Month: January 2025

The GATT, formally known as the General Agreement on Tariffs and Trade but informally known as the Gentleman’s Agreement to Talk and Talk, was first signed by 23 countrie back in 1947. Over the decades, all that talking led to a substantial decrease in tariffs all around the world. By 1994, the GATT morphed into the World Trade Organization. At that time, it has about 125 countries, accounting for about 90% of world trade. From a free trade perspective, it was a considerable success.

Here’s my hypothetical question: Would the GATT have been able to expand the parameters of free trade around most of the world if it had also included the USSR?

Of couse, this did not actually happen. The old Soviet Union perceived GATT as a club of geopolitical and capitalist opponents. In 1949, it started COMECON, the Council for Mutual Economic Assistance, as its counterbalance to the GATT. The original members were in eastern Europe: along with the Soviet Union, it included Bulgaria, Czechoslovakia, Hungary, Poland, Romania and later expanded to include Albania, East Germany, Mongolia, Cuba, and Vietnam. The Soviet Union had its own notion of “comparative advantage” and “gains from trade,” which was that it would organize global trade with non-Soviet countries having only a few major export industries, thus making it harder for those countries to become independent.

Back in the day, the US had a fairly small amount of trade in a fairly small number of goods with the old Soviet Union: for example, we bought Soviet oil and sold them grain. But even though some prominent economists argued back in the 1960s and 1970s that the Soviet economy would outgrow the US economy, I don’t know of a time when American manufacturing workers felt as if their jobs were endangered by a flood of lower-cost imported cars or appliances or steel from Russia. The US worries of the 1970s and 1980s were about trade with Japan, or maybe Korea, but not the Soviet Union.

Nonetheless, imagine an alternative global economy in which the USSR was part of the GATT during the Cold War: say, after Russia invaded Hungary in 1956, or after the Sputnik launch in 1957, or after the Cuban missile crisis in 1961. Would it have been politically possible to sustain a global free trade movement with a growing global membership, like GATT, with the US and the Soviet Union both as members?I suspect not.

Now make the leap to the current day. The US and China have not yet had the equivalent of the old Soviet invasion of Hungary, nor a Sputnik moment (although the recent DeepSeek AI from China may come close), nor a direct confrontation like the Cuban missile crisis. But s the the level geopolitical confrontation rises, the pressures on international trade are rising as well.

Indeed, there’s evidence that for many Americans, worries about international trade in general are actually worries about conflict with China in particular. Germany has had enormous trade surpluses for decades, and Japan has continued to have substantial trade surpluses as well, but it is China’s trade surpluses that have gotten the attention.

In survey data on feelings about trade, Americans are something of a muddle. The answers we give depend on the questions that are asked. For example, a national survey last summer found that a strong majority of 63% favor increasing global trade, and a similarly strong majority of 58% believes that Americans favor having US firms “manufacture and make everything that we need within this country.” However, Americans don’t want to pay substantially higher prices for US products, if imports are cheaper. A majority of Americans are worried about the trade deficit, but if told that the trade deficit represents money invested in the United States, they are OK with it.

In particular, Americans are likely to report that trade with China is “unfair.”

In her own surveys, Laura Alfaro at Harvard Business School has also found that when trade with China is mentioned, any other positive attitudes about trade more-or-less vanish, because all people think about is loss of jobs.

Back in the early 1990s, when Vice-President Al Gore was defending the propossed North American Free Trade Agreement in a prime-time televised debate with Ross Perot, one of the arguments was that Mexico’s economy was just so much smaller than that of the United State, so that fears of trade with Mexico were overblown. Back in 1980, when China began its economic reforms, the US economy was 15 times as large as that of China; by 2001, when Cina became as a member of the World Trade Organization, the US economy was about 8 times as large as that of China; in 2023, the US economy is now less than twice as large as China’s–about 60% larger (measured by current US dollars).

On per capita basis, the US per capita GDP is still about six times as large as per capita GDP in China. But China’s population if of course much larger, and in global affairs, size matters. At least some of the official pronouncements from China suggest that it would like to jolt its economy out of the doldrums with a renewed surge of exporting. But a rate of Chinese export growth that was possible back in 1980 or 2001, given the relatively small size of China’s economy at that time, would be wildly disruptive to the rest of the global economy today. Moreover, the size of China’s economy is correlated with its military spending and defense posture.

I’m in general a big supporter of free trade, as readers of this blog know. But economics happens against a backdrop of politics. I don’t think the GATT could have survived back in the 1950s and 1960s and 1970s if it had included both the US and the USSR. Perhaps if China took steps toward emphasizing domestic-driven economic growth, took steps to enforce intellectual property agreements, and backed off on threatening sounds about the China Sea, then trade agreemement including the US and China could be sustained. But that feels unlikely.

Looking ahead, it seems like advances for free trade will be driven by technology, both ongoing reductions in transportation costs and in particular how the internet has both connected buyers and sellers around the world and made it possible to buy and sell services across national borders. When it comes to trade agreements, China’s presence in the World Trade Organization is one more difficulty for an organization that already was hobbled. Thus, trade agrements seem likely to be regional and bilateral, instead–and the agreements may be as full of rules and restrictions as they are attempts to reduce barriers to trade.

It’s well-known that when a couple has a child, the average woman experiences a “child penalty” in labor market outcomes, while outcomes for the man are largely unchanged. For a discussion of this pattern using US data, here’s an article by Jane Waldfogel from back in 1998 in the Journal of Economic Perspectives. As that paper points out: “As the gender gap in pay between women and men has been narrowing, the ‘family gap’ in pay between mothers and nonmothers has been widening.”

This pattern is widespread around the world. Henrik Kleven, Camille Landais, and Gabriel Leite-Mariante consider data for 134 countries in “The Child Penalty Atlas^* (published online in The Review of Economic Studies). For those who don’t have enough caffeine in their system at present to tackle the academic paper, the authors have set a “Child Penalty Atlas” website, with a useful overview of method and findings.

Here’s the data problem they face. For a number of countries, there is fairly comprehensive annual data on labor market outcomes and births. Thus, a research can track a basic labor market outcome like whether someone is in the labor force or not, and how the pattern shifts when a couple’s first child is born. Here’s a relatively common pattern using data from Chile. In the years leading up to a first child, both men and women are more likely to be holding jobs (perhaps becasue they are leaving school). But when the first child is born, the employment rate for women drops off, while that for men continues rising a bit, but mainly levels off.

However, many countries do not have annual data. Instead, they have occasional data from a government census or household survey. The researchers then take this approach:

In those cases, we know the age of people’s oldest child, so we know what happens to women and men’s employment after having children. But because we do not observe the same people over time, we do not know what their outcomes were before they had children. How do we address this? In a nutshell, we ‘match’ each observed individual who has just had a first child (i.e., they are at t=0) to a childless person with similar characteristics who is n (n varying from 1 to 5) years younger. We then assume that this childless person will have a child in n years from now. Effectively, we create a population of “future parents” from the population of people who don’t have children and who are very similar to the actual parents we observe.

Is this approach a sensible one? You can check it. Take the countries like Chile that have both kinds of data: annual data and occasional census/survey data. Apply this method of choosing people who are similar in observed characteristics based on the occasional data. Then look at the annual data and check whether this method offers accurate projections. It turns out that the method works pretty well.

The result suggests that child penalties vary a lot across countries. As the map shows, the reduction in women’s labor force participation after a first birth is very low in parts of Africa as well as China and parts of east Asia; intermediate level in the US, Canada, Russia, and parts of Europe like France; and higher in Latin America, parts of the Middle East, and parts of Europe.

in many high-income countries the child penalty explains nearly 100% of the gap of the gap in labor force participation between men and women: for example, it explains 84% of the gap in the United States, 95% in Canada, 97% in Germany, and more than 100% of the gap in Sweden. For many other countries around the world, the child penalty is only part of the labor force participation gap: in some cases, because the child penalty is so small (as in certain countrie in Africa and Asia), and in other cases because the gender gap in labor force participation is so large (as in Latin America and the Middle East).

There is of course an ongoing argument in the United States over the extent to which government programs that support first-time parents, from work leave to child care, might reduce the gender gap in labor force participation. The evidence here doesn’t speak to that point directly. After all, many countries across Europe have considerably more extensive parental leave policies and child care support than the United States, but also a greater child penalty. Policies to support new parents probably have a different effect depending on broader social expectations: if the social expectation is that most mothers will return to the labor force, these policies might help the transition out of the labor force and back in; but if the social expectation is that mothers not return to the labor force soon, or at all, then parental leave and other supports may just smooth the path out of the labor force.

Friedrich von Hayek won the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in 1974. For the 50th anniversary of the prize, the IEA published a short collection of essays called Hayek’s Nobel: 50 Years On, edited by Kristian Niemietz. It Includes Hayek’s speech upon acceptance of the Nobel Prize, “The Pretence of Knowledge,” with three essays placing the essay in historical and modern context by Bruce Caldwell, Peter J. Boettke, and Donald J. Boudreaux.

Hayek is perhaps best-known today for the line of argument famously laid out in his 1945 essay, “The Use of Knowledge in Society,” which is also the focus of his Nobel address. He points out that the operation of prices in a market offers a way of coordinating actions. One example focuses on the price of tin. He wrote:

Fundamentally, in a system where the knowledge of the, relevant facts is dispersed among many people, prices can act to coordinate the separate actions of different people in the same way as subjective values help the individual to coordinate the parts of his plan. It is worth contemplating for a moment a very simple and commonplace instance of the action of the price system to see what precisely it accomplishes. Assume that somewhere in the world a new opportunity for the use of some raw material, say tin, has arisen, or that one of the sources of supply of tin has been eliminated. It does not matter for our purpose-and it is very significant that it does not matter- which of these two causes has made tin more scarce. All that the users of tin need to know is that some of the tin they used to consume is now more profitably employed elsewhere, and that in consequence they must economize tin. There is no need for the great majority of them even to know where the more urgent need has arisen, or in favor of what other needs they ought to husband the supply. If only some of them know directly of the new demand, and switch resources over to it, and if the people who are aware of the new gap thus created in turn fill it from still other sources, the effect will rapidly spread throughout the whole economic system and influence not only all the uses of tin, but also those of its substitutes and the substitutes of these substitutes, the supply of all the things made of tin, and their substitutes, and so on; and all this without the great majority of those instrumental in bringing about these substitutions knowing anything at all about the original cause of these changes. The whole acts as one market, not because any of its members survey the whole field, but because their limited individual fields of vision sufficiently overlap so that through many intermediaries the relevant information is communicated to all

Thus, the coordinating action of a market is tightly related to how the price provides signals to producers and users. But Hayek’s point about markets and information operates at a more subtle level as well.

Imagine that an economic planner observes that a supply of tin has been eliminated, and want to adjust economic outcomes accordingly. Presumably, there should be some mixture of efforts to expand production of tin in some ways, and to reduce the use of tin in other ways. In turn, those who reduce the use of tin may with to turn to other materials, and so production of those other materials should be increased as well. But what would be the appropriate mixture of these (and other) changes?

Hayek argues that it is literally impossible for an economic planner to answer this question. The reason is that consumers of tin literally don’t know how much they might conserve on tin (or switch to substitutes) until they are actually forced experiment with different methods of doing so. Similarly, alternative producers of tin (or substitutes) literally don’t know about how they might adjust production in response to a shortage of tin that happens elsewhere until they actually try to do it. The knowledge of how future adjustments might take place if conditions change is predictable in terms of broad patterns–that is, in response to a shutdown of a supply of tin, users of tin will try to conserve and alternative producers of tin will try to increase output–but specifically who will be able to take these steps most easily and cost-effectively is not known in advance.

In Hayek’s Nobel address, he writes:

Unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena, the aspects of the events to be accounted for about which we can get quantitative data are necessarily limited and may not include the important ones. While in the physical sciences it is generally assumed, probably with good reason, that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process, for reasons which I shall explain later, will hardly ever be fully known or measurable. And while in the physical sciences the investigator will be able to measure what, on the basis of a prima facie theory, he thinks important, in the social sciences often that is treated as important which happens to be accessible to measurement.

There is much to be said about the strengths and weakness of Hayek’s theory, which I won’t try to do here. But I will point out one consequence of his theory, which is that it is common for politicians to speak as if economic outcomes are just a matter of political will. Maybe the discussion is about saving a factory at risk of closing down, or saving or creating an industry, creating more well-paid jobs, making housing more affordable, reducing the price of eggs, cutting interest rates, and so on and so on. The language of politics often makes it sound as if these and other economic outcomes are just a matter of whether your favorite politician or party is “fighting” for you. It’s just a matter of whether they “want it enough,” as the sportcasters say of the winning team, suggesting that losing and other unwanted outcomes are just about a weakess of desire.

In his 1988 essay, Hayek referred to this belief in the malleability of economic outcome as The Fatal Conceit: he wrote of “the fatal conceit that man is able to shape the world around him according to his wishes.”

More to the present point, Hayek argued that many people have a tendency to emphasize the role that government plays in economic outcomes, because government actions are large-scale, associated with prominent leaders, and commemorated by writers. The actions of government are the facts that we have written down. But we typically don’t write down, or even observe, the myriad small-scale reactions of individual consumers and producers across an economy as they continually react to changes and shift. Hayek wrote:

The role played by governments is greatly exaggerated in historical accounts because we necessarily know so much more about what organised government did than about what the spontaneous coordination of individual efforts accomplished. This deception, which stems from the nature of those things preserved, such as documents and monuments …

Government economic policy, whatever its announced goals, doesn’t create outcomes. Instead, it changes the context in which economic actors make decisions, which in turn leads to economic outcomes. The distinction matters.

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