Conversable Economist

The US economy, like much of the rest of the world, is headed into the teeth of what economists sometimes bloodlessly call the “demographic transition:” that is, birthrates are falling as life expectancies continue to rise, so that the population is on average aging. Melissa A. Kearney and Luke Pardue have edited a four-paper collection of essays on the subject in Demographic Headwinds: The Economic Consequences of Lower Birth Rates and Longer Lives (Aspen Economic Strategy Group, February 2026).

Some of the consequences are relatively well-known, like the effects on the federal budget as the share of the receiving Social Security and Medicare is on the rise, as discusses in “Low Fertility and Fiscal Sustainability: The Effects of Past and Future Fertility Rates on the US Federal Budget Outlook,” by Lisa Dettline and Luke Pardue. Effects on budgets of state and local governments have been less discussed, but as Jeffrey Clemens points out in “Implications of Low Fertility and Declining Populations for the Operations of US State and Local Governments”: “Education accounts for a substantial portion of direct expenditure by both [state and local] layers of government, at 19 and 38 percent, respectively. For states, this spending is primarily on higher education, while local governments spend primarily on K–12 education.” As the number of children declines, one challenge will be to shrink the physical and employment footprint of the K-12 sector.

But here, I’ll focus on two other implications of the US demographic transition that seem to me even less discussed. One is the shift in the age of workers, and in particular, the ways in which a rising number of older workers has contributed to large gaps between older and younger workers. Nicola Bianchi and Matteo Paradisi discuss “The Age Divide in the American Workplace.” They write:

In the late 1970s, workers over 50 earned roughly 35 percent more per week than workers under 30. Over the following four decades, this gap widened steadily, reaching a peak in the early 2010s, when older workers earned about 55 percent more. It has narrowed somewhat in the last decade, as the oldest baby boomers have begun to retire and leave the very top of the wage distribution, but even in 2024, older workers still earned 47 percent more per week than younger workers on average. And because working lives are now longer, with many employees remaining in senior roles well into their late sixties, there is little reason to expect this pay gap to return to the more modest levels observed in the 1970s without further intervention from firms or policymakers.

There are two additional pieces of evidence that describe how lopsided the career outcomes of younger and older workers have become over the past five decades. First, … over time, younger workers have become considerably more likely than their 1970s counterparts to be in the bottom quarter of the wage distribution and less likely to be in the top quarter. For workers over 50, the pattern is broadly reversed. Relative to 1976, their probability of being in the bottom quartile has gradually fallen, reaching around 15–17 percent below its baseline level during the 2010s, while their probability of being in the top quartile has typically been between 5 and 15 percent higher.

Second, older workers have pulled further ahead of younger workers in access to desirable, higher-paying managerial jobs, as displayed in figure 4. In the mid-1970s, workers over 50 were about 5 percentage points more likely than workers under 30 to be employed in management occupations in the top quarter of the wage distribution. By 2024, this gap had widened to almost 8.3 percentage points. Over the same period, these higher-paying managerial roles grew from just over 5 percent to a little more than 7 percent of full-time private-sector jobs. Therefore, the inability of younger workers to capture a larger share of these positions was not simply due to a shrinking number of these career opportunities.

In conclusion, at a time when their numbers in the labor force were rising dramatically, older workers increasingly occupied higher-paying jobs and leadership positions. Younger workers, by contrast, faced declining representation at the top and limited access to managerial pathways. Bianchi and Paradisi (2024) show that these patterns are common to most high-income economies, rather than being unique to the United States.

My strong suspicion is that these patterns help to explain why so many young adults feel grim about the state of the US economy. They are, in fact, further behind their parents’ generation. Also, there is a considerable body of evidence that lower earnings earlier in life will continue to echo through a lifetime.

Another less-discussed aspect of the US demographic transition is a claim I’ve heard a few times that “at least lower population growth will reduce environmenal harms.” But this belief is not likely to be true, as Kevin Kuruc explains in “The Environmental Benefits of Low Fertility and Population Decline are Overstated“:

The discussion of impending population decline is often dismissed or minimized by arguments that downplay its urgency – or even welcome this development – because of the proposed environmental benefits. This paper argues that the environmental benefits of depopulation are far smaller than widely believed, and that complacency about population decline may be counterproductive to climate goals. First, there is a fundamental issue of timing mismatch. Demographic change unfolds over generations, while effective responses to emissions and environmental harm require immediate action. Second, effective climate strategies, such as carbon capture, require high fixed capital and labor costs. The smaller the economy, the larger the share of national income required to achieve climate goals. Beyond the climate, there is little evidence to suggest that increases in per-capita resource availability from depopulation would materially improve living standards, as modern natural-resource constraints on well-being are limited and declining. In contrast, sustainability depends on policy, human ingenuity, and fiscal capacity, none of which are aided by a shrinking and aging population.

Although I hear a lot about “stablecoins,” it seems at present that in the context of the US or world economy, they remain small in size, and in the context of usage, they remain primarily a tool for facilitating other crypto-asset transactions–rather than acting as a medium of exchange for everyday buying and selling.

The European Systemic Risk Board offers an an overview in “Crypto-assets and decentralised finance: Report on stablecoins, crypto-investment products and multifunction groups” (October 2025). A “stablecoin” is a kind of crypto-currency, run on a blockchain, which is more-or-less guaranteed to keep its value because the company issuing the stablecoins owns safe assets like US Treasury debt to provide backing for the currency.

Total issuance of stablecoins seems to be around $300 billion in early 2026. Here’s a graph showing the rise up through mid-2025. As you can see, the market has been dominated pretty much since its inception by two companies: Tether (USDT) and Circle (USDC). Essentially all of the stablecoin market is backed by US dollar assets.

Given that the value of stablecoin is, well, stable, they aren’t of much interest to speculative investors searching for high returns. Moreover, stablecoins are barely used for ordinary transactions. The ESRB writes (footnotes omitted):

Despite widespread attention, stablecoins continue to play only a limited role in the global payment landscape, although their presence is steadily expanding. According to Worldpay’s 2025 Global Payments Report, stablecoins accounted for just 0.2% of global e-commerce transaction value in 2024. While interest in initiatives such as PayPal’s USD stablecoin via Xoom is rising, stablecoin adoption as a means of payment remains limited. A 2024 BIS survey found that over half of central banks viewed stablecoin use in their jurisdictions as negligible, confined mainly to niche remittance and retail users

Given stablecoins are mostly used as a gateway to the rest of the crypto-asset universe: that is, they are used for buying and selling other crypto-assets. As the report notes: “Stablecoins are still primarily used as a bridge between fiat and crypto-asset trading and as providers of liquidity in decentralised finance and lending. They are essential to decentralised finance ecosystems such as decentralised exchanges and lending protocols, where they help users reduce their exposure to price volatility by providing a more stable settlement asset and are also used as collateral for loans. They are also key to fiat-crypto conversions on centralised platforms.”

Might something go wrong in the stablecoin world that causes problems for the rest of the financial ecosystem? The ESRB suggests some possibilities that deserve attention. For example, stablecoins are held anonymously. What happens if a company is sold for a payment in stablecoins, so that the new owner is not legally visible? What happens if the stablecoin company does not own US Treasury debt directly, but instead holds deposits in a regular bank–and what if those bank deposits are large enough that they aren’t covered by deposit insurance. For example, it turned out that Circle was holding large deposits in Silicon Valley Bank, when that bank cratered in 2023. What if stablecoins start draining deposits out of the banking system, so that banks are less able to make loans? On the other side, if stablecoins start to play a substantially larger role outside the crypto-asset world, then regulators will presumably step up and banks and other financial institutions will have a strong incentive to innovate with comparable products.

All in all, I don’t worry much about stablecoins. But then, I don’t play in the crypto sandbox, either.

Government debt isn’t just up in the US economy; it’s a common pattern around emerging market and developing economies, too. Thus, the most recent Global Economic Prospects report includes a special topics chapter focused on these countries in “Rebuilding Fiscal Space: The Case for Fiscal Rules” (World Bank, January 2026). Here’s a flavor:

At a time when global shocks have become more frequent and government debt among emerging market and developing economies (EMDEs) has climbed to a 55-year high, fiscal rules are an important policy tool for promoting fiscal discipline. More than half of EMDEs have at least one fiscal rule, up from about 15 percent in 2000. Fiscal rules are associated with improvements in budget balances that extend to the medium and long term. Among EMDEs, improvements in the cyclically adjusted primary balance (CAPB) peak five years after fiscal rules are adopted, reaching a cumulative 1.4 percentage points of trend GDP. The gains are more pronounced when institutions are strong and economic conditions are favorable at the time of adoption, and the use of a deficit rule is central to durable improvements. Fiscal rules are also associated with a greater likelihood of fiscal adjustment episodes—multiyear periods of improvement in the CAPB as a percent of trend GDP. During a fiscal adjustment episode, the CAPB in the typical EMDE improves by 1.6 percentage points of trend GDP per year. Fiscal rules with credible enforcement provisions are associated with a higher likelihood of expenditure-based adjustment. Further, fiscal rules need not be complex to be effective. Simple rule frameworks are associated with a higher likelihood of revenue-based adjustment

One of the obvious and intriguing questions here is whether countries that are already in weak economic shape, perhaps already with high levels of debt, are more likely to adopt fiscal rules. Or to put it another way, are fiscal rules a possible way of reducing the risk of high future debt? Or are they a response to already-high government debt?

Surprisingly to me, at least, about half the emerging market and developing economies that have adopted a fiscal rule did so when their existing level of debt was low, rather than high, and when their economies were weak, rather than strong. The effect of adopting a fiscal rule on future debt turns out to be larger for the already healthy economies with relatively little debt. It also turns out that about half of the countries adopting fiscal rules did so with slim rather than large parliamentary control–but this difference did not seem to affect the ability of the rule to reduce future deficits.

At the start of World War II, the US economy relied almost exclusively on imported rubber as the key material for making, among other things, tires for cars and airplanes. The dependency was well-known, but in April 1942, when Japan cut off the foreign supply, the US was unprepared. Synthetic rubber ended up being part of the answer, but the rest of the answer involved a lot of muddling through. Alexander J. Field tells the story in “The World War II US Rubber Famine” (Business History Review, Autumn 2025, 99: 365-390). Field writes:

There were simply no satisfactory substitutes for rubber in a variety of critical uses, particularly tire carcasses and treads, the ultimate end use of 70% of rubber inputs. The severe shortage of natural rubber that resulted adversely affected the ability of the US military to project force and contributed to the disappointing record of wartime manufacturing productivity. … During World War II, the United States never escaped the threat of running out of rubber, which stood as a sword of Damocles over the entire economic and military effort. In 1944 the country almost ran out of natural rubber and would have, had the war continued into 1946.

One fun fact: Apparently the original World War II plan had been to launch a counterattack across the English Channel in 1943, rather than the D-Day invasion of June 1944. But in 1943, the lack of rubber meant that there weren’t enough landing vehicles for a counterattack to work.

A second fun fact: During World War II, “The rubber famine led directly to the imposition of a 35-mph speed limit and nationwide gas rationing in a country that, in the aggregate, was awash in petroleum. The intent was not to save fuel but to reduce tread wear on the tires installed on the nation’s 27 million automobiles and 5 million trucks, almost all of which were otherwise forecast to be off the roads within two years. These restrictions made it more difficult for people to get to and from work, contributing to absenteeism, and impacted the distribution of products by truck.”

The rubber famine is of interest for its own sake. But in addition, it echoes modern concerns about the risks of excessive dependence on key imported products–and thus may offer some food for thought on those issues as well. There were three possibilities for dealing with the rubber famine: grow rubber domestically, build up a stockpile of imported rubber, or produce synthetic rubber. Here’s what happened with each one:

Efforts to grow rubber plants from the far East in the western hemisphere had not worked well in the 1920s, but there was a plant called guayule that was a promising alternative natural source of latex–substitutable for natural rubber in many applications, and better than rubber for some of them. In 1930, a then-obscure Army major named Dwight D. Eisenhower toured the guayule plantations of Mexico, and wrote in his report: “Should our sea communications with [Southeast Asia] be cut in an emergency, shortage of rubber in the United States would rapidly become acute.” Eisenhower proposed in his 1930 report a US program to subsidize guayule. but at the time, nothing happened. On March 5, 1942, the US passed the Emergency Rubber Act, sometimes known as the Guayule Act, to subsidize farming of guayule in the the United States. But since guayule plants take four years of growth before they mature and produce latex, the law made no contribution to the wartime effort.

The US made efforts to build up its rubber stockpile starting in 1939, as war loomed, but by 1942 the total stockpile was equal to about one year of use. One problem was that the powers-that-be in Washington, DC, insisted that imported rubber travel by ship from the far East to New York, by way of the Panama Canal, rather than offloading in San Francisco and having the rubber cross the US by train. Having ships go all the way to New York was slightly cheaper than trains, but as a result, the ships carrying rubber were unable to go back-and-forth across the Pacific as often–and growth of the rubber stockpile was limited as a result..

The powers-that-be in Washington viewed research efforts in synthetic rubber as an overly costly sop to petrochemical companies up through February 1941, and the created a program that ended up costing as much as the Manhattan Project to develop the atomic bomb. As it turned out, total production of synthetic rubber during World War II was roughly two years of wartime quantity demanded–or bout twice as much as the stock of natural rubber entering the war.

As Field writes, the synthetic rubber progrm has been “uncritically lionized” in most writing about World War II, as if it just burst into existence and solved the problems. But aas Field writes: “The eventual availability of synthetic rubber in quantity did not end the rubber famine. Synthetic had to be blended with natural in the manufacture of almost all products, and in some cases synthetic could not be used at all.”

Moreover, the synthetic rubber program depended on a gas called butadiene, could be produced from petroleum or from alcohol. Standard Oil and other big petroleum companies favored a petroleum feedstock, although alcohol-based plants were also started. Field writes:

The first butadiene plant was not completed until April 1943. All three of the big alcohol plants opened that year, and they produced far more than their rated annual capacity. Of the five petroleum-based butadiene plants, the three largest did not begin production until 1944, and consistently produced below their rated capacity. … Without alcohol-based butadiene, it is hard to see how D-Day could have gone forward in June 1944. In retrospect, it is not clear that petroleum-based butadiene was needed at all to win the war. Butadiene from isobutylene was cheaper in the long run because, even though plants using this input were more expensive to construct, required more complex engineering, and relied on untested processes, the feedstock (petroleum) was ultimately cheaper. Due to huge agricultural surpluses accumulated during the Depression, however, the opportunity cost of ethanol was far lower during the war years, an advantage augmented by the much lower capital requirements of the process using it to produce butadiene.

The US government ended up establishing the foundations for a commercially successful synthetic rubber industry in the postwar period, one using petroleum as the principal feedstock, as Standard [Oil] intended. Given that synthetic rubber would be needed during the war, it would have been cheaper and faster to have focused from the outset on ethanol as the feedstock. Standard Oil bears responsibility for the emphasis on petroleum. Whether petroleum or ethanol was to be the feedstock, however, construction on the butadiene plants should have started earlier …

If Eisenhower’s recommendation had been followed back in 1930, or if the US rubber stockpile had been built up more expeditiously, then synthetic rubber would not have been needed to win World War II. If production of synthetic rubber had been alcohol-based from the start, it could have ramped up more quickly. If more rubber had been available, then the war might have ended sooner if the D-Day invasion could have happened in 1943.

The broader lesson is that a serious discussion of critical imported materials should never be a last-minute, against-the-deadline affair. Moreover, a serious discussion will need to resist pressure from domestic companies with a financial interest in the choices made. The US and Allied forces muddled through the rubber famine during World War II, but it was a nearer thing than most people realize. Field notes: “In 1945 the War Production Board forecast that, in the event of an invasion of Japan, the US would simply run out of natural rubber in 1946. Among other consequences, the US would then have been unable to manufacture airplane tires.” That fact surely played a role in the decision to use the atomic bomb.

There are certainly examples where new technology has replaced jobs. The US had 350,000 telephone switchboard operators in 1950, and the job just went away. The tractor played a big role in reducing the number of US farmers in the first half of the 20th century. Almost every village of any size had a blacksmith though much of the 19th century, but with the rise of industrial production, there weren’t enough of them left to be counted as a separate employment category in the 1900 census. And now here we are with the new artificial intelligence tools, and warnings that all manner of jobs that use computers–across a wide array of industries–could be at risk.

It seems obvious to me that many jobs will change as new technologies appear. Many of the tasks involved in my own job, running an academic economics journal since the late 1980s, changed substantially with the arrival of the internet, for example. But the job itself didn’t go away; indeed, the internet probably made me better at my job. For example, it’s a lot easier for me to look up cited articles from my desk than it was to walk over into the library stacks to find the article–and so I check many more articles as a result.

Thus, a key issue here is the extent to which the new AI tools replace workers outright, like telephone switchboard operators, or whether they allow workers to be more productive and effect–or even create the possibility for new and previously unimagined jobs. Daron Acemoglu, David Autor, and Simon Johnson work through these distinctions in “Building pro-worker artificial intelligence” (Hamilton Project at the Brookings Institution, February 2026). They write: “We define pro-worker technologies—including AI—as technologies that make human skills and expertise more valuable by expanding worker capabilities.”

They emphasize a key point about AI tools: such tools may actually be more useful when collaborating with humans. They write:

A modern AI system can ingest drone imagery and soil sensor data from a farm’s every acre, the complete sensor logs from a building’s HVAC system, or the detailed vital signs of a single patient observed over many months to support workers making high-stakes decisions. Drawing on this pretraining, AI tools can think alongside workers, identify relevant context, generate well-informed responses to questions, and present lucid, well-structured data to support decisionmaking. This is collaboration.

You may object: “If AI can behave like an expert, can’t it simply replace experts, thus automating their expertise into irrelevance?” In some cases, the answer is yes. But in many more cases, we think the answer is no: AI will prove more effective at collaboration than
at automation. Precisely because AI is not rule-bound, it is less trustworthy as an autonomous actor than a conventional computer system, and more valuable as a collaborator (Narayanan and Kapoor 2025).

To be useful, an automation tool must deliver near-flawless performance almost all the time. You would not tolerate a spreadsheet that hallucinated values, a robotic surgeon that glitched-out during bypass surgery, an agentic investing tool that squandered your money while you were not paying attention, or an AI-powered vending machine that gave away PlayStations and stocked live fish at the behest of persuasive customers (Stern 2025). For most of these tasks, the stakes are too consequential and the decisions too nuanced to be fully delegated to an automatic system that acts on its own discretion. The AI needs human expertise.

A collaboration tool does not need to be anywhere close to infallible to be useful. A doctor with a stethoscope can better diagnose a patient than the same doctor without one, and a contractor can pitch a squarer house frame with a laser level than they could by eyeballing it. These tools do not need to work flawlessly, because they do not promise to replace the expertise of their user. They make experts better at what they do—and extend their expertise to places it could not go unassisted. Rather than making expertise unnecessary, they render expertise more valuable by extending its efficacy and scope. It is this complementarity between machine capacity and human expertise that we believe imbues AI with vast pro-worker potential.

The authors provide concrete examples of pro-worker uses of AI for teachers, electricians’ assistants, patent examiners and others. They point out that the use of AI-assisted hearing aids might enable some workers to expand their on-the-job capabilities. However, they also worry that economic incentives and business habits may tend to emphasize AI applications that substitute for current jobs, rather than complement them. Thus, they argue for the public sector to nudge the incentives for pro-worker AI tools where possible. One of their examples stuck with me:

Indeed, the public sector already heavily shapes the path of technology in health care and education. For example, the federal Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 dramatically accelerated electronic health record adoption in U.S. hospitals through financial incentives and penalties. Within less than a decade, the
United States went from approximately 10 percent of hospitals with electronic health records to near-universal adoption (Office of the National Coordinator for Health Information Technology 2017). In a similar vein, the federal schools and libraries universal support (E-Rate) program, established by the Telecommunications Act of 1996, provides ongoing subsidies to schools and libraries for Internet connectivity. As of 2021, 95 percent of U.S. public school classrooms had WiFi (Munson 2023).

They also point out that the US corporate tax code treats investment in machinery more favorably than investment in, say, worker training and skills. That policy difference could be at least equalized. In this and other ways, the future uses of AI are not purely determined by technological advance, but instead by the incentives and beliefs of economic players–firms that develop AI technologies, firms that use them, managers thinking about how work should evolve, the the willingness of workers to build new skills.

When thinking about what makes an economy flourish, many of us tend to focus on the success stories of innovation and growth. After all, success stories involve an element of risk, which means a chance of failure. When it’s more expensive to fail, then avoiding the risk of failure–by avoiding innovative but risky business choices–starts to make sense. Yann Coatanlem and Oliver Coste put some meat on the bones of this idea in “Cost of Failure, Disruptive Innovation and Targeted Flexicurity: More evidence supporting targeted reforms” (Institute for European Policymaking at Bocconi University Working Paper, November 2025).

The authors focus on the situation of a large company that took a business risk that did not work out, and wants to restructure significantly–which in turn involves the costs of laying off a large number of employees. They have data on 250 such restucturings across a number of European countries and the United States. For each major restructuring, they go through financial and government reports to estimate the costs of laying a worker off, expressed in terms of “months of average employee compensation.” Thus, the illustrative figures show that in Germany, each layoff cost 31 months of average employee compensation; in France, 38 months; in Italy, 52 months; and in Spain, 63 months.

In comparison, when a similar exercise is carried out for US firms, the average cost was 7 months of employee compensation.

Here’s a concrete US example:

In the United States, the responsiveness of tech companies to a technological shock such as the unexpected success of ChatGPT is quite staggering. OpenAI released ChatGPT to the public on 30 November 2022. This breakthrough AI solution reached 1 million users within five days and 100 million users within two months — taking even the most seasoned stakeholders in Silicon Valley by surprise. The industry’s reaction unfolded within weeks: On 18 January 2023, Microsoft announced 10,000 layoffs, representing 5% of its workforce. The restructuring plan was completed by end of March 2023. On 20 January 2023, Google followed with 12,000 layoffs, or 6% of its workforce. The U.S. based employees were notified individually (by email) on the same day. On 14 March 2023, Meta announced 10,000 additional layoffs — a second round following the 11,000 job cuts announced on 9 November 2022 — bringing the total reduction to approximately 25% of its workforce. Most of the restructuring plan was completed by May 2023. This kind of technological shock is by no means unique. It is a recurring pattern in the tech industry—seen with the advent of cloud computing, smartphones, social networks, e-commerce, mobile phones and the internet.

Agility in laying off also means agility in hiring. U.S. tech companies did not cut tens of thousands of engineering jobs to scale back investment—quite the opposite. These workforce reductions were aimed at reallocating resources to accelerate innovation where most promising. They immediately began hiring thousands of AI engineers and invested heavily in AI computing capacity. Meta provides a striking example: after reducing its workforce by 25% in just six months, as seen before, the company hired approximately 10,000 engineers and ramped up its investment in AI supercomputers from around $1 billion in 2022 to $20 billion in 2023, $37 billion in 2024, and a projected $65 billion in 2025. Microsoft and Google are committing even larger sums to AI infrastructure. … In practice, European Employment Protection Law (EPL) often makes such strategic shifts nearly impossible.

A broader discussion of this subject by Pieter Garicano was titled “Why Europe doesn’t have a Tesla” (Works in Progress, February 7, 2026). If you are fortunate enough to be hired as a worker for Volkswagen in Germany, you are essentially guaranteed a job for life–because the costs of laying off such workers are so very high. But the tradeoff is that no company is like to start a new auto manufacturing facility in Germany–not because the potential rewards are too low, but because the potential costs of failure and restructuring are so high. More broadly, if the costs of restructuring a large technology company will be very high, then spending the R&D money seek out new innovations and products will be less attractive as well.

So are we forced into a harsh choice between protecting workers from layoffs and economic dynamism? Perhaps the tradeoff need not be as severe as one might fear. In the Coatanlem and Coste study, they note that the costs of restructuring are at US levels, or even lower, in Denmark, Sweden, and Switzerland. The reason seems tied to a set of policies they call “flexicurity.” (Michael Svarer and Claus Thustrup Kreiner provide an overview of “Danish Flexicurity: Rights and Duties” in the Fall 2022 issue of the Journal of Economic Perspectives, where I work as Managing Editor).

The basic “flexicurity” idea is that the government provides a package of higher unemployment benefits combined with support for job search and job training–and if a displaced worker find an alternative job relatively soon, the higher unemployment benefits start to decline. To put it another way, a company that wishes to restructure bears a relatively modest share of the total costs of the adjustment, and the government steps in with policies and incentives to hasten the adjustment. These steps are also called “active labor market policies,” which differentiates them from “passive labor market policies” like just paying unemployment benefits for a time, and the US government has lagged far behind other high-income countries in such efforts.

24 Min

Europe’s cutting edge firms are falling far behind the American frontier because of restrictive labor laws.

being hired in a Volkswagen factory in Germany is a lifetime job

what if Volkswagen wants to shift to producing EV’s, and existing workers don’t have the skills?

When recessions hit, the US Federal Reserve lowers its target interest rate–the “federal funds interest rate.” This interest rate applies to extremely safe borrowing: essentially, to overnight borrowing by large and safe financial institutions. The idea is that by altering this ultra-safe interest rate, other riskier interest rates will also be under pressure to adjust, so the lower federal funds rate will be passed through by a country’s financial sector to lower rates for mortgage lending, car lending,and the like (as well as to lower interest rates paid to those with deposits in banks or money market funds).

But in the Great Recession and its aftermath, the Fed ran into a problem: it took the federal funds interest rate to just barely above zero percent. Here, it ran into what economists sometimes call the “zero lower bound.” The Fed felt that it couldn’t make interest rates negative (instead of the bank paying interest to depositors, the depositors would pay interest to the bank?). Thus, the Fed felt as if it needed to try other policies for stimulating the economy with names like “quantitative easing,” “forward guidance,” and the like.

But the European Central Banks, along with central banks in Switzerland, Sweden, Denmark, and Japan, decided to break through the “zero lower bound” (often abbreviated as the ZLB) and try out a negative policy interest rate. I wrote about this when it was happening: for example, “Negative Interest Rates: Practical, but Limited” (March 15, 2021), ”Negative Interest Rates: Evidence and Practicalities” (August 8, 2017); ”What Else Can Central Banks Do?\” (October 24, 2016); and ”Fed Policy: Negative Interest Rates, Neo-Fisherian, or No Change” (August 26, 2016).

But now we have the advantage of a few years to look back at the experience. Michael McLeay, Silvana Tenreyro, and Lukas von dem Berge look back at the evidence in “Negative Rates and the Effective Lower Bound: Theory and Evidence” (Journal of the European Economic Association, 24: 1, February 2026, pp. 1–57). The paper is based on the FBBVA Lecture delivered at the 2025 meetings of the European Economic Association. The second half of the paper is technical stuff (that is, it’s a new model of a heterogeneous, oligopolistic banking sector, with high- and low-deposit banks, embedded in an “open-economy macroeconomic model, featuring exchange-rate and capital market transmission channels). But the basic fact patterns at the start of the paper, which the model seeks to capture, are an easy read.

The short answer is that when central banks moved the key policy interest rate (slightly) below zero, it did seem to provide additional economic stimulus without weird or disastraous consequences. Here are some top-line takeaways.

• “Empirical Observation 1. Pass-through of policy rates to household deposit rates is bounded by the ZLB. Meanwhile, corporate deposit and wholesale funding rates can fall below zero.”
• “Empirical Observation 2. At low or negative policy rates, aggregate pass-through to bank lending rates and volumes still occurs, though it is typically reduced and potentially delayed.” However, the authors cite studies that the pass-through rate of negative interest rates to lower interest rates on loans in the economy is not 1:1. The pass-through of negative interest rates in the euro area seems to have been more than 50%, but in some other countries, it was less than that.
• “Empirical Observation 3. Aggregate banking sector profitability is not necessarily adversely affected by negative policy rates, and may even improve owing to general equilibrium effects.” A concern with negative interest rates was that if banks were pressured to lend at extremely low interest rates, then the banks might go broke. But it turns out that the macroeconomic benefits banks received from lower interest rates stimulating the economy tended to offset the lower bank profits from lending at lower rates.

Of course, none of this means that it would be necessarily sensible policy for a central bank to make its policy interest rate deeply negative. But it turns out that businesses in particular can function with a negative interest rate at their bank–that is, they pay interest to the bank on their deposita at the bank. Also, as we have seen in the US, people can function with banks paying essentially zero interest on their deposits as well. I suspect the reason is that it’s worth something to business and to people to make and receive payments through the banking system (say, a business making payroll, or a person having paychecks automatically deposited). As a result, when a bank is paying zero or even slightly negative interest on deposits, the bank will not lose all of its deposits.

There are three main ways to tax carbon emissions: through a fuel tax (like a gasoline tax), through an explicit tax on carbon emissions, and through an emissions trading system that requires carbon emitters to have marketable permit for doing so. The OECD offers an overview of that is happening across these areas in 79 countries in “Effective Carbon Rates 2025: Recent Trends in Taxes on Energy Use and Carbon Pricing” (November 13, 2025).

Here’s a summary figure: the light-blue bars are the tax imposed on carbon emissions by a fuel tax; the darker blue bars are an explicit carbon tax; and the green bars are the costs imposed by an emissions trading system. The calculations take into account both the level of these taxes, and also the share of carbon emissions actually covered.

One obvious lesson is that fuel taxes remain the primary way in which carbon is taxed. Another lesson is that the countries with the highest carbon taxes tend to be in Europe. If you run your eye down the list looking for the United States, you will find it about three-quarters of the way down, between South Africa and Uruguay. But you will also note that China, by far the world’s largest emitter of carbon, falls even lower on the list. And India, with rapidly rising carbon emissions, is lower than China.

The recent Supreme Court decision that President Trump did not have arbitrary and unbounded powers he has claimed to impose or retrace tariffs came as no surprise, for a variety reasons laid out in earlier posts here, here, and here. But here I want to focus on a different claim: that the reason for tariffs was to reduce the US trade deficit.

Broadly speaking, there are two possible explanations for trade deficits. One possible explanation is that other countries are competing unfairly in some way, that this unfairness penalizes US exports and subsidizes US imports, and thus leads to a trade deficit. The other possible explanation argues that trade deficits are determined by macroeconomic factors like total levels of production and consumption, along with domestic savings and investment. From this view, government policies to subsidize or penalize certain industries will certainly affect those industries, and lead to shifts between industries. However, this second view argues, such policies won’t determine the trade deficit.

Well, President Trump started imposing tariffs last April. The effective tariff rate (that is, the average over all goods) rose substantially. The federal revenue collected by tariffs rose from near-zero to about 1% of GDP in the most recent federal budget projections–call it roughly $300 billion per year.

We have here a test of the two hypotheses about what causes trade deficits. Did the US government actions to disfavor imports in fact reduce the trade deficit? The US Department of Commerce just released the trade data for the full calendar year 2025, and here’s what the overall picture of imports and exports looks like:

It is simply a fact that the US trade deficit in 2025 barely budged, even with Trump’s tariffs. As the Census Bureau reports: “For 2025, the goods and services deficit was $901.5 billion, down $2.1 billion from $903.5 billion in 2024”.

You can break this down separately into goods and services. In goods, where Trump’s tariffs were focused, the US trade deficit got larger. In services, the US trade surplus got bigger. Census writes: “The 2025 decrease in the goods and services deficit reflected an increase in the goods deficit of $25.5 billion, or 2.1 percent, to $1,240.9 billion and an increase in the
services surplus of $27.6 billion, or 8.9 percent, to $339.5 billion.”

Yes, tariffs have effects. For example, they can reallocate where imports come from between countries. For example, imports of goods from China were $438 billion in 2024, and dropped to $308 billion in 2025. But reallocating imports and exports between countries is not the same as changing the overall balance of trade.

Indeed, the erratic and untargeted nature of Trump’s tariffs means that they have not even focused on the kinds of trade that matters most for the US economy. As one of the Census tables, the US economy had a trade deficit in advanced technology products of $297 billion in 2024 (that is, $763 billion in imports and $466 billion in exports). In 2025, with a blast of US tariffs, the US trade deficit in advanced technology products was even larger at $414 billion (that is, $965 billion in imports and $550 billion in exports).

It is a basic lesson in intro economics that trade deficits are determined by macroeconomic forces, not by levels of governmental fairness or unfairness about trade. I have tried to explain the macroeconomic argument on this blog a number of times, and I won’t bother trying again here. My point here is just to focus on the fact that if you believed that Trump’s tariffs would put a big dent in US trade deficits, your belief is being disproved by facts. Tariffs can readjust what goods and services are shipped, and to and from what places. But they are not in fact a way of addressing the overall US trade deficits.

On question about how artificial intelligence will affect jobs and productivity, the honest answer is that the actual empirical evidence is pretty thin. In part, this is because the new AI tools have arrived so recently, and are evolving so quickly, that don’t yet have much experience with their effects–or much data to separate the effects of AI from other changes in the economy. Eric Fruits and Kristian Stout offer an overview of what the emerging evidence has to say in “AI, Productivity, and Labor Markets: A Review of the Empirical Evidence” (International Center for Law & Economics Issue Brief, February 5, 2026). For those just getting up to speed on the subject, one especially useful part of the article is that the reference list is annotated–that is, there’s a brief discussion of what angle is taken by each of the references, with weblinks as possible.

The authors emphasize that most of the predictions about effects of AI are essentially driven by underlying assumptions: what share of tasks in in what share of jobs might be affected by AI; to what extent will AI complement workers and shift the tasks that they do in a given day, or replace them; how quickly will AI diffuse across varying sectors of the economy; how quickly will complementary investments be made to support the effects of AI (in everything from electric power generation to education to robotics); if AI requires large-scale organizational redesign, how long does that take; will AI expand overall output or make the distribution of income more unequal; and so on and so on. Choose your assumptions, and you can choose your outcome. As the authors point out, for example, its also possible to have effects that start in one direction and then swerve to another direction: for example, it’s straightforward to build a “J-curve” theory in which the adjustment to a new technology first has a negative effect during a transition period as firms face the cost of making adjustments, but then later has a very positive effect.

However, some themes are begining to emerge from actual research on the use of AI in different settings, like call centers, accountants, law students doing complex legal tasks, software developers, professional translators, randomized experiments with groups given certain tasks to perform, and others. Here are some themes that emerge:

First, such studies consistently show substantial gains in specific areas of both quantity and quality of output.

Second, the gains can be highly variable, even on tasks that may appear similar. For example, a given AI tool might be enormously helpful and reliable for certain tasks or up to a certain level of complexity–but then become highly unreliable beyond that point. Those using the tool may see that it works well in lots of cases, and then start trying to use it everywhere–even where it doesn’t work well. Thus, several of the studies emphasize the need for “verification protocols” to double-check quality of output and to provide feedback to users.

Third, another theme across these studies is “skill compression,” as the authors note: “Across settings, micro-level evidence points to skill compression. AI tools disproportionately boost output among lower-performing workers, narrowing performance gaps within job categories. This pattern recurs across productivity studies and carries distributional implications: AI can reduce inequality within occupations, even as it reshapes demand for certain entry-level roles.” In other words, access to AI tools may have the biggest benefit for the productivity of workers with less experience or skill, and smaller benefits for workers who already have greater skills. (This pattern may also help to explain some evidence that the number of jobs in sectors exposed to AI for young workers may have fallen: when AI boosts productivity of these younger workers, firms end up hiring fewer of them.)

Fourth, the studies of AI and jobs do not involve a random selection of jobs. Instead, the studies tend to focus on jobs where it seems intuitively plausible that the gains might be substantial. Thus, it would be clearly incorrect to extrapolate the patterns from individual studies across the economy as a whole.

Fifth, the new AI tools seem to be leading to a surge of new companies. Moreover, these companies are often smaller in terms of employment than average start-ups in the past. authors write: “AI substitutes for managerial, operational, and technical tasks that previously required additional hires or cofounders. By lowering the minimum viable team size, AI reduces entry costs, increases the number of entrants, and strengthens downstream competition.” The emerging pattern here may be large and concentrated “upstream” companies doing the foundational research on AI and owning the actual computing power, w while the new startups may be smaller competitive “downstream” users of this technology. Of course, the share of these start-ups that eventually succeed and grow it not yet clear.

Sixth, the growth of the new AI companies depend on other factors in the economy: availability of finance, a non-hostile and non-fragmented regulatory environment, a supply of science and engineering workers, capacity for training new workers, capacity for computing power and supporting infrastructure (like electricity and telecommunications).

I think we don’t yet have a clear sense of what these AI tools will actually be in the medium term of, say, 5-10 years. (See above discussion of how assumptions will determine predictions.) But I strongly suspect that no high-income country, if it wants to remain a high-income country, is going to be able to a to sidestep and avoid the new AI technologies. It’s worth remembering that in the long-run, the average standard of living in a society rises when the average productivity per worker rises. In turn, greater productivity pretty much always requires change, sometimes disruptive change.