Author: conversableeconomist

Tyler Cowen interviews Joseph Stiglitz (Nobel ’01) on his “Conversations with Tyler” podcast: “Joseph Stiglitz on Pioneering Economic Theories, Policy Challenges, and His Intellectual Legacy” (June 26, 2024). It’s impossible to go over Joe’s monumental professional legacy research in a one-hour interview. As Tyler mentions, Joe’s CV now runs to 153 pages, which “is neither complete nor really has any chaff.” But here are a few of the high spots that caught my eye.

(I should note that I am eternally in Joe’s debt, because when he was chosen to be the first editor of the Journal of Economic Perspectives back in 1986, he hired me as the Managing Editor. Joe rotated off as editor after six or seven years and went on to other adventures, but I’ve been very pleased to hold the job ever since. Joe has extraordinary breadth across fields of economics, and I learned an enormous amount from talking about JEP-related papers and ideas, and economics in general. On a personal level, Joe always treated me with openness, friendliness, and fundamental decency.)

How looking at sharecropping practices in Nigeria led to a formalization of principal-agent theory:

[O]ne of the issues that, of course, as public finance economists, we worried about was the adverse incentive effect on taxation. If a government takes 50 percent of your product, we all say, “Oh, that’s a terrible system. It discourages work.” General sense in the United States is that even the top rate shouldn’t be higher than 40 percent. I think that’s wrong, but that was certainly a sentiment, a very strong sentiment.

Well, here you had sharecropping — not only in Kenya but many other countries around the world — where one-half to two-thirds of the produce was taken by the landlord. That was equivalent to a tax of 50 percent to 67 percent, and yet this was a prevalent form of tenancy, the arrangement that people had with a landlord.

One had to ask, why was that? How could this seemingly inefficient system persist for thousands of years? That was what motivated one of my most influential papers. That was the idea that there was a risk-incentive tradeoff, that in the absence of perfect information and the presence of a lot of risk, farmers couldn’t bear the risk of land ownership. If they owned the land, or rented the land more accurately, they’d have to absorb all the residual, the fluctuations in the weather, and all the other fluctuations, disease, that they would confront.

With sharecropping, they divided that risk, and a lot of the risk was borne by the landlord. That was a model of what came to be called the principal-agent problem, and it’s part of the incentive model that now is really fundamental. It was a first formalization of that basic incentive model that is now basic to modern economics.

On the impossibility of informationally efficient markets, a paper written with Sandy Grossman back in 1980:

The title of that paper was “On the Impossibility of Informationally Efficient Markets.” It was an argument against the view that was held by people like Eugene Fama that markets were informationally efficient, that they transmitted efficiently all the information from the informed to the uninformed.

We made the obvious observation that if that were the case, there would be no incentive for anybody to gather information. So the market might be transmitting information, but it would be all free information. It would be information that nobody had done any work to collect.

That idea, actually, in another context worries me very much today, that with Google and AI scraping so much information off of our newspapers, off of our podcasts, off of everything they can get a hold of, they’re trying to appropriate the value of the knowledge that’s been created by other people without paying for it. If they succeed in doing that, of course, that will decrease the incentives for others to produce information of high quality and of value. It’s that kind of interaction that was at the heart of our 1980 paper, and the themes that we talked about there are still the critical themes that we’re talking about today.

On how well the economy allocates credit–or not.

The issue here was that we weren’t very good at credit allocation and that we thought, let the market rip. We lowered interest rates. We deregulated, so we didn’t look at where the credit was going. The bank supervisors the Federal Reserve is supposed to oversee — and there are actually several other supervisors that are supposed to oversee the riskiness of the lending — that’s where the fault came.

Now, one of the things that, when I was at the World Bank and since then, I’ve emphasized very heavily: One of the signs that there’s a problem in the credit allocation is when you see a very rapid increase in the credit in one particular area. It’s a sign that, probably, people aren’t paying enough attention. Particularly, when we saw the increase in credit to housing, we should have been worried.

As it turned out, the banks weren’t doing the kind of diligence that they should have done. They were passing these mortgages on to investors, effectively lying, committing fraud. There have been a lot of cases of this, where they said, “Well, we’ve been very careful. We’ve inspected. These are mortgages originating in owner-occupied homes, people with this income.” They hadn’t done any of that, and all of that contributed to the financial crisis of 2008. So, the issue isn’t the amount of credit. It was the allocation of credit. If they had used that credit for productive uses, how much better our economy would have been.

Joe has left a different legacy in his hometown of Gary, Indiana, which is also the hometown of Paul Samuelson and the Jackson 5. Joe notes:

It was impressive, you might say an impressive trio in the library in Gary, Indiana. There’s a mural that they made recently. I went back to Gary just a few years ago, and they were very proud to show me the mural in which the Jackson 5, Paul Samuelson, and I are all on that mural.

The mural is 50 feet long and includes 22 people and places associated with Gary, Indiana, but here’s Joe standing in front of the part where his image appears behind him.

Claudia Goldin’s Nobel prize lecture, “An Evolving Economic Force,” has now been published in the June 2024 issue of the American Economic Review. Or if you prefer, you can watch the watch the lecture (with more numerous slides!) from the link at the Nobel website. She writes:

Women are now at the center of the world’s economies. Employment rates for women are at historic highs across the globe. Of the 165 nations … almost 60 percent have female employment rates (for those 25 to 54 years old) that exceed 0.70, and 80 percent exceed 0.50. For comparison, in the United States one-half of the women in that age group worked in 1970 and around three-quarters have done so ever since the early 1990s. … Women are at the center of the world’s economies not just because they are engaged in paid employment to a significant degree. They are rapidly becoming the better-educated gender, constituting the majority of college students in every one of the 38 OECD nations. Women do the vast amount of care-work across the world. And they largely determine the birth rate.

Regular readers of this blog will recognize these themes from earlier posts that have discussed Goldin’s work (for example, here, here, here, and here). For example, there is the well-known pattern that women’s work in the paid labor force first diminishes with economic growth, and then expands. This figure, taken from the Nobel lecture, shows the pattern of married US women who worked outside the home over time:

But here, I want to focus on a more recent pattern: the pay gap between men and women over the last 60 years or so. As the figure shows, the ratio of female-to-male earnings doesn’t move much from 1960 to 1980. At that point, there is a rapid rise in the ration, although not up to 1. Also, the earnings ratio for college-educated women levels off around 1995. So what is going on here?

The common economic story about the lack of change in the ratio during the 1960s and 1970s was that this was a time when entry of women into the paid labor force was especially high. Many of these women were older and lacked substantial paid work experience. Thus, the labor market entry of this group tended to hold down wages for women. As Goldin writes about this period:

The persistence of the gender gap in earnings was, in large part, due to the increase in women’s labor market participation, not despite it. As participation rates increased, women, whose job experiences were somewhat distant and brief, were pulled into the labor force. That put downward pressure on the earnings of the average working woman relative to the average working man. The stability of the gender gap in earnings given the increase in female labor force rates was a source of great frustration to those in the resurgent US women’s movement in the late 1960s and early 1970s. Banners at rallies decried the fact that women were working more, yet not being paid more relative to men. Most who interpreted the aggregate statistics as revealing a discriminatory process did not realize that the average job experience of working women was being depressed by the entry of less experienced and generally older women.

But by around 1980, this earlier process had run its course. A larger share women in the labor market had higher levels of education and paid job experience, and the wage ratio begins rising.

The current question is what explains the remaining wage gap–and in particular, the higher wage gap for college-educated women? As Goldin poses the question: “The question is particularly puzzling because today, many of the determinants of earnings are nearly the same between men and women and some, in fact, favor women. If earnings in competitive labor markets are determined by pre- labor market characteristics (such as education and training) as well as those pre-job (such as experience in previous positions), and if these characteristics have become nearly identical by sex and some favor women, what remains?”

Goldin brings some additional evidence to bear on this question. One fact is that the average ratio of female-to-male earning declines with age: “Earnings of women relative to those of men begin closer to parity (almost at 0.95 for the youngest age group in the most recent birth cohort), but that ratio decreases with age and thus with a host of other life cycle transitions. By their late thirties, the ratio for the most recent cohort shown is 0.8. The widening of the gender earnings ratio by years since college or professional school graduation is even steeper among higher income occupations, such as those in the corporate and financial sectors …

A substantial reason for the decline is that having children is for women associated with a substantial decline in hours worked and earnings. Goldin writes: “[T]he weight of the evidence is that the earnings of women plummet with the event of a birth and do not recover. Furthermore, most of the change comes from a reduction in hours of work or in participation, rather than from a reduction in earnings per hour, although that factor contributes somewhat.”

A final fact-based clue is that the difference in the female-to-male earnings ratio is more related to differences within occupations, rather than to men and women ending up in different occupations: “It is important to realize that the majority of earnings differences by occupation are within rather than across occupations (given around 500 occupations and a sufficiently large dataset).”

The last few decades in the US economy have been a time of growing inequality of incomes at the very top of the distribution. These jobs at the very top of the income distribution often involve extraordinary commitments of time, and the people holding these jobs not only work more hours, but their total salaries represent a much higher hourly wage rate, as well. To put it another way, there is a “part-time earnings penalty,” in which those who work part-time not only have fewer hours, but also earn less per hour. As an example, consider a man and woman who attend the same law school and perform equally well. For a few years, they earn very similar pay. But when the woman has children, her hours drop substantially, and she is no longer on the track to be one of the heavy-hour and top-paid partners.

The part-time earnings penalty does not apply across all jobs. Goldin writes:

How can the gender earnings gap be reduced? One part of a solution is to lower the cost of flexibility. The simplest way is to create virtual substitutes between workers. That has been done in various occupations that use IT to effectively pass information and hand off clients. Teams of substitutes could be created, as they have been in pediatrics, anesthesiology, veterinary medicine, personal banking, many tech jobs, primary care medicine, and pharmacy.

The case of pharmacy is instructive. The occupation of pharmacist in the United States today has almost no part time earnings penalty and the earnings gap between male and female pharmacists is small. But that was not always the case. In the 1970s a substantial fraction of male pharmacists owned a pharmacy and many hired female pharmacists. The gender earnings gap was substantial. Several changes occurred in pharmacy that greatly narrowed the gender gap in pay but that had nothing to do with gender issues. Technological changes enhanced substitutability among pharmacists, pharmacy employment in retail chains and hospitals increased, and independent pharmacies declined (Goldin and Katz 2016). Change in other occupations, such as pediatrics, did emanate from the demands of professionals who wanted to spend more time with their own children and formed group practices that facilitated substitutability.

The remaining female-to-male earnings gap, at least in the United States, seems linked to this mixture of motherhood penalty and part-time earnings penalty. Changing the motherhood penalty involves redesigning family interactions, which seems hard, while changing the part-time earnings penalty seems perhaps tricky, but also do-able.

When did the first recognizably modern markets for insurance emerge? Maristella Botticini delivered the 2023 Presidential Address to the European Economic Association. Drawing on a research paper written with Pietro Buri, Massimo Marinacci, she argues that insurance markets were born in 14th-century Italian maritime trading (“Presidential Address 2023: The Beauty of Uncertainty: The Rise of Insurance Contracts and Markets in Medieval Europe,” Journal of the European Economic Association, 21: 6, December 2023, 2287–2326; video of the lecture is available here). From the abstract:

Maritime insurance developed in medieval Europe is the ancestor of all forms of insurance that appeared subsequently. … [W]e show that medieval merchants had to bear more frequently natural risks (they traveled longer distances) and new human risks with unknown probabilities (they faced unpredictable attacks by corsairs due to increased political fragmentation and commercial competition in Europe). The increased demand for protection in medieval seaborne trade met the supply of protection by a small group of wealthy merchants with a broad information network who could pool risks and profit from selling protection through a novel business device: the insurance contract. A new market—the market for insurance—was then born. Next, analyzing more than 7,000 insurance contracts redacted by notaries and about 100 court proceedings housed in the archives of Barcelona, Florence, Genoa, Palermo, Prato, and Venice, we study the main features of medieval trade, the type of risks faced by merchants, and the characteristics of insurance contracts and markets from 1340 to 1500.

An insurance policy requires the ability to estimate risks of bad outcomes. It requires a group ready to pay premiums so that if the bad outcome occurs, they are protected. It also requires a group that expects to make a profit from selling this insurance over time, but has sufficient resources to pay the claims if and when a series of bad outcomes occurs.

The authors emphasize a number of factors that came together at the start of the insurance industry. The authors write:

First, thanks to major progresses in nautical technologies and techniques that punctuated the Commercial Revolution, maritime commerce took place over longer distances and all year round, whereas trade in the Mediterranean during ancient times typically occurred along the coasts and during the safer summer season. Traveling longer distances and all year round entailed having to cope more frequently with natural risks (e.g., thunderstorms). Second, starting from the late 13th and early 14th centuries, corsairs began disrupting trade routes in the Mediterranean, especially the ones along the Italian and Spanish coasts. Unlike pirates who disrupted seaborne trade since antiquity, corsairs were private citizens hired by governments and states to damage commercial competitors. Their presence and the way they conducted their business created previously unknown and much unpredictable risks to merchants who had to cope with a new type of uncertainty.

Using their data on these original insurance contracts, they can show that the price of the insurance premiums reflected the risks of the trip. Longer trips were exposed to more risks of bad weather. Certain routes were exposed to a greater risk of corsairs. Those who had better information about these risks were able to price insurance more effectively.

First, risks related to human activities (e.g., attacks by corsairs, warfare) seem to have had a relatively greater impact on insurance premia compared to natural risks (proxied by seasonal risks). Second, distance mattered but the route seems to have had a greater impact on insurance premia. Longer routes potentially increased the probability of losses from natural risks; however, these risks were mostly avoidable by choosing longer but safer routes. In contrast, regardless of distance, specific routes (e.g., in the Tyrrhenian and the western Mediterranean) were more plagued by human risks (e.g., attacks by corsairs) which were harder to avoid for the majority of sedentary merchants; these merchants did not have a broad information network compared to the few wealthy merchants, who became the key players in pooling risks and selling insurance in the early stages of the development of insurance markets.

Finally, I’ll add that new products can often face social disapproval for a time. For example, in the 19th century there was a time when life insurance was faced with moral disapproval, because it was gambling with God. It took a full-scale marketing campaign by life-insurance companies over several decades, often employing people identified with churches, to argue that actually life insurance was a responsibility that a good person owed to their family. In this case, a question of the time was whether “insurance” was actually a way of making a loan at a high interest rate, in violation of the laws against usury. For a time, some fancy footwork was needed to avoid such a charge.

In Genoa, insurance contracts were first disguised as a way to avoid charges of usury. Initially, an insurance contract was drawn up as mutuum, a fictitious sea loan resembling the foenus nauticum used in ancient times—a loan to be repaid only in the case of safe arrival of the shipment. Then, during the 14th century, the insurance contract took the form of a fictitious sale contract, and only in the 15th century became openly written as an insurance contract. Meanwhile, insurance contracts developed in Florence during the mid-14th century without the need of disguising them under fictitious sale contracts.

Anne Krueger has a remarkable resume, including Chief Economist at the World Bank from 1982-86 (where she is widely credited with substantially upgrading the quality of published research) and First Deputy Managing Director of IMF from 2001-2007. She converses with Shruti Rajagopalan for about an hour about a wide array of topics in “Anne Krueger Reflects on 50 Years of Rent-Seeking, Trade, and Economic Development” (Mercatus Original Podcasts, June 20, 2024).

I can’t do justice to the sweep of the conversation here, but some it focuses on the lead-up to a prominent 1974 paper by Krueger called “The Political Economy of Rent-seeking” (American Economic Review, June 1974). At the time the paper was gestating, in the late 1960s and early 1970s, Krueger was on the faculty at the University of Minnesota. But in the summers, often as part of US AID projects, she found opportunities to travel in places like Turkey, South Korea, and India. She talked with actual business people along the supply chain. For example, in one study she talked with Hindustan Motors in India and 50-60 of its suppliers. This process of gathering background information is wildly different than how most economists conduct research today. For example, she found that “each of these parts businesses have three sets of books: one for the tax man, one for the public and one for themselves to actually understand what was going on.”

Perhaps the key conclusion in the 1974 paper was that corruption wasn’t just a set of transfers or bribes from one group to another. Instead, “competitive rent-seeking” meant that firms were devoting considerable resources to finding ways to beat the government’s prohibitions and licenses and rules. As a result, the costs of those rules were more substantial than had been previously believed. As Krueger says:

At first it seemed amazing, but then after you realize, these guys are smuggling parts or these guys are importing in false pricing or whatever it is they’re doing, you figure out, there’s that. But then after a while when there’s so much of it, you realize this is not just simply a matter of me taking money out of your pocket, that you are indeed making your living doing that when you could be doing something productive instead. That was the fundamental thing, is not realizing it was there, which I think everybody knew. 

I remember a day or two on corruption in graduate school. I think what we were taught was that, when there’s corruption, it doesn’t much matter because it’s simply a transfer from one person to another. That would be true if it were one or two little isolated events, I suppose. Once everybody realizes that if they do this, that or the other thing, they’ll get more, then everybody competes for it. By that time they’re spending time and resources on it. By that time it is more costly.

A common justification for rules and regulations, and blocking imports, was that it was a necessary price to pay to give domestic industries some space to grow and develop. But Krueger argued that those in business didn’t really believed this justification. They just wanted less competition. Here’s an exchange from the interview.

RAJAGOPALAN: Even before 1965, the general consensus in the ’50s and ’60s was that free trade was really for the developed world, the Western world in the post-war period, and developing countries were doing the right thing by being protectionist, by having infant industry protection, import substitution, import licensing—you know the list better than I do. Did you ever buy into that orthodoxy, or were you always skeptical of it? If you did buy into it, what made you change your mind?

KRUEGER: I don’t know as I bought in. I think I recall someone at graduate school saying, “Yes, there might be an industry where you had high cost of startup, but if you then set it up, you would recoup your money and you would be able to take off the protection and be able to produce for world markets and stuff.” What I understood about India and about Turkey, was that they were not doing any part of that. Not only were the ones that were protected not thriving, they wanted more protection, and they were not at all thinking about the international market. They knew they couldn’t compete. There was some dissonance that way. I’m not so sure that the consensus, at least as I perceived it, was quite as strongly pro-import substitution and all that, as you are saying.

Krueger tells a nice story about her attempts to argue for open trade and macroeconomic stability in India at this time, and how her arguments were received.

At some point, I was coming back to India, and one of the secretaries and one of the important economic ministries said, “You’ve been selling this for years, but you’ve never heard the counterarguments. Come give a talk on a Saturday morning at our ministry. I’ll invite in the other chief secretaries and so on, and we’ll have a discussion.”

I went and had the discussion on the Saturday morning and made my pitch, which by that time was a little bit smoother than it was earlier on. And I knew India well enough, so I could apply it to India, no problem there. I was reasonably content with it as it finished up. The first question came from my host, and the first question was, “Now, madam, surely you know that India is a poor country. Surely you know that there are two kinds of goods: There are luxuries and there are necessities. Now, surely it would be criminal for a poor country to produce luxuries, and how could we possibly export necessities?” The discussion did not change very much from that level.

There’s the case for strict government control over the economy (“criminal .. to produce luxuries”) and zero exports (” how could we possibly export necessities?”) in a soundbite. India did not start its patterns of more rapid growth until that kind of thinking changed.

There’s no gain-saying that a substantial share of politicians and the public believe international trade is harmful to the US economy, and accordingly, also believe that enacting high tariffs or other barriers to trade will benefit the US economy. The argument has been ongoing for centuries, and I have no illusions of resolving it here. But I can provide some facts bearing on two of the more outrageous claims.

For example, back in 2018, then-President Donald Trump tweeted: “When a country (USA) is losing many billions of dollars on trade with virtually every country it does business with, trade wars are good, and easy to win. Example, when we are down $100 billion with a certain country and they get cute, don’t trade anymore-we win big. It’s easy!”

President Trump used executive orders to enact higher tariffs and other barriers to trade, and on trade policy, President Biden has largely followed in the same footsteps. So has this trade war in fact been easy to win? The upper figure shows patterns of US imports (blue line) and exports (red line) since 1980, measured as a share of GDP. The lower level shows “net exports,” which is the gap between the two lines, which can be used as a measure of the US trade deficit.

Anyone with a morsel of curiosity might wonder: “We’ve had much higher tariffs with 2018 or so, in particular with China but with other parts of the world as well. But the trade balance hasn’t changed–if anything, it’s a little worse than it was How can it be (one might wonder), that trade wars apparently aren’t so easy to win?

One obvious answer is that trade has many pathways through the world economy. Limits on direct trade with China can easily lead to China instead exporting to third countries (say, Vietnam), which then export to the US.

But the less obvious and more fundamental answer, taught in pretty much every intro econ class, is that a country’s balance of trade is not about whether other countries trade “fairly” or not. Looking at the trade deficit graph above, there’s no evidence that the up and down movements of the trade deficit track patterns of trade “fairness” by other countries. When the trade deficit getting smaller in the second half of the 1980s or from 2005-2010, you didn’t see major headlines about “global fairness in trade improving.” That’s because trade balances are about big macroeconomic factors.

If the trade balance is zero, then the value of foreign currencies earned by US exporters is equal to the value of US dollars earned by those who sell imports to the US economy. If a country has a trade deficit, like the US, then the value of foreign currencies earned by US exporters must be less than the value of US dollars earned by those who sell imports to the US economy. If a country has a trade surplus, like China, then the value of value of foreign currencies earned by China’s exporters–including the US dollars they earn–must be greater than the value paid in renminbi yuan by China’s importers.

How is this imbalance possible? To put it another way, countries like China around the world are earning US dollars as the US economy imports goods and services from them. We know that these economies are not using all of those US dollars to purchase US exports–if they did, the US would not have a trade deficit. So what are they doing with those US dollars? The answer is that they are investing the US dollars in financial assets, including US Treasury debt.

The US Bureau of Economic Analysis keeps track of total US holdings of foreign assets, and total foreign holdings of US assets. Because of the trade deficit, the gap between these two, the “net international investment position” for the US economy, is steadily dropping (as shown in the figure).

Is it possible for the “net” line between US liabilities to foreign investors and the assets of US investors in foreign liabilities to keep declining? There’s some controversy here, but the answer seems to be “yes.” The reason is that foreign investors in the US economy tend to buy bonds, which on average pays a lower interest rate, while US investors in foreign economies tend to buy ownership of a company, which on average pays a higher interest rate. As a result, US investors who hold foreign assets, as a group, earn more than foreign investors who hold US assets, as a group (for detail on these arguments, see this post from 2021).

I fear for some readers that what I just wrote is a blur of words, and didn’t carry sufficient clarity. It’s easier to explain in about the sixth lecture of an intro macro course, when I’ve had a chance to lay some groundwork! But the bottom line is that trade deficits are a macroeconomic outcome, based on whether a nation consumes more than it produces, or not, and on the types of financial investments that happen in different economies around the world. Broadly speaking, the US has a trade deficit because runs its macroeconomy in such a way that it consumes more than it produces (for example, with large budget deficits), while China has a trade surplus because it produces more than it consumes (for example, by pressuring household to have extremely high rates of saving).

But set aside the question of whether it’s easy to use tariffs to “win” a trade war (as opposed to just starting a trade war). It turns out that Trump was “burying the lede” back in 2018, as the news-people say. According to Trump, higher tariffs can also allow the US to get rid of the US income tax! Perhaps this potential benefit of tariffs might have been mentioned back in 2018? But for this proposal, both the arithmetic and the economics are a mess.

The basic arithmetic is that personal income in the United States was about $23 trillion in 2023. The US income tax collected $2.2 trillion. This is roughly half of US federal revenue: the other half is mainly payroll taxes to support Social Security and Medicare and the corporate income tax, along with smaller sources like the federal excise taxes on gas, alcohol and tobacco, the estate tax, and others. Imports of goods and services in 2023 (the number behind what’s in the chart above) was $3.8 trillion.

A simple-minded calculation would suggest that if you tax $3.8 trillion at a tax rate of around 60%, you could raise the $2.2 trillion. But of course, imposing a tariff of 60% would lead to dramatically fewer imports, and so you would need to tax the remaining imports at a higher rate to collect that $2.2 trillion. Thus, this idea of using tariffs to offset the US income tax would surely require a tariff rate approaching 100%, or more.

Proponents of tariffs seem to think of them a way of taxing foreign companies, without consequences for US households, but that belief is of course incorrect. Tariffs are essentially a form of a sales tax–a sales tax on imports. It would be possible to use revenue from a national sales tax (or a value-added tax) to replace the US income tax, but very few middle-income or low-income households would see that as a win, because they know that a higher sales tax leads to a higher purchase price at the cash register. Similarly, a 100% tariff on all imported oil, for example, would lead to a parallel increase in the price of gasoline.

Moreover, other countries would surely use higher US tariffs on their exports as an reason for imposing countervailing tariffs on US exports. In this scenario, firms and workers in US industries that depend heavily on the $3 trillion in US exports in 2023 (everything from farmers to pharmaceuticals) would see their global sales crater. The disruption to the US economy in this tit-for-tat tariff scenario would be dramatic at best, and depression-inducing at worst.

Again, I cannot hope to make the broader case for the benefits of international trade in a short post. But if you are left with grave doubts that tariffs are a useful way of reducing trade deficits and that tariffs can be a painless tool for eliminating the US income tax, my work for today is done.

The “Great Resignation” refers to a rise in the rate at which people were quitting their jobs starting in late 2021. It may look like much on a graph. This graph shows the monthly rate at which workers quit jobs voluntarily (thus, not counting retirements, health issues, or being laid off). You can see the blue line peaking at 3% per month, which if you work out the arithmetic, would me that over a 12-month period, a number of workers equal to 40% of the entire workforce would have quit their job. You can also see a gradually rising “quit rate” from the end of the Great Recession in 2009.

Ryan Michaels lays out three possible explanations in “What Explains the Great Resignation?” (Economic Insights: Federal Reserve Bank of Philadelphia, 2024: Q2, pp. 10-18).

In the graph above, the blue line is a “quit rate” calculated from the Job Openings and Labor Turnover (JOLTS) Survey, which is a survey of 21,000 establishments. The red line is data from the Longitudinal Employer and Household Dynamics (LEHD) data set, which includes nearly all workers and firms, but only comes out quarterly rather than monthly. An advantage of the LEHD is that you can track whether someone switches directly from one employer to another: a disadvantage is that you don’t know in the LEHD data if the worker quit voluntarily to take another job, or was laid-off and just found another job very quickly.

Michaels suggests three reasons why the quit rate may have risen:

According to the fast-growth narrative, the rise in quits was a byproduct of the fast economic recovery in 2021–2022. According to the telework narrative, quits rose because more workers transitioned to remote-work occupations. And according to the wealth narrative, the sharp increase in household savings during the pandemic enabled workers to spend more time away from paid work, and thereby induced quits.

After breaking down the labor market movements by industry and demographic groups, Michaels concludes:

Higher quit rates were observed for all industries and demographic groups, but the rise in quits was particularly sharp for younger, female, nonwhite, and non-college-educated workers. Many of these workers transitioned directly to another employer, but a majority left the workforce altogether. This suggests that changes in both the supply of labor (as illustrated by the wealth narrative) and the demand (as illustrated by the fast-growth narrative) contributed to the rise in quits. … The rise in quits was fueled by both stronger labor demand and weaker labor supply—a combination that should put upward pressure on wages. The acceleration in wage inflation appears to have in turn fed into higher price inflation.

US federal debt (that is, the accumulation of annual budget deficits) has been rising sharply. Here, I’ll sidestep the big-picture arguments about how this contributes to a slowdown in US growth rates and the risks of sustained inflation, and raises the longer-term risk of more dire financial crises. Instead, let’s just spell out some facts.

This figure shows the “gross” debt-to-GDP ratio, and the “held by the public” debt-to-GDP ratio. The distinction is that the federal government holds a lot of federal debt itself–especially in the trust funds for Social Security and Medicare, which are legally required to hold US Treasury debt. It’s often more useful to focus on debt held by the public, because this represents what the US government is drawing from capital markets outside the government itself.

As you can see, federal debt held by the public rose in the 1980s, with a combination of high Reagan-era budget deficits and interest rates. But it sagged back in the late 1990s. Federal debt held by the public was around 35% of GDP as recently as 2008. Now it’s up around 95% of GDP–a rise of about 60% of the ginormous US GDP in less than two decades.

Who is the “public” that is holding federal debt? Here’s a breakdown from the Peterson Foundation, based on the underlying US Treasury data. As you can see, about two-third of the debt held by the public is held by those in the US. Some of these holders are who you would expect: mutual funds, banks, pension funds, insurance companies, other investors. US Treasury debt is sometimes called the “safe asset,” so it will be a natural part of an investment portfolio for many institutions.

However, one substantial change in recent decades is the rising amount of US debt held by the Federal Reserve system. This figure shows federal debt held by the Fed, as part of its “quantitative easing” program. As you can see, the usual pattern of the last half-century was for the Fed to hold US federal debt equal to about 5% of GDP–an amount used for the Fed’s day-to-day financial duties. But from 2008 to about 2014, when the overall debt-to-GDP ratio rose by about 30 percentage points, the Fed ended up holding about one-third of that debt.

The Fed started to phase down its holdings of federal debt as a share of GDP, but then the pandemic hit, and the Fed stepped in once more, holding even more federal debt. Now, the Fed is again trying to phase down its federal debt holding–the Fed’s appetite for federal debt is not limitless–but it’s still far above the 5% of GDP baseline level that prevailed for the half-century or so before 2008.

Another big change is the amount of US debt held by foreign investors. The holdings of US federal debt as a share of GDP had been rising over time for a few decades. This isn’t a surprise: again, US debt is the world’s “safe asset,” so it’s a natural part of the portfolio for central banks and private investors in a globalizing world economy. You can also see that when the US debt-to-GDP ratio takes off around 2008, the holdings of foreign investors rise sharply–from about 15% of GDP to 35% of GDP, before sagging a little since then. T In what seemed like an increasingly risky world economy after 2008, investors around the world wanted to hold more of the “safe asset.” In that sense, paradoxically, the financial crises of 2008 made it easier for the US government to borrow. To put it another way, US debt held by the public rose about 30 percentage points of GDP from 2008 to 2014, and about two-thirds of that was accounted for by increased foreign holdings of federal debt.

However, since then, and even taking into account the additional rise in the US debt/GDP ratio associated with the pandemic, foreign holdings of US debt as a share of GDP have fallen. The appetite of foreign investors for US debt is not limitless.

There was an argument back in the 1970s, when I was first delving into economics, that the US didn’t need to worry overmuch about federal borrowing, because “we owed it to ourselves.” Well, the rise in foreign holdings of US government debt mean that we now owe about one-third of that federal debt–and the associated interest payments–to others outside the US economy.

The interest payments owed by the federal government (again, shown as a share of GDP) were relatively low for most of the time since 2000, because of the low interest rates. But with federal debt rising higher and interest rates rising as well, interest payments are spiking. The Congressional Budget Office says that in 2024, federal interest payments will exceed defense spending; by 2025, federal interest payment will exceed Medicare.

Back in 1628, Ambrose Acroyd published a book called Tables of Leasses and Interest. Acroyd was an administrator at Trinity College from 1615-1625, including a role as senior bursar–the modern equivalent might be “chief financial officer”–for several years. The first table of his book focused on a specific situation:

Acroyd’s unusual Table i envisions a very specific situation: one party owns an annuity with a full term of twenty-one years, several of those years have already expired, and the annuity owner wishes to pay to extend that annuity’s term back to a full twenty-one-years. Imagine someone possessing an annuity paying £1 annually for twenty-one years and fourteen years had already expired, leaving seven remaining. Acroyd’s first table states how much should be paid to “fill up” that annuity back to twenty-one years: in this case, £3 5s.11d., found on row “14” in the table.

This is an example of a “net present value” or “present discounted value” calculation: that is, how much does one have to pay in the present to receive a stream of income for some years into the future? Acroyd’s book is just one of several printed in England in the first few decades of the 17th century that included these kinds of formulas. These authors did not invent the formulas for translating a stream of future payments into a present value: the mathematics goes back at least to the Leonardo of Pisa (called “Fibonacci”) several centuries before. But why does this calculation become important at this place and time? Indeed, authors were still using his book and asking “who was Acroyd, anyway?” a century and more later.

William Deringer tells the story in “Mr. Aecroid’s Tables: Economic Calculations and Social Customs in the Early Modern Countryside,” Journal of Modern History, March 2024, 96:1). Peter Dizikes offers a readable short overview in MIT News (June 6, 2024).

The social problem came up as a result of a surge of price inflation. As Deringer writes:

Compared to prices in the decade 1501–10, average prices for foodstuffs in England were 3.0 times greater in 1551–60, 5.0 times greater in 1601–10, and 6.5 times greater in 1651–60. This constituted a radical break from prior experience, when prices had generally been stable, even falling slightly in the period from 1400 to 1500.

Thus, rents paid by tenant farmers had been essentially stable for a century or more. Raising those rents would have been viewed as an act of social aggression, and tenants could and did push back against it with protests and through the courts. But as price inflation arrived, the nominal rental payments became smaller and smaller. The landowners, including the Church of England and church institutions like Trinity College, were squeezed. Again, raising the nominal rents seemed socially and politically impossible. So the landowners reacted by raising the fee for entering or renewing a lease, called a “fine.” The size of these one-time “fines” was linked to the profitability of the land over the number of years of the lease, which in turn was determined by some combination of past experience and land surveys.

As Deringer emphasizes, 17th century England is a time and place when the logic of supply and demand and market outcome was not even in the social discussion. Instead, this was a time when payments were judged in terms of fairness, given the social roles and obligations of the parties. In this setting, the books of net present value formulas became the solution to a social bargaining problem faced by landowners and tenants after inflation had dramatically disrupted their earlier fixed-nominal-rent annual payments. Acroyd’s 1628 book was almost all tables, with very little text, but apparently, a number of the surviving copies had a Latin epigraph written into the beginning of many copies. them. Deringer explains:

The poem, comprising four elegiac couplets, does not appear to stem from any earlier source. A fairly literal translation would be:

Deviant fraud often afflicts us, but the forthright rule of the law of arithmetic teaches [us] what is useful and just. Nature taught mortals to cheat; perhaps it will be for art to prevent the treachery of frauds. Let the crowd of tricksters, the quarrelsome people cry out in protest [against this book]. Buyers, sellers, consult [it]! You will be prudent [to do so]. Those who promote fair exchange among people, if they wish to avoid praise for doing so, should be able to avoid ill-will for it as well.

This epigraph frames Acroyd’s book as an instrument for combating fraud and promoting just exchange. This tool is presented as accommodating buyers and sellers, landlords and tenants—whoever sought fair, prudent, and honest commerce. In leasing conflicts, both landlords and tenants might be guilty of certain frauds and deceits (fraus): landlords, by demanding exorbitant fines or deploying coercive tactics like selling reversionary leases; tenants, by concealing the value of their property or denying landlords their fair share by unreasonable appeals to custom. Arithmetical calculation could advance justice and harmony by enabling reasonable economic practices and curtailing fraudulent ones.

To put it differently, those of us in the modern age tend to think of formulas for net present value as part of financial decision-making, which it is. When an investor thinks about buying a stock, the investor needs to think about what present payment would be equal to the returns expected from owning that stock over time. When a bank lends money, it is calculating how much of a present payment (to you) would be equal to the amount you will repay over time. When you buy a home, you are thinking about whether the price you are paying can be justified by the stream of benefits you expect to receive while owning the home, along with an expected resale price in the figure. When government considers a program for building infrastructure or improving child nutrition or reducing pollution, part of the analysis is to compare the amount spent in the present to the value received over time. Deringer quotes a 2016 book by financial historian William N. Goetzmann to the effect that “the method of ‘net present value’ is the most important tool in modern finance.”

But back in the 17th century, the net present value formula served a different social function: most people didn’t understand the details of the math, but they understood enough of the basic idea to believe that the math represented a rule that placed limits on opportunistic behavior by both parties.

But as Deringer points out, this interpretation is both true and incomplete. The specific interest rates used by Acroyd to determine the present value payments made by “fines” were pretty high–in the range of 11-13%. When relatively high interest rates are used to discount future benefits, the present value of those payments will be relatively small. Thus, it turned out that well-to-do people and political favorites were often able to lease land from the Church of England at these preferential rates, while the less powerful who were trying to lease land in the marketplace were less protected. Deringer draws a trenchant parallel between the use of “net present value” in the 1600s and the use of algorithms to make decisions today. He writes:

Yet there is also something strikingly modern about how “Mr. Aecroid’s Tables” encoded economic equity in abstruse calculations. Particularly remarkable is how those recondite pages of figures were empowered to make judgments about what was fair and equitable on behalf of people with no understanding of how the mathematics worked. … We can assume that the vast majority of church tenants found those cryptic tables largely inscrutable. Based on evidence … so too did some of the church officials tasked with using them. If, from one direction, we might see Acroyd’s Tables as the evolution of medieval conceptions of just price, from another perspective we might see their adoption as an important early chapter in what Rodrigo Ochigame recently called “the long history of algorithmic fairness.”

Today, the use of opaque computational procedures, “black box algorithms,” to make socially contentious decisions is a familiar phenomenon. Across many fields, complicated questions about what is fair—a fair interest rate, a fair use of public resources or distribution of public benefits, a fair punishment—are delegated to algorithms, bolstered by a belief that they are less susceptible to human bias and error. …

From one perspective, those calculative techniques did succeed in creating a mutually acceptable, sustainable solution to the problem of dividing agricultural revenues on church lands. They provided landlords a way to increase their revenues over time to accommodate inflation, while protecting tenants against arbitrariness and exploitation. Ossified in institutional routines, Acroyd’s Tables came to function like a synthetic custom.

At the same time, the adoption of discounting on church lands was a component of other profound transformations in landlord-tenant relations, most notably the shift from a system in which tenants paid based on fixed “ancient” rents to one based on the surveyed profitability of the land. This seismic transformation was unquestionably to tenants’ detriment. The institutionalization of discounting tables on church lands shielded tenants on church lands from the worst consequences of this change. At the same time, though, we can reasonably speculate that the church’s policies served to legitimate this shift and exacerbated the erosion of customary protections for tenants on nonchurch estates. Insofar as many of those who benefited from favorable church leases were comparatively wealthy and well connected, the institutionalization of Acroyd’s algorithm might actually have served to harden preexisting disparities—a recurrent pattern in the subsequent history of algorithmic judgment. To put it another way: calculated fairness was a form of collusion, a bargain struck between a subset of interested parties without the input—and often to the detriment—of many others who never got to be part of the equation.

If you are someone from a high-income country, or even just a high-income city, and you travel to other places, you are familiar with finding that, at least sometimes, many items are considerably cheaper in the low-income country: food and meals, entertainment and even health care. As a result, $100 of buying power in the US economy seems to buy more goods and services in a number of other places around the world.

The World Bank International Comparison Program attempts to do an adjustment for prices around the world: that is, what would it cost to buy the same “basket of goods” (as economists say) in different countries. The result of this adjustment is to calculate a difference between the “market exchange rate” and the “purchasing power parity or PPP exchange rate. The market exchange rate tells you how much of one currency you receive in exchange for another. The PPP exchange rate adjusts for what that goods and services that currency can actually buy. Doing these “purchasing power parity” comparisons is a huge task, and so the ICP only updates the PPP numbers every few years. The 2021 comparison is now available.

When comparing the size of national or regional economies, a standard result is the economies of high-income countries look relatively smaller when measured in PPP terms than when measured in market exchange rates, because prices tend to be lower in the low-income countries. For example, the third panel of this figure shows that the high-income countries are 62% of global GDP if measured in market exchange rates, but 46% of world GDP measured in “what you can buy with it” PPP exchange rates.

Using PPP exchange rates to compare the sizes of economies means that China is already the largest economy in the world. The ICP report notes:

The largest economy in the world in 2021 was China, recording a PPP-based GDP of $28.8 trillion, reflecting 18.9 percent of the global GDP. The United States was the second largest, with nearly $23.6 trillion or 15.5 percent of the global GDP. India’s economy was the third largest at $11.0 trillion, accounting for 7.2 percent. Also in the ten largest economies group were the Russian Federation ($5.7 trillion and 3.8 percent), Japan ($5.6 trillion and 3.7 percent), Germany ($5.2 trillion and 3.4 percent), Brazil ($3.7 trillion and 2.4 percent), France ($3.6 trillion and 2.4 percent), the United Kingdom ($3.5 trillion and 2.3 percent), and Indonesia ($3.5 trillion and 2.3 percent).

Here’s a snapshot of the world economy through a PPP lens. The vertical axis shows the share of global population for each country (thus, China and India are the largest), while the horizontal axis shows per capita GDP as measured using the PPP exchange rates. If you go to the link, you can click on each of the individual bars to see what country it represents.

To have a sense of the differences in price levels across countries, and thus a sense of the size of the adjustment made by the PPP exchange rate, the ICP sets the average for world price level across countries equal to 100. From that baseline:

An index over 100 indicates prices are higher relative to the world average, while a PLI [Price Level Index] of less than 100 indicates lower prices. The most expensive economy in the world was Bermuda, with a GDP PLI of 194, followed by the Cayman Islands, Switzerland, Israel, Iceland, and Australia. The United States ranked ninth in the world with a GDP PLI of 158. High-income economies had a GDP PLI of 136 … . Upper-middle-income economies had a GDP PLI of 81 … For lower-middle-income economies, … while in low-income economies, the average GDP PLI was 50 …

It may have occurred to the reader that measuring price levels in a comparable way across all the countries of the world, in a way that adjusts for differences in quality and availability of various goods and services, is a Herculean task. There’s a reason why the estimated PPP exchange rates for 2021 are being published in 2024–it takes time to put all this together. The report describes the methodology in some detail, but there is room for skepticism. Indeed, back in in 2010 Angus Deaton devoted his Presidential Address to the American Economic Association (freely available on-line here) to detailing the ”weak theoretical and empirical foundations” of such measurements. But for anyone who has read this far, it will come as no surprise that imperfect economic statistics can still be useful, when applied with context and caution. 

Evidence supports the belief that if people had better access to jobs and income, they would be less likely to commit crimes. But there is a caveat. The reduction occurs in property crime–which, to be clear are more than 80% of all crimes–but not in violent crimes. Jens Ludwig and Kevin Schnepel describe the pattern in Does “Nothing Stop a Bullet Like a Job? The Effects of Income on Crime” (April 2024, Working paper 2024-42, Becker-Friedman Institute at the University of Chicago, as submitted for a future issue of the Annual Review of Criminology). They write in the abstract:

The best available evidence suggests that policies that reduce economic desperation reduce property crime (and hence overall crime rates) but have little systematic relationship to violent crime. The difference in impacts surely stems in large part from the fact that most violent crimes, including murder, are not crimes of profit but rather crimes of passion – including rage. Policies to alleviate material hardship, as important and useful as those are for improving people’s lives and well-being, are not by themselves sufficient to also substantially alleviate the burden of crime on society.

This study is a review of existing evidence, not a new set of evidence. The authors focus on how randomized studies that provide jobs or income, or studies that look at “macro” variations in jobs and income across geographic areas or over the business cycle. This table summarizes the findings: The point in the middle shows the central estimate of a given study, with the bar showing the range of statistical uncertain around that central estimate. The evidence across studies is mixed, unsurprisingly. But there are a bunch of studies showing an effect on property crimes, and not many showing an effect on violent crime.

As the authors point out, programs to improve access to jobs or income may be worthwhile for their direct benefits to people, as well as their effects of reducing property crime. They are not arguing that such programs are not worthwhile, only that they don’t much affect violent crime.

This finding may feel counterintuitive. After all, don’t we all “know” that violent crime is more likely in low-income neighborhoods? The authors point out that what we “know” is only partially correct. Yes, some low-income neighborhoods have high levels of violent crime, but many others do not. Why some areas are violent but others are not is a question going beyond issues of jobs and income. The authors write (references to figures omitted):

Note what these results can and can’t tell us. It’s possible that much larger, massive changes in income could have different effects. This sample of studies can’t speak to that. But we would mention, as an aside, that arrest rates among NFL players ($2.7 million is a frequently mentioned average salary) are lower than among the general population for property crimes, but that’s not true for violent crimes (Leal et al. 2015). Taken together, the best available data and evidence suggest that economic conditions contribute importantly to property crime but are not the key driver of the crime problem itself–that is, of violent crime. The things that matter for violence seem to be correlated with income poverty but are not the same thing as income poverty.

To see this, examine the pattern across Chicago neighborhoods. Every rich neighborhood is safe. And every one of the high-gun-violence neighborhoods is poor. But there is enormous variability across low-income areas in their rates of gun violence. We see a similar pattern across countries: Almost every rich country (except the US) is quite safe with respect to their murder rates, while all the most unsafe countries – Mexico, Brazil, Nigeria – are quite poor. But it’s not true that every poor country is dangerous. With respect to violence, poverty is not destiny. Something else is clearly going on.

If anything, the evidence seems to be at least as strong for the reverse relationship: Uncontrolled violence exacerbates poverty and joblessness. Exposure to community violence harms children’s schooling outcomes and the mental health of both parents and children (Sharkey, 2018). … Local economic development is hard when people and businesses are fleeing to safety. The flip side is that anything that helps control violent crime problem can be a massive tailwind for community development efforts.

In short, the flow of causality is not that lack of jobs and income leads to violent crime in a given area, but rather that violent crime in an area contributes to a lack of jobs and income in that area. Addressing violent crime seems likely to require non-economic tools.