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

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

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

Then it got a model.

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

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

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

The Waterbed Breaks Before Consumers Do

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

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

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

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

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

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

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

The Policy That Outruns Its Math

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

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

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

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

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

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

I reject all these criticisms.

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

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

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

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

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

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

Economic Incentives and Drug Use

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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Price theory, he argues,

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

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

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

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

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

Reasoning vs. technique

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

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

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

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

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

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

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

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

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

What reasoning produces

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

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

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

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

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

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

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

360,000 factors

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

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

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

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

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

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

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

Cowen proves his own point.

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

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

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

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

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

The real repricing

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

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

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

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

It does.

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

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

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

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

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

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

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

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

We have the following simple, causal structure.

Structure → Conduct → Performance

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

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

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

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

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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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5 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.

Other times this phrase is meant to be taken literally. In that context, the phrase is meant to evoke the idea that whatever is being described is not something that you could purchase. It is often used to describe an experience or a moment or something of natural beauty.

For a price theorist, this use of the phrase is confusing. For example, as Thomas Sowell writes in Knowledge and Decisions:

To say that we “cannot put a price” on this or that is to misconceive the economic process. Things cost because other things could have been produced with the same time, effort, and material. Everything necessarily has a price in this sense, whether or not social institutions cause money to be collected from individual consumers.

Nonetheless, one often hears people use the phrase in reference to things like the value of human life. This is often meant to convey the idea that a human life is in some sense priceless. It is understandable why people might use the phrase in that context. Often this is attached to religious belief. There is a saying from the Talmud, for example, that “whoever saves one life, it is as if he saved the whole world.” Christians believe in the inherent dignity of man, that man was created in the image of God, for a purpose, and that human life is sacred and must be protected.

At the same time, one can think of many everyday experiences in which people are tasked with estimating the value of human life. Insurance companies must make such determinations. Legislators create rules designed to save lives and regulators must decide how and when to to enforce rules. This requires making a determination about whether the cost of saving an additional life would be worth the cost. The legal system allows the families of victims to seek compensation in the event of a wrongful death.

In this week’s newsletter, I want to focus on estimates of the value of life. In cases where one needs to put a value of human life, how does one do so? As always, price theory can be our guide.

The Statistical Value of Life

If we are trying to estimate the value of a life, we can get estimates from the economic decisions that people make. In this respect, we need to differentiate between two types of behavior. The first is the decision to purchase insurance. Someone who purchases insurance is someone who is trying to smooth out consumption. Absent insurance, a major health issue will shift a lot of consumption towards health care. During those times, the marginal value of a dollar spent on other goods is much higher. Insurance effectively transfers dollars from times when the marginal value of the dollar spent on those goods is lower to a time when the marginal value is higher.

A second type of behavior is the decision to invest in one’s health. These types of expenditures are designed to increase the probability of survival. This isn’t about reallocating disposable income over time. This is about living for a longer period of time.

It is important to note that there is feedback between both of these objectives. A longer lifespan has implications for consumption smoothing. There is also a moral hazard problem associated with health insurance. A person that has health insurance might not invest as much in health as the person would have if he or she had not purchased insurance.

Price theory can help us sort all of this out. Divide one’s life into two periods. Suppose that the consumer has actuarily fair insurance. The consumer would like to consume in both periods. The consumer must divide income between consumption goods (in both periods) and health expenditures. Greater health expenditures increase the probability of survival (with diminishing returns; the maximum probability is one, after all), but reduce consumption on other goods. The consumer has to decide how much to spend on health expenditures relative to other goods.

Price theory tells us that the consumer will choose health expenditures such that the marginal benefit of the last dollar spent is equal to the marginal cost. It is this marginal condition that gives us insight into the value that people put on their life. When you invest in health, this increases the likelihood of survival. This increases one’s average utility. At the same time, this increases the expected benefit from insurance since it increases the expected value of smoothing consumption over time.

Recall from my previous post on the price level that even though we cannot measure utility directly, we can use price theory to determine what observable variables make it possible to measure average utility. It is straightforward to show that we can capture average utility by measuring the value of consumption and leisure in the second period. Recall, however, that there is an additional benefit from being able to smooth consumption over time. Nonetheless, we can use price theory to show that the total effect is just some multiple of average utility, where the multiple depends on one’s willingness to substitute consumption across time.

There is still a challenge here. The multiple that I referenced above is a function of a parameter that measures the curvature of the utility function. It is possible to estimate that parameter. We have estimates. We can estimate the statistical value of life by calculating the present value of the product of that parameter and our measure of average utility.

However, there is also an equivalent measure of the value of life if we ignore this curvature. In fact, if we ignore the curvature of the utility function, it turns out that the marginal condition described above is equivalent to the marginal condition that one gets by choosing health expenditures to maximize lifetime wealth. This should make some intuitive sense since expenditures on health are made to extend one’s life. There is a natural correlation between increasing one’s lifespan and increasing one’s lifetime wealth.

This alternative measure gives us an easy way to measure the statistical value of life. The marginal condition from the wealth maximization problem measures the marginal benefit as the change in the probability of survival from an additional dollar spent on health multiplied by one’s wage.

What makes this alternative measure attractive is that one can back out such calculations by estimating wage regressions for workers. Dangerous jobs can result in death. People who work in those jobs know this in advance. They also know that they have alternatives. Dangerous jobs therefore require a wage premium.

Since the wage premium communicates something about how the person evaluates risk, one can use information in the wage premium to figure out the statistical value of life.

It might be easiest to do this by way of example. Back in 2004, Dora Costa and Matt Kahn estimated wage premia for workers between ages 18 and 45 based on the probability of dying on the job. They found that for 1980, one death per million hours of work in a profession led to a wage premium of about $5.34 per hour (in 1990 dollars). The price per change in probability is $5.34 million (in 1990 dollars), which is the implied statistical value of life.

This estimate is important. Going back to the discussion in the introduction, another common phrase you hear from people is that “if it saves just one life, it would be worth it.” That might be true in a certain metaphysical sense — and is certainly true to the family of person saved. However, in a world of finite resources, policymakers face trade-offs. How much should policymakers be willing to spend to save one life? The answer we have provided is about $5 million (in 1990 dollars). The figure is ultimately important for evaluating workplace safety rules, for deciding how much to spend on medical research, environmental regulations, etc.

The Statistical Value of Life Over Time

Since the statistical value of life is priced in terms of dollars, one might be interested to know how that value changes over time. Their estimates are in 1990 dollars. Changes in the purchasing power of a dollar certainly would affect these estimates. Nonetheless, this isn’t all that price theory has to say.

If we can estimate the statistical value of life using wage premia, then the statistical value of life might change in predictable ways over time. For example, if the quality of life improves over time, then workers will demand a higher wage premium. Changes in life expectancy matter as well. If non-job-related mortality declines, the wage premium should rise as well (this reflects a higher value of life because there is more life to live). At the same time, the demand for safety is likely a normal good. That means that as incomes rise, on average, the demand for safety will rise along with it. This is likely to lead to fewer workplace deaths. Yet, this effect could be somewhat ambiguous. Typically, we would expect workers would pay for this increase in safety with a lower wage premium. Nonetheless, people could create more safety is by moving out of risky sectors and into less risky sectors. All else equal, to attract more workers into the dangerous jobs, this would tend to push the premium higher.

In their paper, Costa and Kahn estimate the statistical value of life for 1940, 1950, 1960, 1970, and 1980. They find that workplace fatalities did indeed dramatically decline over this period. The trends were particularly dramatic in industries like mining and construction. They also find that the wage premium associated with fatality risk increased pretty significantly over that time.

For 1940, their linear model implies a statistical value of life of around $1 million (in 1990 dollars). This estimate more than doubles by 1960 and rises to $5.3 million for 1980. Thus, even though fatality risk declined pretty dramatically, the wage premium (and thus the statistical value of life) rose over this period.

Another important conclusion is that they find that the statistical value of life rises by approximately $1.50 to $1.70 per $1 increase in real GNP per capita. As people see their income rise, the value of their life rises faster than their income.

The implications are important. Costa and Kahn point out that many treat estimates of the statistical value of life as a constant, at least in real terms. Even those that update it over time with income, consider it relatively unresponsive to income compared to their estimates. Their argument is that this is not only incorrect from a theoretical point of view, but also that it has important implications for policymakers. Relying on old estimates can therefore lead to a significant underestimation of the value of life thereby adversely influencing rule-making and enforcement.

Concluding Thoughts

The main implication here is that price theory can be used as a tool to put a price on something even if that something does not have a market price. This is important. To argue that we cannot put a price on something ignores what economists mean by price. A price is a measure of cost. Even when things aren’t formally priced in markets, people’s actions still reveal the value they place on them. Decisions to work in a risky job and the compensation received for doing so tell us a lot about the value that people place on their life. Price theory allows us to take those actions and translate them into dollar values. In fact, a pretty basic formula for estimating the statistical value of life follows directly from price theoretic insights.

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For example, places like Chik-Fil-A charge the same price for a chicken sandwich regardless of the time of day and the length of the line. Last week, Brian tried to answer the question, “Why aren’t Pappy (van Winkle bourbon) and other rare whiskies sold at market prices?” that was first asked by Jeremy Horpedahl. And there is currently a big debate about why employers won’t simply raise their wages when they are having a hard time finding workers to fill their job openings. Each of these examples is about prices that are not clearing the market. Why doesn’t Chik-Fil-A raise its price at lunchtime? Why don’t stores increase the price of rare whiskey? Why won’t firms raise their wages?

In this post, I would like to argue that all of these questions can be answered by an appeal to the insights of the great Armen Alchian when it comes to thinking about costs.

Armen Alchian had two particular views that are valuable when thinking about market decisions. First, Alchian’s view of cost was primarily in terms of opportunity cost. What you are giving up when you make a choice is the next best alternative. This is the cost of an action. Second, Alchian thought it best to think about costs in terms of present value. Taken together, this view of costs can generate a lot of insight.

When thinking about investment, it is easy to understand why one might think about the costs and benefits of an investment in terms of present value. Some of the costs occur now. Some of the costs occur later. Almost all of the benefits accrue later. If one values the present more than the future, then thinking about this in terms of present value seems somewhat obvious.

However, Alchian’s insight about costs was not only about investment. Many of the models that we use in class are static models, but the effects of particular actions are dynamic. How can we think about dynamic issues in a static model? By thinking about things in terms of present value! To think about why this might be useful, let’s start with the Chik-Fil-A example.

In a previous post, I asked why prices are sticky. When I walk into a Chik-Fil-A at noon, the lines are long. When I walk in at 2 p.m., the lines are short. Yet, I pay the same price at both times of day. Doesn’t this violate supply and demand? Shouldn’t the price be higher when demand is high than when demand is low? What explains this?

Think about the costs to the consumer. The cost of buying Chik-Fil-A is the opportunity cost. It is what I could have purchased if I had not bought Chik-Fil-A plus what I could have done with my time if I had not waited in line. By this measure of costs, the cost of the Chik-Fil-A sandwhich is higher at noon than at 2 p.m.

Of course, that does not explain why Chik-Fil-A doesn’t raise its price. The restaurant does not gain any benefit from the cost associated with waiting. Why not turn that non-monetary cost into a monetary cost? Conceivably, Chik-Fil-A could monetize this willingness to pay by charging a higher price. This is where Alchian’s insights matter.

From the perspective of the customer, a constant price throughout the day creates a reliability in terms of monetary cost per unit of quality. Those with different time costs can therefore choose to eat at different times per day. Furthermore, consumers not only have reliable information about price per unit of quality, but also about the approximate wait times at various different times per day.

Now imagine that Chik-Fil-A installed menu boards in which the price was constantly adjusted based on the length of the line. This would eliminate costs associated with waiting because some people would get out of line. However, this strategy would create a cost associated with searching. If it is noon and I want Chik-Fil-A, this pricing strategy allows me to show up and avoid a line. However, I have a maximum willingness to pay for a chicken sandwich. If I show up and the price is above my willingness to pay, I have to eat somewhere else or I have to come back later to check the price.

One might think that solving this problem is easy. For example, why not just show up at 2 p.m. when you know that demand should be lower? You should be able to get your sandwich at that time with no line and even at a lower price. Well, everyone else knows that this is the case too. Thus, everyone might try to eat at 2 p.m. because that is when they expect the price to be lower. However, if everyone thinks this way and shows up at 2 p.m., the price will be high and if I don’t want to pay that price I likely won’t eat lunch.

The relevant question for thinking about which alternative that consumers would prefer appears to be whether the monetary costs and time costs with constant pricing are greater than the monetary costs and search costs with variable pricing. What Alchian tells us is that we should think about this in terms of the present value of these costs. I don’t just care about whether search costs are high today or the cost of waiting is high today. I care about the present value of the search costs, the present value of the waiting costs, and the present value of the monetary cost.

Now think about these two alternatives. Searching requires balancing a trade-off. All else equal, the longer I search, the lower the price that I will pay. I can balance this trade-off optimally so long as the marginal cost of my search is equal to the marginal saving that I get from searching. However, since the price is a random variable, even if I optimize my search, the time spent searching will also be random. Thus, even with optimization, I cannot completely minimize my costs because some fraction of my costs are randomly determined. With constant pricing, I do not have to search on price because prices are constant throughout the day, but I might have to wait, depending on when I show up. In this case, I can minimize my costs simply by showing up during downtimes. Furthermore, regardless of when I show up, the cost is predictable, unlike in the world of searching and constantly change prices. Evidently, the expected present value of the costs of searching and the resulting monetary price are greater than the present value of a constant monetary price and the cost of time.

The reason that firms choose this strategy is similarly based on the present value of the costs. If searching is more costly, then the demand will be lower for the product; not just today, but in the future. Thus, even if the higher price could generate more revenue today, the firm is stuck with lower demand in the future. The cost to the firm of these price adjustments is therefore related to the present value of the decline in demand that would result. The firm can prevent these reductions in demand by keeping the price constant. Although it cannot monetize the time cost of waiting, the firm is better off. What the firm is offering is reliability.

This seems to be the same point that Brian was making in his post about reliability. But while I think that the lesson about the price of whiskey is related to the above example, I don’t think that the answer has to do with reliability in this case. I think it has to do with firms thinking about costs in terms of the present value of opportunity cost; in this case, the present value of foregone demand due to a bad pricing strategy.

What the Chik-Fil-A example hopefully demonstrates is that what firms care about is not just the current demand for their product, but the present value of demand. A firm might be willing to trade-off a higher demand now for a lower demand later, or vice versa, if the present value is higher. However, what firms do not want to do is boost demand today if it means a lower present value of current and future demand.

As I alluded to above, rare whiskey tends to sell for a lower price at a store as it does in secondary markets. Why do firms forgo these higher prices?

These stores could simply auction it off to the highest bidder. Auctions aren’t that costly. As a result, an auction would clearly seem to generate more revenue to the firm. This seems like a no-brainer. Why don’t they do things this way?

I think the answer is related to thinking about demand in terms of the present value of demand. With an auction, only those with the highest willingness to pay will be given the opportunity to purchase the whiskey. Suppose that a competitor offers an alternative. A competing liquor store offers to allocate the rare whiskey through a lottery. By doing so, this liquor store will likely attract the business of customers who are interested in buying the whiskey, but who are unlikely to win the auction. Since entering the lottery will require visiting the store, this increases the present value of demand at the liquor store offering the lottery.

This strategy seems similar to the strategies used by companies who make video game systems. When first released, these systems regularly sell at prices on secondary markets that are much higher than the retail price. Why are these companies forgoing higher profits by selling these gaming systems below their market value? Again, the answer seems to be that some people are indifferent between one gaming system and another. However, the purchase of one gaming system locks-in the customer to that ecosystem of games. If one company charges the higher price, they could lose all of the customers indifferent between systems.

Of course, lotteries aren’t the only alternative to auctions. Another way to allocate the whiskey is to offer it to certain customers. When a shipment of rare whiskey comes in, the owner or the manager of the store calls a select number of people and offers to sell it to them. This, of course, seems somewhat corrupt. Why would the store do this?

Just like customers might prefer certain stores, stores might prefer certain customers. This type of allocation is like a “customer rewards” program for loyal customers. Those customers who already have a high present value of demand are rewarded with the opportunity to purchase rare whiskey. This offer isn’t available to everyone. It therefore encourages loyalty to the store. While these customers might not have the highest willingness to pay for the rare whiskey, they do have a higher present value of demand for the store’s products than possible auction winners. The cost to the store of losing these customers is the present value of these customers’ demand. This is almost certainly higher than the foregone revenue from selling the bottle for $200 rather than through an auction.

This same basic idea about costs can be applied to the current state of the labor market. Last week, the jobs report showed that the number of net new jobs created in the U.S. was considerably below the market forecast. What made this news a bit confusing to some is that the tepid job growth occurred at the same time as a bunch of anecdotal evidence that firms are having a hard time finding workers and that many firms are offering what are effectively signing bonuses to new employees.

The jobs number immediately sparked debate among economists, pundits, and policy makers about precisely what was going on. Some argued that the generous unemployment benefits created during the pandemic are holding back job growth since some workers can earn comparable income by staying home as they would returning to work. Others argued that with schools and day cares still closed in certain areas of the country, some people simply cannot return to work because they need to take care of their children. A number of economists noted that if firms are really having such a hard time finding workers, they should raise wages.

While it might not be immediately clear, this debate is actually related to the very concepts I have discussed thus far. The debate is about why wages are not being set to clear the market. Both those who blame unemployment compensation and educational/child care issues are arguing that the problem is with the labor supply. There is not enough labor willing and/or able to work at the going wage. There is excess demand. So why doesn’t the wage simply go up?

The answer is that these are temporary factors. As our discussion of Alchian’s idea of costs makes clear, when firms offer wages to workers, they care about the present value of those wages over the course of the worker’s employment. Unemployment compensation will go back to normal soon enough. Schools and day cares will continue to re-open as vaccination rates go up and case numbers go down. Given this knowledge, why would firms offer a higher wage to workers right now when they know that they can hire the same worker at a lower wage in the near future? In other words, if the labor shortage is driven by policies that will expire soon, the market-clearing wage today is higher than it will be in the future. In present value terms, the cost of paying higher wages now relative to paying lower wages later can be quite costly depending on how durable to the employer-employee relationship will be.

If firms know that the wage necessary to clear the market is higher now than it will be months from now, one thing that firms could do is offer higher wages now and then cut wages when the labor supply starts to increase. Of course, that is hard to do. Or is it? That actually seems to be precisely what firms are doing right now! They are offering bonuses for returning to work to workers who come to back to work now. These bonuses are one-time payments. However, they allow workers to earn more income now, when the market-clearing wage is higher, than they will later, when the market-clearing wage is lower. This is no different than hiring workers at higher wages now and then cutting their wages later when the labor supply increases.

So while many people are puzzled by what is going on in the labor market, price theory and some careful thinking about costs can provide us with insights that help us understand the world.

More generally, when we ask the question as to why firms seem to be pricing things in a non-market-clearing way, the answer offered by Economic Forces always seems to point back to Alchian. If we want to better understand the world, we have to restructure how we think about costs and supply and demand toward that Alchianian approach.

1. Labor’s share of income will go to zero.

2. Given that, a global progressive capital tax is “essentially the only way to prevent inequality from growing extreme.”

But reading through the essay (and follow-up discourse), I kept losing track of different parts of the argument. There’s a lot happening in these big, world altering scenarios: how easily machines can replace workers, how fast equipment wears out, differences in investment returns, who actually pays taxes, international coordination.

So instead, as is my wont, let me slow down and revisit supply and demand. In this case, supply and demand for capital. I start with a baseline growth model and ask: which assumptions need to hold for their conclusions to follow?

In particular, their analysis jumps to an extreme endpoint (an economy where labor contributes nothing). Séb Krier has a new piece that reaches basically the same conclusion, that they are looking at a knife-edge.

Getting labor’s share all the way to zero—not from 60% to 30% but to basically zero—requires one of two conditions:

One option is that capital and labor are perfect substitutes. Not just easy to substitute but perfectly interchangeable. This means there’s not a single task in the entire economy where humans have a comparative advantage. Labor becomes worthless regardless of how much capital exists, so its share goes to zero. I touch on this briefly.

The other option—and the focus of their piece through its connection to Piketty—is that capital accumulates without bound faster than labor. Workers still earn something per hour, but capital income grows so large that it dwarfs labor income.

For this to happen, we also need an extreme (from my perspective) assumption. It’s not enough for capital to become more substitutable with labor. For capital accumulation to obliterate labor share, the return on capital must always exceed depreciation plus what savers require to delay consumption, not just now or during a transition, but at every level of capital accumulation, forever. That is a problem if there are ever diminishing returns (say you need land).

Even if there are no land/labor/energy/anything constraints, a different problem arises. The same technological progress that makes substitution easy also makes last year’s AI obsolete. Fast progress raises depreciation through obsolescence. Your GPU doesn’t depreciate only because circuits break. Instead, it depreciates because next year’s model renders it less valuable. And investors have to be rewarded for that loss of value.

Economics is about the process of markets, how they adapt and change prices. The zero-labor endpoint is a knife-edge. Getting there requires passing through regions where standard economic principles apply and where multiple margins adjust simultaneously.

In those regions where labor still plays a role, I don’t think their policy conclusions achieve their ends. Capital taxation hurts workers. If you start with the world as it is today (with capital and labor both contributing) and think about the possible path to a world with zero labor share, their policy proposal to tax capital will only hurt workers. The features of AI they highlight are exactly the features that make capital taxes horrible for workers.

Especially as someone who works on policy and sees knife-edge ideas trickle into policy discourse as if it’s the real world, I want to keep us in that world, while I’m glad others are thinking about other futures.

[I’ve tried to make this as jargon-free as possible. All math is relegated to the footnotes along the way. It should look almost like an appendix at the bottom for people who are interested in the math.]

How the Economy Divides Its Output

We have tools to think through this. Supply and demand. In particular, I want to think about the supply and demand for capital. Since we are trying to understand how we get to a world with zero labor share, we don’t want to assume that. We want to derive whether it’s plausible.

Start with the basics. Total economic output gets divided between workers (as wages) and capital owners (as returns on their investments). These two shares add up to 100%; the whole pie gets divided between workers and capital owners.1 To simplify things, this is how I’m going to think about inequality. I think that’s a big assumption that I’d like to drop but their piece is heavily framed around the share. And I’ve already written about wage inequality, although as Philip and Dwarkesh point out, capital inequality could be a big deal.

Production today (maybe not at the singularity but today) uses capital and labor. How do firms choose what combination? As always, it depends on prices. When the relative price of labor (wages) rises compared to the cost of renting equipment, firms substitute toward equipment and away from workers. They switch to using more machines and fewer people to produce any amount of output.

The key technological question is how responsive is this substitution? When relative prices change, how much do relative quantities change? If wages rise 10% relative to equipment costs, and firms respond by using 20% more equipment relative to labor, firms are very responsive. High responsiveness means firms aggressively substitute toward whichever factor got cheaper. Low responsiveness means firms are stuck with roughly fixed proportions regardless of prices.

As Philip and Dwarkesh point out, this responsiveness determines how income shares change when the economy accumulates more capital. Most empirical estimates find that firms don’t substitute very easily. The “elasticity of substitution” between capital and labor is well below one, which is below what is needed for the Piketty capital accumulation spiral.2

That’s why so many economists pushed back on Piketty’s original argument: capital accumulation has been self-correcting. As societies accumulated more capital, returns fell fast enough that capital’s share of income didn’t explode. This newsletter isn’t a piece about Piketty, but this is the background context.

Trammell and Patel argue AI changes this. When firms substitute easily between capital and labor, adding more capital doesn’t bid down returns as fast as the quantity increases. Capital’s slice of the pie grows. Even if substitution was hard historically, it will become easy once robots can do enough tasks that workers do. I find this plausible.

But how far does this take us? So far this is all technological. Where are the markets? Where is supply and demand?

Supply and Demand for Capital

I’m going to hold the number of workers fixed. This rules out some things people care about in this discourse like workers starving, but as we will see workers don’t starve so that doesn’t really matter in this model. So I can think of everything in per worker terms. How much capital per worker?

Start with demand. In the basic model firms rent capital; they don’t own it. Why do firms demand capital? It helps them produce output that they get paid for. The return they’re willing to pay depends on how much that capital adds to output.

When capital is scarce, firms bid aggressively for it. The return is high. As capital becomes abundant, the standard assumption is that each additional machine adds less value. That’s what we call diminishing returns. The return firms will pay falls as the supply of capital rises.

This gives a downward-sloping demand curve. More capital, lower returns.

Remember that we are always talking ultimately about consumer value. There is decreasing demand for capital because the VALUE that capital generates for the firm is falling. It could be that the capital is just as physically productive, but consumers are willing to pay less for the trillionth semiconductor produced. I think this is crucial point as we start thinking about the slope. But we will get to that in a minute.

Now consider supply. Where does capital come from? Capital comes from investment. Investment comes because people save instead of consuming. They’re willing to do this if the return compensates them for waiting.

The standard assumption (and nothing in the AI story changes this) is that savers require a minimum return. Below that threshold, they’d rather consume today. Above it, they’ll supply as much capital as the economy wants.

This threshold has two components. One is patience: how much return do savers need to delay consumption? Call it 3-5% per year. The other is depreciation: capital wears out, so savers need to earn enough to replace what’s lost before they see any real return. We will spend a chunk of time on that later.

I’m going to assume the supply of capital is upward sloping for now. The first dollar saved requires a lower return than the millionth. I’ll discuss later how this could be upward sloping, downward sloping, or flat, and what drives each case.

To find the capital stock that exists in equilibrium, we need to understand where supply and demand intersect. It doesn’t matter if (I said there’d be no math. I didn’t say no graphs.)

The horizontal axis is the capital stock. The vertical axis is the return on capital.

The demand curve slopes downward; more capital means lower returns. How steeply it slopes depends on a few things, but one that Trammell and Patel focus on and that matters for this discussion is how easily capital substitutes for labor.

If capital and labor are easily substituted, then diminishing returns don’t bite as hard and the demand curve is flatter. That means we will end up with more capital. But if we are still in the elasticity less than 1 world, the demand curve goes toward zero, so will cross the supply curve.

If, however, we are in the world with even easier substitutability (elasticity of substitution greater than 1), then the demand curve will not go to zero but go to some other positive return. No matter how much capital we accumulate, there is still finite value being generated. Remember this is all in value terms.

For now, the supply curve is upward sloping, so it will intersect the demand curve at some point, even in the Piketty world.

Where these curves cross determines how much capital the economy accumulates.

Rising vs. Going to 100%

I’m glossing over a bit by jumping to that equilibrium amount of capital, k*. After all, we want to take the process seriously. We might start below the equilibrium (more properly, steady state) capital and build toward the steady state. That will make more sense when we think about building a capital stock and the role of depreciation below.

It’s important to make clear that capital share rising is not the same as capital share going to 100%. The first requires fewer assumptions. The second requires more.

Claim 1: Capital share rises as capital grows relative to labor (when substitution is easy).

When capital and labor substitute easily, adding capital doesn’t bid down returns as fast as the quantity increases. Capital’s slice of the pie grows, as you’re moving to the steady state. If AI makes substitution easy, capital accumulation raises the capital share instead of being self-correcting. I find this plausible for some range. Absolutely.

But notice: this tells us the direction of change, not the destination. Capital share could rise from 30% to 40% to 70% and then slows down and reach some steady state. That’s a tranformative change but it’s very different from reaching 100%.

Claim 2: Capital share approaches 100%.

In this case, labor becomes negligible. For this to happen, either labor becomes worthless or labor stays somewhat valuable and capital grows without bound.

For labor to be worthless, the elasticity of substitution has to be infinite, not just high (above 1). There’s a big gap between 1 and infinity, even when AI is counting.

Remember this is a model of the whole economy, so that would mean there’s not a single thing produced that humans have a comparative advantage. This would also mean the labor share goes to zero even if output overall isn’t growing.

The other possibility for 100% capital share is that workers still have some pay but capital accumulates and outstrips labor income. That’s the Piketty lens. And not just that, but for capital share to reach 100% capital has to grow without bound. Since this would be the more standard way to generate economic growth (and what I assume will happen under any reasonable model of AI), we should examine how plausible that is.

When does capital grow without bound? Only if the demand curve is always above the supply curve. To see when that happens, we need to be more careful about the shapes.

Where Does Capital Come From?

I’ve glossed over a distinction between investment and capital. Savers invest. That investment possibly accumulates to determine that capital stock.

I say possibly because capital involves depreciation. Every period, a fraction of your machines break down, become obsolete, or wear out. This is depreciation. You need to invest just to stay in place by replacing what’s lost before you can grow.3 The bigger your capital stock, the more you lose to wear and tear each period.

Think of it as a leaky bucket. You’re pouring water in (savings and investment) while water leaks out (depreciation). The leak is proportional to how full the bucket is. A small bucket loses a little water. A big bucket loses a lot. Eventually you reach a level where the water pouring in exactly equals the water leaking out. That’s the steady state.

When we talk about capital’s marginal product, we mean the net return after accounting for depreciation. (We could also flip it around and think about the saver getting a net return). A machine might add $100 to output, but if $10 of that value gets eaten by wear and tear, the net marginal product is $90. Depreciation isn’t a choice or a tax; it’s a physical fact that happens whether you like it or not. Gamma rays hit your semiconductor and mess something up.

This matters because for capital to grow without bound, the net marginal product must stay above what savers require.4 Savers have a patience threshold; they’d rather consume today unless compensated enough to wait. Let’s put that at 3-5% per year. If the net return falls below that threshold, they stop accumulating and consume instead.

When does capital grow without bound?

Only if the demand curve NEVER crosses the supply curve. Only if returns stay above the hurdle rate forever, no matter how much capital accumulates. That requires understanding whether the demand curve ever dips below the flat supply curve.

What Determines Savings, aka the Supply Curve of Capital?

Where does the savings rate come from? Households decide between consuming today versus saving for tomorrow.

A household values consumption in both periods. Patience determines how much they discount future consumption. If a household values a dollar of consumption next year at 95 cents today, they’re fairly patient.

If the household saves a dollar today, it earns a net return from the productivity of capital after depreciation has already taken its cut. The household trades off happiness from consuming today against happiness from the larger consumption tomorrow.

In steady state, consumption is constant over time. This pins down the required net return: it must equal the rate of impatience.5 Households will supply any amount of capital as long as the net return meets this threshold. Below this rate, they consume instead of saving.

This makes the supply curve flat at the rate of impatience.

Why is the supply curve flat? Two assumptions drive this result, both plausible for thinking about long-run capital accumulation.

The first is that the ability to turn dollars into capital units doesn’t change. A unit of output not consumed becomes a unit of capital; the transformation rate stays constant as you scale up investment. If instead producing investment goods had rising marginal costs—if the tenth billion dollars of investment were harder to create than the first—then the supply curve would slope upward. But with a single consumption-investment good, the trade-off between consuming today and investing for tomorrow stays fixed in the steady state.

Second, the rate of impatience doesn’t shift with wealth.. This means that as capital accumulates and households get richer, they don’t suddenly become more patient and accept lower returns, or vice versa.

Remember this is forever. We are wondering what happens out at the far right. It’s not just whether people will get more or less patient from today but whether impatience eventually stabilizes.

Neither assumption is a deep truth about the world. Both could fail. If producing investment goods has rising marginal cost and you need increasingly expensive inputs as you scale up production, then capital supply slopes up instead of staying flat. If wealthy people become more patient (the rate of time preference falls with wealth), then supply could even slope down. Wealthy households would accept lower returns, which, as we will see, would actually make capital taxation even worse for workers.

But the flat supply curve is a reasonable benchmark for long-run analysis. The supply curve sits flat at the rate of impatience, again maybe 3-5% per year. Remember, this is the net return savers require, after depreciation has already been accounted for in what capital actually delivers.

Paths to Zero Labor Share

The zero-labor-share outcome can happen two ways. Each requires different conditions.

Path 1: Labor becomes worthless.

I went over this quickly, but it’s worth mentioning again. This happens if capital and labor are perfect substitutes. Not just easy to substitute—perfectly interchangeable. Not just elasticity above one—infinity.

In this world, if a robot can do a task 1% cheaper than a human, firms switch entirely to robots. No mixing. No comparative advantage for humans in anything. Labor’s marginal product falls to zero regardless of how much capital exists.

Path 2: Labor stays somewhat valuable, but capital grows without bound.

This is the Piketty-style path. Workers still earn something per hour, but capital accumulates so fast that capital income dwarfs labor income. The ratio of capital to labor goes to infinity.

This requires the elasticity of substitution to exceed one (which we don’t see in the data), but it doesn’t need to be infinite. Capital and labor can still complement each other somewhat. The question is whether capital keeps accumulating forever.

For that to happen, the demand curve for capital must NEVER cross the supply curve. Returns on capital must stay above the hurdle rate (depreciation plus impatience) no matter how much capital exists. If returns eventually fall below that threshold, accumulation stops at some finite level. Capital share might be high—60%, 70%, 80%—but not 100%.

Three Scenarios

I’m going to again not worry about the perfect substitution case.

This gives us three paths for the capital share, not two:

Scenario A: Historical. Capital and labor complement each other. The demand curve slopes steeply from diminishing returns, crosses the supply curve, and we get a finite steady state. Capital share is stable around 30-40%. This is the world Piketty’s critics described.

Scenario B: Transition. Capital and labor substitute easily. The demand curve is flatter. Capital share rises as capital grows relative to labor. But depreciation is high enough that the curves still cross at a finite point. Capital share might reach 50%, 60%, even 70%, but not 100%. Labor still matters. Standard economics still applies.

Scenario C: Zero-labor endpoint. The demand curve stays above the supply curve and depreciation forever. Capital grows without bound. Labor share falls toward zero. This is the Trammell-Patel world.

The essay jumps from “substitution will become easy” directly to Scenario C. But Scenario B is a logical possibility they don’t address. And Scenario B is where we’d spend a lot of time on the transition path, even if we’re eventually heading toward C.

What Does “Robots Building Robots” Mean?

This phrase appears throughout discussions of AI and capital. But what does it actually mean for the model’s parameters?

The phrase could involve several distinct mechanisms. All four imply a higher equilibrium capital stock. All four make it more likely the demand and supply curves never cross.

Interpretation 1: Easier substitution between capital and labor.

Robots building robots could mean capital brings its own complementary inputs. Historically, adding capital required proportionally more labor to maintain, operate, and coordinate the machines. Every new machine needed workers to run it. If AI capital maintains itself, the “not enough hands” constraint weakens. Diminishing returns slow. This is about production technology.

This is the interpretation the essay emphasizes. This flattens the demand curve. Returns stay higher as capital accumulates.

Interpretation 2: Weaker diminishing returns overall.

Even holding labor fixed, capital might face weaker diminishing returns if it doesn’t hit other bottlenecks. Traditional capital runs into scarce energy, rare materials, specialized components. If robots can substitute for these inputs too—mining their own materials, generating their own power, fabricating their own parts—then returns don’t fall as fast.

This also flattens the demand curve, for reasons beyond labor substitution, in a fuller model with land and energy.

Interpretation 3: More efficient investment.

Robots building robots could mean capital is cheaper to produce and replace. Installation costs fall. Adjustment frictions disappear. Replication accelerates.6

This lowers and possibly flattens the supply curve. If replacing depreciated capital costs less, savers don’t need as high a return to keep accumulating. The hurdle rate falls.

This is what I think Trammell and Patel mean with their “self-replicating robot factories”—capital that can reproduce itself without hitting bottlenecks. Each dollar of saving deploys more effective capital. The wedge between “dollars saved” and “machines working” shrinks.

Interpretation 4: More mobile capital.

Robots building robots could mean capital is easier to relocate. If robots can be shipped anywhere, built anywhere, operated remotely, capital flows freely to wherever returns are highest.

This flattens the supply curve. Capital faces fewer local bottlenecks, so any profitable opportunity gets funded.

Each interpretation works through a different mechanism: two flatten demand, two lower or flatten supply. But they all push in the same direction: more capital in equilibrium, and a better chance the curves never cross.

This is the interpretation the essay emphasizes. Fair enough. But notice: for unbounded growth, you need these effects to be strong enough that the curves never cross. That’s a quantitative claim, not just a qualitative one.

Will AI Depreciate Faster? Depreciation Question

Fast technological progress is what makes substitution easier. AI improves and replaces more labor. But fast progress also means last year’s AI becomes obsolete. Think about how dated AI models feel from a few months ago, let alone years.

The same force that flattens the demand curve raises the hurdle rate.

Think about what it costs to own a machine for a year. An investor who buys equipment today and rents it out must earn enough to cover three distinct costs:

There’s an opportunity cost. The money tied up in the machine could have earned interest elsewhere. If you spent $100,000 on a robot, that’s $100,000 not sitting in a bond earning 5%. Remember, we are already accounting for this through household impatience.

There’s physical wear. The machine breaks down, parts wear out, maintenance accumulates. A truck’s engine has finite miles. A robot arm’s joints degrade.

There’s obsolescence. The machine becomes outdated. Next year’s model is faster, cheaper, more capable. Your equipment still works, but nobody wants to rent last year’s technology when this year’s does the job better.

This third component is the killer for the “robots building robots” story.

Your GPU doesn’t depreciate just because circuits break. It depreciates because next year’s model renders it worthless. Physical decay might be 5% per year. But if last year’s AI model is uncompetitive against this year’s, obsolescence could add another 20-30%. Value depreciation of 30% per year is plausible for cutting-edge AI hardware.7

Here’s the tension: fast progress flattens the demand curve but also raises the supply curve. Whether the curves cross depends on which effect dominates.

If depreciation is 30% and impatience is 3%, the hurdle is 33%. The limiting productivity of capital must exceed 33% for capital to grow without bound. That’s a high bar.

And it’s not enough to clear that bar once. You have to clear it forever.

For capital to grow without bound, the return on the next machine must exceed the hurdle rate no matter how much capital already exists. Not just when capital is scarce and returns are high. Not just during a transition period. At every point along the path, and out to infinity.

If there’s any capital stock at which the marginal return dips below depreciation plus impatience (even far in the future, even at capital levels we can barely imagine) accumulation stops there. The curves cross. You get a finite steady state. Maybe a very high one, with capital share at 70% or 80%. But finite.

The zero-labor-share outcome requires that capital always generates at least a 30% return and the lines never cross.

Maybe that’s plausible in a different model, but if we are taking the benchmark economic growth model off the shelf, it’s a lot to ask.

Should we tax capital?

Suppose they win the race. Suppose AI does make substitution so easy, and depreciation stays low enough, that capital grows without bound and labor’s share falls toward zero. Grant them the inequality story.

What about the decades before labor share goes to zero? (Grant me it won’t happen for two decades). Does the policy conclusion follow?

Their answer is a global progressive capital tax which is ”essentially the only way to prevent inequality from growing extreme.” If capital owners are capturing an ever-larger share of income, tax that income and redistribute it.

The logic seems straightforward. But it runs into a problem: the same features that make capital accumulation explosive are exactly the features that make capital taxation ineffective.

The essay focuses on how easily capital substitutes for labor in production. Thousands of words on this parameter. But who bears a tax depends on a different question: how responsive is capital supply to changes in the required return?

These are separate concepts doing separate work.

Substitutability in production measures how much the return on capital falls as you add more capital. It’s the slope of the demand curve. High substitutability means a flat demand curve. You can add a lot of capital before returns fall much.

Responsiveness of capital supply measures how much the capital stock changes when the required return changes. If savers demand a fixed after-tax return regardless of how much capital exists, supply is perfectly responsive. The supply curve is flat. Any return above that level brings forth unlimited capital; any return below it causes capital to disappear.

Who bears a tax depends on which factor can escape. Same tax, same production technology, same substitutability, but different burden depending on what can move.

For the AI scenario Trammell and Patel describe, responsive capital supply is the relevant case. They’re thinking about long-run dynamics where capital accumulates over generations. And their own language supports it. They describe “self-replicating robot factories” that “can easily go anywhere.”

This points toward increasingly elastic supply, maybe even perfectly elastic like the benchmark model.

So what happens when you tax capital with elastic supply?

Households won’t supply capital unless the after-tax return equals their required return. They have a patience level. If returns fall below it, they consume instead of saving. In the long run, accumulation adjusts until returns exactly meet their requirement.

This means if you impose a tax, the pre-tax return must rise to compensate. Capital owners end up with the same after-tax return as before. They’ve escaped the tax.

But the pre-tax return is what firms pay. If firms must pay higher returns to attract capital, something else has to give. That something is wages.

This is a standard result: with responsive capital supply, a capital income tax lowers wages. Capital escapes. Labor pays.

What role does substitutability play? It determines how severely the economy contracts. Yes, high substitutability means firms can aggressively expand capital

It also means that firms aggressively shed capital when they are taxed. The capital stock shrinks MORE!. Output falls more. Workers lose more.8

High substitutability means workers are easily replaced by machines. That’s the inequality story. But it also means firms respond aggressively to any increase in capital costs. A flatter demand curve means capital flees faster when taxed, and labor bears a heavier burden through lost wages.

The features Trammell and Patel invoke for their inequality story are the same features that undermine their policy story.

The Policy Tension

For capital share to approach 100%, Trammell and Patel need capital to accumulate aggressively. They describe robots that replicate, factories that relocate, capital that flows freely to wherever returns are highest. Elastic supply is doing work for their inequality story.

For capital taxation to help workers, they need the tax burden to stick to capital owners. This requires inelastic supply. If capital can escape—by flowing elsewhere, by not being accumulated, by being consumed instead of saved—then taxing it shrinks the economy and wages fall.

These cannot both be true. The features that make capital accumulation explosive are the same features that make capital taxation ineffective.

The authors recognize this partially when they call for international coordination. If all jurisdictions tax capital equally, there’s nowhere to flee. But this only solves the mobility problem. It doesn’t solve the accumulation problem.

Even with perfect global coordination, savers can still escape the tax by not saving. If the after-tax return falls below their patience threshold, they consume instead of accumulating. It’s not that capital doesn’t flow to another country. It simply doesn’t get built.

The Path to Singularity Matters

The essay treats easy substitution as if it immediately implies an economy where labor contributes nothing. But easy substitution is a local condition about how factor shares respond to capital growth. Getting to zero labor requires passing through a transition where labor still matters, where multiple margins adjust simultaneously, and where standard economics applies.

In Scenario B—where substitution is easy but we don’t reach the zero-labor endpoint—standard policy tools still work. Labor income still exists to tax. Human capital investment still has returns. Wage subsidies still have a target. The economy looks different from today, more capital-intensive, but it’s not a different planet.

In Scenario C—their world—labor income approaches zero, and “tax capital” becomes the only option by process of elimination. (At least, according to Trammell and Patel. As Basil Halperin points out, we can still tax consumption or a fixed factor like land). But taxing capital in a world of responsive capital supply doesn’t help workers. The features that get you to Scenario C are the same features that make capital taxation fail.

I don’t know the answer to most of the predictive questions here. How easily will capital substitute for labor? How much will consumers switch away from human services? How fast will depreciation rise? How mobile will capital actually be? How patient are the rich? I know estimates from today and they are far off from the assumptions.

But the baseline model helps me see what’s at stake. The jump from “substitution might become easy” to “global capital taxation is the only way” requires being on the right side of multiple separate conditions simultaneously. Some plausible, some unknown, some in tension with each other.

Trammell and Patel ask a genuinely important question: what happens to factor shares and inequality if AI can do everything humans can do?

I have no idea. But I’m confident that the path matters and economics can help us think through things. The endpoint where labor contributes nothing is a limiting case. Getting there (if we ever do) involves passing through regions where standard economic principles apply and where multiple margins adjust. That’s where we actually live, and that’s where policy has to operate.

Transaction costs can be somewhat tautological. If it appears that there are foregone gains from trade, the explanation can always be framed as due to some type of transaction cost. Simply blaming transaction costs, however, doesn’t tell the whole story. We need a precise definition of transaction costs. Furthermore, depending on how broadly we define the term, we need to make sure to differentiate between different types of transaction costs. Failure to do so will provide us with an explanation, but little gained understanding of the world.

With that in mind, this week I wanted to write about a type of cost emphasized by Earl Thompson. In an unpublished working paper, Thompson argued for the important role of what he called “cooperation costs.” Broadly, what he meant by cooperation costs are costs associated with communication, coordination, and commitment among economic actors. His argument was that cooperation costs were important for thinking about sequential decision-making. In particular, he argued that when people engage in strategic, sequential decision-making, cooperation costs become really important.

He thus coined the term “potential cooperation” as a contrast to the idea of “potential competition” more familiar to price theorists and students. The idea behind potential competition is that even a firm with market power might behave more like a competitive firm when there is the threat of entry by a potential competitor. Whereas potential competition might push society toward greater gains from trade, Thompson’s idea of potential cooperation tends to limit gains from trade or lead to wasteful allocations of resources.

The basic idea is as follows. In a world of zero cooperation costs, all sequential decision-making would maximize the gains from trade. This is because anyone could communicate what economists would call a reaction function, “if you do Y, I will do X.” If the cost of communicating, coordinating, and committing are all zero, and assuming both Y and X would expand the gains from trade, these actions would be taken and make everyone better off. However, when these actions are costly, sequential decision-making can lead to foregone gains from trade. The reason is that, given a fixed cost of cooperation, it might be difficult at the outset of any sequential decision-making for everyone to agree to cooperate. Yet, once beneficial sequential actions start taking place, it becomes possible for a subset of decision-makers to cooperate and expropriate the gains from the people who have already taken action. Because they not only get the private gains they would have gotten otherwise plus the gains to other decision-makers, this subset is able to overcome the fixed cost of cooperation. Of course, if any of the early decision-makers know of this possible future cooperation, they will be unlikely to take beneficial actions because they expect their personal gains to be expropriated.

He argues that this concept can explain why certain places seem to end up in development traps, why sometimes our policies to deal with externalities seem inconsistent with lessons from introductory economics, as well as why the criminal justice system is structured in the way that it is.

Let’s dig in and think about this idea in a little bit more depth by applying it to the examples that Thompson provides.

Underdevelopment

Thompson starts with a simple example. Suppose that there is a site where one could open a mine and a site where someone could open a port. If both the mine and the port were opened, then one would need a road to connect the mine with the port so that the production from the mine could be transported to the port. An important question that we might think about is why some places seem to have no problem getting these investments made and why other places seem to get stuck in development traps in which these investments are not made.

To think about this question, Thompson first goes through conventional arguments for coordination failure. For example, if the decision to invest in the road, the mine, and the port were all made simultaneously, it is easy to show that you could get two Nash equilibria. In one equilibrium, all of the investments get made. In the other equilibrium, none of the investments get made. In other words, since the productivity and the value of any one of these investments is dependent on whether the investments get made, whether all three investments are made depend on whether each investor believes that the other investors will make their investments.

As Thompson correctly points out, this might be an interesting technical argument for development traps, but it doesn’t survive close scrutiny. Investments need not be undertaken all at the same time. The investments could occur sequentially. In fact, pecuniary externalities would seem to imply that sequential decision-making would lead to investment and prevent development traps. For example, suppose that someone decides to open the port. Subsequent to this, there is greater value in building a road that connects the port to the possible site of the mine. Furthermore, once the port and the road already exist, now the value of opening the mine is substantially greater than it was prior to any of these investments.

If cooperation costs are zero, this is precisely what we would expect to happen. One investment will lead to a subsequent investment.

However, suppose that cooperation costs are large enough to prevent these investors from being able to agree to these sequential investments, but less than the total surplus of the investments. Now, consider the same example. An investor decides to open the port in anticipation that the others will invest. It is possible that the road will subsequently get built. Yet, once the port and the road are built, the investor in the mine might refuse to construct the mine unless he can extract all of the surplus of the first two investments. Alternatively, investors in the road and the mine might cooperate to extract all of the surplus from the port investor. If the investor who opens the port knows that this is a possibility, he never opens the port in the first place because he doesn’t stand to earn any of the returns on the investment. Hence, the development trap.

Thompson argued that states that escape such development traps are those that are able to find solutions to these investment problems and create institutions that prevents the expropriation of gains from trade from early investors. He also argued that this is one reason why states that have substantial internal social conflict might be more susceptible to development traps: the cost of initial cooperation and ex post expropriation is substantial.

Externalities

Another application of his logic is to think about externalities. The standard textbook argument is that negative externalities should be taxed at a rate equal to the marginal external cost. Despite that standard argument, the real world is filled with quantity regulations and zoning rules to deal with pollution. Perhaps that is because an insufficient number of policymakers have successfully learned introductory economics. Thompson disagreed. He argued that policymakers clearly understood something that is ignored by the introductory textbook: cooperation costs.

As I have written about before in my post “Externalities and the Numbers Problem,” how we deal with externalities seems to depend on the number of victims and the number of imposers. Thompson argues that this is because of the costs of communication, coordination, and commitment.

For example, suppose that the state puts a Pigouvian tax on pollution that is equal to the marginal external cost of pollution. This is what the introductory textbook tells you to do. Now imagine that a factory that produces a lot of air pollution decides to locate in a large neighborhood. The residents of the neighborhood do not want to live by the factory and its pollution. Moving to another neighborhood will be costly, not only because moving is costly, but also because the value of the house and property is now lower due to the proximity of the factory. Knowing this, the factory could purchase the land and commit itself to building the factory. It could then offer the residents of the neighborhood a chance to buy the land back from the investor so that he cannot build in the neighborhood.

Like the underdevelopment example, the potential factory builder is able to expropriate some of the value of the housing in the neighborhood simply by committing to build if the transfer is not made. To prevent this sort of unproductive behavior from going on, governments tend to impose zoning rules that prevent factories from building in a neighborhood.

Crime and Punishment

A final issue that can be addressed in this context is the system of crime and punishment. Brian wrote about this recently in reference to David Friedman’s work on punishment. Brian frames the issues as follows:

As I said, Friedman’s paper is about punishment. He asks why we use prison instead of fines. From your textbook economics perspective, fines are much better. The defendant loses money, the state gains money, and no resources are destroyed in the process. Prison is horrible from this perspective. The defendant loses years of freedom, and the defendant’s potential economic output is wasted. Unlike fines, where the state at least gets some money, the state here pays the costs. That’s the opposite of getting money.

There’s a somewhat standard law and economics answer that fines cannot adequately punish poor defendants. If someone commits a crime worth $100,000 in damages but only has $10,000, a fine can’t deter them. Prison fills the gap. There’s definitely something to that.

Thompson poses a similar question in his paper. Economic theory would seem to suggest that fines would be more efficient forms of punishment than things like imprisonment. Furthermore, the issue of solvency referenced in Brian’s framing doesn’t seem to be an adequate answer. For example, if someone commits a crime with a social cost of $1 million, but only has $10,000 to their name, clearly they won’t be able to pay the appropriate fine. Nonetheless, the criminal could be made to pay some of the costs via a fine and the remainder in prison. That is often not the case. Also, we do allow for both criminal and civil procedures. If solvency is the problem, these seem to occur in the wrong order. The criminal case is prosecuted first and the civil case follows. Knowing this, the wealthy criminal has an incentive to spend as much as possible on defense to try to assure his freedom, leaving little residual wealth to be claimed by the victim(s) in a subsequent civil trial. If this was about solvency of the criminal, wouldn’t it be the opposite? Or wouldn’t they be combined into one process?

Instead, anticipating David Friedman’s argument by about a decade and a half, Thompson argues that the use of imprisonment is designed to deal with his idea of cooperation costs. His argument is that it only appears that imprisonment is the socially wasteful punishment because it assumes the objectivity of all judges, juries, and witnesses and ignores financial incentives to law enforcement and prosecutors in the alternative system. Punishments like imprisonment are used instead of fines because otherwise the criminal justice system would have an incentive to excessively enforce crimes and even bring dubious cases against people. They might also have an incentive to frame people for crimes they didn’t commit.

He also argues that this explains other elements of our criminal justice system. For example, since imprisonment is socially costly, one way to recoup some of the cost would be to lease the labor of prisoners to the private sector. However, this practice was prohibited by statute in the twentieth century. This was done to limit the incentive of the state and its private sector partners to use the criminal justice system to expropriate the human capital of prisoners.

As further evidence of this claim, Thompson notes that the elimination of the “murder fine” in England and its replacement with imprisonment in the twelfth century also coincided with the use of juries. He argues that both imprisonment and the use of juries fix the same problem. Just as fines would tend to lead to over-enforcement and potential extortion, it is much easier for a judge to collect a bribe from a defendant in exchange for a lesser or zero punishment than it would be for a jury to all cooperate to extract a similar bribe. Furthermore, the bribe is likely to be diluted by the fact that it must be shared by members of the jury. Even just the possibility of extracting a bribe makes it more likely for police to make an arrest and prosecutors to pursue a case if the police and the prosecutor are able to cooperate with the judge who collects the bribe. The jury not only makes it more difficult to extract the bribe, it would also be more difficult and thus more costly for a jury to cooperate in such a bribe scheme with others in the criminal justice. Unlike judges, jury members are unlikely to have repeated dealings with police and prosecutors thereby making cooperation costs much larger.

In short, potential cooperation creates the conditions for members of the criminal justice system to steal real capital from the wealthy and human capital from those who lack wealth. By using imprisonment rather than fines as a punishment, jury trials instead of single judge presiding, and prohibiting the lease of prison labor to the private sector, the system increases the costs of potential cooperation and therefore limits the corresponding expropriation.

Conclusion

Thompson’s idea of cooperation costs is an example of the sort of analysis that UCLA price theorists were known for. By focusing on particular types of costs and how those costs influence the nature of exchange, he was able to formulate hypotheses about why countries end up in development traps, why quantity regulations are sometimes used to deal with externalities, and why the state uses imprisonment rather than fines as punishment for crime. He has other examples as well. He argued that these types of costs might explain elements of anti-trust law and were at the root of the public provision of certain services, like firefighting and flood insurance. This argument is also indicative of Thompson’s style, both provocative and thought-provoking.

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One of the good problems of growing as a newsletter is that recent subscribers miss our old stuff. About 7,000 of you are too new to have received Josh’s February post on “What’s going on with these tariffs?,” the earliest piece on the list, which was about pre-liberation day tariffs. While we sometimes rerun pieces, the end of the year gives us an excuse to point you toward some highlights you might have missed.

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This was a fun one to write because I got to push back on a claim that sounds historical but isn't really about history. It was from April 7th. That seems like a lifetime ago in tariff land but it wasn’t even the earliest tariff piece on this list… My sweet summer child.

People always love hearing about the Nobel winners. This year I liked writing about them :)

The number one lesson in price theory.

This was back when people were mad and trying to do smoke and mirrors with the bad Q1 data. I wonder what they think after the initial Q3 data.

You never know what posts will be popular…

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The most Hendricksonian post of the year. And I think you could write basically the opposite post from a Hendricksonian perspective as well.

What was your favorite post of the year?

Most likely, that number comes from the famous “China Shock” research by David Autor, David Dorn, and Gordon Hanson. It’s become a fixture in trade debates. In that original paper, Autor, Dorn, and Hanson compared regions with different exposure to Chinese import competition. Some areas manufactured goods that competed directly with Chinese imports. Others didn’t. By comparing employment changes across these regions, the researchers identified how relative employment shifted; high-exposure regions lost jobs compared to low-exposure regions.

In a benchmarking calculation, they estimate that Chinese import competition accounts for approximately 21% of the U.S. manufacturing employment decline from 1990 to 2007, corresponding to roughly 1.5 million manufacturing jobs.

How seriously should we take that number? Answering that question is actually quite informative for answering many issues in empirical economics.

Price theory makes empirical work hard

One of the core premises of price theory is that prices coordinate people. Firms don’t exist in isolation; they’re competing with other firms. When Honda’s costs rise, it may produce less, which affects the demand for Toyotas. Firms are also connected across markets. They buy inputs from other firms, sell outputs to consumers and other firms, and compete for workers with every other employer in the economy.

Because prices coordinate behavior across all participants, when you shock one part of the system, the price mechanism transmits that shock everywhere else. Wages adjust. Workers relocate. Capital flows to different uses. Industries expand and contract.

This is an eternal nightmare for empirical work in economics. We rarely observe a clean experiment where only one thing changes. We see the whole interconnected mess moving together. Clever identification strategies try to isolate specific causal effects by finding variation that hits some units but not others. That’s valuable. But it also means we’re often measuring something specific: how the treated units changed relative to the untreated units.

And that’s not always what we want to know.

Robert Minton and Casey Mulligan have been developing what they call a “market interpretation of treatment effects” that clarifies what’s happening here. Their key insight is the simple idea above: in markets, prices coordinate behavior across all participants. When you shock one part of the market, the price system transmits that shock everywhere. The control group is RARELY unaffected. The treatment spills over onto them.

Their framework distinguishes three things that people often confuse:

• Difference-in-Differences (DiD): The gap between the treated and the controls. This is what your standard regression estimates.

• Treatment on the Treated (ToT): What happened to the treated compared to the baseline. This includes any spillover effects.

• Scale Effect: What happens when you treat the entire market. This is often what policymakers actually care about.

These three are not the same thing. Usually, when we talk about spillovers, economists will point to a small treatment size, meaning spillovers are small. But ToT and DiD become less informative about the scale effect as the share of treated units falls. In the limit of a tiny treated share, ToT and DiD coincide with each other (which is often what microeconomists want) but can differ from the scale effect by an arbitrary amount (which is often what the public discussion is about).

Before getting into the details, let’s think about the intuition for why these are different. You’re at a concert. You stand up to see better. The person behind you is now blocked, so they see worse.

A researcher measures the difference: “Standing up improves your view by X relative to the person behind you.” Correct! Now the researcher scales up: “If everyone stands up, everyone will see better by X.” Obviously wrong. If everyone stands up, nobody sees better, and everyone just has tired legs.

Econometricians have long grappled with spillovers or externalities, but more as a complication.

As Minton and Mulligan point out, this is the norm in markets. They illustrate this with a simple industry model that makes the problem precise.

Start at the market level. You have a downward-sloping demand curve and an upward-sloping supply curve. In the baseline, before any treatment, the market clears at some equilibrium price and quantity. Now imagine some suppliers get a productivity treatment so their output rises, a new technology like AI perhaps. The supply curve shifts outward. A new equilibrium emerges at a lower market price and higher total quantity, moving from E to E’ in the figure below. If the full market were treated, you’d move to E’’.

Now look at the firm level. In the baseline, treated and untreated firms have the same output and revenue. After the treatment, the treated firms can produce more. But the additional output from the treated drives down the market price that all suppliers receive. The untreated firms haven’t changed their production at all, but they now get paid less for every unit they sell.

After the treatment, the price is P’, so the treated firms receive revenue R_3 + R_4 + R_5 + R_6. The untreated firms have a revenue of R_3 + R_5. The DiD picks up the difference: R_4 + R_6.

If we ask what was the effect on the treated, we also have to account for the fact that prices fell from P to P’. The net effect on the treated is their new revenue (R_3 + R_4 + R_5 + R_6) minus their starting revenue (R_1 + R_3 + R_5). So ToT is R_4 + R_6 - R_1.

If we want to ask what would happen if all firms got the technology, now prices would go down to P’’. Revenue would go from R_1 + R_3 + R_5 to R_5 + R_6, so the net effect would be R_6 - R_1 - R_3, which is less than the ToT.

Notice what happened. DiD is positive (R_4 + R_6). But the scale effect (R_6 - R_1 - R_3) can be negative if R_1 + R_3 > R_6. That happens when demand is price inelastic, so that the price falls so much that even higher output doesn’t compensate.

A statistician might say the control group is “contaminated” because the treatment spills over through competition. But the contamination thinking doesn’t help us. For example, shrinking the treated share, which we generally think helps contamination issues, doesn’t fix this. The divergence between DiD and the scale effect persists no matter how small you make the treatment group. In fact, it gets worse: a smaller treated share means more of the market remains untreated, so the gap between “treating those we treated” and “treating everyone” grows larger.

The Missing Intercept

That’s the price theory framing. There’s an econometric way of seeing the same thing for the nerds who want that.

Your regression gives you a slope: how outcomes vary with treatment intensity across units. High-exposure regions lost more jobs than low-exposure regions. That slope is your DiD estimate.

The treatment effect on the treated isn’t just the gap between treated and controls. It’s that gap plus whatever happened to the controls because of the treatment. If competition from displaced manufacturing workers pushed down wages in services, that’s a spillover onto the “control” sector. The controls moved too.

So: ToT = DiD + the spillover onto controls.

Your regression identifies the slope. But that level shift—how much everyone moved because of the treatment—gets absorbed by the intercept (or maybe time fixed effects). It’s invisible to cross-sectional variation. Macro people sometimes call this the “missing intercept.” In Minton and Mulligan’s framing, it would be called the treatment effect on the untreated.

Notice that the scale effect is still different. ToT tells you what happened to the treated when you treated some of the market. The scale effect tells you what would happen if you treated all of it. The difference is the counterfactual spillover on the treated from additionally treating everyone else. When your treated group is small, that’s a lot of “everyone else.” The additional spillover can be large.

Formally, ToT is a weighted average of the scale effect and DiD. The weight depends on the fraction of the market that is treated. As that fraction shrinks toward zero, ToT converges to DiD, but both can differ from the scale effect by an arbitrary amount.

Two Distinct Problems

There are (at least) two conceptually different reasons you can’t just multiply the cross-sectional slope by aggregate treatment intensity to get the aggregate effect. Both matter in different proportions, depending on the problem.

Problem 1: Aggregate Shocks That Hit Everyone

At least in theory, some responses have literally zero cross-sectional variation. They hit everyone identically.

Did the Fed respond to rising unemployment by easing monetary policy? Did the dollar weaken? Did fiscal policy respond?

These affect everyone equally. Time fixed effects absorb them completely. Your regression cannot see them. There’s no cross-sectional variation to exploit.

Problem 2: Spillovers and Reallocation

When workers lose their jobs in one sector, they don’t vanish. They move—geographically or across industries—to find new work. They flood into other sectors.

The cross-sectional estimate captures substitution within the market. Basically, how activity shifted between treated and control groups. But some of that gap is zero-sum reshuffling that doesn’t affect the aggregate at all.

Minton and Mulligan push this further. Even if you could perfectly measure the spillovers—how much wages fell in the control sector when treated workers flooded in—you still wouldn’t recover the aggregate effect. The reason goes back to the distinction between ToT and the scale effect. Measuring spillovers onto controls gets you from DiD to ToT. But the scale effect is what happens when there’s no control sector left to absorb the shock.

If you want to know what happens when the entire economy is hit—when there’s no unaffected sector to flee to—you need a different elasticity entirely. Not the elasticity of substitution between the treated and control sectors, but the elasticity of substitution between working and not working. Between participating in this market and leaving it entirely.

The cross-sectional design identifies substitution within the market. The aggregate effect depends on substitution out of the market. These can be completely different numbers.

Back to the China Shock

Now we can return to that 1.5 million jobs number with clearer eyes.

The China Shock is different from the development economics case. When a development economist runs a microfinance pilot in a few villages, they may want to know: what would happen if we scaled this up nationally? That’s the scale effect question.

Autor, Dorn, and Hanson aren’t asking that. China has already happened to the whole economy. They’re trying to measure an aggregate shock that already occurred. The question is: what did Chinese imports actually do to American employment?

The problem is that they’re using cross-sectional variation to get at an aggregate effect. High-exposure regions lost jobs compared to low-exposure regions. That’s the DiD. Solid identification.

But to get the headline number, they multiply that regional slope by the national change in import penetration. Benjamin Moll calls this the “naive scale-up”:

Aggregate effect ≈ (micro coefficient) × (aggregate change in treatment)

This arithmetic looks innocent. But it goes against the basics of price theory. (To be fair, they recognize the problem and frame it as a benchmark.)

It assumes the aggregate effect is just DiD times the size of the shock. It assumes no spillovers, no reallocation, and no general equilibrium. This isn’t a secret in the paper, but it’s maybe missing by those who just casually know it.

What’s actually missing? The common component—how much everyone moved because of the China shock. Did displaced manufacturing workers flood into services, pushing down wages there? Did the Fed respond to rising unemployment? Did falling demand in factory towns hurt local retailers everywhere? These effects hit high-exposure and low-exposure regions together. They’re absorbed by time fixed effects. The cross-section can’t see them.

This is the “missing intercept” problem. It’s not that we want to know the intercept per se (when the treatment is zero) but that the research design identifies relative effects, or the slope. But the level shift that’s common to all regions disappears.

If manufacturing workers who lost their jobs moved into services, that displacement shows up as a huge gap between sectors, even if total employment barely changed. The DiD is large. The actual aggregate effect could be much smaller. Or if the common shock was large—if macro policy failed to respond, if there really was nowhere for workers to go—the aggregate effect could be larger than the cross-sectional estimate suggests.

I’m not saying the aggregate effect is zero. I’m saying the research design can’t tell us what it is. The naive scale-up assumes no spillovers. But the whole point of price theory is that spillovers are everywhere.

The Intercept Can Cut Either Way

I’ve been emphasizing what the cross-section misses. But I haven’t told you which direction it cuts. The missing intercept could make the aggregate effect smaller than the naive scale-up—or larger.

The cushioning intuition is natural. Workers displaced from manufacturing flood into services. The shock gets absorbed. The aggregate effect is smaller than the local one because there’s somewhere else to go.

But the intercept can also amplify.

Recent work by Adão, Arkolakis, and Esposito tries to recover the missing intercept using a structural general equilibrium model. They find that the China Shock eliminated 2.2 million jobs, a number even higher than the original estimate by Autor et al. In their model, regions are linked through trade and production chains. When a manufacturing hub shrinks due to import competition, it ceases to generate the agglomeration benefits that make nearby firms more productive.

On the other side, Lyon and Waugh incorporate aggregate labor supply responses, producing a positive aggregate employment effect from the China shock of about 1.5 million additional jobs. They explicitly state the limitation: difference-in-difference approaches identify only differential effects, not levels. The aggregate labor supply response is not identified. This is more about the aggregate shock that I mentioned above.

Now, I’m not claiming the 1.5 million number is too high. I’m not claiming it’s too low. Or I think it's below 1.5 million, but I’ll save sorting through that literature for another newsletter.

The point is that the research design of comparing high-exposure to low-exposure regions can’t adjudicate between these possibilities. The cross-section identifies the slope. The literature itself is an iterative attempt to reconstruct what cross-sectional designs omit. Even time variation is problematic. For example, Alessandria, Khan, Khederlarian, Ruhl, and Steinberg argue that the job losses we observed in the 2000s were largely the slow, grinding adjustment to earlier decisions. You can’t just difference that out. In a sense, the past is contaminating the WTO time period. Or in price theory language, this is markets interacting through time, not just across place.

Missing Intercepts Everywhere

I started out with how general this problem is. Yes, I tried to lure you in with the China shock number, but then it’s everywhere. The Moll slides that I linked about list a bunch of areas that fall prey to this. Let me add one more case where you need to worry about comparing treated and control groups: airline mergers.

Airline mergers have the "missing intercept" problem built in from the start.

The standard approach compares “treated” routes (where both merging carriers operated before) to “control” routes where only one carrier flew. Classic DiD.

But airlines operate networks. When Delta and Northwest merged, Delta didn’t just adjust fares on overlap routes. They reoptimized capacity, frequencies, and pricing across hundreds of routes. That’s the whole justification for the merger (maybe overblown, but still). Your “control” routes are connected to the treated routes through the network. They’re partially treated or contaminated.

Any common effect that hits all routes together, such as network-wide pricing changes, capacity discipline, or coordinated conduct, gets absorbed by time fixed effects. It’s invisible to the cross-sectional comparison. The DiD identifies the differential change between treated and control routes, not what happened to the overall level of fares.

A recent paper by Aryal, Chattopadhyaya, and Ciliberto is basically an attempt to grapple with these exact critiques. There are really two parts to their analysis. The first uses synthetic DiD to reweight controls to better match the pre-trends of the treated group. This addresses a parallel trends problem, a separate issue from the missing intercept. After this fix, the results shift dramatically. The baseline DiD suggests mergers reduced prices by 4-8%, but with synthetic weights, those effects vanish. If anything, prices rose.

But synthetic weights don’t solve the missing intercept problem. They ensure treated and control routes were on similar trajectories before the merger. They can’t tell you about common shocks that hit all routes together. If the merger generated efficiency gains that spread across the network, lowering prices on all routes, we wouldn’t pick that up. On the flip side, if the merger facilitated anticompetitive coordination across all routes, raising prices everywhere, we wouldn’t pick that up either. The intercept can cut either way.

The authors know this and are extremely clear about it. They explicitly flag that the standard merger DiD approach relies on no spillover effects across markets and note this is questionable when firms compete across markets.

That’s why the second part of their paper turns to a structural model, separately identifying efficiency gains and changes in “conduct.” This gives you a framework for thinking about what the missing intercept might contain. But the identification still comes from cross-sectional variation. The structure helps you interpret, but it doesn’t conjure new information from thin air.

So what are we to do? The authors try to rescue the analysis with a structural model, but I’m not sure it solves the fundamental problem. Novel methods in a single application should make us more cautious, not less. In the case of the airline mergers, I think this paper is the best we have on these mergers, and it looks like these mergers have raised prices.

But we should be quite humble at this point. The cross-sectional estimates require no-spillovers assumptions that are especially heroic for networked industries. The missing intercept problem is the norm when you’re dealing with networks.

The key point is that distributional effects and aggregate effects are different questions. Most of the time, the coefficient is a slope. The aggregate effect requires the slope and the intercept, which requires an economic model.

Markets connect people through prices. That’s what makes them so good at coordinating activity. It’s also what makes them so hard to study in pieces.

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On a Thursday in early September, more than 40 strangers logged into Instacart to buy eggs and test a hypothesis. They all selected the same store, the same brand, and the same pickup option. The only difference was the price they were offered: $3.99 for some, $4.79 for others.

We are, of course, supposed to not only be shocked by this, but also outraged. The Groundwork Collaborative, which organized the study, warned that companies like Instacart are jeopardizing trust in markets.

The New York Times ran it as the hook for a larger piece on algorithmic pricing: “the notion of a single price, offered to all customers for a predictable period, is breaking down.” The Times piece ends with Groundwork Executive Director Lindsay Owens saying: “This isn’t about managing scarcity or efficient markets… [It’s about] pushing to figure out the maximum amount you are willing to pay and squeeze it out of you.”

Both pieces blur together several distinct ideas: that prices vary, that prices are set by algorithms, that algorithms use your personal data, and that all of this harms consumers. But these are not the same thing.

Importantly, the study actually found no evidence that Instacart based prices on individual characteristics. As the Times piece correctly notes: “The Groundwork study found no evidence that Instacart was basing different prices on customers’ individual characteristics like income, ZIP code or shopping history.” Yet Groundwork uses the findings to call for Federal Trade Commission (FTC) action against “price discrimination not justified by differences in cost or distribution.” The Times frames the study as evidence of a trend toward algorithmic price discrimination.

To see why this leap fails, consider that there are at least four distinct reasons you might see different prices for the same product.

Four Reasons You Might See Different Prices

Randomized pricing experiments (A/B testing)

Some shoppers randomly see $3.99 for eggs; others see $4.79. The purpose is to learn how price-sensitive the market is across different product categories. This is standard A/B testing, the same methodology that virtually every online company uses to improve its products.

Netflix tests different thumbnail images. Retailers test prices. Random YouTubers, even tiny ones, test thumbnail placements. Over on Substack, I can A/B test article titles.

In the case of pricing, the randomization means the prices are not based on any individual characteristics. You get $4.79 for eggs because a random number generator assigned you to that treatment group, not because an algorithm profiled you.

Why would a company do this? When I teach my students about monopoly pricing, I draw the demand curve and assume the monopolist knows that curve and picks the optimal price. But real companies don’t see the demand curve. They want to learn how quantity demanded changes with price. Randomized experiments are how you learn.

Strategic randomization

Even without learning motives, firms might randomize prices as a competitive strategy. The intuition: if you always charge $5, your competitor charges $4.99 and steals your customers. If you always charge $4.99, they charge $4.98. In the jargon of game theory, no pure strategy equilibrium survives.

Instead, sellers randomize. Each seller picks one probability distribution over prices—not tailored to individuals, just one strategy for the whole market. Different customers end up paying different prices, but not because anyone profiled them. They just happened to show up when the price was high or low.

In practice, it can be hard to tell strategic randomization from price discrimination. A “sale” could be a random price variation, or it could be targeting price-sensitive shoppers who wait for discounts.

Dynamic pricing

Prices adjust over time in response to supply and demand. Uber’s surge pricing is the standard example: when demand spikes after a concert, prices rise to attract more drivers. Airlines do this. Hotels do this. The prices adjust based on observed market conditions, not based on individual profiles.

It’s not always easy to tell what is true dynamic pricing. Is an early-bird discount reflecting lower costs, lower demand for food services, or a strategic targeting of older people or people who are more flexible in terms of time? In practice, it can be both, but we can conceptually separate the ideas.

Price discrimination

Actual price discrimination is charging different prices for goods that are identical from the seller’s perspective, based on consumers’ willingness to pay. Senior discounts, student pricing, and coupons are all forms of price discrimination. The fear driving the Instacart coverage is that algorithms will profile you and charge you the maximum you’ll pay, which is an extreme version of price discrimination. This is the one that everyone seems worried about (I’ll say more on that later).

Again, it may not always be possible to tell these apart in practice. But we should be clear that these four phenomena have different causes, different welfare implications, and require different policy responses, if any.

The Narrative vs The Evidence

This brings us back to Instacart. The company says its pricing tests are “short term, randomized,” “never based on personal or behavioral characteristics,” and “never change in real time, including in response to supply and demand.” That’s a description of A/B testing. I’m not saying we should necessarily believe what they say, but the Groundwork study—despite its framing—found no evidence to the contrary.

The evidence, which is always subject to revision, points to category one. The narrative assumes category four. After reporting that the study found no evidence of personalized pricing, the article pivots: “But there is little doubt that Instacart and other online sellers have the ability to do so.” A volunteer speculates: “If they know how you shop, even on other items, they can use all that information to say, ‘We’re going to tailor the price to you… We’re going to get the maximum amount of money out of you that you’re prepared to pay and drain your pocketbook.’”

The article then notes that “Companies including Delta Air Lines, Amazon and Home Depot have been accused of experimenting with such personalized pricing, only to retreat after consumer backlash.” And it cautions that frequent price changes “could make it easier for companies to adopt personalized pricing strategies in the future.”

This is a story about something that might happen, not something that did happen. The study found randomized A/B testing. The narrative is about algorithmic profiling.

Would Banning Price Experiments Help Consumers?

Grant for a moment that A/B testing itself could be a concern. The implicit assumption in the discourse is that banning price experiments would help consumers. Stop the randomization, get stable prices, and consumers win.

But which stable price would Instacart use?

Let me pose a question that I posed on Twitter. For what classes of downward-sloping demand curves would consumers prefer the stable price to randomized prices around the same average?

The answer: none.

The intuition is that when prices are high, you buy less and limit your losses. When prices are low, you buy more and capture gains. The gains from low-price periods outweigh the losses from high-price periods.

The intuition that price stability being good helps consumers implicitly sneaks in an assumption about risk aversion that isn’t part of standard consumer-surplus analysis. Maybe you want to make that argument. Fine. But you should be aware that you’re making an argument that contradicts basic consumer theory.

Why Price Discrimination Isn’t Always Bad

So, the Instacart study found A/B testing, and I gave a simple (silly) counterexample where banning A/B testing wouldn’t help consumers anyway. That’s not the only thing at play, but we can’t just assume our intuition about constant pricing will ultimately help consumers.

Here’s what I find strange about the whole episode: we know companies engage in price discrimination. Senior discounts, student pricing, coupons, and loyalty programs are all price discrimination, out in the open, every day.

Spotify charges students $5.99 and everyone else $11.99 for the same service. Amazon Prime costs $6.99 per month if you’re on Medicaid, $14.99 if you’re not. Airlines have turned price discrimination into an art form. If Groundwork wanted to document price discrimination, they could walk into any grocery store and photograph the signs, or just open any app on their phone.

And even if they had found true personalized pricing (which they did not), the framing would still be overblown. Price discrimination sounds bad. The word “discrimination” does a lot of work there. It’s almost as scary as “algorithm.” Alexander MacKay of the University of Virginia, quoted in the Times, made this point: “Many pricing strategies resulted in higher costs for less price-sensitive, and therefore potentially higher-income, consumers, while lower-income consumers paid less.” Sure, that’s discrimination, but is it bad for consumers?

Remember the basics of monopoly theory. A monopolist charging a single price faces a tradeoff: lower the price to sell more units, or keep the price high and sell fewer. The monopolist leaves some transactions on the table; consumers who would pay more than the cost of production but less than the monopolist’s chosen price. This generates the standard deadweight loss from monopoly, and it’s why antitrust cares about market power in the first place.

The ability to use price discrimination changes the calculus. If the monopolist can charge different prices to different consumers, it can sell to those marginal buyers without lowering the price for everyone else. Output expands. The transactions that were left on the table now happen. In the extreme case of perfect price discrimination (charging everyone exactly their willingness to pay), the monopolist produces the efficient quantity.

People may worry that consumers don’t get the benefits of that output expansion. But that’s just the textbook case with a single seller. With competition, the results can be even better for consumers. In my theoretical research, I’ve found that price discrimination with competing firms can maximize consumer surplus. Competition forces firms to fight over price-sensitive consumers, while still serving the broader market.

Whether price discrimination helps or hurts consumers is an empirical question with a long literature. The answer varies by context. The empirical work is mixed but hardly supports blanket opposition. What you don’t find is evidence that price discrimination is systematically bad for consumers.

The Real Question

There are legitimate questions about algorithmic pricing that are only going to become more important. How do algorithms affect competitive dynamics? When does price variation reflect learning versus extraction? How much transparency should consumers have about how prices are set?

But the Instacart study doesn’t begin to touch these questions. It found that a company runs A/B tests on prices, something that’s been standard practice in retail for decades, just done now with digital tools. The innovation is speed and scale, not the basic practice.

Economics is not just an intuition pump. When someone says “this feels unfair,” we need to ask: compared to what? Compared to everyone paying the lowest price? That’s not happening. That’s not a plausible counterfactual. Compared to everyone paying the same price? Consumer-surplus theory says randomization around the same average price actually benefits consumers.

Price theory exists to cut through exactly this kind of confusion. Let’s use the tools we have.

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