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 abovee), 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 at Stanford makes the sharpest point. 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. If the worry is that the tax system tilts toward automation, fix that asymmetry before inventing a new tax on the machines.

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.

Economic Forces

Is Concentration Driving Inflation?

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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.

This month it’s Citrini Research’s “THE 2028 GLOBAL INTELLIGENCE CRISIS” (yes, all caps, so you know it’s serious). The post imagines a 2028 scenario: AI automates white-collar work, companies collapse, private credit blows up, mortgages default, unemployment hits 10%.

Stocks fell earlier this week. People panicked, based on a random blog post.

If you’re terminally online, you’re probably sick of hearing about this one already. I don’t want to go through point by point. Way too many people have already done that (and I’ll link some throughout.)

But if there’s a niche here, Economic Forces is about taking that step back and thinking through the economics, through the price theory.

So why do these types of arguments keep happening? How do they keep fooling smart people, but at the same time basically none of the economists fall for this line of thinking? More importantly, how can we avoid these pitfalls?

As usual, basic economics can help us a lot.

Because the Citrini post is not a new type of error. It’s the same error we see reading all sorts of news stories, this time dressed up in new clothes as fantasy. No economist or economics-adjacent person was blown away by it.

We have a few basic principles that rule it out before the conversation starts. As usual, we don’t need to invent new theories every time a new technology comes along. The old ones work fine.

Basic economics cuts against Citrini

The piece is a narrative around a few companies. That’s fine. The company-level stuff is worth thinking through. ServiceNow mechanics? Well observed. DoorDash losing its moat because AI agents don’t have a home screen? Maybe. Whether any particular company survives AI disruption is a fine question. Maybe the scenario is right for some of these firms.

But Citrini doesn’t stop at companies. They leap from “these businesses will be disrupted” to “the economy will enter a crisis.” And that’s a different kind of claim. Firm analysis is about what happens to one company.

That leap requires economics. Economics is about what happens in markets: where prices adjust, resources get reallocated, and one person’s lost revenue becomes someone else’s cost savings.

And every basic principle we have cuts against their narrative. Mankiw lists ten principles in chapter one of the intro textbook. They’re a little hokey. But they’re in the textbook because people keep violating them.

Let’s go through the red flags.

Ghost GDP (accounting identities)

Right at the start, Citrini introduces a concept called “Ghost GDP”: output that “shows up in the national accounts but never circulates through the real economy.”

Ummm… yea, that’s not a thing.

GDP is not a number someone estimates and hopes is roughly right. I mean they do estimate it, but that’s not what matters here. It’s an accounting identity. Every dollar of output is, by definition, a dollar of income to someone. There is no output that “doesn’t circulate.” If a GPU cluster in North Dakota does the work of 10,000 white-collar workers, someone owns that output. Someone earned that revenue. The money went somewhere.

Where does the money go? That’s the question Citrini never asks.

If labor’s share of income falls from 56% to 46%, that’s a massive shift. But it’s a shift to capital owners, not a shift into thin air. Capital owners do things with that income: they consume some, save the rest, and those savings finance investment. Citrini treats income accruing to capital as though it disappears from the economy. Their own numbers show it doesn’t. Hyperscalers are spending hundreds of billions on data center capex. That is enormous investment demand absorbing savings, which is the opposite of a demand collapse.

The essay needs the consumer economy to collapse and the investment economy to boom at the same time. It never reconciles the tension because it can’t. You cannot have GDP going up while every component of spending is going down. The identity won’t let you. As Steinberg puts it, “it only makes sense to invest in AI if there is income to buy the things the AI is generating.”

The spiral with no brake (supply and demand)

The same section describes a “human intelligence displacement spiral”: AI improves, fewer workers needed, layoffs increase, displaced workers spend less, more AI investment, repeat. “a negative feedback loop with no natural brake.”

There is a natural brake. It’s called prices.

This is the most basic idea in economics. Before accounting identities, before GDP, before any of that. Supply goes up, price falls. Price falls, quantity demanded rises. The system has built-in dampening feedback. That’s what equilibrium means. That’s what markets do. More basic, that’s what incentives do.

Are there exceptions? Sure. Sometimes feedback reinforces instead of dampens. Economic growth can build on itself: more capital, more productivity, more savings, more capital. Supply curves can slope downward. But that’s growth begetting growth.

Citrini is describing the opposite, a doom spiral where disruption begets disruption with no floor. Does that sometimes happen? Sure. We have panics and negative spirals, but the dampening forces are the default. You need a specific story to override them, and Citrini never provides one.

Citrini’s scenario freezes the economy at step one. Companies cut workers. Workers lose income. Spending falls. Recession. Full stop. They never ask what happens to the price of the services those workers used to provide, or what happens to demand when intelligence gets radically cheaper. It’s a simple point, but people forget one side of the market all the time. Citrini forgot the demand side entirely.

From a company to the economy (micro =/= macro)

Then the company stories begin. In “How It Started,” ServiceNow loses seats because its Fortune 500 clients are cutting headcount. “Each company’s individual response was rational. The collective result was catastrophic.”

What’s true for one company is not a prediction about the economy. There’s all the feedback we talked about above.

Also, the fact that the economy is huge. 160 million workers. 6 million firms. $28 trillion in output. Citrini tells stories about a handful of SaaS companies and leaps to the whole thing collapsing.

You can’t narrate the aggregate from the most-exposed slice. Even granting Citrini’s claim that white-collar workers are 50% of employment, the other 50% — healthcare, construction, government — operates on entirely different timescales. A hospital can’t replace nurses over a mid-year budget review. I just had a converation with someone in healthcare that was talking about the new tech of SHAREPOINT!

You can’t get to 10% unemployment by telling stories about software companies.

Even within those stories, though, look at what’s actually happening. ServiceNow’s revenue falls, but its customers’ costs fall too. The price of routine cognitive work drops. The price of human judgment in areas AI handles poorly? That barely moves. Some prices fall, others rise, and relative prices shift all over the place. That’s a market adjusting. And when relative prices change, new comparative advantages show up.

“For every new role, dozens obsolete” (comparative advantage)

Then comes the labor section. Citrini’s implicit logic: AI is better than humans at cognitive work, so humans lose those jobs. “For every new role AI created, though, it rendered dozens obsolete. The new roles paid a fraction of what the old ones did.”

We’ve heard this a bunch of times.

Again, let’s put on our Econ 101 hats. The assumption buried in there is that being worse at something means you don’t get to do it. That’s absolute advantage thinking. It ignores the most powerful idea in economics: comparative advantage.

Even if AI has absolute advantage in every cognitive task, humans still have comparative advantage in something. That’s a mathematical necessity, not a hopeful guess.

Here’s how it works. Suppose AI is 100x better than humans at coding and 10x better at in-person care. The cost to AI of doing one hour of care is 10 hours of coding foregone. The cost to humans of doing one hour of care is only 1 hour of coding foregone. Humans are the cheaper option for care, even though AI is better at both.

Comparative advantage is about relative costs, not absolute capability. Wages may fall in those tasks. But lower wages are the price adjustment that makes humans competitive, not a sign that humans are useless.

In the here and now, and I’m willing to bet in 2028, there is labor. And so comparative advantage is real.

The pie isn’t fixed (lower costs, more demand)

Comparative advantage tells you humans aren’t useless. But it goes further. The total amount of work to be done is not fixed. It has never been fixed.

And Citrini’s own essay shows this. In “When Friction Went to Zero,” they describe AI agents handling travel booking, re-shopping insurance, assembling itineraries. Token consumption rising 10x in a year. Stablecoin-based machine-to-machine commerce. Agentic shoppers running in the background.

They’re describing massive new economic activity while arguing the economy is stagnating.

Automation lowers costs, which lowers prices, which gives consumers more purchasing power, which creates demand for things that didn’t used to clear the market.

The ATM was supposed to eliminate bank tellers. The number of bank tellers increased after ATMs because cheaper branch operations meant more branches, and tellers shifted to customer service and sales. Every round of automation has done some version of this. (I’ll address the horse example in another post; I promise.)

The question is always “what new jobs will emerge when these tasks become cheap?” The essay doesn’t touch it.

An economy generating new forms of consumption is the opposite of a stagnating one. The essay assumes maximal disruption on the supply side and zero creation on the demand side. Its own content contradicts that assumption.

Again, if I wanted to sum up, its the price theory principle that markets are connected, and the seen/unseen asymmetry. The Citrini post is a very detailed, very vivid first link in the chain. It never gets to the second. And in economics, the second link is where the action is.

Nothin’ new under the sun

This error happens all the time. It’s a pattern that becomes easy to spot (with varied repitition). Different details, same structure. Someone observes something true about a company and leaps to a conclusion about the entire economy, without thinking through how prices adjust along the way.

During the post-Covid inflation: PepsiCo executives told investors they were raising prices. Kroger admitted to pricing above costs. Therefore, corporate greed caused inflation. But asking companies why prices rose told you nothing about inflation.

Kroger raising prices is the seen. The unseen is all the other markets. Those are relative prices: Kroger got more expensive relative to everything else, and that changes behavior everywhere. We need to remember to trace that out. traced how one price rising meant others adjusting.

Price theory as antidote

Economists make mistakes. I’m sure I’ll make one someday. But we’re relatively immune to this type of mistake, and the responses to Citrini show why. Noah Smith, Alex Imas, a month earlier, and Steinberg all applied standard tools, and the scenario fell apart. These responses are routine. That is precisely the point.

Yes, AI is genuinely new. What it can do is impressive and a little unsettling. I understand the impulse to treat it as something that requires a whole new economics.

It doesn’t!

The economy has absorbed general-purpose technologies before. The transitions were painful for real people, and this one will be too. But the analytical tools that help us think through those transitions are the same tools we’ve always had. Ben Thompson made a similar observation from the business side: the Citrini post is “grounded in a fundamental lack of belief in dynamism, human choice, and markets.” He’s right. But believing in dynamism isn’t enough. You have to reason through the logic of dynamism, choice, and markets.

You don’t need to spin stories out of thin air or write sci-fi from 2028. Supply and demand. Accounting identities. “And then what?” That’s the whole toolkit.

As Josh put it, price theory is the antidote to bad arguments. It was the antidote during Covid. It was the antidote for trade deficits. It is the antidote now. It will be the antidote next time.

I think that it is worth having a conversation about these issues, but that the discourse largely misses the point. Sometimes missing the point is deliberate because people are acting in bad faith.

Thus, in today’s newsletter, I would like to think through some of these issues in a much more serious way. First, I would like to talk about why replication matters. Then, I would like to address some problems with the criticism. For example, by appealing to certain statistics, it is easy to weave a narrative. However, it is important to recognize that these statistics reflect equilibrium outcomes. In addition, it is important to understand what replication failures do and do not mean for the usefulness of economics. Finally, I would like to conclude by discussing the importance of price theory (of course).

Setting the Stage

Let me begin by saying that I think replication is important. In part, it is important because it helps us to root out fraud in the profession. If people are making up data or manipulating results, it is important to know that this is happening and who is doing it. But even when people are doing work to the best of their ability, they will still occasionally make mistakes in collecting data or merging data from different sources. They might have errors in their code. Although these are just honest mistakes, it is still important to know that they exist. In fact, they give us an excuse to revisit the questions asked in that research not only using the same data, but also data from other places and times.

It is also important to keep in mind that we don’t always need literal replication. In other words, we typically think of replication as simply collecting the same data and estimating the same models to see if we get the same results. However, the literature often features similar analyses using different data or a longer sample. The accumulation of that research can be just as important as making sure a particular paper or result can be replicated.

Replication can also be done in different ways, including ways that aren’t easily identifiable to people searching for replications. For example, one of my first publications was designed to see how particular empirical results in monetary economics would hold up if researchers used properly measured monetary aggregates rather than the simple sum aggregates used in prior research and provided by the Federal Reserve. In that paper, I estimated the same models with an updated sample of data. I did so using both the simple sum aggregates used in previous research and the theoretically correct Divisia monetary aggregates. What I wanted to know is whether the results of those previous studies would hold up in a new, larger sample, and if so, whether replacing the simple sum aggregates with the Divisia aggregates would overturn the previously puzzling results. I found that the earlier work did hold up for the most part in the larger sample. I also found that the Divisia aggregates overturned the puzzling results.

I bring this up because this is a form of replication. As part of this project, I used the same methodology of the previous papers from the literature and I tested to see whether the results held up in a longer sample. But my starting point was to assume that the results would hold up. My hypothesis was that their puzzling results were caused by the use of an imperfect measure of money, not that these results wouldn’t hold up to an attempt at replication. Nonetheless, in the process, I did perform a type of replication. Similar types of work are often neglected by those searching for replication studies.

On the Importance of Solving for Equilibrium

One statistic that I have seen bouncing around is a time series of comments, replies, and rejoinders. What it shows is an upward-trajectory of published papers in economics that are comments, replies, or rejoinders to previously published papers beginning in the early 20th century. These types of articles peak by the late 1960s and early 1970s and subsequently decline. This dramatic reversal in these types of articles has been used to contribution to the debate by saying that this demonstrates that economists are no longer interested in debate, or in the validity of empirical results. I am not sure that this is the correct interpretation. I think it reflects a change in the publication process.

Economists in academia are by now well aware of the inanity of the peer review process. But you don’t have to be a member of the profession to notice this. Peer review of empirical research often goes through multiple rounds of revisions. The final product often consists of very long appendix, filled with robustness checks. The paper presents the main results. The appendix presents all of the other results that either the author or the reviewers (but mostly the reviewers) wanted to see to make sure that the result held up to other assumptions and model specifications. In some cases, the appendix can be longer than the paper itself.

In talking to older generations of economists, they lament that this has become the norm. When they were younger, this wasn’t the case. Although I haven’t studied it closely, it certainly seems like this presents a theory that would produce an observationally equivalent time series. In other words, whereas in the past people would submit comments in response to a published paper, many of those comments are now incorporated into the review process. Thus, it is not necessarily the case that economists have become less interested in debate or engagement with existing research, but rather that the comments and replies are happening behind the scenes with authors, reviewers, and editors.

In fairness to critics, I would prefer that the profession go back to the model of comments and replies. The review process is becoming more arduous, and unnecessarily slow. Why not allow reviewers who think the paper is worthy of publication to get published with the option to submit a comment that examines the robustness of the results to alternative model specifications? This is more difficult than it sounds, as it creates a different standard for publication and people will adjust on various margins. Nonetheless, it might be worth pursuing.

In addition, critics miss the mark on a valid criticism. One issue with the economics profession is that it is very hierarchical. One notable economist, let’s call him OB, once wrote a paper using a framework that he had previously rejected as too insignificant for publication. When the author of the original work inquired into what had changed, OB is said to have replied, “an idea isn’t discovered until it is discovered in Cambridge.” If you think that the profession stifles debate, one would be better served by focusing on the research networks and the snobbish tendencies of a subset of the profession. (But such criticism is also difficult to do from within the profession, lest there be retaliation. Fortunately, I’m a tenured, full professor.)

Do Not Fall for a Bait and Switch

Discussions of replication also seem to confuse frontier research with the core fundamentals of economics as a discipline. People who are publishing economic research in journals are engaged in conversations at the frontier of the field. They are trying to move the field forward and gain more understanding. Independent of issues related to replication, the quality of research at the frontier should be expected to be quite noisy. Some people are going to publish novel theories and empirical results that challenge conventional wisdom. Some of those publications will have a significant impact on the field. Most will not. Theories will be found not to match the data, or only to match the data under particular circumstances. Provocative empirical results will be found to only apply in a particular sample of data, but not robust to data from other places or time periods.

The moral of the story is that one individual paper or one particular empirical result is unlikely to change the way that people think about a particular topic. At the frontier, it tends to be the case that people are working on similar ideas and topics. Over time, some of those research agendas turn out to be disappointing or dead ends. Other research agendas move the field in a particular direction.

The importance of one particular paper is therefore not as significant as some people would lead you to believe. For replication failures to truly affect one’s views of the state of the literature, such failures would have to be widespread within that literature. If there is a large body of research that says policy X causes outcome Y, the failure of one study to replicate is unlikely to change many people’s minds.

Nonetheless, some critics seem intent on saying that failures to replicate indict the profession as a whole. This is a charge that economics is useless and the research untrustworthy. But one does not follow from the other. The fact that one particular study at the frontier doesn’t replicate — or even that a lot of those studies don’t replicate — does not tell us anything about the usefulness of supply and demand, for example.

The Importance of Price Theory

This is one reason why I am so passionate about sharing my appreciation for price theory. I will let Brian speak for himself, but one reason that I really wanted to write this newsletter is that I think the profession occasionally loses touch with the importance of basic price theory and the importance of simple models. I also think that the profession has swung too far in the direction of pure empirics. There is very much an attitude among younger scholars that we can’t say much of anything without using particular research methods and that we must let the data speak. However, as I often write in these newsletters, data do not speak.

We need price theory to disciple our thinking. Price theory provides us with a guide for thinking about topics. Someone who understands price theory well will ask better research questions and be in a better position to evaluate empirical results, regardless of whether they have been replicated.

At the frontier, we love surprising results. We love clever arguments. But there is often a reason why surprising results are surprising. They often don’t hold up well to scrutiny.

But some do hold up to scrutiny. In fact, I would argue that it is typically the result of a solid foundation in price theory. On the other hand, surprising results from theory-free empirics and ex post storytelling of an empirical result do not.

This shift towards letting the data speak likely means that there is a greater need for more widespread replication. At the same time, a good price theorist can often point you in the direction of what is most in need of attention.

There is a (possibly apocryphal) story that might be of some value. Supposedly, in the heyday of UCLA price theory, economists would show up and present their work and the empirical results would be subjected to the Alchian test. This test essentially consisted of the faculty looking at Armen Alchian and asking, “Armen, does that sound right?” This is an amusing story to tell, but there is an actual lesson here. Armen Alchian was such a good price theorist that his colleagues knew that he would immediately be able to say whether a particular marginal effect estimated in someone else’s paper was reasonable. This wasn’t blind faith. This indicated such mastery of price theory that they assumed Alchian would be able to put a ballpark figure on what the estimated elasticity should be. Perhaps that is an alternative form of replication.

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Chicken farmers gassed, drowned, and suffocated roughly a million baby chicks. “It’s cheaper to drown ‘em than to put ‘em down and raise ‘em,” one Texas farmer explained. Dairy farmers slaughtered cows. Hog farmers culled breeding stock.

Why did any of this happen? Good ol’ price controls.

This isn’t another “price controls are bad” post. Well, they are. But I have a new paper with Alex Tabarrok and Mark Whitmeyer that has genuinely new stuff: a theorem explaining why price controls produce exactly this kind of “chaos,” as we call it, and a new way to measure the costs that doesn’t require assuming a demand curve.

Instead of chickens, most people probaly think about the gasoline lines for 1970s price controls. I’ve written before about the 1970s gas crisis, about odd-even rationing, about violence at filling stations, about the lines disappearing overnight when controls were lifted. Lines stretched miles in Maryland and Connecticut. Over 90 percent of stations in Connecticut and Massachusetts were rationing fuel. Some had run out entirely.

But what gets less attention is the other side: in Idaho, Montana, Utah, and Wyoming, not a single surveyed station reported any problem, according to AAA survey data presented to President Ford during the crisis. Zero. Texas, the Deep South, and the Great Plains were, as Time magazine put it, “virtually awash with gasoline.”

Why would a 9 percent national gasoline shortfall produce over 90 percent of stations rationing in Connecticut and zero problems in Idaho?

The answers point to the same mechanism that explains why farmers destroy livestock rather than gradually reducing output. And the key to understanding it, as always, is price theory.

Why Arbitrage Dies

Our paper lays out why price controls produce exactly this kind of “chaos,” as we call it.

First, start with how prices normally coordinate markets. Suppose gasoline is more valuable in Connecticut than in Idaho. Under market prices, a trader profits by redirecting supply eastward. The price premium in Connecticut rises just enough to offset the cost difference. A slight cost advantage for one destination captures only the marginal tanker loads, because the price in the underserved market pushes back.

Notice what this means in practice. If it costs one cent more per gallon to deliver fuel to Connecticut than to Idaho, a supplier might redirect a few tanker loads to take advantage of the lower cost. But the price in Connecticut rises as those loads leave, and the price in Idaho falls as extra loads arrive. The price gap narrows until it just equals the cost difference. The system settles on a marginal adjustment, a few tanker loads shifted, and both markets remain well-supplied. Prices provide the pushback that limits how far any reallocation can go. This arbitrage drives markets.

Now freeze the price at the same level everywhere.

Every unit earns identical revenue regardless of destination. I have to sell at 50 cents in Idaho or in Connecticut. The government says I can’t charge more. Where do I ship? The price mechanism that usually pushes back and levels stuff out is gone. There is no premium in Connecticut to attract supply, no discount in Idaho to redirect it.

Under market prices, a one-cent cost advantage captures a marginal reallocation. In the extreme, with no transaction costs, a one-cent cost advantage under a price ceiling redirects the entire flow. Because there is no price in the destination market pushing back, a one-cent advantage is as good as a one-dollar advantage. Both redirect everything. What would have been a marginal adjustment becomes a categorical one. As Thomas Sowell stressed in Knowledge and Decisions, the economy loses its capacity for incremental adjustment and instead lurches between all-or-nothing extremes.

A bit of geometry makes this precise. The set of feasible allocations, all the ways you can divide a fixed total supply across markets, forms a shape with sharp corners. Under market prices, the objective (maximizing consumer surplus) is curved, so the optimum lands in the smooth interior. Under price controls, the supplier’s problem reduces to cost minimization, and cost is linear in quantity. A linear objective over a shape with corners always lands on a corner. Some markets get filled to capacity. Others get nothing. There is no “split the difference” outcome.

The figure below makes this concrete. The blue segment shows all feasible ways to split a fixed supply between two markets. It has two endpoints: E1, where market 1 is filled to capacity and market 2 gets the residual, and E2, the reverse. The dashed orange lines are the supplier’s cost curves, and because cost is linear, they are straight lines. A straight line sliding across a line segment always lands on an endpoint. When it is slightly cheaper to serve market 1, cost minimization selects E1. If that cost ranking flips by even a fraction of a cent, the allocation jumps to E2. There is no gradual slide from one endpoint to the other.

The all-or-nothing outcome emerges from the incentive structure price controls create.

The Chaos

Corner allocations are bad enough on their own. But it gets worse. Which corner the economy lands on is unpredictable.

When two markets have nearly equal delivery costs, the economy sits on a knife-edge. A refinery outage, a pipeline repair, a regulatory tweak, any small change in relative costs can flip which market gets filled and which gets starved. The allocation jumps discontinuously even though the parameter change is vanishingly small. This is what we call the Chaos Theorem.

Why “chaos”? Because under normal market conditions, small changes produce small effects. If a pipeline shuts down, prices in nearby markets rise a little, a few tanker loads get rerouted, and the system settles into a new equilibrium close to the old one. The price mechanism provides pushback that keeps adjustments gradual. Price controls eliminate that pushback. Once every gallon earns the same controlled price everywhere, the supplier’s problem reduces to pure cost minimization, and cost is linear in quantity. Linear problems do not adjust gradually. They jump between corners.

Under market prices, a pipeline repair might shift where the last few tanker loads go. Under price controls, a pipeline repair can shift where all the tanker loads go.

Look at that map above again. The dark red Northeast versus the white Mountain West. A natural interpretation is that Idaho got lucky and Connecticut got unlucky. But think about what efficient allocation would look like. With a 9 percent national shortfall, every market should experience roughly a 9 percent reduction, or at least some reduction. Zero rationing means those states received more than their efficient share. The states with abundant fuel are a sign of the problem, not an escape from it. Under an efficient allocation, the marginal value of gasoline, what economists call the “shadow price,” would equalize across markets. Some stations would limit purchases slightly. Nobody would have five-mile lines, and nobody would be completely unaffected.

This instability shows up whenever the price mechanism is suppressed across any segmented market. Our paper proves that whenever costs are nearly equal across two destinations, arbitrarily small changes in those costs can move welfare up or down by a fixed, discrete amount. The allocation becomes hypersensitive to “nuisance parameters,” transportation costs, regulatory discretion, historical consumption patterns, things that would be irrelevant under market clearing.

The most dramatic illustration played out in the Soviet Union over decades. Soviet citizens carried avoska bags, from avos’, meaning “perhaps” or “just in case.” You carried the bag everywhere because shortages were unpredictable: shoes today, soap tomorrow, nothing next week. People joined any queue they encountered, often without knowing what was being sold, because the queue itself signaled temporary availability. Factories “stormed,” alternating between slack periods when little happened and frantic bursts of activity as plan deadlines approached, because input deliveries were erratic and uncoordinated with production schedules. The chaos theorem applies good-by-good. Extend it across an entire economy, goods as inputs to other goods, each with its own ceiling, and corner allocations compound across stages of production. Communism is universal price controls. The avoska bag is what life looks like when allocation depends on which pipeline happens to be under repair.

As I discussed in People Act, Markets Clear, markets always clear one way or another. Under price controls, clearing happens through queuing, quality cuts, rationing schemes. The chaos theorem adds something to that picture: which market gets rationed is itself unstable.

What misallocation costs

If price controls push allocation to corners, a natural question is: how much does that cost? To answer it, we need the concept of a “shadow price,” which is a measure of what consumers would actually pay for one more gallon if they could. When goods are allocated efficiently, shadow prices equalize: the last gallon is worth roughly the same to a driver in Connecticut as to a driver in Montana. The familiar Harberger triangle assumes this equalization. It measures the cost of having less total supply, taking for granted that reduced supply still goes to the right places. That is the best case. The actual cost, driven by misallocation, was several times larger.

The map makes the misallocation visible. Idaho had zero problems, receiving more than its efficient share, even more than its uncontrolled amount, at Connecticut’s expense.

The top row is what every textbook draws: shadow prices equalize across markets, and the deadweight loss is the familiar Harberger triangle. The second row is what actually happens when cost minimization selects the allocation. One market gets everything it wants at the controlled price. The other gets whatever is left. The welfare loss in Panel B is nearly an order of magnitude larger than in Panel A. Same total supply. Same controlled price. The only difference is how that supply is distributed.

As Josh has written before, competition for scarce goods does not stop under price controls. It just takes other forms: waiting in line, quality degradation, black markets. Our paper reveals an additional mechanism. The allocation itself goes haywire, even before you account for queuing or quality cuts. The distribution of goods across markets is driven to extremes by the very same cost minimization that, under normal circumstances, would be disciplined by price variation.

States with zero rationing are a sign of misallocation, not an escape from shortage.

At the artificially low controlled price, a homeowner in Idaho might burn cheap gas heating a swimming pool while a commuter in Connecticut can’t get enough fuel to drive to work. At market prices, the commuter’s willingness to pay would easily outbid the pool heater, and supply would flow eastward. Under a binding ceiling, both pay the same price. There is no mechanism to redirect supply. The rationing in Connecticut and the abundance in Idaho are two sides of the same misallocation.

Harberger is the minimum

Every intermediate microeconomics textbook draws the Harberger triangle as the cost of a price ceiling. The quantity falls, the triangle measures the deadweight loss, the instructor moves on. This analysis is correct as far as it goes. The problem is that it assumes the reduced supply is allocated efficiently across markets, with shadow prices equalized. That is the best case.

For the 1973-74 gasoline crisis, the Harberger triangle is roughly 2 percent of baseline consumer spending on gasoline. That is the cost of having 9 percent less fuel, assuming that reduced fuel goes to all the right places.

It did not go to all the right places. Using station-level AAA survey data presented to President Ford during the crisis, we find exactly the corner-solution structure the chaos theorem predicts: 62.3 percent of stations operating normally, 27.6 percent limiting purchases, and 10.1 percent completely out of fuel. Open stations satisfied their customers at the low, controlled price. Closed and limiting stations received the residual.

But measuring how much this misallocation costs raises a problem. Normally, you need to know the demand curve to compute welfare losses, which means knowing what consumers would have paid at various prices. Price controls destroy exactly that information. When every transaction occurs at the same controlled price, observed behavior tells you almost nothing about marginal valuations. The standard approach (assume a demand curve, compute the surplus loss) smuggles in precisely the knowledge that price suppression makes unavailable. Elasticity estimates identify demand locally, near equilibrium. But rationed stations received 68 percent of baseline quantity, and many received nothing at all. We are not near equilibrium. Extrapolating a demand curve from equilibrium estimates to these depressed quantities means the answer is only as good as the assumed functional form. Assume a different elasticity or functional form, and you can get any number you want.

That is why we had to develop a new bounding approach. Rather than assume a demand curve and compute a point estimate, we ask: across all demand curves consistent with what we can actually observe (the allocation data, the ceiling price, and plausible ranges for how responsive consumers are to price changes), what are the largest and smallest possible welfare losses? The answer is a robust interval, not a point estimate that depends on getting the elasticity exactly right.

Partial identification, bounding what the data can tell you without committing to a functional form, is well-established in econometrics. What we show is how this complex problem across many states and stations collapses to a much simpler one, using the basic economic idea—really just mere accounting—that the quantities have to add up across markets.

The intuition is simple but a little counterintuitive. For the upper bound, we see some markets that are overflowed and some that are a shortage.

What would be the worst possible underlying demand curves consistent with the observed behavior? Think of a station that ended up with a lot of fuel: “open.” The worst case is that those stations should get very little in the efficient allocation. Then the gap between what they got and what they should have gotten is as large as possible. For the lower bound, we do the opposite: choose admissible shapes that compress those gaps.

The real novelty for the multi-market case is the adding-up restriction. Total quantity is fixed, so what one market gets, another must lose. That accounting identity links all markets through a common shadow price and collapses what looks like a high-dimensional demand-shape problem into a one-dimensional search.

The nerds can check out the math.

Out of that bounding, we get shadow prices, which tell the story in a nice picture. In the upper-bound case—the configuration that maximizes welfare loss across admissible demand curves—Connecticut’s state-average shadow price was 2.5 times the pre-crisis price. Montana’s was 0.7 times baseline. Connecticut consumers valued gasoline at 3.5 times what Montana consumers were paying. Shipping a barrel from Montana to Connecticut would have more than tripled your money. That is the arbitrage opportunity that price controls created and simultaneously prevented anyone from exploiting. Even in the lower-bound case, the geographic dispersion persists. We get the same pattern, just less extreme. But still significant misallocation.

The geographic pattern is striking. The Northeast, with its high population density and commuter dependence on gasoline, shows the highest shadow prices. The Mountain West, with its sparse population and proximity to refineries, shows the lowest. Under market prices, this gap would attract supply eastward until the shadow prices converged. Under a ceiling, the gap persists because no one can profit from closing it. The queuing that Deacon and Sonstelie documented, with hour-long waits routine at hard-hit stations, corresponds to exactly these shadow price levels: when the money price cannot rise, the time price rises to fill the gap.

The misallocation loss is 1 to 9 times the Harberger triangle. That range is not a confidence interval or something like that. It is the set of welfare losses consistent with the observed allocation data and our transparent assumptions about demand slopes. The data cannot pin it down more tightly without assuming a specific demand curve, which is exactly what we are trying to avoid.

Even at the conservative lower bound, misallocation roughly equals the quantity-reduction loss. The total cost is AT LEAST DOUBLE what the textbook says. At the upper bound, the Harberger triangle accounts for barely one-tenth of total welfare cost.

Misallocation dwarfs the quantity reduction. The real cost of price controls is where the goods go. The quantity reduction is almost beside the point.

Controls beget controls

Faced with shortage-chaos, the political system was pushed toward direct quantity management. This is a natural response. If “the market” is delivering erratic, feast-or-famine outcomes, the political temptation is to step in and dictate allocations directly. And that is exactly what happened.

The Emergency Petroleum Allocation Act of 1973 ratified Nixon’s earlier executive orders with legislated price controls and an allocation system based pro-rata on 1972 consumption levels. If total supply fell to 90 percent of 1972’s volume, each buyer would receive 90 percent of their 1972 allocation. The system was then adjusted with numerous exceptions, prioritizing national defense, essential services, agriculture, and independent refiners, all under an overarching “fair and equitable” guideline. By 1979, when an even smaller national quantity reduction of about 3.5 percent led at its height to the closing of almost every gasoline station in New Jersey, Connecticut, and New York City, the Department of Energy was threatening yield regulations requiring refiners to produce a specified share of heating oil per barrel of crude. Bureaucrats instructed refiners on inventory accumulation ahead of summer gasoline demand.

Price controls tend to metastasize into quantity controls. But quantity mandates do not escape the chaos theorem. The mandated allocations are themselves fragile, because small errors in the parameters the regulator uses to set them can flip the outcome between corners. The regulator faces the same problem the price system would have solved: which markets need more, which need less, and how should those answers change when conditions shift? Without price signals, the regulator is flying blind. The regulator’s 1972 baseline was already obsolete by 1974. Population had shifted, driving patterns had changed, and the baseline bore little resemblance to efficient allocation.

As I wrote recently in the Washington Post, the new love affair with price controls is not partisan. Trump wants credit card interest rates capped at 10 percent. Sanders and Hawley have introduced legislation to do the same. New York’s mayor won his race promising to freeze rents for 2 million residents.

Whenever someone proposes a price ceiling on groceries, gasoline, rent, or anything else, the instinct is to draw the Harberger triangle and argue about how big or small it is. The triangle is the least of it. Price controls suppress the very mechanism that prevents chaos, and the resulting chaos fuels demands for ever more control, in an escalating cycle that the price system, left to operate, would have prevented from starting.

Remember the baby chicks. Remember Connecticut and Idaho. A million chicks drowned. Over 90 percent of stations rationing in one state, zero in another, schools closed for lack of heating oil. That is what happens when prices cannot do their job.

Recently, Brian wrote a long post examining some rather provocative claims about AI and economic growth. The post is long, but worth reading in detail. At the same time, it is important to recognize the underlying purpose of the post. When we start thinking about a speculative world, we need something to discipline our thinking. We need a model. Thus, what I want to do this week is to provide some very basic lessons for framing the debate around AI and economic growth.

Complementary Tasks and Production

Production often requires a number of complementary tasks. Construction might require framers, carpenters, plumbers, electricians, and bricklayers. Restaurants produces meals that require cooks, servers, bartenders, and dishwashers. Manufacturing is often organized using an assembly line, in which each worker on the line is assigned a particular task. In these types of production, each task is important to the production of the final output good.

Michael Kremer’s influential paper, “The O-Ring Theory of Economic Development,” is useful for organizing our thinking about these types of production. The motivation for that paper is to think about the explosion of the space shuttle Challenger. Although the space shuttle consisted of thousands of components, the failure of just one component, the O-rings, led to its explosion. As Kremer pointed out, this concept seems fairly generalizable to many different types of production. Furthermore, once we start thinking about production in this way, we can learn a lot of lessons.

Consider a simple model. Suppose that every worker has a type. The worker can either be a high-skilled type or a low-skilled type. The difference between types can be understood in terms of the percentage of time that the worker successfully completes a task without error. Let this percentage be denoted as q. To keep the math simple, suppose that for the high-skilled worker, q = 0.75, and for the low-skilled worker, q = 0.25. Now, imagine that production, y, can be characterized by the following production function.

Where k is capital and f(k) is a function such that f ’, -f ’’ > 0, f(0) = 0, and f(1) = 1. Note that production is increasing in the number of workers and the amount of capital. However, production also depends on the skills of the workers. To see why this is important, assume that the amount of capital used in production is normalized to 1 and assume that two workers are used in production.

When two high-skilled workers are employed together, total production is 1.125. When one high-skilled worker is matched with a low-skilled worker, production is 0.375. Finally, when two low-skilled workers are matched together production is 0.125. Note the importance of the skill levels of the workers. Skilled workers are 3 times more productive than low-skilled workers, as measured by their ability to successfully complete a task. Yet, when two high-skilled workers are matched together, they produce 9 times more than when two low-skilled workers are matched. Furthermore, although high-skilled workers are 3 times better than low-skilled workers, using 3 low-skilled workers and 1 high-skilled worker is not equivalent to 2 high-skilled workers. To see why, plug those values into the production function. Such a firm would produce ~0.05 units of production. This is lower than the 2 low-skilled workers alone. Quantity isn’t a substitute for quality.

So why does this matter?

Well, one important implication is that if one considers a firm that has this sort of production function, one would find that wage that firms are willing to pay is an increasing function of the skills of the workers they already employ. Given that, one would expect high-skilled talent to be collected at particular firms since those firms will have the highest willingness to pay for high-skilled workers. In other words, we should not observe the intermediate example of firms in the numerical example above. We should expected high-skilled workers to be matched with other high-skilled workers. We should also expect a secretary at Goldman Sachs to get paid a higher salary than a secretary that works for a local bank. This should be true even if their job responsibilities are the same. Similarly, we should expect different levels of skills at jobs based on the quality of the product offered. Fast food restaurants don’t hire world famous chefs. Similarly, and perhaps most importantly for the discussion to come, this conclusion can provide an explanation for why we see such large productivity and wage differences between rich and poor countries.

The lesson of the model is that complementarity in production is important. Quantity is not a substitute for quality. The importance of complementarity has broader implications.

Weak Links

Building on this work, Chad Jones has a paper on “weak links” and intermediate goods. The basic premise of his paper is as follows. The typical neoclassical model of economic growth has capital, labor, and technology being used to produce an output good. There is a multiplier effect associated with capital. More investment in capital leads to more output, which itself leads to more capital.

There is reason to believe that intermediate goods, which are absent from this standard model, have the same sort of multiplier effect. This is potentially empirically important in explaining just why rich countries are so much richer than poor countries. For example, the multiplier effect associated with capital is inversely proportional to the labor share of income. Since the labor share is 2/3, the multiplier is 3/2 or 1.5. Thus, if total factor productivity doubles in the economy, real GDP should increase by about 2.8 (2^1.5). This isn’t big enough to explain the differences between countries. The inclusion of intermediate goods implies that the multiplier should equal the product of the inverse of the labor share and the inverse of the intermediate good share of gross output. Since the intermediate goods share of gross output is about 1/2, this implies a multiplier of 1.5 * 2 = 3. Thus, when total factor productivity doubles, real GDP should increase by a factor of 8 (2^3). Now, we are getting somewhere.

These are back-of-the-envelope calculations based on a modified standard growth model. The precise calculations aren’t that important. Instead, what is important is the general multiplier effect. Intermediate goods matter a great deal for production in ways that are likely to multiply. More intermediate goods means more output, which means more intermediate goods. The same can be said in reverse. (This multiplier effect is really what is behind the Diamond-Mirrlees result in economics that one shouldn’t tax intermediate goods, as Brian has discussed with regard to tariffs.) For example, electricity is an intermediate good in the production of both the construction industry and the banking industry. Thus, if the electrical infrastructure is good, you get more production in construction and banking. At the same time, more production in construction and banking means it is easier to build and finance electrical infrastructure.

Jones then combines this insight with Kremer’s work on complementarity. However, he generalizes the method. Rather than assuming a production function like Kremer’s above, he assumes that intermediate inputs enter the production function via a constant elasticity of substitution. This provides additional flexibility to the model. If the elasticity of substitution is greater than 1, then the intermediate goods are substitutes. On the other hand, if the elasticity of substitution is less than 1, then the intermediate goods are complements. Jones assumes that the elasticity of substitution is less than 1. This allows him to consider what happens when there are “weak links” in the production process. If one thinks of the intermediate goods as things like electricity and transportation, this makes a lot of sense. You cannot substitute transportation for electricity. Instead, it is much more natural to think of them as complements in production.

Growth and AI

These types of growth models give us something to work with when we think about AI. For example, what these models suggest is that the critical parameter is the elasticity of substitution. Let’s think about why this is.

The world has a number of general purpose technologies, such as electricity or the internet. We would expect these general purpose technologies to have a multiplicative effect on output. Electricity makes construction more productive. As construction becomes more productive, one can build new and better electrical infrastructure, which further increases output.

At the same time, these general purpose technologies tend to be complements in the production process. Think about transportation and the internet. A business with a presence on the internet will expand its market. In order to get its products to market, it will need transportation infrastructure and services. The internet and transportation are complements rather than substitutes.

The complementarity has potentially important implications because it creates “weak links” in the production process. The production of an internet-based business is limited by the potential weak links of poor transportation infrastructure and/or services. It is also limited by poor infrastructure for electricity production. If electrical power is down frequently, then people will not have access to the website storefront.

A natural question to ask is whether the development of AI should be seen as a complement or a substitute to other intermediate goods. The more provocative predictions about AI seem to imply that AI will lead to greater automation and that automation via machines, robots, and other forms of capital will substitute for boring, old human labor. As this automation becomes more and more important, economic growth will explode and the capital share of income will go to 100%.

A natural question to ask, however, is what happens when some things cannot be automated — or even if certain things are just much harder to automate than others. What if automation is subject to comparative advantage?

Let’s consider this scenario. Suppose that there are two intermediate goods used in the production of the final output good. Also, assume that certain types of intermediate goods production cannot be automated. Finally, assume that these intermediate goods are complements in production.

The constant elasticity of substitution production function can be written as

where y is output, h is the input that is hard to automate, e is the input that is easy to automate and sigma is the elasticity of substitution.

Since we are assuming that these inputs are complements, let’s make things really simple and set the elasticity of substitution equal to 1/2. This implies that our production function can be written as

Now, suppose that automation means the production of the intermediate good for which production is easy to automate becomes infinitely large over time because of the rapid growth that comes from automation. It follows that total production will just be equal to the production of the hard-to-automate input (y = h).

Complementarity creates weak links that limit the ability of production to explode as a result of automation. Furthermore, in this case, the share of income that goes to the easy-to-automate intermediate good would go to zero as the production of this intermediate good becomes infinite. Thus, complementarity implies that the weak link input will reap all of the rewards from automation.

This is in some sense a dramatically oversimplified example. Nonetheless, it does seem to highlight the importance of complementarity and weak links in the production process — characteristics without which it would be hard to explain dramatic differences in productivity and wages across countries as well as patterns of production.

A more serious exercise would be to take the model of Chad Jones and Christopher Tonetti. They start with a constant elasticity of substitution production function in which output is a function of capital and labor. They then allow a fraction of tasks to be automated and allow automation to change over time. In their empirical exercises, the elasticity of substitution less than 1 proves to the important factor. It creates a weak link. As a result, they show that when tasks are automated through rapid improvements in capital productivity, overall economic growth is still constrained by labor, which is improving at a much slower rate. The reason is that these labor tasks are the weak links in the production process. As a result, in their baseline case, although economic growth increases in their numerical simulations, it is only 19% higher in 2060 than the counterfactual without automation. In addition, the capital share in their model remains relatively constant. The reason for this latter result comes from the fact that as automation makes capital more productive, less capital is needed for a given level of “effective capital.” This helps to keep the capital share of income relatively constant.

Thus, the crucial factors are (a) how much can be automated, and (b) whether we should think of AI as a complement to other inputs in the production process.

Some Concluding Thoughts

When we think about economic growth, it is hard to explain the vast differences in productivity and wages between rich and poor countries without appealing to things like complementary inputs and weak links in production. These represent important building blocks when thinking about growth.

When thinking about AI, it is important to discipline our thinking by starting out with a model. Ideally, that model should capture some important characteristics of reality. In other words, we shouldn’t just pick any old model off the shelf. We need to think about models that have something to say about issues presented by AI.

Historically, general purpose technologies have dramatically changed the lives of human beings. However, when we look at the long-run trend in economic growth in places like the U.S. or the U.K., the trend seems pretty linear over a long period of time. In other words, it is hard to identify the effects of general purpose technologies like electricity and the internet in observed growth rates.

Why is that?

It is possible that these general purpose technologies have a large effect that declines over time. Perhaps the reason we don’t see this in the data is that the creation of general purpose technologies cause an increase in the growth rather that tapers off over time. However, another general purpose technology comes along and thus it is hard to detect the speed-up and slowing-down of growth as the technology is diffused throughout society.

Another possible explanation is that general purpose technologies tend to be things that are complementary to other inputs in the production process. When that is the case, these other inputs potentially create weak links that limit the ability of growth rates to change very much.

When we think about AI, there is very much a “this time is different” mantra surrounding the technology. AI is simply going to eat the world. But what if AI cannot automate everything? What if AI is once again a general purpose technology that is complementary to other inputs in production that cannot be automated? If that is the case, AI is likely to have a significant impact on growth. However, the wildly speculative and provocative claims will prove incorrect.

This is why it is important to discipline our thinking with well-worn theory. Perhaps the theory will be proven wrong. Nonetheless, it at least can help define the terms of the debate and identify exactly why predictions differ.

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His advice seems entirely hokey. Use cash envelopes. Grocery budget in one envelope, entertainment in another. When the envelope is empty, stop spending. Pay off your smallest debt first, regardless of interest rate. Build momentum. Feel the wins.

There’s one type of economist (me, that’s me), who winces at this. Money is fungible—a dollar in one envelope is worth the same as a dollar in another. Paying low-interest debt before high-interest debt leaves money on the table. These rules violate basic principles of optimization.

There’s another type of economist (me, also me, when I’m being a better economist), who says wait. Millions of people swear by the envelopes. And it’s not just Ramsey. Rules of thumb are everywhere: round down small purchases, treat sunk costs as sunk, set aside money before you see it. Personal finance is full of these heuristics that look wrong to an optimizer but seem to be popular in practice.

So let’s flip it around: what constraints make this behavior sensible? What problem does it solve? Why do people rely on heuristics when making decisions?

Cognitive Costs Are Constraints

A core of economics is about people maximizing subject to constraints. Usually, economists focus on budget constraints. Sometimes we add time constraints. But there’s another constraint that price theorists have long taken seriously: cognitive costs.

Decision-making takes effort. Comparing options requires attention. Tracking spending causes fatigue. This is a huge part of Thomas Sowell’s Knowledge and Decisions, which we are always mentioning here.

Deliberate decision making is not a free good; that is why there are thermostats and payroll deductions. Decision making has costs, including time, stress, fatigue, insomnia, and heart attacks. Clearly, it is something that must be economized.

Armen Alchian made similar points about how market institutions economize on information costs. The insight isn’t new.

How do we economize on decision-making? Rules of thumb. Mental shortcuts. Categories that reduce complex comparisons to simple ones. Instead of evaluating every dollar against every possible use, we create buckets: “entertainment,” “clothing,” “eating out,” and optimize within them.

Think about what “rational” budgeting would require. Every dollar has an opportunity cost—the best alternative use across your entire lifetime. We introduce opportunity cost as some simple idea in Econ 101 but taking it literally in some omniscient sense would be crazy. To optimize perfectly, you’d need to compare this jacket against next year’s vacation, your retirement savings, and every other possible expenditure. Nobody does this. Nobody can.

We can see this made explicit in AI systems, where complicated reasoning literally costs more tokens, and therefore more money. Human cognition isn’t priced so precisely, but the constraint is just as real.

Someone who puts grocery money in one envelope and entertainment money in another probably understands fungibility just fine. They know they can move the dollars between envelopes, and probably have many times, breaking their own rules.

But the envelope system reduces the cognitive cost of evaluating purchases. Mental accounting—separating money into categories like “entertainment” or “clothing”—creates a simpler optimization problem than tracking every dollar against every possible use.

Cognitive costs can be modeled as real constraints, with testable implications. In my work with Mark Whitmeyer, we explicitly add information acquisition costs to a model of consumer choice.

This is just bread-and-butter constrained maximization, with a twist. The constraint happens to be monetary plus cognitive rather than just monetary.

Institutions Shape Cognitive Costs

Once we recognize cognitive costs as constraints, we can ask how institutions affect them. Some institutions raise cognitive costs; others lower them.

Markets, through prices, economize on information. They compress enormous amounts of information into a single number. When you see a price of $50 for a jacket, you don’t need to know anything about the supply chain, the cost of materials, the wages of workers, or the scarcity of retail space. The price summarizes all of that for you.

Friedrich Hayek made this point about the price system as a whole. No one needs to know why copper is scarce—whether a mine collapsed or demand surged in China. The price increase alone tells people to economize. Prices allow coordination without requiring that anyone understand the full picture.

At the individual level, this is the idea that prices reduce the cognitive cost of making decisions. A world without prices—where you had to evaluate every good based on its intrinsic properties and your own needs—would be cognitively overwhelming. Prices provide a common metric for comparison.

As always, prices are never the full story. For example, brand names add another layer. Sowell uses the example of a Holiday Inn sign. It doesn’t promise the best hotel in town, just a predictable one. One to lower cognitive costs for the buyer. A traveler might find a better, cheaper independent motel if they searched long enough. But the brand reduces the variance of the outcome. You pay a premium to avoid inspecting five unknown motels. The brand has already done the sorting.

Alchian and Allen make the same point more precisely: brand names matter most for purchases that are “harder to evaluate before purchase” and where “subsequent surprising defects would cause more severe damage.” The brand reduces the shopper’s cost of estimating reliability.

These types of information-economizing institutions are everywhere. Culture works the same way. Sowell argues that culture provides “pre-packaged” beliefs that save the costs of reinventing the wheel on every decision. No one invents these heuristics from scratch. They circulate socially, get updated through use, and spread because people find them useful.

Competition Disciplines Beliefs

If cognitive constraints lead to systematic mistakes, what keeps those mistakes from overwhelming the whole system, leading to chaos? The answer is that reality eventually intrudes.

Start with the Alchian point about market competition. Firms that make consistently bad decisions, whether based on wrong beliefs about costs, demand, or competitors, tend to lose money and exit. This selection process doesn’t require any individual manager to be perfectly rational. It just requires that firms with better decision-making tend to survive longer. The market selects for behavior that approximates profit maximization, regardless of the actual decision processes inside firms. You don’t need to assume rationality to get rational-looking outcomes. You need selection pressure.

For individual consumers, the selection mechanism is weaker but still present. The budget constraint is real. You can use whatever mental accounting system you like, but you can’t spend more than you have, at least not for long. People whose mental accounting systematically exceeds their actual budget will eventually face consequences (overdrafts, debt, inability to pay rent) that force adjustment.

Wrong beliefs don’t get corrected immediately or completely. Competition works slowly and imperfectly. Some mistakes persist because the stakes are too low to justify the cognitive cost of fixing them. If your envelope budget is off by 20%, but the category only matters for $50, the expected loss is $10. Spending an hour doing precise calculations to save $10 isn’t worth it.

The heuristics that survive are the ones that work well enough in the domains where people actually use them. These heuristics persist because, for everyday discretionary spending, they produce acceptable results at low cognitive cost.

David Levine makes a related point in Is Behavioral Economics Doomed?. His complaint about behavioral economics is that it’s “obsessed with people being dysfunctional” and overlooks why behavior is functional—sometimes for subtle reasons, sometimes for obvious ones. “Seemingly dysfunctional behavior is often quite sensible when the circumstances and incentives are understood properly.” These are experienced consumers making familiar decisions in domains they’ve navigated for years. This isn’t a novel lab setting where we’d expect anomalies. It’s exactly the domain where standard economic analysis predicts behavior will look sensible.

When Are Heuristics Actually Wrong?

Taking cognitive constraints seriously lets us go further. Not just “behavior is functional" but why and in which domains. Not everything is a familiar environment.

Bohren, Hascher, Imas, Ungeheuer, and Weber have a recent paper where they show how attention and memory constraints can generate actual mistakes. By mistake, they don’t mean failure to optimize some ideal function, but beliefs that are measurably wrong about objective probabilities. But you would only do this with some sort of cognitive constraint. The important part is that the constraint exists.

When information arrives all at once, attention is the binding constraint. You can’t process everything, so you focus on what stands out. Rare but dramatic events get overweighted. When information arrives over time, memory is the binding constraint. You can’t remember everything, so unusual occurrences fade. The same objective information produces opposite biases depending on how it’s presented. That’s a testable prediction: change how information arrives, change the direction of the error.

Here I circle back to: Institutions matter. In markets, the budget constraint provides a hard backstop. As I’ve written before, even Gode and Sunder’s “zero-intelligence traders” making random choices converged to a competitive equilibrium—the constraint did all the work. Becker made the same point about consumers: imagine people choosing randomly, subject only to their budget. When prices rise, the budget constraint rotates, forcing them to buy less of the expensive good on average. You get downward-sloping demand without any optimization. Whatever heuristics people use, they can’t systematically spend more than they have.

Reality bats last.