Conversable Economist

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

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

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

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

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

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

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

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

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

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

I don’t actually expect that writing about US budget deficits and the accumulating federal debt, as I do from time to time, will have any near-term effect. But given the current trajectory, a time will come when decisions need to be made, and having a grip on the underlying dynamics is always a useful starting point. In that spirit of humble and diminished expectations, here are some figures from the just-released Congressional Budget Office report, “The Budget and Economic Outlook: 2026 to 2036” (February 2026).

This figure shows federal deficits as a percent of GDP, going back a half-century to 1976 and projected forward through 2036, based on current law. You can see that the current level fo deficits, and those projected for the next decade, are higher than any years except for the spikes during the Great Recession of 2007-09 and the Covid recession. If you are able to remember the concerns about the size of budget deficits during President Reagan’s term of office in the 1980s, we are comfortably above that level, and projected to stay above it.

The figure also divides the deficit into “primary” and “net interest”: that is, the “primary” deficit is what the deficit would look like if the federal government didn’t need to pay interest on past borrowing. The problem for the future, as you can see, is that the primary deficit is falling, but interest outlays are rising. This pattern creates a bad dynamic, where rising future deficits are being driven by rising interest payments.

I occasionally see some social media boasting about how revenue from the tariffs are going to fix federal deficit problems. Here’s a breakdown of federal revenues. Customs duties are tshow by a dark line near the bottom. You can see that they have definitely risen. This increase is taken into account, and projected into the future, by the deficit estimates given above.

On the spending side, it’s conventional to divide it into what is classified as mandatory, discretionary, and interest payments. Mandatory includes Social Security, federal health care programs, income support programs, retirement for federal employees, and some other items. Discretionary spending is at present about half made up of defense spending, and the other half made up of many smaller programs, including federal spending on education, transportation, veteran’s benefits, and other items. As a share of GDP, discreitional spending is falling over time, while mandatory spending is rising. In 2026, it’s already true that federal interest payments on past borrowign exceed national defense spending. Byt 2036, interest is projected to be more-or-less equal to all discretionary spending.

The costs of excessive federal debt are gradually becoming apparent in various ways. The inflation spike a few years ago was, in part, driven by the spike in federal deficits. The arguments between the Trump administration and the Federal Reserve over interest rate policy are driven, in part, by the recognition that high federal interest payments are crowding out other possibilities for greater spending or tax cuts. Concerns over large US trade deficits are created, in part, because the very large US budget deficits are creating an ongoing surge of US demand for imports. Concerns over slow rates of US economic growth are driven, in part, by concerns that high federal borrowing is crowding out some opportunities for private-sector investment. The current trajectory of federal deficits and debt seems sustainable for awhile longer, in the sense that it doesn’t seem likely to cause a near-term crisis, but that doesn’t make it a desirable path for the US economy to follow.

In the modern world, many of the topics about which people have opinions are some distance away from any actual experience or expertise that these same people have. In this situation, people are unlikely to form their opinions with a deep dive into relevant history, data, and academic research. Instead, they are more likely to turn to a mixture of personal experience and mental models. In a 2018 paper in Behavioral and Brain Sciences, Pascal Boyer and Michael Bang Petersen referred to this as “folk economics” and discuss “Folk-economic beliefs: An evolutionary cognitive model.”

More explicitly, the authors write in the abstract for their paper:

The domain of “folk-economics” consists in explicit beliefs about the economy held by laypeople, untrained in economics, about such topics as, for example, the causes of the wealth of nations, the benefits or drawbacks of markets and international trade, the effects of regulation, the origins of inequality, the connection between work and wages, the economic consequences of immigration, or the possible causes of unemployment. These beliefs are crucial in forming people’s political beliefs and in shaping their reception of different policies. Yet, they often conflict with elementary principles of economic theory and are often described as the consequences of ignorance, irrationality, or specific biases. … Here we propose that the cultural success of particular beliefs about the economy is predictable if we consider the influence of specialized, largely automatic inference systems that evolved as adaptations to ancestral human smallscale sociality. These systems, for which there is independent evidence, include free-rider detection, fairness-based partner choice, ownership intuitions, coalitional psychology, and more. Information about modern mass-market conditions activates these specific inference systems, resulting in particular intuitions, for example, that impersonal transactions are dangerous or that international trade is a zero-sum game. These intuitions in turn make specific policy proposals more likely than others to become intuitively
compelling, and as a consequence exert a crucial influence on political choices.

To give a sense of this line of thinking, here is a (nonexhaustive) list of eight folk-economic beliefs from the authors:

• International trade is zero-sum, has negative effects.
• Immigrants “steal” jobs.
• Immigrants abuse the welfare system.
• Necessary social welfare programs are abused by scroungers.
• Markets have a negative social impact.
• The profit motive is detrimental to general welfare.
• Labor is the source of value.
• Price-regulation has the intended effects.

This list is intended to be descriptive, rather than critical. Many of these beliefs have some mixture of truth and falsehood, depending on the setting. Folk economic beliefs need not be consistent with each other: for eample, there is some conflict between the beliefs that immigrants are simultaneously taking a large number of jobs while also not working and receiving welfare benefits. The beliefs need not govern the personal behavior of individuals, either: for example, someone who believes that labor is the source of value might also rent out an extra room to a college student. The central point is that these themes are part of how a given culture has evolved, and are widely believed in a way that influences political and policy beliefs. The authors write:

As an illustration, we can consider that optimization problems such as hunting, foraging, choosing the best mate, selecting nutritious foods, and garnering social support were present, and relevant to fitness, throughout human evolution. By contrast, urban life, mass communication, rapid long-distance travel, and mass-market economies only occurred for a small duration and only in some places at first. So it is more plausible that human minds were selected for systems geared to the first kind of adaptive problems, than to the second. … The human mind, in other words, contains a rudimentary exchange psychology, evolved by natural selection to help facilitate transactions. Although it evolved within ancestral small scale hunter–gatherer groups, the cues inherent in modern markets economies (transactions, bargaining, prices, etc.) also bring it online. However, market economies are a novelty at the scale of biological evolution, so we should not expect specific adaptations to their features, as the differences between ancestral exchange and the market are vast (Rubin 2003).

As an example of how this analysis works in practice, consider the first belief that that international trade is zero-sum or negative in its effects. The authors argue that this folk-economic belief is built on beliefs about coalitional dynamics that have been relevant for much of the history of homo sapiens. They write:

[T]he statement that international trade has negative consequences … contains several pieces of information likely to activate specific inference systems. Let us consider a news headline like “China sells more to the U.S. than to Russia.” Selling involves receiving resources and, importantly, resources in this case transfer from one nation to another. In psychological terms, nations are “imagined communities” (Anderson 1983) or, with the vocabulary presented above, nations are coalitions to the mind and, hence, mention of nations activates the coalitional psychological machinery (Gat 2006; Hechter 1987a). Nations are exclusive groups, citizens of a nation are assumed to have common interests, and nations are equipped with armies to fight each other. The activation of this machinery has the downstream consequence, we argue, that Americans will evaluate the transfer of resources to China – and, hence, the headline – negatively. As argued above, one key assumption of the coalitional system, once activated about two categories or groups, is that there is a zero-sum interaction between the mutually exclusive groups. As a consequence, there is a strong prior belief that any advantage to another group is detrimental to one’s own (Hiscox 2006). Any information to the effect that other groups are prosperous, or getting better, is equivalent to a threat-cue, indicating that our group stands to lose out. … To maintain stable and efficient coalitions, humans in many different contexts must have assumed that other groups’ advantage was a potential loss.

Viewing the “international trade is bad” belief as supported by coalitional psychology does not just explain the belief but also suggests novel testable predictions. In particular, we should expect the view that trade is bad to be particularly attractive when the trading crosses perceived coalitional boundaries. It is predicted to invariably occur in the context of, precisely, debates about trade between countries. American consumers may find it intuitive that the United States might suffer from Chinese prosperity, but, on this theory, they would find it less compelling that development in Vermont damages the economy of Texas. Similarly, the survival value of the belief might depend on the relationship between the countries. Trading between long-term allies (e.g., trading between Great Britain and the United States) should be viewed as less problematic than trading between rivals (e.g., trading between China and the United States), even if all else were equal.

The notion that human brains are still adjusting to a world of impersonal exchange makes sense too me. From this perspective, one of the challenges for economics in the world of public policy is to explain economic insights in a way that sidesteps and does not trigger folk economic beliefs.

In editing two papers on Asian American immigration for the Winter 2026 issue of the Journal of Economic Perspectives (where I work as Managing Editor), I found myself musing over two alternative histories: one about more such immigration, one about less.

Hannah Postel describes “Asian Immigration to the United States in Historical Perspective” (Journal of Economic Perspectives 40:1, 191–214) from the later decades of rhe 19th century up to the present. As she points out, Asians were the first group to be singled out for discriminatory treatment by US immigration laws, and the first to be excluded from immigrating. Postel writes:

The Chinese became a target of American racial animus and the nation’s first federal immigration laws during the late nineteenth century. By the 1870s, about 15,000 to 20,000 permanent Chinese immigrants were arriving each year … For perspective, the total US population in 1870 was about 38 million, or one-ninth its current level. The 1875 Page Act excluded Chinese women, thereby precluding Chinese family formation for almost a century. The 1882 Restriction Act limited future Chinese immigration to the United States and was extended in 1888 to “cut off circular Chinese migration, [pour] ten times the funding into enforcement, [eliminate] many avenues for fraud, and [develop] new systems of management and deportation” (Lew-Williams 2014, p. 27). New Chinese immigration then dropped to near-zero for decades. The Chinese immigrant population in the United States peaked at 106,688 in 1890, falling to a low of 45,757 in 1920.

US employers then turned to Japanese immigrants in the early 20th century, but that flow of immigration was shut down by a 1907 agreement, and then expanded to a continent-wide “barred zone” in 1917. The Phillipines had been ceded from Spain to the United States at the end of the 1898 Spanish-American war. By the early 1930s, more than 100,000 Filipinos had emigrated to Hawaii (which doesn’t become a US state until 1959) and another 30,000 had emigrated to California. This Asian immigration was then shut down by the 1934 Tydings-McDuffie Act, which as Postel writes “deemed Filipinos `foreign aliens’ subject to a quota of 50 new immigrants per year.”

Thus, my first alterate history is how the United States would have been shaped for several decades after the 1870s if the Asian exclusion laws had not been passed. For example, Asian immigration during this period would surely have been much larger. The population of west coast states like California (becomes a US state in 1850), Oregon (1859) and Washington (1889) might have grown much more rapidly. The share of seats for these states in the House of Representatives might have been substantially large, and the economies of the western states might have grown much more rapidly. The inherited cultural history of the western United States might have diverged substantially from US states that fought for the North during the US Civil War, as well as states that fought for the South.

The other paper in the JEP symposium by Gaurav Khanna, “From Asia, with Skills” (Journal of Economic Perspectives 40:1, 215–40) focuses on more recent experience of Asian American immigration. Overall, as Postel notes, “The Asian-identifying population is now nearly 8 percent of the US population, having more than doubled since 2000.” Khanna focuses on the insight that this wave of Asian American immigration has been predominantly high-skilled. He write:

Migration from countries in Asia in the last three decades or so—and especially China and India, as well as South Korea, the Philippines, and Japan—has occurred with unprecedented scale and impact. This wave of migration to the United States has been predominantly high-skill. In 2019, among working adults in the US labor force, 78 percent of Indian-born and 63 percent of Chinese-born workers held at least a four-year college degree, compared to 39 percent of US-born workers (ACS 2019). Between 1990 and 2019, the share of college-educated workers in the United States from these five Asian countries doubled to 7.3 percent. Over this period, migration from these countries contributed to over 38 percent of the growth in US employment of software developers, 25 percent of the increase in scientists and engineers, and 21 percent of the growth in physicians (ACS 2019).

Khanna goes into some detail about the conditions of demand for skilled workers in the US economy, the supply of skilled workers in these five Asian countries, greater economic opportunities in the US economy, changes in immigration laws since 1965, and other factors and events driving this change. My second alternative history involved musing about the shape of the US economy in recent decades if this supply of Asian immigration had remained highly constrained.

The overall picture of US colleges and universities would look rather different if international students from China and India had not increased by about 400,000 from 2007-2023. In technology fields, “In 2010, 27 percent of all foreign-born workers and 35 percent of foreign-born information technology workers in the United States initially arrived on a student visa. Asian-born students are heavily concentrated in science and engineering fields, such as computer science, engineering, and mathematics. … By 2010, one-third of US information technology workers were foreign-born, predominantly from India. By 2019, Indian-born individuals were 29 percent of all workers in science, technology, engineering, and mathematics jobs in the United States.”

In the area of entrepreneurship and corporate management, Khanna notes (citations omitted):

Asian-born immigrants have also played prominent roles in entrepreneurship, particularly Chinese and Indian immigrants in the tech sector. In the late 1990s, Chinese- and Indian born engineers were running one out of every four US tech startups, and a similar fraction had at least one immigrant founder. More generally, immigrants are twice as likely to start new businesses as natives. In 2023, 44.8 percent of US Fortune 500 companies were founded by immigrants (from anywhere in the world) or their children (AIC 2023); in 2022, 55 percent of America’s `unicorn’ startups (privately held startups with total stock market capitalization over $1 billion) had an immigrant founder. As of 2024, chief executive officers of giants like Google, Microsoft, Adobe, Cognizant, and IBM were born and educated in India. Indian-born CEOs of Fortune 500 companies are also found in other sectors of the economy, including technology (Micron Technology, Microchip Technology), consumer goods and retail (Starbucks, Chewy, Albertsons), technical services (FedEx, Cognizant, Honeywell, Jacobs Solutions), and pharmaceuticals (Vertex). … Looking ahead at the race for artificial intelligence startups, a recent analysis found that nearly two-thirds of the Forbes top 50 artificial intelligence startups had an immigrant founder, with Indian entrepreneurs leading the pack with 10 startups …

In the provision of health care, as Khanna notes, “In 2023, according to the Association of American Medical Colleges (2024), one in four active US physicians were international medical graduates. In 2016, migrants from Asia made up 17.7 percent of all US physicians and surgeons (Patel et al. 2018). Physicians are more likely to be from India, with recent growth in the number of physicians from China. The Philippines, on the other hand, is an important source of the nursing workforce in the United States. Just before the Covid-19 pandemic, there were 136,000 Filipino nurses and 78,000 Indian-born physicians in the United States. Migrants from Asia—and in particular, China, India, and the Philippines—represent 20 percent of the growth in the US physician population and 11 percent of the increase in the US nurse population between 1990 and 2019.”

When noting the actual contributions of immigrants to the US economy, one common response is along the lines that, if only the immigrants had not been here, those jobs would have been filled by native-born Americans. In some situations, for some jobs, I’m sure that’s true. But the opportunities and flexibility of the US economy is also a magnet for those with high skills from all over the world–including especially the two largest-population countries in the world of India and China. It does not seem to me that US medical schools, for example, have been standing by, ready and waiting to train massively more native-born doctors and nurses. I am dubious that a legion of native-born entrepreneurs or AI-talented managers have highly profitably business plans in hand–but are somehow being shut out by immigrants with skills in these areas.

Frankly, it doesn’t matter to me if potential native-born CEOs are displaced by immigrant talent. (I don’t spend much time worrying about the income and welfare of CEOs.) But there is strong evidence that new and successful companies increase the wage and job opportunities available to a wide swath of middle-class and lower-income workers, which matters to me a lot more. Asian American immigration seems clearly to be one reason why the sustained and solid growth of the US economy has in recent decades exceeded that of other high-income countries, like those in the European Union. I would be delighted to see the US education system, from K-12 and up through higher education, produce a sharp rise in the US share of high-skilled workers. A high-income economy is driven forward by high-skilled workers, and the more of them the better.

I have been the Managing Editor of the Journal of Economic Perspectives since the first issue in Summer 1987. The JEP is published by the American Economic Association, which decided back in 2011–to my delight–that the journal would be freely available online, from the current issue all the way back to the first issue. You can download individual articles or entire issues, and it is available in various e-reader formats, too. Here, I’ll start with the Table of Contents for the just-released Winter 2026 issue, which in the Taylor household is known as issue #155. Below that are abstracts and direct links for each of the papers. I plan to blog more specifically about some of the papers in the few weeks, as well.

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Symposium on Fertility Rates

“The Likelihood of Persistently Low Global Fertility,” by Michael Geruso and Dean Spears

     For the world as a whole, average birth rates have been falling for decades—from about 5 in 1950 to a little above 2 today. Two-thirds of people today live in a country where the birth rate is below an average of two children per two adults, which means below the fertility level needed to sustain population sizes (without net migration). In this paper, we assess whether low fertility is likely to persist as a global phenomenon. We distinguish cohort birth rates, which matter for generation-to-generation population change, from period birth rates, which present a snapshot of birth rates at a point in time, but may offer less insight on longer-run possibilities. Where cohort birth rates have fallen low, they have not subsequently rebounded. We show that both increasing rates of lifetime childlessness and smaller family sizes among parents have contributed to falling cohort birth rates. Pronatal policies, we discuss, can have large effects on the annual fertility data without substantially changing the average number of children women have over their lifetimes. Although future birth rates remain uncertain, we conclude from the evidence that, over a long horizon, persistent low fertility is a likely future.

“How Much Would Continued Low Fertility Affect the US Standard of Living?,” by David N. Weil

     I assess the effect of continued sub-replacement fertility on age-adjusted consumption per capita. Channels assessed include transfers from working-age adults to children and the elderly, the effect of the labor force growth rate on required capital investment, sustainability of government debt, the interaction of population size with fixed natural resources (including a clean environment), and the effect of population size on the speed of technological progress. To isolate the effect of low fertility from other ongoing demographic changes, I use simulation models as well as projections from the United Nations and Social Security Administration that vary fertility rates while holding other factors constant. My main finding is that the impact of low fertility is likely to be negative but small. In addition, this negative impact arrives only after a long adjustment period. An increase in fertility back to the replacement rate would lower the standard of living for several decades.

“Family Institutions and the Global Fertility Transition,” by Paula E. Gobbi, Anne Hannusch, and Pauline Rossi

     Much of the observed cross-country variation in fertility aligns with the predictions of classic theories of the fertility transition: countries with higher levels of human capital, higher GDP per capita, or lower mortality rates tend to exhibit lower fertility. However, when examining changes within countries over the past 60 years, larger fertility declines are only weakly associated with greater improvements in human capital, per capita GDP, or survival rates. To understand why, we focus on the role of family institutions, particularly marriage and inheritance customs. We argue that, together with the diffusion of cultural norms, they help explain variations in the timing, speed and magnitude of the fertility decline. We propose a stylized model integrating economic, health, institutional and cultural factors to study how these factors interact to shape fertility transition paths. We find that family institutions can mediate the effect of economic development by constraining fertility responses.

“Global Labor Mobility between Shrinking and Growing Labor Forces,” by Lant Pritchett

     Falling fertility and improved mortality create a powerful and inexorable demographic arithmetic of ageing in the coming decades around the world, with three patterns. The richest countries, along with China and the Former Soviet Union, will see absolute declines in the labor force aged (15–64) population and absolute rises in those 65 plus. All major developing country regions except for Africa: Latin America, South-East Asia and Pacific, South Asia, and West Asia/Middle East, will experience modest labor force growth to 2050 (less than 30 percent) aged combined with rapid growth of those over 65 (doubling or tripling). The fall in fertility in Africa (Sub-Saharan and North) started later and has fallen much less and hence, in standard scenarios for 2050, Africa will account for 80 percent of all global net growth in the world’s labor force aged. A fundamental feature of the global economy over the medium-run to 2050 is that that highest labor productivity countries will have absolutely fewer native-born workers and Africa, home to many of the world’s lowest productivity countries, will have 800 million more labor force aged. The combination makes possible gains on the order of trillions of dollars to policies that creating legal pathways to allow people, particularly youth, to move from low productivity, labor abundance places to high productivity, labor scarcity places. But, so far, politics has not found the way to “yes” for this win-win scenario.

Symposium on Competition in Labor Markets

“Labor Market Power: From Micro Evidence to Macro Consequences,” by David Berger, Kyle Herkenhoff, and Simon Mongey

     The traditional theoretical and empirical “micro approach” to studying labor market power (or monopsony) requires that firms are small and atomistic. This is at odds with the reality of labor markets in which monopsony potentially matters most. Empirically, many markets are concentrated and characterized by large, dominant employers. The actions of large employers in an occupation or industry affect local and national wages, employment and output. Employers that understand their largeness may then act strategically when hiring and setting wages, generating misallocation and harming workers. This paper advocates for a “macro approach”: (1) directly model equilibrium behavior of large employers, (2) combine macro data and empirical estimates of employers’ responses to policy changes—obtained using the “micro approach”—to estimate the model, (3) use the model to compute the aggregate costs of monopsony, and optimal policies. This approach provides new perspectives on minimum wage and antitrust policy.

“Antitrust Enforcement in Labor Markets,” by Elena Prager

     Until recently, antitrust laws were rarely enforced in labor markets. Although the existence of labor market power has long been recognized, evidence only recently emerged that such market power regularly arises from sources that are actionable under antitrust law. Since 2010, antitrust agencies have substantially increased labor market enforcement actions. However, many questions relevant to enforcement remain unanswered, such as how to conduct market definition for labor markets and how best to incorporate concentration into models of the labor market. This article reviews how antitrust is beginning to be used in labor markets, the evidence for and against its use, and the remaining evidence gaps standing in the way of more effective use.

“The Economics of Noncompete Clauses,” by Evan Starr

     For over 600 years, debates over noncompete clauses have centered on whether they function as efficient contracting tools or anticompetitive restraints on workers. This article reassesses that debate in light of recent policy attention and new empirical and theoretical research. Proponents argue that noncompetes are necessary to protect investments in training and trade secrets, increasing productivity and wages. However, recent studies indicate that the widespread use of noncompetes—frequently extending beyond roles involving sensitive information—and their enforceability lower mobility, wages, innovation, and entrepreneurship. Moreover, in many cases, less restrictive contractual terms appear to safeguard firm interests. Evidence of spillovers to other workers and across state boundaries, as well as behavioral effects even when noncompetes are unenforceable, raises questions about whether existing state-level enforcement regimes adequately address their observed impacts.

“Occupational Licensing in the United States,” by Janna E. Johnson

     Occupational licensing—the requirement that individuals attain a license to legally perform a specific job—is now necessary for over a fifth of the US workforce. The policy is intended to protect consumers by ensuring members of licensed occupations meet a minimum quality standard but comes at the cost of higher prices for their services. Economic theory and research support the argument that at least in some cases the costs of licensure exceed its benefits. Incumbent members of licensed occupations gain from the higher wages caused by licensure policies, creating a strong incentive for them to push for stricter regulations and resist any efforts to remove or loosen licensure requirements. However, despite bipartisan interest in licensure reform, data limitations and vast heterogeneity in licensure policies limit the usefulness of existing research in guiding its design.

Symposium on Asian Americans

“Asian Immigration to the United States in Historical Perspective,” by Hannah M. Postel

     Asian Americans are the fastest-growing immigrant group in the United States, yet Asian immigration remains relatively understudied in quantitative social science. This paper reviews the historical evolution of Asian immigration, focusing on six major origin countries—China, Japan, India, the Philippines, Korea, and Vietnam—to show how US immigration and foreign policy shaped the size and composition of immigrant arrivals. It then examines subsequent patterns of demographic composition, geographic settlement, and socioeconomic characteristics. Taken together, the evidence highlights the enduring influence of US policy regimes on Asian immigration over time.

“From Asia, with Skills,” by Gaurav Khanna

       This paper examines the rise of high-skill migration from Asia to the United States since 1990 and its consequences for sending and receiving economies. Over 1990–2019, migrants from India, China, South Korea, Japan, and the Philippines accounted for over one-third of US growth in software developers and a quarter of the increase in scientists, engineers, and physicians. Using census microdata, visa records, and administrative sources, I show how growing US demand for talent in information technology, higher education, and healthcare interacted with Asia’s demographic and educational transformations. Policy reforms in the H-1B, F-1, and J-1 programs and sectoral shifts—such as the internet revolution and aging-related healthcare demand—generated persistent needs for foreign students and workers. Asian economies were uniquely positioned to meet this demand through tertiary expansion, strong STEM institutions, English proficiency, and diaspora networks. These inflows boosted US innovation while fostering “brain gain” and “brain circulation” in Asia.

Features

“Recommendations for Further Reading,” by Timothy Taylor

A useful way to look at the generousity of Social Security payments is to look at the “replacement rate” of benefits. This can be calculated in various ways, but a standard approach is to compare Social Security payments that would be received by someone at the “normal retirement age” as an average of that person’s top 35 years of pre-retirement earnings–with those earnings adjusted for the growth rate of national average wages over time (and in this way capturing an aspect of both inflation and growth over time). In the early 1970s, the replacement rate went haywire, which is part of what caused the Social Security system to need rescuing in the early 1980s, and what will cause it to need rescuing again by the early 2030s. Andrew Biggs tells the story in “It’s Fine to Embrace FDR’s Vision of Social Security, But You Don’t Need to Embrace Nixon’s and Carter’s” (Little Known Facts, January 29, 2026).

This graph tells the basic story of what happened. Focus first on the line showing replacement rates for a “medium” wage-earner. At the start of the system, the medium wage-earning had a replacement rate of about 24%, but Congress raises this level in the 1950s, and by the late 1960s it’s about 29%. Then there is a dramatic spike: the Social Security replacement rate for a medium earner rises from 29% around 1970 to 51% by 1980. It then sags back down to about 40% by the mid-1980s, where it has stayed since.

Since the beginning of Social Security, the system has been set up so that while those with high incomes did and do receive higher monthly payments, the share of income replaced is higher for those with low incomes. The graph above shows the higher replacement rates for those with lower levels of income. It also shows how the spike in Social Security replacements rates applied to all groups.

So what happened? Biggs lays out details of the timeline, and who knew what. His basic story is that Congress wanted to adjust Social Security for rising inflation, but the formula that was enacted into law (obviously) did a lot more than just keep benefits aligned with inflation. He argues that many members of Congress literally didn’t know what they were doing.

He digs into the background staff reports at the time. For example, who can forget that timeless 1973 blockbuster from the actuaries, “Some Aspects of the Dynamic Projection of Benefits Under the 1972 Social Security Amendments”? Biggs makes the case that, behind the scenes, many staffers knew that the new formula would spike the Social Security replacement rate, but because the staffers wanted that to happen, they didn’t spell it out to Congress. The chief actuary for Social Security said in 1970: “Certain of the top policy-making officials at the Social Security Administration (who are holdovers from the Johnson Administration) have strong beliefs in the desirability—even the necessity—of the public sector taking over virtually all economic security provisions for the entire population and thus eliminating private efforts in this area.”

But the dramatic rise in replacement rates had consequences. According to calculations by Biggs: “Had Social Security replacement rates simply been kept at their 1969 levels, and therefore missed the large benefit increases of the 1970s, the average benefit for a new retiree in 2026 would have been about $20,375, about 25 percent less. That difference alone would have almost certainly avoided Social Security’s near-death experience in 1977, its brush with insolvency in 1983 and the projected exhaustion of the trust funds in 2032.”

Biggs also grasps the nettle of a more difficult policy question: Does this history have lessons for the necessary reform of Social Security finances by the early 2030s? After all, the Social Security reforms of the early 1980s put the system on a path it has followed for the last half-century, andt the reforms of the early 2030s may establish a path all the way to 2100. There are shorter-term and longer-term issues here.

In the shorter-term, there are elderly folks already relying on Social Security, and others who have been planning their retirement based on often-repeated promises of the Social Security income they will receive after retirement. It would seem grossly unfair, even cruel, to break those promises.

But now consider those born since, say, 2020. That generation will not enter the workforce in a significant way until 2040, and some of them (say, those who attend college and graduate school) until later than that. At this point, and by early 2030, they will not have paid any taxes into the Social Security system, and they have not built their financial futures based on, say, what their retirement income will look like when they turn age 65 in 2085. It would not be unfair or cruel to redesign Social Security for that generation.

Biggs puts forward his own view of what should happen. He points out that that when Social Security was first enacted in the 1930s, it was in an economy where very few people had private pension, and self-owned contributory retirement accounts like IRAs and 401k accounts didn’t exist. As he writes, it is “far, far easier for middle- and high-income Americans to save for retirement on their own today than it was in the world of 1935.” Thus, Biggs suggests that for this next generation, Social Security reforms should transition to a flat benefit, received by every elderly person, that would provide a robust above-the-poverty-line safety net for all seniors.

I’m uncertain about that recommendation. Part of the allure of Social Security is that it has not been a pure safety-net program for the elderly poor. Instead, even though it has always involved some redistribution from high- to low-incomes, the benefits you receive are linked, to some extent, to what you paid into the system.

However, Biggs seems clearly correct on three points. First, the financial ecosystem that helps those with middle- and high-incomes save for retirement is quite a bit different than in the 1930s. Second, the current replacement rates and tax rates in Social Security are not the original vision of the program. Instead, the current replacement rates were foisted on the public without discussion in the early 1970s, and the current tax rates were enacted in the early 1980s to as necesary to support those higher replacement rates. Third, when Congress gets around to redesigning the future of Social Security–probably in the early 2030s–for the future generations who are born only recently, or not yet born at all, we should not treat the current structure of Social Security as sacrosanct.

One of the predictions made by economists when President Trump announce the start of his freewheeling tariff policies in April 2025 was that the costs of the tariffs would ultimately be passed through to consumers, leading to overall higher inflation. Well, President Trump has been tossing out tariff threats, keeping some and withdrawing others. However, the Consumer Price Index showed a rise in total prices of 2.7% for the 12 months up through December 2025. So where’s the inflation?

The short answer goes like this: Imports are about 14% of the US economy. Say that the tariffs, with all exceptions and delays factored in, are imposed at an average rate across all imports of about 10%. If 14% of the economy has a 10% increase in tariffs, then the pass-through to consumer prices would be 1.4%. The evidence suggests that’s roughly what’s happening.

For an overall picture of the rising US tariffs, the Yale Budget Lab has published an update of its “State of Tariffs” report, dated January 19, 2026. The “average effective tariff” is calculated across all imports of goods. You can see the high tariff rates of the 19th century, and the much lower rates since the end of World War II. In the graph, the “pre-substitution rate” refers to the effective tariff rate before patterns of imports adjust to the higher tariffs, while the “post-substitution rate” refers the average rate after the adjustment of imports has happened.

For a focus on what happened in 2025 in particular, Gita Gopinath and Brent Neiman have published “The Incidence of Tariffs: Rates and Reality” (University of Chicago, Becker-Friedman Institute, BFI Working Paper 2025-151, January 2026). They differentiate between the announced or “statutory” rates and the actual rates with exceptions and timing lags included. The “weights” and “moving weights” are their version of the pre-substitution and post-substitution lines in the previous figure. As they point out: “A key reason why the price impact of the tariffs remains below many forecasts made in April is that the implemented policy remains much smaller than the announced policy.”

Gopinath and Neiman also look at the extent to which tariffs are being passed through in US prices. Their method involved looking at detailed breakdowns of US prices charged for specific goods. Then, they can trace back whether the goods that had tariffs imposed on them rose in price by more than other goods that did not have tariffs imposed. They carry out this exercise in a bunch of ways, focusing in some examples on tariffs on goods and in other on tariffs by country. They find: “When a 10 percent tariff is imposed on an imported good, U.S. importers appear to pay 8-10 percent more, including the tariff, for that good.”

The Yale Budget Lab calculates that the additional cost of the tariff so far work out to about $1400 per year for the median household. However, if the cost of the tariffs is expressed as a percent of income, rather than dollar amount, the negative effect is biggest for those with the lowest income levels, because they rely more heavily on less-expensive imported products.

The Yale Budget Lab also points out that the Federal Reserve has easy access to these kinds of estimates, and thus when the Fed looks at the 2.7% annual rate of inflation in December 2025, it mentally does the calculation: imports are 14% of the economy, tariffs are up about 10%, and 80-100% of the tariffs are passing through to prices. Put it together, and about 1.1-1.4 perentage points of the 2.7% inflation rate is probably due to the tariffs. To put it another way, without President Trump’s tariff policy in place, the measured inflation rate would probably be below the Fed’s target rate of 2% per year. Affordability concerns for the general public would be slightly diminished, and the Fed would be more willing to reduce policy interest rates more quickly. Meanwhile, President Trump’s bigger promises about how tariffs would save the US economy are not aging well.

The US Congress has not enacted major changes to US immigration policy since the 1990s, three decades ago. Presidents of both parties have thus become accustomed to enacting immigration by decree, or “executive authority.” However, such policies are easily amended or erased. The result is that millions of immigrants live in a liminal status–that is, an uncertain space where the rules are unclear, where someone crossing the border may be allowed to enter the country under one set of rules, but then have those rules change. Pia M. Orrenius delivered the Presidential Address of the Southern Economic Association in 2025 on the subject “Temporary Fixes, Permanent Problems: Implications of the Growing Reliance on Liminal Status in U.S. Immigration Policy” (co-authored with Madeline Zavodny, Southern Economic Journal, October 2025, pp. 181-193).

This figure shows the number of monthly “encounters” between the US Border Patrol and potential border-crossers with the blue line, measured on the left-hand axis. The red line and the right-hand axis shows the share of potential border-crossers released into the United States. Perhaps unsurprisingly, a rising share of border-crossers being released into the US (red line) was accompanied by a rising number of border encounters (blue line).

Set aside questions over the merits of this policy for a moment. My theme here is that the Biden administration chose to alter US immigration rules not as a result of a law debated and passed by Congress, but purely a matter of “executive authority.” The Trump administration has pushed back with its own alteration of immigration rules, again through its own “executive authority.” In the US Constitution, Article 1 lays out the structure and powers of Congress. In Section 8, Clause 4, Congress is given the authority “To establish an uniform Rule of Naturalization.” Presidents and the executive branch are supposed to be carrying out the rules, not making them.

Orrenius and Zavodny offer some estimates of the size of this liminal population of immigrants, which has risen dramatically in the last decade or so. The categories are a reminder of how immigration policy has been evolving over time.

Temporary Protected Status (TPS) was created by the Immigration Act of 1990 “and was first designated to help migrants who had fled civil war in El Salvador remain in the U.S.” The Department of Homeland Security can designate countries where it would be unsafe to return. “As of the end of fiscal year (FY) 2024, almost 1.1 million immigrants from 17 countries were protected under TPS.”

Deferred Action for Childhood Arrivals (DACA) was created by the Obama administration in 2012 “in order to provide temporary legal presence to unauthorized immigrants who were children when they entered the U.S.” The Trump administration partially rescinded it, but courts blocked them from going further; the Biden administration tried to reinstate the original version, but courts blocked them from going further. “Almost 835,000 young people have been approved for DACA since 2012, including over 537,000 active beneficiaries as of the end of FY 2024.”

The Special Immigrant Juvenile (SIJ) program is so that “child immigrants who are unauthorized and have been abused, neglected, or abandoned may be eligible for a permanent resident visa.”

The Nonimmigrant T/U adjustment group refers to “immigrants who have been victims of criminal activity or trafficking,” who under certain circumstances “are eligible for a U or T nonimmigrant (temporary) visa, which allows them to apply for permanent residence after 3 years and to work in the meantime. The number of U visas for principal applicants is capped at 10,000 a year, and there is a substantial backlog—almost 400,000 petitions by victims and their family members were pending at the end of FY 2024. While they wait, victims with bona fide claims and their family members are protected from deportation and can apply for work authorization, but even that process takes years …”

Asylum seekers and the paroled are, at least in my mind, closely related groups: “A growing number of people with temporary legal presence in the U.S. are asylum seekers. Some of them presented themselves at the border, asked for asylum, and were paroled into the U.S. Others were pre-approved for parole status and sought asylum upon arrival. Yet others asked for asylum after illegally entering the U.S. and being apprehended by U.S. Border Patrol and were released into the U.S. to await determination of their asylum claim. There were so many of the latter that the Biden administration effectively eliminated the ability of apprehended migrants to ask for asylum in the summer of 2024. Regardless, it will take years for asylum officers and immigration courts to weigh the merits of pending asylum claims. As of the end of FY 2024, there were over 2.4 million asylum claims awaiting adjudication. In the meantime, asylum applicants can apply for work authorization. If their asylum claim is approved, they can adjust to permanent resident status. Typically, only about 20% to 30% of asylum claims are approved. If their claim is denied, they are supposed to leave the U.S., but evidence suggests many remain in the U.S. without legal status, adding to the unauthorized population.”

In addition to those with liminal status under these various rules, the number of those with explicit status under a temporary visa is also on the rise.

As the authors write: “The other large category of temporary migrants is those with temporary work visas, who make up the great majority of employment-based immigration. Over the last 10 years, the annual average ratio of temporary to permanent work-based visa issuances is 35 to 1. The U.S. issues temporary worker visas in an alphabet soup of categories. The most notable temporary worker visa categories are the H-1B visa for high-skilled professionals in specialty occupations; the H-2A visa for agricultural workers; the H-2B visa for non-agricultural workers; and the J-1 visa for exchange visitors. Together, they accounted for over 800,000 workers each year since 2018.”

In addition, there are F-1 visas for students: “International students who hold an F-1 visa can work under certain circumstances, including for up to 3 years after graduation under the Optional Practical Training (OPT) program if they majored in a STEM field. That post graduation work period gives STEM graduates multiple shots at winning a visa in the H-1B lottery. In 2023, almost 400,000 international students who had graduated from a U.S. university had work authorization, and another 140,000 were authorized to work off-campus while completing their studies.”

In a big-picture sense, the US population is entering a period of demographic transition, with lower birthrates and longer life expectancies. The ratio of retirees per worker is likely to rise. The US is overdue for a rethinking of immigration policy that doesn’t depend on temporary categories or on the caprice of whoever is president. Orrenius and Zavodny argue for a rise in the total levels of legal and permanent immigration, and I agree.

I’m confident that a Biden-style immigration policy of just releasing most people who show up at the US border into the US economy for indeterminate periods of time is not optimal. About two-thirds of all legal and permanent US immigration is based on family unification, with the other one-third is employment related. As a starting point, I would suggest keeping the levels of immigration related to family unification at the same level, but doubling or tripling the employment-related immigration. One starting point would be to make it more straightforward for foreign students at US colleges and universities to find a pathway to US citizenship. There is a global contest to attract talent, and the current US immigration system is a hindrance in that competition.

Lending from state-owned Chinese banks to developing countries took off around 2010. From China’s view, there were several justifications. The public name given to the wave of lending was the Belt and Road Initiative, with the broad idea of building infrastructure and trade connections across Asia, the Middle East, and Africa (for some posts in the last few years on the initiative, see here, here and here). The increasing trade ties would help to assure China’s economy of access to raw materials, while also providing additional market for China’s exports. In addition, these deals often involved other countries borrowing money from China in substantial part to hire Chinese companies to come and build the infrastructure. Hovering just behind these economic arguments was a hope or belief that China would deepen its political connections with the borrowing nations, and its influence over these nations.

Sebastian Horn, Carmen M. Reinhart, and Christoph Trebesch bring the story up to date in “The China’s Lending to Developing Countries: From Boom to Bust” (Journal of Economic Perspectives, Fall 2025). Here are two figures to show the trends:

This figure shows net lending by China and its state-owned banks to developing countries. As you can see, lending takes off around 2010, dries up around 2019, and in the last few years turns into repayments.

This companion figure shows total “official” debt of developing countries–that is, not debt to private banks or companies. The red line showing “debt to China” exceeds total debt to the IMF by around 2007 and has been roughly equal to total debt to the World Bank since about 2016.

A recent report by Mengdi Yue, Diego Morro, Nicolo Capirone, and Yiyuan Qi, published by the Global China Initiative at Boston University, focuses on Chinese loan commitments to Africa (“Selective Engagement and Strategic Retooling: The Chinese Loans to Africa Database, 2000–2024, January 2026).

As the United States learned back in the 1980s and 1990s, it’s easy to be popular during the opening act of international lending. But when the loans aren’t getting repaid in time, the lender can become unpopular pretty quickly.

When countries are having a hard time repaying debt, the standard policy response is to get all the lenders together in a room, and have them all agree that they will refinance the loan and take less money. (This is essentially the “Paris Club” shown in the figure above.) The logic is that no single lender will agree to take less if everyone else is being repaid in full. However, if everyone insists on getting repaid in full, and the result is an economic collapse in the borrowing country, then the lenders could end up with very little indeed. Thus, better to agree on taking a moderate loss now, rather than risking a much larger loss later. However, China’s lenders have been largely refusing to participate in these arrangements, instead holding the position that everyone else should take the losses instead.

It will take a few years to sort out the legacy of China’s foreign lending and the Belt and Road Initiative from about 2010 to 2019. Some of the infrastructure plans work out just fine; others looked better on the drawing board than in reality. Some countries have closer ties to China as a result; others are now looking at Chinese influence and money with a wary and even hostile eye. How the debts to China’s lenders are ultimately resolved, for better or worse, will be the climax of the story.

Imagine a situation where a substantial area is run by a heavy-handed organized crime group. Those at the top of the organized crime pyramid live like royalty, with palatial housing and the highest-end food, drink, and clothing. Those who live in this area pay for this luxury in a variety of ways: payoffs for starting or running a profitable business, limits on jobs, higher higher prices for goods and services, and an ongoing shadow of violence.

Now imagine that someone argued that organized crime was a great success for the local economy. For evidence, this person adds up the value of economic activity within the organized crime empire, and points to the high incomes, wealth, and political influence of the organized crime leaders.

But of course, the fact that this hypothetical organized crime organization makes money, especially for its leaders, doesn’t make it economically beneficial. Similarly, the fact that slavery was the basis for a larger agricultural economy and was profitable for slaveowners doesn’t make it economically beneficial, either. To know if something is “beneficial,” one needs to engage in what economists call “counterfactual” reasoning: that is, what would the economy have looked like otherwise?

As an example of one way to make this comparison, Hoyt Bleakley and Paul W. Rhode consider “The Economic Effects of American Slavery, Redux: Tests at the Border” (June 2024, NBER Working Paper 32640). They take inspiration from the famous voyage of Alexis de Tocqueville and Gustave de Beaumont to America in 1831-32. When they were travelling down the Ohio River, with the free state of Ohio on one bank and the slave state of Kentucky on the other bank, it seemed obvious that the Ohio side was flourishing, while the Kentucky side was not. De Tocqueville wrote (translated from the French): “It is impossible to attribute those differences to any other cause than slavery. It brutalizes the black population and debilitates the white. One can see its deathly effects, yet it continues and will continue for a long time. […] Man is not made for servitude.”

Many analyses of slavery look at state-level or regional-level averages of South vs. North. Instead, Bleakley and Rhode focuses on a long narrow stretch of land on both sides of the border between free and slave states. They write: “We take the testing ground of de Tocqueville and de Beaumont — the upper Ohio River valley– and extend the comparison east to cover the borders dividing Pennsylvania and New Jersey from Virginia, Maryland and Delaware and west to contrast free states of Illinois and Iowa with the slave state of Missouri. The border was the dividing line between slavery and free labor institutions within the same
country, with a common language, national laws, and shared heritage.”

The pattern they find is that land is only about half as likely to be utilized on the slave side of the border; on the free side, investments in land-clearing and farm buildings were much higher. This represents a dramatic reduction in potential economic output in slave states–extreme enough that it was visible from the deck of ships going down the Ohio River. (And yes, the authors also do roughly a gazillion statistical checks to see if the results might be accounted for by soil erosion, river access, soil composition, timing of earlier settlement, earlier glacial coverage, the existence of state borders, and more.)

So why didn’t free labor move across the border to the available land in the slave states? Bleakley and Rhode emphasize that a slave society was dominated by rich slaveowners, who were focused on their own source of wealth. These states did not invest in infrastructure or institutions to benefit the middle- and lower-class of free workers; instead, they were likely to impose requirements that free workers participate in the enforcement of slavery. Slave-owning states were much more likely to have institutions that also affected free whites: indentured servitude, public whippings, debt bondage (in which someone in debt is legally required to work for another until the debt is repaid–which had a nasty habit of taking a very long time), or the leasing of prisoners to private companies as forced labor. The slaveowners’ idea of “property rights” was very much about their own personal property, not anyone else’s. For free labor, the political, economic, and social institutions of slave-owning states were largely unattractive. The authors quote a 1854 speech by Abraham Lincoln, who said that “slave States are places for poor white people to remove FROM; not to remove TO. New free States are the places for poor people to go to and better their condition.”

An alternative way of comparing slavery to Emancipation can be done with statistical modelling. Treb Allen, Winston Chen, and Suresh Naidu take this approach in “The Economic Geography of American Slavery” (NBER Working Paper 34356, October 2025). They use data from the 1860 Census to “assemble a comprehensive dataset of the spatial and sectoral distribution of economic activity in the U.S. in the year 1860.” This includes a division into agricultural, manufacturing and service sectors, along with data on measures of output, wages for free workers, prices of slaves, whether the area was likely to have malaria infestations, and much more.

The model then allows a thought-experiment: what if the enslaved workers and their families were emancipated? Where would they relocate, and in what sectors would they work? The authors write: “Combining theory and data, we then quantify the impacts of slavery. We find that complete emancipation has large effects on the U.S. economy, inducing an expansion of manufacturing (26.5%) and services (21.0%) and a contraction of agriculture (-5.4%). The welfare of formerly enslaved workers increases by almost 1,200%, whereas free worker welfare declines 0.7% and slaveholders’ profits are erased.” In their calculations, Emancipation causes overall GDP to rise by 9.1%.

Of course, the authors can then compare the effects of Emancipation in their statistical model with the actual effects of Emancipation after the Civil War: “Finally, we show that the counterfactual changes in labor allocations from emancipation are strongly correlated with observed patterns of White and Black reallocations across all sectors following the Civil War, although the comparison offers suggestive evidence of substantial migration frictions for recently emancipated Black workers.” In other words, the statistical model allowed former slaves to reallocate quite easily, and it wasn’t in fact that easy, so the actual economic effects would have been smaller than the model predictions–but in the same direction.

In the lead-in to the US Civil War, it was common to hear arguments from the pro-South side that slavery had been essential to the growth of the US economy. This argument has been resuscitated in recent years, but has received a markedly unamused response from scholars in the field. The pro-North side argued that the US economy would do just as well, or better, without slavery. These studies, and others, help to explain why that argument was correct. Back in 1804, after Napoleon damaged his own political reputation by executing a political opponent, one commenter remarked: “It’s worse than a crime; it’s a mistake.” In a similar spirit, one might say that slavery was not just a moral abomination; it was also an inefficient and low-growth economic outcome.