Cement is an intermediate ingredient for making concrete, which in turn is an ingredient for construction of roads and buildings all around the world. The price of cement and the quantity produced is thus a marker for the extent of economic development. A few years ago (I haven’t seen more recent data), China was producing more than half of the cement in the world as part of its ongoing modernization. Moreover, cement is an interesting product, because it exhibits both economies of scale and high transportation costs–so that there tend to be relatively few producers in a given geographic area. For all of these reasons, Fabrizio Leone, Rocco Macchiavello, and Tristan Reed found it worthwhile to investigate “The High and Falling Price of Cement in Africa” (American Economic Journal: Applied Economics 2025, 17(2): 1–40).
For those not fully up to speed on their construction materials, cement is not the same as concrete. The authors explain:
Portland cement (hereafter cement) is the most widely used type of hydraulic cement, which hardens when combined with water. The main inputs to cement production are limestone, clay, and gypsum, which are heated in a kiln to form clinker. Clinker is ground into a fine powder, which is finished cement. In turn, cement is a major input to ready-mix concrete, which is cement mixed with gravel, sand, and water, and delivered to a construction site.
The authors point out that an efficient cement plant has high fixed costs: about $150 million for every million tons of capacity. That is, a larger plant will have a lower per-unit price than a smaller plant, and thus will tend to drive the smaller plant out of business. Cement is heavy, and transporting it by land is costly. Put these two factors together, and markets for cement tend to be localized and concentrated, with a few larger-sized plants dominating each local market. (Economists call this a “natural monopoly,” in which the physical characteristics of the product itself tend to create a lack of local competition.) The exception is a port city with access to water transportation, because moving cement by water is considerably cheaper than moving it over land, so it is possible for such a city to receive cement from competing suppliers.
The authors provide evidence for three facts:
Fact 1: The average price of cement in Africa was the highest of any continent in 2011, and consumption was the lowest.
Fact 2: African economies have on average fewer cement firms and less production capacity than other continents.
Fact 3: The average price of cement in Africa fell by more than in any other continent between 2011 and 2017, coinciding with entry and capacity installation.
The authors seek to disentangle the reasons behind expansion of cement capacity and falling prices across nations of Africa. For example, is it improved technology pushing down costs of production? Greater competition so that cement firms are pressured to charge lower markups above cost? Are the lower prices a sign of anticompetitive or cartel-like behavior among the relatively few cement companies? Have regulatory barriers make it harder to start a cement company in many countries of Africa? Perhaps government rules or corruption in some form had been keeping cement prices high? They write:
Contrary to common belief, our model estimates show that cement was not more expensive in Africa due to anti-competitive conduct or high entry barriers (e.g., due to corruption). Instead, the small size of many national markets limited competition and enabled incumbents to sustain higher markups. Consistently with this view, rapid entry and a decline in marginal cost occurred in Africa at a time of rapid economic growth. … Our findings have implications for public policy, specifically the long standing program to reduce entry barriers and increase competition in low- and middle-income countries. … [O]our results challenge the hypothesis that in the cement industry such policies could have a substantial impact on markups and prices.
In short, cement in Africa is a story of economic fundamentals for a product of particular characteristics in a growing economy.
There’s a fundamental misconception at the root of President Trump’s tariff policies, which is the mistaken claim that the existence of a US trade deficit proves that trade is unfair. There are two related mistaken claims. One is a claim that if tariff and non-tariff barriers to trade were removed, then trade would be balanced. Another is that if the US trade deficit persists, then it proves that trade barriers remain.
As an illustration of this mindset, President Trump said to reporters in the aftermath of his tariff announcement a few days ago: “I spoke to a lot of leaders — European, Asian, from all over the world. They are dying to make a deal, but I said ‘we’re not gonna have deficits with your country’ … to me a deficit is a loss. We’re gonna have surpluses or at worst we’re gonna be breaking even.”
But tariffs do not in fact cause trade deficits. The existence of a US trade deficit does not prove in any way that other countries have larger (or smaller) tariffs. Whether the end result of Trump’s trade negotiations is higher tariffs or a return to the pre-existing tariffs, it’s not going to fix the US trade deficits.
A first key insight here is that tariffs (and other trade barriers) can shift the composition of an economy, but these shifts in composition are not related to the existence of trade deficit or surplus. Think of it this way: When a national economy starts to engage in international trade, it will alter the shape of that economy. Sectors of the economy that are well-suited for exporting will expand; sectors of the economy where other countries are well-suited for exporting will contract.
These trade-induced shifts away from some sectors and toward others can be painful. Indeed, many economic shifts–like automation or other new technologies–can be painful as well. But shifts toward higher-productivity areas is the source of economic growth, and trade-induced shifts toward the areas where an economy has an advantage and away from areas where other countries have an advantage is actually why both sides benefit from trade. Conversely, the proposed new tariffs would cause a high level of economic pain to the US economy, because they are also an attempt to shift sectoral patterns. However, the tariffs seek to shift the sectoral patterns toward areas where the US has less or no global advantage and–necessarily–away from areas where it does.
Whether you agree with my negative view of tariffs or not, here’s the key point: the trade-induced shifts across the size of economic sectors do not require there to be an imbalance of trade. Even if US imports and exports were equal, there would still be US domestic producers who feel a competitive threat from foreign producers of very similar goods. (For example, in the early 1970s when US trade was roughly balanced, or in the late 1980s when trade was near-balance for a few years, there were still concerns over imports.)
In short, trade shifts the mixture of good and services produced in a domestic economy. Conversely, when a nation imposes barriers to trade like tariffs, it shifts the national economy back toward the sectoral patterns that would have existed in the absence of trade. But although these shifts will make some sectors relatively larger or smaller, the shifts are not actually related to trade deficits–not at the bilateral level and not at the overall level.
The misconception that differences in tariffs are the cause and the solution of trade deficits comes in a silly version and a deeper version. The silly version is that if all countries removed their trade barriers, the US would then have a bilateral trade balance with every individual country. But in a global economy, there is no reason why every single pair of countries should have balanced trade–as opposed to countries having trade surpluses with some partners and trade deficits with others. Indeed, although the US has consistently had overall trade deficits since the late 1970s and early 1980s, it has bilateral trade surpluses with a number of economies. In 2023, for example, the US had a surplus in goods trade with Belgium, the United Kingdom, Australia, and others. Indeed, the US had an overall trade surplus in 2023 with the South/Central America region, including trade surpluses with Argentina, Brazil, and Chile.
If you believe that bilateral trade imbalances are caused only by trade barriers, then you need to look at the mixture of US trade surpluses and deficit across countries, and believe that all the countries where the US has a with bilateral trade deficits are treating the US unfairly, and also that all the countries where the US has a bilateral trade surplus are being treated unfairly by the US. But there is literally no evidence that levels of trade barriers match up to trade surpluses. The US has trade deficits with countries where it has already negotiated free-trade agreements. Even the Trump administration doesn’t believe this is true: it has sought to impose tariffs on all US trading partners, not just those where the US has bilateral trade deficits. And one suspects that the Trump administration would be deeply unamused if the countries where the US has bilateral trade surpluses used that as a reason to limit US exports.
So let’s set aside the peculiar and ridiculous claims about bilateral trade deficits and surpluses, and focus instead on the overall US trade deficit. Maurice Obstfeld tackles these issues in “The U.S. Trade Deficit: Myths and Realities” (Brookings Papers on Economic Activity, Spring 2025, presentation of this paper, along with comments and discussion is available here).
As a starting point, consider the pattern of the overall US trade deficit over time. As you can see, the US trade deficit as a percent of GDP was near-zero from the 1950s up through the mid-1970s, and has been mostly in deficit since then.
If the US trade deficit is caused by the tariffs and barriers to trade from other countries, then changes in the trade deficit must be caused by changes in barriers to trade. Thus, the larger trade deficits from the mid-1970s to the mid-1980s must reflect greater barriers to trade from US trading partners, followed by lesser barriers to trade as the US trade deficit declines in size from the mid-1980s to the early 1990s. As the trade deficit then gets larger through the 1990s, this must reflect greater barriers to trade at this time, and the decline in US trade deficits around the time of the Great Recession from 2007-09 must reflect smaller barriers to trade.
Just to be clear, no one actually believes that movements in unfairness of trade explain movements in the US trade deficit. Concerns about unfair Japanese trade barriers were strongly expressed in the early 1970s when overall US trade was close to balance. No one was saying in the late 1980s or in the Great Recession–times when the US trade deficits declined in size–that the cause was a sharp reduction in foreign trade barriers. When US trade deficits rose in the 1990s, the concern was that trade barriers had been reduced because of the North American Free Trade Agreement–not that global trade barriers had gone up. When US trade deficits rose in the early 2000s, the concern was that trade barriers had been reduced when China entered the World Trade Organization, not that global trade barriers had gone up.
More broadly, the the overall argument that the US has larger trade deficits than a half-century ago is because trade barriers around the world are higher than a half-century ago doesn’t pass a basic reality check. As any anti-globalization protester will be happy to tell you, the overall thrust of trade policy around the world in the last half-century has been toward reducing barriers to trade: the World Trade Organization, the US-Mexico-Canada Agreement (USMCA, offspring of NAFTA) and the other 13 “free trade agreements” the US has signed, along with any number of trade-encouraging treaties on issues from taxation to intellectual property.
So if tariffs (and other trade barriers) are not the cause of US trade deficits, what is the cause? If it’s not tariffs, what causes US deficits to rise and fall. The key point here (and this is a standard intro-econ argument, not a personal theory of mine) is that a trade deficit reflects a macroeconomic imbalance. Obstfeld goes through the argument in some theoretical detail. Here, let me illustrate the theory by offering some potential alternative (and partial) explanations changes in the US trade deficit that are unrelated to tariffs.
Here’s a first episode: Back in the 1980s, the federal government ran budget deficits which at the time looked quite large, and the US trade deficit also got larger. At the time, these were sometimes called the “twin deficits.” The intuition went like this: the buying power from the large budget deficits of the 1980s could in theory have gone to buying domestically produced goods, but in fact a lot of it went to buying imported goods. The high US budget deficits of the 1980s were thus a primary cause of the US trade deficits of that time
As a second episode, consider the sharp decline in the size of the US trade deficit around the time of the Great Recession of 2007-09. During a recession, household buying and investment decrease, and as part of that, imports also fall, which leads to a reduced trade deficit. (The recession of 1990-91 also helps to explain the pattern of a smaller trade deficit at that time.)
As a third episode, consider the larger trade deficits that the US economy experienced in the 1990s. This was during the “dot-com boom,” when investors all over the world were eager to put dollars into US-based information technology startups for this new thing called the World Wide Web. To put it another way, the rest of the world shifted to some extent at that time toward investing in the US economy, and to some extent away from buying US-produced goods and services.
Of course, each of these episodes is considerably more complex than my quick discussion here. But My hope is to illustrate that there are a variety of macroeconomic reasons why trade deficits rise and fall that have nothing to do with levels of tariffs in other countries, like surges of US government borrowing, US recessions, and surges of capital inflows from other countries. Conversely, one can also look at countries with consistent trade surpluses and find explanations in their patterns of domestic saving and borrowing, business cycles, and changes in flows of foreign capital. (And in addition, tariffs bring with them factors like retaliation from other countries and shifts in exchange rates that will greatly reduce any effect they can have on trade balance.)
Again, my point in this particular post is not to argue whether tariffs are good or bad. It is just to point out that tariffs are not the likely cause of US trade deficits, nor are they a likely answer. The US government is running enormous budget deficits, and like in the 1980s, the buying power of these deficits as they flow into the economy is keeping purchases of imports high–along with the trade deficit. This is one reason why Obstfeld writes: “U.S. trade deficits are high and likely to rise, notwithstanding new and prospective tariffs.”
Of course, it’s a lot easier politically to blame the unfairness of dastardly foreigners, rather than to get serious about the details of an agenda to reduce US budget deficits or to increase US productivity.
But it’s perhaps worth noting that trade surpluses are not necessarily a sign of economic success, and trade deficits are not necessarily a sign of economic failure. To cite just one prominent example, Japan’s economy has had trade surpluses for most o the last half-cnetury decades and also ultra-slow and near-stagnant growth since the early 1990s, while the US economy has had trade deficits and has been leading the high-income countries of the world in its growth rate.
In the US system of government, the federal government has shifted its empahasis toward becoming a pass-through device for money: it passes through funds to individual through Social Security and various safety net programs; it passes through money to the health care industry through Medicare and Medicaid; it passes through money in the form of interest payments to those who loaned money to the government by purchasing Treasury securities; and it passes through money to state and local government. (About one-fifth of all federal spending goes to state and local governments; about one-third of state revenues come from the federal government.)
But while much of the public attention to government focuses on the federal level, many activities of “government” actually happen at the state and local level: for example, K-12 schools and public higher education, roads and bridges, public transit, airports, water and sewage treatment, policing and traffic rules, firefighters, housing policy, homelessness, regulating the production and transmission of electrical power, regulating insurance companies, direct administration of programs like Medicaid and unemployment insurance, and others. The overwhelming majority of “government” workers in the US are employed by state and local governments, not by the federal govermment.
To what extent do state and local governments have the capacity to handle the tasks they face? David Schleicher and Nicholas Bagley tackle this question in “The State Capacity Crisis” (Niskanen Center, January 1, 2025). They write: “The old joke is that the federal government is really an insurance company with an army. It doles out checks for old people through Social Security and Medicare, but does not much involve itself in service provision.” Here’s a flavor of their argument (footnotes omitted):
Three areas are of particular concern to us. First, the linchpin of the usual story about the lack of state capacity is the claim that Congress is broken. … State legislatures, however, aren’t broken in the same way that Congress is. Polarization notwithstanding, in 39 out of 50 states, both houses of legislatures and the governor come from the same party and only rarely have institutional limits like the filibuster. As a result, majority parties can usually do what they want. Gridlock is not the problem. Yet state legislatures are in an even worse spot than Congress. Voters know almost nothing about what is happening in state politics, and increasingly vote for the same party for state legislature that they do for president and Congress. This pervasive nationalization of state and local elections means that state legislative performance has little connection to electoral outcomes. Gerrymandering is also a much worse problem at the state level than at the federal level .. as is the lack of staff capacity and resources. The natural consequence is inattention to genuine public priorities.
Second, a key plank of the state capacity literature is that administrative law imposes too many procedural rules on government agencies. However well-intentioned, these rules bog agencies down in often-senseless red tape, augment the power of narrow interest groups to twist agency outcomes to private ends, and license courts to halt agency action for ticky-tack or partisan reasons. But while this “procedure fetish” is a big problem for the federal administration … it is in many ways a bigger problem for states and localities. State administrative law is as strict, and often stricter, than federal administrative law, both with respect to the procedures it imposes and the intensity of judicial review. State and, in particular, local governments have extremely powerful rules requiring lots of public participation in administration. Because small groups with members that care intensely about state and local decisions are much easier to form than groups representing a diffuse public interest, unrepresentative private interests—whether that’s the Chamber of Commerce or neighborhood NIMBYs—overwhelm administrative process at the expense of majoritarian preferences.
Third, there’s a blind spot in the state capacity literature about budgets. The reason is that the federal government has extraordinary fiscal capacity, including the ability to deficit-spend during recessions. … The picture looks very different at the state level. Every state (save Vermont) is legally required to balance its budget, no state can print money to inflate away its deficits, and all states face both legal and market limits on their capacity to borrow. When a recession depletes tax revenue, states have few choices except to increase taxes or reduce spending—right when public services are needed most. States’ limited fiscal capacity thus contributes mightily to poor governance, especially during recessions. Budget constraints are becoming increasingly salient as Medicaid consumes ever-larger fractions of state budgets, the costs of state and local public services increase faster than inflation, and states and localities deal with the consequences of underfunding their pension obligations.
Taken together, these forces—the unaccountability of state and local politics, the excessive strictures of state and local administrative law, and the sharp limits on state fiscal powers—dull officials’ incentives to govern well, privilege narrow interest groups at the expense of the majority, and frustrate efforts to build capacity.
In short, your state and local government are not run by the federal government. If you care about the practical side of state and local government actually getting things done, and in a timely and cost-effective manner, then you need to pay attention to the practicalities of what happening and vote accordingly. A classic example here would be the heavily Democratic New York City electorate sometimes voting for Republican mayors like Rudolph Giuliani or Michael Bloomberg. (If you react to those names based on their actions in the federal political arena, rather than their performance and actions in local government, you are illustrating the problem.)
Personally, I wince every time I hear state or local policymakers taking a stance on what’s happening at the federal level, because to me, it suggest that they are not focused on their actual jobs. When the streets are safe and in good repair, the K-12 schools are educating students at a high level, and the public pension funds are all well-financed, then I’m willing to hear the opinions of state and local policymakers about national politics–but not before then. Schleicher and Bagley conclude:
Whatever the right approach may be, our point is that reformers seeking to build state capacity need to think about where to concentrate their efforts. In our view, you won’t make that much headway in the Beltway. You need to go to Lansing and Hartford, Sacramento and Austin, Los Angeles County and New York City. State capacity—in America at least—is about states and localities.
It will take some time for the effects of President Trump’s “Liberation Day” announcement of higher US tariffs to become apparent. Here, I’ll just offer some notes and quick reactions.
1) The announced tariffs represent a very large increase. Here’s a figure showing historical US tariff rates. You will notice that the average rates have been under 10% for the entire post-World War II era. If you squint, you can see the Trump tariff increases from his first term in 2017. The jump in 2025 represents tariffs already announced earlier this year, which by historical standards were already substantial. Yesterday’s announcement of a universal tariff on US imports of 10%, plus more for many countries, comes on top of all earlier announcements. I’m sure that estimates of the average US tariff rate are being calculated even now, but it will surely be above 10%, perhaps in the range of 20%. In short, Trump is taking US tariff levels back to the time of the Great Depression, and the late 19th century.
2) For better or worse, the announced tariffs are fully the political responsibility of the Trump administration. This tariff increase was not a bill proposed within Congress, debated and analyzed, and then subject to a vote. It was concocted behind closed doors.
3) It is not clear that President Trump actually has authority to impose these tariffs by decree. Article 1 of the US Constitution–which lays out the structure and powers of the legislative branch–states in Section 8: “The Congress shall have Power To lay and collect taxes, Duties, Imposts and Excises, to pay the Debts and provide for the common Defence and general Welfare of the United States …” On its face, this certainly seems to suggest that new tariffs need to start in Congress and be signed into law. Over time, Congress has passed laws that give the president the power to impose tariffs in specific settings for specific industries, but Trump is in effect claiming that these partial and piecemeal laws have delegated him complete power over tariffs, because it is a “national emergency” that the US economy has trade deficits–which it has had since the 1980s. Maybe! But Trump’s declaration of “national emergency” to claim of complete power over tariff-setting is contrary to the plain text of the Constitution.
4) The amount of the tariffs seems arbitrary and unclear, because the US import tariffs are, in theory, set at half the foreign level, or 10%, whichever is higher. But James Suriowecki reports that the Trump administration apparently took the trade deficit in goods with each country, divided by total US imports from that country, and called the result the “tariff rate” for that country. A minor problem with this calculation is that it involves only trade in goods, not including services. A major problem is that this isn’t actually the tariff rate that other countries are charging. My guess is that there will be a blizzard of adjustments to these announced tariff rates, which means the uncertainty surrounding them will continue.
5) Promises have been made by the Trump administration about the benefits of this proposal. For example, there have been promises that tariffs on imports are all paid by foreigners, so the new tariffs will not cause price increases for US consumers. There are promises that the new tariffs will raise $600 billion per year in additional federal revenue, promises that the number of US manufacturing jobs with high wages will climb dramatically, and promises that US trade deficits will be eliminated. For example, when President Trump was talking about the tariffs to come, he said: “All I know is this: We’re going to take in hundreds of millions of dollars in tariffs, and we’re going to become so rich, you’re not going to know where to spend all that money! I’m telling you – you just watch. We’re going to have jobs, we’re going to have open factories, it’s going to be great.”
6) These predictions about the positive effects of tariffs need to be remembered. The predictions do not fit with standard economic beliefs about the effects of tariffs. (Indeed, given all these promised benefits, one wonders why Trump did not set the tariffs at a much higher level?) If the benefits are realized, Trump will deserve enormous credit; conversely, if they are not realized and more dire economic outcomes emerge instead, Trump will deserve enormous blame.
7) Whether one like it or not, US multinational firms have in fact invested in global networks both for purchasing supplies and exporting products in the last few decades. With much higher inport tariffs, the value of those investments by major US firms takes a real hit. If and when other nations retaliate against US exports, these major firms–and all US exports, including farm products–will take a hit as well. The costs of reorganizing supply chains and export sales for US firms will be very real. The costs of losing a share of the existing gains from trade will be very real.
8) I’m no political savant, but it seems to me that President Trump is making a potentially enormous unforced error by raising tariffs so dramatically. For many of Trump’s policies–say, tougher immigration, pushing back against the so-called “DEI” efforts, hunting down government waste and abuse, and others–he has a considerable wave of popular support behind him. But I have not observed a similar popular outcry for substantially higher tariffs. Instead, many Trump voters expressed strong concerns over a rising cost of living. These voters will not be amused when they find that prices of imported goods are rising (or that such goods are much less available) and that with a lack of competition from imported goods, prices of domestic producers will tend to rise as well. Trump voters who are working in industries that rely on exports will not be amused to see their international markets reduced, either. In addition, by enacting these tariff policies near the start of his term, the effects of the policies will play out while Trump is still president. The credit or blame will be his.
9) The US enacted high tariffs during the Great Depression–the infamous Smoot-Hawley tariffs of 1930. Those tariffs were not a primary cause of the Depression, but they didn’t help, either. My sense is that the experience of those failed tariffs was part of the shared US political consciousness for some decades. However, that experience eventually faded in popular memory. My expectation about Trump’s tariffs is that there will be waves of lobbying and renegotiations, and with each one, Trump will claim another victory for his approach. But I confess to a darker thought. Part of me hopes that Trump will keep his tariffs in place until the costs are broadly apparent to all, so that a modern consciousness of why this approach doesn’t work can take effect for the next few decades.
10) President Trump’s claims about the benefits of tariffs seem based on demonstrably false beliefs. For example, he seems to believe that trade imbalances are the result of tariffs, that the existence of trade balances proves that other nations are imposing unfair trade imbalances, and that reciprocal unfairness by the United States will eliminate trade imbalances. He seems to believe import tariffs won’t affect prices to US consumers. He seems to believe that although manufacturing jobs are declining all over the world, including in China, tariffs will make manufacturing jobs resurgent in the United States. None of this is plausible. Trump also seems to that that the US economy will be stronger with more limited connections to global trade. But I am unaware of any real-world examples of countries that made themselves rich by withdrawing from the world economy.
The ocean covers 71% of Earth’s surface, comprises 90% of the biosphere, provides food security for over three billion people, enables the transportation of over 80% of global goods, and hosts sea cables carrying 98% of international Internet traffic. … If considered a country, the ocean economy would be the world’s fifth-largest economy in 2019. From 1995 to 2020, it contributed 3% to 4% of global gross value added (GVA) and employed up to 133 million full-time equivalents (FTEs). The global ocean economy doubled in real terms in 25 years from USD 1.3 trillion of GVA in 1995 to USD 2.6 trillion in 2020, growing at an annual average rate of 2.8%.
Unsurprisingly, the growth in the ocean economy has mainly been in east Asia in the last 25 years or so, and is related to the rapid growth of China’s economy during that time. I was struck by this breakdown of the ocean-related economy by industry:
The report goes into some depth in analyzing various factors that will affect the future course of the ocean economy: population, environmental, legal, energy demand, technological, and others. It’s striking to me that the top industry on the list is marine and coastal tourism.As I read through the report, one key tradeoff for the ocean economy will be over environmental protections. Future success for some of these industries–tourism, fishing–depend heavily on environmental protections, while others like offshore oil/gas or overfishing pose enviromental dangers.
But more broadly, it feels to me as if thinking of the ocean as an economy can conceal as much as it reveals. For example, the economic function of ports and ship-building is different from the issues raised by freedom of the seas for shipping and by national defense. The importance of undersea data connections and ocean shipping in the global economy is connected to the gains they bring to users–not just the amount that is paid to the providers.
For users of AI tools, the discussion often focuses on the newest version of ChatGPT or Gemini or DeepResearch. But the point of contact of the AI industry with users is of course just one part of the AI industry as a whole. as a whole. Leonardo Gambacorta and Vatsala Shreeti lay it out in “The AI supply chain” (Bank of International Settlements Papers No. 154, March 2025). Here is their version of the AI supply chain:
Based on this structure, here are some of the key player in the overall AI ecosystem:
Of course, these graphs are an overview rather than a detailed presentation, but that’s often a useful way of gaining perspective. Gambacorta and Shreeti provide more detail in the text. For example (citations and references to graphs omitted):
Hardware. Consider the most important hardware for AI applications, namely microprocessors like GPUs. Nvidia – headquartered in Santa Clara, California, in the United States – serves most of the market for GPUs, with its market share reported to be larger than 90%. It has gross margins of over 70% and has seen its revenues increase by 405% between 2023 and 2024. Initially serving the video game market, Nvidia had a head start in leveraging the parallel computing capacity of its GPUs for AI models. Over time, it has built-up substantial intellectual property and a significant reputation, solidifying its position as the market leader for GPUs. Apart from the GPUs themselves, Nvidia also produces complementary software. Nvidia’s GPUs come in an exclusive bundle with CUDA, its parallel computing platform, which enables programmers and software developers to simplify the process of using GPUs and to enhance their performance. CUDA has become the industry standard for programmers and can only be used with Nvidia’s GPUs. …
To be sure, several other firms, including startups and big techs, are also active in the market for AI hardware. Advanced Micro Devices (AMD), Intel and big techs like Microsoft, Google and Amazon are all producing AI microprocessors to compete with Nvidia’s GPUs, both for training AI models and for inference. Chinese companies like Alibaba, Baidu and Huawei are also starting to produce their own microprocessors, especially in light of geopolitical constraints. …
Cloud computing layer. Globally, the cloud computing market is dominated by three big tech companies: Amazon Web Services (AWS) with a market share of 31%, Microsoft Azure with 24% and Google Cloud Platform with 11%. In the European Union (EU), the estimated combined market share of AWS and Azure in 2020 was over 80% and their profit margins were also reported to be high, at 30% and 38%, respectively. In the case of the IaaS segment – the most relevant one for AI models the market is even more concentrated. In 2023, AWS, Microsoft Azure and Google Cloud Platform together accounted for nearly 74% of the global market. …
Training data. So far, frontier AI models have been trained using vast troves of publicly available data. However, as the stock of public data rapidly declines, firms are turning to other data sources. …
Foundation models. At first glance, the market for foundation models is dynamic and rife with competitors. There are over 300 foundation models in the market, provided by 14 different firms. There are also competing business models – while some firms choose to offer proprietary foundation models (like OpenAI and Google DeepMind), others have adopted a relatively more open approach (notably Meta with its open source Llama models and, more recently, DeepSeek). … Nevertheless, the market for foundation models is currently dominated by only a handful of firms like OpenAI, Google DeepMind, Anthropic and Meta. In 2023, despite numerous competing foundation models, OpenAI’s GPT-4 accounted for 69% of the market for generative AI in terms of global revenue. Given the dynamic nature of the market and the potential to realise efficiencies, the hierarchy may shift rapidly. …
AI applications and user-facing layer. The last stage of the AI supply chain, the user facing layer, follows the playbook of digital platforms and mobile applications. Since the “ChatGPT moment” of AI, applications built on top of foundation models have been proliferating in various sectors of the economy including health, education, backend processing and compliance, software development and others. Nonetheless, and as with digital platforms, there can be a risk of “winner takes all” dynamics emerging in the markets for AI applications. While it is a Herculean task to trace the market for AI applications in every sector, the market for chatbots can be instructive. … [D]espite a flurry of similar interfaces, ChatGPT still accounted for 60% of the chatbot market (measured by the total number of monthly visits) in 2024, highlighting the importance of being first to market.
One lesson from this overview is that if one believes that AI tools are going to be important drivers of productivity going forward, then a leading economy like the United States should be interested in strengthening the entire ecosystem for developing and using AI. A harder lesson is that with a very rapidly evolving technology, the best approaches for firms, users, and policymakers are not at all obvious in advance. Firms that seem to have a dominant market position in early 2025 may not have one in six months or a year or two, whether policymakers take any action or not. Lessons are learned about a technology as firms and strategies rise and fall, and to a substantial extent, policymakers should let the process play out so tha these lessons can be learned. In particular, policymakers have a tendency to listen to the loudest complainers, rather than giving equal weight to the beneficiaries who are too busy building and using these tools to spend time lobbying about them.
Industrial policy can be defined as government policies that seek to shift the sectoral structure of an economy: for example, toward a certain high-technology industry, or toward a manufacturing industry, or in a lower-income country away from a heavy production of raw materials and agricultural goods and toward a greater emphasis on processing those outputs. Tools for industrial policy can involve direct subsidies, indirect subsidies (like low-interest loans or building key infracture), subsidies based on export performance, reducing import competition via tariffs and other methods, reducing barriers to trade to make it cheaper to purchase key inputs and goods, rules requiring domestic content in production of certain goods, direct government purchases of goods, and others. As one might expect, such policies have sometimes been successful, and sometimes have resulted in pouring resources down a rat hole.
A group of IMF economists–Sandra Baquie, Yueling Huang, Florence Jaumotte, Jaden Kim, Rafael Machado Parente, and Samuel Pienknagura–have published a Staff Discussion Note attempting to summarize some lessons in “Industrial Policies: Handle with Care” (IMF, SDN/2025/002, March 2025). Here are a few of their themes that caught my eye.
The standard examples cited as success stories for industrial policy tend to be countries from East Asia: Japan, Korea, Taiwan, China. The standard examples cited as failures of industrial policy are often countries from Latin America. One intriguing distinction here is that the success stories were outward-focused, requiring that industries meet export targets in the global economy to receive subsidies, while the failures often involved import substitution, in which imports were blocked to support domestic consumption for domestic use. The IMF economists write:
The debate around the effectiveness of IPs [industrial policies] has centered around two somewhat opposing narratives. On the negative side, there is the experience of Latin American countries with import-substitution, which, after two decades of favorable economic performance between the 1950s and the 1960s, have struggled to achieve high-productivity growth. On the positive side, there is the experience of Asian economies, such as Hong Kong SAR, Japan, the Republic of Korea, Singapore, and Taiwan Province of China, which focused on export-led growth and provided a blueprint for proponents of IPs. The divergent paths of Latin American and Asian economies have led many observers to stress the importance of design. For example, Cherif and Hasanov (2019) contrast the limitations of IPs focused on the development of domestic markets through import protection, which was the Latin American model, with the virtuous cycle fostered by the export-driven IP model pursued in Asia.
Another theme is that if one compares broad-based “structural” policies to more targeted “industrial” policies, the structural policies often have substantially larger and longer-lasting effects. Indeed, structural reforms may be a precondition for industrial policies to succeed. the IMF economists write:
Structural reforms have, on average, much larger effects than IPs [industrial policies], pointing to their fundamental role. IPs are accompanied by smaller economic benefits than “horizontal policies” focused on lowering corruption, improving governance and enhancing access to credit. Even when IPs may be desirable, horizontal policies are key. IPs are more effective in countries with better institutions, business environment, and financial market conditions, and a more educated workforce. Good institutions limit the capture of IPs by interest groups and facilitate their successful implementation. A strong business environment eases the flow of factors of production to targeted, fast-growing, firms, and pushes them to remain competitive. Efficient financial markets allow targeted firms to get a double boost to unlocking their potential, as IP support can be combined with private credit to seize profitable projects.
Industrial policies that reduce trade barriers tend to produce greater gains than import tariffs that limit trade. The IMF discussion note says:
Relatedly, trade-liberalizing IPs—those that reduce trade restrictions—are associated with higher firm productivity and value added in the medium term, with negligible change in the stock of capital. … An additional liberalizing policy is associated with improved medium-term performance of firms: 1.6 percent higher productivity, 1.2 percent higher value added, 0.8 percent more payroll (a proxy for wages and employment), and 0.4 percent more capital stock although the latter is not statistically significant (Figure 8, panel 3). The positive association between liberalizing trade conditions and firm productivity and value added relates to a long-standing literature on how lower trade barriers can strengthen competition in the liberalized sectors, inducing firms to leverage economies of scale, improve efficiency, and innovate (Helpman and Krugman 1985; Melitz 2003, Aghion and others 2005). Differently from export incentives and domestic subsidies, which are targeted in nature, liberalizing trade barriers yield a uniform impact across firms within the targeted sector. The results are in line with the finding that industrial subsidies targeting high-externality sectors yield smaller welfare gains compared to trade liberalizing measures such as broad-based tariff reductions (Bartelme and others 2019).
Lowering import barriers also favors technological transfers in the medium and long term. Although well-targeted protectionist IPs may temporarily boost received technological transfers, lifting trade-restricting policies unlocks larger and potentially longer improvements. Lifting an additional import barrier increases the number of received patent applications by 5 percent on average after four years …
Finally, industrial policies aren’t free, but impose both monetary and nonmonetary costs. The IMF economists write:
On the fiscal front, IP [industrial policy] expenditures in the 2019–21 period in a sample of OECD countries amounted to about 1.4 percent of GDP (Criscuolo and others 2023). Thus, in the context of high debt levels, IPs can limit governments’ ability to save and/or redeploy resources to tackle other challenges. IPs can also affect sectors or firms that are not targeted, through the reallocation of sales or resources to the supported entities. This action may not be welfare enhancing if IPs are not well targeted and this reallocation harms more productive sectors or firms. Moreover, the current geoeconomic landscape adds to the complexity. IPs can lead to cross-border spillovers, raising the risk of retaliation by other countries, which can ultimately weaken the multilateral trading system and worsen geoeconomic fragmentation. This, in turn, can also limit global welfare by stifling innovation incentives and the flow of new technologies across countries.
From my own perspective, arguments in favor of industrial policy often devolve into a list of injustices purportedly faced by US firms in global markets. The injustices sometimes seem real to me; other times, not so much. Also, I am perplexed by anyone who expects global markets to reflect concerns of justice–especiallly if these concerns somehow lead to cutting off support for the trade dispute resolution mechanisms of the World Trade Organization.
But perhaps more to the point, the arguments over what is “fair” in global trade often seem to me a way of blaming others while failing to tackle domestic policy issues. For example, the US economy has real challenges with K-12 education and worker training, with its oversized budget deficits and growing levels of government debt, with keeping its research and development at the cutting edge of technological progress, with rules and regulations that give small pressure groups a way to stifle both good and arguably harmful development, and with other issues as well.
It’s crystal-clear that decision-making by algorithm can be imperfect. It’s also crystal-clear that decision-making by humans can be pretty imperfect, too. However, the imperfections across these two types of decisions are probably not the same. How and when should society make use of decision-making by algorthm? Cass R. Sunstein provides a thoughtful overview in “The use of algorithms in society” (Review of Austrian Economics, December 2024, 37: 399–420).
Sunstein emphasizes two considerable advantages of decision-making by algorithm: it can avoid bias and noisiness. Bias arises when a certain judge or doctor is overly influenced by certain factors, like whether they saw what seemed like a similar case recently. Noisiness is just the human attribute of inconsistency, where decision rules might be applied different ways if the weather is good or bad, or before or after lunch. (For an overview of decision-making in bail cases, with reference to bias, noisiness, and a possible role for algorithms, a useful starting point is “The US Pretrial System: Balancing Individual Rights and Public Interests,” by Crystal S. Yang and Will Dobbie, in the Fall 2021 issue of the Journal of Economic Perspectives, where I work as Managing Editor.)
Here, I want to focus on some of the reservations that Sunstein summarizes about algorithmic decision-making–some of which might be overcome with better systems over time, some perhaps not.
1) Even if the algorithm does a better job than most humans, some humans will do better than the algorithm. Sunstein writes:
Some important work suggests that while algorithms outperform 90% of human judges in the context of bail decisions, the top 10% of judges outperform algorithms. The reason appears to be that the best judges have and use private information to make better decisions. They consider factors that algorithms do not. They appear to have something like local knowledge – an understanding of the defendant or the circumstances that algorithms lack. We could easily imagine a similar finding for doctors. It is possible that the best doctors know whom to test for heart disease, because they see something, or intuit something, that algorithms do not consider.
2) The gain from using algorithms in many contexts is relatively small in percentage terms, although a small percentage gain applied to a large number of people can certainly be meaningful. Sunstein writes:
As I have noted, algorithms do better than people do, but they do not do spectacularly better. The impressive aggregate figures, in terms of welfare gains, come from the fact that very large populations are involved. If algorithms show a modest percentage increase in accuracy as compared with human beings, we might find seemingly major improvements. If an algorithm can produce a slight increase in the accuracy of screening for heart disease, we might see a significant reduction in deaths. But a slight increase in accuracy remains slight. I have said, for example, that formulas do better than clinicians. But in the median study, formulas are right 73% of the time, while clinicians are right 68% of the time.
3) People are more unforgiving of algorithmic error than of human error. Sunstein:
In short, people are less forgiving of algorithms than they are of human beings. … Is that rational? If people want to make the correct decision, it is not. If their goal is to make money or to improve their health, they should rely on the better decider. But one more time: if people enjoy making decisions, a preference for making one’s own decisions might be perfectly rational. Perhaps people find the relevant decisions fun to make. Perhaps they like learning. Perhaps decision-making is a kind of game. Perhaps they like the feeling of responsibility. Perhaps they like the actuality of responsibility. If so, algorithm aversion is no mistake at all.
4) Greater complexity will limit the benefit of an algorithm. For example, the algorithm behind a dating app might offer recommendations that are slightly more likely to be successful than those who went unrecommended–but certainly no guarantee of true love. Sunstein tells an interesting story of a prediction competition called the Fragile Families Challenge., which teaches humility about the predictions of algorithms.
There’s a dataset called the Fragile Families and Child Wellbeing Study. It collected data on thousands of families with unmarried parents, where the mother gave birth to a child around the year 2000. The study collected a LOT of data about these families at the time of birth, and then followed up at ages 1, 3, 5, and 9. The challenge was whether this earlier data could be used to predict certain outcomes about the child or the family when the child turned 15. Hundreds of analysts applied to this contest, and 160 teams were selected. As it turned out, many of the predictions were similar to each other, but literally none of them were very accurate. Sunstein writes:
The central question was simple: Which of the 160 teams would make good predictions? The answer is: None of them. True, the machine-learning algorithms were better than random; they were not horrible. But they were not a lot better than random, and for single-event outcomes – such as whether the primary caregiver had been laid off or had been in job training – they were only slightly better than random. The researchers conclude that “low predictive accuracy cannot easily be attributed to the limitations of any particular researcher or approach; hundreds of researchers attempted the task, and none could predict accurately.” Notwithstanding their diverse methods, the 160 teams produced predictions that were pretty close to one another – and not so good. As the researchers put it, “the submissions were much better at predicting each other than at predicting the truth.” A reasonable lesson is that we really do not understand the relationship between where families are in one year and where they will be a few years hence. … You can learn a great deal about where someone now is in life, and still, you might not be able to say very much at all about specific outcomes in the future.
5) Algorithms are not good, and perhaps cannot be good, at what are sometimes called path-dependent events. The question when a certain political movement will rise or fall, or whether a certain musical act or movie will become popular, will rely on the unfolding of a chain of events. Algorithm rely on patterns from the past events, and may not be good at predicting the timing or probability of future chains of events.
The challenge is consider both the benefits and tradeoffs of algorithms in different settings. If someone I love is facing a bail decision, and I don’t know who the judge will be, I personally would prefer that an algorithm make the decision. When it comes to the algorithms that govern self-driving cars, many people are clearly much less forgiving of errors and accidents caused by an algorithm than they are of errors and accidents of human drivers. In many contexts, from love to future possibilities, the guidance from following an algorithm may be positive, but quite small.
To me, algorithms are always interesting because they specify reasons for an underlying decision. Sometimes the reasons will expose bias and noise in human decision-making; sometimes the algorithm itself can display bias. But when you know the reasons, you can evaluate the decision more clearly.
For those not familiar with the work of Henry George (1839-1897), he is best-known today for the thesis of a book called Progress and Poverty, which after its original publication in 1879 became a best-seller in the late 1880s and into the 1890s. He argued for a land tax as a practical method of financing government in a way that would also be more fair and efficient.
For a modern take, there is a Henry George Foundation today, and its website offers an overview of a proposal for a land tax in the United Kingdom: “LVT [land value taxation] is an annual, nationally determined, nationally collected, percentage tax, paid by the freeholder, on the open market value of all land with no exceptions.” Notice that a land value tax is not identical to a property tax—which includes both the value land but also the value of the housing or commercial buildings, or land improved in various ways (say, for agriculture or recreation purposes). Thus, a land value tax does not rise when you build something on a given property; conversely, a plot of land with nothing built on it, right next to a similar plot of land with a house or factory built on it, would be taxed the same amount.
In the late 19th century, when George was writing, very large amounts of land in the United Kingdom were owned by those who were noble or rich or both. They could block this land from being developed, and thus limit the ability of towns to expand, for either housing or industry. By taxing what that undeveloped land would sell for on the open market, there would be an incentive to sell off some of that land. Moreover, if the government built, say, a railroad link through a certain area, then the value of the undeveloped land close to the railway would rise–thus providing an even greater incentive to sell off some of the land.
A land tax would work somewhat differently today, of course. But one can imagine a situation where a suburb has very restrictive zoning–say, one house per acre. However, if it was possible to develop that land with, say 16 small houses or an apartment building on that acre, that property could be be taxed on what the underlying land was worth–not just on the value of the single home on the property.
I will not try here to argue the case for and against a land tax in any detail. Instead, I’ll point to an historical episode that illustrates some of its practical difficulties. Samuel Watling tells the story in “The failure of the land value tax” (Works in Progress, Issue 18, March 13, 2025).
In the UK circa 1900 , the national government was primarily funded by an income tax, which paid for the military and the civil service. About one-quarter of the income tax revenue was passed along to local governments, which were responsible for “poverty relief, the police, education, and sanitation.” In what we would today call “unfunded mandates,” the central government has passed laws requiring that the local governments provide certain levels of poverty relief, police, education (for ages 5-12) and sanitation, but without sufficient funding to do so.
Local UK governments of this time had property taxes available to them as an option. A main use of property was renting the land, either for housing or business. Thus, a tax on property was largely a tax on rental income: according to Watling, “three quarters of funding for local government activities – poverty relief, the police, education, and sanitation – came from taxing rental income. Since urban rents added up to about 10 percent of GDP at the time, this meant that one tenth of the economy was responsible for financing almost the entirety of local authority budgets.” As one would expect, a tax on rental income is largely passed along to the renters. For well-to-do cities, these taxes on rental income raised enough money for the public services they were obligated to provide. For poorer cities, with greater needs for welfare spending and less valuable property to tax, the situation was more difficult.
In the predictions of Henry George, a land tax could address these issues, and in a sweeping way. Watling explains:
But these marginal improvements – reducing the disincentive to improve land and providing more funds for urban councils – were only part of the reason Liberal Georgists favored land value taxation. George had promised his followers nothing short of Utopia. George argued that since all production needs land, competition would push labor and capital returns down to minimum levels, leaving all remaining economic surplus to accumulate as land rent. Therefore, he concluded taxing land rent alone could fund all government activities since it captured society’s total surplus value. Intercepting this entire social surplus with the land value tax, he argued, would provide not just all the money the government needed but enough to end poverty and create a harmonious society in which all humans could fully satisfy their innate needs and desires.
Events happened, as they do. Watling provides details. For my purpose, the key fact is that the Liberal Party ended up enacting a set of land taxes in 1911, and set about the task of placing a value not on a given property–which could be valued based on the rent paid or by comparison with similar properties nearby–but only on the land. For properties that provided revenue via mining, this calcualation was reasonably straightforward. But a land tax based on what the land would be worth, if it was developed more fully or at all, was a harder calculation. Watling again:
There were close to ten million properties in the country that needed valuing, and for the majority of these properties, the land and structure had been traded together, meaning that there was no distinct market valuation of land to draw from. What’s more, in line with Georgist theory, the tax was supposed to credit owners for improvements they made to the land. But this meant calculating several hypotheticals, many of which had never been measured or recorded, including building and structure value and value contributed from plumbing, access to railways, and other infrastructure contributions. The process was beyond the capacity of the government. In August 1910, the Liberals sent out 10.5 million copies of the notorious ‘Form 4’, which required owners to submit specific details on their income and the use and tenure of their properties. It also required them to estimate the site value themselves. Failure to return the document carried a fine of £50, about £7,500 in current prices.
The pushback was extreme. There were lawsuits galore, until one of the land taxes (there were several) was invalidated entirely. Setting aside the tax complianc costs being imposed on landowners, the costs to government of implementing the tax were considerably greater than the additional revenue raissed. Moreover, although the promise of a land tax was that it would encourage use of underutilized land, it instead imposed immediate costs on underutilized land, so that owners and potential builders of that land lacked funding for construction. Rates of building dropped, rather than rising.
Perhaps all of this could have been worked out over time, but events contineud to happen, as they do–in this case, World War I. By the time the Great War was over, the Liberal party had lost its appetite for pursuing land taxes further, and abolished them in 1922. The difficulties for local governments across the UK to raise funding for their required activities continued–and in some ways continue to the present day. For the land tax in particular, Watling notes:
[T]he pure land value tax is chimerical. Those countries that raise substantial amounts of tax from land, such as Japan and the USA, do so through taxes on property … A pure tax on the unimproved value of land has never been successfully implemented anywhere. Land value taxes introduced in Australia and New Zealand have been repealed. Denmark’s land value tax is a minor quirk of the system, comprising less than two percent of total revenues.
Henry George was writing in the context of his time and place–the United Kingdom at the tail end of the 1800s. One can argue that a pure land tax could have been a sensible approach in that time and place. In other times and places–and with the property tax as a proven and practical alternative–it’s harder to make the case.
The “multipolar” world has been a reasonably popular framework for thinking about the global economy. The idea was that the world economy was sorting itself into three geographically defined regions: a North American group, a European group, and an East Asian group. Each group of countries had a combination of traits: a sufficient amount of industry and technological leadership in many areas, a mix of skilled and unskilled labor, access to natural resources. In addition, the three regions had the advantages of geographic proximity, and relatively free trade within the area, so that goods and services could flow back and forth with some ease.
From this perspective, a main concern with President Trump’s threats to set off a trade war with Canada and Mexico is that it threatens to fracture the North American bloc. However, Germany and western Europe will remain at the heart of the European bloc, while a combination of China, Japan, and Korea will remain at the heart of the Asia bloc.
After all, President Trump negotiated and signed the United States-Mexico-Canada Agreement (USMCA) in 2020 during his first term, to address his concerns about the earlier North American Free Trade Agreement. It’s worth quoting some of Trump’s comments during the signing ceremony in 2020:
The USMCA is the largest, fairest, most balanced, and modern trade agreement ever achieved. There’s never been anything like it. Other countries are now looking at it, but there can’t be a border like that because, believe it or not, that is by far the biggest border anywhere in the world, in terms of economy, in terms of people. There’s nothing even close.
This is a colossal victory for our farmers, ranchers, energy workers, factory workers, and American workers in all 50 states … The USMCA is estimated to add another 1.2 percent to our GDP and create countless new American jobs. It will make our blue-collar boom — which is beyond anybody’s expectation — even bigger, stronger, and more extraordinary, delivering massive gains for the loyal citizens of our nation.
For the first time in American history, we have replaced a disastrous trade deal that rewarded outsourcing with a truly fair and reciprocal trade deal that will keep jobs, wealth, and growth right here in America. And, in a true sense, it’s also a partnership with Mexico and Canada and ourselves against the world. It’s really a trade partnership, if you look at it that way. And it’s a day of great celebration in all three countries.
For some perspective on what can be lost by fracturing the North American trading bloc, the Brookings Institution has published USMCA Forward 2025, a collection of seven essays and additional short comments about some of the positive gains from Trump’s trade agreement. As Joshua P. Meltzer and Brahima Sangafowa Coulibaly point out in their introduction to this volume, U.S. exports to Mexico and Canada have increased by 46% since the USMCA agreement was signed in 2020.
This set of essays focuses on a few key areas of interest for the North American Trade bloc. One is critical minerals. Meltzer and Coulibaly note:
Critical minerals and rare earths are key inputs into the production of many technologies, such as batteries, mobile phones, and semiconductors and needed for defense purposes. … The challenge for North America is the heavy dependence on many of these minerals from China, particularly when it comes to processing. The Trump administration has also made secure supply chains a focus, and this will require addressing the heavy reliance on China for critical minerals. China’s recent announcement that it will restrict exports of various critical minerals to the U.S. in response to U.S. tariffs further underscores the strategic need for the U.S. to reduce this dependency. … [T]he U.S. is 100% reliant on imports of 16 critical minerals such as graphite and more than 50% reliant on imports for another 29 critical minerals, including rare earths, zinc, and nickel. About 40% of U.S. import of critical minerals come from Canada and Mexico. Moreover, the U.S., Canada, and Mexico have largely complimentary resources, meaning that U.S. support for the development of critical minerals and rare earths in Canada and Mexico does not compete with U.S. production but can replace existing dependencies on China.
In short, if the US is going to be a world leader in technologies like batteries, mobile phones, and semiconductors, it needs easy access to minerals across the North American trade bloc. Moreover, if US manufacturing in these and other areas is to set the standard for global productivity, the US business sector needs to be able to locate different pieces of the goods and services supply chain across the US, Canada, and Mexico in ways that can boost efficiency.
I was pleased back in 2020 by the passage of the USMCA, since it seemed to assuage President Trump’s worries about the NAFTA agreement, while still supporting the North American trading bloc. Import tariffs aimed at China at least have the rationale of being aimed at a country where the US is involved with economic and geopolitical competition. But in Trump’s words from 2020, USMCA is “a partnership with Mexico and Canada and ourselves against the world” and “a truly fair and reciprocal trade deal that will keep jobs, wealth, and growth right here in America.”
