Conversable Economist

I admit this story is insider stuff for those with experience of academic seminars for economists. But it made me laugh, so I pass it along. It’s how Jesse Tack, Jisang Yu, and Roderick M. Rejesus introduce their review essay “Recent approaches in agricultural production economics: Where the heck are the prices?” (Food Policy, May 2026).

A scene is unfolding. It’s 2005 and an excited young PhD candidate applied for a job and has been invited to give a job market seminar at a prominent department. They are nervous, but well prepared. Nice new outfit, a couple practice presentations under their belt. Their voice cracks just a smidge as they introduce themselves to the audience and share some personal background on why they are so excited about this job opportunity. By the time they get to the title of the paper they have recovered and are settling in. As the final word of the title rolls off their tongue, they are about to click to the introduction slide when a hand shoots up in the audience…

“I read the paper linking the new technology to crop production, but…[dramatic pause]…where the heck are the prices? Those would seem to be really important variables to be included in the analysis.”

The speaker is rattled. The questioner seems genuinely upset, and this was not expected so early in the presentation. They stammer a bit but aren’t panicking yet. They decide to go with a succinct reply in hopes of moving on. Remember, be polite…

“Great point, thank you. We agree that prices are important drivers of production but we do not need them in order to estimate the effect of interest for reasons that I will discuss later.”

The questioner doesn’t like this answer, they are a little offended…

“Well you want a job here and I asked you a question. I would appreciate an answer now.”

The speaker is again surprised and fidgets a bit. They had everything perfectly planned out. Number of slides. Length of presentation. Leave enough room for questions along the way, sure, but this soon in the presentation? Not sure what exactly to do, they summarize their strategy succinctly in the hopes that they can move on…

“One of the really cool aspects of this project is that we spent a large amount of time setting up the experimental design in which the technology was randomly assigned to different producers and thus plausibly exogeneous to both input and output prices.”

Experimental design??? Sensing shenanigans, they dig back in…

“Okay…hmmm…I don’t know about all that but I do know that you’re trying to explain production outcomes and prices are really important in that context. We are economists after all, not agronomists”.

It’s clear the questioner is well practiced in this slight against agronomists, putting just the perfect amount of snark into the comment to elicit a chuckle from the audience. Damn! After an internal sigh, the speaker wades back into the madness….

“I appreciate your concern and we have thought a lot about it, I suppose there might be some incidental in-sample correlation of prices and technology adoption so to check that this isn’t indeed a major concern we did consider a robustness check where we controlled for prices with annual fixed effects and the estimates remained stable.”

“A what effect?”

“A fixed effect. Like a dummy variable for each year in the sample.”

“You controlled for prices with a dummy variable?”

“Yes, a different one for each year.”

“Like a zero-one variable? Prices are continuous variables how can that possibly work?”

“I know right, its super cool. As long as the producers all face the same prices it is observationally equivalent to putting prices directly in the model.”

“Aha! But the economic environment is different by regions so that can’t possibly work.”

“Agreed! That’s why we also used region-by-year fixed effects!”

“What the what???”

“Yeah, our results actually seem to be pretty robust to a wide range of possible confounders.”

Baffled, and sensing they might be a bit behind on current empirical approaches as some of their colleagues in the audience seem to be nodding along in agreement with the speaker, they make one last attempt….

“But prices are a really important drivers of production and I want to see what their effect is in this study”

“That’s not our focus here”

“Well…it should be!”

“Ummmm…okay…”

OK, to appreciate the humor, perhaps you have to have been in this kind of seminar room. For those who were not, it’s probably useful background to know that for a long time, agricultural economists had a heavy focus on “production functions”–that is, a function where the inputs would be land, labor, seeds, irrigation, fertilizer, farm machinery and the like, and the question was how changing various inputs would affect output.

The goal seems straightforward, but as has been well-recognized for a long time, drawing inferences in this approach can be messy. For example, if one observes a bunch of farmers using more farm equipment, and producing higher output, is the farm equipment causing the higher output? Or is the greater use of farm equipment a reaction to some factor not included in the analysis, but also affecting output? For example, perhaps some land is just much better-suited to farm equipment, and if you don’t have that kind of land, buying farm equipment won’t benefit you as much. What if certain inputs are not well-measured in the analysis? For exmaple, perhaps some farmers may be more entrepreneurial, risk-taking, and knowledgeable than others, but you can’t just take the inputs those farmers are using, apply them to farmers with different managerial characteristics, and expect the same result.

The question of how to get more plausible causal estimates is at the center of lots of economic empirical work in the last few decades, including agricultural economics. As a straightforward example, imagine a study of several hundred or several thousand farms. Half of the farms are randomly chosen to receive a certain intervention: perhaps seeds that produce more if given particular care, or loans to buy additional fertilizer before planting, or insurance against future fluctuations in crop prices, or conservation payments, or a sophisticated weather app. Later, one can then compare output of farms that got randomly got the technology and those that didn’t.

A researcher can also look for events that introduce a random component for agricultural production, so that out of a group of farmers who are similarly situated and growing similar crops, some of thosse farmers randomly face a situation that others do not. Variations in weather conditions can offer such randomization: more specifically, it’s now becoming possible to look at detailed variation in humidity, wind speed, solar radiation, evaporation, and so on. If you want to know, for example, how global warming might affect agricultural output, these sorts of studies offer a place to start.  Farmer may also vary in what technologies are available to them, or by variations in the policy environment.

And yes, it turns out to be fairly straightforward to add effects of prices into these models, as our pestering seminar participant in the story above wanted back in 2005–although the methods for doing so weren’t yet well-developed 20 years ago. The essay is a good example of a pattern that has happened fairly often over the years, in which agricultural economics raises problems that lead to new econometric techniques: for example, the birth of instrumental variable estimation and fixed-effects methods.

Kevin Warsh was nominated by President Trump in January 2026 to replace Jerome Powell as Chair of the Federal Reserve. He was confirmed by the US Senate last week and sworn in as the new Fed chair earlier this week. What are some of his views and priorities as he takes office? For a clue, I looked at a just-published collection of essays by economists and central bankers, Finishing the Inflation Job and New Challenges for Monetary Policy, edited by Michael D. Bordo, John H. Cochrane, and John B. Taylor (Hoover Press, 2026, scroll to the bottom of the link and the advance page proofs of book can be downloaded for free in sections). The book described papers and comments from a conference held a year ago in May 2025–that is, well before Warsh was nominated. Here are some of his comments that struck me:

On central banks and causes of inflation:

Inflation is a choice. The world’s central bankers get to choose the inflation rate. … The central bank establishes the policy rate and steers in the direction of an inflation objective. Central banks … are not victims. … Inflation is not caused by pandemics or autocrats around the world. The inflation level is set by the world’s central bankers. And without going too far afield, in my view, it is principally determined by government spending and printing. …

It would be nice if one could say bygones are bygones. It would be nice if households and businesses believed that past errors had no bearing. But the precondition for stable prices is confidence on the part of households and businesses that central banks will deliver stable prices. And the best way to give them that confidence is to have achieved it. It is up to the central bank to ensure that whatever shocks occur outside are one-off effects. The inflation
rate, that is, the second- and third-order consequence of changes in prices, not the first-order change in the price level, is up to the world’s central banks.

If central banks assert that outsize changes in the price level affect inflation and then drive a set of inflation outcomes, the banks are, in a way, admitting something against their own interest. They’re saying, in a sense, that their credibility has been impaired and that inflation will occur because they don’t have the credibility to stop it.

On the aftermath of earlier decisions to conducting monetary policy with quantitative easing, and its interaction with conducting monetary policy by adjusting the federal funds interest rate:

In a broad sense, we should acknowledge now what was acknowledged at QE’s creation in 2008: The Federal Reserve established a second monetary policy instrument, a supplementary instrument that has an important effect on inflation. … [W]ith a very active, large, and often growing balance sheet, we have two policy instruments that are imperfect substitutes for each other, sometimes working at cross-purposes and at other times working together. But if the printing press could be quieted, we could have lower policy rates, because a $7 trillion balance sheet is affecting inflation. There are many benefits of a small balance sheet, including lower rates. However, a better economic outcome is probably the most important. …

First, a surge in the balance sheet is understandable in periods of great shocks. The Fed was created after a panic early in the 20th century. So nothing I say should be taken as any direct criticism of what happened to the balance sheet in 2008, or what happened in the darkest days of 2020. One should give the benefit of the doubt, I think, to central bankers in harm’s way. Second, I think it’s strange to say in 2008 and 2020 that the balance sheet expansion was monetary policy by other means, but not in more benign times. It’s odd not to have any rhetorical or real symmetry. In my view, the balance sheet can’t be construed as monetary policy in crisis times, but the balance sheet has nothing to do with monetary policy in any other circumstance. That kind of asymmetry goes against the very spirit of policy rules and moves us to a policy of full discretion.

Finally, when I joined the central bank in 2006, we had about an $800 billion balance sheet. If you were to try to scale that to the growth of the economy, or to the growth of financial markets, one might end up with a $2.5 trillion or $3 trillion balance sheet today. As we sit here, the balance sheet is about $7 trillion. The right question was raised earlier about transitions between policy regimes. The transition from a scarce reserve system—in which banks were relying predominantly on each other for liquidity, with the central bank entering the market more rarely in periods of extreme illiquidity—to an excess reserves regime was not sudden. Going back to some status quo ante, or adopting a new, third-way model, will take time. The transition is not something that could or should happen overnight. But banks will grow accustomed to the liquidity regime around them. And if the central bank has a permanently larger role, not just in crises but in normal times, and is in some sense providing liquidity to the banks during all seasons and for all reasons, then one has fundamentally changed the role and responsibility of the central bank.

The transition to what I think is a more prudent system will take time, deliberation, and an excess of communication with the public and the institutions in the banking system itself.

For those of us who live in the world of editing, writing, and publishing, the ability of the newest generations of AI tools to produce rivers of grammatically correct prose is a deep shock. But is there actual evidence on AI and the quality of published work? Imke Reimers and Joel Waldfogel offer a starting point in their research paper, “AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books?” (NBER Working paper 34777, May 2026, also available here).

The obvious questions here are how to measure quantity and especially quality of new books. Quantity is easier. The authors offer some highly suggestive evidence: “Using data on new books offered for sale at Amazon, we document that the number of new titles appearing each month nearly tripled between 2022 and late 2025 and rose by a factor of nearly ten in some categories; and the increase in new titles 2022-2025 coincides with both the diffusion of LLMs and the incidence of detected AI in books.”

The Amazon data also provides information on the number of ratings for any given book, the sales rank, and the number of “stars” for any given book. These kinds of measures offer a data-driven way of getting at the question of quality. In addition, Reimers and Waldfogel describe:

[W]e take a direct approach employing AI detection on over 50,000 randomly selected titles. We document the growth of AI usage and compare the quality of “AI books” and “non-AI books” (books with and without detected AI). We have four findings. First, the timing of AI growth tracks the growth in releases: Detected AI use is roughly zero through 2022, rises to 30 percent in 2023, to 45 percent in 2024, and surpasses 60 percent during 2025. Second, AI books garner substantially less usage per title. Most have very little usage, and a modest share is somewhat used, both when measured by the number of ratings and eventual sales ranks. AI books are also worse in the sense of having lower star ratings. Third, the human-AI usage gap narrows substantially between 2023 and 2025. Finally, the number and quality of human-authored books has remained stable.

Perhaps unsurprisingly, authors with relatively low sales and rankings in the past much more likely to take up the AI tools.

At some level, the results of the study are unsurprising. AI seems to have led to many more books, but the average quality of these AI-assisted or -generated books is lower than the previous average. The authors write: “The effects of this influx [of new books] on consumer welfare depend on the quality of the additional books. The average quality of new books has fallen with the LLM-induced influx, and books with detected AI are substantially worse than human-authored books, so that much of the new work is of little value to consumers. Still, the LLM influx has delivered some books in the middle range of the usage/quality distribution …”

Consumers vary in their tastes, and more books means more to choose from. At least so far, AI-generated fiction is not hitting the top of the measures of quality. But it’s worth remembering that a large proportion of book says are in the middle range of the quality distribution, including categories like romance, SF, and mystery. Personally, I read a lot of mysteries, and while I try to pick high-quality ones, I read a fair number that could be fairly characterized as in the middle range of quality; indeed, some of the mysteries I read could probably be improved with an assist from AI.

This broad pattern probably characterizes a lot of AI-assisted work in creative areas. It can produce vast quantities very quickly, which will mostly be low-quality, but some of it will reach middle-quality. If the low-quality output can be ignored at low cost, a greater choice among middle-quality output is a modest social gain.

The US and other high-income countries have their own set of concerns about China’s role int the world economy–but the concerns for low- and middle-income countries may be even more severe. A standard way of thinking about the process of global economic development is as a sort of ladder. Low-income countries start out with economies that are heavy on agriculture and subsistence farming. However, they gradually take a step up into low-skill manufacturing (textiles is a classic example), and then with additional capital investment they can take another step into higher-skill manufacturing. As manufacturing displaces agriculture, their service economy begins to expand as well, and the service jobs in everything from logistics to health care, from finance to education, and the broad category called “professional and business services,” all expand as well.

But what happens if the bottom step of the development ladder, the move into low-skilled manufacturing, is blocked by the presence of China in the global economy? Shoumitro Chatterjee and Arvind Subramanian argue that China’s ongoing global presence in low-skilled manufacuturing is shutting off the early steps to economic development for low- and middle-income countries around the world. They make the case in “China’s Mercantilist Squeeze on Developing Countries” (Peterson Institute for International Economics, Working Paper 26-7, May 2026).

For example, consider China’s share of global markets in the kinds of low-skilled production that has in the past often been an early rung on the development ladder. The size of global market is measured by domestic value-added of those exports (after some inputs were imported ). Notice that high-income countries dominated these global markets in the 1960s, but then moved on to other industries. However, despite China’s rapid growth in the last few decades, it continues to dominate these markets.

If China’s share of these low-skilled goods should be in decline, by how much? The authors tackle this question in a few ways. For example, they look at China’s share of low-skill/low-paid workers in the world economy, and how it has declined over time, and they look at the history of how today’s high-income countries reduced their global presence in these industries. No matter how they slice it, China is competing very directly in world markets with the low- and middle-income countries of the world in precisely the industries that have often been an early step toward economic development. Moreover, China imports very little of these goods, so it does not act as a buyer in international markets in these areas. As the authors write:

What matters is that China is occupying a larger share of the global value chain in precisely the sectors where poorer countries would otherwise expect to compete. … Not enough attention is being paid to China’s persistent, even rising, occupation of export space in low skilled goods and the attendant impact on the development prospects of low- and middle income countries—the China Squeeze. This impact—hundreds of billions of dollars in foregone LMIC exports—has been felt most acutely in global markets but also in China’s imports in LMIC markets and low access to China’s markets as well. The magnitudes are sizable enough to stymie their structural transformation and ability to escape from low- and middle-income status.

At some level, it doesn’t matter whether China’s position, blocking ability of developing countries around the world, arises “naturally” from China’s economic growth or “unnaturally” from policies of China’s government that offer its manufacturing and exporting firms a direct or indirect subsidy. But the question of “why” is nonetheless intersting.

As one piece of data, Chatterjee and Subramanian look at wages in the apparel industry across countries. Wages in China are way up–but this has not meant a shift of global apparel exports to countries with lower wage levels, as one might expect.

As another piece of data, the authors look at changes in manufacuturing productivity over time. China’s productivity growth in manufacturing has slowed, while other competing countries have seen sharp rises. But again, contrary to what one might expect, this has not led to a lower share for China in low-skilled manufacturing.

Comparing direct and indirect government subsidies across countries is an enormous task, and this paper doesn’t take it on. But the authors do suggest that, in their judgement, perhaps the most likely reason for China’s ongoing global dominance in these low-skilled manufacturig jobs is that China central bank acts to keep its exchange rate artificially low. The result is that China’s exports to the rest of the world are cheaper than they would otherwise be, while China’s imports are more expensive than they would otherwise be. Of course, this also helps to explain China’s consistent pattern of large trade surpluses.

The go-to source for comparing national levels of defense spending arount the world is SIPRI, the Stockholm International Peace Research Institute, which has just published its data on defense spending in 2025. Here are a few snapshots from “Trends in World Military Expenditure, 2025,” co-authored by Xiao Liang, Nan Tian, Diego Lopes da Silva, Lorenzo Scarazzato,  Zubaida A. Karim, and Jade Guiberteau Ricard (April 2026).

This figure shows the inflation-adjusted level of defense spending for the world going back to 1988. The decline after the fall of the Soviet Union in the early 1990s, the rise in the “Asia and Oceania” share as China’s economy and defense spending have grown, and the rise in European defense spending in the last few years are all visible. Moreover, the “Americas” defense spending–dominated by US spending–has been fairly flat since about 2010, so the growth in defense spending is happening in other regions of the world.

It’s interesting (to me, at least) that the growth rate of defense spending over this time has been slower than the growth of the world economy more broadly. As a rough average, world GDP has been growing at 3.5% annually in recent decades. As compounded growth rates take place, the world economy has grown by about 180% in the last 30 years. If you look back at the trough of defense spending in the mid-1990s at about $1.2 trillion, it has risen by “only” about 120% during that time. Thus, the share of world GDP going to defense spending has been falling.

The US continues to have by far the largest level of defense spending of any country, accounting for about one-third of the total.

Finally, for fans of large tables, here’s a list of the countries of the world ranked by total spending on national defense. You can pick out the data-points that interest you the most. (Numbers in brackets mean that the totals were estimated by SIPRI based on government sources.) For example, the total rise in US defense spending from 2016 to 202 was 11%, compared with a rise of 62% for China, 96% for Russia, 118% for Germany, and 39% for India.

Or in the column on defense spending as a share of national GDP, it’s heartbreaking to see that Ukraine’s share was 3.7% in 2016 and 40% in 2025, even as Russia’s defense spending as a share of GDP has risen from 5.4% in 2016 to 7.5% in 2025. However, the percentage of GDP going to military spending hasn’t changed much for the US, China, or India; to put it another way, the large increases in China’s defense spending in the last decade or so are a reflection of the large increases in the size of China’s overall economy.

Ranking cities by population require some choices: in particular, do you focus only the legal boundaries of the city, or on a metropolitan area? If it’s a metropolitan area, how do you decide on the city limits? According to the World Urbanization Prospects 2025 report from the United Nations Department of Economic and Social Affairs, the most populous city in the world is Jakarta, Indonesia. Their process involves dividing the area into square kilometers,and estimating population density in each square. Here’s their graphic showing the top 10 cities in the world by population in 2000, 2025, and projected for 2050.

It’s interesting to note that Tokyo, the world’s largest city by population in 2000, has grown in population only modestly since then, and isn’t projected to grow further by 2050. However, Jakarta, Dhaka, Shanghai, Karachi,Cairo, and a few others are taking off. I find it hard, with my 20th-century brain, to imagine what it means to have “city” of more than 50 million people.

What got me thinking about Jakarta was an essay by Nithin Coca: “Jakarta’s Remarkable Urban Transit Transformation” (In Development, April 30, 2026). She begins:

For many years, the first word most foreign visitors learned upon moving to Jakarta was macet, traffic jam. Traffic was so bad that transport experts warned in 2013 that if nothing was done, the city could achieve total gridlock, with every part of the city experiencing a traffic jam. In 2014, Jakarta was crowned the world’s most congested city by the Stop-Start Index and a year later was ranked far below other Asian cities on livability by the Economist Intelligence Unit.

Ten years later, Jakarta has the world’s largest and one of the most used bus rapid transit (BRT) systems. The old, crowded diesel commuter trains, famous for allowing passengers to ride on the roofs, are now electrified, air conditioned, and run on regular schedules linking the suburbs to the city center. There are multiple subway and light rail lines crisscrossing the city. The transformation has been remarkable: in 2015, less than 20% of residents were within walking distance of transit. Now, nearly 90% of the city has access to BRT or trains.

How did this happen? One answer is that traffic and pollution concerns had gotten really bad. “Jakarta had also become one of the world’s most polluted cities. By 2011, 58 percent of all illnesses among people living in the city were related to air pollution. … Residents were resigned to spending an average of 16 days stuck in traffic each year.”

Then a former governor of Jakarta was elected president of Indonesia, and used the position to push for a mass transit system. Indonesia then got a low-interest loan from Japan’s development agency. The deal was: “Japan would play a role in basic design, construction, and introduction of transportation systems, including trains, signals, and gate systems, as well as their operation and maintenance. But Japanese contractors were insistent that, while they might build the railway, it was up to Indonesia to run it. Much of the technology would come from Japanese companies like Sumitomo and Nippon Sharyo, but construction, operations, and maintenance would all have to be done by Indonesian companies or the government.” As the system has been built out, a South Korean consortium has become involved as well.

But the story is only a partial success. Jakarta’s population is growing so fast that what has been built so far is far from adequate. Even with expanded mass transit, the number of cars continues to expand as well. “Danny Djarum, an Air Quality Senior Research Lead at WRI Indonesia says that PM 2.5, the measurement of inhalable airborne particulate matter, is now eight to ten times higher than World Health Organization guidelines. `We’re still one of the top 5 most polluted cities in the world, he said.”

Jakarta’s mass transit system continues to expand. But in addition, one intriguing suggestion is to designate certain parts of the city as “low emissions zones,” where access by private vehicles is restricted, green space is expanded and walkability improved.” Another policy suggestion is some form of congestion pricing.

But overall, Jakarta is showing that if you have a local political determination and are also willing to hire and to heed the world’s top experts to manage the project, a big start toward working mass transit system for the world’s largest city can happen in a decade. “In the latest TomTom traffic index, measuring average congestion—the percentage increase in travel time compared to free-flow conditions— the city ranked 24^th, just ahead of the United States’ most famous traffic clogged city, Los Angeles.”

When I was a young teenager, changing diapers for my baby brother and on babysitting gigs, it was all cloth. I could change a sleepy baby’s cloth diaper in the dark, large safety pins and all. A couple of decades later when I was a parent, it was all disposable. What happened? Virginia Postrel tells the story in “Engineering the disposable diaper: Benjamin Spock told mothers in the mid-twentieth century to buy six dozen cloth diapers and a covered pail. Within a decade, both were obsolete” (Works in Progress, April 24, 2026).

Back in 1957, disposable diapers had about 1% of the diaper market. They were expensive, and mainly used for situation where diapers would need to be changed while travelling. Postrel takes up the story:

After buying Charmin Paper Company in 1957, Procter & Gamble began looking for ideas for new paper products.  Motivated by the less pleasant aspects of spending time with his new grandchild, the company’s director of exploratory development, Victor Mills, suggested disposable diapers. After analyzing existing products and conducting consumer research, P&G created a dedicated diaper research group.

The research this group conducted, like that of its successors and competitors, wasn’t glamorous. It didn’t advance basic science. It wasn’t even an obvious route to profit. (One percent of the market!) It was a high-stakes gamble that required solving difficult engineering problems. How that happened represents the kind of hidden progress that leads to everyday abundance.

P&G’s first design flopped. Tested in the extreme heat of a Dallas summer, the pleated absorbent pad with plastic pants made babies miserable and left them with heat rashes. Starting over, the group had a one piece diaper ready for testing in March 1959. With an improved rayon moisture barrier between the baby and the absorbent tissue wadding, the new diaper was softer and more comfortable. An initial test of 37,000 hand-assembled prototypes went well, with about two thirds of the parents deeming the disposables as good or better than cloth. The next step was mass production.

Designing one well-functioning disposable was hard enough. Turning out hundreds a minute was practically impossible. ‘I think it was the most complex production operation the company had ever faced’, an engineer recalled.

Eventually, the diaper team mastered the process. In December 1961, Pampers went on the market in Peoria, Illinois. Once again, the test failed. This time mothers liked the diapers. But the price was way too high for a single use item: ten cents a diaper, equivalent to about one dollar today. By contrast, diaper delivery services, which served about five percent of the market, charged no more than five cents a diaper. Home laundry costs ran to one or two cents.

Lowering the price of a diaper required much larger volumes. Aiming at about six cents a diaper, P&G engineers spent several years developing what Harvard Business School’s Michael E. Porter described as ‘a highly sophisticated block-long, continuous-process machine that could assemble diapers at speeds of up to a remarkable 400 a minute’. After successfully testing Pampers at 5.5 cents each, P&G began a national rollout in 1966. By 1973, disposables accounted for 42 percent of the US diaper market.

Other firms first entered the diaper market in the 1970s, and then left: Scott Paper, International Paper, Union Carbide, Johnson & Johnson. The competitor that did gain a foothold was Kimberley-Clark, the innovator who had created “Kleenex tissues and Kotex feminine pads … in the 1920s.” After a false start or two, the Huggies diaper, with elastic around the legs and an improved tape closure, swept into the diaper market. It cost 30% more, but for a lot of buyers, the premium price was worth it.

I love a good product development story, and Postrel has lots more details: how new absorbent materials made diapers slimmer over time, reducing logistics costs like storage, handing, and retail shelf space; the environment arguments about disposable vs. cloth diapers; disposable training pants that little ones could pull up by themselves; some well-chosen modern cultural references to disposable diapers; and more. Here, I’ll just offer three takeaways.

First, the creation of a workable disposable diaper, and then improving on that diaper, and being able to take it to mass production at an affordable cost, were all genuinely difficult tasks. The innovations took investment measured in time, money, and varied kinds of expertise. The time-path to disposable diapers having 95% of the diaper market took decades, with a number of failures along the way both within and across companies.

Second, a non-breakthrough innovation like disposable diapers may be nndervalued, in part because Americans take so much for granted that competitive companies will be trying to provide new and improved versions of so many products. But over time, the accumulaton of many such innovations makes day-to-day life so much easier. It’s an enormous benefit of living in a dynamic market-oriented economy that can also be nearly invisible.

Third, when new mothers are asked about the essential needs for their babies, they tend to focus on milk and diapers. But although diapers are affordable in the mass market, they aren’t cheap. I wrote a few years ago in “Some Economics of Diapers” (September 29, 2022) about what an author called a “leaky” part of the US social safety net, along with discussions of diaper “banks” and other methods of assuring that low-income parents and their babies have access to diapers.

Under pressure from US tariffs and geopolitical conflicts, US imports of most good and services has been fairly flat, but with one big exception: imports related to artificial intelligence. Michael Waugh of the Minneapolis Fed provides evidence in his working paper “Trade in AI-Related Products”(Minneapolis Fed Staff Report 684, April 8, 2026). Jeff Horwich, also from the Minneapolis Fed, provides a shorter and readable overview of the findings in “Much more than microchips: Trade soars in AI-related goods, driving U.S. trade deficit” (May 4, 2026).

As background, it’s useful to know that that all US imports are classified by what is called the Harmonized System, and the most detailed level of classification is called HS10. Waugh uses a large-language model to determine how closely linked different inputs are to the broader notion of what is needed to build AI. He explains:

What are AI-related products? The classification identifies the obvious computer hardware inputs such as data processing units and storage devices. These products account for about half of all AI-related trade and imports of them have grown by triple digits since 2023. But the classification also identifies a broader set of products tied to electrical infrastructure, networking, cooling and HVAC, and specialty materials. These ancillary products account for the other half of AI-related trade and have also experienced strong import growth.Through 2025, AI-related products account for 23 percent of all U.S. imports, up from 15 percent in 2023. Leading into the start of 2024, AI-related products grew no differently than non-AI-related products. But since early 2024, import growth in AI-related products has accelerated sharply. As of January 2026, trade in AI-related products had grown by 73 percent relative to 2023, compared with only 3 percent for non-AI-related products.

Where are these products coming from? Two countries play an important role in the sourcing of AI-related products. Not surprisingly, Taiwan is an important source for computer hardware components (e.g. semiconductors) and Taiwan accounts for about a quarter of imports of AI-related products. The surprising source is Mexico. It accounts for another quarter of AI-related trade and its reach extends to computer hardware and other products related to electrical power, networking, and cooling HVAC. China is less important in AI-related trade and its overall share has diminished over the past two years.

Here’s a figure to illustrate the pattern. The blue line shows AI-related imports to the US. The black line shows all imports. The red line show imports not related to AI. To compare trends, the levels of all three are set equal to 100 for the monthly average in 2023. Looking back to 2022, you can see that the three lines move much the same. But starting in 2024, they separate quite a bit.

Waugh notes that many of the AI-relevant goods are wholly or partially exempt from Trump’s tariffs. He also offers a rough calculation that if AI imports had not risen as they have, the US trade deficit would have been about $200 billion less in 2025. If policies are judged by their outcomes, not their stated rationales, the Trump administration has decided that imports of AI-related items are important enough that the usual rationales for tariffs (cut the trade deficit, it should all be immediately built in the US, and so on) do not apply.

At a global level, it seems plausible that artificial intelligence technologies will cause an increase in world trade, no matter the tariff and geopolitical issues. The World Trade Report 2025 produced by the World Trade Organization (September 2025) is subtitled “Making trade and AI work together to the benefit of all.” The report notes:

WTO simulations suggest that AI could lead to significant increases in global trade and real income. These simulations are based on an extension of the standard WTO Global Trade Model with AI services and incorporate trade cost reductions, a shift in tasks from labour to AI and productivity gains related to this shift. They suggest that AI could lead to significant increases in trade and GDP by 2040, with global trade projected to rise by 34 to 37 per cent across different scenarios. The largest growth occurs in the trade of digitally deliverable services (42 per cent), including AI services. This trade increase reflects (i) reduced operational trade costs, (ii) the strong projected growth of AI services combined with the high tradability of AI services, related to its geographic concentration of production in a few regions, and (iii) the aboveaverage productivity growth in more tradable sectors, in particular digitally deliverable services. The development and deployment of AI are also projected to generate substantial global GDP increases, ranging from 12 to 13 per cent across scenarios.

There have been other times in history when the political winds were leaning against international trade, but underlying economic forces created large increases in trade anyway. As one example that I wrote about here a few months back, the wave of globalization that happened from about 1870 to 1914 happened in the face of tariff levels that were remarkably high by modern standards. However, this was also a time of dramatic improvements in transportatation, communication, and infrastructure, which combined to drive trade higher even in the face of high tariffs. Perhaps AI will become another example in which technological force that drive down the costs of trade and increase the benefits of trade will have bigger global effect than efforts and events that should seem to have a negative effect on trade.

I have been the Managing Editor of the Journal of Economic Perspectives since the first issue in Summer 1987. The JEP is published by the American Economic Association, which decided back in 2011–to my delight–that the journal would be freely available online, from the current issue all the way back to the first issue. You can download individual articles or entire issues, and it is available in various e-reader formats, too. Here, I’ll start with the Table of Contents for the just-released Spring 2026 issue, which in the Taylor household is known as issue #156. Below that are abstracts and direct links for each of the papers. I plan to blog more specifically about some of the papers in the few weeks, as well.

__________________

Symposium on Competition in Health Care

“The Emerging Role of Competition in Health Care,” by Paul B. Ginsburg

    This essay, commissioned to serve as an introduction to the JEP symposium on current competition in health care, provides a historical perspective on the role of both competition and regulation in the financing and delivery of health services since the implementation of Medicare and Medicaid in the mid-1960s. At the beginning of this period, few could perceive a role for competition in healthcare given the key role played by physicians in providing and ordering care and concerns that lower prices might signal lower quality. Initial attempts to slow rapidly rising costs involved various regulatory tools, but over time, regulation increasingly incorporated incentives for providers, to control costs. Competitive approaches began to develop in the late 1970s, in part reflecting broad changes in the nation’s political culture. Competitive approaches are now quite widespread, but regulation plays an important role in the structuring of competition and in addressing areas where competition is seen as having less potential.

“Competition in Health Insurance Markets,” by Martin Gaynor and Amanda Starc

    The United States relies primarily on private health insurance markets, yet these markets are highly concentrated and becoming more so over time. We document concentration across commercial, Medicare Advantage, and Medicaid markets. We then examine how asymmetric information—particularly adverse selection—interacts with market power to shape premiums, plan design, and consumer welfare. Empirical evidence confirms that insurer consolidation raises premiums. We discuss how antitrust enforcement, risk adjustment, regulation, and informational interventions shape competition and consumer welfare in these markets.

“Regulated Competition in Health Insurance Markets on Two Sides of the Atlantic,” by Lukas Kauer, Thomas G. McGuire, Sonja Schillo, and Richard C. van Kleef

    Many high-income countries implement their policy of universal health insurance by individual health insurance in combination with regulated competition among insurers. Supported by public intervention, regulated competition can, in principle, address market failures in health insurance and smooth out some inequities in the financial consequences of ill health and in the ability to pay for health insurance. We compare the national systems in Germany, the Netherlands, and Switzerland to the US Marketplaces, all of which use versions of regulated competition. While they show many similarities (for example, open enrollment, community-rated premiums with subsidies, comprehensive benefit package, risk adjustment), we focus on three major differences and their implications for market functioning: (1) mandatory and universal versus voluntary and partial (applying to only one sector of health insurance); (2) greater or lesser profit orientation of insurers; and (3) reliance on markets or regulation to contain costs.

“Anticompetitive Contracts between Insurers and Providers in Health Care,” by Anna D. Sinaiko

    For people with private health insurance in the United States, contracts between insurers and providers are important to fostering health care competition and improving efficiency. However, insurer-provider contract provisions do not always advance competition and consumer welfare. This essay discusses the contracting strategies used by insurers to increase competition, and four anticompetitive contract terms: anti-tiering or anti-steering, all-or-nothing, most favored nation, and gag clauses, that may be used by dominant health systems to protect themselves from competition. I conclude with a discussion of policy responses that can be used to address provider use of anticompetitive clauses and that can reduce the negative impacts of provider market power. Understanding anticompetitive contract provisions and the potential policy responses to limit their impact is critical to health care competition.

“The Current Era of Health Care Consolidation,” by Michael R. Richards and Christopher M. Whaley

    Consolidation in the last few decades has reshaped the organization and structure of US health care markets, among both providers and insurers. Nearly all US hospital and insurer markets exceed established regulatory thresholds for competitive markets, and over half of physicians are now employed by a hospital or health system, which can increase spending for patients, payers, and taxpayers. Increased supply-side concentration can alter the balance of negotiations between providers and insurers. Prices for patients with commercial insurance are approximately 2.5 times the prices paid by those with public insurance. High and variable prices have minimal link with higher quality, and the United States leads peer nations in health care spending. These dynamics have created ongoing national debates over an “affordability crisis” and generates frustrations with the US health care system. This article discusses sources of rising health care spending and potential policy solutions.

“Physician Competition: Entry and Substitution,” by Joshua D. Gottlieb and Sean Nicholson

    We describe competition in the physician market, focusing on how entry barriers and substitution possibilities have changed in recent decades. Regulatory caps on medical school seats and residency slots—especially for high-paying specialties—continue to ration entry, generate high returns for those who gain these slots, and direct the most academically accomplished trainees toward lucrative fields. But trained physicians increasingly compete with nurse practitioners, physician assistants, and other mid-level practitioners in the market for patients. Training of these substitutes has expanded far more rapidly than physician supply. We present key facts about the physician pipeline, a conceptual framework linking specialty earnings to entry barriers, and describe the rise of mid-level providers. These facts mean that effective competition policy in physician markets must look beyond conventional concentration measures and focus on the institutions and laws that govern who can provide medical care.

“Substitutes for Success? Public versus Private Competition in Medicare Advantage,” by Tim Layton, Luca Maini, and J. Michael McWilliams

    We assess the evolving role of competition in Medicare Advantage and its implications for beneficiary welfare. We describe how competition from the public option, traditional Medicare, and other private insurers within Medicare Advantage act as substitutes in incentivizing plans to deliver value. We show that while historically the choice between traditional Medicare and Medicare Advantage provided a vital competitive dynamic, traditional Medicare’s strength as a competitor has declined significantly, driven by generous payments favoring private plans. Consequently, the burden of ensuring value for enrollees has shifted to competition within the Medicare Advantage market. While county-level competition among private insurers has increased, this growth is primarily driven by the expansion of large national carriers rather than new entrants. Insurers still wield substantial market power due to significant barriers to entry, raising concerns about the ability of the program to incentivize private insurers to use public dollars to maximize value for beneficiaries.

“Understanding Medicaid Managed Care: The Procured Competition Model,” by Mark Shepard and Jacob Wallace

    Medicaid is one of the largest public programs in the United States—providing health insurance to over 75 million low-income Americans—and over three quarters of its enrollees receive care via private “managed care” insurers. In this article, we make three central points about the economics of contracting out Medicaid to private insurers. First, the empirical evidence on Medicaid privatization is mixed: contracting out has not meaningfully reduced public costs or improved quality of care. Second, we propose a framework, which we call “procured competition,” to describe the unique structure of Medicaid managed care as a hybrid of public procurement and regulated competition. Third, we discuss the key policy levers across procurement, competition, and consumer choice in this model. Throughout, we highlight open research questions, arguing that the enormous variation in how states design these programs—combined with limited evidence on what works—represents a promising area for high-impact scholarship.

Articles

“A Users’ Guide to Uncovering Worker and Firm Effects: The ABC of AKM,” by Stéphane Bonhomme, Thibaut Lamadon, and Elena Manresa

    The AKM model introduced by Abowd, Kramarz, and Margolis (1999) has become a workhorse to study worker and firm heterogeneity, and to understand the sources of wage dispersion in the labor market using linked employer-employee data. In this article, we introduce the model and estimator, discuss some best practices for estimation, and review some empirical findings on the role of worker and firm heterogeneity in wage dispersion. While the AKM methodology has proven useful to analyze a host of questions in a variety of settings within labor economics and beyond, we also point to the need for methodological developments.

“The Economics of Paid Sick Leave,” by Stefan Pichler, Christopher Prinz, Stefan Thewissen, and Nicolas R. Ziebarth

      This article examines the economics of paid sick leave from both theoretical and empirical perspectives. Research on paid sick leave has evolved dynamically over the last decade, primarily driven by the spread of US sick pay mandates, which have increased paid sick leave access from 63 percent to 77 percent in all US jobs. We begin by discussing the economic rationales for government regulation of paid sick leave, particularly the negative externalities associated with contagious diseases when individuals work while sick. After that, we discuss the key trade-offs in the general design of paid sick leave schemes, along with the trade-offs when setting specific policy parameters. Finally, we review economic modeling approaches to study optimal paid sick leave policies.

Features

“Retrospectives: The Great Dollar-Shortage Debate,” by Harris Dellas and George S. Tavlas

      The dollar shortage debate—Paul Samuelson called it “the big open question of our time”—dominated international macroeconomics in the 15 years following the end of World War II. There were two main views regarding its cause: financial frictions that limited capital flows to Western Europe (Kindleberger); and overvalued fixed exchange rates versus the US dollar (Friedman). According to Kindleberger the dollar shortage was attenuated by two real factors that contributed to current account deficits: a large technological gap between the United States and Europe; and European impatience to improve living standards. Kindleberger believed that the current account deficit would prove chronic because of the persistence of the productivity gap, a view that was challenged by Bloomfield who argued that it would dissipate through income growth in Europe. We argue that Kindelberger’s analytical framework is closely connected to the modern intertemporal approach to current account determination; and, also, that the international reserve function of the US dollar—the Triffin dilemma—did not play a role in the dollar shortage.

“Recommendations for Further Reading,” by Timothy Taylor

There’s certainly no economic reason why every national economy should expect to have a balance between its exports and imports. But it’s also true that sustained and large trade imbalances have sometimes been a forerunner of economic problems, and often been a forerunner of political problems. It’s also true that global trade imbalances have been higher in the last 15 years or so than in the pre-2000 period.

Here’s a snapshot of global trade balances over time from a recent IMF report, “Understanding Global Imbalances” (April 6, 2026). The not-easy-to-read bars in the figure show trade surpluses and deficits for large economies and also for other advanced economies (AE) and emerging market and developing economies (EMDE). For present purposes, one key takeaway is that the dark blue US bar represents a large part of the global trade deficits while the red China bar represents a large part of global trade surpluses (especially in the decade from about 2000-2010).

Of course, a trade deficit for one country is always necessarily reflected in a trade surplus for some other country. So the IMF adds the total of all trade surpluses and trade deficits to get its “overall balance” dark black line. The letters along the black line refer to various economic events: specifically, (a) Collapse of Bretton Woods System (1971); (b) Dollar Crisis (1977); (c) Plaza Accord (1985); (d) Louvre Accord (1987); (e) Asian Crisis (1997); (f) China WTO accession (2001); (g) GFC (2007); (h) COVID-19 Pandemic (2020). The second key takeaway here is that overall trade imbalances have been drifting up over time. From the mid-1970s up to the mid-1990s the overall balance line was typically about 2-3% of global GDP. After about 2000, the imbalance rises to above 4% of global GDP, following China’s joining the World Trade Organization. After the global financial crisis (GFC) of 2007-08, the global trade imbalance comes back down a bit, but has remained above its pre-2000 levels.

The overall IMF take goes like this: “While current account surpluses and deficits can be appropriate when they reflect economic fundamentals and desirable policies, the buildup and persistence of large imbalances raise concerns when they are driven by policy distortions and unwind in a disorderly manner. The expansion of industrial policies and the rise in trade restrictions—often motivated by imbalances themselves—has intensified the debate on the causes and consequences of global imbalances, despite limited analytical and empirical clarity on how both policies affect the current account.”

However, for those who would like a deeper dive, I recommend the 17 essays collected in Paris Report 4: The New Global Imbalances, and edited by Hélène Rey, Beatrice Weder di Mauro, and Jeromin Zettelmeyer (Centre for Economic Policy Research, 2026). Here, I’ll just emphasize some thoughts from the introductory lead essay by di Mauro and Zettelmeyer:

In sum, global current account imbalances reflect domestic saving–investment gaps.
They can support growth when financed sustainably and directed toward productive
uses, but they become risky when large, persistent, and tied to rising leverage or asset
bubbles. What matters for these risks is not bilateral trade balances but the underlying
macroeconomic conditions. Durable adjustment therefore requires domestic policy
changes, not trade measures alone.

dfjas

The authors emphasize that episodes of trade imbalances sometimes end badly, but sometimes not. For example, in the 19th and early 20th century, the US economy typically ran trade deficits, while Britain and other European countries were running trade surpluses. But one basic insight about trade imbalances is that a trade deficit means a net inflow of foreign investment, while a trade surplus means a net outflow. During this time, as the author write:

At the time, Britain and other European economies ran sustained current account surpluses, while capital flowed to the United States and other ‘new world’ economies – Canada, Australia, and Argentina. These capital inflows largely financed productivity-enhancing infrastructure, including railways and ports, which expanded export capacity and
supported debt servicing.

On the other side, trade deficits in Mexico, Argentina and across Latin America in the 1970s reflected large net inflows of foreign capital, where did not finance productivity-enhancing investments, and thus were not repayable when they came due. The buildup of unsteady credit in the US economy before the economic meltdown of 2007-09 was in part financed by inflows of foreign capital for the very large US trade deficits of that time.

So at present, is the US trade deficit good or bad? On one side, the US economy is investing a lot in AI and all the supporting technologies, like computing power and electricity. Overall, this is likely to be productivity-enhancing. On the other side, the long and consistent stream of US trade deficits means that have been continual inflows of foreign investment into the US economy. If you add up all the foreign investments that US firms and individuals have abroad, and compare it to all the US investments that foreign parties have in the US economy, the “the United States’ net international investment position (NIIP) reached about 90% of US GDP, or 24% of world GDP, by end-2024.”

As one looks more closely at the situation, there are some yellow flags waving. For example, it used to be that the US economy could pay low interest rates when borrowing (for example, when governments or central banks in other countries purchased US Treasury bonds), and then US investors putting money abroad would instead tend to buy higher-return and riskier investments. From an overall macro point of view, the US economy was borrowing cheap and investing for a higher return.

But this pattern is shifting. Instead of foreign governments buying US Treasury bonds, it’s becoming more common for non-government foreign investors to put money into the US stock market and other investments. As a rsult, a share of the future gains from US productivity-enhancing investment will be flowing to these foreign investors. Also, these non-government foreign investors are likely to be more willing to sell and flee if returns on these riskier US investments take a turn for the worse. Thus, even though the annual trade imbalances aren’t far from historical norms (as shown in the figure above), the accumulated effect of those imbalances raises some cause for concern.

How might the world economy reduce concerns about this situation? As the authors explain:

The ideal adjustment would involve the main systemic economies – at least the United States, China, and Europe – rebalancing simultaneously and in a coordinated manner. Such an approach would reduce the risk that adjustment in one economy simply shifts imbalances elsewhere or triggers destabilising spillovers. In simple terms, the required policy mix is well known. The United States would raise national saving, primarily through credible fiscal consolidation, thereby reducing its reliance on external financing. China would lower excess saving by rebalancing toward household consumption – strengthening social safety nets, boosting disposable income, and shifting away from investment- and export-led growth. Europe, for its part, would increase investment, particularly in infrastructure, defence, and the green transition, thereby absorbing more domestic and global savings. …

Ultimately, the choice is between gradual, policy-led adjustment and disorderly correction under stress. Addressing domestic distortions that give rise to external imbalances is in each country’s own interest. In a world of high leverage and weakened trust, imbalances are unlikely to unwind smoothly. They are more likely to correct through financial stress, protectionist escalation, or both. The costs of such an outcome – lower growth, fragmented trade, and impaired financial stability – would be substantial and widely shared.

For those who might be interested in digging further into these issues, here’s the Table of Contents of the book: