcome while simultaneously being a Pareto improvement?

In order to think about this, we need to think about the welfare of the other people in the market, producers and those outside the market who suffer the costs of the externality. For simplicity, let’s lump both these groups together. In addition, let’s think of this group’s welfare as equivalent to the total surplus to the group. We can define the “other surplus” that goes to others in the market as follows:

Where the total surplus is the combined surplus to producers minus the external cost to those outside the market. To think about how this can be illustrated on our graph, consider the following example. Suppose that the marginal external cost is 1/2. It is possible to think about combinations of p and q that provide the same surplus. To see, why let’s consider some examples. Imagine that when the p = 1, q_u = 48. This implies the surplus is -24. Now, consider that if the price is 1/2, the quantity that provides the same amount of surplus is 24. If the price is 3/10, the quantity that yields the same surplus is 20. If the price is 0, the quantity that produces the same surplus is 16. Thus, one can illustrate the “other surplus” in the figure as the curve OC.

Now, we are prepared to think in Coaseian terms. Note that for any given quantity, the “other surplus” is increasing in p. It follows that any point above the line denoted OC, the combined group of producers and those who suffer the cost of the externality are better off. At the same time, we know that any point that lies below U_1 makes consumers better off. It must therefore be true that the area between the indifference curve labeled U_1 and the OC curve represents potential gains from trade between the consumers and the others.

The Pareto optimum solution is achieved at the point at which the others’ surplus curve is tangent to the consumer’s indifference curve. Consider the following example. For the OC curve as it is drawn, a lower indifference curve tangent to the OC curve would make consumers better office without making the others worse off. The tangency point between these two curves is a Pareto optimal outcome because in comparison to that tangency point, there is no other point that can make one group better off without making the other group worse off.

It is also important to note from the graph that there are many points on the graph between U_1 and OC associated with the efficient quantity. This not only tells us that the efficient outcome is possible, but that we can get to the efficient outcome without the Pigouvian tax and in a way that consumers would prefer to the Pigouvian tax.

Of course, there are a couple of things to note here. The efficient outcome is not possible if the marginal price is set below 1. If the marginal price is uniform to all consumers and set below 1, the quantity demanded would be even greater than q_u. Implementing the efficient equilibrium outcome without Pigouvian taxation would require that the marginal price is greater than 1 and the average price is less than 1. This entails some non-linear pricing scheme.

To turn this back to Coase, there might be some voluntary solution via bargaining that implements a non-linear pricing scheme that is Pareto improving. This would likely require setting up some type of organization that can impose this type of non-linear pricing in order to generate a Pareto-improving outcome.

Whether or not the efficient quantity can be achieved and how close such an organization can get to the efficient quantity depends on cooperation costs. As Coase alluded to with regards to transaction costs more broadly, coordination costs limit the ability to achieve the efficient outcome.

Some Concluding Thoughts

The standard argument that negative externalities can dealt with by implementing a Pigouvian tax ignore some of the welfare implications of the tax. Such taxes might produce the socially efficient quantity, but these taxes are not Pareto improving.

In fact, externalities are substantially more complicated if we take Pareto optimality seriously. The Coaseian logic that bargaining can produce a better outcome is illustrated by thinking about the total surplus from trade of all parties involved, including those who bear the external costs. Furthermore, as Coase himself emphasized, the ability to reach the efficient outcome through a voluntary solution is limited by transaction (in this case, coordination) costs.

This is just one more example of why we spend so much time on externalities here at Economic Forces. The average introductory textbook often makes externalities seem straightforward and the policy solutions seem easy. When it comes to providing caveats to the Pigouvian policy solution, textbooks might acknowledge the difficulty of estimating the true marginal external cost. Some textbooks might even mention that there can be a tension between the Pigouvian tax that implements the efficient quantity of output and the Pigouvian tax that generates the most revenue. Nonetheless, this is only part of the story. A bigger limitation is that Pigouvian taxation isn’t Pareto-improving.

If you want the efficient quantity of output and you want to make sure there is a Pareto improvement, a Pigouvian tax simply won’t do.

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One idea is to tax computer processing capabilities, sometimes called a “compute tax.” Andrew Yang is pushing it, so you know it must be serious. John Arnold captured the general sentiment on Twitter:

As Anton Korinek told the WSJ: “Half a year ago, it was something you would hear about only in very select circles. It really has become much more mainstream in the last three months.” That piece has a lot of great quote, so I will keep comping back to it. It also has Nobel laureate Simon Johnson saying a compute tax is a “sensible policy lever to slow down automation.”

Is it sensible?

We agree that when you tax something, you get less of it. That would slow automation down. “Sensible” is not the word I would use, though. If you just hate AI and want less of it, then maybe Simon Johnson is right, and a compute tax is sensible.

A compute tax is about as bad as a tax can be, by the standard benchmarks used in public finance. If you care about things like increasing output, total surplus, minimizing deadweight loss, those types of things, stay away from this.

This newsletter explains why. In many ways, this will mirror what we’ve discussed before about tariffs, so I’m going to keep bringing them up in this piece. I know. Sorry. But, as Herbert Spencer said, “Only by varied iteration can alien conceptions be forced on reluctant minds.”

What is a compute tax?

A compute tax is a levy on computational resources. Think GPU hours, processing power, data center electricity, or some similar proxy for AI work. But “compute tax” bundles two quite different proposals, and the difference matters.

Korinek and Lockwood have written the most careful framework on this in a Brookings working paper on public finance in the age of AI. They draw the key distinction: there is a stock version and a flow version.

A stock tax is if you tax the GPUs, the data centers, the training clusters. This is basically a form of capital taxation (which we will talk a lot about below). My reading is that this is the most commonly proposed tax. The idea of banning data centers is an extreme tax on the stock of compute.

In principle, you could also tax the output. This is a tax on the flows, through the tokens generated, the images produced, the AI services consumed. Korinek and Lockwood are more sympathetic to this form of taxation. But once you add the B2B exemption that they suggest, you’ve described a regular sales tax or VAT on AI consumer services. I don’t think we should call that a compute tax.

Taxing the wrong thing

There are a few main results in optimal taxation that show up again and again. People can quibble over the exact ordering but I’d say the most important comes from Diamond and Mirrlees.

The takeaway is don’t tax intermediate goods. Keep the production side of the economy as efficient as possible, then redistribute from the output. That’s why it’s called Diamond-Mirrlees production efficiency.

Compute (again, as a stand-in for computer processing) is huge now but going to be the intermediate input of the modern economy. It goes into drug discovery, weather forecasting, fraud detection, medical imaging, logistics, customer service, and, yes, generating cat pictures. When you tax compute, you raise the cost of every single one of those downstream applications. You shrink the economic pie before anyone gets a slice.

Korinek and Lockwood have a great line about how a compute tax is like “taxing steel during the industrial revolution.” You don’t make people richer by making steel expensive.

This was also the core problem with tariffs on intermediate goods. As I’ve explained before, a 10% tariff on imported car parts compounds through the supply chain and becomes a 30% effective tax on the finished car.

The supply chain aspect may not be as extreme as if you’d say tax something even further up the supply chain, like semiconductors, but a compute tax compounds in the same way. Tax the GPU hours that train an AI model, and the cost cascades through every product that uses the model. The drug company, the weather service, the fraud detection startup, the accounting firm, they all pay more. Pascual Restrepo told the WSJ: “Why do you want to increase the cost of all of that?”

This feeds directly into another one of the main results in optimal taxation: don’t tax capital. The original Chamley-Judd result says that you shouldn’t tax capital. The original result said “in the long run,” but, really, even in the short run.

Data centers are capital. Training clusters are capital. The entire AI infrastructure buildout is a massive capital investment.

A compute tax discourages precisely the investments that make AI cheaper and more widely available over time. This is the equivalent of taxing ladders to protect the people climbing trees with their bare hands. You don’t help the bare-handed climbers by making ladders expensive. You help them by making ladders cheap enough that everyone can afford one. Then, if you want to redistribute, tax the coconuts.

It also doesn’t make sense to tax capital from a redistribution point of view, as John Arnold seems to worry about in his tweet. I made this exact argument about AI and capital taxation in my response to Trammell and Patel. The features of AI that people worry about (easy substitution between capital and labor, mobile capital, self-replicating infrastructure) are exactly the features that make capital taxation counterproductive. With elastic capital supply, a capital tax doesn’t stick to capital owners. It falls on workers through lower wages. The more mobile and substitutable capital becomes, the worse taxing it gets for the workers you’re trying to help.

The optimal rate is below zero

So far, Diamond-Mirrlees and Chamley-Judd say a compute tax is a bad tax at any rate. It’s not obvious that we shouldn’t even go further. Maybe we should subsidize compute?

The Pigovian principle (1920) says the optimal corrective tax on an activity equals its marginal external damage. When an activity generates negative externalities (pollution, congestion), tax it at the marginal external cost. When an activity generates positive externalities, the optimal “tax” is negative. A subsidy.

It’s plausible that compute has positive externalities from at least two distinct sources.

There’s a learning-by-doing element. If we’re training models and other people are learning from that process, the industry supply curve can be downward sloping, so my production actually helps your production. The firm producing the millionth GPU does not capture the full social value of pushing the technology further down that curve. Everyone who uses cheaper GPUs in the future benefits from that producer’s accumulated experience without paying for it. That’s an externality. We may want to push producers down the supply curve to lower everyone’s cost

There seems to be something to that when we see GPU performance improving, but it could be this is almost all internal to NVIDIA so its not ’s not an externality.

There’s a slightly different angle around general-purpose technology (GPT) spillovers. Bresnahan and Trajtenberg (1995) defined a GPT as something that is pervasive across sectors, improves over time, and has the capacity to spawn complementary innovations. So things like steam, electricity, semiconductors, and now AI compute all qualify. GPTs generate two-sided externalities, where users don’t capture the value of pushing the technology down its cost curve, and producers don’t capture the value of enabling downstream innovators they never anticipated. So it’s like learning-by-doing combined with when people use your good, you don’t get all the benefit.

In practice, I don’t think we have a good enough sense of exactly how to subsidize it to make it work. I’m skeptical of externality-based arguments. If we are going to start saying externalities for anything we want, that becomes a huge mess of just asserting AI is wonderful or going to kill us.

I’m saying it’s not obvious, from a Pigovian perspective, that we want to tax.

You can’t even define it

Above is all in the land of standard economic theory. It’s at a level of abstraction. But let’s take practice a bit more seriously.

Now suppose the legislature sits down to write the statute. What counts as taxable compute?

It seems like we definitely want to count a GPU training a large language model. How are we going to track that? What about a GPU rendering a video game? When my laptop is running code or my phone is autocorrecting, is that compute? Probably not. What if it is happening on the cloud through data centers?

Tax practitioners flagged this same problem with robot taxes (also a dumb idea for the same reasons). Are we going to tax ATMs?

Any definition will be either so narrow that firms route around it by relabeling their compute, or so broad it taxes everything with a processor. Both outcomes are terrible.

Let’s not forget about all those lovely exemptions. Will where get to the point where we have that “U.S. data” (undefined in statute) must be “processed” (also undefined) on “U.S.-made motherboards” (also undefined). Will medical AI get a carve-out? Climate modeling? National security applications? Each exemption is a lobbying target.

A broad consumption tax doesn’t have this problem. It taxes the final transaction. It doesn’t need to know whether an AI or a human produced the good. It doesn’t need to distinguish between “AI compute” and “regular compute.” It’s technology-neutral by design.

The revenue isn’t there

If you want to just slow down automation like Johnson, that’s one thing. But if you want revenue, say for redistributing to labor (ignoring the problems above), that’s another thing. Here’s the part that compute tax advocates don’t want to talk about. Korinek, who takes the AI displacement concern seriously, points out that the numbers don’t work, and a compute tax “would raise a little bit of money but would not really make a significant impact.”

U.S. compute spending is large in absolute terms but small relative to GDP. A tax high enough to fund meaningful UBI payments would need astronomical rates, generating enormous distortions. I’ve worked through the math before. When a tax base is narrow, you need sky-high rates to raise real revenue, and the deadweight loss grows with the square of the rate. That’s bad enough for tariffs, where imports are about 10% of consumption. For compute, the base is even smaller.

And let’s not forget those pesky elasticities, always causing problems for central planners. Compute infrastructure can relocate (unlike land, unlike most labor), a unilateral U.S. compute tax pushes training runs to Canada, the Gulf states, or wherever electricity is cheap and taxes are lower.

So what are we to do?

It’s not like we are starting from some optimal tax code. So can we fix it? Erik Brynjolfsson argues the current U.S. tax code already implicitly penalizes labor relative to machines. A company with 1,000 workers pays more in total taxes, including payroll taxes, than a company making the same revenue with 1,000 machines. I’m not sure that’s even true—and to the extent it is, we may want it, since capital taxes should be zero for the reasons above. If you still think the system tilts too far toward automation, the fix is to cut the tax on labor, not to add one to a particular form of capital.

Perhaps it is different now, but when I lived in Michigan, there was a combination of two laws that really confused me. On the one hand, it was legal to buy and sell fireworks. On the other hand, it was illegal to set off fireworks. Violations were subject to a fine. (In fairness, this isn’t completely true. There were certain days in which one was exempt from fines. I believe that New Year’s, Memorial Day, and Independence Day celebrations were exempt from fines.) It seemed contradictory to allow people to buy and sell fireworks, but that lighting off the fireworks on most days of the year resulted in a fine. Why allow people to buy something and then fine them when they consume it?

Another thing that I’ve long found perplexing is the parking situation on university campuses. I’ve spent a lot of years on university campuses and the one thing these campuses have in common is that there never seems to be enough parking to satisfy demand. This is such a problem that universities often have meetings about how to solve the parking problem that involve discussions about whether to build a new parking garage, but rarely about price. One would think if the objective was to solve an excess demand problem, the obvious solution would be to increase the price. Yet, somehow this never comes up in the meetings.

What is going on here? My conjecture is that both of these examples represent applications of something like a two-part tariff.

In price theory, a two-part tariff is a type of price discrimination. This particular type of price discrimination refers to a scenario in which the price of a good includes two parts: a lump-sum amount and a per-unit price. This type of arrangement applies to a variety of circumstances. Some major retailers like Costco or Sam’s Club are known for selling large quantities of goods at lower prices per unit than their competitors. However, they also charge a fixed annual fee to be a member and receive that lower per-unit pricing. Another example would be to think about bars that have a cover charge. The cover charge allows you to get in the door, but then they charge you for each individual drink you order.

Okay, but how does this relate to the examples that I started the post with?

University Parking

Let’s start by thinking about university parking.

One thing about university parking is that there always seems to be excess demand. This would seem to suggest that parking passes are priced below their market-clearing level. However, that doesn’t really make sense. Why would the university do this?

For example, imagine that the university has a fixed number of parking spaces available. In addition, suppose for simplicity that each person with a parking pass comes to campus every day and demands one parking spot. It would make sense to charge a price that is just high enough that the number of people who buy a parking pass is equal to the number of spots on campus. Charging a higher price will result in empty parking spaces and foregone revenue. Charging a price below this amount will lead to excess demand. There will be more people on campus each day who want to park than there are parking spots available.

Of course, if there are more cars on campus than there are spots to park them, these cars will have to park somewhere. Universities tend to have a common way of dealing with that problem: parking tickets.

Herein lies the solution to the puzzle of pricing for parking. The university is price discriminating when it comes to parking in order to maximize revenue. The university price discriminates via a two-part tariff: a fixed entry fee (the parking pass) plus per-unit pricing (parking tickets).

Let’s think about how this might work. Let’s imagine that there are a fixed number of of parking spots, denoted by Q_s. Furthermore, the inverse demand curve for parking spots is given as p = a - bQ.

Let p* denote the price that clears the market such that

Now suppose that the university sets the price equal to p_u such that

Note that by lowering the price of a parking permit from p to p_u, the university has the following foregone revenue:

where the first term is the lost revenue from lowering the price at the previous quantity and the second term is the increase in revenue from increasing the quantity sold at the lower price.

The excess demand for parking is a source of parking tickets. Suppose that there is some probability, $\lambda$, that the people parking illegally get caught and that enforcement is costless. Those caught parking illegally must pay a fee f. It follows that the university will generate revenue from parking tickets equal to

The university benefits from the two-part tariff if the revenue from parking tickets exceeds the foregone revenue from the lower price of the parking pass:

Given the demand curve, this simplifies to

Recall from the definition of the price elasticity of demand, evaluated at the market-clearing price, we have:

Thus, in order for the university to generate more revenue from the two-part tariff than from the market-clearing price for parking permits, the university simply needs to set the expected fine such that

where $\delta$ is the difference between with market-clearing price and the actual price of the permits. What this shows is that if the fines and/or the probability of getting caught is sufficiently high, then the university is actually better off with the two-part tariff. The necessary magnitude of the fine depends on the elasticity of demand for permits and the magnitude of the discount. All else equal, the bigger the discount, the bigger the necessary fine. Furthermore, as demand becomes more inelastic, the magnitude of the fine has to get bigger. The intuition is easy to understand. The university is selling a number of permits that is equal to the quantity demanded at the price it sets. When demand is inelastic, they lose revenue from reducing the price of permits because the percentage decrease in the price is bigger than the percentage increase in the quantity demanded. Thus, one needs a bigger fine per violation to make up for the lost revenue from permit sales.

As a result, it is possible for the lump-sum permit and the per-violation fines can maximize revenue. The basic idea here is that the university is able to price discriminate. Those who have a higher willingness to pay will not only buy the permit, but they will continue to park on campus even when there are no spots available and incur the fine whereas those with a lower willingness to pay will tend to arrive earlier to make sure that they get a spot.

Interestingly, this also seems to have an added benefit for the university. If parking is in short supply, high parking permit prices might draw the ire of students and their parents. High permit prices might make it prohibitively costly to park on campus. However, by reducing the permit prices and instituting fines for violations, the cost of parking on campus is now endogenous to the behavior of the driver. Parents are therefore likely to blame their children for the high cost of parking due to repeated violations instead of the university since the fines could have been avoided with better preparation. This tendency of parents to blame their child for the high cost of parking will be true even if the university has itself engineered excess demand with lower permit prices, which increases the likelihood of violations.

Fireworks

When it comes to the fireworks example, our analysis should consider the objective of the state government. Why would they allow you to buy fireworks, but fine you for lighting them? Aren’t they sending mixed signals?

Suppose that their objective is to raise the maximum amount of revenue possible. One way for the state to generate revenue would be for the state to charge a tax on every unit of fireworks that they sell. However, there are limits to how much tax revenue can be raised.

For example, consider the case in which all consumers are identical and the supply and demand curves are linear. In the absence of the tax, quantity supplied is equal to quantity demanded and the market clears at some equilibrium price. In this case, the total surplus from trade (consumer surplus + producer surplus) is maximized.

When the government levies a tax per unit of fireworks sold, this drives a wedge between the price that consumers pay and the price that sellers receive that is equal to the size of the tax. Because of this wedge, both consumer surplus and producer surplus are lost. The distribution of the foregone surplus depends on the relative price elasticities of supply and demand. For our purposes, we don’t need to worry about the burden of the tax. The more important point is that the foregone surplus is exactly equal to the amount of tax revenue raised plus the deadweight loss.

Tax revenue is easy to understand. The tax revenue generated is the tax per unit multiplied by the number of units sold given the tax. Tax revenue increases in proportion to the tax.

The deadweight loss is the residual. It is the total loss in surplus to consumers and producers minus the revenue that went to the government. Given this definition, it is not hard to understand why it is referred to as a deadweight loss. It is the foregone surplus that is lost and goes to no one. It is the value of the gains from trade that are simply foregone because of the tax.

What is particularly important about the deadweight loss is its relationship to the tax rate. With linear supply and demand curves, we know that the deadweight loss can be illustrated graphically as a triangle. What we know about triangles and this triangle in particular can actually generate an important insight about the costs of taxation.

Recall the following. The area of a rectangle is (Base x Height)/2. The base of the triangle in this example is equal to the size of the tax. The height of the triangle is the change in the quantity traded because of the tax. We can then use the definition of elasticity to derive a precise equation for the deadweight loss of a tax as a function of the tax itself. Allow me to explain.

Note that the price elasticity of demand is defined as follows:

Solve this expression for dQ and note that dP = t, where t is the tax. It follows that

Now, using our definition of the area of a triangle, we have:

Note the important insight here. Tax revenue is proportional to t, but the deadweight loss is proportional to t^2. What this means as a practical matter is that the deadweight loss from taxation grows much faster as the size of the tax grows than tax revenue grows. This is where the Laffer curve comes from. For low levels of taxes, an increase in the tax will tend to increase revenue. However, as the magnitude of the tax gets larger, the deadweight loss grows faster, which implies that tax revenue will eventually start to decline.

Now, think about this from the perspective of the government. Imagine that their objective is to collect as much revenue as possible from fireworks. What they would want to do is choose the size of the tax that maximizes the amount of revenue that the tax generates. In the state of Michigan, they collect the normal 6 percent sales tax, but also collect an additional 6 percent tax that applies to fireworks.

Nonetheless, it is important to note that when choosing the tax to maximize revenue, there is still consumer surplus and producer surplus to be captured. If you are a government and your objective is solely to maximize tax revenue, you would also like to figure out a way to collect all of that surplus as well. Of course, you know that you cannot vary the size of the tax to generate more revenue because it would distort behavior in the market. If the state has already set the tax rate at the height of the Laffer curve, then this would be self-defeated.

All is not lost for this revenue-maximizing government. What it could do is institute a two-part tariff. The government could combine a lump-sum tax with the per unit tax. Given that the per unit tax maximizes revenue, the government could levy a lump-sum tax on consumers less than or equal to the remaining consumer surplus and a lump-sum tax on producers less than or equal to the remaining producer surplus.

Again, the state of Michigan appears to do something like this. The fine for setting off fireworks is the same no matter the quantity of fireworks that have been lit. One way to interpret the fine is as a lump-sum tax to collect at least some of the remaining consumer surplus. Michigan also levies a lump-sum license fee on retailers who sell fireworks. In order to sell fireworks, retailers effectively have to pay an entry fee. This is a flat fee that is again independent of the number of fireworks sold.

Concluding Thoughts

When I lived in Michigan, the rules on fireworks were initially confusing to me. Why would the state allow people to purchase them, but then turn around and fine people for actually consuming them? Furthermore, one persistent problem on college campuses is that universities tend to lack adequate parking. Yet, despite the excess demand, there is little discussion of raising the price of a parking permit.

Hopefully, what I have shown in this post is that both of these examples can be explained through some very basic price theory. If the objective of the state and of the university is to maximize revenue, then something like a two-part tariff can be optimal. In the case of the university, this is a straightforward application of price discrimination. The two-part tariff allows them to charge more to those with a higher willingness to pay. For the state, this is a way for the state to capture as much revenue as possible from a sometimes dangerous recreational activity.

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So, a little context. In 2006, Boeing and Lockheed Martin combined their launch divisions in a joint venture into United Launch Alliance. That’s what I mean when I say they combined. It’s not actually a merger, and there are important differences when you get into the weeds of antitrust, but let’s think of this as a merger to a monopoly for simplicity. This seems bad and extra bad in an industry that handles national security payloads.

By every static measure, this is the textbook nightmare.

Economic Forces readers will know I think the textbook metrics are a bad starting point. So I was intrigued to see a recent paper by Ruibing Su, Chenyu Yang, and Andrew Sweeting, which argues it was probably a good idea.

Their key mechanism that is missing from the basic model is that every launch teaches you something. The engineers who fly the fiftieth mission know things the engineers on the first mission didn’t. And before the “merger,” that asset was being duplicated across two separate programs with separate engineering teams, separate supply chains, separate everything. So we have a clear possibility (not sure if its plausible yet) for an efficiency in a specific sense.

Is that efficiency enough to outweigh the standard monopoly problem?

Su, Yang, and Sweeting argue yes. They use a full structural model, lots of bells and whistles, modern industrial organization stuff. They look at the space launch industry from 1985 to 2024 and argue the learning synergies were real and large enough to outweigh the market power. They also find that when the government committed to multi-year block buys rather than shopping launch by launch, costs fell dramatically. Forward-looking procurement gave the supplier incentive to invest in getting better, even without a competitor pushing them.

That’s a serious empirical study for one industry. The harder question is what regulators were supposed to do at the time.

We usually punt on dynamics

Every merger in a learning-intensive industry raises the same questions. Will the merged firm keep innovating, or get comfortable? What happens to entry when the incumbent has a decade of accumulated know-how and the challenger starts from scratch? Will the firm use its position to lock up inputs and foreclose rivals?

In practice, regulators mostly punt. They evaluate mergers on the static margin and treat “dynamic efficiencies” as a vague thing out in the ether, too speculative to count for much. That bias always cuts the same way. It cuts against mergers in the industries where dynamic effects are biggest.

It’s fair. Studying dynamic efficiency is hard. Full dynamic models can deliver answers, but the people who build them admit they come with lots of costs. Doraszelski and Pakes in their handbook chapter on “Applied Dynamic Analysis in IO” caution the framework “delivers very little in the way of analytic results of applied interest.” Berry and Compiani, in another summary of the dynamic literature, aren’t really any more positive. “[T]he attempt to add dynamics may create enough compromises that the result is not better than the static model.”

So, yeah, if the only way to engage with dynamics is a five-year structural project, most merger reviews will keep ignoring them.

Turning static to dynamic to static

Let’s work through some other options. Instead of going full structural IO, let’s think in simple price theory times. Strip away the details. I think there’s a simple way to capture a lot of the interesting dynamics here.

The key idea here is that output today affects costs tomorrow. The firm that produces more accumulates something.

That’s a reduced form way of capturing lots of different things. The learning-by-doing example of rockets makes sense, but it’s only one case. Retailers do the same thing with distribution density. Airlines do it with routes. A manufacturer that keeps producing refines its tooling and supplier relationships in ways a competitor running smaller volumes can’t match. The platform version is an installed base.

The engineering details differ. Don’t let that obstruct things. The economics is the same. Output today builds a productive stock, and that stock lowers future cost.

Notice this is not standard capital accumulation. That is a separate decision that sort of fights with production. Cash spent on a new machine is cash not spent directly making product. Time spent building the next semiconductor line is time not spent producing the current one. In your basic dynamic model with capital accumulation, production and investment compete for the same scarce resources in a more direct way.

The productive stock here works differently. You can’t write a check for a year of launch experience or a denser route map. The only way to build it is to produce. So output and investment don’t compete. They’re the same decision. Producing more today is investing more today.

Circling back to mergers in particular, of course, this doesn’t cover every dynamic merger claim. Some dynamics are about patent races, product repositioning, entry timing, demand-side network effects, or strategic investment chosen separately from output. Those may require different tools.

But most merger-efficiency claims are more mundane. They’re about scale, experience, density, know-how or having some installed base. For example, when T-Mobile and Sprint argued in 2019 that combining their spectrum holdings and cell sites would let them build a higher-quality 5G network than either could alone, that’s a network-density claim. The “stock” is network capacity. Producing more output builds that.

Always some tradeoffs

A merger with this type of capital does two things at once, pulling in opposite directions.

Of course, we still have the standard worry of the drop in competition. The new firm doesn’t worry about product diversion. If Boeing’s launch business raises price before common control, some lost business goes to Lockheed. After common control, those sales stay inside the same organization. The combined organization recaptures customers it used to lose. That weakens the incentive to fight for the marginal customer, which means higher markups and less output.

We can draw this out. Common control rotates the marginal benefit curve in. Before the combination, winning a launch from your rival is a gain. After the combination, winning a launch from your other division is partly stealing from yourself. The combined firm internalizes cross-product diversion, so the marginal benefit of expanding output is lower. Quantity falls.

That’s the normal merger logic.

But here we have a competing force. If producing today builds capability for tomorrow, each extra launch is also a little more experience and operating capability. That lowers the effective marginal cost of producing today. When the effect is large enough, the supply curve slopes down. The firm is paying the current cost of the launch, but it is also buying future cost reductions.

If consolidation means the combined organization captures a larger share of the returns from building productive capability, it has a stronger incentive to produce today. Combined production volumes mean faster learning. More internal coordination means the cost savings actually show up rather than getting duplicated across separate programs.

Draw the picture

To see the two forces in one picture, we need to think about the marginal cost of this productive stock. How does it change the marginal cost curve? What’s the price?

Capital has a rental rate, what you’d pay per period to use it. Jorgenson called it the user cost of capital; it’s the implicit rental on an asset the firm owns rather than rents. We can think of experience and operating capability have the same structure, even though no one cuts you a check, just as most firms own their capital so don’t pay a rental/user cost.

What’s the value of owning that capital? Each unit of output today adds to the stock and lowers tomorrow’s cost. The present value of that future saving is the implicit rental rate on the stock. Producing one more launch is cheaper than the accounting cost suggests, because part of what you pay “buys” future productivity.

Call the normal accounting cost per launch the static marginal cost. Call the static cost minus the rental rebate the dynamic marginal cost “net of rental”. Static MC is what shows up on the invoice. Dynamic MC is what actually drives the firm’s output decision once it values the stock.

Pre-merger, in the top panel, the firm faces residual demand for its own product and a single-product marginal revenue curve. Static MC sits at 3. Dynamic MC, net of the rental rebate, sits closer to 2. The firm produces where MR meets dynamic MC. We can read off the price and quantity.

Post-merger, two things change at once. MR rotates inward to “owned-product MR.” This is the standard diversion effect in picture form. If that was all that happened, mechanically, the merger would reduce output and raise prices.

Here, second force is that the dynamic MC rotates further down to “post-merger dynamic MC.” The combined firm captures more of the rental value of producing today. Less duplication across two engineering organizations. Higher combined volume moving up a learning curve (or moving down the cost curve). More of the future cost reduction stays inside the firm rather than leaking to a competitor. The rebate gets bigger, so the firm’s perceived marginal cost falls.

In the figure, the dynamic-MC shift dominates.

The picture also tells you when the result flips. If the learning curve is flat, dynamic MC barely moves and the MR rotation wins. Quantity falls, price rises, cats and dogs, living together, mass hysteria.

In some sense, this is all trivial. If the force that pushes up quantity is greater than the force that pushes down quantity, quantity goes up. Not exactly a deep insight. The figure can help us think through when the result flips.

But we can think even more about how learning works and how that maps to the figure. If experience spills over to competitors, the merger doesn’t change how much of the rebate the firm captures, and again dynamic MC barely changes. The industry already has that downward sloping curve, even if each firm doesn’t. Which one dominates depends on the slope of learning, how private it is, and how durable. The theory can guide your investigation into these questions.

Then we can start thinking about alternatives. A merger is one way to change the return to producing today. It is not the only way. Su, Yang, and Sweeting find that when the government switched from buying launches one at a time to committing to multi-year block buys, which is a way of guaranteeing the supplier a stream of future orders, costs fell sharply. The mechanism is the same one the figure shows. A bigger committed order book raises the value of investing in learning today, because the firm knows it will be producing the launches that benefit from that learning. The rental rebate gets bigger. Dynamic MC rotates down.

Is the stock the harm?

The figure shows when a merger generates real cost savings: steep learning curve, private experience, durable stock.

We need to be careful here. Those same parameters make entry hard. A new entrant starts at zero experience while the merged firm sits on years of accumulated know-how. If learning is steep, the cost gap is large. If learning is private, the entrant can’t catch up by hiring engineers away. If the stock is durable, the gap persists.

It’s tempting to call the entry barrier the offsetting cost of the efficiency and try to net them out. I’ve argued before that this gets the analysis wrong. Achieving scale is not an antitrust harm. Preventing a rival from achieving scale through better products or lower prices is not an antitrust harm either. Both are what competition on the merits looks like.

The same goes for an accumulated learning stock. If ULA’s experience makes it harder for an entrant to win contracts, that’s the productive stock doing its job. The merger that built the stock faster did so by combining output, not by doing anything to competitors.

The harm has to be a specific mechanism. The restraint is the harm, not the stock. Foreclosing key inputs. Locking up distribution. These raise rivals’ costs in ways that have nothing to do with the merged firm’s own productivity.

In this case, it turns out ex post that entry was possible and SpaceX completely changed the game and this all seems almost irrelevant. But not every market will have that type of entrant to switch things up.

Again, we have the tools to think carefully through it. This quick analysis doesn’t replace the structural model but gives you a way to think about the question before you build one, and a way to see what the model is doing once it’s built. It forces you to be specific about trade-offs and mechanisms. Which curves are shifting? What’s moving which way? Which ones can you see ex ante, and which ones only show up after the fact? If we can even clearly articulate that, we are a long way toward understanding a market.

What if demand isn’t downward-sloping? Isn’t this a problem for price theory? After all, if both the supply and demand curves are upward-sloping, it is possible to get very different price responses when the supply or demand curve shifts. For example, for a Giffen good, it would be possible that sudden reduction in supply might cause a reduction in the price, depending on the slope of the demand curve relative to the supply curve. This seems counterintuitive and empirically false. Nonetheless, the fact that this is a theoretical possibility seems like it could be a pretty significant problem for price theoretic analysis.

A lot of people dismiss Giffen goods as sufficiently rare that we don’t have to think about them. Others suggest that it is a theoretical possibility, but not not empirically relevant. I would like to take a much stronger stance on the issue and argue that upward-sloping demand is entirely the result of certain assumptions of the price theoretic framework. If we take uncertainty about prices seriously or if we allow for intertemporal decision-making, then the demand for Giffen goods is actually downward-sloping like all other demand curves.

Where Does the Theoretical Possibility Come From?

Let’s start by thinking through the theoretical possibility of a good for which the quantity demanded increases in conjunction with the good’s price. Recall that in price theory, there are two ways of deriving demand curves. One way of deriving demand is by starting with a utility-maximizing framework. In that framework, the set of possible choices available to the consumer depends on their income and the prices of the goods that the consumer would like to purchase. The consumer will choose his or her basket of consumption, given those prices and given income, so that the choice maximizes the consumer’s utility.

From this framework, we can use a hypothetical set of all possible prices of a particular good and determine the precise quantity demanded for that particular good at that price (holding all of the other prices constant). The plot of those combinations of prices and quantities demanded is referred to as the Marshallian demand curve. The Marshallian quantity demanded depends on the prices of the goods and the consumer’s income. In other words, as one moves along the demand curve, utility is changing, but income is not.

Alternatively, one could derive a demand curve by starting with the idea that the consumer wants to achieve a particular level of utility. Given the prices of the goods, the consumer chooses the quantities of each good to buy that minimize the expenditure necessary to achieve that level of utility. Holding all other prices constant, one can figure out the quantity demanded by the consumer at each possible price. The plot of this combination of prices and quantities demanded are referred to as the Hicksian demand curve. The Hicksian quantity demanded depends on prices of the goods and the utility of the consumer. In other words, as one moves along the demand curve, utility isn’t changing.

The reason that all of this is important is that there is a distinct relationship between Marshallian and Hicksian demand. In particular, let e^m denote the Marshallian elasticity of demand (the percentage change in the quantity demanded from a 1 percent change in the price). It follows that

where e^M is the Hicksian elasticity of demand, s is the share of expenditure on the good, and N is the income elasticity of demand (the percentage change in the quantity demanded from a 1 percent change in income). This is known as the Slutsky equation (in elasticity form). What it shows is that the Marshallian price elasticity consists of two effects. There is a substitution effect (captured by the Hicksian elasticity) and there is an income effect.

To understand these two effects, think of an increase in the price of apples. The substitution effect says that apples are more expensive and so the consumer should buy fewer apples. The income effect says that the higher price of apples makes the consumer feel poorer, which creates an additional effect on the quantity demanded.

How the quantity demanded responds to income depends on the type of good. Most goods are “normal” goods. By that, we mean that when income goes up, people buy more of the good. However, some goods are inferior goods. For those goods, when income rises, people demand a lower quantity of those goods.

The theoretical possibility of an upward-sloping demand curve should now be apparent. Note that when goods are normal, we don’t have to worry about a Giffen good situation. e^H is negative. As long as N is positive, we know that e^M is negative. However, consider the case of an inferior good. In that case, N is negative. Now, whether the Marshallian elasticity is positive or negative is going to depend on the magnitude of the Hicksian elasticity and the income elasticity. If the income elasticity is sufficiently large (in absolute value) or if the share of expenditures spent on the inferior good is sufficiently large, then the Marshallian demand curve could be upward-sloping.

Now that we have seen the theoretical possibility, we should assess how seriously we should take this claim.

The Demand for Giffen Goods is Actually Downward-Sloping

As I stated earlier, some people are dismissive of Giffen goods as a mere theoretical possibility, but the conditions are unlikely to be met (even in theory). After all, how many people are spending a large enough share of their expenditures on an inferior good? How sensitive are inferior goods to changes in income? Others concede that Giffen goods are a theoretical possibility, but not empirically important.

I think that both of those arguments concede too much. Instead, we should recognize that the demand for Giffen goods is downward-sloping like all other goods. However, to make that point, we need to discuss what is missing from our analysis.

The exercise that I just described for generating both Marshallian and Hicksian demand curves generates demand curves by considering the quantity demanded associated with a set of hypothetical prices. If the price equals Y, then the consumer will choose a quantity X. If the price equals Z, then the consumer will choose a quantity W.

It is important to note that this is not always the same as saying that when the price changes from Z to Y, the quantity demanded will change from W to X. That might be true, but it is not guaranteed. Whether or not these two things are the same depends on the expectations of the consumer. Only in the case in which a change in the price is completely unexpected would these things be the same. If, on the other hand, the consumer knows that there might be a change in the price, we should expect that to factor into the consumer’s decision-making process and that the consumer makes contingency plans.

It might help to illustrate this with an example.

Consider a Giffen good. Suppose that the prices of all other goods are constant, but that the price of the Giffen good could be high, with some positive probability. Otherwise, the Giffen good’s price is low.

Given the uncertainty over prices, the consumer might want to purchase insurance against the fluctuations in price. The reason is as follows. In the state of the world in which the Giffen good’s price is high, this will make the consumer feel poorer (and thus the marginal utility income will be high). In the state of the world in which the Giffen good’s price is low, this will make the consumer feel richer (and thus the marginal utility of income will be low). However, an optimizing consumer would like to equalize the marginal utility of income across these states. This requires transferring income from the low price state to the high price state. It is better to have an extra dollar to spend in the high price state than the low price state. How does one do that? Buy insurance.

Now, let’s think about the implications of the insurance.

If the consumer insures against these price changes, then income will be high when the price is high and income will be low when the price is low. Recall also that Giffen goods must also be inferior goods. One consumes less of the inferior good when income goes up and more of the inferior good when income goes down. Thus, with insurance, since income is high when the price is high and since the good is inferior, the consumer will consume less of the Giffen good when the price is high and more of the Giffen good when the price is low. In other words, the demand for the Giffen good is downward-sloping.

Critics might argue that this logic only applies when there is insurance to buy. There are two responses to this. First, this moves the goalpost. An upper-sloping demand curve would now require not only the typical Giffen good characteristics, but also would require incomplete markets.

Second, and more importantly, insurance isn’t even necessary here. Suppose that we allow for intertemporal decision-making rather than one-period decision-making. Now consider a world in which the Giffen good is storable. If you can store the Giffen good, then storage actually replicates the insurance policy. Why? Buffer stocks are a form of insurance. In periods when the price of the Giffen good is high, the person storing Giffen good now has more “income.” In periods when the price of the Giffen good is low, the person storing the Giffen good will now have less “income.” Since the good is inferior, we get the standard result that quantity demanded is lower when the price is high than when the price is low.

This can be extended further to intertemporal substitution more generally, although it requires more technical details. As Yoram Barzel and Wing Suen show in their paper on the topic, if a consumer is maximizing over both different goods and time, one of the maximizing conditions is that the marginal utility of income is constant across all periods. Thus, what we need to think about is how the price of the Giffen good affects the marginal utility of income. Barzel and Suen show that the marginal utility of income is high when the price of the Giffen good is high because this is true of inferior goods more generally. Thus, just like the case of insurance, the quantity demanded of the Giffen good is lower when its price is high than when its price is low.

It is important to note, however, that this intertemporal result is not deriving a structural demand curve. It is a time series of price-quantity pairs that shows a negative relationship between price and quantity demanded for the Giffen good.

There is one final caveat here is that the change in the price must be anticipated. People cannot plan for things they don’t expect to occur.

Concluding Thoughts

The theoretical possibility of a Giffen good is something that many students are taught, even in introductory courses. Economists are often quick to dismiss this theoretical possibility as unlikely to hold or empirically irrelevant. Yet, there is a broader lesson to be learned. People optimize on many margins. If prices fluctuate over time, people might take actions to mitigate the costs of such price fluctuations. The very actions that people take to mitigate those costs actually result in negative relationships between the price and quantity demanded of a Giffen good. All of this follows from straightforward applications of price theoretic logic and concepts that any student who has learned about Giffen goods would understand. The problem with the analysis of Giffen goods seems to be that it doesn’t take the price theoretic logic far enough.

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And every major AI builder seems to agree that humans are next. Musk says AI will “replace all jobs.” Amodei is out there talking all the time about everyone losing their jobs, grounded his framing of AI as “a general labor substitute.” OpenAI investors are out there talking about “80% of all jobs by 2030.”

While these are important people in the space, not some random blogger, they may not exactly be a random sampling of the most knowledgeable people. But it’s certainly not a new fear, and not just an AI thing. Wassily Leontief of input-output fame (more on input-output in a minute) had a few pieces in the early 1980s expressing this worry.

If AI really is a perfect substitute for human labor, any cost advantage drives to 100% AI. You don’t need an essay to prove it. But “AI will eventually be a perfect substitute” is doing all the work.

But that’s hiding a lot, lots of margins of adjustment and differences, heterogeneity that makes the world the world and not a simple model. How substitutable is AI right now? What would it take for that to rise high enough? What else has to hold?

Even the historical example that “tractors could do what horses did, therefore horses became obsolete” sounds like one step, but it’s actually several. And “AI can do what humans do, therefore humans become obsolete” is hiding even more.

So let’s go through those steps. This newsletter is based on a new working paper that goes through the math and economics, really MOSTLY basic accounting, in detail.

What collapse of human labor actually means

For a quick recap for those unaware of the history of horses in the US, the US horse population rose from 4.3 million in 1840 to 27.3 million in 1920. Then we get the fall with farm horses and mules dropping to roughly 3 million by 1960.

Horses basically had one job that came and went. For humans, we need to be more careful. Let’s make sure we have our accounting right and be clear about what a collapse actually means.

For simplicity, suppose human labor demand goes to zero. Not low. Zero. What does that require? It means no dollar you spend, anywhere in the economy, passes through a human hand at any point in its supply chain. Not the person who made the thing. Not the person who shipped it. Not the person who designed it, sold it, maintained it, or cleaned the building where it was assembled. Zero human labor embodied in final expenditure. That’s the target. That’s what I’m going to take “humans become horses” to mean, stated precisely.

This is the input-output idea Leontief built his career on. You can trace any final purchase back through its supply chain and add up all the human labor that went into it, direct and indirect. A cup of coffee has the barista, but also the roaster, the trucker, the farmer, the person who made the truck. “Embodied labor” means all of it. For labor demand to collapse, every one of those links has to go to zero, in every product anyone buys

The economy is not one production function. It is many activities. When AI makes some of them cheaper, people don’t just buy more of the same thing. They buy something else.

Every dollar you spend lands somewhere. Some dollars land in activities with lots of human labor inside them: a restaurant, a therapist, a roofer. Some land in activities with almost none: a streaming subscription, an automated checkout, cloud storage. So when we are tracing out what happens when AI gets cheaper, it’s not just “Can AI do my job?” It is “When everyone saves money because AI did my job cheaper, what do they buy next?”

Aggregate labor demand depends on three things: how much people spend in total, how much of that spending lands on activities with human labor inside them, and how much labor is embodied in each of those activities. For human labor demand to collapse, it’s not enough for AI to displace workers inside some activities. Every dollar of spending, wherever it lands, must lose all its embodied human labor. That’s three channels, and the horse argument needs all three to go wrong simultaneously.

The important starting point for thinking about labor is te idea that nobody wants labor. A restaurant doesn’t want waiters; it wants orders taken, customers reassured, mistakes corrected. So labor demand is derived demand. How does AI change how much firms demand?

When AI can do the things firms are actually buying, cheaper AI does two things at once. Firms substitute AI for workers, which reduces labor demand per unit of output. But cheaper AI also lowers output prices, output expands, and the expansion pulls labor demand back up. Whether labor demand rises or falls depends on which effect is larger. This is the Hicks-Marshall decomposition of derived demand into substitution and scale effects.

This is going to be the organizing principle for everything. When a dollar is saved, where is it redirected? To new tasks? To new jobs? To new sectors? It must go somewhere.

“AI can do the tasks.”

This is obviously true for many things. Early models had this even. For example, the early GPT exposure paper by Eloundou, Manning, Mishkin, and Rock estimated that roughly 80% of the U.S. workforce could have at least 10% of tasks affected by LLMs. With complementary software, 86% of occupations cross the 10% exposure threshold.

And lots has been done on this. The task-level evidence backs this up. In a large customer-support setting, access to generative AI raised issues resolved per hour by about 15%. In a professional writing experiment, ChatGPT reduced average task time by 40% and raised measured output quality by 18%. In a controlled GitHub Copilot experiment, developers completed a coding task 55.8% faster. These aren’t tiny effects.

But they’re effects on tasks. The saved dollar doesn’t vanish when a task gets automated. It creates new tasks within the same job, such as more review, more client management, more judgment calls. Just as there’s not some fixed amount of demand so the scale effects matter, there is not some fixed job.

“A job is more than a task list.”

There’s a ritual in AI discourse where someone posts a demo, the demo does a task associated with a job, and people conclude the job is doomed. Sometimes they’re right. But the inference skips about fifteen steps. What does it actually cost to deploy, errors included? Do customers trust it? Does management know how to reorganize around it? A chatbot demo can appear overnight. A hospital reorganizing clinical liability around AI cannot.

We need to think not just about jobs but organizations. Often the result is a team, not a replacement. A human-AI pair produces output. But complementarity is not free. A pair that produces only slightly more than the AI alone doesn’t justify the human wage. The human has to add something the AI can’t replicate cheaply.

Surgery, aviation, structural engineering, fiduciary advice, for legal reasons alone are areas where we can expect the damage from an error dwarfs the savings from cheaper production. Again, that can always change one day but not soon. When failure on one component destroys the value of all others, you don’t care about the sticker price. That’s the O-Ring logic. You care about cost per unit that actually works. When damage stakes are high enough, human-supervised production wins regardless of how cheap AI becomes.

“Fine, the current jobs go. Where does the spending go?”

Suppose substitution wins inside most jobs. The saved dollar escapes the workplace entirely. Where does it go? Most standard models aggregate into a single final good, so this question plays no role. The real economy has many sectors, and the dollar has to land somewhere.

Start with software as a microcosm. This is a sector that has already been heavily automated by digital inputs for decades. If substitution were going to drive labor out of a sector, this is where you’d see it first. The chart below groups industries by how much software they purchase relative to their value added — low, medium, and high software intensity. What do you see?

The most software-intensive industries don’t just retain human labor;they have a higher labor share (67%) than the least software-intensive ones (55%). Heavy digital inputs didn’t drive out human labor. If anything, the industries that automated the most are the ones that spend the most on workers. BLS projects U.S. employment to increase by 5.2 million from 2024 to 2034. Software-developer employment? Up 17.9%, despite direct AI exposure.

The scale effect won within the sector most exposed to digital automation. The BLS could be completely off but the evidence so far points strongly toward the scale effect dominating in software-intense industries.

Software is one extreme but we basically have the same pattern holds across the whole economy, over a much longer period.

For another angle on the problem, let’s go bigger and look across the biggest sectors in the economy: services vs. goods. In 1929, most consumer spending went to physical goods. Today, roughly two-thirds goes to services. As manufacturing got cheaper, people didn’t just buy more stuff. They shifted spending toward healthcare, education, restaurants, personal services. That’s the saved dollar in action at a more not-quite macro but close level — the savings from cheaper goods flowed toward services.

In terms of our guiding decomposition, coods got cheaper. I’m going to be a bit fast and loose here but the scale effect didn’t show up in goods. Demand for physical stuff didn’t explode. Instead those freed-up dollars migrated to services, and the scale effect showed up there. The substitution effect won inside goods-producing industries. The scale effect won across sectors. Output overall expanded. So if you’re thinking as a macroeconomist, the scale effect dominated.

But migration alone doesn’t help workers unless the destination still has human labor inside it. Did it?

This chart tracks what fraction of each sector’s value goes to workers — employee compensation as a share of value added. Services consistently pay a higher share to labor than goods-producing industries. Spending didn’t just migrate. It migrated toward sectors where more of each dollar ends up in someone’s paycheck.

Again, so far, yes. We are moving toward services.

So you say sure, this actually supports the horse outcome. The price of goods fell and we bought fewer goods. What I’m saying is that there is a margin of adjustment, there is an escape hatch when you are looking at an economy as diverse as the modern U.S. economy.

And comparative advantage always pops up fighting against this. When automation makes some things cheap, the things that remain expensive tend to be the things that are hard to automate. And the things that are hard to automate are, almost by definition, the things where humans still have comparative advantage. The saved dollar drifts toward where humans are still worth paying. That’s not optimism. That’s what comparative advantage means.

Bessen showed this sector by sector. In early textiles, power looms cut labor per yard of cloth. But cloth got so cheap that demand exploded, and total employment in textiles rose for decades. Same in early steel, early autos. Eventually demand saturated, prices stopped falling fast enough, and automation reduced employment in each sector. The question for AI isn’t “does automation destroy jobs?” It’s “which phase are we in, for which sectors?”

Where might the AI-saved dollar land today? Healthcare is already 18% of GDP and rising. Elder care will grow as populations age.

Mokyr, Vickers, and Ziebarth have a great JEP piece that makes the historical case for why this time isn’t different: new tasks appeared, comparative advantage held, products we couldn't imagine created new work.

Horses had no equivalent destination.

“Fine, spending chases automation.”

The saved dollar found human-intensive sectors last time. The best argument for why this time is different comes from Philip Trammell’s “Is labor a luxury in the long run?“ His answer is probably not. Even if people initially spend more on human services as they get richer—live music, handmade goods, personal care—four forces erode that over time. AI-produced variety keeps expanding, competing for every dollar that lands on human-made goods. Consuming human services has an opportunity cost: time spent at a live concert is time not spent on a superior AI experience.

Other scarce goods—beachfront land, status goods, R&D—compete with labor for the “scarce thing people pay a premium for” slot. And capital goods keep getting cheaper to produce, so the investment share of spending can grow without limit.

Trammell’s Coca-Cola analogy is the sharpest version. Original Coke held 50% of the soda market. Then Diet Coke, Cherry Coke, Pepsi Max, energy drinks, sparkling water. Even with brand loyalty and supply restrictions, the share fell below 20%. If AI keeps inventing new varieties of goods that compete with human-produced ones, even a strong initial preference for human labor gets diluted by expanding choice.

I take this seriously. It’s a possible scenario.But notice what it requires. Not just that AI-produced variety expands (which it will) but that it expands fast enough and broadly enough to pull spending away from every human-intensive category at once. The question isn’t whether AI competes with some human goods. It’s whether any human-intensive island survives. Does anyone still spend money on something with a person inside it?

The numbers still have to be extreme. Suppose AI eats 85% of the economy. Software, accounting, law, medicine, logistics, most management, most media. All gone or nearly gone as human labor categories. Suppose the remaining 15% of spending goes to things with at least 30% human labor inside them. Elder care, in-person education, surgery, live performance, skilled trades, therapy, status goods. Then the aggregate human labor share is at least

S ≥ 0.15 × 0.30 = 0.045

That may not sound great but I’m literatlly just putting a bound. Knowing nothing else, we can sustain this. Not large. Not utopia. But not zero, and that’s the absolute lowest possible bound. And remember, labor share declining is not the same thing if the pie is growing much larger.

But is it just sentimentality to think spending stays on human-intensive stuff? Alex Imas argues no. As AI makes commodities cheap, real incomes rise, and richer people systematically shift spending toward what he calls “relational” goods.

There's a huge literature in economics on structural change, the long-run pattern where spending shifts from agriculture to manufacturing to services as countries get richer. The big question is why. Is it because prices change and people buy more of whatever got cheaper? Or is it because incomes rise and people just want different stuff? Comin, Lashkari, and Mestieri, for example, decompose the two and find that income effects account for over 75% of the shift. That matters here. If spending migration were mostly about chasing cheap goods, AI making things cheaper would pull dollars toward AI-produced stuff. But it's mostly about what richer people want. And richer people have consistently wanted more services with humans in them.

In experiments, when subjects learn that others will be excluded from purchasing an identical product, willingness to pay roughly doubles. Pure exclusivity premium. Anonymous, no status signaling possible. The premium is stronger for human-made goods. Human-created artwork gains 44% in value from exclusivity, versus 21% for AI-generated artwork. AI-made goods feel copyable. Human-made goods feel scarce even when they aren’t. People want what other people can’t have. That wanting doesn’t run out, and it sticks to things a person made.

Maybe the point is to wait long enough and AI variety erodes even that. Maybe. But the structural change evidence says income effects dominate price effects by three to one. When basic needs get cheaper, humans don’t say “good, I’m done wanting.” They invent new ways to compare themselves with neighbors. Whether the new wants land on human-made goods or AI-made goods is the open question, and the experimental evidence so far favors humans.

A falling labor share is not falling labor demand. There is a range where labor’s share of income is declining but total labor demand is still rising, because the pie is growing faster than labor’s slice is shrinking. That range may be where we are right now. It would look like “AI is taking over” in share terms while employment keeps growing. The popular argument runs these together and they are not the same claim.

We already see that. Higher income people consume more services. Services tend to be high labor share. Again, that can always flip in the future but this is the evidence we have.

I think humans have a shot

Going through all of the layers, from tasks (where we are just starting to see some substitution) all the way up to the macroeconomy, leaves me pretty skeptical of the horse outcome. I know I’ve hidden it really well up to this point, but that’s where I’m at.

AI will do many tasks. It will reorganize jobs, probably painfully. Some sectors will lose most of their human labor. Spending can chase automation. All of that can happen and still not get to zero. Because at every step, there’s a saved dollar looking for somewhere to land. And the question is always the same one. Where does it go next?

For the horse outcome, you need that saved dollar to find nothing with a human attached to it.

That’s a very specific future. It might happen. But it has to happen everywhere at once, and the evidence we have, the structural change evidence, the revealed preferences, the experimental results, keeps pointing the other way.

I also like examining topics like this precisely because they highlight what price theory is and is not. Price theory is a framework for understanding, explaining, and predicting human behavior. Price theory is not a theory of mind.

A different way of thinking about this is as follows. In the early days of behavioral economics, a number of researchers focused on finance. The reason that they focused on finance is that financial markets tend to be populated by professional finance people who have an incentive to get things right and acquire information. Financial markets are also characterized by frequent trading and highly liquid markets. Given those characteristics, the thinking was that if behavioral assumptions could survive financial markets and not simply be arbitraged away, this would be strong evidence in favor of these behavioral assumptions. We can debate whether that turned out to be true. Nonetheless, the sentiment itself is useful.

I take a similar view of using price theory to analyze things like drug use. The stereotypical view is that drug users don’t behave in predictable ways. If price theory can explain how people respond to changes in drug policy or even what types of drugs that people use, then this would seem to be a pretty strong indicator of the usefulness and broad applicability to price theory.

With that in mind, I would like to continue with this theme of exploring price theory in a context where one might otherwise find it lacking: panhandling.

Competition, Entry, and Profits

Price theory, at least the Chicago brand, tends to focus a lot of its attention on the so-called competitive model. A basic idea of the competitive model is that competition will tend to drive economic profit to zero. This does not mean that competitive firms don’t earn a profit. Instead, what this means is that the accounting profit earned is equal to the opportunity cost to the owner(s) of operating the firm.

In finance, there is the idea that variation in returns of different assets can be explained by the risk characteristics of the different assets. A higher expected return is simply compensation for taking on more risk. A useful concept that follows from this logic is that equilibrium is characterized by the absence of risk-free arbitrage. Again, competition should see to it that this is true. If one can earn income from risk-free arbitrage, then we should expect people to enter the market to capture some of those profits. Furthermore, we should expect that people will continue to enter the market until the only arbitrage opportunities left require taking on some degree of risk.

This is not to say that these outcomes will always prevail. The basic assumption lurking in the background, but unacknowledged explicitly to this point, is that these outcomes are predicated on free entry. The presence of positive economic profit or risk-free arbitrage could be the result of the fact that it is costly to enter the market. This could be true for a variety of reasons. There could be legal or regulatory barriers to entry. There could be significant fixed costs associated with entering the market. In that case, one has to make sure that the profits from day-to-day operations are large enough to offset that initial fixed cost.

Suppose that you wanted to test this idea that, all else equal, competition tends to equalize rates of return on particular types of investments or activities. How could you do it?

One way to test the theory would be to estimate the rate of return on investment in various industries. In a competitive market with free entry, one would expect that the rate of return on investment would equalize across industries. Any failure to equalize these rates of return should be explained by some sort of barrier to entry.

This is easier said than done. For one thing, there is no reason to think that at any particular moment in time, we are in equilibrium. This is a challenge in finance. Sure, there are a lot of papers that seem to show evidence of clear arbitrage opportunities. However, the fact that trading is happening by the second in financial markets seems to indicate that there are a lot of people who believe they have an arbitrage opportunity. Even if one were to observe that an arbitrage opportunity exists at a moment in time, this would not tell us whether the absence of risk-free arbitrage is a useful equilibrium concept because we might simply be on the path back to equilibrium.

Although I’m not sure that David Laidler ever wrote this down, I’ve heard him say that every economist should have to revisit their dissertation at the end of their career and see if it holds up. In our context, this would mean that if you had evidence of risk-free arbitrage or the failure to equalize returns across industries, you would have to replicate your result after approximately 30 additional years of observations. If you could replicate it, that would be pretty strong evidence that you were correct.

But all is not lost. We need not wait 30 years for people to update their data. There are alternative ways to test the implications of the competitive model.

For example, think of a situation in which there is (a) free entry, but (b) some reason to believe that the competitive model might not apply. If one finds evidence in favor of the competitive model under those conditions, then that is pretty strong evidence in support of the model.

One such example is that of panhandlers. It seems pretty clear that there are places in which panhandling is perfectly legal and there is no restriction on panhandling. The absence of those restrictions suggests that anyone can show up and panhandle. As a result, if panhandling turns out to be quite lucrative, we would expect to see people switch to panhandling from some alternative use of their time. In fact, competition in panhandling should drive down the rate of return on panhandling until it reaches the opportunity cost of the marginal panhandler.

At the same time, casual observation suggests that panhandlers might suffer from the same erratic behavior attributed to drug users. This shouldn’t be surprising since many panhandlers tend to be drug users. Given their erratic behavior, a common retort would be that panhandling isn’t the best example. Sure, there is free entry. However, we are not dealing with rational actors. We are dealing with people who are on drugs or desperate. They aren’t calculating where to panhandle to get the highest rate of return. They’re panhandling because they need all the help that they can get, possibly to buy a meal or perhaps even to get their drug fix.

It is not despite those objections, but precisely because of those objections that this is a useful testing ground. There is free entry, but panhandlers aren’t crude, calculating maximizers. So what happens?

Evidence on Panhandlers

Fortunately for you, dear reader, there are people who get paid to think about such things. A recent paper by Peter Leeson, August Hardy, and Paola Suarez used data they obtained by observing panhandlers in Washington, D.C. to test this very hypothesis.

Specifically, they observed panhandlers outside the D.C. Metrorail stations. The places where the panhandling takes place around these stations appears to be legally permitted based on local statutes. Regardless, at the very least, it is de facto permitted. They were able to observe the panhandlers and get information about how much they collected by asking the panhandlers to count their earnings in exchange for five dollars. They are also able to get data on the number of customers that pass through that area using publicly available data from the Metro system. They also used a standardized process to examine the friendliness of passersby.

One possible barrier to entry is the ability to get to a particular Metro station. Not all homeless are panhandlers, but most panhandlers are homeless. If that is the case, then a basic barrier to entry might be the ability to get to a particular Metro station.

Setting aside whether competition equalizes rates of return across different stations, one simple test would be to see if panhandlers follow basic economics incentives. For example, one would expect that there would be more panhandlers at the busier stations and the friendlier stations. One would also expect that there would be more panhandlers where the barriers to entry are low, such as at stations where there is a shuttle stop for the homeless nearby.

This is precisely what they find for the full sample.

Nonetheless, it is important to remember that the main thing that they want to test is whether the rates of return equalize. Yes, all else equal, one should expect busier stations to have more panhandlers. When Willie Sutton was asked why he robbed banks, he replied “because that’s where the money is.” The same principle applies here. Busier stations mean more people to ask for money. The more people one asks, the more likely one is to receive some money from a passerby. At the same time, this tendency will cause rate of return equalization between Metro stations. Busier stations have more competition, which drives down returns. Less busy stations will have less competition, which boosts returns.

Because they were able to obtain data on how much the panhandlers earned per hour directly from the panhandlers themselves (and verify the information by having the panhandler count for them), they are able to test whether the mean, median, and variance of the returns are similar across stations. Using the data that they obtained, they cannot reject the null hypothesis that the mean, median, and variance of returns across the stations are equal.

This is especially strong support for the competitive model. Panhandlers are much more likely to suffer from mental health disorders and substance abuse problems than the rest of the population. In addition, panhandlers are often thought to have less self-control and be deemed erratic or irrational. Yet, the prediction of the competitive model prevails even in those circumstances.

Conclusion

I bring up examples like this because it allows me to address criticisms of price theory that largely miss the mark. Critics of economics generally, and price theory in particular, tend to argue that we assume that everyone is a hyper-maximizer, only concerned with self-interest and that the world is more complicated than that. People aren’t walking around all day solving utility- and profit-maximizing problems in their head. Not everyone is a rational calculator.

I think that we can reject these criticisms. I am not saying that we should reject them on the grounds that they are false characterizations of the real world, but rather that they are a false characterization of price theory. As I wrote in my previous posts, price theory is about providing rational frameworks to understand, explain, and predict human behavior. It is about rational frameworks, not rational people.

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A familiar concern in antitrust-adjacent debates goes like this: when a company such as Walmart grows large enough, it can strong-arm suppliers into steep discounts. Suppliers, in turn, recoup those lost margins by charging smaller grocery stores more. Those smaller stores raise prices. The big chain’s gains come at everyone else’s expense—prices fall on one end because they rise on the other. That’s the “waterbed effect.”

It’s a—maybe not compelling—but a story. A 2023 New York Times op-ed argued that this mechanism drives high grocery prices, noting that “as suppliers cut special deals for Walmart and other large chains, they make up for the lost revenue by charging smaller retailers even more, something economists refer to as the water bed effect.” The Organisation for Economic Co-operation and Development (OECD) has raised concerns about it for years. The United Kingdom’s Competition Commission has investigated it.

Regulators that have actually examined the waterbed effect tend to be skeptical. In its 2008 groceries investigation, the UK Competition Commission considered the theory and declined to rely on it, finding the evidence insufficient. Two years earlier, the UK Office of Fair Trading concluded that “there are theoretical questions that would need to be resolved before concluding that the price differentials observed are evidence of a waterbed effect.” As Eric Fruits put it on this blog, the waterbed was a notion without a model—and without a model, it was headed the same way as the real-world waterbed.

Then it got a model.

In 2011, Roman Inderst and Tommaso Valletti published a paper in the Journal of Industrial Economics that gave the waterbed a formal theoretical foundation. Their model features a supplier selling to a large buyer and smaller rivals. The large buyer’s scale gives it bargaining leverage, so the supplier compensates by charging the smaller rivals more. In the model, that is exactly what happens: the large buyer pays less, and the smaller rivals pay more. That result is straightforward.

The paper goes further. It claims the waterbed harms consumers—not just smaller firms, but shoppers at the checkout, who face higher prices on average. That is the result that matters for antitrust, which turns on consumer harm. It is also how the authors close their abstract.

I have a new working paper that shows consumer harm is impossible in their model, which is a Hotelling model. Seems relevant to the discourse? So let me explain.

The Waterbed Breaks Before Consumers Do

The waterbed operates through the small firm’s wholesale price. More precisely, it does not turn on the supplier needing to “make up” lost profits. It turns on bargaining. Once the small firm faces a lower-cost rival, its bargaining position with the supplier weakens, and the supplier can charge it more.

But the small firm is not captive. It can reject the offer and source inputs elsewhere. The supplier may want to squeeze harder, but push too far and the small firm walks.

For the waterbed to harm consumers overall, the squeeze has to be extreme. The small firm’s retail-price increase must outweigh the large firm’s price decrease. The walk-away option blocks that outcome. The supplier hits a ceiling before the waterbed grows strong enough to raise average prices.

In the Inderst and Valletti model, these forces pull in opposite directions. The small firm’s cost disadvantage must stay limited so the firm prefers the supplier’s deal over walking away—that is what keeps the waterbed in place. But consumer harm requires a large enough disadvantage that the small firm’s price increase swamps the large firm’s decrease. The first constraint caps the disadvantage below what the second requires. The waterbed and consumer harm cannot coexist.

This is not a math error. Inderst and Valletti’s consumer-harm result takes the form of an if-then: if a certain condition holds, then consumers are worse off. The logic is sound. The condition never holds. No equilibrium in the model satisfies the “if.” Nowhere in their model does the waterbed harm consumers.

The wholesale waterbed is real in their setup. Large buyers pay less, and smaller rivals pay more. The model even allows the small firm to raise its retail price—despite competing against a lower-cost rival, it still chooses to do so.

You can call that a waterbed. But a higher price at the small firm is not the same as consumers being worse off.

The Policy That Outruns Its Math

A throwaway line in a 15-year-old paper would not usually matter. This one does. The waterbed effect has hovered over antitrust for two decades. As Eric Fruits has noted on this blog in the context of the proposed Kroger-Albertsons merger, it surfaces in merger review and policy debates. It came up repeatedly in congressional hearings on that deal.

Its largest policy footprint may be the push to revive the Robinson-Patman Act. The RPA prohibits suppliers from charging competing buyers different prices for the same goods. That is the waterbed: a supplier charges Walmart less and the corner grocery more. If that price discrimination harms consumers, you have a consumer-welfare case for enforcement.

The logic chain runs like this: the waterbed harms consumers; supplier price discrimination is the mechanism; RPA enforcement is the fix. If the waterbed does not harm consumers in the very model that supplies its theoretical foundation, the first link in the chain breaks. What remains is a statute that bars supplier discounts to large buyers without a consumer-welfare rationale. As Alden Abbott has argued, reinvigorated RPA enforcement risks raising prices for the consumers it is supposed to protect.

None of this proves the waterbed could never harm consumers. A different model, with different assumptions, might get there. You could all a simple price discrimination model that raises average prices “waterbed” if you want.

The point is narrower. The specific claim that this model—the one people cite—shows consumer harm does not hold. The general idea that there is this tension in waterbed-type effects between squeezing retailers and them also being able to exit is hard to avoid. Hard is not impossible.

This ruffled some feathers. Some people contacted me to tell me that the post was silly. We cannot use price theory to explain drug use, they said, because drug users aren’t rational. These are not people moving along a demand curve. Their behavior is erratic. Some criticized me for ignoring actual price elasticities of demand to downplay the success of Narcan. Others told me that I was ignoring the psychology of drug users. Still others noted that addiction is a biological condition, detached from economic incentives.

I reject all these criticisms.

First, I never assumed that drug users are rational. They need not be rational for the theory to apply. Price theory is about applying rational frameworks (not rational people) to explain human behavior. It does not require understanding what is going on in the decision-maker’s brain.

Second, the typical negative relationship between price and quantity demanded doesn’t even rely on rationality. As Gary Becker explained, this type of relationship between price and quantity demanded only requires that the person has a budget constraint. This is something that Brian wrote about previously.

In addition, while I didn’t mention actual elasticities, I did describe several scenarios. I described what happens in the inelastic case and what happens in the elastic case. As a benchmark, if the price elasticity of demand for opioids is -1, then there would be no effect on deaths. There is a relatively recent paper by Olmstead, et al. in the Journal of Health Economics that estimates the price elasticity of demand for heroin. Their baseline estimates suggest a price elasticity of demand for heroin of -0.8 with a standard error of 0.23. The point estimate itself suggests that the presence of Narcan would reduce opioid-related overdose deaths modestly. However, from these estimates, we cannot rule out that there is no effect on deaths. (Note that the relevant elasticity is the Hicksian elasticity. Since they control for income, the estimates are best interpreted as Marshallian elasticities. Whether this biases the implications toward or away from “no effect” depends on whether heroin is a normal or inferior good and the share of the average consumer’s income that goes to drug consumption. The authors own estimates of the income elasticity are quite small, 0.1, but positive.)

Finally, I don’t deny that there are psychological and biological aspects to drug use. However, that isn’t relevant to the analysis. What the price theoretic analysis does is it asks, holding other things constant, if price theory can inform our understanding of the market for opioids.

This gets to a broader point here at Economic Forces. We think price theory is broadly applicable. Yes, it is applicable to the market for oranges or grapes, but it is also applicable to other types of goods, like illicit drugs.

With all that being said, this week I thought I would double-down on the discussion of drugs in the context of economic decision-making. Let’s push the boundary of what price theory can explain by trying to explain what types of drugs that people use based on economic incentives. To this, I’m going to rely on the work of my colleague, Henry Thompson, and his coauthor, Justin Callais, and their work on “Doing Drugs.”

Economic Incentives and Drug Use

There are many different types of drugs. One might wonder whether price theory has anything to say about the types of drugs that people decide to use. This isn’t to say that there are not other factors that determine which drugs people use. Nonetheless, price theory might be able to explain an aspect of these choices.

There is certainly reason to believe that economic incentives play a role in the decisions that people make about what types of drugs to use. For example, not long ago, Major League Baseball went through quite a scandal about steroid use in baseball. The scandal was due to the fact that steroids are considered performance-enhancing drugs. Steroids help athletes to develop larger muscles and improve recovery times. As a result, a player who uses steroids has an advantage over a player who isn’t using steroids. Given that contracts for professional baseball players are quite lucrative, it is not surprising that a number of players were alleged to have used steroids.

But what if that principle applies more generally? What if labor market contracts in a variety of settings explain the types of drugs that people decide to do? Thompson and Callais present a theory of drug use consistent with this idea.

Think about two different types of labor contracts. Sometimes compensation is based on output. The worker is paid depending on what they produce. Other times, compensation is based on inputs. This is a standard wage-based job in which the worker is paid based on the number of hours worked. All else equal, if someone is doing drugs, one would expect that the decision about what drugs to use will depend on their labor market contract.

For example, suppose that I have an output-based contract. This might be something like a sales job in which the employee earns a commission on every sale. All else equal, to the extent to which a person in an output-based contract does drugs at all, one would expect them to be more likely to use stimulants than depressants or psychedelics. Stimulants might help the worker to stay alert longer, work longer hours, and have the energy necessary to make a hard sale at the end of the day.

Although this argument might sound somewhat controversial, the argument is really no different than the Major League Baseball example. Players are compensated with salaries, but those salaries are based on performance. If you hit more home runs, then you will receive a more lucrative contract. If there is a drug that contributes positively to one’s performance on the job, people are more likely to use that drug than a drug that doesn’t help with performance. (And less likely to use drugs that would harm their performance.)

You even see evidence of the price theoretic prediction in popular media. Movies about out-of-control guys on Wall Street, known for its long hours and commission-based income, almost always includes scenes in which the drug of choice is something like cocaine.

But let’s not get ahead of ourselves. Although I’m using the word “drugs,” I’m doing so quite liberally. I’m not necessarily talking about illegal drugs. Sure, stimulants include illegal drugs, like cocaine, but nicotine is also a stimulant and is sold in every gas station in America, in many forms.

The theory also doesn’t say that labor contracts make someone more or less likely to consume drugs, illegal or otherwise. The theory simply predicts that if the person does drugs and if the person is compensated in a particular way, we should be able to predict which types of drugs the person chooses.

This isn’t even necessarily a causal argument. Suppose a non-drug user becomes a drug user. If that person has an output-based labor contract, all else equal one would expect that the drug of choice for the new user would be a stimulant. However, one could just as easily argue that someone who is already using stimulants might self-select into a job with output-based compensation.

It is more difficult than one might think to test the prediction. If output-based compensation is associated with more stimulant use and input-based compensation is associated with less stimulant use, it is possible that these people in these different contracts are also systematically different in other, observable ways.

For example, maybe output-based compensation is higher on average. We couldn’t rule out that it is actually the level of income itself that determines the stimulant use. Or maybe there are systematically different patterns of drug use and different patterns of compensation among different age groups. Then the use of stimulants might simply be driven by age, even though it varies by compensation type. Furthermore, if those with output-based compensation use more stimulants, it could also be the case that people in these type of labor contracts are just more likely to be drug users independent of the type of drug.

To test this prediction about labor market compensation and the type of drug use, Thompson and Callais use data from the National Survey on Drug Use and Health, a comprehensive and representative, national survey that relies on anonymous responses. Using the data available from the survey, they match people in the survey with others who are most similar in terms of observable variables like income, education, and age. They do this through both distance matching and propensity score matching.

As their baseline approach, they match people who are self-employed (to proxy for output-based compensation) with those who work at not-for-profit firms (to proxy for input-based compensation). [Note: They also test the robustness of the results, but I won’t go into that here. You can read the paper.]

They have an extensive list of observable controls related to both characteristics of the individual as well as the individual’s risk attitudes and access to different types of drugs. By matching people in these two groups using these observable characteristics, it makes it more likely that observed variation (to the extent it exists) in the type of drug use in these pairs is primarily explained by the remaining difference: the difference in their labor market contracts.

Using a nearest neighbor approach (matching an individual with 1-3 of his or her closest neighbors in terms of observables), they find that the self-employed are 0.3 - 0.5 percentage points more likely to use prescription stimulants. They also find some evidence that the self-employed are more likely to use cocaine. By contrast, the self-employed are no more likely to use prescription sedatives, hallucinogens, or ecstasy. Each of these results are confirmed by their propensity score matching approach as well.

What these results seem to indicate is that the terms of labor market contracts do seem to have some effect on the choice of which drugs to use. Those with output-based compensation, like the self-employed, are more likely to use stimulants, but not more likely to use other types of drugs.

One could argue that the same type of person who uses stimulants is also more likely to self-select in self-employment. That’s fine. Again, they claim no causation. And remember, they are controlling for risk-taking attitudes and the self-employed are no more likely to use other types of drugs than the control group, so this isn’t just a preference for drug use generally or a difference in risk-taking attitudes more generally.

Price theory, it seems, has something to say here.

Conclusion

What this post hopefully illustrates is that economic incentives matter for decision-making. There might be a number of other factors that matter. When talking about drugs, there are entire academic literatures outside of economics that have studied drug use and addiction. An analysis of drug use within the context of price theory does not imply that this other work is wrong. It also doesn’t imply that the behavior explained by price theory is more important or more informative than those other approaches.

The basic lesson is that price theory can allow us to think through different types of decision-making, even if they are things that people might consider outside the typical purview of economics. Nonetheless, any decision that people make that involves costs can be understood through the lens of price theory. This is true even when the people making the decisions are erratic or otherwise unpredictable. Real-world constraints discipline decision-makers because those real-world constraints are the source of costs. As long as those constraints exist, price theory has a role to play in understanding human behavior.

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Price theory, he argues,

has moved to being a niche interest in economics. To some economists, especially from a few decades ago, that may sound almost contradictory, almost like saying “economics has become a niche field within economics.” Well, that is a bit true as well.

He quotes Steve Levitt, who retired from the University of Chicago at 57, on price theory: “I gotta say that the Chicago price theory really has lost.” And “I think it is essentially lost to posterity at this point.”

Not so fast. There’s a big difference between mostly dead and all dead. Mostly dead is slightly alive.

Sure, he’s probably right about the academic market. As he points out, theoretical papers in the top journals fell by 30% percentage points from 1963 to 2011. This has been documented for decades now and applies to theory more broadly than just price theory. He describes how graduate programs increasingly recruit math and computer science majors, on the theory that “the economics you can figure out along the way, or for some topics you may not need to know much of it at all.” Kevin Murphy no longer teaches the Chicago PhD price theory course. Kevin also no longer teaches the Chicago Price Theory Summer Camp has trained hundreds of PhD students over 14 years, including being foundational for my graduate time. (The 2026 edition has Garicano, Glaeser, Hurst, and Mulligan on the faculty, so the rearguard is fighting.) Even the framing of needing this type of camp, Cowen describes as “a rearguard action.”

In some sense, this is good for me personally. Product differentiation and all that. But I’m not so negative. Cowen is focused on one part of the production process: publishing formal research. Yes, it’s the high-status part, but it’s only one part.

Reasoning vs. technique

Cowen defines price theory as “the view that the basic intuitive economic concepts, as would be taught in intermediate microeconomics, are highly useful and for advanced problems too.” A hypothesis should be “intelligible in terms of microeconomic concepts that you can hold in your mind and understand.” You should be able to explain it to a smart non-economist.

That’s what we’ve been doing at this newsletter for six years. And what Cowen describes is real; the profession has moved on.

It’s important to note that he’s measuring the market share of price theory as a research technique, and finding it has fallen. Fair enough. The credibility revolution raised the bar for publishable empirical work. Machine learning generates predictions from 360,000 factors. Structural estimation recovers parameters from computational models. Mechanism design proves theorems. In all of these, the technique itself is a large part of the contribution. Price theory doesn’t work that way. You can’t build a career around “I thought clearly about the problem using supply and demand.” I would be much more high status if one could. But the profession rewards new techniques, it always has, and broad, basic price theory isn’t one in 2026.

Instead, price theory plays a different, hidden role in research.

Price theory is the reasoning that tells you whether the technique was pointed at the right question and whether the answer makes sense. It’s upstream of the identification strategy, upstream of the structural model, upstream of the theorem, heck even upstream of data collection. It’s the discipline that hears “corporate profits rose during inflation” and immediately asks, relative to what, as a share of what, and is that consistent with the standard model, or does it require a special story?

The profession has gotten extraordinarily good at technique. The tools are more powerful than they were 30 years ago. The results are more carefully identified. But every one of those results still needs someone asking whether the mechanism is plausible, whether the magnitudes are realistic, whether the finding generalizes or is specific to one context, and what it means for policy. Those questions are not answered by running the technique again with better data. They require economic reasoning. Price theory.

The profession does reward this reasoning, just not on its own. Kevin Murphy is the archetype, sure. Someone who understood price theory deeply enough to extend it into genuinely new territory on addiction, human capital, inequality. The combination of deep reasoning plus a frontier contribution gets you tenure at a top department. But you still see that today. Top people are combining data and theory to ask economic questions. Yes, lots of people ask non-economic questions but people still care about prices. In any seminar I’ve been to, the reasoning matters a lot.

And that reasoning is useful even when it doesn’t produce a paper. Most of the price-theoretic work happening in the world isn’t published. It’s an economist reading a paper and thinking “that can’t be right, because...” It’s someone writing a newsletter explaining why a popular argument about inflation is incoherent. It’s going about your day thinking through causality, prices, what happens at the margin. The academic market doesn’t reward this directly. It rewards the downstream output. But the downstream output is worse without it.

Cowen looked at the downstream output and concluded that price theory is in decline. That’s fine, but we can’t forget the upstream reasoning. And—shocking for a guy with a newsletter—I think the broader reasoning matters a lot. Unfortunately, I do believe the reasoning has declined too but not as much as looking at research would suggest.

What reasoning produces

Let’s talk about COVID inflation again. Someday I will have a new example, but today is not that day.

Consider what happens when people actually need to understand the world in real time. People come up with all sorts of theories. We got the greedflation, we got sellers’ inflation, all sorts of stuff made up ad hoc.

Price theory has a clear framework for this, and it doesn’t require some machine learning or a structural demand model or a novel identification strategy. When marginal costs increase, profit-maximizing firms reduce their markups because they can only pass along part of the cost increase. Rising markups are the signature of demand-side pressure. If you see profits rising alongside prices, the most natural explanation in the standard model is that demand shifted out. The greedflation argument got the causation exactly backward.

Josh wrote a whole newsletter called “Price Theory as an Antidote,” making exactly this point. Price theory disciplines one’s thinking, and the greedflation episode is his prime example. As Josh put it, if rising markups caused the rise in inflation, “one would need to explain why firms, across the board, suddenly and simultaneously increased markups.” They couldn’t, because the standard price-setting model says rising marginal costs compress markups, not expand them. If markups are expanding, demand is doing the work. That’s a price theory question.

We understood this pretty quickly using basic reasoning, before the formal research caught up. But the initial price theory reaction was basically right. As Christopher Conlon’s recent piece argues , “prices rising faster than costs is a generic feature of markets with market power and does not, by itself, indicate a change in the nature of competition.”

None of the credibility revolution’s tools resolved this debate. What resolved it was economic reasoning. Someone asked whether the mechanism made sense, and it didn’t.

Scott Sumner, in his response to Cowen’s book, makes a related point from a different angle. He describes how smart scientists routinely say foolish things about economics, like insisting that greedy firms won’t pass on lower input costs to consumers. Sumner’s example: tell a room of high-IQ scientists that reducing fees on property developers will lower home prices, and a significant proportion will roll their eyes. “These developers are greedy, and they won’t pass on lower costs to consumers.” But as Sumner puts it, they are greedy, “which is precisely why they’ll pass on lower input costs to consumers.” If the profit-maximizing price falls when input costs fall, a profit-maximizing firm charges less. It’s the symmetry. Economics, Sumner argues, is “extremely easy but also quite difficult.” The models are simple. The intuitions are hard to hold onto without them.

360,000 factors

Cowen is particularly worried about machine learning. He describes a paper that built a model with 360,000 factors to predict stock returns, reducing pricing errors by 54.8 percent compared to the classic six-factor model. He asks: “What kinds of intuitions do you think possibly can be supported by those 360,000 factors?”

He asks the question and walks past it. But it’s the right question.

When you have 360,000 factors, you can’t reason about the model using the model. You need something outside it. A framework for asking whether the factors are capturing real economic mechanisms or statistical noise. Whether the out-of-sample performance will hold. Whether the returns are compensation for risk or artifacts of the sample.

Those are not questions that more data answers. They require economic reasoning about what’s generating the patterns in the first place.

“Marginalism will not die,” Cowen writes, “but we will automate it, and in the process drain marginalism away from the human intuitions of most economists.” But automating the technique doesn’t automate the reasoning about whether the technique’s output makes sense. It increases the volume of output that needs reasoning applied to it.

Lynne Kiesling, in her response to Cowen, makes this argument with price theory’s own tools. AI, she argues, is a shock to the relative prices inside the academic knowledge economy. It lowers the cost of routine cognitive labor: literature search, coding, specification exploration, data cleaning. Those tasks are becoming cheap. What becomes relatively more scarce? Judgment. The ability to decide what’s worth studying, which mechanism is first-order, which assumptions are plausible, which results travel across contexts.

Kiesling’s point is that AI doesn’t weaken the case for economic reasoning. It raises the return to it. If execution becomes cheap, the binding constraint is knowing what to execute and whether the result means anything. That’s a price-theoretic argument about the profession itself, and it points in exactly the opposite direction from Cowen’s conclusion.

Cowen proves his own point.

I actually think that Cowen’s book is evidence against this thesis.

The first chapter is full of price-theoretic reasoning. He explains why under some Chinese compensation schemes, drivers who hit pedestrians face a perverse incentive. The fine for killing a pedestrian can run $30,000 to $50,000, but lifetime disability payments for a surviving victim can reach hundreds of thousands. At the margin, the financial penalty falls if the victim dies. He explains why homeless people cluster in high-amenity cities, since they don’t pay rent, and the high prices signal good amenities rather than costs to them. He analyzes abortion clinic access through the lens of marginal valuations. He examines the Obamacare subsidy debate by asking what a large quantity response implies about how much people actually valued the insurance.

All of this is exactly the “explain to a well-educated non-economist” reasoning he says is in retreat. He does it throughout the book he wrote to announce its decline. Sure, he and I are not the journal-centric heart of the profession, but we exist.

And then in chapter 2, he explains why price theory is declining, using price theory. The mechanism, he argues, is competition. “Once you reach a certain level of microeconomic prowess, it is hard to ‘understand marginalism better than the other person.’ So competition moves into other fields of endeavor.” When a margin is exhausted, competition shifts to other margins. The same framework that explains why firms diversify explains why economists stopped differentiating on price theory.

He applies competitive market logic to explain the decline of competitive market logic. For an economist, the reasoning is so deeply embedded it operates even when you’re writing its obituary. We can’t lose that and need to protect it in education going forward, but it is not lost yet.

The real repricing

As a research technique, price theory’s market share has fallen. Cowen documents this honestly.

But as economic reasoning, the upstream discipline that tells you whether a question is well-posed and an answer makes sense, the demand has grown. More empirical results, more ML predictions, more computational models, all producing outputs that someone needs to check against basic economic logic. Does the mechanism work? Does the sign make sense? What’s happening on the other margins?

Kiesling sees AI pushing in the same direction whereby cheapening execution, raising the return to reasoning. Sumner sees the continued difficulty of the reasoning itself, the “seeing around corners” that even smart people fail at without the model.

The question I’d focus on, and the question this newsletter exists to answer, is not whether price theory is a competitive research technique. It is one input but not the showy part. I’d focus on whether the world still needs people who can hear a claim about the economy and ask whether it makes sense.

It does.

Econ 101 is behind the times. No, I don’t mean because it’s too free-market or doesn’t include behavioral economics, feminist economics, game theory, or any of the other things every article complaining about Econ 101 mentions.

The major problem with most Econ 101 courses is that they take an outdated and successfully refuted approach to market structure and how prices are determined. As a price theory guy, that makes me sad.

Here’s a standard course or textbook: After focusing on perfect competition for one-third of the semester, the focus moves to the monopoly model. What supposedly differentiates these two situations is the market structure. Competition involves many buyers and sellers; monopoly has only one seller. From that starting difference, all the other results flow: competition is efficient, monopoly is inefficient, etc.

The problem? This approach, which used to be common in economics research, has been thoroughly discredited within the relevant economics subfield: industrial organization (IO). 50 years ago, Harold Demsetz taught economic researchers the errors in a little paper, “Industry Structure, Market Rivalry, and Public Policy.”

It’s time for Econ 101 to learn the lesson.

Pre-Demsetz: The Old Structure-Conduct-Performance Paradigm

Before getting to how we teach 101, it’s worth doing a quick recap on the history of industrial organization. Pre-Demsetz, the structure-conduct-performance (SCP) paradigm had an intellectual monopoly in IO. Monopoly. Get it?

The big name in this area was Joe Bain, but sometimes you also see it associated with Edward Chamberlin or even Joan Robinson. Under SCP, the performance of the market was something like consumer welfare. That’s what we care about. Performance was determined by the conduct of the firms in the market, say their prices. Ultimately, the conduct was determined by the structure of the market: how many firms are there? How high of a market share do the top 4 firms have?

We have the following simple, causal structure.

Structure → Conduct → Performance

There are multiple mechanisms for why this makes sense. In a Cournot model, the number of firms (structure) determines the equilibrium price (conduct) and, therefore, the consumer welfare (performance). It’s not restricted to Cournot, though. George Stigler’s 1964 “A Theory of Oligopoly” was really an SCP model of collusion. Fewer firms (structure) makes it easier to collude (conduct), which hurts consumers (performance). Basically, in all of these models, the core result—really the core assumption— was that fewer firms ultimately led to higher profits. In the applied work, you had a bunch of regressions of profit on the number of firms or price on a concentration measure.

What did they learn? A whole lot of nothing. They found zero, positive, negative, and everything under the sun correlations. A plurality of papers did find that higher concentration was associated with a higher price, but that was in no way a sure thing.

Today, we still find similar regressions being run outside of the field of industrial organization. For example, last year, there was an online debate about the relationship between concentration and inflation. Was concentration to blame for our recent inflation? Hal Singer ran one regression that found a positive relationship: higher concentration correlated with higher price increases. Jason Furman found a negative relationship. More recently, Conlon, Miller, Otgon, and Yao found no correlation between concentration and markups (not exactly prices but part of the same debate).

The whole debate is a mess, as I’ve discussed before.

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4 years ago · 15 likes · 2 comments · Brian Albrecht

Problems with Structure-Conduct-Performance

The fundamental problem with this type of regression and the SCP paradigm, more generally, is that both variables are endogenous; they are the outcome of some competitive process. Demsetz was the first to hammer this home.

Demsetz argued that market structure was an outcome and could be shaped by firm behavior and innovation. For example, he starts out his article by discussing the possibility of "concentration through competition." Instead of concentration falling from the sky (as it does in Cournot), concentration is an outcome of some previous competition. For example, high prices and profits can induce entry so that you will find less concentration.

Or maybe one firm is just more efficient. They will capture most of the market, but it doesn’t make sense to say the market was more competitive before they entered. If the threat of entry is real and we have what Demsetz called “competition for the field” or what Baumol and coauthors called “contestable markets.” The market price can be the competitive price, and profits can be zero.

The exact relationship between structure and performance depends on lots of features of the competition that exists in the market. This is especially true once we consider the dynamic nature of any of these markets. Prices and profits today drive dynamism tomorrow.

As I’ve cited before, Chad Syverson nicely summarizes the problem with the SCP approach:

Perhaps the deepest conceptual problem with concentration as a measure of market power is that it is an outcome, not an immutable core determinant of how competitive an industry or market is… ​​As a result, concentration is worse than just a noisy barometer of market power. Instead, we cannot even generally know which way the barometer is oriented.

Another way to see the problem with SCP is to think about the comparative statics approach that economists love to use. With comparative statics, you imagine something outside of the system you are studying (in jargon, an exogenous parameter) changing: the tax rate rises, oil prices spike, or an outsider develops a new technology. You then trace the effects of the parameter on some outcome variable (an endogenous variable).

For a basic comparative static, sometimes we assume buyers are passive price-takers, and then we can imagine the price being exogenous to the buyers and derive how their behavior changes. That is how we get the law of demand; when the price goes down (exogenously), the quantity demanded goes up.

But we should never do comparative statics on price for the whole market since the price is determined within the market. Price is an outcome determined by the interplay of the supply and demand system; it’s not outside.

What does it mean for the market price, not just the buyers’ price, to drop exogenously? What forces the price to go down? By assumption, we are fixing supply and demand, which means the price is determined already. But we are forcing price to change??? It’s an incoherent exercise.

This is the same reason we never reason from a price change. Sometimes we can consider the effects of the price of oil on the price of natural gas. That’s not what we mean. But we cannot reason from a price change for the market we are studying. The reason is that the price change in the market we are studying is not out of that market. Our predictions about the effects of a price change depend on the exact cause. For example, if the price goes down, we need to know whether that was a supply shift or a demand shift. Depending on what changed, the predictions about what happens to quantity are the opposite! That’s why we can’t start the story at “price change.”

In the same way, we can’t start the story with five firms simply existing in the market, and we can't do comparative statics on the number of sellers without imposing a very particular form of competition like Cournot.

Suppose a firm drops out of the market. What happens to the price? It matters why the firm left. If it is a Cournot model, the assumption is lightning strikes, and the firm dies. That begs the whole question; there is no real cause. But in the real world, there is a cause (or many). Did other competitors cut costs? Then we will see firms exiting, but prices drop. Did the government say a firm cannot produce anymore? Then we will see prices rise. The first step in the causal chain to prices and consumer welfare matters, and that first step is never simply “structure.”

In reality, firms are constantly innovating and adapting to changes in the market. For example, they may invest in new technologies to reduce costs, or they may develop new products to differentiate themselves from competitors. None of those key features of markets show up in SCP.

Demset’z insight absolutely destroyed SCP and became core to the IO approach that came to prominence next, the so-called new learning approach. But even when new learning was supplanted by the game theory revolution and what is now called modern industrial organization, this insight from Demsetz remains prominent. The dynamic nature of competition matters, and the structure is not exogenous. In fact, I’d say game theory was such a success in IO exactly because it was able to capture aspects of this dynamic competition. Game theory emphasizes the strategic interactions between firms, which opens up the theory of contestable markets mentioned above. As Berry, Gaynor, and Scott Morton put it, "the structure-conduct-performance approach has been discredited for a long time."

This is not one of those areas where I am just crazy and out of step. Demsetz’s insight is mainstream IO today.

Structure-Conduct-Performance in Econ 101

With that whirlwind history of IO behind us, we can now turn back to Econ 101. (That’s enough spinning metaphors for one newsletter.)

Despite the significance of Demsetz's contributions, introductory economics courses have largely ignored the evolution of IO over the past 50 years. Instead, they continue to present the structure-conduct-performance approach as the primary framework for comparing market structures.

As I said above, the introductory study of economics has traditionally focused on two types of market structures: perfect competition and monopoly. Introductory economics courses often present these structures as polar opposites, with perfect competition representing the ideal market and monopoly representing a market with significant inefficiencies. Under perfect competition, firms are assumed to be price-takers with no ability to influence the market price. In contrast, under a monopoly, firms are assumed to be price-setters with significant market power that allows them to charge high prices and earn large profits.

We should now recognize that this is just the discredited SCP paradigm with all the problems listed above. Despite the advances from Demsetz and others, introductory economics courses have failed to incorporate these new ideas. This is partly due to the inertia of the textbook publishing industry, which often takes years to incorporate new research findings into textbooks. Some of that lag is good. I would never say that Econ 101 should follow the latest fads in the research community, even those rare fads that I like.

Instead, the goal of 101, in my eyes, is to provide lasting knowledge that the students can take with them. Something being central to IO for 50 years crosses the “lasting” threshold. Getting rid of the SCP approach also has the benefit that, unlike adding something like behavioral economics to Econ 101, post-SCP is actually accepted within the relevant economics community. Unlike adding more game theory, it requires no new tools, so it is easy for a 101 course.

The biggest way to improve on the current approach is to remove the monopoly/competitive framing. It’s a language issue. The structure or number of firms is not the important feature. It’s even extremely misleading. The monopoly model doesn’t apply only to monopoly but to any firm with market power. Firms can have market power even when there are infinitely many of them. Similarly, the competitive model doesn’t require many sellers but sometimes goes through with two sellers (as in standard Bertrand) or even one active seller (if the market is contestable). The structure is largely irrelevant!

Economic Forces

Common Chicanery about Competition

Over my next few newsletters, I plan on digging more into the details of our favorite model, good ol’ supply and demand. Shocker. I know. Before doing that, I want to make sure we are on the same page about what the baseline model actually says. In particular, I want to quickly go through three myths/fallacies/willful-misreadings-for-someone’s-political…

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6 years ago · 12 likes · 4 comments · Brian Albrecht

Moreover, many (though not all) of the insights of supply and demand go through even in a world that isn’t perfectly competitive. This is why I focus so much on supply and demand. For example, when oil prices rise because of an invasion by Russia, the quantity drops, and the price rises across many models. It’s just easier to show that using supply and demand, so that’s where we show it.

Why would we undersell the main topic of our course and the main tool economists have (supply and demand) by filling students’ heads with nonsense like “this holds with many buyers and sellers with identical goods and perfect knowledge,” all of which are wrong claims in theory? They are also wrong empirically. Competition outcomes occur all over the places with few sellers and imperfect knowledge.

If we need a new framing, let me suggest the old UCLA distinction between price-takers and price-searchers. This is the distinction Alchian and Allen make. While not perfect, it has two advantages over the competitive/monopoly dichotomy. It removes the focus on the structure of the market for all the reasons above. It also naturally leads into questions of why Firm X is a price-taker while Firm Y is a price-searcher. Ultimately, it must be that it is more profitable for each firm to take this approach. But why? That’s a result to derive and discover about the market. It is an invitation to study, not an assumption.

Ultimately, I’m not perfectly satisfied with Alchian and Allen’s framing either. I am all ears for better approaches. Let me know in the comments. But I am sure we can’t keep going the way we have been in Econ 101.

In a previous post, for example, Brian discussed Casey Mulligan’s work on the topic. Casey’s works focuses on the idea that there are prescription opioids and illicit opioids and these tend to have different prices. As a result, consumers have a non-convex budget set and a change in the price of prescription opioids can have dramatic effects on consumption since consumers must substitute away from the higher-priced prescriptions to a lower-priced illicit alternative. Rising (prescription) prices can lead to more consumption. This has important implications for policy and highlights the important role that price theory can play in the evaluation of policies designed to reduce opioid use.

In this post, I would like to address a particular type of policy response that requires a different type of analysis. In my home state of Mississippi, the state legislature has approved public schools to keep Narcan in stock. Narcan is a medication that can be used to reverse an opioid overdose. The University of Mississippi, where I work, even offers a training program through the School of Pharmacy that trains people on how to respond to opioid overdoses as well as how to administer Narcan.

News accounts discussing this legislation and these training programs tend to highlight success stories of people saved from opioid overdoses by receiving the medication in a timely fashion. Proponents of this law argue that it will save lives. In fact, to make this argument, a number of stories cite the number of reported uses of Narcan that have saved people’s lives.

But is this true? Does Narcan save lives? There is a sense in which it obviously does. The stories of people whose lives have been saved from receiving the medication are real and these people are alive today due to the existence and use of the medication. However, this is not how an economist would answer the question. The relevant question is not whether we can identify people who have received the medication and survived. Rather, the relevant question is whether fewer people are dying of opioid overdoses relative to the counterfactual in which this medication was unavailable. This is a more difficult question to answer, but price theory can help us sort some things out.

And although it is too early to empirically measure the effects of the policy, price theory can serve as a useful guide for thinking about how to measure the effect of the policy relative to the counterfactual.

A Brief Diversion About Price Theory and Taxes

Although the policy I just described has nothing to do with taxes, I nonetheless think that we can draw lessons from the price theoretic approach to taxes. Please stick with me while I review the price theoretic approach to taxation. I will then apply the same logic of this approach taxes to the market for opioids via a useful analogy. I promise it will be worth it.

When we think about taxes on a particular good, there are two types of costs that are created for participants in this market. There is the cost of paying the tax (the tax revenue that goes to the government) and there are the foregone benefits of the gains from trade that could have been had, but are no longer feasible.

For example, consider a simple example. Suppose that the price of milk is $3 per gallon and people buy 10 gallons of milk. Now suppose that the government levies a tax of $1 per gallon on milk. For typical supply and demand framework, we get the following results. The price paid by consumers goes up. The price received by sellers goes down. The difference between the two prices is $1. The quantity traded declines. Suppose that the quantity that is traded falls to 7 gallons of milk. It follows that the tax revenue generated by the tax is $7. But this isn’t the total cost. There are people who are wiling to pay an amount of money that is above the marginal cost of producing another gallon of milk. However, the difference between their willingness to pay and the marginal cost is less than $1. Thus, without the tax, these buyers and sellers would trade. With the tax, they do not. This is a cost also. We call this additional cost the deadweight loss. Chicago price theorists call this loss the “excess burden” of the tax.

Taxes and the Expenditure Function

Now that we have taken this very brief diversion to think about taxes, I would like to add a little wrinkle to the analysis. A lot of times when we are discussing demand, we are focused on Marshallian demand curves. These Marshallian demand curves can be derived from a utility maximization problem. A consumer chooses how much to consume of each good in order to maximize his or her utility subject to the consumer’s budget constraint. The net result of this approach is to derive the demand for each particular good as a function of its prices, the prices of other goods, and the person’s income. Every point on a Marshallian demand curve is a combination of price and quantity demanded holding other prices and income constant. Every point on an individual’s demand curve is a utility-maximizing point.

As I discussed in a previous post on price theory and the price level, there is an alternative approach to demand. This approach views the consumer’s problem as choosing amount to consume of each good in order to minimize the expenditure required for a given level of utility. This approach derives Hicksian demand curves in which the quantity demanded of a good is a function of its price, the prices of other goods, and the level of utility. It follows that when we plot Hicksian demand curves, we are plotting combinations of price and quantity associated with a particular level of utility, holding all other prices constant.

As I mentioned in my post on the price level, what makes the Hicksian approach useful is that it allows us to utilize something called the expenditure function. The expenditure function can be thought of as follows. Suppose that you know all of the prices of all of the goods. You can plug those prices into the Hicksian demand curve for each good and get the quantity demand for every good, given that set of prices. Then you can take the price of a good multiplied by its quantity demanded, given those prices, to estimate the expenditure on that good. When you add up these expenditures across all goods, you get a measure of expenditures for a given level of utility. What is important about this expenditure function is that you can then consider what happens when things change. In particular, you can think about what happens to total expenditures as the price of one particular good rises and everything else stays the same while simultaneously holding utility constant.

The expenditure function is not only useful for the measurement of the “cost of living,” as I illustrate in my post on the price level, but it has more broad applicability. In fact, the Chicago brand of price theory emphasizes the importance of the use of the expenditure function for understanding things like the cost of taxation.

For example, suppose that we think of our tax example. Consider again the market for milk and let’s suppose for the time being that the supply curve is horizontal, such that milk suppliers are willing to supply unlimited quantities at the going price. Or, put differently, the amount traded is demand-determined. The imposition of the tax on milk will cause the price that consumers pay to rise by the amount of the tax. We thus move along our Hicksian demand curve to a new lower quantity demanded as the price rises from say p to p+t. Now consider the difference in the expenditure function from the imposition of this tax. It must be true that:

Expenditures after the tax - Expenditures before the tax = Tax Revenue + Excess Burden

In other words, when the tax is imposed and the price increases by t, the additional amount of expenditures necessary to keep utility constant would be equal to the amount of taxes paid plus the foregone gains from trade. I will now re-write this equation as follows:

The cost of the tax = Tax revenue + Excess Burden

Why is this useful? Well, it is useful because it allows us to focus on costs while holding utility constant. As a result, we can focus on the composition of the costs associated with taxation. For example, suppose that the government decides to raise the tax on milk from $1 per gallon to $2 per gallon. We know that this will increase the total cost of the tax. We know this because the total cost of the tax change will be measured by the area to the left of the Hicksian demand curve in p-q space between prices p + 1 and p + 2. However, sometimes what we are worried about is the composition of the cost. Is the change in the cost largely in the form of greater tax payments? Or is it largely excess burden?

More importantly, the change in the excess burden can be greater than the cost of the tax! This is not hard to understand why. Consider that tax revenue is measured by the tax per gallon multiplied by the number of gallons purchased. When you increase the tax on milk, the tax per unit goes up. However, the quantity demanded of milk goes down at the the higher after-tax price. Thus, the effect of the increase in taxes on tax revenue is going to depend on the elasticity of demand. If demand is very elastic, a small increase in the tax on milk will result in a larger percentage reduction in the quantity demanded and tax revenue (taxes paid) will decline. But since the cost of the tax must rise, this implies that the increase in the excess burden is even greater than the cost of the tax. On the other hand, when demand is inelastic, the rising cost of the tax will be split between higher taxes paid and a larger excess burden.

Using this Framework to Think About the Narcan Policy

Okay, so what in the world does this have to do with the Narcan policy I mentioned at the beginning of the post?

Consider the market for opioids. There is a Hicksian demand for opioids, just like anything else that people want to consume. Even if we abstract away from all other drug-related public policies, we should recognize that there is an implicit tax associated with opioid use: there is some chance that it will kill you.

Again, let’s think about Hicksian demand and a horizontal supply curve.

We can think of the implicit tax rate on opioids as the number of opioid overdose deaths divided by the quantity consumed. This is the probability of an overdose death per unit of consumption. If people using opioids take this into account when they are making decisions, then this is equivalent to a tax on opioid use equal to the probability of death associated with each unit consumed.

Note that this implies that “tax revenue” in this example is given as

Tax revenue = Deaths/Quantity * Quantity = Deaths

Or, to think about this in terms of the cost of the implicit tax compared to the world in which it was impossible to overdose. This cost is given as:

Cost of the implicit tax = Deaths + Excess Burden

In other words, the cost of this implicit tax to those in the market is the number of deaths caused by opioid overdoses plus the foregone gains from trade that would have occurred if people could use opioids without this implicit tax.

Now, let’s consider the Narcan policy. If there is Narcan on hand, anyone experiencing an opioid overdose can be administered Narcan and the drug user’s death can be prevented (I’m of course abstracting from efficacy rates and timely administration for simplicity of analysis). The increased prevalence of Narcan in public places therefore reduces the probability of death from an opioid overdose. Put differently, the policy lowers the implicit tax associated with opioid use. From our discussion of taxation, we know that the lower tax means a lower price, which necessarily means a lower cost of taxation. What is the composition of this tax cost reduction?

The critical issue here is what happens to deaths when the implicit tax declines. The answer is not obvious. A lower implicit tax implies that the quantity demand of opioids will increase. Whether deaths rise or fall will depend on the elasticity of demand for opioids. If the demand for opioids is relatively inelastic, then deaths will decline since the implicit tax will decline by a greater percentage than the quantity demanded rises. However, if the demand for opioids is elastic, then the percentage increase in quantity demanded will exceed the percentage decline in the implicit tax rate and deaths resulting from an opioid overdose will actually go up.

This is important for thinking about the implications of the policy. For example, if the demand for opioids is elastic, this implies that the benefit of a reduction in excess burden is greater than the overall benefits. This means that the increased benefits of risk-taking behavior exceeds the total benefits of the policy itself and overdose deaths rise. Surely, that is not what policymakers are intending.

But even if the demand for opioids is inelastic, this means that only some of the benefits come in the form of a reduction in the number of opioid-related deaths. Some of the benefits still come from the enjoyment people get from increasing their opoid use.

This is what I meant at the beginning of the post when I said that it is unclear whether Narcan reduces deaths. It reduces the “seen” deaths because we see people’s lives saved by the administration of the medication. However, there are potential “unseen” deaths as a result of the reduced cost of such risky behavior. The number of lives we observe being saved from the availability of the medication overstates the number of total lives saved.

Nonetheless, what this analysis does is give us a way of measuring the extent to which this policy can be successful. The key measurement variable would be to determine what happens to the number of opioid-related deaths after the policy is implemented. If the reduction in the number of deaths is large, then the policy is having its intended impact. On the other hand, if the reduction in the number of deaths is quite small, or if the number of deaths actually rise, then this tells us that the primary beneficiaries of the policy are those who simply enjoy using opioids.

The important point here is that price theory not only gives us a guide to thinking through the effects of the policy, but it also tells us what we should be measuring to judge the effectiveness of the policy in relation to its intended goals.

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A lot of news stories have turned that into a discussion about inflation and recessions. I think we need to be careful about how we think about this, especially if we are just thinking about the U.S. economy.

It seems that the people doing the debating haven’t updated their model of the U.S. energy economy. As I’ve explained before, it’s kind of an artificial divide between supply and demand. From another perspective, the U.S. hasn’t had a supply shock, at least not directly. We need to be careful. The country they’re worried about, the one that hemorrhaged hundreds of billions of dollars every time OPEC sneezed, doesn’t exist anymore.

How big of a deal for the U.S.? Let’s use price theory to do some basic calculations.

Who Gains and Who Loses?

When energy prices rise, every country has consumers who lose and producers who gain. High prices or low prices aren’t blanket good or bad. The national effect depends on which side of the trade you’re on.

Think about extreme examples. Take Saudi Arabia. When oil hits $118, Saudi consumers pay more for gasoline, just like everyone else. But the Saudis now export about 7 million barrels a day to the rest of the world.

Clearly, the export revenue dwarfs the consumer cost. When we add it all up, an oil price spike is a massive windfall for Saudi Arabia. This changes terms of trade and other aspects, but the key is that ultimately those exports are more valuable and give Saudis control over more real resources in the global economy. That’s why nobody runs a headline asking whether Saudi Arabia is “hurt” by high oil prices. Again, yes some people are (especially in a brutal regime that doesn’t share gains), but at a conceptual level it makes sense that we shouldn’t think of a negative oil shock as hurting them.

On the flip side, think about Japan. Japan produces almost no oil. Every barrel is imported. When the price doubles, money flows out to foreign producers and no Japanese producer captures the other side. They are poorer and control fewer resources. Japan is unambiguously worse off.

In general, when the price of oil rises, consumers pay more and producers earn more. And consumers here means anyone using oil as an input, not just people at the gas pump. In a closed economy, when a Saudi consumer pays more for gasoline to a Saudi producer, money changes hands inside Saudi Arabia. Nothing enters or leaves the country. It’s a transfer, not a national gain or loss. The same is true inside the U.S., inside Japan, inside anywhere. Domestic transactions wash out in the aggregate.

The part that doesn’t wash out is the cross-border piece.

When a Japanese consumer pays more per barrel to a Saudi producer, that money leaves Japan and enters Saudi Arabia. No Japanese producer captures the other side. So the national welfare effect—to a first-order approximation—for any country is just: how many barrels cross the border on net, times the price change per barrel.

where M̄ is the average of net imports before and after the price change.

If you’re a net importer, M̄ is positive and the welfare change is negative. You lose. If you’re a net exporter, M̄ is negative and it flips. You gain.

Think of it like owning your house when house prices double. As a homeowner, you’re richer. As someone who needs housing, you’re poorer. If you’re staying put, the two effects cancel.

The nice thing about this simple calculation is that you need just two numbers: how much do you import or export on net, and how much did the price move?

How can we possibly say that with so little information? What about elasticities? Aren’t those crucial?

Remember this is a first-order approximation. When prices rise, consumers and producers both respond. Consumers buy less. Domestic producers supply more. Imports shrink. But the value of those marginal barrels, the ones no longer traded, is approximately equal to their cost. The first barrel you stop importing was barely worth importing in the first place. The welfare effect of the quantity response is second-order, a triangle.

The first-order effect is the full rectangle, equal to the price increase multiplied by the volume traded. That rectangle is pure transfer, dollars moving from buyers to foreign sellers on every barrel still traded. Yes, you need elasticities to calculate the triangle. You only need quantities and prices to calculate the rectangle. We will say more about this later.

So, where does the U.S. fall?

For most of the post WWII period, firmly on the importer side. For example, in the 2000s, the U.S. was importing 10 to 12 million barrels a day. Let’s keep easy numbers. From April 2009 to Feb 2011, oil price doubled, from $50-100. doubling of oil prices meant a national loss on the order of M̄ × ΔP ≈ 10 million barrels/day × $50/barrel × 365 days ≈ $180 billion per year, about 1% of GDP transferred to foreign producers. The true loss was somewhat less, because Americans reduced their imports in response. We will say more about that. But the sign was obvious and the magnitude was nothing to sneeze at.

I think this is everyone’s starting point for thinking about oil prices. We saw the importer story play out again in Europe after Russia invaded Ukraine in 2022. Europe depended on Russian pipeline gas.

But the U.S. is not in that position anymore.

The shale revolution, which accelerated through the 2010s, turned the U.S. into a net petroleum exporter. In 2023, the U.S. exported 10.15 million barrels per day of petroleum products while importing 8.51 million, a net export position of 1.64 million barrels per day.

So, today, the formula has flipped. A price increase on goods you sell to the world is a terms-of-trade gain. At $50 per barrel above the pre-war baseline and using pre-war export volumes, the U.S. comes out roughly $30 billion per year ahead. As shale producers ramp up and consumers cut back, net exports grow, and the true gain is larger. That’s small relative to a $30 trillion economy, but the direction matters. The U.S. gains from this crisis. Not by a lot. But it gains. It certainlty doesn’t lose like

And we can follow this through to other markets to get beyond the simple baseline. Petroleum is only part of the story. The U.S. is also the world’s largest exporter of liquefied natural gas, shipping 15 Bcf/d in 2025.

Between petroleum and natural gas, the U.S. net gain from the crisis is in the range of $60 to $70 billion per year. Small relative to GDP, but taken together, the U.S. is better off, not worse. Again, this is the first cut. There are more complicated stories about supply chain networks and what not and any adjustment is takes time to adjust to.

You can see it in prices. I’ve talked about the oil price as if there is only one but that’s not quite right. The Brent-WTI spread has blown out to roughly $10, more than double the usual $2 to $5 gap. Brent reflects every barrel exposed to the Strait of Hormuz. WTI reflects a domestic market backstopped by shale.

Now, this first-order logic is usually applied to small price changes, where the triangle is negligible, just looking at one side. But here, a 73 percent increase in the price of oil is not small. The triangle matters.

But we still have two offsetting triangles. So which way the bias runs. For a net importer, the quantity response shrinks imports: consumers cut back, domestic producers ramp up, and the country buys fewer foreign barrels. The rectangle calculated at the original import volume overstates the loss, because some of those barrels are no longer being purchased. The triangle correction makes the loss smaller.

For a net exporter, the same responses work in reverse. Higher prices mean more domestic production and less domestic consumption, so net exports grow. The rectangle calculated at the original export volume understates the gain, because the country is now selling more barrels at the higher price. The triangle correction makes the gain larger.

What about inflation/stagflation?

The above was about quantity. What about the price level? Does this perspective change anything?

Let’s think through a simple AS-AD model. When I used to teach the 1970s oil shocks, I’d talk about that as a negative supply shock. Because of oil prices rising, production costs rise, so firms produce less at every price level while charging more. Prices go up, output goes down.

But that’s a bit too simplistic. I know. Shocker that literally the first model doesn’t capture everything. But we can build from it.

The nasty thing about oil is that it hits everyone. It’s not just a supply shock. As we worked out above, oil really hits both sides of the market. (HT: Pedro Serôdio for flagging this inflation connection.) It’s a demand shock, and which way demand moves depends on whether the money stays home.

For a net importer, an oil shock shifts AS left unambiguously. Energy is a production cost, and the price increase is a pure drain. That’s the story I told. But AD shifts left too, because income flows to foreign producers. You get the supply dilemma plus a demand recession on top. The AD shift makes the quantity drop worse but actually tempers the inflation part.

For a net exporter, we still have the AS shock left. Energy is still a cost of production, so there is cost-push pressure. But the export windfall raises income, which works the other way. You have a bigger inflation spike but less of an output fall.

We can see this with the trust ole graphs. Both panels below start from the same equilibrium. AS shifts left by the same amount in both. The difference is entirely on the demand side. For the importer, income drains to foreign producers, so AD shifts left too. For the exporter, the revenue stays home. AD shifts right.

Quantitatively, the AD effect may not be too large for the United States, but it is definitely large for large importers.

I think this helps us make sense of oil shocks in recent years. Europe after the Russian invasion in 2022 is more classic stagflation, both curves shifting the wrong way. The U.S. in 2022 wasn’t really affected.

This complication illustrates the difficult spot the Fed is in with “seeing through supply shocks.” PCE includes gasoline and heating oil directly, so it will look alarming. But the GDP deflator, which tracks domestic production prices, tells a calmer story. If the Fed reacts to the PCE number and tightens aggressively, it risks creating the recession that the oil shock itself would not have caused.

Shocks still hurt

If the country gains, why does $118 oil feel so terrible?

The big thing is that producers and consumers are not the same people. When oil prices rise, energy companies and mineral rights holders capture enormous gains. Workers take a hit to their real wages. Sure, the national pie doesn’t shrink. But any price change has winners and losers.

Again, we can think through the logic in a simple example. Start constant returns to scale model of production. If you can replicate a business by doubling all its inputs, then doubling inputs doubles output. There are no fixed factors collecting rents. Every dollar a firm takes in goes out the door as payments to labor, capital, or energy.

Revenue equals costs, always. So if the energy bill goes up by a dollar, wages or capital returns must go down by a dollar. The question is which one gives. Write this in percentage changes: the percentage change in the output price is a weighted average of the percentage changes in input prices, where the weights are each input’s share of total cost.

Who eats the cost?

As we stressed in the context of capital taxation, in the long run, capital is mobile. It flows to wherever the return is highest. If a factory in Ohio offers a lower return than one in Germany, investment goes to Germany. Capital won’t accept a lower return just because American energy got expensive. It leaves, and keeps leaving, until the return is back to normal.

Capital can escape. Workers can’t. They live here. They work here. So labor eats it.

We can work out the number. The real wage decline equals the ratio of energy’s cost share to labor’s cost share, multiplied by the real increase in energy prices.

Today, the energy cost share in the U.S. is about 6 percent. The labor share is about 58 percent. The ratio is roughly one-tenth. Oil is up about 70 percent, but natural gas — half of U.S. energy consumption — has barely moved, so the composite energy price is up maybe 35 percent. Have I mentioned this is back-of-the-envelope? One-tenth times 35 percent. We’re talking a real wage hit of 3 to 4 percent.

That’s nowhere near the 1970s. Back then the economy was far more energy-intensive. Suppose energy’s cost share was double what it is today. Natural gas was regulated and pegged to oil, so when oil quadrupled, everything quadrupled. A bigger price shock hitting a bigger cost share produced real wage declines on the order of 15 to 20 percent. Way worse than today (so far).

Of course, nobody is only a worker. You might take a 3 percent real wage cut and also hold Exxon shares that are up 40 percent. People own mineral rights, have 401(k)s heavy in energy, heat their homes with cheap natural gas. How much the shock hurts you personally depends on your whole mix of income, not just the paycheck. But most people’s mix is mostly paycheck.

But, again, we are neglecting the exporting. Some workers are the producers. Their wages are going up, their overtime is going up, their industry is hiring. The cost-share identity gives you the average. The average hides the fact that oil-patch labor is on the winning side.

All of the above came from a handful of simple models. One equation for national welfare. One cost-share identity for real wages. One AS-AD diagram for inflation. None of them required a computer. None of them required forecasting anything.

The gut reaction to $118 oil is that it’s bad. The price theory reaction is instead to work through the models, bad for whom, through what channel, and are you sure? The answers turn out to be more interesting than the panic.

I was wrong. People really seem to be mad at grocery stores and policymakers in certain parts of the country seem intent on figuring out ways to punish them.

What is Going on in Washington?

Politicians in the state of Washington seem particularly upset with grocery stores. A recent story in the Seattle Times summarizes what is going on there. In short, there is an exodus of retail stores from Seattle and the surrounding area. Fred Meyer, a chain operated by Kroger, announced it is closing five stores. This comes in the aftermath of smaller stores like Rite Aid announcing closings of stores in the area as well.

The stores say that they are leaving because of regulatory costs and rampant shoplifting that are raising their costs and making stores unprofitable. Critics claim that these stores took advantage of the pandemic to raise prices and that removing stores from particular neighborhoods will leave people without access to food, creating a so-called “food desert.”

Politicians have responded by promising action. Some have advocated publicly-owned grocery stores. The current Mayor Katie Wilson has said that the city will not allow grocery stores to leave. It is not entirely clear what that means. However, there is legislation introduced by the Washington legislature that would allow the city to use eminent domain to seize the property of grocery stores for use as publicly-owned grocery stores.

I think it is important to note that it is a long-running political trick used by Marxists to impose unbearable costs on firms to create the pretext for expropriation. Nonetheless, this is a newsletter about price theory and not politics. Thus, I will not assume bad faith. Instead, I will take the politicians at their word and use price theory to explain why their proposed solutions are unlikely to produce their desired outcome.

The Underlying Price Theory

Let’s start with basic price theory.

We experienced high inflation. Some people blamed supply-side factors. Some people blamed demand-side factors. But this isn’t a hopeless debate. There are various things that we can do to evaluate which is the more likely outcome. In fact, this is a question for which some basic price theory is especially useful.

For example, if this is a supply-side problem, then rising costs are to blame. If this is a demand-side problem, then fiscal and monetary stimulus are to blame because such policies increase the demand for goods and services, generally. What does price theory have to say about this?

Those who blame grocery stores tend to take a supply-side view. Goods are in shorter supply, it is more costly to supply those goods to the market and the grocery store is “taking advantage” of these cost increases by raising prices and earning higher profits.

Unfortunately for those making this argument, price theory says this explanation cannot be true. While it is true that unexpected increases in costs do lead to higher prices, they also lead to smaller margins. Demand curves slope down. Firms cannot passthrough the entire increase in costs and certainly cannot raise prices by more than the increase in costs. The supply-side story is therefore inconsistent with higher profit margins.

The only way to get higher prices and higher margins is if there is a broad increase in demand. These are caused by things like expansionary monetary and/or fiscal policy, both of which occurred during (and in the aftermath of) the pandemic-related shutdowns.

Thus, much of the backlash against grocery stores seems to be driven by a lack of understanding of basic lessons of price theory.

Optimal Stopping

One of my favorite concepts in all of economics is the study of inaction. The economics of doing nothing. What do I mean by this?

Well, a lot of economic decisions involve significant fixed costs that cannot be recovered later. There are a lot of examples, like the decision to build a new factory or store and the decision to move a long distance. Most of the time, the optimal thing to do is nothing. Behavior thus appears lumpy. If you look at the data, the spending of individual firms for things like capital investment tends to be relatively low or even zero in most periods. But then there are periods when investment spending is very high. This is because the firm has the option, but not the obligation to invest in a new factory or a new retail store. Because the investment entails a large fixed cost and the fixed capital investment might not be a perfect substitute for other firms, the firm must take care to make sure it is really worth building, given the uncertainty of the market. In other words, you don’t want to build the establishment if an immediate downturn in the market would render it unprofitable. The firm would want to wait until it has a significant cushion in the value of the investment that the firm could absorb in a downturn without squandering profitability.

But how can we think about that cushion or how big it would need to be?

It turns out that this is just a standard pricing problem. Since the firm has the option, but not the obligation to invest and this option doesn’t typically expire, the value of a potential project can be priced in the same way that an American financial option would be priced. Since it doesn’t expire, there is an additional problem to be solved, which is the decision about when to exercise the option (which itself affects the value).

The reason that this idea is important for the discussion of grocery stores is that it relates to entry and exit decisions of firms. Think about the decision to shut down. Doing so is costly. The firm is giving up the present discounted value of future profits. The firm also incurs direct costs of shutting down. Maintaining the real estate until one can find a buyer is costly. Finding a buyer is costly because the physical establishment was designed for the purpose of the firm. It is a particular type of establishment. Those who would have the greatest use are those that are in the same business. Given that your firm was unsuccessful, it is unlikely there will be competing firms jumping at the chance to operate in your location if you shut down.

But there is some basic logic here. If policymakers create a regulatory environment that is costly or if policymakers adopt a lax attitude on crimes like shoplifting, this significantly reduces the present discounted value of expected future profits. As a result, it makes it more likely that a firm that never would have left (or would have possibly left at some point in the distant future) will exit now.

Even policies like using eminent domain are likely to have perverse incentives. Eminent domain requires compensation to the party whose property has been taken. To the extent to which the fixed cost of exit is related to the maintenance cost of the real estate left behind, a policy of eminent domain might hasten the decision to exit of the marginal firm.

In addition, firms considering entering the area will take into account the costs of exiting before they even enter. This is something that Brian wrote about last week in labor markets. If it is hard to fire workers, then it is more costly to employ workers. If it is more costly to employ workers, firms will employ fewer workers. The same concept applies here. If a firm sees that the state is trying to raise the cost of leaving, then a firm might never enter in the first place. There is already uncertainty about profitability. That uncertainty coupled with the large, fixed cost of investment is what prevents firms from entering in the first place. Punishment costs associated with leaving thus increase the cost of entry because they lower the option value of exit. As a result, fewer firms are likely to enter.

We’ve Seen This Show Before

The public grocery store issue isn’t new. We have seen this show before. Brian previously wrote about Boris Yeltsin’s famous visit to a Houston grocery store. Even as a member of the Soviet elite, Yeltsin was amazed at the organization, scale, and scope of American grocery stores.

A lesser known story is that of the one-time famous fund manager, Jim Rogers, who took a trip around the same period of time in the opposite direction. Having retired young and wealthy, Rogers decided to take a tour of the entire world on his motorcycle. He documented the trip in his book Investment Biker. What he found when he visited Soviet Russia were stores with long lines and empty shelves. There was little product variety or access to basic things Americans take for granted.

In more rural areas, he met shopkeepers who told him that they had never experienced inflation. Prices hadn’t changed in years. Of course, prices had changed elsewhere. Poles were known to come into Russia, buy whatever was available, and return to Poland to sell the goods at market prices. Shelves were empty. And when global prices, like the price of oil, increased elsewhere, the Russians sold the oil at higher prices outside of Russia and rationed whatever was left inside of Russia.

The ability of prices to coordinate economic activity is what explains the abundance of options and the fully-stocked shelves at American grocery stores. Yet, this is precisely what policymakers want to eliminate. They want command and control over prices. They think that bureaucrats will do better at setting prices than the people like grocery store owners and managers, who possess a particular knowledge informed by past experiences and circumstances.

In fact, they give away the game when they say that they will simply use existing prices to control the profit margins. They’re simultaneously admitting that they need markets to operate effectively while noting that, unlike traditional grocery stores, they don’t need to make a profit to survive. That is apparently why one needs taxpayers. But if taxpayers are subsidizing these stores, the prices in the public grocery store might be lower, but the cost to the marginal consumer will be higher.

Costly Policies Are Not the Cure for Other Costly Policies

It is understandable why people are frustrated. Experiencing the highest inflation rate of the last 40 years was not pleasant for anyone. The prospect of losing neighborhood grocery stores, especially for those who lack their own transportation is not desirable. However, it is important to recognize that these unpleasant things are not inherent problems with our economic system, but rather the result of policy choices made by elected officials and central bankers. The solution is not to fight the costs of previous policies with new costly policies. This whack-a-mole policy environment will only compound the costs.

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I have my hobby horse about tech regulation and horrible antitrust laws, but I don’t think those are THE biggest reason. Instead, I agree with a recent piece by Pieter Garicano that points to labor market regulations. The timing and the magnitude fit much better than for other theories. See also their podcast discussion of it.

Lots of places have regulations. As the piece points out, California has lots of regulations. Labor regulations—specifically those that generate rigidity, compared to like labor safety regulations—are fundamentally different.

When it’s hard to fire workers, you never hire them in the first place. That’s the big idea. Yes, it’s super simple.

Still, in this newsletter, I want to augment Garicano’s piece a bit by going more explicitly through a basic theory to show the problem with rigid markets. There’s a bit more to it than “don’t hire since you can’t fire.”

Don’t worry; there’s no math this week. But we will work through a model, one that recognizes that workers become a type of “capital” stock. For businesses, stocks are slow to adjust. Worse than that, businesses are very hesitant to adjust their stocks (read labor force) in the face of uncertainty.

The Everything is Housing Theory

To explain the theory, let’s first talk about something a bit more standard first: housing.

When demand for housing rises, both prices and quantity adjust. Prices jump, construction ramps up. That makes sense. The market works the way the textbook says it should.

Now flip it. Demand falls. Prices drop. But quantity? The houses are still there. A wood-frame house left alone takes roughly fifty years to fall apart. Concrete lasts a century. Once the structure exists, the decision isn’t “build or don’t build.” It’s “use it, mothball it, or pay to remove it.” You can’t un-build them. Well, you can, but removal is usually the worst deal.

You can see this asymmetry across cities that experience a bust: the ghosts of demand that vanished, rotting in slow motion.

With a positive demand shock, prices and quantity both rise. But with a negative demand shock, prices fall but quantity can’t really drop. Yes, the flow of new construction can drop, but the stock of housing won’t. When people say “markets will adjust,” they’re imagining supply can move both ways. But if the only fast adjustment is “build less,” you don’t just get up and down, nice and smooth.

The cost of uncertainty isn’t that we could end up being wrong. That’s fine. Any market has risk and people cope. The unique problem here is that, anticipating this inability to un-build, you’re hesitant to build in the first place.

I’m focused on housing, because this is where we see it most explicit. There are something like 75 million single-family homes in the United States. About one million new ones get built each year. The flow of new construction is around a percent or two of the existing stock.

The key is that when demand falls, the market can kill the flow instantly: construction goes to zero. But it can’t kill the stock. Notice that the stock is a backward-looking object. It’s the accumulated result of every building decision made over the past fifty years. Prices crash immediately because they’re forward-looking: they capitalize the bad news right now. But the stock just sits there, because it only knows about the past. The adjustment margin is one-sided.

So you get a lot of unique things about housing, like it has much higher volatility in purchase prices than rental price, or than you get for other goods. This isn’t a housing post. We can dig into that another time.

The strength of this effect depends on how durable the asset is. Housing depreciates at two percent per year, so a negative shock lingers for decades. Software depreciates at thirty percent and a negative shock washes out in three years. The more durable the capital, the more uncertainty depresses investment.

As we’ve argued, chaos freezes economic calculation. This is the most explicit way we see it in a simple setting. It’s not that the calculation isn’t possible in these simple examples, but instead uncertainty is harmful because of irreveribilities. This also is closely related to the ideas. As Josh has written before, there’s an option value to waiting. This means uncertainty raises the bar before anyone pours concrete. People build later and build less. The two mechanisms stack.

Labor force as a rigid stock

Now let’s circle back to Europe and labor markets.

Imagine you run Siemens. To highlight the mechanism, let’s make it extreme. You can hire workers, but you can’t fire them. They can only quit. Let’s make it more extreme: workers only quit when they retire. So a company can only get turnover when workers turn 65. Well, we’re talking about France, so 50ish. Now, for your stock of workers, you’re looking at a turnover/depreciation rate that is more like housing than it is like software.

Now imagine there is a surge for Siemens products. Do you hire a ton of workers to fill that demand? No, you’re worried about having to fire them in the future but being stuck until they retire.

But it’s even worse than that. Suppose you knew demand would stay high forever. You know with certainty, so you aren’t worried about needing to fire them in the future when demand cools. We’ve shut down the simple mechanism that everyone knows about.

Even in this case, hiring is still really costly in the short run.

Where is Siemens getting those workers from? In our hypothetical, you can only hire people entering the labor market for the first time, since no one quits their job early. Not only is it a problem for Siemens that they won’t be able to fire people down the road, the fact that BMW (I really need to be more creative with my European manufacturing companies) doesn’t fire anyone means you can’t hire people.

This is the equivalent of it being extremely costly to increase the housing supply in a short time frame, even if you know demand will be there in the future because of demographics. There aren’t the people out there to hire. The short run supply curve is very steep.

In reality, both are going on. Siemens doesn’t know that demand will stay high forever. They can’t tell how long it will last. So they don’t want to hire because there aren’t the workers and because you will have to keep them forever. That’s the don’t hire because you can’t fire. You can’t un-build a house. You can’t unwind a European factory line. In the US, you can.

This simple mechanism explains how recessions can be so catastrophic for rigid markets. Let’s think about an increase in uncertainty, say coming out of the Euro Crisis. That uncertainty will be scarring; you’ll REALLY do not want to hire new workers, even if they are lying around (high unemployment) if you can’t fire them if it’s a double-dip recession. So the spiral perpetuates. Even if wages fall and you have a bunch of unemployed people, the uncertainty is too drastic in a world where you’re committed to workers.

We can also think about high risk industries. Think VC heavy industries. Suppose you KNOW AI will be a big deal and transform everything. (We almost got through a newsletter without AI.) That’s not the same thing as knowing your AI company will be a big deal. How do you hire? How do you invest? The forces discussed above say you don’t. So, yes, Europe’s issues far predate AI, but it’s going to get even worse for Europe without changes.

The big picture is that turning labor markets into housing markets grinds everything to a halt. And the key to economic competition and growth is that churn, that turnover to more productive firms. That’s not to say the ideal is where everyone is a freelancer. There are real economic gains to certainty and investing in the relationship. But there’s a downside too and when economic uncertainty rises, that downside increases. That’s what Garicano documents so well. As always, it’s basic economics :)

Building can be fast. Un-building must be slow. So volatility reduces the amount of durable capital the economy dares to create. The economy dislikes uncertainty because you can’t un-build a house. Europe has a labor market with the problems of the housing market.