Conversable Economist

In arguments over industrial policy, there’s often a moment where someone makes an assertion like: “Every nation has industrial policy. Even not having an industrial policy is a type of industrial policy. The only relevant question is what kind of industrial policy we should choose.” In my experience, the people who make this argument then jump immediately to why a specific kind of industrial policy should be very aggressive indeed, including tools like subsidies and constraints on imports aimed at assisting specific domestic industries or companies.

It’s true, of course, that every nation has some type of industrial policy, if that term is very broadly understood. But I find it usefult think of economic policy and its effects on industry in layers.

The most basic layer is an economy with a legal system that enforces contracts, a functioning financial system, functional bankruptcy laws, low inflation, moderate government borrowing, good transportation and communications infrastructure, and a solid educational system from K-12 up through colleges and universities, workforce training for adults, and so on. These features surely support a more robust development of industry, but without taking sides in which industries will emerge.

As a next step, one can imagine the insight that long-run growth in the standard of living has, in the last 2-3 centuries, been closely related to advances in science and technology. It’s a standard belief among economists that an unfettered free market will tend to underinvest in innovation, in large part because innovations can be copied, and much of the benefit of an innovation goes to users rather than to the inventor. Thus, high-income countries subsidize innovation in a number of way: through protection of patents and intellectual property rights to help raise the reward for successful innovators, through tax breaks for research and development done by firms, and through direct funding of science and innovation at research institutions. These kinds of steps seek to to shape the direction of an economy toward a greater emphasis on technology-based growth. I have argued that despite a recent moderate increase in US R&D spending, there is a plausible case for increasing these incentives with an aim to doubling US research and development spending.

However, one can draw a conceptual line between general support for R&D and targetted support by industry. For example, a society might identify certain technological priorities: say, carbon-free energy production, anti-cancer drugs, stronger domestic production of semiconductors, artificial intelligence, and others. A certain amount of government support of R&D might be aimed at the desired areas. In addition, government might take other steps: perhaps prizes for certain kinds of inventions (think Operation Warp Speed for creating the COVID vaccines), or allow firms to cooperate, without fear of antitrust laws, to fund research jointly, or to build up joint ventures with the highest-performing firms in other countries. But all of these steps are focused on support for research and development of knowledge.

The next level is direct support for industries, or even for certain specific companies. This support might take the form of direct government subsidies or tax breaks for certain firms and/or industries. It might also involve government becoming involved in transportation infrastructure or workforce training that is aimed quite specifically at industrial development in a particular location.

The final layer of “industrial policy” is not just to build up domestic firms and industries with subsidies, infrastructure, and workforce development–as well as support for the underlying technological and scientific expertise–but to hinder international competition with tariffs and import quotas.

There are probably other sensible ways to divide up these categories, but the point I’m trying to make is that using the term of “industrial policy” to refer to all of these steps seems to me to stretch the term so far that it stops being useful. My sense is that most of the economists who would view themselves as against “industrial policy” are also supporters of at least the first two or three levels of policy above–that is, the basic underpinnings of a strong economy including support for research and development. Instead, I would focus th term “industrial policy” on subsidies or trade barriers aimed at certain companies or industries.

Sometimes this kind of industrial policy has worked. There are plenty of local examples where support (or at least not active opposition) from government was necessary for a large-scale firm to thrive, including specialized training for workers, infrastructure investment, making land available, a local research center, local tax breaks (“tax-increment financing”) and so on. Of course, there are also plenty of cases where local government tried to roll out the red carpet for a firm, and blew a lot of money without much success. As one of many examples, some will remember back in 2018 when President Trump announced to m much fanfare that Foxconn was going to build a giant manufacturing facility in central Wisconsin, which never happened.

Similarly, there are some examples around the world of where countries used tariffs and import quotas–along with all the other technology, workforce, and infrastructure steps mentioned here–to help build a domestic industry, which over time became a global leader. But in the cases that seemed to work, like certain industries in South Korea, the government support for these industries was tied to the industry meeting certain goals for exports that would be cost-competitive in world market. If industries did not meet the goals, the subsidies were cut off. And there are many examples of countries that blocked imports simply to support domestic producers

But all of these types of industrial policy happen through politics, and thus are more likely to be responsive to a combination of powerful incumbent special interests and to wishful thinking (after all, politicians aren’t putting their own money on the line). A lot of prominent industrial policy efforts have turned out badly. I write a few years ago about my qualms about industrial policy:

For example, back in 1991 Linda Cohen and Roger Noll published a book called The Technology Pork Barrel, which was based on case studies of US attempts to build infant industries in supersonic planes, communications satellites, a space shuttle, breeder reactors, photovoltaics, and synthetic fuels. I remember back in the 1980s when Japan announced with great fanfare the “Fifth Generation” computer project, which then went away with out fanfare. I remember when Japan was the shining example of how industrial policy worked in the 1970s and into the 1980s, but somehow it abruptly stopped being a shining example when Japan’s economy entered three decades of stagnation starting in the Brazil decided that it would become a computer-producing power in the 1970s and 1980s, and when Argentina decide that it would become a global electronics superpower. I remember the economic disaster that was the industrial policy of the Soviet Union. I remember the places around the world that have tried to be the next “Silicon XXXX,” generally without success.

Ultimately, every proposal for industrial policy must grapple with the problem of political discipline. As the levels of industrial policy move beyond the basic steps like health institutions and support of research and development, and start to focus on particular industries and companies, how likely is the policy to work? What are the intermediate goals that will be used to judge whether the policy affecting the industry as desired? Will the policy be cut off if the intermediate goals are not being met? The closer that industrial policy can be captured by firms at a certain company or industry, the political tensions

There is often a heavy dose of irony in industrial policy. Back in the 1950s, the head of General Motors was nominated to become Secretary of Defense. The story goes that when he was asked if he could separate the interests of General Motors from the broader nation interest, he answered: “What’s good for General Motors is good for the country.” The line was quoted for decades to show as an example of an excessively pro-business attitude. (The story isn’t accurate, as I described here.) But when General Motors needed a government subsidy to survive during the Great Recession, a lot of people then argued that what was General Motors was good for the country. Similarly, current US industrial policy favords multi-billion subsidies directly for companies on Intel and TSMC, on the grouds that “the interests of domestic semiconductor manufactures are good for the country.”

There’s an old line that “government should steer, not row.” The idea is that the useful role is to set up policies like appropriate institutions, as well as incentive for innovation in general and for specific industries. But when government gets into the business of direct subsidies and tariffs, it has moved into rowing rather than steering, and the danger of political incentives starting to override sensible economic policy begins to become a greater risk.

These issues and others have been top-of-mind for me lately, because the most recent issue of the Journal of Economic Perspectives, where I work as Managing Editor, published a “Symposium on Industrial Policy” in the Fall 2024 issue. As with all JEP content and archives, the papers are freely available online:

• “Export-Led Industrial Policy for Developing Countries: Is There a Way to Pick Winners?” by Tristan Reed
• “The Political Economy of Industrial Policy,” by Réka Juhász and Nathan Lane
• “Industrial Policy: Lessons from Shipbuilding,” by Panle Jia Barwick, Myrto Kalouptsidi, and Nahim Bin Zahur
• “Semiconductors and Modern Industrial Policy,” by Chad P. Bown and Dan Wang
• “Alexander Hamilton’s Report on Manufactures and Industrial Policy,” by by Richard Sylla

Want more? The most recent Annual Review of Economics also includes a couple of articles on industrial policy:

• “Industrial Policy and the Great Divergence,” by Réka Juhász, and Claudia Steinwender
• “The New Economics of Industrial Policy,” by Réka Juhász, Nathan Lane, and Dani Rodrik
• “Modern Industrial Policy and the World Trade Organization,” by Chad P. Bown

President Trump set off a wave of protectionist trade policies about seven years ago, back in 2018, and those policies were mostly extended and followed during President Biden’s term of office as well. But unsurprisingly to most economists, trade restrictions have done a poor job of producing the desired results.

Michael Strain provides a trenchant critique of the move to protectionism since the first Trump term in “Protectionism is Failing and Wrongheaded: An Evaluation of the Post-2017 Shift toward Trade Wars and Industrial Policy.” The essay appears in a collection of six essays from the Aspen Economic Strategy Group titled Strengthening America’s Economic Dynamism, edited by Melissa Kearney and Luke Pardue, and published late last year.

As Strain points out, there are typically three concrete benefits claimed for protectionism: more US jobs in manufacturing, reducing US economic ties with China, and reducing the trade deficit. The author goes into these arguments in more detail, but here are some of the highlights.

First, here’s a graph showing manufacturing jobs as a share of total US employment since 1939. There’s a boom-and-bust in manufacturing jobs looking at World War II production, but after that, the line drops steadily until the last decade or so. In particular, the share of manufacturing jobs is falling well before the forces of globalization take hold in the 1970s or 1980s, and well before China joins the World Trade Organization and enters global markets in force in the earyl 2000s. A similar pattern of decline in the share of manufacturing jobs holds all over the world. The key underlying factors here over the decades seem to be steadily growing productivity in manufacturing (think automation and robotics, along with just-in-time inventory), along with a general shift to an economy more oriented around services than around goods. Those productivity gains flattened out for a few years after the Great Recession of 2008-09, and the decline in the share of US manufacturing jobs correspondingly eased off for a few years. But lower productivity growth isn’t a path to future prosperity.

As Strain points out, there are several effects of trade barriers on US manufacturing jobs: a certain domestic industry is protected against competition, but higher prices in that industry can lead to problems for other domestic industries, and foreign countries may retaliate by shutting out US-produced exports. Put these together, and Strain suggests that the Trump tariffs of 2018 may even have led to a reduction in US manufacturing jobs.

Second, consider the goal of reducing US economic ties to China. The US can trade with China either by directly importing from China, or indirectly by having China export to a country like Vietnam or Japan, and then having the US import from those other countries. In recent years, direct US trade with China has declined, but indirect trade through other countries has increased. A standard measure here is to look at “value added”–that is, what portion of US imports of manufacturered goods was created in China.

This figure is based on looking at overall US demand for manufactured goods, then calculating what share of that demand comes from foreign value-added, and finally what share of that foreign value-added comes from China. The upward trend levelled off somewhat after the Great Recession. But seven years of protectionism has not led to any meaningful drop in China’s value-added share.

Finally, consider the goal of reducing the US trade deficit. The graph shows the trade deficit since 1999. President Trump focused on the trade deficit in manufactured goods. This measure of the US trade deficit didn’t move much after about 2011 until the pandemic, when it dropped off and then partially recovered.

The “current account deficit” is a broader measure of the trade deficit. It includes trade in goods and also services, as well as certain income flows related to foreign investments or remittances across borders. This measure also doesn’t change much in the years after the Great Recession, and then gets much worse during the pandemic. In short, seven years of protectionism hasn’t “fixed” the trade deficit, either.

There is a lot more to say about tariffs and protectionism than this quick overview. Strain has more to say in his essay, and I’m sure I’ll have many excuses to return to the topic it the next few years. But for the moment, the main point is simply that judged in terms of its own main justifications, the surge of protectionism since 2018 has not been achieving its goals.

One can of course offer reasons for this failure. A common pattern in politics–and not just in trade issues–is that the failure of past policies to achieve their stated goals then becomes a new justification for more of the same. In this case, the failures of past protectionism become a reason for additional protectionism.

As one example. after Trump renegotiated the North American Free Trade Agreement (NAFTA) back in 2018, transforming it into US-Mexico-Canada Agreement (USMCA), he said in his press conference: “Once approved by Congress, this new deal will be the most modern, up-to-date, and balanced trade agreement in the history of our country, with the most advanced protections for workers ever developed.” Seven years later, Trump now apparently views the agreement that he renegotiated and lauded as a failure, and promises to dial up tariffs against Mexico and Canada–along with the rest of the world–to new heights.

Learning that a problem is widespread may, paradoxically, cause people to view the problem as less dangerous. Kasandra Brabaw offers a readable overview of this dynamic in “The ‘Big Problem Paradox'” (Chicago Booth Review, December 10, 2024). Brabaw writes:

 If you want to get people’s attention to address a problem, making it seem as big as possible is a nearly universal reflex.

But it’s almost certain to backfire, according to Northwestern’s Lauren Eskreis-Winkler, Cornell predoctoral scholar Luiza Tanoue Troncoso Peres, and Chicago Booth’s Ayelet Fishbach. In a study, they name this the “big problem paradox. Across more than a dozen experiments, they find that describing how big a problem is tends to lessen people’s estimates of its severity. “When you learn there are many people who don’t finish college, you say, ‘Probably it won’t affect their lives that much,’” Fishbach says. “When we remind you that air pollution is common, you say, ‘Well, I guess it’s not so bad.’” Big numbers often give you a false sense of security, and the way a problem is communicated is often at odds with the intended message, according to the study.

In their experiments, the researchers told participants the size of a range of problems, including city-wide building code violations; children who aren’t vaccinated against measles, mumps, and rubella; patients who don’t take their medications; drunk driving; adultery; and positive screenings for a breast cancer gene mutation. No matter the problem, people who learned it was prevalent inferred that it caused less harm.

The underlying research appears in “The Bigger the Problem the Littler: When the Scope of a Problem Makes It Seem Less Dangerous,” by Lauren Eskreis-Winkler , Luiza Tanoue Troncoso Peres, and Ayelet Fishbach (Journal of Personality and Social Psychology, online publication October 24, 2024). The research approach here is to do two surveys side-by-side: one asks about a problem, with no information about how common the the problem is; the other asks about the problem, and also provides quantitative evidence that the problem is widespread. It turns out that concern expressed about the problem is consistently lower with the additional quantitative data provided.

The authors offer this explanation:

Yet severity has two dimensions: breadth, which refers to the number of people the problem affects, and depth, or the harm felt by an individual experiencing the problem. We propose that, psychologically, these dimensions affect each other. Our main hypothesis is that people who consider the prevalence of a problem infer it causes less harm, a phenomenon we dub the big problem paradox. The bigger the problem, the littler.

The authors argue that when you hear about a very large number of people affected by a problem, one reflexive reaction is to think: “Well, how bad can it really be?” One of their examples discusses “medication nonadherance”–that is, not taking medications correctly. The potential harms here are enormous. But focusing on the overall number of people who don’t always take medications correctly is likely to make many people think about the time they forgot to take a pill on time, or took an extra dose by mistake, and nothing all that terrible happened.

To the extent that such a tradeoff exists between how people perceive depth and breadth of problems, it may be that when when trying to raise public concern over an issue, it may be more productive to focus on how it represents an especially deep problem for a smaller group of people, rather than how the problem “affects” in a milder way a larger number of people.

A word of warning here. These conclusions summarize the results of 15 different studies with a total of 2,636 participants– so on average, fewer than 200 participants per study. A number of the studies are based on participants from websites that recruit people to participate in online research, but others involve asking people on the street in downtown Chicago, a survey taken of participants at a pharmaceutical conference, and the like. As the authors note, there are issues of “external validity” here–that is, are the result from the kind of people who choose to participate in these surveys representative of the broader population? On the other hand, the fact that the “big problem paradox” seems to apply across a wide variety of settings gives it some credence.

The US Trade Representative has filed a “Report on China’s Targeting of the Maritime, Logistics and Shipbuilding Sectors for Dominance” (January 16, 2025). In the lingo of US trade law, this is a “Section 301” report, which comes from a 1974 law delegating the authority to the USTR to investigate “unfair” trade practices by other countries and to impose tariffs or other trade restrictions in response.

There is zero doubt that China has targetted its shipbuilding industry with major subsidies. But part of what is interesting in this case is that the US has not been a major player in global shipbuilding for decades. Thus, the USTR report reads strangely to me, because while it is phrased in terms of effects on US shipbuilding, hat industry has been dominated by Japan and South Korea.

Either fortuitious or thanks to excellent editorial decision-making, the Journal of Economic Perspectives, where I work as Managing Editor, published a paper on Chinese ship-building subsidies in the Fall 2024 issue as part of a symposium on industrial policy. Like all JEP papers back to the first issue, it is available free and ungated. Panle Jia Barwick, Myrto Kalouptsidi, and Nahim Bin Zahur describe “Industrial Policy: Lessons from Shipbuilding.” 

They present a figure showing global patterns of shipbuilding. As you can see, the UK(blue) and other nations of Europe (red) dominated global shipbilding for most of the first half of the 20th century. The US has surges of shipbilding in each World War, but is generally not much of a factor. Then Japan (orange) takes a large share of the global shipbuilding market after World War II and Korea (green) enters the market in force in the 1980s. China’s share begins to rise rapidly in the early 2000s.

As the figure illustrates, it would be impossible for China’s shipbuilding to have affected the US shipbuilding industry before about 2000. Thus, when the USTR report discusses the low levels of US shipbuilding in, say, the 1970s or 1980s, the causes are necessarily elsewhere.

The USTR report has only a few mentions of shipbuilding in Japan and Korea, mostly in footnotes, but it does drop in an occasional sentence. For example, USTR (pp. 116-117) notes in passing: “For China to achieve its targeted dominance, including as demonstrated by explicit global market share targets, Chinese companies must displace foreign companies in existing markets and take new markets as they develop. Such displacement affects China’s current top competitors in Korea and Japan, as well as U.S. shipbuilders, which continue to see their smallmarket share decline and are unable to compete with China’s artificially low prices and massive scale.” At another point, USTR (p. 60) quotes an outside study stating: “Chinese yards often force ship buyers to source engines and other subcomponents in China when they order vessels. Otherwise, ship buyers interviewed by the authors indicate, they would favor Korean and Japanese made engines and other internal parts.” In short, this is not a case where a large or cutting-edge US industry is being challenged by China’s subsidies.

Shipbuilding has been a highly subsidized industry in Europe, Japan, and Koreaa before it became subsidized by China, as Barwick, Kalouptsidi, and Zahur point out in JEP. They write:

First, why do governments subsidize shipbuilding? Our narrative suggests a wide variety of reasons: the connection between trade, shipping, and shipbuilding; the development of heavy manufacturing as a strategy for promoting economic growth; employment; national security and military considerations; and the desire for national prestige (or “pride and machismo,” as Stråth (1987) puts it). Yet, in none of the historical cases is it self-evident exactly what mix of objectives led to industrial policy in shipbuilding.

Second, was industrial policy successful? It is challenging to evaluate if industrial policy worked. There are certainly examples of “apparent success” in Japan, South Korea, and China, where a country with a negligible initial share of the global industry embarks on a program of industrial policy and rapidly becomes a global leader. But the history of shipbuilding is also filled with examples of unsuccessful industrial policy, such as the long- standing US policy of protecting its shipbuilding sector through cabotage laws, European governments’ prolonged and costly attempts to subsidize their shipbuilders in the face of Japanese and Korean competition (Stråth 1987), or an earlier attempt by South Korea to promote shipbuilding in the 1960s (Amsden 1989). Other countries have failed to launch a shipbuilding industry as well, as in the case of Brazil’s failed attempt to launch its own shipbuilding sector in the late 1970s (Bruno and Tenold 2011). Even the apparent success stories required massive support, leading to the question (rarely answered in the literature) of whether the benefits from subsidizing shipbuilding are worth its large cost.

They seek to estimate the full range of China’s subsidies for the shipbuilding industry: cheap land near the ocean, cheap low-interest long-term loans, subsidized inputs (like steel), subsidies for exporting ships, subsidies for ship-buyers, and streamlined licenses. China opens literally hundreds of shipyard from about 2006-2013. They estimate that these government subsidies were equal to about half of total revenue for China’s shipbuilding industry during these years.

Should China’s shipbuilding subsidies be counted as a “success”? They write:

[A]lthough China’s shipbuilding subsidies were highly effective at achieving output growth and market share expansion, we find that they were largely unsuccessful in terms of welfare measures. The program generated modest gains in domestic producers’ profit and domestic consumer surplus. In the long run, the gross return rate of the adopted policy mix, as measured by the increase in lifetime profits of domestic firms divided by total subsidies, is only 18 percent, meaning that for every $1 the government spends, it gets back 18 cents in profitability. In other words, the net return when incorporating the cost to the government was a negative 82 percent, with entry subsidies explaining a lion’s share of the negative return.

They discuss how one might estimate a higher return. For example, if China had targeted its shipbuilding subsidies to larger and more efficient firms, rather than encouraging entry–as it eventually did–the return to its subsidies would have been higher. Also, if one takes into account that China’s massive shipbuilding program was probably large enough to drive down global costs of transportation, then China (and other exporters around the world) would have also benefited from being able to trade more cheaply.

The current situation in global ship-building is that if the US penalizes Chinese ship-building, most of the benefits will go to Japanese and Korean shipbuilders. But let’s try to look beyond that. Why has the US has played such a small role in global ship-building? What would be involved in changing that?

For most economists, the travails of US ship-building go back to laws in the 19th and early 20th century–for example, the Jones Act of 1920–which sought to protect US shipbuilding from foreign competition. The law requires that shipping between two US ports can only be carried by ships built in the United States. But when US shipbuilders no longer faced global competition, their efficiency fell behind. Current estimates are that the US cost for building a large ocean-going ship is about 300-400% higher than a ship built in Japan or Korea. Thus, the US ship-building industry has become focused on smaller ships for domestic purposes, not ocean-goign vessels. The USTR writes: “U.S. shipbuilders delivered 608 vessels of all types in 2020, including 15 deep-draft vessels and 5 large oceangoing barges. The majority of these 608 vessel deliveries were inland dry cargo or tank barges and tugs and towboats. U.S. shipbuilders delivered only four bulk vessels in 2024 …”

If US ships were much, much cheaper, the US transportation system could look quite different: for example, it would be much cheaper to transport cargo and bulk goods up and down the east coast and west coast, rather than using overland rail or trucks. For example, US lumber companies complain that they are at a disadvantage in shipping lumber between US locations compared to Canadian lumber firms–because the Canadian firms can use cheaper international shipping.

I struggle to imagine the US economy becoming an important global ship-building nation. In a big-picture sense, the country would need to develop the domestic expertise to drive down the cost of building large ocean-going vessels by, say, 75%. This would involve a building managerial and corporate expertise, along with worker expertise, and developing the supply chains of specialized products to support th is effort. But a more basic starting point, imagine the problems in a US context of acquiring land and permitting by the ocean or a large enough river to make launching hundreds of ocean-going ships possible.

It’s perhaps easier to imagine a newly board US shipbuilding industry focused on particular tasks, like top-level maintenance and repair of big oceangoing vessels, or focusing as a starting point on a particular part of the market. As the JEP authors point out: “The major types of ships currently produced include containerships, (oil) tankers, bulk carriers, as well as more niche products like cruise ships, liquefied natural gas carriers, and “Ro-Ro’s,” which are ships that allow vehicles to be rolled on and off the ship.” The USTR report also points out the specialized ships need to install offshore wind turbines.

I’m sure that shipmakers in Japan and Korea are perfectly happy for the US to take a stab at reining in Chinese subsidies for ship-building. But I confess that when I think of orienting the US toward key industries for 21st century prosperity, pouring in the government subsidies and attention to create a globally competitive shipbuilding industry would not be high on my list.

Back in the 1971, Herbert Simon (Nobel ’78) published an essay on the “attention economy.” It famously noted that “a wealth of information creates a poverty of attention.” He offered insights about how economic organizations (and people) needed mechanisms to receive and process large amounts of information, and then pass only the relevant portion of that information. (Simon won the Nobel prize “for his pioneering research into the decision-making process within economic organizations.”)

Yaqub Chaudhary  and Jonnie Penn suggest that artificial intelligence may shift the parameters of the tradeoffs between information and attention, and instead might lead to what they call an “intention economy.” They describe this prospect in “Beware the Intention Economy: Collection and Commodification of Intent via Large Language Models,” published December 30, 2024, in a Special Issue of the Harvard Data Science Review with papers on the theme “Grappling With the Generative AI Revolution.”

From the abstract:

The rapid proliferation of large language models (LLMs) invites the possibility of a new marketplace for behavioral and psychological data that signals intent. This brief article introduces some initial features of that emerging marketplace. We survey recent efforts by tech executives to position the capture, manipulation, and commodification of human intentionality as a lucrative parallel to—and viable extension of—the now-dominant attention economy, which has bent consumer, civic, and media norms around users’ finite attention spans since the 1990s. We call this follow-on the intention economy. We characterize it in two ways. First, as competition, initially, between established tech players armed with the infrastructural and data capacities needed to vie for first-mover advantage on a new frontier of persuasive technologies. Second, as a commodification of hitherto unreachable levels of explicit and implicit data that signal intent, namely those signals borne of combining (a) hyper-personalized manipulation via LLM-based sycophancy, ingratiation, and emotional infiltration and (b) increasingly detailed categorization of online activity elicited through natural language.

This new dimension of automated persuasion draws on the unique capabilities of LLMs and generative AI more broadly, which intervene not only on what users want, but also, to cite Williams, “what they want to want” (Williams, 2018, p. 122). We demonstrate through a close reading of recent technical and critical literature (including unpublished papers from ArXiv) that such tools are already being explored to elicit, infer, collect, record, understand, forecast, and ultimately manipulate, modulate, and commodify human plans and purposes, both mundane (e.g., selecting a hotel) and profound (e.g., selecting a political candidate).

I confess that I am only partially persuaded that the “intention economy” is fundamentally new and different from the “attention economy.” The classic book by Vance Packard, The Hidden Persuaders–about how our wants and desires can be and are manipulated by business, media, and politicians–was written back in 1957. As Chaudary and Penn write: “At time of print, the intention economy is more aspiration than reality.” But here’s an example of what they have in mind:

[A] concrete example helps to illustrate how the intention economy, as a digital marketplace for commodified signals of ‘intent,’ would differ from our present-day attention economy. Today, advertisers can purchase access to users’ attention in the present (e.g., via real-time-bidding [RTB] networks like Google AdSense) or in the future (e.g., buying next month’s ad space on, say, a billboard or subway line). LLMs diversify these market forms by allowing advertisers to bid for access both in real time (e.g., ‘Have you thought about seeing Spiderman tonight?’) and against possible futures (e.g., ‘You mentioned feeling overworked, shall I book you that movie ticket we’d talked about?’). If you are reading these examples online, imagine that each was dynamically generated to match your personal behavioral traces, psychological profile, and contextual indicators. In an intention economy, an LLM could, at low cost, leverage a user’s cadence, politics, vocabulary, age, gender, preferences for sycophancy, and so on, in concert with brokered bids, to maximize the likelihood of achieving a given aim (e.g., to sell a film ticket). Zuboff (2019) identifies this type of personal AI ‘assistant’ as the equivalent of a “market avatar” that steers conversation in the service of platforms, advertisers, businesses, and other third parties.

In short, imagine persuasive messages that are far more individualized, in several senses. These messages could be based on a considerably wider range of data about you: where you live and work,travel patterns, family status, past purchases, past internet searches, and the like. Your personal data could then be compared with personal data of others to find statistically similar people. The messages you receive, based on how you are categorized based on your personal data, would also be phrased in the language most likely to appeal to you–again, based both on how you have personally responded in the past and how others who are statistically similar to you have responded. These messages could also be “dynamically adjusted,” meaning that instead of getting the same message over and over, you would receive an ever-changing series of messages.

Chaudary and Penn recognize that some of this just sounds like better-targeted advertising, but they argue that there are “possibilities of intervening on—and commodifying—a higher order of user intentionality than that seen in the attention economy.” Perhaps the bottom line is that AI tools are already starting provide back-and-forth interactions, sometimes in the series of advertisements you see, sometimes in the form of chatbots, and sometimes even forms like providing medical advice or therapy. As these interactions multiply, it’s important to remember that AI is both a tool for you to use, and also a tool for others to use in communicating with you. In neither case is the AI your friend, with nothing but your best interests at heart.

Jon Hartley interviews Myron Scholes (Nobel ’97) on “Academic Finance, Black-Scholes Options Pricing, and Regulation“(“Capitalism and Freedom in the 21st Century” podcast, January 5, 2025). The interview includes insights about what was happening in economic finance in the 1960s and 1970s after the “big bang” represented by the work of Harry Markowitz. Here are a few points that caught my eye:

The interview has considerable detail on the development of Scholes’s work with Fisher Black in creating the Black-Scholes option pricing formula. Here’s a taste:

And so Fisher and I started talking about options … And we started working together, and we very quickly came to a theory of how to solve the option by setting up the replicating portfolio. But we tried to think about how to do it for myriad state variables. And even though the theory was correct and could be done, it was basically the state variables would be multiple, and then figure out how to integrate. Once you had a differential equations with all these state variables made it impossible to come to a conclusion or solution quickly.

So then Fisher and I said, well, let’s make an assumption, which is false, that the interest rate is constant, and that the volatility is constant. And we got and the option was European, and therefore, we can get a closed form solution. So that we got a closed form solution and that became known as the Black Scholes option pricing model.

The underlying theory was published in the Journal of Political Economy with the model or given its assumptions. Now we know that every model has an assumption, every model has an error, every model is an incomplete description of reality. How well does the model do in making predictions? And that’s the key. Basically the model has done very well over time. There’s a lot of people who say the model doesn’t do this, the model doesn’t do that, but it does pretty darn great. …

At the time the Black-Scholes model was published was coincident with the birth of the first listed options trading in the Chicago Board Options Exchange in Chicago. So there was 16 options were traded on calls, call options at that time on 16 securities.

That was in 1973. Then it was the case that there was the old grizzly traders who thought they had the experience from the over the counter market and the new young turks who were going to be market makers and trade on the floor of the Chicago Board Options Exchange. So here’s an idea with experience only and intuition versus a model. And the young guys had the model … Fisher Black made sheets of paper which talked about the Delta and the pricing at different levels of the stock price relative to the exercise price. And they could look at the sheets. And there was a war between the grizzly intuition people and the model people, the young turks who had no intuition, but they had the model. And in a matter of about six months or so, the young turks had wiped out the grizzlies, okay, the intuition people.

Merton Miller apparently used to refer to criticisms of the the Modigliani-Miller theorem (that the value of a company is based on future profits, not capital structure) with an analogy I had not heard before, about horse and rabbit stew Scholes tells it this way:

When I got to Chicago at the time Merton Miller had come to Chicago in 1960, I came there first as a student in ’62. And Merton came from Carnegie Mellon, having worked with Franco Modigliani to develop the idea of capital structure equilibrium. Because it was felt, prior to Merton and Franco Modigliani’s work, that how you finance your activity was determinant what the cost of capital was on investment. So if you use more debt, it was cheaper than equity, and therefore there would be a level of debt you would use that would reduce your overall cost of capital.

Merton Miller and Franco Modigliani said, no, that’s ridiculous because economically, if you think about the pie, it’s how you’re dividing up the pie is not necessarily what you wanna think about it. What the pie is itself, how the pie is going to grow, and that means that the risk of the underlying investments of the firm are the risk of the assets, and not how they’re financed. And they prove that rigorously by arbitrage models and the like, and showed that basically that was true, which was a great innovation.

Obviously, over time, Merton’s work and Franco’s work was criticized simply because people thought about bankruptcy costs and other things that would interfere. And Merton’s summary was very good, he said, my theory is a little bit like horse and rabbit stew. There’s one horse and one rabbit in the stew, and what my ideas are, obviously the horse and all these conundrums and critic of the horse as the stew is the rabbit.

For most of us, January 1 is New Year’s Day. But for the copyright lawyers among us, it is Public Domain Day, when a new batch of copyright materials first published back in the 1920s lose their intellectual property protection. Jennifer Jenkins and James Boyle, who direct the Duke Center for the Study of the Public Domain, provide an overview of some of the best-known works that are now in the public domain, along with an argument for the importance of having intellectual property protection eventually expire, in “January 1, 2025 is Public Domain Day: Works from 1929 are open to all, as are sound recordings from 1924!” They also try to answer some of the big questions, like: “Popeye was already in the public domain, but he does not eat spinach until a 1933 cartoon. So is “Popeye-eating-spinach” in the public domain yet?

Here is a very limited selection from Jenkins and Boyle of some works that have just entered the public domain. With links! Because now and into the future, these works in the public domain.

Books and Plays

• William Faulkner, The Sound and the Fury
• Ernest Hemingway, A Farewell to Arms
• Virginia Woolf, A Room of One’s Own
• Dashiell Hammett, Red Harvest and The Maltese Falcon (as serialized in Black Mask magazine)[4]
• John Steinbeck, Cup of Gold (Steinbeck’s first novel)
• Richard Hughes, A High Wind in Jamaica
• Oliver La Farge, Laughing Boy: A Navajo Love Story
• Patrick Hamilton, Rope
• Arthur Wesley Wheen, the first English translation of All Quiet on the Western Front by Erich Maria Remarque
• Agatha Christie, Seven Dials Mystery
• Robert Graves, Good-bye to All That
• E. B. White and James Thurber, Is Sex Necessary? Or, Why You Feel the Way You Do
• Rainer Maria Rilke, Letters to a Young Poet (only the original German version, Briefe an einen jungen Dichter)
• Walter Lippmann, A Preface to Morals
• Ellery Queen (Frederic Dannay and Manfred Bennington Lee), The Roman Hat Mystery

Films

• A dozen more Mickey Mouse animations (including Mickey’s first talking appearance in The Karnival Kid)
• The Cocoanuts, directed by Robert Florey and Joseph Santley (the first Marx Brothers feature film)
• The Broadway Melody, directed by Harry Beaumont (winner of the Academy Award for Best Picture)
• The Hollywood Revue of 1929, directed by Charles Reisner (featuring the song “Singin’ in the Rain”)
• The Skeleton Dance, directed by Walt Disney and animated by Ub Iwerks (the first Silly Symphony short from Disney)
• Blackmail, directed by Alfred Hitchcock (Hitchcock’s first sound film)
• Hallelujah, directed by King Vidor (one of the first film from a major studio with an all African-American cast)
• The Wild Party, directed by Dorothy Arzner (Clara Bow’s first “talkie”)
• Welcome Danger, directed by Clyde Bruckman and Malcolm St. Clair (the first full-sound comedy starring Harold Lloyd)
• On With the Show, directed by Alan Crosland (the first all-talking, all-color, feature-length film)
• Pandora’s Box (Die Büchse der Pandora), directed by G.W. Pabst
• Show Boat, directed by Harry A. Pollard (adaptation of the novel and musical)
• The Black Watch, directed by John Ford (Ford’s first sound film)
• Spite Marriage, directed by Edward Sedgwick and Buster Keaton (Keaton’s final silent feature)
• Say It with Songs, directed by Lloyd Bacon (follow-up to The Jazz Singer and The Singing Fool)
• Dynamite, directed by Cecil B. DeMille (DeMille’s first sound film)
• Gold Diggers of Broadway, directed Roy Del Ruth

Characters

• E. C. Segar, Popeye (in “Gobs of Work” from the Thimble Theatre comic strip)
• Hergé (Georges Remi), Tintin (in “Les Aventures de Tintin” from the magazine Le Petit Vingtième)

Musical Compositions

• Singin’ in the Rain, lyrics by Arthur Freed, music by Nacio Herb Brown
• Ain’t Misbehavin’, lyrics by Andy Paul Razaf, music by Thomas W. (“Fats”) Waller & Harry Brooks (from the musical Hot Chocolates)
• An American in Paris, George Gershwin
• Boléro, Maurice Ravel
• (What Did I Do to Be So) Black and Blue, lyrics by Andy Paul Razaf, music by Thomas W. “Fats” Waller & Harry Brooks (a song about racial injustice from the musical Hot Chocolates)
• Tiptoe Through the Tulips, lyrics by Alfred Dubin, music by Joseph Burke
• Happy Days Are Here Again, lyrics by Jack Yellen, music by Milton Ager (the theme song for Franklin D. Roosevelt’s 1932 presidential campaign)
• What Is This Thing Called Love?, by Cole Porter (from Porter’s musical Wake Up and Dream)
• Am I Blue?, lyrics by Grant Clarke, music by Harry Akst
• You Were Meant for Me, lyrics by Arthur Freed, music by Nacio Herb Brown
• Honey, lyrics and music by Seymour Simons, Haven Gillespie, and Richard A. Whiting
• Waiting for a Train, lyrics and music by Jimmie Rodgers

Sound Recordings from 1924

• My Way’s Cloudy, recorded by Marian Anderson
• Rhapsody in Blue, recorded by George Gershwin
• Shreveport Stomp, recorded by Jelly Roll Morton
• Lazy, recorded by The Georgians
• Krooked Blues, recorded by King Oliver’s Creole Jazz Band featuring Louis Armstrong
• Deep Blue Sea Blues, recorded by Clara Smith
• The Gouge of Armour Avenue, recorded by Fletcher Henderson and his Orchestra featuring Big Charlie Green
• Mama’s Gone, Good Bye, recorded by Ray Miller and his Orchestra
• It Had To Be You, recorded by the Isham Jones Orchestra and by Marion Harris
• California Here I Come, recorded by Al Jolson

Joe Walker interviews Eugene Fama (Nobel ’13) with the title “For Whom is the Market Efficient?” (The Joe Walker podcast, December 31, 2024). Here are some bits and pieces of their exchange that caught my eye.

Are financial markets efficient?

WALKER: Gene, I was talking with a few friends who work in high finance in preparation for this conversation. And one of my impressions is that a lot of people think of you as holding this extreme position that markets are perfectly rational. But I know that you don’t believe that, and I’ve also heard people who’ve taken your classes at Chicago say that you repeat ad nauseam that models aren’t real and the question is really: how efficient are markets?

FAMA: There’s a different way of putting it, actually: who is it efficient for? That’s another way to put it.

WALKER: Can you elaborate on that?

FAMA: Well, for almost everybody, the market is efficient in the sense that they don’t have information that’s not already built into prices. People who have special information, the market’s not efficient for them. So let’s say insiders, for example, typically have special information. So as far as they’re concerned, this stock is not priced totally efficiently because they have information they know will change the price. But for everybody else, assuming it’s efficient, it may be a really good approximation. … So if you say, tell me about professional investors, I’ll say a very small fraction of them show evidence of having information that isn’t already built into the price. So that’s going to the top of the food chain, saying even among the professionals, there are very few that have information that isn’t already in the price. If I go out to the public, alright, the market’s efficient for everybody out there.

Why all tests of market efficiency involve a “joint hypothesis,” because they are tests of the underlying model of price formation in financial markets.

So the problem is, this is what I call the joint hypothesis problem. You can’t tell me that prices reflect all available information unless you take a stance on what the price should be. So you have to have some model that tells me, for example, what is risk and what’s the relation between risk and expected return. And then we can look at deviations from that and see if the market is efficient. So there is what I call this joint hypothesis problem. You need a model that tells you how prices get formed. So in the jargon that’s called a model of market equilibrium. You need to join that with efficiency, then I can test it in the context of whatever model you tell me is determining prices. …

Okay, so you cannot test market efficiency without a story about risk and return, which is a market equilibrium issue. The reverse is also true. You can’t test models of market equilibrium without market efficiency. So these two things are like joined at the hip. They can’t be separated. People who do market efficiency, they almost don’t exist anymore. Everybody takes it for granted in the academic sphere. It’s considered uninteresting to test. But everybody that does market efficiency understands the joint hypothesis problem. But it’s not that widely recognized among the people who do asset risk and return models. It’s implicitly assumed, but they never make it explicit.

Why Fama, and others who argue for market efficiency, are the most important figures in behavioral economics.

FAMA: Behavioral finance, well, that had its time, but everything is behavioral after all. … Now the problem is that behavioral finance, behavioral economics, doesn’t have any models of their own. It’s just a criticism of other models.  So I’ve always chided Dick Thaler and told him, “Hey, it’s easy to criticize my models if that’s what you guys do. Give me a model of yours that I can criticize.” Never happens.  I really get under his skin when I say, well, there’s no real behavioral economics. It’s just a branch of efficient markets. You don’t have a model of your own. You just have a criticism of efficient markets. So they’re really just my cousin.

WALKER: I heard a debate between you and Thaler where you said that you were the most important person in behavioral finance.

FAMA: That’s what I said. That’s another one of my lines. Without efficient markets they’d have nothing to criticize.

Back in 1956, the federal government created the Highway Trust Fund. The basic idea was to use money collected from the federal tax on gasoline and diesel fuel to pay for construction and maintenance of federal highways. When the interstate highway system was completed back in the early 1990s, this obviously meant that less funding was needed. But the Congressional Budget Office projects maintenance spending for the federal highways at nearly $60 billion in the next few years, gradually rising over time. Meanwhile, fuel-tax revenues for the Highway Trust Fund are below $40 billion per year, and gradually falling over time with the rise of higher-mileage cars in the short-run, and also the rise of electric vehicles in the long run.

Of course, one option is just to fill the gap with general tax revenues, which is what we have in fact been doing in the last few decades. But if we would prefer to return to something closer to the earlier “user pays” approach, in which the main users of highways pay for their maintenance, then there is an argument, laid out by Michael E. Gorman, for “A Vehicle Mileage Tax for Heavy Trucks?” (Regulation magazine, Winter 2024-2025).

Why a mileage tax just on heavy trucks?

1) Heavy trucks do most of the damage to highways. Gorman discusses the evidence form the most recent Federal Highway Administration report.

A 2000 Federal Highway Administration (FHWA) report estimated that for each mile traveled, combination trucks (a tractor with one or more trailers) imposed a road repair cost average of 66¢ a mile (FHWA 2000). For all trucks, the estimated road maintenance cost per mile traveled ranged from 4¢ to 40¢ (in 2024 dollars) depending on the weight of the load. According to Bureau of Transportation Statistics (BTS) calculations, trucks today effectively pay 4¢ per mile (calculated at 24.4¢ per gallon and 6.1 miles per gallon average).

In short, the fuel taxes paid by trucks don’t come close to covering the highway maintenance costs that they impose. As Gorman points out, heavy trucks also have additional social from their contributions to air pollution and traffic congestion.

2) There are relatively few heavy trucks compared to passenger vehicles, and the truck already need to keep track of their mileage for business and regulatory purposes. Indeed, most trucks already have transponders for when they are passing through toll plazas and weighing stations. Thus, the practical task of applying a mileage tax to heavy trucks is relatively straightforward. Several states have been conducting pilot programs with a vehicle-mileage tax for trucks–New York, New Mexico, Kentucky, and Oregon–with the general pattern that “[w]hile the initial set-up costs were significant, the ongoing administrative costs were slight.”

3) A worry about applying a mileage tax to personal vehicles is that it potentially invades the privacy of users. This privacy argument carries little or no weight when applied to the business of heavy trucks.

Of course, the trucking industry hates this idea. At present, heavy trucks receive an implicit subsity, and a vehicle-mileage tax would raiase the cost of trucking to reflect the actual maintenance costs. It would have other effects as well. For example, it would probably would cause a certain amount of freight to shift to rail, but since the environmental and traffic congestion costs of rail are lower, this shift should be viewed as a social benefit. (If the US was able to reform the Jones Act rules that have raised the cost of ocean-shipping between US ports, then a certain amount of freight could also move across water, rather than over land.)

One way or another, the costs of road maintenance will be paid, either out of general revenues, or if the maintenance is postponed, it will be paid out of additional wear-and-tear on vehicles. A vehicle-mileage tax on heavy trucks is a better choice.

Andrew Peaple interviews “Barry Naughton on the State of the Xi Jinping Economy” at The Wire China website (January 5, 2025). The interview is subtitled: “The economist discusses Beijing’s recent stimulus efforts, and the long-term problems building up as China’s leader implements his model for the country.” The interview is packed with insights. Here are a few that caught my eye:

Chinese echoes of Japan’s stagnation

[N]one of the things that we’ve seen to prop up demand and keep institutional structures intact have yet involved a substantial resolution of large amounts of debt. He [Xi Jinping] keeps refinancing, kicking the can down the road, injecting some funds into the system to keep anybody from failing, but without resolving any of the problems. That’s really a problem, because at a certain point you have to clean up the mess. You mentioned earlier whether China was becoming Japan-like: this is one respect in which it certainly is. Japan spent almost a decade trying to painlessly restructure a financial system that had suffered a huge reduction in the value of its assets. It was the fundamental problem that lay behind the so-called ‘Lost Decade’ in Japan; and now China seems to be repeating some parts of that.

China’s high levels of inefficient investment

When we look at total factor productivity growth, which is the economists’ attempt to figure out what you’re getting from pure efficiency gains, China’s not really experiencing significant productivity growth. That is astonishing, because if we look at this economy that’s implementing all these new technologies, we think, wow, that’s gotta produce some kind of explosive growth in productivity. But we don’t see it.  And it’s fundamentally because, for example, China is investing in lots of semiconductor equipment plants that are losing immense amounts of money; it’s investing in thousands of miles of high speed rail that go where nobody wants to go. There are just these huge, long run implicit costs from not improving the efficiency of your society. Now, of course, on some level, Xi Jinping is making a gamble that all these technologies will at some point come together and produce a sudden surge of productivity. And he might be right. We can’t say for sure that he’s not. But thus far, he’s very much not.  …

Outsiders are much too prone to hear Chinese policy makers say we want to increase domestic demand, and interpret that as being them wanting to rebalance the structure of the economy towards consumption. As far as I can determine, China has never, since 2008, said we’re going to rebalance the economy away from investment, towards consumption.  … Do they really want to do that rebalancing, or do they enjoy sitting atop an economy that can invest trillions of dollars? I think they rather like it. 

Can the US and China reach “reasonably efficient protectionism”?

We need to get beyond the sort of ‘protectionism good, protectionism bad’ argument, and try to craft a set of policies that amounts to reasonably efficient protectionism, that works in the American interest. 

There’s two parts of that which we can lay out pretty simply. One is, let’s decide what are the sectors that we want to protect, and what are the sectors where we can benefit from opening up in the way that you suggest. EVs is a sector that we’re going to want to protect, because we have a big automobile industry that employs a lot of people. It’s behind the Chinese industry in terms of electric vehicles, but it clearly is capable of catching up. By contrast, solar panels or batteries are areas where we don’t really have a significant industry. Chinese producers have meanwhile become very good, and very low cost. We should encourage Chinese investment in the United States to produce these products, both to create jobs and so that we can learn how to produce at that kind of scale and efficiency. We’re going to need a more explicit industrial policy.

The second thing is, we don’t want to trigger a chaotic scramble for protectionism among all the different countries in the world. We want to signal that we have certain interests, but they’re reasonably limited, and we are also interested in reaching a mutually beneficial equilibrium, especially with our allies, but also with China. 

That’s difficult to do, but not impossible. We should keep our minds open to the idea that the U.S. will move towards more protectionist policy, but that it will be articulated in a way that is less disruptive, and benefits the United States’s interests. … This might, with a little bit of luck and wisdom on both sides, lead us to a new equilibrium that’s not terrible.