Month: September 2025

Maybe it’s just because I live in Minnesota, a state where the differences between immigrants from Sweden, Norway, and Finland are still apparent in the names of towns and the surnames of people. But when I run into people who would prefer that the US distribution of income be more equal, they often point to the economies of northern Europe as a real-world example of what they have in mind.

How do these countries do it? Magne Mogstad, Kjell G. Salvanes, and Gaute Torsvik explore the evidence in “Income Inequality in the Nordic Countries: Myths, Facts, and Lessons” (Journal of Economic Literature 2025, 63:3, 791–839).

In thinking about why greater inequality of income prevails in the Nordic countries, it’s useful to divide possible reasons into redistribution and predistribution. An example of redistribution after income is received would be public policy decisions like higher marginal tax rates on the well-off, or greater support for those less well-off, or some combination of those two. In contrast, predistribution involves affecting what income is received in the first place, before taxes and transfer payments. Examples might include minimum wage laws, greater workers representation (though unions or other mechanisms), or rules that affect the ability of top executives to be paid in the form of bonuses and stock options. Thus, Mogstad, Salvanes, and Torsvik write:

We argue that the contemporary Nordic model is built on four principal pillars: (i) significant public investment in family policies, education, and health services; (ii) coordinated wage setting within and across industries; (iii) substantial expenditure on social insurance to safeguard against income losses due to unemployment, disability, and illness; and (iv) high and progressive taxation of labor income, complemented by subsidies for services that support employment. …

A key finding is that a more equal predistribution of earnings, rather than income redistribution, is the main reason for the lower income inequality in the Nordic countries compared to the United States and the United Kingdom. While the direct effects of taxes and transfers contribute to the relatively low income inequality in the Nordic countries, the key factor is that the distribution of pretax market income, particularly labor earnings, is much more equal in the Nordics than in the United States and the United Kingdom. Another key finding is that equality in hourly pay, not work hours, is the primary explanation for why the Nordic countries have much lower inequality in labor earnings than the United States and the United Kingdom. … Quantitatively, the compression of hourly wages matters the most, explaining a large majority of the difference in earnings inequality between the Nordic countries and the United States and the United Kingdom.

The authors go through possible alternative reasons for why the four elements of the Nordic model might lead to greater equality. For example, “Nordic governments spend heavily on children and families through heavily subsidized day care, education, and health programs. Although these programs are typically universal, they could help equalize the distribution of skills and human capital if the take-up or the positive effects of the program are concentrated among children from poor or disadvantaged families. We argue that most of the available evidence suggests that this is not the key explanation for income equality in the Nordics. A substantial body of research evaluating the causal effects of day care, education, and health policies in the Nordics suggests that these policies have a relatively modest impact on inequality in skills, educational attainment, and labor market outcomes.”

It’s important here to be clear on how the minds of economists operate. The authors are not arguing in an overall sense either for or against these universal social programs. They are only making the very specific argument that the evidence about the effects of these programs does not support the claim that they are a primary cause of the greater income equality that exists in the Nordic countries. For the authors, the key difference is that those with higher education and skills are paid a substantially higher premium in the US and UK economies than in the Nordic economies, compared with those who have lower levels of education and skills.

Each year, the US Census Bureau publishes three overview reports to update the annual data on income, poverty rates, and health insurance. Here, I focus on some figures from Income in the United States: 2024, by Melissa Kollar and Zach Scherer (September 2025, P60-286). Here’s a figure showing several measures of pre-tax income inequality.

It’s perhaps useful for most readers to start at the bottom. US households as a grouap are divided into five parts, or quintiles. The share of income going to the top quintile has been rising for decades, with an especially sharp jump in the 1990s. The middle figure offer several ratios with the income at the 90th percentile of the income, the 50th percentile. The 90th percentile is rising substantially compared to the 10th percentile, and rising but less so compared to the 50th percentile. The ratio of the 50th to the 10th percentile hasn’t moved much. Both of these figures suggest a rise in pre-tax income concentrated at the top of the income distribution.

The top panel shows the “Gini coefficient,” which will be less intuitively clear to many reader. I offered my own explanation of it here. But basically, it is a way of measuring the extent to which an income distribution departs from perfect equality of incomes. On this scale, perfect equality of incomes has a Gini coefficient of zero, while perfect inequality of incomes–that is, all income going to one person–has a Gini coefficient of 1. Again, this measure shows a steady rise over time.

A few thoughts here:

1) This is a measure of inequality in money income. It includes wages and salaries, rent payments, interest and dividends, as well as government cash payments like Social Security, and cash forms of public assistance. It does not include capital gains. It does not include after-tax effects, including both taxes paid and assistance to those with low incomes that happens through tax credits like the Earned Income Tax Credit. It also does not inclued the value of non-cash assistance programs like food stamps or Medicaid. But it seems safe to say that the rising inequality at the top of the distribution is being primarily driven by wage and salary payments at the top of the income distribution–which is a pattern of interest all by itself.

2) The steady rise in income inequality over time suggests that the driving factor is not something short-term, like the policies of a given president. I won’t try to run through a list of possible candidates for causal factors here. But it does seem worth noting some data on education and income also just released by the Census Bureau. This figure only goes back two decades. As the Census Bureau notes:

Overall, the income gap between householders with a bachelor’s degree or higher and those with a high school degree but no college widened during the 20-year period. In 2004, households headed by those with at least a bachelor’s degree had about twice as much income as those headed by someone with a high school degree but no college. By 2024, householders with a bachelor’s degree or higher had median household income 2.3 times higher than those with a high school degree. The makeup of educational attainment groups also changed over time. In recent decades, growth in the population with a bachelor’s degree or higher has been concentrated among racial and ethnic groups with historically low attainment. This growth has also disproportionately come from increasing educational attainment among women. 

One of the driving forces between rising inequality over time lies in the race between demand for skilled labor and supply of skilled labor. If the supply isn’t keeping up with demand, then the wage gap between the two groups will tend to rise. This is surely not the entire story of rising pre-tax, pre-transfer US income inequality, but it’s a part.

Economic growth and productivity growth across the nations of the European Union has been lagging the US economy. What are the reasons and what might be done? A group of essays in the June 2025 issue of Finance & Development offers some insights. A common theme is that EU economic integration has not proceeded as planned. As a result, EU firms are selling into smaller national markets, rather than a continent-wide market, and their incentives to attract finance and to invest in economies of scale and new technologies are accordingly reduced.

For example, here is Alfred Kammer in “Europe’s Integration Imperative.“

The EU has made significant progress freeing up trade between its member states, but plenty of obstacles remain. High trade barriers within Europe are equivalent to an ad valorem cost of 44 percent for manufactured goods and 110 percent for services, IMF research shows (2024). These costs are borne by EU consumers and companies in the form of less competition, higher prices, and lower productivity.

The EU is also a long way from capital market integration, with cross-border flows frustrated by persistent fragmentation along national lines. The total market capitalization of the bloc’s stock exchanges was about $12 trillion in 2024, or 60 percent of the GDP of the participating countries. By comparison, the two largest stock exchanges in the US had a combined market capitalization of $60 trillion, or over 200 percent of domestic GDP. Limited EU-level harmonization in important areas, such as securities law, hampers growth by preventing capital from flowing to where it’s most productive.

This is one reason Europe has fallen behind in the adoption of productivity-enhancing technologies and its productivity levels are low. Today, the EU’s total factor productivity is about 20 percent below the US level. Lower productivity means lower incomes. Even in the EU’s largest advanced economies, per capita income is about 30 percent lower than the US average (see Chart 1). 

Kammer points out:

Not only do Europe’s leading companies lag their US competitors, but they are falling further behind over time. This is true across all sectors, but especially for tech. While the productivity of US-listed tech firms has increased by about 40 percent over the past two decades, European tech firms have seen almost no improvement. One reason could be that US firms are simply trying harder: They have tripled their research and development spending to 12 percent of sales revenue, three times European companies’ ratio, which has languished at an average of 4 percent in recent decades.

The future would look brighter if Europe could hope for young high-growth firms to reduce the innovation and productivity deficit. Alas, the EU has few such companies. And they have a substantially smaller economic footprint than those in the US, where younger firms account for a far larger share of employment. In other words, the EU has too many small, old, and low-growth companies. About a fifth of European employees work in microfirms with 10 people or fewer, about double the US figure. And while the average European firm that has been in business 25 years or more employs about 10 workers, comparable US companies employ 70 (Chart 2).

Issues for the EU may be especially acute for young tech companies. As Kammer points out, banks are typically the primary source of capital for EU companies, and banks typically want to lend to companies with collateral–not a company based on a few patents and an idea: “[T]here is a troubling trend of innovative European firms taking their talents to more dynamic markets elsewhere, with future “unicorn” companies valued at more than $1 billion leaving the EU for the US at a rate that is 120 times faster than the other way around, according to research by Ricardo Reis, of the London School of Economics.”

Other essays in the issue focus on what would be needed for an EU savings and investment union that could support innovative new companies, as well as essays with more details on Germany, Poland, Greece, and Spain. But for now, in an essay that offers qualified optimism about the future for innovative EU firms, Alessandro Merli begins:

“The US innovates, China replicates, Europe regulates” is how critics summarize the continent’s approach to innovation. Exhibit A of the European Union’s regulatory overreach is the now infamous Artificial Intelligence Act, which governs AI—even though the region has not yet produced a single major player. Productivity in US technology firms has surged nearly 40 percent since 2005 while stagnating among European companies, according to IMF research. US research and development spending as a share of sales is more than double what it is in Europe. No European company ranks among the 10 largest tech companies by market share. 

Back in November 2022, Dean Karlan took the job as the first “chief economist” for USAID. In that position, he had a staff of about 30 whose task was to figure out the benefits and costs of different aid programs, with the goal over time of refocusing aid on problems and situations where the payoff was highest. In February 2025, Karlan resigned his position as USAID when he felt that political opposition made it impossible to do the job for which he had signed on.

Santi Ruiz interviews Karlan in “How to Fix Foreign Aid: USAID’s former Chief Economist reflects on DOGE” (Statecraft, July 31, 2025). Here are a few of the points that caught my eye:

On the role of the Chief Economist at USAID

There had never been an Office of the Chief Economist before. In a sense, I was running a startup, within a 13,000-employee agency that had fairly baked-in, decentralized processes for doing things. … [T]he reality is, we were running a consulting unit within USAID, trying to advise others on how to use evidence more effectively in order to maximize impact for every dollar spent. We were able to make some institutional changes, focused on basically a two-pronged strategy. One, what are the institutional enablers — the rules and the processes for how things get done — that are changeable? And two, let’s get our hands dirty working with the budget holders who say, `I would love to use the evidence that’s out there, please help guide us to be more effective with what we’re doing.’ There were a lot of willing and eager people within USAID. 

On the challenge of Congressional earmarking

[T]he number that I heard is that something in the ballpark of 150-170% of USAID funds were earmarked. … Congress double-dips, in a sense: we have two different demands. You must spend money on these two things. If the same dollar can satisfy both, that was completely legitimate. There was no hiding of that fact. It’s all public record, and it all comes from congressional acts that create these earmarks. … There’s an earmark for Development Innovation Ventures (DIV) to do research, and an earmark for education. If DIV is going to fund an evaluation of something in the education space, there’s a possibility that that can satisfy a dual earmark requirement. That’s the kind of thing that would happen. One is an earmark for a process: “Do really careful, rigorous evaluations of interventions, so that we learn more about what works and what doesn’t.” And another is, “Here’s money that has to be spent on education.” That would be an example of a double dip on an earmark.

How the Department of Government Efficiency (DOGE) intervention operated

There was not really any looking at any of the impact of anything. That was never in the cards. There was a 90-day review that was supposed to be done, but there were no questions asked, there was no data being collected. There was nothing whatsoever being looked at that had anything to do with, “Was this award actually accomplishing what it set out to accomplish?” There was no process in which they made those kinds of evaluations on what’s actually working. You can see this very clearly when you think about what their bean counter was at DOGE: the spending that they cut. … Throughout the entire government, that bean counter never once said, “benefits foregone.” It was always just “lowered spending.” Some of that probably did actually have a net loss, maybe it was $100 million spent on something that only created $10 million of benefits to Americans. That’s a $90 million gain. But it was recorded as $100 million. And the point is, they never once looked at what benefits were being generated from the spending. What was being asked, within USAID, had nothing to do with what was actually being accomplished by any of the money that was being spent. It was never even asked.

Francisco Flores also interviewed Karlan for the Economics that Really Matters (ETRM) website in “ETRM Interview Series – Dean Karlan”  focused on “the future of research in development economics, and for their advice to young researchers.” Here’s Karlan on the topic of broad political support for foreign aid:

[H]onestly, I’m not convinced that a lack of evidence is the main reason [foreign] aid isn’t more supported. It’s a bit of an oversimplification to say, “People don’t see the benefits, so they don’t support it.” There are many things governments do that only benefit a small segment of the population—like specific research initiatives or industry subsidies—and yet we still do them. If our standard were that every policy has to directly benefit 51% of people to be justified, we’d hardly get anything done. So, I don’t think that’s a fair criticism of foreign aid.

Also, the best evidence we can provide is about whether aid is effective—not whether it tangibly benefits, say, a middle-income family in Kansas. Sometimes there are material connections—like if USAID buys wheat from Kansas and a local farmer benefits—but those are exceptions. Most aid programs don’t have a direct economic payoff for Americans. Instead, the benefit is about soft power, about global leadership, and most importantly, about doing the right thing.

And that moral stand—that’s something a lot of Americans already live by. Most Americans donate to charity. Most care about others. We talk about ourselves as a generous, giving nation. So what’s wrong with living up to that identity as a country? Why shouldn’t our foreign policy reflect those values? … So I don’t think we need to show a financial return on foreign aid to justify it. And I don’t think a lack of direct benefit to Americans is the reason it sometimes loses support. 

Economists have been thinking for a long time about the operation of buying and selling in markets. However, they have traditionally spent less time studying what happens inside a firm–a setting in which forces of supply and demand are replaced by managerial decision-making. Anyone who has had both a good boss and bad boss knows that it makes a difference, but how and why? Alan Benson and Kathryn Shaw tackle the research on this question in “What Do Managers Do? An Economist’s Perspective” (Annual Review of Economics, 2025, 17: 635-664).  They write:

Economic activity requires motivating and coordinating individuals to work toward a common goal. These aims are the purview of managers. What, however, do managers actually do? We outline three defining principles of economic research on managers—technological determinism, skill distinction, and managerial self-interest—and relate them to the set of skills reported by managers on LinkedIn. We highlight “managers of people” and “managers of projects” as a useful distinction for categorizing theoretical, empirical, and descriptive accounts of managers. In light of our three principles, we review research on how managers can create value—namely, by hiring, retaining, training, monitoring, evaluating, allocating, and supervising. We propose that managers apply these skills in different proportions depending on the production technology in which they are embedded …

Empirical studies in this literature often involve finding data from within companies. For example, consider a company with a group of middle managers, all at the same level in the hierarchy, who oversee groups of workers. Moreover, say that workers sometimes are shifted from one manager to another, as business needs evolve. It may become apparent that most workers perform with higher productivity under some managers than others. What are some of the main themes that emerge from this research?

In hiring decisions, the evidence suggest that few managers are good at screening potential workers. A fairly robust literature finds that more productive workers are hired by a process that involves some mixture of highly structured interviews (so that answers are more comparable across applicants) or specific testing, or by direct observation of the person doing the job, when that is possible. But managers do a better job of hiring if the have incentives to overcome the biases that lead them to prefer hiring from their own friend-groups, social groups, or ethnic groups.

In retention of existing workers: “Perhaps the clearest evidence linking people skills and retention is provided by Hoffman & Tadelis (2021). Using data from a large high-tech firm, they find that survey-measured people management skills are highly correlated with greater subordinate retention: Replacing a manager at the 10th percentile in measured people skills with one at the 90th percentile corresponds to a 60% reduction in overall turnover and to declines in turnover among workers estimated to be high performers.” Retention is often easier when a worker and manager share a characteristic: for example, female managers are generally better at retaining female workers. There is also evidence that managers who are encouraged to focus on retention can often improve on this dimension.

In training and mentoring: “Sandvik et al. (2020) provide one of the most comprehensive recent field studies of how managers create value through training. They examine sales agents whose productivity may be tracked by revenue per call. Managers are responsible for improving sales agents’ performance through formal training, probationary screening, and ongoing feedback. Importantly, managers can encourage development by managing workplace knowledge flows, including by setting up policies that encourage peer learning from the best performers.” When it comes to mentoring, the approach that produces more productiv workers seems to be regular, mandatory, and broad-based mentoring, rather than selecting a smaller number of people for mentoring.

In the area of motivation: “For instance, Lazear et al. (2015) estimate a two-way fixed effect model in the context of supervisors of workers doing routine tasks. They find that the difference in productivity under a 90th-percentile manager and a 10th-percentile manager is equivalent to the productivity from an additional worker. Benson et al. (2019) estimate manager value added from the manager fixed effects in a regression with salesperson productivity. They find large differences in the productivity of sales workers under different managers: A worker under a 75th-percentile manager has nearly five times the sales of one under a 25th-percentile manager, which is approximately half the raw sales gap between workers at these quartiles.” Some of these differences in managerial ability seem traceable to differences in the “prosocial” skills of managers.

In the area of evaluating and monitoring, the research takes a certain need to limit cheating and shirking for granted, but focuses more broadly on how a manager can improve productivity fair process of evaluation can help in “providing workers with greater autonomy, enablement, and incentives for reaching prespecified outcomes, except in situations where a manager’s monitoring and supervision are required to check moral hazard Much of what economists refer to as monitoring also falls under what practitioners refer to as performance management, highlighting contemporary organizations’ emphasis on using evaluations for the dual purpose of evaluation and professional development (i.e., identifying and training high-ability workers).”

In the area of allocating, economists are familiar with the idea of “good matches” between workers and jobs that happen through markets, but managers often have the challenge of matching existing worker within the firm to the tasks that need to be done. For example: “Using data featuring manager job rotations at a large multinational company, Minni (2023) finds that good managers, defined as those revealed to be good by quick subsequent promotion, more actively move their subordinates both laterally and vertically and enhance their productivity and future advancement. Adhvaryu et al. (2022a), using data from an Indian garment plant, find that the most attentive managers enhance productivity by reassigning workers in response to particulate matter pollution.”

Economists probably still focus more on buying and selling within markets than on what happens inside firms, but digging into the inner workings of firms is becoming more common. This makes sense. Herbert Simon (Nobel 1978) wrote an essay on “Organizations and Markets” for the Journal of Economic Perspectives (where I work as Managing Editor) back in 1991, argued for the importance of looking inside the organizations of firms with a (to me) memorable metaphor. Simon wrote:

A mythical visitor from Mars, not having been apprised of the centrality of markets and contracts, might find the new institutional economics rather astonishing. Suppose that it (the visitor—I’ll avoid the question of its sex) approaches the Earth from space, equipped with a telescope that reveals social structures. The firms reveal themselves, say, as solid green areas with faint interior contours marking out divisions and departments. Market transactions show as red lines connecting firms, forming a network in the spaces between them. Within firms (and perhaps even between them) the approaching visitor also sees pale blue lines, the lines of authority connecting bosses with various levels of workers. As our visitor looked more carefully at the scene beneath, it might see one of the green masses divide, as a firm divested itself of one of its divisions. Or it might see one green object gobble up another. At this distance, the departing golden parachutes would probably not be visible.

No matter whether our visitor approached the United States or the Soviet Union, urban China or the European Community, the greater part of the space below it would be within the green areas, for almost all of the inhabitants would be employees, hence inside the firm boundaries. Organizations would be the dominant feature of the landscape. A message sent back home, describing the scene, would speak of “large green areas interconnected by red lines.” It would not likely speak of “a network of red lines connecting green spots.” …

When our visitor came to know that the green masses were organizations and the red lines connecting them were market transactions, it might be surprised to hear the structure called a market economy. “Wouldn’t ‘organizational economy’ be the more appropriate term?” it might ask. The choice of name may matter a great deal. The name can affect the order in which we describe its institutions, and the order of description can affect the theory. In particular, it may strongly affect our choice of the variables that are important enough to be included in a first-order theory of the phenomena.

In the old days of publishing the Journal of Economic Perspectives on paper–like last year–I didn’t worry about publishing an issue in August. Even if potentially interested readers were on vacation or away from the office, I figured that the physical paper copy of the journal would hang around in their mailbox, or perhaps in the piles of paper littering their desktop, and readers would eventually discover the issue.

But in 2025, the JEP and the other journals published by the American Economic Association shifted to online-only publication (although you can purchase a paper copy if you wish). Yes, the American Economic Association announced the Summer 2025 issue of the JEP back in mid-August, and yes, I announced it on this website. But were there potentially interested readers who missed or skimmed over those late summertime announcements, but who are now back at the grindstone of a new academic year? I don’t know, but there seemed little harm in sending out a reminder that the Summer 2025 issue of the JEP (where I work as Managing Editor) is freely available online–as it has been for more than a decade now. You can download individual articles or entire issues, and it is available in various e-reader formats, too. To entice you to take a look, here’s the Table of Contents.

Bitcoin and cryptocurrencies in general are no longer the bright shiny new thing. The Journal of Economic Perspectives, where I work as Managing Editor, was describing and discussing the crypto world a decade ago. What happened to all those predictions that Bitcoin would rapidly displace existing currencies? In “Crypto, tokenisation, and the future of payments.” Stephen Cecchetti and Kermit L. Schoenholtz discuss what has held crypto back, and argue that the momentum for “stablecoins” is unlikely to improve upon the possibility of “tokenization” run by ginormous global financial firms like JP Morgan and Black Rock (CEPR Policy Insights 146, August 2025).

Why have cryptocurrencies like Bitcoin not taken off as their enthusiasts predicted? Cecchetti and Schoenholtz write:  

By some estimates, there are over 20,000 cryptoassets – instruments whose ownership is recorded on a ledger based on some form of cryptography (FCA 2023). At this writing, these have a cumulative value of about $4 trillion, with Bitcoin accounting for roughly 60% of the total. While it functions as a store of value, outside of the crypto world Bitcoin is still neither a common means of exchange nor a popular unit of account. … When historians look back at the decades following Bitcoin’s introduction, they will ask: “Why has crypto not ‘taken off’ in the way its creators and early backers hoped?” We offer three tentative answers.

First, despite the hype about the speed and efficiency of digital transactions, it turns out that transfers of Bitcoin and Ether – the leading cryptoassets – remain slow and costly. On 14 August 2025, it took an average of more than 15 minutes to confirm a Bitcoin transaction. And that time varies widely: on several days in September 2024, it took more than 2,000 minutes! This variation makes settlement and finality difficult to predict. Small retail payments are especially costly (say, 5% for a payment of $20) in part because even the limited number of retailers who are willing to accept Bitcoin
in payment typically do not wish to hold it.

Second, the competition from traditional finance is intense, helping to lower costs and speed up payments. Consider, for example, the world of cross-border remittances. Critics argue that costs in the traditional sector are stubbornly high. In fact, for a standard-sized remittance, the average cost faced by a savvy consumer has halved over less than a decade to less than 3% (World Bank 2024, Figure 3). And there is strong evidence that further gains are coming. Indeed, for a range of recipient countries, Figure 3 shows how much less than the average cost (black bars) the cheapest provider (red bars) charges. The message is that as consumers gain familiarity with what is available, the benefits of competition among traditional providers are likely to intensify, further lowering average costs.

Third, while both governments and private groups are expanding their efforts to track illicit crypto payments, the reputational damage from criminal activity lingers. In addition, spectacular failures in the past – such as the collapse of the FTX exchange (Cecchetti and Schoenholtz 2022) – encourage consumer doubts about the reliability of crypto custodians. Similarly, dire headlines about crypto-related kidnapping and torture probably deter potential crypto users who do not trust custodians and instead would consider owning a digital wallet (Horvath 2025).

So what is taking off? The answer seems to be “payments stablecoins.” The key difference is that the value of a cryptocurrency like Bitcoin isn’t tied to anything else: indeed, some of those who buy Bitcoin are hoping for its price to rise. In contrast, the value of a stablecoin is based on the ownership of an underlying asset, like US Treasury bonds or a mutual fund that invests in high-quality bonds. Thus, the value of stablecoins is neither going to rise or fall by much–which makes them useful for transactions. Cecchetyi and Schoenholtz write:

‘[P]ayments stablecoins’ … are reserve-backed tokens with value pegged to government-issued currency, predominantly the US dollar. Smart contracts on the Ethereum blockchain control the two largest stablecoins, Tether’s USDT and Circle’s USDC. These originated as a stable-valued means of payment for people trading inside the crypto world. They quickly turned into the primary bridge between the traditional financial system and the crypto world, allowing investors and speculators to shift funds between traditional financial instruments (equity, bonds, bank balances, and the like) and crypto assets (Bitcoin, Ether, Solana, etc.). At this writing, this remains stablecoins’ primary use.

Ironically, stablecoin issuers (and some other promoters of crypto) are now strong advocates of government regulation. Their goal is to legitimise crypto in ways that can draw participants from the traditional financial system. Put slightly differently, the dream of a fully decentralised system operating without intermediaries or governments has given way to a far less radical vision that requires government oversight and the legal enforcement of property rights.

Thus, the Guiding and Establishing National Innovation for U.S. Stablecoins Act, for obvious reasons usually called the GENIUS Act, was signed into law by President Trump in June. It creates a short list of safe assets in which stablecoins are allowed to invest. It requires that stablecoins do not pay interest, although they can offer “rewards” to holders of stablecoins that look at lot like interest. It requires that stablecoins comply with rules like know your customer (KYC), anti-money laundering (AML) and anti-terrorist financing (ATF) standards–which is to say that they aren’t very anonymous.

But again, stablecoins are basically a halfway house for investors to move money between cryptocurrencies like Bitcoin and more conventional financial assets. They aren’t going to rise and fall in value, and they aren’t a useful method for carrying out other everyday transactions, either. So their ultimate usefulness seems limited.

Thus, Cecchetti and Schoenholtz point to the new kid on the block for financial technology: “tokenised deposits and tokenised money market funds.” In particular, they discuss “JPMorganChase’s tokenised deposit (JPMD) and BlackRock’s tokenised money market fund (BUIDL).” The first is still experimental; the second has just started. The idea here is that these products will not just be available to those with accounts at JPMorganChase and BlackRock, but any institutional (or approved) customer will be able to use these products to make deposits/withdrawals within the financial ecosystem of these giant firms.

Outside of China, JPMorganChase is the largest global bank (with assets of roughly $4 trillion) and BlackRock is the largest global asset manager (with assets under management of more than $12 trillion). When these gigantic institutions offer customers a product, they do it inside an ecosystem with tens of millions of existing customers and a wide array of complementary products and services. In this context, as the number of customers using JPMD or BUIDL increases, the internal (‘on us’) market will grow more liquid, with the potential for instant settlement both within and across borders at minimal cost. …

These tokenised assets differ from existing deposit accounts and money market funds in two important ways. First, they clear and settle around the clock. And second, the plan is that they will allow for programmable settlement and automated functions through smart contracts. They also can trade either on a proprietary centralised ledger or, using smart programming to provide access only to approved clients, on a public, distributed ledger. … Imagine, for example, that a few internationally active systemic banks decide to accept each other’s tokenised deposits instantly at par. In effect, they would be implementing a digital version of the 19th century US cheque clearinghouses that assured the expeditious settlement of most payments, imposed credit standards, and even acted as private lenders of last resort (Bernanke 2011). Such a 21st century clearinghouse would be a too-big-too-fail juggernaut.

In short, the financial technology revolution has come a long way since the Bitcoin enthusiasts of 10-15 years ago imagined circumventing national currencies and government regulations. The next iteration may be that a central method of settling everyday payments starts to happen with “tokenized” deposits and money market accounts run by financial megacorporations.

Compared to workers in most other high-income countries, Americans tend to work more hours per year. Here’s a figure from the OECD, which is based on taking the total number of hours worked in an economy and dividing it by the number of workers for the most recent year available. Because different countries will measure categories like “hours” and “workers” somewhat differently, the results should not be taken as precise.

But look at the size of the gaps! An American worker is at 1,811 hours/year, while a German worker is at 1,340 hours/year. If one thinks in terms of 40-hour work weeks, the German worker is working about 12 weeks per year less.

Juliet Schor offers a rumination on this issue in “Americans Are Overworked. Could AI Change That?” (Behavioral Scientist, August 2, 2-025). She writes:

[F]or many decades, the United States was a place where people worked less. Before 1900, American hours were lower than in a number of European countries, such as Belgium, France, Germany, the Netherlands, and Italy. The U.S. was first to go to the five-day week. In 1950, Germany, France, the U.K., Italy, and Spain all had longer hours. Even through the 1960s, work schedules in Europe exceeded those in the U.S. Then the two regions took different paths. U.S. hours stagnated and rose. Europeans continued a century-long trajectory of reducing work time.

This divergence seemed to start happening in the 1970s–which suggests that it is not the result of some deep-seated cultural difference going back a century or more, but instead resulted from more recent political and social choices. Schor suggests several underlying factors that might lead the American labor market toward more hours per worker.

As one example, many full-time workers in the US labor market get their health insurance through their employer. Most economists believe that although the employer writes the check to pay the cost, the economic value of health insurance is actually paid by workers in the form of wages that are lower than they would otherwise have been. Schor writes:

It [employer-paid health insurance] functions like a tax on employment, giving employers an incentive to hire fewer people for more hours. This was an accidental and unfortunate pairing; during World War II, employers began offering health insurance to attract workers because wages were controlled by the government to keep wartime inflation at bay. Little did anyone expect this would distort the labor market eighty years later.

Another reason, Schor argues, is that many US jobs are paid a salary, rather than a hourly wage. Of course, salaried workers do not receive additional pay if they work additional hours–and so employers have an incentive to push such workers for additional hours.

As Schor points out, the overall question is whether increases in productivity translate into higher wages or fewer hours worked. Through a variety of mechanisms like higher levels of unionization, European countries in the last half-century have generally used higher productivity to mean fewer hours worked, while the US has generally used higher productivity to mean higher wages. Schor writes:

 In recent decades, digitization has transformed work in many occupations and industries, but in the U.S. hours haven’t fallen. I’ve argued that’s due to biases in the economy that have operated against hours reductions. Europe has some of these biases, but stronger unions and welfare states and a more equal income distribution have reduced those pressures, so European countries have continued to translate productivity growth into free time. Since 1973, I’ve calculated that the U.S. has taken less than 8 percent of its increased productivity to reduce hours, while western European countries have taken much more—generally three to four times that amount.

Of course, there are tradeoffs for a society that makes choices to take productivity gains in the form of leisure, rather than in the form of increased income. Schor advocates for a gradual move to a four-day work week. Whether one agrees with that goal or not, her essay is a reminder that, often without any explicit consideration of the range of tradeoffs between leisure and income, political and social arrangements can strongly affect this choice over a few decades.