Question: Over the past several decades, the inflation-adjusted price of healthcare has increased. Based on this information alone, can you infer the source of the higher price–lower supply or higher demand? If not, what additional data would you need to determine whether higher prices are being driven by changes in supply or demand? Solution: I use this question, and others like it, in my principles of microeconomics class to emphasize a central lesson: you should never reason from a price change alone. As Scott Sumner has emphasized repeatedly, higher prices can result from an increase in demand, a decrease in supply, or some combination of the two. Observing a price change by itself is therefore not enough to identify the underlying cause. To determine why prices have changed, we must also examine what happened to quantity. Before turning to the analysis, it is useful to clarify the framework. In what follows, healthcare is treated as a composite good. While this introduces some measurement complications, it is a standard and appropriate simplification for a principles-level analysis. Likewise, the quality of healthcare has improved substantially over time. This does not undermine the supply-and-demand approach. Improvements in quality affect production costs and consumers’ willingness to pay, and therefore operate through shifts in supply, demand, or both. As a result, changes in quality can be incorporated within the same price-quantity framework used here. Suppose, for example, that we observe both the price of healthcare and the quantity of healthcare consumed rising over time. In this case, the data indicate that higher prices are driven primarily by an increase in demand. Importantly, this conclusion does not require that supply has remained constant. Rather, it reflects the fact that any supply-side changes were dominated by a sufficiently large outward shift in demand, resulting in a higher equilibrium price and quantity. By contrast, if we observed that the price of healthcare was rising while the quantity consumed was falling over time, we could conclude that higher prices were driven primarily by a contraction in supply. This reasoning follows directly from the logic of supply and demand, which treats each observed price-quantity pair as an equilibrium outcome. At any point in time, the market price and quantity reflect the intersection of the prevailing supply and demand curves. When we compare outcomes across time, we are therefore comparing different equilibria generated by shifts in supply, demand, or both. Observing how price and quantity move together across equilibria allows us to infer which shift was dominant, even though we do not directly observe the underlying curves themselves. This is why changes in prices must be interpreted alongside changes in quantities: together, they reveal the direction of the forces reshaping the market. Note that we do not need to identify the specific underlying factors—such as demographics, regulation, preferences, or technology—before drawing conclusions about whether supply or demand has changed. While these factors are important for explaining why supply or demand shifts, they are not necessary for identifying which side of the market shifted. In supply-and-demand analysis, such factors matter only insofar as they shift the supply curve, the demand curve, or both. By observing how equilibrium price and quantity change, we can infer whether demand or supply was the dominant force, even without knowing the precise source of the shift. In short, price and quantity data identify the direction of the change, while information about underlying determinants explains its cause. It is also important to emphasize that total spending—price times quantity—on healthcare cannot tell us whether higher prices are due to changes in supply or demand. An increase in demand would raise total spending, since both price and quantity increase. However, a decrease in the supply of healthcare could also raise total spending if demand is relatively inelastic, because price may rise by more than quantity falls. For this reason, total spending on healthcare does not allow us to identify the underlying source of higher prices. In short, a rise in the inflation-adjusted price of healthcare, by itself, does not tell us whether demand or supply is responsible. To identify the dominant force, we must examine how quantity changed alongside price. (0 COMMENTS)

Psychologist Gerd Gigerenzer explains the power of intuition, how intuition became gendered, what he thinks Kahneman and Tversky’s research agenda got wrong, and why it’s a mistake to place intuition and conscious thinking on opposing ends of the cognition spectrum. Topics he discusses in this wide-ranging conversation with EconTalk’s Russ Roberts include what Gigerenzer calls […] The post In Defense of Intuition (with Gerd Gigerenzer) appeared first on Econlib.

All major American professional sports have a time of year when they capture the eyes of the nation. America’s pastime, baseball, has the ‘Fall Classic’, the NFL dominates Thanksgiving, and the country has an entire weekend dedicated to the Super Bowl. Christmas Day is the NBA’s time to shine with action from noon to midnight (though the NFL tries to get in on the action). When Americans tune in to watch Lebron James and Kevin Durant battle it out on the court, they usually aren’t thinking about the referees, but it’s impossible to play without them.  The creation and enforcement of rules within sports is often overlooked despite their significance in determining the outcome of games and championships, at least until the referees make the wrong calls. In a 2010 World Cup knockout round match, English midfielder Frank Lampard shot a missile that rocketed against the crossbar and beat German goalkeeper Manuel Neuer. Nearly everyone in the stadium was sure that the match had been equalized 2-2. Everyone except the referees. Images in real time showed that Lampard’s shot had indeed crossed the goal line, but nevertheless, the goal was not counted. Germany went on to win 4-1, sparking a conversation among fans around the world about refereeing in the beautiful game, and resulting in the implementation of goal-line technology soon after. Nearly a decade later, the English Premier League instituted Video Assistant Refereeing, or VAR, out of a similar ambition to reduce the human error of refereeing and increase fairness in the game. However, in the six seasons since its introduction, VAR has produced more controversy than it has solved. How could that be? Don’t fans want more correct decisions?  Maybe not, says Daisy Christodoulou, author of the book I Can’t Stop Thinking about VAR, and guest of the February 2025 EconTalk episode, Coase, the Rules of the Game, and the Costs of Perfection. Christodoulou and host Russ Roberts apply economic theory to understand why the desire for perfection often leads to unsatisfactory results, why continuums are often more helpful than categories, and how comparative judgment can improve consistent rulemaking that accommodates individual preferences As the title suggests, the Coase theorem is a core theme of this episode. The Coase theorem states that in certain instances, individuals can resolve disputes involving externalities more efficiently than a governing body. This is partially because the attempt at making perfect rules that supposedly “solve” externalities leaves little room for the complexity of individual situations. However, Russ Roberts argues that rulemaking techniques like VAR can muddy the waters of refereeing, needlessly overcomplicating disputes that can be resolved through common sense. In his words, “We all know what a goal is. We all know what a handball is…and, yet once we get down to these details of making sure…somehow it gets harder.” Christodoulou agrees, adding that although VAR was brought in to make refereeing decisions clearer, it has thus far primarily served as a force of confusion and frustration. She finds that through the implementation of VAR, English football has smashed a top-down rulemaking structure on top of a once bottom-up process, and this has led to the conflict of accuracy with other preferences of fans. There is bound to be a trade-off between the accuracy of refereeing and the excitement of a game. Agonizingly long video review sessions may produce the correct result, but oftentimes they reduce the electricity of scoring a goal.  Christodoulou believes this is representative of the trade-off between consistency and common sense. Using the justice system as an example, Christodoulou argues that people often value a certain level of discretion in enforcing and interpreting the law. However, discretion inevitably leads to inconsistencies, accusations of bias, and potential injustices. In other words, beating tradeoffs is impossible, and trying to do so often results in the worst of both worlds.  This has been the result with VAR: Overcomplicated rules, applied inconsistently.  Christodoulou states that the handball rule increased from 11 words to 121 words since the institution of VAR, and yet what’s deemed a handball differs widely even from minute to minute within the same match. Christodoulou believes the handball rule misidentifies a continuous variable as a categorical one. Categorical variables describe concepts that are mutually exclusive, while continuous variables exist on a spectrum. Many decisions that referees make during matches are categorical: an incident is either a foul or it is not. The problem is when the line between two is blurry. For example, it’s exceedingly difficult to describe what is and what is not a handball in plain language. Indeed, many aspects of everyday life are almost indescribable through language itself. But this does not mean that it is impossible to determine what is or isn’t a handball. Christodoulou argues this can be done through comparative judgment and tacit knowledge. Christodoulou uses the example of grading papers to illustrate. make this point. It is far easier to decide which piece of two is better than the other, as opposed to deciding how good a piece of writing is on its own. Determinations of quality within a vacuum vary wildly both between individuals and, crucially, within the same individual. Comparisons, on the other hand, can be combined to create a quality distribution. According to Christodoulou, grading through comparative judgment leads to more agreement and consistency than grading based on a rubric. “So, you have this weird paradox, in that what feels like an incredibly subjective method of assessment, the data shows it is actually really quite objective. And the flip side is true: That when you have this very objective measure–seemingly very objective measure of assessment—which has all these tick lists, and you can say, ‘Does it feature this? Does this piece of writing feature that? Does it feature this?’ But when you crunch the numbers, people do not agree at all….it’s actually very subjective.” To apply this insight to refereeing, first, technology can create a collection of potential handballs, for instance. Then crowdsourcing can be used to prompt fans, players, and referees to decide which of two clips in this dataset is more of a handball. This, done repeatedly, creates a distribution for judging handballs. Here, Christodoulou suggests that AI can be trained to recognize patterns from examples that were considered handballs that can be relayed to referees during handball disputes. Referees would then determine whether the instance in question lies above or below the set line of handball. As always, there are no solutions, only trade-offs. Perfect rules don’t exist. Attempts to impose them, top-down, result in unclear rules applied inconsistently—and unhappy fans (and players). Counterintuitive as it might seem, the key to encouraging goodwill towards referees is less likely to come from more oversight or attempts to override referee judgment than through recognizing the power of common sense and tacit knowledge.   Kevin Lavery is a graduate student in the M.S. in Economics program at Georgetown University. He holds dual Bachelor of Science degrees in Economic Analysis and Political Science from Western Carolina University. (0 COMMENTS)

With the holidays upon us, what could be better than Christmas movies? And Christmas songs? And Christmas movies with great Christmas songs, like “Silver and Gold” as sung by Burl Ives in Rudolph, the Red-Nosed Reindeer? And of course, there’s the profit-seeking entrepreneur-prospector, Yukon Cornelius, obsessed with finding silver and gold. If silver and gold are so great, why don’t we use them for everything? Silver conducts heat and electricity more efficiently than any other metal. Why don’t we wire our houses with it? Make electric stoves out of it? Or why don’t we do these things with gold? Gold is less conductive than silver or copper but has many other valuable properties: it is highly ductile and can be drawn into the thinnest wires, for example, and it does not corrode or tarnish. It’s also pretty, which is why Burl Ives sings, “Silver and gold/Mean so much more when I see/Silver and gold decorations/On every Christmas tree.” The case of conductivity illustrates the importance of prices and monetary calculation. IT also explains why most silver and gold decorations on Christmas trees are fake. The “best” metal to use for a particular task is context-dependent. A bit of noodling around with Google and ChatGPT reveals several drawbacks to using gold for connections that require frequent plugging and unplugging, for example. Gold is conductive, ductile, and doesn’t tarnish; however, it’s also a lot softer than metals like chromium, nickel, and tin. It may not be what you want if you’re making the plugs for USB-C cables or vacuum cleaners. Sometimes, I hear people object to the argument that raising labor costs with things like minimum wages and other regulations, because companies “will want to use the best technology they can.” But “best” depends on prices. Look at the computers people have in your workspace. Is there a top-of-the-line supercomputer on every desk? No, because you don’t need the Frontier system at Oak Ridge National Laboratory to check email or make spreadsheets–or, if you’re McDonald’s or Dollar General, to run self-checkout lines. It all comes down to a version of the Great Economic Problem. The market is a continuous conversation among thinking, feeling agents, not a computational problem. When Ludwig von Mises and F.A. Hayek explained the problems with economic calculation under socialism, they were making an epistemic argument: rational economic calculation—perhaps, unfortunately, defined and described—is not a problem that can be solved with a sufficiently powerful computer, even the Frontier system. Resources have alternative uses, and competing bids and offers in free markets enable us to use broadly dispersed knowledge as we allocate resources among those uses. The best metal to use to make an electrical connection depends on people’s ideas about how to make jewelry and cookware. If we find enough gold—say, through advances in asteroid mining and harvesting—then perhaps we can look forward to futures where robots cook our meals in pots and pans made of gold and silver (right before they decorate our trees with silver and gold decorations). (0 COMMENTS)

A world-class physicist makes a shocking claim: across 2,500 years and every kind of society, there has been a recurring moral exception carved out just for Jews–the idea that hurting Jews is, in some sense, legitimate. Most of the time, this doesn’t erupt into pogroms. Instead, it lives as a background permission: a readiness to excuse, […] The post David Deutsch on the Pattern appeared first on Econlib.

Every line trending upward, every drop in cost, every additional ounce of efficiency we can squeeze from a bundle of inputs is the product of deliberate effort—of thousands of workers, engineers, factory managers, and line supervisors redesigning products, rearranging factories, testing and exploring new ways to do things. —Brian Potter, The Origins of Efficiency (304) Economists look at productivity gains in the aggregate. Rather than examine where they come from, we simply utter the phrase “technological change.” Brian Potter’s recent book, The Origins of Efficiency, 1 takes a bottom-up approach to looking at productivity improvement. Potter describes various ways that firms lower the cost of production. His many historical examples serve to illustrate and clarify the analysis. Potter looks at production in terms of transforming inputs into outputs, in which the efficiency of the process depends on five factors: the transformation method; the production rate; the cost of inputs; the size of the buffer (work in process); the variability of the output. For example, in a bread bakery, the transformation method is the set of instructions for making a loaf of bread. The production rate is the number of loaves per hour. The cost of inputs is the cost of flour, yeast, sugar, salt, energy, labor, and so on. The work in process consists of loaves that have been formed but not yet put in the oven. If the loaves are allowed to rise for different amounts of time, this will cause variability in output. “Through theoretical study as well as trial and error, enough of the problems get solved, and a dominant design emerges that attracts many tinkerers who proceed to improve the technology.” Potter points out that new transformation methods tend to follow an S-curve of improvement. A new technique may seem promising, but it starts out with a low level of performance and progress is slow, because there are many problems that make it costly or impractical. Through theoretical study as well as trial and error, enough of the problems get solved, and a dominant design emerges that attracts many tinkerers who proceed to improve the technology. Progress then accelerates rapidly. Eventually, there are fewer remaining opportunities to wring improvement out of the technology, and productivity levels off. “An S-curve pattern means that early on, a new technology often performs significantly worse than an established technology along the most important measures of performance, even if its theoretical performance ceiling is much higher.” (50) Mechanization can dramatically lower costs. Potter uses the example of glass bulbs for electric lights being blown by machines rather than by humans. But he points out that humans have better ability to adjust to different conditions and to work with softer and more variable materials. “Successful mechanization has thus historically required reducing or otherwise limiting the amount of information processing that must be performed and the environmental variation that must be considered.” (70) One interesting source of efficiency is removing unnecessary steps in the production process. For example, in an assembly line, if you raise the conveyor belt, the workers will not have to bend and lift objects. Modern writers often use scare quotes to describe “scientific management” or “Taylorism,” creating the impression that time-and-motion studies were instruments of oppression aimed at individual workers. But from Potter I learned that time-and-motion studies were used to discover ways to improve manufacturing processes. Raising the height of the conveyor belt is an example of scientific management that is a win-win for workers and for the manufacturer. As I was reading The Origins of Efficiency, I saw many ways in which the analysis applies to the latest developments in artificial intelligence. For example, the release of ChatGPT attracted capital and inventors to similar models using neural networks and the “transformer” algorithm. We are now on the steep portion of the S-curve of improvement. Although ordinary machines lack flexibility and adaptability, artificial intelligence may enable machines to overcome this limitation. Self-driving cars are one example. The field of robotics could improve dramatically using AI. Today, a nurse or phlebotomist is needed in order to start an intravenous drip for a hospital patient. Perhaps by using AI, a robot could handle this task. Construction workers today rely on subtle knowledge and experience that is beyond the capacity of ordinary machines. But perhaps in the future AI-enabled robots could perform more tasks in construction. I can see ample opportunity for AI to eliminate unnecessary steps in the provision of goods and services. For example, a corporation does not need to design an elaborate menu on its web site. Instead, users can rely on an AI interface to find the information that they need. But in important lesson from Potter’s book is that applications of promising technologies are slow to develop. “Fixing one problem with a nascent technology tends to simply reveal more problems, so significant time and effort might be invested without any noticeable increase in performance.” (40) For more on these topics, see “Two Steves and One Soichiro: Why Politicians Can’t Judge Innovation,” by Michael Munger. Econlib, October 2, 2006. Matt Ridley on How Innovation Works. EconTalk. “Innovation,” by Timothy Sandefur. Concise Encyclopedia of Economics. As of this writing, early adopters of AI may be feeling this pain. The Origins of Efficiency is a book that defies easy summary. The many useful concepts and well-chosen illustrative examples give it a richness that is best appreciated by taking it in as a whole. Footnotes [1]Brian Potter (2025), The Origins of Efficiency. Stripe Press. *Arnold Kling has a Ph.D. in economics from the Massachusetts Institute of Technology. He is the author of several books, including Crisis of Abundance: Rethinking How We Pay for Health Care; Invisible Wealth: The Hidden Story of How Markets Work; Unchecked and Unbalanced: How the Discrepancy Between Knowledge and Power Caused the Financial Crisis and Threatens Democracy; and Specialization and Trade: A Re-introduction to Economics. He contributed to EconLog from January 2003 through August 2012. Read more of what Arnold Kling’s been reading. For more book reviews and articles by Arnold Kling, see the Archive. As an Amazon Associate, Econlib earns from qualifying purchases. (0 COMMENTS)

Shoppers are filling their carts, both literally and digitally, with last-minute gifts. One tempting purchase, whether for gifting or for showing up in style at a holiday sweater party, is ultra-cheap clothing from Shein. Like many around the world, the French hunt for deals in December. During a recent interview with journalist Thomas Mahler, I learned that fast fashion has become a political flashpoint in France, the country known for haute couture. French lawmakers are considering measures aimed at threatening the economic viability of Shein, the Chinese company that dominates ultra-cheap clothing globally.  Millions of French consumers shop through Shein regularly. Mahler asked me: Can politicians persuade consumers to buy domestically-made clothes instead, in a country with a proud tradition in domestic fashion? My reply was that this dilemma extends beyond France. Wealthy countries do not manufacture much apparel at home. It’s cheaper to produce at scale in lower-income countries, and residents of rich nations rarely aspire to work in garment factories.  The French government’s proposed intervention is an “eco-penalty” on fast-fashion items, a tax that could eventually add €10 per garment. The purpose is to make French-made clothing more competitive while also discouraging the environmental excesses of disposable fashion. Fast fashion generates garbage. Trend cycles in cheap apparel last for only weeks instead of seasons. Shein adds many new items daily, while a traditional French fashion house releases only a few designs each year. Much of the clothing is so cheaply made that it’s worn only a few times before being thrown away. Even charities struggle to accept donated garments because of the flood of unwanted clothes. Discarded polyester shirts pile up in landfills at best, or pollute rivers and beaches at worst. Synthetic fibers shed microplastics. These are real externalities.  France’s approach thus combines modern environmentalism with a familiar protectionism. It may be politically easier to sell a tariff when it’s framed as discouraging “wasteful” consumption rather than only protecting domestic producers. Revealed preferences, meaning the preferences people demonstrate through their actual purchasing behavior rather than their stated ideals, show that consumers want affordability and variety. That puts them at odds with protectionist policymakers. When a Shein dress costs €15 and a French-made equivalent costs €100, even patriotic consumers face a hard trade-off. The price gap reflects not just labor costs, but supply-chain efficiencies, economies of scale, and a fundamentally different business model. Even if these measures succeeded in reducing the amount of clothing bought from Shien, would the French people take garment manufacturing jobs that are “brought back” to France? French youth unemployment hovers above 17%, but garment work doesn’t match the aspirations of an educated workforce. In the United States, the few apparel factories that remain largely employ recent immigrants. Is trying to rebuild a mid-20th-century industrial base like trying to resurrect typewriters? Nostalgia is not an economic strategy in a technologically advancing world. Furthermore, would robots soon “take” most jobs that could be done by French people today in garment manufacturing?  Mahler also asked me whether people could simply buy fewer clothes to help the environment. It’s an interesting question because we also see this dilemma with food in the rich world today. Calories were once expensive; now the binding constraint is waistlines, not income. Clothing has followed the same pattern. After the Multi-Fiber Arrangement ended in 2005, global textile trade boomed. For example, one Chinese city now produces more than 20 billion pairs of socks per year, which they can export at low prices. For many consumers, the price of clothes is not the main constraint on how many garments they purchase. The result has been the democratization of style and abundance. Reasonable people can debate the appropriate policy response. A Pigouvian tax on new garments to fund recycling or reduce waste, akin to a carbon tax, is worth considering. Better labeling, such as durability ratings, could help consumers make more informed decisions on how long garments will last so they can appropriately trade off price versus quality. Cultural norms are shifting such that some consumers brag about thrift-store finds rather than new purchases, somewhat reducing the flow of new clothes to landfills.  While addressing the excesses of cheap fashion, we should resist romanticizing the past. We should not return to a world in which only the rich could afford variety and comfort. Countries like Bangladesh and Vietnam have reduced poverty by joining global garment supply chains. Bangladesh’s GDP per capita was under $500 in 1998; today it is over $2,500.  Fast fashion is neither a triumph nor a catastrophe. It is the outcome of solving an important problem: how to clothe billions of people affordably. The French, along with many of us around the world, now face a more pleasant question: how much is enough once basic scarcity has been conquered? Someone from 1850, wearing his one patched coat, would be astonished to learn that we are debating whether people buy too many clothes. That we have the luxury to ask is evidence that, despite its problems, the system has delivered something extraordinary. (0 COMMENTS)

From the editors: We have an in-depth discussion about tariffs across two Liberty Fund sites today. EconLog contributor David Hebert has a piece on the consequences of America’s new, more protectionist trade policies on our sister site, Law and Liberty, this morning. This piece makes a good complement to today’s EconLog post by Jon Murphy, No Manufacturing Jolt from Tariffs. From Hebert’s piece: Who Really Pays the Tariff? Lynn’s central argument rests on a fundamental confusion between what economists refer to as the “legal incidence” and the “economic incidence” of a tax. Legally, because tariffs are a tax on imports, it is the US importers who must write the check to Customs and Border Protection. But this says nothing about who actually pays the tariff. For example, when landlords’ property taxes go up, who pays? The landlord will obviously write the check to the county assessor, but unless Lynn thinks that landlords are running charities, that cost gets passed on to tenants in the form of higher rent, less frequent maintenance, or fewer included benefits (utilities or access to designated parking, for example). The legal incidence falls on the landlord, but the economic incidence falls disproportionately on renters, i.e., young Americans already besieged by high housing costs. Tariffs work the same way. US Customs and Border Protection bills the American importer directly, which is the legal incidence of the tariff. But the economic burden gets distributed among American consumers, American importers, and foreign exporters, depending on the particulars of the individual markets. Read Hebert’s full essay here, and Jon Murphy’s EconLog post here.   (0 COMMENTS)

Writing on Facebook, AEI economist Mark Perry points to evidence that the tariffs imposed in April by the Trump Administration have not resulted in job creation for the manufacturing industry (Mark’s graph is recreated below for those of you who do not have access to Facebook. The solid red line indicates the day the tariffs were imposed). Prima facie, this decline indicates the tariffs are not working to “bring back” jobs.  Some folks have objected that this graph is deceptive. Truncating the Y-axis makes the decline appear worse than it is. This objection misses the point: the trend is negative. Trump et al predicted the trend would be positive; the tariffs were supposed to reverse this declining trend. They got the direction wrong. A more reasonable objection is that it takes time for manufacturers to hire. Even six months in, there’s no particular reason to think that manufacturing hiring would suddenly jump. What about future hiring trends? Fortunately, the Bureau of Labor Statistics publishes that data. The BLS publishes their Job Openings and Labor Turnover Survey (JOLTS). Job Openings (series IDJTS300000000000000JOL) would be an indicator of future hiring as firms need to post their jobs before they can hire people. Since the tariffs were imposed, job openings are down, too. Excluding the post-pandemic recovery years (2021–2022, when openings and hirings were unusually high as firms ramped back up after the lockdowns), manufacturing job openings averaged 543,000 job openings per month in 2023 and 2024. In other words, over the past two years, approximately half a million open manufacturing jobs were listed each month. These were not necessarily new job openings; they were just unfulfilled jobs. The BLS doesn’t track how long job postings are up. Since Trump’s inauguration and the initial tariffs declared in February, job openings have averaged 410,000. In other words, there are approximately 130,000 fewer job openings in manufacturing since the tariffs were imposed than before. The fewer openings indicate that firms are slowing their hiring, not increasing it. (Note: these data are seasonally-adjusted. In theory, there shouldn’t be seasonal swings.) As recently reported by the Wall Street Journal, China is merely shifting exports from the United States to other countries.  The US tariffs haven’t done much to reduce Chinese export share, and certainly aren’t doing anything to help increase US exports.  Indeed, these results indicate the “optimal tariff model,” invoked by certain members of this administration, such as Peter Navarro, to justify tariffs, likely doesn’t apply. The point here is that US tariffs do not seem to be improving US manufacturing in the global sphere. Do these data prove that tariffs are failing? Not necessarily. To do a causal analysis would require far more data, time, and research. But they are indicative that the tariffs are failing to accomplish their (oft-contradictory) goals. Why are the tariffs failing to accomplish these goals?  In theory, tariffs should shift jobs to the protected industries.  If these tariffs protect manufacturing, why aren’t jobs shifting there?   The argument for tariffs to protect manufacturing relies on an assumption that the imports are of final goods and that the protected country has tariff-free access to intermediate goods (the goods used in manufacturing).  In 21st-century America, that assumption doesn’t hold.  According to Dartmouth University trade economist Doug Irwin, approximately 75% of US imports are these intermediate goods (Free Trade Under Fire (5th ed) Table 1.1, pg 14).  This means that tariffs raise the cost of manufacturing in the US, and thus make US manufacturing less competitive against both foreign imports and in the global market.  As Irwin explains: “Any trade restriction that increases the price of an intermediate good raises the costs of production in downstream user industries with an adverse effect on employment in those industries. In other words, when domestic firms have to pay a premium on their productive inputs, particularly when they are competing with foreign rivals that do not pay those taxes, employment in those industries suffers” (ibid, pg 100, emphasis added). But let us steel-man and consider what other factors could cause a similar pattern: For one, these tariffs face legal challenges. As of this writing, a legal challenge to the tariffs has been argued before the Supreme Court (Trump v. VOS Selections, consolidated with Learning Resources v. Trump). These cases have been ongoing since the spring, with an initial challenge filed in April 2025 in the US Court of International Trade. Given the costs of hiring, firms may be hesitant to hire while this case is pending. It’s possible that a resolution in favor of Trump on tariffs could set off a hiring bonanza. A major problem I see with this argument: many of the plaintiffs in VOS are manufacturers themselves. It’d be odd that they are arguing against tariffs and, if the tariffs hold, they’ll just start hiring. There may be other reasonable claims out there, but I cannot think of any (share in the comments if you have thoughts). (0 COMMENTS)

Are we truly characters with agency, or are we just playing out our programming in the great video game of life? Contrary to those in his field who claim that free will is an illusion, neuroscientist Kevin Mitchell insists that we’re agents who wield our decision-making mechanism for our own purposes. Listen as the author […] The post Free Will Is Real (with Kevin Mitchell) appeared first on Econlib.