Conversable Economist

Employers will often choose to provide paid sick leave to some of their employees. About 15 years ago, neither the US federal government nor state governments required paid sick leave, but about 63% of US employees worked in jobs that had at least paid short-term sick leave. Since then, 18 US states and lots of cities and counties have started requiring paid sick leave, and by 2023, about 77% of US employees had jobs with at least short-term paid sick leave. Stefan, Pichler Christopher Prinz, Stefan Thewissen, and Nicolas R. Ziebarth describe the evidence from the US experience, along with international comparisons and the underlhying economic tradeoffs, in “The Economics of Paid Sick Leave” (Journal of Economic Perspectives, Spring 2026,  40:2, 215–42. I work as Managing Editor of the JEP.)

Before the rise of US state and local government mandates for paid sick leave, the benefit was not distributed equally across the workforce. The authors write:

In 2011, based on calculations from the National Compensation Survey by the Bureau of Labor Statistics, 63 percent of all US jobs came with (voluntary) paid sick leave provision for short-term sickness. However, heterogeneity by job type was substantial: 76 percent of full-time employees but only 28 percent of part-time employees had paid sick leave coverage. Further, private-sector employees in large firms with more than 100 employees were more likely to be covered by paid sick leave than those in firms with fewer than 100 employees (74 percent versus 54 percent). Unionized jobs had a higher probability of coming with paid sick leave than nonunionized jobs (72 percent versus 63 percent). Notably, the income gradient was steep: only 33 percent of jobs in the bottom quartile of the wage distribution offered paid sick leave, compared with 86 percent in the top quartile.

Even in a situation without government requirements, employers have two main reasons for providing paid sick leave: it can help to attract certain kinds of employees (if the employees place a high value on paid sick leave), and it can discourage sick employees from coming to the office and infecting other workers. To put it another way, sick leave for some can lead to less need for sick leave by others. On the other side of the tradeoff, paid sick leave raises the possibility that workers will use it for an extra day off, now and then. In particular, firms may be concerned that if they offer a paid sick leave benefit, while other firms do not, they are more likely to attract employees with a greater likelihood of being sick.

In an effort to balance these concerns, countries have different rules about paid sick leave, as shown in the table. For example, a number of countries require being sick and staying home for several days before you can claim paid sick leave. A number of countries require a doctor’s note to verify the sickness. The “paid” part of paid sick leave can be 100% of normal pay, or less, and it can either be paid by the employer, or paid by an insurance fund into which the employer has already paid. Most countries have a line between short-term and long-term paid sick leave, with the amount received by the sick worker being reduced when the long-term category is reached.

Paid sick leave in the US (and Australia) operates differently than in most other countries. The standard design for sick leave policies in the US has the employee build up credits for sick leave over time, and then use those credits when claiming sick leave. (A common rule is to accumulate one hour of sick leave credits for every 40 hours worked.) This approach has the advantage that a sick employee is, in a way, spending their own resources from their own personal account. It also protects employers against hiring someone who, soon after being hired, starts taking extensive sick leave for which the employer has to pay.

In contrast, most other countries have a social insurance fund for the costs of paid sick leave: a standad pattern is that employers pay into the fund for short-term leave and general tax revenues covers long-term sick leave.

The overall challenge for design of paid sick leave, as the authors write, is to minimize “inefficient absenteeism,” when workers are out of the workplace with paid sick leave and don’t really need to be, and “inefficient presenteeism,” where sick workers come to the office even though their productivity may be quite low and they have a risk of infecting others. As the authors point out, there isn’t a consensus on the best constellation of rules for defining paid sick leave; for example, requiring employees to take a few unpaid days of sick leave before claiming sick leave is intended to discourage overly casual use of paid sick leave, bu tin practice, ti can lead to the worker being out of the office longer–after the sick leave payment kicks in.

But it’s interesting to note variations across countries in the amount of sick leave that is claimed. Say that a worker spends 240 days/year at the workplace (five days per week, but with some holidays and vacation). Thus, a sick leave rate of 1% per year means the average employee misses 2.4 days/year of work; a sick leave of 4% means that the average employee is missing about two weeks of work each year.

Comparing large economies at the extremes these sick leave rates, the authors write: “Thus, when employees have no access to paid sick leave, as continues to hold true in many states and professions in the United States, many employees will work sick and the costs of presenteeism will be high as workers spread contagious diseases and have low work productivity. On the other hand, a generous sick leave system like Germany’s, where employer mandates require a 100 percent replacement rate for up to six weeks per sickness episode, leads not only to high employer costs, but also to an inefficiently high share of workers who call in sick, thereby raising the costs of absenteeism.”

For the last eight years or so, going back before the pandemic, Mexico’s economy has been growing at 1% per year or less, which is barely faster than the population of Mexico has been growing. It is a fact of arithmetic that an upper-middle-income country, as Mexico is classified by the World Bank, will not become an upper-income country unless it grows faster than the upper-income countries. Tony Payan, Gabriela Siller Pagaza, José Iván Rodríguez-Sánchez explore the dimensions of the problem in “Locked in Low Gear: Mexico’s Struggling Economy” (Rice University Baker Institute for Public Policy, April 15, 2026).

As a starting point, it’s useful to recognize that Mexico’s economy is readily divided into “formal” and “informal.” The informal economy is essentially not taxed or regulated by the government, and workers in this sector tend to be lower-skilled, with less equipment or machinery to do their jobs, and less connection to the modern economy. In addition, informal workers are not eligible for many government benefits. This formal/informal division is common in developing economies. The hope is that over time, the formal sector will include the more modern, productive, and expanding firms, so there will be a gradual shift to the formal economy.

But survey data for 2024 suggests that over half of Mexico’s workforce is still in the informal economy. Indeed, a combination of higher tax rates, higher minimum wages, and more government-mandated vacation days seems to be causing a number of smaller firms to move from the formal to the informal economy. There’s also some belief that informal firms may be less exposed to official corruption and to organized crime.

A vicious cycle emerges here. If workers know that most of the jobs available to them will be low-skill jobs in the informal sector with low-wage employers, their incentives to gain education and skills are reduced. Firms expecting to be in the informal sector have little reason to invest in machinery or equipment, and no reason to invest in research and development, because they have little desire to expand or to become prominent. There is little pressure from the business community for the government to improve infrastructure or institutions, because a large part of business community is hiding in the shadows. With a large informal economy, it’s harder for the government to collect revenues that could go into improvements in education, health care, infrastructure, or technology diffusion. Instead, Mexico’s government spending is heavily aimed at pensions and corporate subsidies, and government debt continues to rise.

Of course, the Trump administration tariffs haven’t helped to improve Mexico’s economic outlook either–although Mexico’s economy has been somewhat protected from the tariffs by the pre-existing US-Mexico-Canada Trade Agreement (the update of the earliet North American Free Trade Agreement).

This diagnosis is not especially new. Back in 2019, I was writing in “Mexico Misallocated” about how Mexico’s institutional framework does not create a situation in which higher-productivity firms are likely to expand, thus expanding the formal economy. There is no magic-bullet policy solution either; instead, an array of legislative, budgetary, regulatory, and judicial policies need rethinking.

Here’s a modest puzzle: The quantity supplied of milk is way up, but the price is also way up. One usually expects higher supply to drive down the price. What’s happening? Here’s a meandering commentary on supply and demand in US dairy.

On the supply side, Jeffrey Gillespie  and Eric Njuki of the US Department of Agriculture discuss “Fewer Farms, More Milk: The Changing Structure and Costs of U.S. Dairy Farming” (Amber Waves, Februar 23, 2026).

The pattern over the last couple of decades is fewer herds of dairy cattle, but bigger herds, along with a substantial rise in milk output. Gillespie and Njuki write: “Increasing adoption of new technologies and advanced management practices are among factors that have contributed to increasing productivity of cows on U.S. dairy farms. Between 2000 and 2021, advances in genetics, nutrition, and technology increased average milk yields per cow, and the market share of sales coming from farms using those advanced technologies and practices increased. Some of these technologies may be “scale-dependent” and are better suited to larger farms that need to exceed a minimum size in order to feasibly adopt the technology.”

A result of these changes is that annual milk prodution per cow continues to rise.

Gillespie and Njuki also provide this discomforting graph for milk producers. The USDA measures dairy production into four main categories, where the later and larger categories include what is in the earlier and smaller categories: feed cost, operating cost (including feed cost), and operating and ownership cost; and total cost (which also includes “unpaid labor, land cost, and general farm overhead expenses”). TThey write: “Between 2000–24, feed costs were covered by the average dairy farm in all years. Operating costs, which include feed as well as labor, inputs, and services, were covered by the average dairy farm in all but 2 years, 2009 and 2012. Total operating and ownership costs (related to assets such as buildings, equipment, and insurance) were covered by the average dairy farm in 13 of the 25 years. Total economic costs, which include opportunity costs such as unpaid labor and land, were covered by the average dairy farm in only 4 years.”

This figure shows what share of farms cover these costs, according to size of the farm, using annual data from 2000-2024.

In short, dairy production is up, and dairy is not a high-profit industry (no big surprise there). However, milk prices are up. The blue line shows the price for a gallon of whole milk in the last 20 years or so, while the green dashed line shows an overall price for low-fat and skim milk. But show a rise beginning back around 2018–that is, before the inflation of 2022. They also show a price that has remained fairly high in the last couple of years, despite increases in milk production.

Karen Bohnert tackles the question of what’s happening with milk prices in “The Great Rebalancing: Why 2026 Milk Prices are Defying the Supply Tsunami” (Dairy Herd Manageemtn, May 1, 2026). The subtitle for her article gives the punchline: “2026 milk prices are defying a massive supply surge as a revolution in protein demand and steady exports create a great rebalancing for U.S. dairy producers navigating market volatility.” As she reports, US milk production in the second half of 2025 was up about 4% over the same time in 2024; European milk production rose by a similar amount during that time.

In the State of the Dairy Industy 2026 Report from the Farm Journal, Bohnert reports in more detail. On one side, the profit expecations of dairy farmers are low this year. Bohnert writes: Top-line sentiment is bleak; profit expectations have cratered … Skyrocketing input costs, political volatility and labor scarcity have created a perfect storm. Essential infrastructure costs have nearly doubled in five years while land prices hit the ceiling. … Yet, inside this pessimism lies a remarkable paradox: 45% of dairy operators still plan to expand in the next five years.”

The underling reason here is a fundamental shift in the demand for milk: specifically, the demand for liquid milk has been falling for some time, but the demand for milk-based products as been shifting and rising. A moderate shift is that international demand for cheese is rising, which has been absorbing the increase in US cheese production in the last few years. A much bigger shift is the rising demand for high-protein products, which includes dairy-based products like whey power, yogurt, and others.

Bohnert describes several interrelated changes. One is the “cow number paradox.”

Perhaps the most interesting part of this story is the cow number paradox. Historically, more milk meant more cows. But in 2026, we are seeing a decoupling of those two metrics. While total milk production and total components are up, cow numbers are remaining relatively stable — and even declining in some regions. “For the first time in history, the dairy industry is growing its output while the heifer supply is at a 20-year low,” says Phil Plourd, president of Ever.Ag. “We are seeing a new math where the value of the components — not the number of head — is the only metric that matters. Producers are doing more with less because they have to.”

A related change is that dairy farmers are not optimizing for the total volume of milk produced–the traditional goal–but instead focusing on producing the components of milk that will be used for other purposes.

[A] staggering 89% of producers are now actively and surgically adjusting their rations to target specific milk components — such as fat and protein — rather than just total volume. This isn’t just a trend; it is a response to a market that is hungry for solids. As the domestic fluid milk market continues its long-term decline, the demand for cheese, butter and high-value protein powders has skyrocketed. For the modern producer, the milk check is no longer driven by the weight of the water, but by the percentage of the components. Producers are leveraging high-precision metrics, bypass fats and amino acids to push butterfat levels toward 4.5% and 5%. These are numbers that were once the exclusive domain of small Jersey herds but are now being achieved more and more by large Holstein operations. This component boom allows a farm to produce higher value while caring for fewer cows.

The third change is a surge of investment in dairy-processing. Again, Bohnert explains in the 2026 State of the Dairy Industry report:

The milk produced on these high-efficiency dairies has to go somewhere, and the U.S. landscape is currently seeing a processing renaissance. From the High Plains of Texas to the rolling hills of Idaho and the heart of Kansas, massive amounts of capital are being poured into new processing plants. These processing plants are hightech manufacturing centers designed for the global market. We are seeing the emergence of plants capable of processing millions of pounds of milk a day into cheese, ultra-filtered milk and specialized whey ingredients. This boom in processing infrastructure provides a home for the increased production and allows the U.S. to compete on a scale that was previously impossible. In 2026, the industry is no longer limited by how much milk we can produce, but by how much stainless steel we have in the ground to convert that milk into exportable goods.

In the modern US economy, where we spend an inordinate amount of time obsessing about technology and AI, it’s perhaps useful to remember that the extraordinary adjustments in response to shifting consumer demand happen all the time, even in an industry as ageless as dairy cattle.

The “baby boom” generation, made up of Americans born from after World War II up into the early 1960s, is sometimes called the “pig in the python” of US demographics. The pig started moving into retirement age around 2010, when the earliest of the boomers hit age 65. At present, the tail end of the boomers (like me) are hitting retirement age. This shift has some well-known implications, like the rising financial pressures on Social Security and Medicare. But here, I want to focus on a different implication: With large numbers of retirees, and lower birthrates after the baby boom generation, it seems as if the supply of labor in the US economy should be growing at a relatively slow pace for the next decade or two. In addition, basic economics suggest that a slowdown in supply should tend to raise the price–in this case, raise the wage for young workers entering the job market. How likely is this scenario?

Steven Ruggles offers some calculations and insights in “The pig in the python: US decennial labor flows and economic opportunity, 1910–2040” (PNAS, May 15, 2026, vol. 123, #20, e2601716123). Here’s one illustrative figure. Let’s calculate the “net” entrants to the US labor force in each decade, and we will do the calculation as a share of the size of the pre-existing workforce in that decade.

During the high-immigration period at the start of the 20th century, net entrants to the labor force were in the range of 8-10% of the of the working-age population. After the clampdown on immigration in the 1920s and the miserable economic period of the 1930s, this dropped a bit, and the rises slightly again in the 1950. But then you see the big spike of new entrants into the US labor force in the 1960s, whihc is the boomers entering the labor force. After this boom, new entrants sag ove time. The level is currently down to below the Great Depression levels, and in the decades from 2030-2040, net entry into the labor force is projected to turn negative (with a combination of lower US birthrates and recent clampdowns in immigration to the US).

What are the implications for younger workers. Ruggles summarizes in this way:

We are on the verge of a fundamental transformation of the labor market. Baby boomers began retiring in large numbers during the 2010s, and immigration began declining in the same period. The number of births went down 17% between 2007 and 2024, which will contribute to a reduction in the number of new entrants to the labor force in the 2030s.

In the current moment of political turmoil, any economic predictions spanning the next 15 years are highly uncertain. We can predict with some confidence, however, the general demographic configuration of the 2040 working-age population: the births have already occurred, mortality change is ordinarily gradual, and a massive surge of immigration seems unlikely. Other factors may affect the prospects of new workers, including recessions, sudden shifts in federal policies, and long-running economic trends like globalization and mechanization. It is possible, as some prognosticators contend, that artificial intelligence will dramatically reduce the demand for workers. It is also possible—or even probable—that the magnitude of the coming demographic transformation may be great enough to swamp such economic changes.

Barring revolutionary changes in the economy, the drop in labor-market competition over the coming two decades will have profound consequences. There is likely to be an unprecedented shortfall of new workers, creating strong upward pressure on wages. … [W]e are already seeing signs of an uptick in the wages of young workers, and as the demographic shortage accelerates we may finally see real wages of the young exceed the levels of the early 1970s. Americans born in the 2020s might be the first cohort in a half century that earns significantly more than their parents did. Labor-force participation will probably increase as more workers are pulled into the market by higher wages, and some workers will postpone retirement. The decline in labor competition should be a boon for labor organizing. There will be increased incentives for automation of both manufacturing and services. We can expect reduced inequality, especially generational inequality. High demand for labor will create pressure to expand immigration. The unprecedented drop in employment competition may also have adverse consequences. Labor shortages may constrain economic growth, although there is some evidence to the contrary.

The movement of the boomer generation through education, the labor market, and now into retirement has been one of the dominant factors for the US economy for decades. But that period is, gradually, coming to an end.

The standard definition of “behavioral economics” is that it brought insights from psychology to bear on economic decision-making: that is, concepts like using rules-of-thumb in situations with limited information, valuing the present over the future, difficulties in perceiving probabilities accurately, habit formation, peer effects, and more. But the most recent edition of the Behavioral Economics Guide 2026, edited by Alain Samson, includes two essays suggesting that it’s time for additional steps. In a similar spirit, Ulrike Malmendier argues that it’s time to take personal experience effects into account; Isabelle Brocas argues for applying insights from the biological sciences.

In “Homo Experiens: Why Behavioral Economics Needs the Life Sciences,” Malmendier points to a body of research which finds (perhaps unsurprisingly to anyone but economists) that past experience influences behavior. For example, the stock-market returns that people have experienced during their lives have a strong effect on people’s likelihood of investing in the stock market. Those who have lived through periods of higher inflation are more likely to expect higher rates of future inflation. Even experts, well-acquainted and up-to-date with evidence, are not immune to these experience effects. In a study of members of the Federal Reserve Board of Governors, those who had lived through periods of higher inflation were less likely to favor lower interest rates in the future. The beliefs of fund managers and doctors are shaped by life experiences.

Malmendier suggests that economic decision-making should expand to consider homo experiens, as she puts it. She writes:

Yet, the behavioral economics revolution has stalled at a decisive point. The human element I appealed to above is still missing, or at least incomplete. To put it in stark terms, human behavior still seems rather robotic. Where previously, in the neoclassical model, it was the output of a perfectly programmed computer, in the behavioral economics model it became the output of a not-quite-so-perfect computer program, one prone to systematic bugs. And while the discovery of heuristics and biases was a genuine improvement, the resulting models remained mechanical. Once we identify the biases and model them, the program is assumed to run the same way for everyone, at every point in their lives. What you have lived through, what you have felt, what has happened to your body and brain along the way—none of that enters the model.

In practice, how might such a research agenda be carried out? A starting point is just to take life experience into account as a variable. But Malmendier has more in mind:

The implication is that the biological channels through which experiences affect us—stress hormones, neural rewiring, immune and endocrine responses—are real, measurable, and consequential for the economic outcomes we care about. And they are almost entirely absent from economic models, whether behavioral or not. … I believe that the next chapter of behavioral economics lies in a turn towards the life sciences—in taking seriously what neuroscience, psychiatry, medicine, and biology already know about human beings and how they are shaped by what they live through. This means, first, that economists should start collecting data on stress, emotions, hormones, and physical health alongside the economic variables we traditionally measure. So far, economists have rarely gathered data along these dimensions, and the standard sources of survey data—the ACS [American Community Survey], the Michigan Survey of Consumers, the Survey of Consumer Finances—contain few variables that are related. …

In another essay in the same volume, Isabelle Brocas takes a related by different angle in “Beyond Preferences: The Biological Foundations.” Her focus is that behavioral economics can often seem like a list of possible biases and heuristics, operating with a various domains of decision-making, but with little sense that the domains are interrelated. She offers a useful diagram. The peach-colored labels around the outside focus on areas often studied by behavioral economists. The green labels show the brain mechanisms behind these areas of decision-making. As the graph usefully illustrates, mechanims that affect one domain are likely to affect several domains.

Perceiving this system as a unified whole, rather than a one-off list of individualized bits and pieces, seems like a worthy goal. Brocas writes:

One of the [economics] discipline’s great strengths has been its ability to isolate dimensions of behavior. Risk preferences, intertemporal preferences, ambiguity attitudes, social preferences, strategic sophistication, and self-control have all generated productive literatures. Yet, this partitioning has also come at a cost. It has encouraged the idea that these domains correspond to separable psychological objects, when many may reflect overlapping underlying mechanisms.

Take risk and time. Economists often treat them as distinct dimensions of preference: one concerns uncertainty, the other delay. But that separation becomes less convincing once we think mechanistically. Both types of decisions recruit valuation, affective anticipation, future representation, attention to salient outcomes, learning from feedback, and inhibitory control. A person who is impulsive may also appear more willing to take risks, not because impatience and risk-loving are identical constructs but because both may be shaped by common patterns of reward sensitivity, limited future simulation, stress, or weaker self-regulation. Similar overlaps likely exist between strategic reasoning and working memory, between cooperation and emotional regulation, between trust and threat perception, and between consumer choice and attentional bottlenecks. The traditional language of preferences can therefore obscure as much as it reveals. It describes regularities at the level of outcomes, but it does not tell us whether those regularities arise from common mechanisms or genuinely distinct systems.

Brocas argues that fruitful work can be done by looking at biology, and perhaps especially work in neuroeconomics.

A biologically informed economics does not require economists to abandon preferences or reduce behavior to neurons and genes. Preferences remain useful reduced-form representations when behavior is stable and prediction is the objective. The point is different: when economists seek to explain heterogeneity, diagnose why interventions fail, or design policies that work across contexts and populations, it is often necessary to model the mechanisms that generate the outward pattern of choice. Biology can enter economics not only empirically, but also theoretically through models that derive observed behavior from underlying brain processes and resource constraints. This perspective clarifies what is malleable, when it is malleable, for whom, and through which policy levers. Taking biology seriously is therefore not a detour from economic theory—it is a step toward a more explanatory and more useful science of human behavior. …

Neuroeconomic work has been especially valuable here because it shows that value-based choice relies on a sequence of interacting computations rather than on isolated behavioral modules. The act of choosing involves representing the problem, assigning subjective values, selecting an action, experiencing an outcome, and updating behavior through learning (Rangel et al., 2008). This logic already pushes against the idea that risk, time, social choice, and self-control should be understood as self-contained domains.

I can barely keep up with the broad field of economics as it is. I frankly despair of becoming knowledgable in the neuroeconomics of brain-based decision-making, including “stress hormones, neural rewiring, immune and endocrine responses.” But I’m always intrugued by the work of economists like Malmendier and Brocas who are willing and able to make the effort.

Finance & Development has published a five-paper symposium on “Geoeconomics” (June 2026)., which is the idea that in a global economy, national security policy needs to be incorporated into economic policy-making. Christopher Clayton, Matteo Maggiori, and Jesse Schreger contribute “Understanding Geoeconomics in a Volatile World.” They write:

The academic study of geoeconomics dates most prominently to 1945, when economist Albert Hirschman published National Power and the Structure of Foreign Trade. In it, he examines how Nazi Germany had structured its economy to maximize leverage over its neighbors during the interwar period. He rejected the naive view that because trade is voluntary and mutually beneficial, it is geopolitically harmless. Benefits can be mutual, Hirschman argues, without being symmetrical. And asymmetry is how power builds. Since Hirschman’s time, economists have left the study of global power dynamics largely to political scientists and historians, who have led the development of this area of research. Though almost every economics student encounters the Herfindahl-Hirschman Index, few know it was invented to measure the economic power of nations, not firms. 

Clayton, Maggiori, and Schreger discuss some recent episodes of the use of economic sanctions, including those imposed on Russia, and draw some general lessons. For example:

Inputs are called choke points, or critical dependencies, if the hegemon controls a dominant market share of the input in the targeted economy and it is difficult to find alternatives to the hegemon’s inputs. For example, the US and its allies control an overwhelming share of global financial services, upward of 80 to 90 percent in many countries. Payment systems, settlement infrastructure, and dollar-denominated lending are basic inputs in a functioning economy. The lack of viable alternatives to the US financial infrastructure gives the country considerable geoeconomic power. Recently, it has wielded this power by imposing comprehensive financial sanctions on Iran and Russia, putting pressure on HSBC to disclose transactions linked to Huawei, and cutting Russian banks’ access to the SWIFT messaging system for international financial transactions.

However, there is a catch. The relationship between control over a sector and geoeconomic power is not linear; rather, power increases disproportionately as a hegemon approaches complete control. The difference between controlling 95 percent and 85 percent of an input is disproportionately large. At 95 percent, a target economy has almost no viable alternatives and must accept whatever terms the hegemon demands. At 85 percent, there is enough of an alternative to give the target meaningful options, and the hegemon’s leverage dissipates rapidly. …

Our work shows that there is a trade-off between gains from trade and economic security. The same mechanisms that are the classic foundations of the gains from trade—economies of scale and specialization—also generate economic dependence. The domestic alternatives that countries did not build up are poor substitutes for globally dominant inputs, such as Chinese manufacturing or US financial services and technology. This lack of alternatives leaves the countries exposed to coercion. As the global economy increasingly relies on goods and services that have strategic complementarities and economies of scale, these mechanisms are likely to increase in importance.

At one level, geoeconomics is just common sense: no one would want to see a nation’s security weakened to make some academic point about the merits of free trade. Thinking more seriously about diversificaition of global supply chains, and about interdependencies between nations, seems fully worthwhile. But as a matter of practical politics, claims about “national security” also deserve some skepticism. But when you run “national security” through the corridors of Washington, DC, you will find that it collects a lengthy wish-list of subsidies and tax breaks for industries, K-12 education, national health care, climate change, and government access to personal information and much more–are of which, amazingly enough, turn out to be core problems of “national security.” The “national security” label is no excuse for not considering whether the proposed policy is actually appropriate, the full costs of each policy, and the full range of alternative policy choices.

Jerome Powell was Chair of the Federal Reserve from February 2018 to May 2026. Of course, the Fed Chair does not have dictatorial power over setting monetary policy: instead, such policy is set by a vote of the 12-member Federal Open Market Committee, which in turn is made up of the seven DC-based members of the Fed Board of Governors and five presidents of the regional Federal Reserve banks (chosen on a rotating basis). However, the Chair does have, by tradition, position, and leadership, disproportionate influence over the monetary policies that are selected. How should Powell’s record as Chair be evaluated? Christina D. Romer  and David H. Romer discuss “An early retrospective on monetary policy in the Powell era” (Hutchins Center Working Paper #106, Brookings Institution, June 2026).

Rather than trying to do a month-by-month or meeting-by-meeting evaluation, Romer and Romer quite sensibly focus six main episodes in monetary policy during Powell’s tenure as Chair:

We begin our study with a selective chronology of monetary policy since 2018. We don’t attempt to provide a comprehensive blow-by-blow description of Federal Reserve actions. Rather, we focus on what we see as six relatively distinct policy episodes. These are: (1) the interest rate and balance sheet normalization in 2018 and early 2019; (2) the reversal of both these policies in mid- and late-2019; (3) the aggressive expansionary response to the COVID 19 pandemic in 2020 and early 2021; (4) continued loose policy in 2021 as inflation surged; (5) the rapid tightening in 2022 and 2023 to fight inflation; and (6) the interest rate cuts starting in mid-2024 and severe threats to Fed independence.

This table shows the dates of the six episodes, along with the two main policy tools that the Fed was using during this time: changes in the federal funds interest rate (the specific interest rate that is targetted by the Fed) and changes in Fed holdings of securities–US Treasury bonds and mortgage-backed securities.

Here’s a figure showing the path of the target for the federal fund interest rate and for Fed holdings of securities over time. Remember that coming out of the Great Recession of 2007-09, the Fed was doing everything it could to support the economy, including a target interest rate set barely above zero percent and a willingness to hold $4 trillion in securities. Thus, when Powell is named Chair by President Trump in 2018, there is already a goal underway to move interest rates and Fed holdings of securities back to a more usual level.

The first two monetary episodes mentioned by Romer and Romer thus involve continuing the process of raising interest rates and gradually reducing Fed holdings of securities. However, in mid-2019, the Fed decided on a mild reversal of both policies. Romer and Romer write:

Attempts to shrink the Fed’s balance sheet, however, led to a temporary loss of interest rate control and a return to balance sheet expansion. The directional shift from interest rate hikes to cuts in mid-2019 can be understood as a response to slightly worse economic forecasts and a few low inflation readings, but it perhaps missed the more fundamental fact that unemployment was at historic lows.

Monetary policy is hard, and hindsight is 20:20. But as the charts above suggest, the monetary policy choices in 2018-2019 were first to raise interest rates by 1 percentage point, and then to cut them by .75 percent points. With Fed holdings of securities, the first step was to reduce holdings by $593 billion, while the second step was to raise them by $253 billion. These changes are not enormous ones, and they are overshadowed by the dramatic challenges of monetary policy that soon followed during the pandemic.

But as the Romers write about this time: “We see an increasing focus among FOMC members on theories that inflation was relatively impervious to economic conditions …” In other words, there was a belief that interest rates should just stay low, even when unemployment rates were also very low, because inflation was permanently under control. My own view is that these beliefs did not serve the Fed well during the period after the pandemic recession, and in some ways set the stage for Fed confrontations with the Trump administration in the last 18 months or so.

The pandemic recession hits in 2020. The Fed pulls out all the stops. Interest rates drop again to near-zero, and Fed holdings the US Treaury securities rises dramatically. There are also several severe strains in financial markets during this time, including a near-meltdown of the global market for US Treasury bonds in March 2020. Behind the scenes, “the Federal Reserve took aggressive actions to stabilize financial markets and ensure smooth market functioning.” This period (the third episode in the Romer and Romer chronology) is where Powell’s record as chair truly shines. The economic consequences of the pandemic are severe, but could have been so much more severe and longer-lasting without the Fed actions.

Conversely, the period right after the pandemic is the most controversial time for Powell’s legacy. Inflation rises, and the Fed was slow to respond, in part because members of the Fed continued to believe “that inflation was relatively impervious to a very strong labor market,” and that the surge of inflation would be only temporary. Romer and Romer note: “Mistaken forecasts that inflation would disappear quickly also factored into their policy choices. However, given that even the mistaken forecasts had inflation above target for an extended period and unemployment well below the natural rate, the decision to leave the funds rate unchanged for so long does not appear reasonable and had unfortunate consequences.”

In the fifth stage, the Fed finally acted to raise interest rates and reduce inflation, but the end result is that inflation remained above the target of 2% annual inflation since then. About this episode, Romer and Romer write:

It is very possible that the forceful moves and clear communication played a role in taming inflation, and did so with historically modest damage to the labor market. The gradual reduction in interest rates starting in mid-2024 was motivated by the Fed’s forecasts that it was closing in on both its inflation and maximum employment targets, and so could move toward its estimate of the neutral rate. However, similar to what we find for the 2019 cuts and the 2021 inflation forbearance, the FOMC arguably lost some sight of the bigger picture. In this case, the most important missing fundamental was simply that inflation had been well above target for years.

The final episode is the gradual interest rate cuts since 2024 and the pressure on Powell and the Fed from the Trump administration to cut rates faster. On one side, Powell has offered a strong defense of the independence of the Federal Reserve, despite sustained and very public personal and legal attacks. For this, he deserves considerable credit. But on the other side, the inflation rate has remained above the 2% annual target set by the Fed for several years now, while unemployment rates have been low by historical standards. Rather than keep interest rates at a higher level to drive out the remainder of inflation, the Fed has, albeit in a halting and episodic way, brought down interest rates and again started expanding Fed securities holdings.

Romer and Romer make no secret that their view is highly sympathetic to Powell; indeed, they note up front that they view him as a “hero.” My personal view toward Powell’s record is positive, but less so than that of the authors. My own sense is that Powell deserved considerable credit both for Fed actions to support the economy during the worst of the pandemic recession and also for his robust defense of Federal Reserve independence from President Trump. On the other side, it seems to me that the Fed under Powell has consistently leaned toward a belief that interest rates could be reduced or stay low with little or no risk of higher inflation: this was true in the 2019, in the aftermath of the pandemic recession, and again in the last 18 months or so. Moreover, one after-effect of the pandemic recession is that Fed holdings of securities, which were already viewed as too high when Powell assumed the Chair, are markedly higher now. Thus, in sustaining the inflation-fighting credibility of the Fed and also in how the Fed has become intertwined with US budget deficits by holding larger amounts of US Treasury securities, Powell’s two terms as Chair of the Fed have left some real challenges for his successor.

Gross domestic product measures the economic transaction in an economy, according to quantities bought and sold and market prices. But does this method work for AI? Anton Korinek and Patrick McKelvey work through the question in “Where is AI in GDP Statistics (Peterson Institute for International Economics, May 2026, both a readable Policy Brief and an underlying research paper are available).

Here’s the basic issue. The authors calculate that the total amount currently spent on AI computing power is (roughly, depending on underlying assumptions) about $250 billion per year. This measures the AI bought and sold in the market, and thus is clearly part of GDP.

However, the AI chips are becoming more efficient:”As chips became more efficient, each dollar of compute spending bought more physical computing capacity. Measured in H100 equivalent units, US AI computing capacity grew at more than 200 percent per year, outpacing nominal spending.” Also, the AI algorithms are becoming more efficient, so that the quantity of computing power needed to achieve a fixed level of AI has been falling by about two-thirds per year. Put these together, and the quality-adjusted increase in AI is more than 2000% (that is, a twenty-fold increase) per year.

So here’s a situation where the amount spent on AI and captured by GDP is rising quickly, but the actual capabilities of that AI spending are rising much, much faster. How should measures of the size of the economy deal with this situation?

The question isn’t totally new. Government statisticians have for some years now used “hedonic” adjustment. The idea is to define a good, like a computer or a television or a car, as a set of characteristics. The qualities of these characteristics are improving over time. Thus, when we say whether the “price of a computer” or the “price of a car” has changed, we want to do an apples-to-apples comparison holding the characteristics of that product constant. Thus, the Consumer Price Index measure for computers and related goods looks like this:

Just to be clear, this graph doesn’t mean that the amount you personally paid out of pocket for a computer fell by three-quarters from 2005 to 2018. It means that the price of buying a computer with the same characteristics as a standard 2005 computer fell by three-quarters by 2018–but of course, few of us in 2018 would have wanted to buy that version of the computer from 2005, even if it was available. The authors explain:

That AI’s growing footprint is so faintly visible in national GDP statistics has, in part, a straightforward accounting explanation. Nominal AI revenues grow only moderately because per-unit prices for any given level of AI capability fall almost as fast as quality-adjusted output rises. In the semiconductor industry, this pattern played out for decades: each generation of chips was dramatically cheaper per unit of performance than the last, so the semiconductor share of GDP remained modest even as quality-adjusted output expanded enormously.

This kind of hedonic adjustment could readily be used for AI as well, but for purposes of measuring GDP, it has limitations as well. GDP is a measure of market transactions, which happen when the technology behind supply-side production of goods and services meets the preferences and tastes behind demand-side purchases of those goods and services. Hedonic adjustments focus on adjusting for changes in the quality of what is produced, but don’t directly take into account what value buyers may place on these quality changes.

For example, perhaps the speed and power of AI tools have considerable value to users up to some point, but then additional speed and power have diminishing marginal value beyond that point. Just because quality-adjusted increase in output of AI has risen by 2000% per year in the last couple of years doesn’t mean that the value of AI tools to users has risen by 2000% per year.

Beyond the details of questions like these, Korinek and McKelvey are making a broader point: sensible thinking about the economic effects of the emerging AI tools needs to be rooted in data, but economic data about a very fast-changing industry can be hard to collect and can involve delicate issues of interpretation.

Consider a person who is convicted of a misdemeanor. It is their first offense. Should that person be punished to the full extent of the law, or dealt with more leniently?

Depending on one’s prejudices, a theoretical case can be made for either approach. Lenient treatment has a risk of causing the offender, along with anyone who hears about the lenient treatment, to believe that penalties for transgression are low or nonexistent, and in this way may encourage future transgressions. Harsh treatment, including jail time, has a risk of leading to additional consequences like loss of a job and restricted future employment possibilities, as well as exposing the first-time offender to seasoned recidivists.

On average and for the population as a whole, what does the empirical evidence say? Jennifer Doleac presents some evidence in “What Becomes of Second Chances?” (Behavioral Scientist, March 24, 2026). She points to a study that she has conducted with Amanda Agan and Anna Harvey based on data from Suffolk County, Massachusetts–where Boston is located. She writes: “In Suffolk County, once police make an arrest or issue a summons, and then determine that probable cause exists for the charge, the case goes to an arraignment hearing. In that hearing, an assistant district attorney (ADA) representing the government decides whether to pursue the charges or dismiss the case. They are essentially deciding whether they think the case is a good use of prosecutors’ time. This is the decision we were interested in. What if more cases were dismissed up front? Would that lead to more recidivism, or less?”

Their methodology relies on an underlying fact about this decision-making process. There are a bunch of assistant district attorneys. The nonviolent misdemeanor cases that are the focus of this studey are assigned to them pretty much at random, and given the volume of cases, the ADAs have limited time to make decisions about them. However, some of the ADA’s tend to be more strict, while others tend to be more lenient. To put it more bluntly, a given case is more likely to be either pursued or dismissed as a result of the random decision about which ADA gets the case.

From a standpoint of justice, the idea that the outcome has an element of randomness seems objectionable. From the standpoint of a researcher, it’s catnip. Using a statistical method (for the stat-minded, it’s a kind of instrumental variable called a leniency design), researchers can look at whether a higher number of cases pursued as a result of randomly being assigned to stricter ADAs (or equivalently, the higher number of cases dismissed as a result of being assigned to more lenient ADAs) affects later behavior. Doleac writes:

It turns out that leniency at this early stage—having your case dismissed rather than pursuing prosecution—reduced the likelihood of showing up in court again with new charges by 53 percent, and it reduced the number of future charges by 60 percent. The effects were larger for first-time defendants—those with no prior arrest or conviction on their record.

In other words, for nonviolent misdemeanor cases with no prior arrest or conviction, leniency works in the most practical and empirical sense. For those with prior arrests and/or convictions, leniency becomes less likely to work.

What about nonviolent felony cases, like burglary and car theft? Here, Doleac discusses a  study by Michael Mueller-Smith and Kevin Schnepel using data from Harris County, Texas, which includes the city of Houston. They were able to find two sources of underlying randomness.

On September 1, 1994, a Texas law went into effect that made it much less likely that prosecutors would offer “deferred adjudication”–basically, allowing the accused to go on probation for a period (say, six months) rather put off being tried for a nonviolent felony, and if the person had no further problems with law enforcement during that time, the felony charge would be reduced. Doleac writes:

This created the first natural experiment. The date of the policy change—September 1, 1994—sorted defendants into treatment and control groups, as if at random, based on the date of their offense. Nothing else changed at that date. The only difference between these defendants was whether they got a second chance to avoid a felony conviction. It turns out this second chance was very helpful. First-timers who got lucky and received a deferred adjudication committed fewer crimes going forward. They were 31 percentage points less likely to be convicted of any new crime over the next ten years—a 44 percent reduction compared with the control group.

The other natural experiment involved jail overcrowding in Houston. There was a referendum to expand jail space, but that referendum unexpectedly failed, which meant that a much larger share of those charged with nonviolent felonies were

Again, this set up a beautiful natural experiment. Mueller-Smith and Schnepel could compare defendants sentenced on either side of the election on November 6, 2007. The only difference between those sentenced before and after this date was that those sentenced after were much more likely to avoid a conviction. This difference wasn’t because of underlying differences between these defendants or their cases; it was because of the failed ballot initiative. This gave the researchers confidence that any future differences in recidivism or employment would be due to the diversion decision and not to something else about those defendants. Just as in 1994, there were big benefits to greater leniency. As the likelihood of diversion suddenly increased, the likelihood of new, future convictions fell, by 26 percentage points (46 percent). This is a dramatic change. Nearly half of the first-time offenders who would have committed another crime in the future if they’d been prosecuted and convicted as usual cleaned up their acts and avoided future crime when their cases were dropped or they received a deferred adjudication. 

This specific evidence on benefits of leniency has its limits. For example, it does not say that police activity or arrests should be reduced. As Doleac points out of those who received lenient treatment for nonviolent misdemeanors: “That person had likely been arrested and booked in jail, and had to show up in court for that initial hearing. This might mean taking time off work, and it certainly meant worrying about what might happen during the hearing. All this isn’t nothing—it is an inconvenience at best and a costly and stressful event at worst.” in addition, the focus of this evidence is on nonviolent misdemeanors and felonies, as well as on first-time offenders. Indeed, one additional practical reason for leniency in such cases is to focus the limited resources of the criminal justice system on repeat offenders and violent crimes.

I’ve written a number of times in this space about the “attention economy” (for example, here, here, and here). When people consume content on a screen, we are in effect selling our attention. Decisions about purchasing many goods and services are shaped by what gets our attention. It may look as if employers are paying workers for their time, but in many jobs, they are paying workers in substantial part for the level of attention they bring to the job.

In reading the philosopher Simone Weil, and her 1951 essay “Reflections on the Right Use of School Studies with a View to the Love of God” (available here and here, for example) I found myself thinking about the nature of “attention.” As the title implies, Weil is focused on how attention in the sense of studying in school can build attention for a deeper form of prayer. My own focus here is on her idea that “attention” can come in different forms. Weil writes about attention and studying in school:

In order really to pay attention, it is necessary to know how to set about it. Most often attention is confused with a kind of muscular effort. If one says to one’s pupils: “Now you must pay attention,” one sees them contracting their brows, holding their breath, stiffening their muscles. If after two minutes they are asked what they have been paying attention to, they cannot reply. They have been concentrating on nothing. They have not been paying attention. They have been contracting their muscles.

We often expend this kind of muscular effort on our studies. As it ends by making us tired, we have the impression that we have been working. That is an illusion. … This kind of muscular effort in work is entirely barren, even if it is made with the best of intentions. Good intentions in such cases are among those that pave the way to hell. Studies conducted in such a way can sometimes succeed academically from the point of view of gaining marks and passing examinations, but that is in spite of the effort and thanks to natural gifts; moreover such studies are never of any use.

Will power, the kind that, if need be, makes us set our teeth and endure suffering, is the principal weapon of the apprentice engaged in manual work. But, contrary to the usual belief,_ it has practically no place in study. The intelligence can only be led by desire. For there to be desire, there must be pleasure and joy in the work. The intelligence only grows and bears fruit in joy. The joy of learning is as indispensable in study as breathing is in running. Where it is lacking there are no real students, but only poor caricatures of apprentices who, at the end of their apprenticeship, will not even have a trade.

Weil emphasizes a certain form of attention, which she describes better than I can.

Twenty minutes of concentrated, untired attention is infinitely better than three hours of the kind of frowning application that leads us to say with a sense of duty done: “I have worked well!” But, in spite of all appearances, it is also far more difficult. Something in our soul has a far more violent repugnance for true attention than the flesh has for bodily fatigue. …

Attention consists of suspending our thought, leaving it detached, empty, and ready to be penetrated by the object; it means holding in our minds, within reach of this thought, but on a lower level and not in contact with it, the diverse knowledge we have acquired which we are forced to make use of. Our thought should be in relation to all particular and already formulated thoughts, as a man on a mountain who, as he looks forward, sees also below him, without actually looking at them, a great many forests and plains. Above all our thought should be empty, waiting, not seeking anything, but ready to receive in its naked truth the object that is to penetrate it. … Although people seem to be unaware of it today, the development of the faculty of attention forms the real object and almost the sole interest of studies. Most school tasks have a certain intrinsic interest as well, but such an interest is secondary. All tasks that really call upon the power of attention are interesting for the same reason and to an almost equal degree.

As I mull this over, it seems potentially useful to identify three forms of attention. (I leave outright inattention for another day.) In one form of attention, you become lost in what you are you are doing. You may be reading or studying or doing a job, or doing something physical like walking in a natural setting on a spring day, but there is a rhythm and a directedness where your attention is deeply engaged. Later, you may say that you “lost track of time” or “the time just flew by.” Entering this form of attention isn’t simple, and for most of us, it isn’t possible to sustain this kind of attention all day–or even for more than a few hours at a time.

A second form of attention, which Weil downplays as “muscular contraction,” seems useful to me as well. Sometimes tasks need to be completed. Sometimes you need to grind from one task to the next. Sometimes you just do the work now, under pressure from an employer or a teacher, and trust that you are building a base of experience and knowledge. At least for me, this “muscular contraction” form of attention can form a base for deeper attention in the future.

But when economists write about the attention economy, it feels to me as if the “attention” they have in mind is not necessarily either of these categories. If I am binge-watching a TV series or an afternoon of football games, while also reading a mystery on my tablet and flicking through some cookbooks to plan the grocery-shopping that needs to happen later, there is a sense in which my “attention” is engaged, but one might also say that my attention is splintered. When I’m being influenced by advertising and product placements, or if I’m surfing a social media website, my “attention” is engaged.

A challenge of the modern world, it seems to me, is that the various forms of attention are all necessary and useful in making one’s way through life–but given that every person on Earth faces the constraint of 24 hours per day of living, the different forms of attention will necessarily tend to crowd each other out. Moreover, the broader and deeper forms of attention may be most important for both personal happiness and long-term career development, but the narrower and shallower forms of attention seem easier to commoditize.