Wealth250 and Chicago Economics

View Working Paper View on SSRN View HBR Article

Why do managers matter for firm performance? This paper provides evidence of the critical role of managers in matching workers to jobs within the firm using the universe of personnel records from a large multinational firm. The data covers 200,000 white-collar workers and 30,000 managers over 10 years in 100 countries. I identify good managers by their speed of promotion and leverage exogenous variation induced by the rotation of managers across teams. I find that good managers cause workers to reallocate within the firm through lateral and vertical transfers. This leads to large and persistent gains in workers’ career progression and productivity. My results imply that the visible hands of managers match workers’ specific skills to specialized jobs, leading to an improvement in the productivity of existing workers that outlasts the managers’ time at the firm.

A good (or bad) manager can make (or break) a business. What makes a good manager? Company policies, or individual traits? Identifying the channels by which management impacts business performance is important for designing strategies to improve performance, as well as for determining the appropriate pay for good managers, and estimating how much improving management might bolster productivity on a national scale. Past efforts to study this issue have been hindered by empirical challenges; identifying manager effects has typically required observing performance across several different businesses, making it difficult to isolate the role of managers separate from other dynamics that vary across firms. In this paper, the authors study manager moves across different retail storefronts that share mutual ownership to clearly identify how managers drive performance.

The authors use data on store-level operations for two large retail companies that each run many locations. Noting that retail managers tend to change jobs relatively frequently, the authors track manager moves across stores and study how productivity changes as managers come and go. They find the following:

Individual managers matter for productivity. The impact of replacing a manager who is at the 10th percentile in terms of their team’s productivity with a manager from the 90th percentile could increase productivity by 50% to 100%, an improvement roughly equivalent to that of adding a fifth employee to a team of four. Overall, managers explain between 25% and 35% of the variation in productivity across stores.
High-productivity managers tend to work in low-productivity stores (that is, stores that tend to have lower average productivity regardless of who the manager is), and vice versa. The authors offer several explanations for this surprising result, including the possibility that companies prefer to place high-performing managers in low-performing stores, perhaps because they view store failures as especially costly for the firm. Or it could be that firms are unaware of the benefits of placing high-performing managers in more productive stores, which they calculate could raise company-wide sales by 2-6 percent. The authors also note that statistical bias (explained further in the paper) may drive the negative correlation observed here.
Manager changes impact both stores and managers. At one company studied here, old managers’ earnings fall in the months prior to their exit. At the other company, stores’ productivity and sales fall before manager departure, but this trend reverses once a new manager arrives.
Female managers are less likely to move stores than male managers. This pattern is not explained by tenure, features of their initial store (size, revenue, number of employees, format), or the quality of the store-manager match, and is consistent with the literature showing that women’s family responsibilities often constrain them to a single geographical area.
Manager quality is hard to explain using the data studied here. Manager tenure, gender, distance to the nearest competing store, and even wages do not have a statistically significant association with manager quality, suggesting that less measurable characteristics of managers (such as their leadership style and charisma) could play an important role.
Manager quality and energy efficiency are positively correlated, meaning that managers who drive high labor productivity also tend to drive high energy productivity. This suggests some breadth in managers’ skills applicability.
Managers who perform highly during stable growth times also tend to perform well amidst turbulence. In addition, managers who were observed as high quality before COVID-19 also performed better than average during COVID-19.
Disruptions have a strong negative impact on productivity, but higher-quality managers can mitigate these effects. In addition, managers of all quality levels who were exposed to more disruptions pre-COVID were better at limiting COVID-related productivity disruptions. This was especially true for lower-quality managers, raising the possibility that lower-quality managers learn more from experience.

The upshot is that managers have an impact on productivity that is distinct, quick, and separate from company-level management practices. These results point to the crucial role of managers and suggest possible avenues for unlocking substantial productivity increases through, for example, better allocation of managers across stores.

Rural Americans have worse health outcomes, yet doctors are disproportionally concentrated in large cities. For many, this long-observed phenomenon indicates that doctors are not distributed appropriately across space. Many healthcare policies have sought to “correct” this distribution. However, this new research shows more at play when considering the optimal delivery of medical services. A more complete evaluation considers two economic mechanisms crucial to understanding spatial patterns of US healthcare delivery: economies of scale and trade costs.

When the authors discuss economies of scale in medical services delivery, they are referring to classic ideas in urban economics about the benefits of geographically concentrated production. If many hospitals and doctors are located near each other, they can see more patients, specialize, and gain experience that benefits patients. They can disseminate information on the latest innovations and share the cost of specialized equipment. In other words, this spatial concentration has benefits—and especially for the people who live nearby and can easily access this high-quality care.

What about those living in rural areas, far removed from large medical centers? One way to get these patients healthcare is to distribute medical service production, including doctors, to those rural areas and forgo the benefits of scale. This is natural for time-sensitive emergency care. However, what about most other types of health care, including specialty treatments, that are scheduled in advance? Do we need a hospital with specialty practitioners in every town? Or is it better for patients to travel to big cities to see more experienced, specialized providers? If patients can travel, medical care faces a proximity-concentration trade-off like other tradable industries. In other words, patients who travel for medical services produced elsewhere incur travel costs, but they also benefit from economies of scale.

The authors assess these issues by employing Medicare claims data to quantify the roles of increasing returns to scale and trade costs in medical services. They show that larger markets produce higher quality medical services. They also show that “imported” medical procedures—defined as a patient’s consumption of a service produced by a medical provider in a different region—constitute over one-fifth of US healthcare consumption. Patients in smaller markets are the largest consumers of imported healthcare. It follows that “exports” of medical services—including specialized care—are disproportionately produced in large markets. These patterns reflect economies of scale: larger regions produce higher-quality services because they serve more patients.

The authors employ a rich dataset of millions of patient-provider interactions. They quantify how production subsidies and travel subsidies affect patients’ access to care and the quality produced in each region; the working paper describes these methods in detail. Their findings include the following:

Production is more geographically concentrated in large markets than consumption. Since trade constitutes the difference between production and consumption, trade reduces geographic inequality in medical care access. A key implication is that common measures of healthcare production (e.g., doctors per capita) will overstate inequality in the healthcare people actually receive.
In a theoretical model, local increasing returns to market scale can generate a home-market effect, i.e., exports of medical care rise as a region grows larger, even when prices are fixed. The authors’ model predicts that larger markets will become net exporters of medical services when local increasing returns to market scale are sufficiently strong.
This phenomenon is borne out in the data. Local increasing returns to market scale are so strong that greater demand induces a larger increase in exports than imports. This makes larger markets net exporters of medical care and means that healthcare can serve as an export base for large urban economies.
Larger markets produce higher-quality services thanks to economies of scale. How do we know these services are higher quality? Patients are willing to travel more to get services in these regions, all else equal. In addition, patients’ willingness to travel (revealed preference) corresponds with other measures, like US News hospital rankings.¹
A region’s quality rises considerably with the regional volume of production. While there could be many mechanisms driving this, the authors find that, in large regions, doctors are more specialized, procedures are performed by more experienced doctors, and more unique services are offered.

The authors emphasize differences between the markets for rare and common procedures.
For example, compare patients with heart failure who have left ventricular assist devices (LVADs) implanted to augment cardiac function—a rare procedure—with those who have routine screening colonoscopies. Half of the patients receiving LVAD implants come from outside the surgeon’s region, but only 15 percent of routine screening colonoscopies are performed on patients outside their home region. Their analysis reveals the following about rare procedures:

Trade and market size play a larger role for rare procedures: The imported share of consumption is 22% for common procedures and 35% for rare procedures.
The home-market effect is substantially stronger for rare procedures: a larger residential population drives a greater increase in exports for rarer services.
The geographic scope of the market for a medical procedure depends on its national scale: doctors performing rare procedures export their services across a broader geographic scope, sometimes serving patients who reside thousands of kilometers away. Rarer procedures are disproportionately produced and exported by large markets.

Next, the authors explore the trade-off created by putting providers proximate to patients, which also fragments the production of medical services. They find that reimbursement policies vary in how they affect patients and providers. They also affect regions differently depending on their size and trade patterns. In particular:

A nationwide increase in reimbursements generates the largest increases in local medical care quality in the smallest regions. However, these regions’ patients experience the smallest increase in the value of market access because they consume less of their care locally.
Reimbursement increases generate the highest return when spent in the largest cities.

But this finding comes with an important caveat: the higher-quality care available in larger markets may not benefit all patients equally. The authors show that:

Socioeconomic status predicts how patients trade off travel costs and the benefits of scale. Patients residing in lower-income neighborhoods are less likely to travel farther for better medical care. This finding reveals that all patients do not benefit equally from local increasing returns to scale.

Bottom line for policymakers: Healthcare produced in large regions is higher quality. Policies to reallocate care to smaller regions may impact patients’ access to healthcare in unexpected ways. Traditional production subsidies in small, underserved areas help healthcare producers (e.g., doctors) more than patients in those areas. Patient travel also plays a meaningful role in enabling access to higher-quality, more experienced, and specialized care. Policymakers should consider travel subsidies rather than only production subsidies to increase access to care for underserved patients.

¹health.usnews.com/health-care/best-hospitals/articles/faq-how-and-why-we-rank-and-rate-hospitals

We introduce a model of oligopoly dynamic pricing where firms with limited capacity face a sales deadline. We establish conditions under which the equilibrium is unique and converges to a system of differential equations. Using unique and comprehensive pricing and bookings data for competing U.S. airlines, we estimate our model and find that dynamic pricing results in higher output but lower welfare than under uniform pricing. Our theoretical and empirical findings run counter to standard results in single-firm settings due to the strategic role of competitor scarcity. Pricing heuristics commonly used by airlines increase welfare relative to estimated equilibrium predictions.

For economists, the term “secular” means persistent over time, suggesting that the post-Great Recession years of slow growth were unlike a typical business cycle recovery. Something unexpected was happening. Some even suggested that this stagnation was inexorable, a “new normal” that would shape the economy for years.

A recent paper by Ernest Liu, postdoctoral student at Princeton University, Atif Mian, professor at Princeton University, and Amir Sufi, professor at UChicago’s Booth School of Business, “Low Interest Rates, Market Power, and Productivity Growth,” offers a new explanation for the phenomenon of long-term sluggish growth. The authors acknowledge that very low interest rates, initiated by policymakers in response to a large economic shock, drive people to save and discourage borrowing. However, they offer a supply-side explanation for the resulting slow growth: persistent low interest rates favor larger companies, which—over time—increases market concentration and stifles productivity growth. These insights offer new considerations for researchers and policymakers on the causes of—and cures for—secular stagnation.

Chart

How Lower Interest Rates Can Stall Growth

Notes: This figure illustrates how a shift in consumer demand lowers the interest rate and increases concentration. If the prevailing interest rates are low, that is, when economy is in the upward sloping region of the production-side curve, then a fall in the interest rate is also contractionary as productivity growth slows. This figure describes the authors’ alternative explanation for secular stagnation. As in traditional secular stagnation explanations, an initial inward shift in the consumer-side curve can lower equilibrium interest rates to very low levels. However, the resulting stagnation is not due to monetary constraints such as the zero lower bound or nominal rigidities. Instead, as the authors’ model reveals, a large fall in interest rates can make the economy more monopolistic, thereby lowering innovation and productivity growth.

Low interest rates are not created equal

Persistent low interest rates are not just a post-Great Recession phenomenon; rather, interest rates have fallen globally since the 1980s. This means that the effects of low interest rates—on both the demand and supply side—have been felt for some time. As described above, low interest rates are presumed to be expansionary; monetary policymakers, for example, typically lower interest rates in response to an economic downturn to stimulate investment and spending. In accounting terms, declining interest rates increase the net present value of future cash flows (money today is worth more than money tomorrow), which leads firms to invest.

Most economic models assume that this effect is always in place, regardless of market structure. For example, imagine a market where most companies are roughly the same size and are in competition with each other. If one of the companies makes a capital investment to improve productivity, the others have a strong incentive to make similar investments or risk falling behind. In this case, low interest rates have the expected effect.

However, now imagine a market with a large player and many smaller companies. In such a scenario, when the larger company makes capital investments, the smaller companies may become discouraged and, in effect, drop out of competition. There is no way that they can keep up with their larger, and relatively wealthier, competitors. In such a scenario, over time, a market could become dominated by one or a few players. These large companies might have little incentive to keep improving because all of their effective competition has left the market. Why continue to invest if you have no further market share to gain? Ultimately, society is worse off because without the incremental investments that companies make under competition, productivity gains are lower than they otherwise would be.

To test this hypothesis, the authors construct a model where these changes happen gradually over time as two competitors’ make decisions based on the productivity gap between the two. If the productivity gap is small, the two companies are in a competitive region and both make investments to keep up with the other firm. The authors describe this as the traditional effect of lower interest rates. If the productivity gap gets too large, though, the firms enter a monopolistic region, where a leader is closer to achieving a higher-payoff position and therefore has even more incentive to increase investments. This incentive to invest marginally increases with reductions in interest rates. Lower rates, then, favor those with the incentives and the resources to be patient—larger firms. The authors term this the strategic effect of persistent low interest rates.

One analogy that broadly describes this relationship is a race between two runners. When the runners are next to each other, or one is just slightly ahead of the other, they both have incentives to keep up their pace to ensure that they don’t lose stride with the other. If one of the runners pulls ahead, they will both still have incentive to keep pushing; in the leader’s case because she wants to increase her lead, and in the follower’s case because he doesn’t want to fall further behind. Should the gap between the leader and the follower become too large, though, the follower may see no chance of winning and become discouraged, to the point of slowing down or even quitting. At that point, with no competition, the leader may also slow down or stop running. With no competition there is no improvement. The race is over.

One explanation for the US economy’s low-level growth rate is “secular stagnation,” which posits that such factors as persistent and very low interest rates, and/or wages and prices that remain at consistent levels, weigh down the economy’s growth rate.

A key insight of the authors’ model is that the traditional effect of low interest rates—wherein all companies invest—occurs when interest rates are reduced from a relatively high level, say from 7 percent to 5 percent. However, if interest rates continue to fall and approach zero, the benefits increasingly accrue to larger firms and the strategic effect described above takes hold. In a result that is incongruous with the traditional effect, economic growth slows as the interest rate decreases and larger firms exploit their relative market position. Importantly, if the initial interest rate level is relatively low and then falls, the benefits that accrue to leading firms is larger than if the initial rate were higher. In other words, in a world where interest rates start low and drop even further, larger firms benefit at a higher relative rate.

Finally, the authors conduct an empirical analysis of their hypothesis based on company data dating to 1962, and the results confirm their model’s prediction: leading companies within industries benefit more from reductions in interest rates, and those benefits are higher when starting from a lower level interest rate.

Conclusion

The slowdown in productivity growth is a phenomenon that began well before the Great Recession and persists throughout most advanced economies. To this point, answers to this complex riddle have focused on the negative demand effects from price and wage stickiness and the effect of the zero lower bound on nominal interest rates. However, these answers appear incomplete. By modeling competition within industries and analyzing how lower interest rates affect that competition, this new research offers a counterintuitive answer: the effect of persistent low interest rates on economic growth can be negative.

CLOSING TAKEAWAY
By modeling competition within industries and analyzing how lower interest rates affect that competition, this new research offers a counterintuitive answer: the effect of persistent low interest rates on economic growth can be negative.

The authors don’t claim to have the only—and final—explanation for persistent low interest rates, but their research suggests that the answer may encompass more than demand-side considerations. Also, the authors offer no policy prescriptions except to encourage economists and policymakers to focus attention on the production side of the economy and to raise new questions about the role and effect of persistent low interest rates on an economy. Better questions, of course, mean more helpful answers.

We document the extent of price dispersion for identical products in the U.S. retail industry. Our analysis isbased on “big data” that allow us to draw general conclusions based on the prices for close to 50,000 products (UPC’s) in 17,184 stores that belong to 81 different retail chains. Both at the national and local market level we find a substantial degree of price dispersion for UPC’s and brands at a given moment in time. We document that both persistent base price differences across stores and price promotions contribute to the overall price variance, and we provide a decomposition of the price variance into base price and promotion components. There is substantial heterogeneity in the degree of price dispersion across products. Some of this heterogeneity can be explained by the degree of product penetration (adoption by households) and thenumber of retail chains that carry a product at the market level. Prices and promotions are more homogenous at the retail chain than at the market level. In particular, within local markets prices and promotions are substantially more similar within stores that belong to the same chain than across stores that belong to different chains. Furthermore, the incidence of price promotions is strongly coordinated within retail chains, both at the local market level and nationally. We present evidence, based on store-level demand estimates for 2,000 brands, that price elasticities and promotion effects are also more similar within stores that belong to the same chain. Hence, the limited level of store-level price discrimination by retail chains reflects, in part, that their stores attract customers with similar demand.

Wages, Profits & Rent

Between April 2021 and May 2023, the cumulative price level in the United States rose by over 14%. This inflationary period was characterized by low unemployment and historically high job vacancies. Policymakers and economists attributed these trends to a “hot” labor market, where demand for workers outpaced supply. At the same time, however, real wages: the amount of money a person receives for their work after adjusting for inflation. fell sharply, challenging this notion. In this paper, the authors offer a new explanation: They argue that inflation, not labor market strength, drove these dynamics, simultaneously increasing vacancies while pushing down real wages.

The authors construct a model to examine how inflation affects workers and labor markets. By reducing real wages, inflation prompts workers to renegotiate pay, search for better jobs, or quit, driving job-to-job transitions and increased vacancies while keeping unemployment largely unchanged. Using this framework, the authors explore the consequences of inflation for labor market outcomes and worker welfare, finding the following:

The temporary inflation during 2021-2023 significantly reduced worker welfare across all income levels, with higher-wage workers experiencing the largest losses. The welfare losses equated to approximately 75% of monthly real income for workers in the bottom decile, 85% for the median, and 110% for those in the top decile.
Firms gained from inflation due to increased market power, as reflected in the historically high corporate profit-to-GDP ratios during 2021-2023.
While most of the welfare losses stemmed from real wage declines, workers incurred additional costs searching for jobs and renegotiating wages. However, these losses were nearly offset by the gains from reduced layoffs.

Building on these results, the authors validate their model using historical labor market data from 1950-2019. They also examine other high-inflation contexts, and find the following:

Historical periods of high US inflation (e.g., early 1950s, mid-1970s, and late 1970s) consistently show increases in vacancy rates and vacancy-to-unemployment ratios, even when unemployment remained stable or high.
Inflationary periods caused upward shifts in the Beveridge Curve: a graphical representation of the relationship between unemployment and the job vacancy rate, the number of unfilled jobs expressed as a proportion of the labor force. It typically has vacancies on the vertical axis and unemployment on the horizontal and slopes downward, as a higher rate of unemployment normally occurs with a lower rate of vacancies. , meaning there were more job openings relative to the number of unemployed workers. This pattern was also observed during the current inflationary episode.
International evidence, such as Argentina’s inflation surge in the early 2000s, reveals similar increases in vacancies and labor market tightness during inflation.

The authors conclude by distinguishing between their model’s predictions and alternate “hot labor markets” theories. They show that alternative explanations for high labor market tightness, such as productivity gains, do not align with the observed declines in real wages or the specific patterns of labor market flows that were observed during the recent inflation period.

This research challenges traditional interpretations of labor market tightness during inflationary periods, demonstrating that inflation can create the illusion of an overheated labor market while eroding real wages and worker welfare. The findings are consistent with recent supply chain disruptions, energy price increases, and pandemic-related demand pressures that drove up prices without significantly increasing labor demand. Policymakers should exercise caution in interpreting high labor market tightness as a sign of economic strength and consider policies that mitigate the welfare losses caused by inflation, particularly the erosion of real wages and the costs of job transitions.

Written by Abby Hiller • Designed by Maia Rabenold

This paper develops the nonparametric identification of models with production complementarities, worker-firm specific disutility of labor and search frictions. Mobility in the model is subject to preference shocks, and we assume that firms can write wage contracts. We develop a constructive proof for the nonparametric identification of the model primitives from matched employer-employee data. We use the estimated model to decompose the sources of wage dispersion into worker heterogeneity, compensating differentials, and search frictions that generate between-firm and within-firm dispersion. We find that compensating differentials are substantial on average, but the contribution differs greatly between the lowest and highest types of workers. Finally, we use the model to provide an economic interpretation of several empirical regularities.

Large firms in the United States frequently grow by expanding into new regions, such that local labor markets are increasingly dominated by a small number of large firms that operate in many areas (service-related chains, for example).¹ Given their many locations across heterogeneous labor markets, how do these national firms set wages? This is more than just an academic question, as the answer concerns such issues as wage inequality, the growth of labor market power, and the response of the economy to local shocks. However, little is known about national firms’ influence on these phenomena. This work addresses that gap by employing a novel combination of datasets and a theoretical framework to test empirical findings.

The authors’ primary dataset contains online job vacancies provided by Burning Glass Technologies, including roughly 70% of US vacancies, either online or offline, between 2010 and 2019, of which the authors focus on the 5% that provides posted point wages for detailed occupations across establishments within a firm. These data contain detailed job level information that allow the authors to control for changes in job composition across regions, and they include hourly wages for non-salaried workers and annual wages for salaried workers, which allows them to distinguish between wages and earnings. The authors supplement these data with survey data from human resource professionals, with self-reported salary data, and with reports from firms applying for foreign worker visas, to reveal the following facts:

There is a large amount of wage compression within firms across space; 40-50% of postings for the same job in the same firm—but in different locations—have exactly the same wage.
Identical wage setting is a choice made by firms for each occupation—for a given occupation, some firms set identical wages across all their locations, while the remaining firms set different wages across most of their locations.
Within firms, nominal wages are relatively insensitive to local prices.
Firms setting identical wages pay a wage premium.

The authors compare wage growth in the same job across different establishments over time, and they study the effect of a local shock to wages to provide evidence that the identical wages described above are due to national wage setting. They also survey firms to discovers a range of reasons for why they choose to set wages nationally, including hiring on a national market, simplifying management, and adhering to within-firm fairness norms. Of note, government policies such as minimum wages do not appear to drive national wage setting. These reasons point to a mix of firm and occupation specific factors that matter more for higher wage workers, and which suggest that nominal pay comparisons matter to workers.

The authors also develop a model-based exercise to measure the profits at stake from setting wages nationally. Please see the working paper for details, but this theoretical exercise reveals that in the absence of national wage setting, wages for national wage setters would vary across establishments by a median of 6.1%, and profits would be 3 to 5% higher. If firms set wages nationally to raise productivity, the authors’ estimate bounds the increase in profits that is needed to make national wage setting optimal.

Finally, this work has three key implications with policy relevance. National wage setting:

Reduces aggregate nominal wage inequality by roughly 5% by compressing nominal wages across space.
Raises employment in low-wage areas. Likewise, national wage setters seem to reduce aggregate wage and earnings inequality without dis-employment effects, through raising wages in low-wage regions.
Raises regional nominal wage rigidity, meaning that regional wages (absent inflation) are more resistant to change.

Money & Banking

Do asset prices and interest rates significantly influence investment? They should, according to economic theory. Since the early 2000s, asset prices have risen sharply and interest rates have fallen, indicating that financial investors are now willing to provide capital at lower returns, thus reducing firms’ cost of capital. The standard economic view is that such reductions in the cost of capital should lead to increased investment by firms, as they should pursue projects with returns higher than their cost of capital, adjusting their discount rates: A discount rate for firms is the minimum return a company wants to earn on an investment to make it worthwhile (also known as “hurdle rate”), considering the risk associated with the project and the time value of money. A related term is the weighted average cost of capital (WACC), which is the return expected by financial investors in return for providing capital to the firm. In standard economic models, the discount rate and the cost of capital are equal. In practice, they differ strongly, as this research has shown. (or hurdle rates) accordingly. This implies that firm investments should have surged as discount rates fell, driven by lower capital costs.

However, it is possible that firms do not necessarily align their discount rates with changes in the financial cost of capital. To do so, firms would need to estimate their perceived cost of capital and adjust their required return on capital accordingly, which—it turns out—does not happen consistently. Likewise, fluctuations in financial costs might have only a limited impact on firm investment, at least over certain periods of time. Why is this so?

To address this question, the authors examine the relationship between corporate discount rates, investment, and the cost of capital by constructing a new firm-level database consisting of firms’ discount rates and perceived cost of capital, matched to investment rates. These data were gathered from 74,000 paragraphs in corporate conference calls between 2002 and 2021, where managers discussed discount rates and perceived cost of capital. The authors’ analysis covers approximately 2,500 large firms across 20 countries.

Using their novel database, the authors interrogate the traditional economic view that firms immediately integrate cost of capital shocks into their discount rates using newly constructed data. They find the following:

While firms’ perceived cost of capital reflects financial cost variations, significant deviations and heterogeneity exist. Firms often report discount rates higher than their perceived cost of capital.
In the short and medium term (up to 4 years), changes in perceived cost of capital have minimal impact on discount rates, which rarely change within these periods. This phenomenon results in “discount rate wedges,” or the difference between discount rates and the perceived cost of capital, that vary over time and are negatively related to firm investment.
Only over the long term (more than 10 years) do discount rates align with the perceived cost of capital, challenging the idea that cost-of-capital shocks directly affect investment.
Discount rate wedges have increased by approximately 2.5 percentage points between 2002 and 2021, especially after 2010, when perceived costs fell but discount rates did not follow suit.
These persistent wedges impact investment. For example, a 1 percentage point increase in the wedge correlates with a 0.9 percentage point decrease in investment rates over the following year.
Firms with higher discount rates also report higher realized returns, supporting the idea that discount rates reflect required returns and influence investment behavior.

Bottom line: The existence of discount rate wedges indicates that changes in the cost of capital do not straightforwardly impact investment in the short and medium term, challenging traditional models of the investment-finance relationship. What, then, explains this behavior? The authors suggest that managers on conference calls may be ensuring prudence in their investment decisions by keeping discount rates stable, especially when the cost of capital is falling. The benefit of conservative financial behavior seems to outweigh the cost of investing less, at least in the eyes of managers. Further, the authors’ data indicate that firms with high market power in 2002 were largely responsible for the secular increase in wedges, suggesting that weak competition makes it less costly for firms to introduce high discount rate wedges.

Written by David Fettig • Designed by Maia Rabenold

For more on Harald Uhlig’s related research:

Benigno, Pierpaolo, Linda Schilling and Harald Uhlig (2019). Global (Crypto-)Currencies and Currency Competition. Crypto Review, 1, 01-03.

Benigno, Pierpaolo, Linda Schilling and Harald Uhlig (2022). Cryptocurrencies, Currency Competition and the Impossible Trinity. Journal of International Economics, 136.

Uhlig, Harald (2022). A Lunatic Stablecoin Crash. BFI WP 2022-95, Becker Friedman Institute, University of Chicago, Chicago.

Uhlig, Harald (2023). Review Article: Eswar S. Prasad: How the Digital Revolution is Transforming Currencies and Finance. Business Economics, 58, 201–204.

Uhlig, Harald and Taojun Xie (2021). Parallel Digital Currencies and Sticky Prices. BFI WP 2020-188, Becker Friedman Institute, University of Chicago, Chicago.

For general background on cryptocurrencies:

Among a vast literature on the subject, the author recommends the following three articles and one book:

Allen, Franklin, Xian Gu and Julapa Jagtiani (2021). A Survey of Fintech Research and Policy Discussion. Review of Corporate Finance, 1 259-339.

Chiu, Jonathan, Mohammad Davoodalhosseini, Janet Hua Jiang, Francisco Rivadeneyra and Yu Zhu (2023). Central Bank Digital Currencies and Banking: Literature Review and New Questions. Bank of Canada, Staff Discussion Paper 2023-4.

Kosse, Anneke and Mattei, Ilaria (2023). Making Headway—Results of the 2022 BIS Survey on Central Bank Digital Currencies and Crypto. BIS Papers, No. 136.

Prasad, Eswar S. (2021). The Future of Money How the Digital Revolution Is Transforming Currencies and Finance. Harvard University Press, Boston, MA.

Interest is growing among monetary authorities to begin promotion of digital currencies, which disincentivize the use of cash and could increase financial inclusion. However, little is known about the potential of cryptocurrencies to become a widely used payment method. This paper studies a unique natural experiment: On September 7th, 2021, El Salvador became the first country to make bitcoin legal tender, which not only established bitcoin as a means of payment for taxes and outstanding debts, but also required businesses to accept bitcoin as a medium of exchange for all transactions.

To ease transition to this new payment system, El Salvador also launched an app, “Chivo Wallet,” which allows users to digitally trade both bitcoin and dollars without transaction fees. As an incentive, citizens who downloaded this app received a $30 bitcoin bonus from the government, a significant amount in this dollarized Central American country with a per capita GDP of $4,131, along with discounts for gas.

Given these and other incentives, to what degree was bitcoin adopted? As El Salvadoran government restricts access to information, this research employs a nationally representative survey to answer this question. The survey, which involves 1,800 households, was conducted via face-to- face interviews to avoid the selection issues that may emerge if the survey conditioned respondents on owning a phone or having internet access. The authors’ findings include the following:

While most citizens in El Salvador have a cell phone with internet, fewer than 60% of them downloaded Chivo Wallet, and only 20% continued to use the app after spending their $30 sign-up bonus.
Without the $30 bonus, 75% of the respondents who knew about the app would not have downloaded it.
Most downloads took place just as Chivo Wallet was launched; 40% of all downloads happened in September 2021, with virtually no downloads in 2022. Likewise, remittances in the first quarter of 2022 were at their lowest point since the app’s launch.
Five percent of citizens have paid taxes with bitcoin, and despite its legal tender status, only 20% of mostly large firms accept bitcoin, and just 11.4% report having positive sales in bitcoin. Further, 88% of those businesses that report sales in bitcoin transform money from sales in bitcoin into dollars, and do not keep it as bitcoin in Chivo Wallet.
The fixed cost of technology adoption was high, on average, 0.7% of annual income per capita.

This research should give pause to policymakers advocating for the adoption of digital payment systems. Even after a big governmental push and under favorable circumstances, a digital currency’s viability as a medium of exchange faces big challenges.

The introduction of a central bank digital currency (CBDC) allows the central bank to engage in large-scale intermediation by competing with private financial intermediaries for deposits. Yet, since a central bank is not an investment expert, it cannot invest in long-term projects itself, but relies on investment banks to do so. We derive an equivalence result that shows that absent a banking panic, the set of allocations achieved with private financial intermediation will also be achieved with a CBDC. During a panic, however, we show that the rigidity of the central bank’s contract with the investment banks has the capacity to deter runs. Thus, the central bank is more stable than the commercial banking sector. Depositors internalize this feature ex-ante, and the central bank arises as a deposit monopolist, attracting all deposits away from the commercial banking sector. This monopoly might endangered maturity transformation.

View Working Paper View on SSRN

Capital & Growth

This paper explores the symbiotic relationship between transformative entrepreneurs and inventors, which is crucial for economic growth. We utilize microdata from Denmark to demonstrate that while the relationship between IQ and general entrepreneurship tends to be negative, it is strongly positive among transformative entrepreneurs. Transformative entrepreneurs, often with higher IQ and education levels, significantly drive R&D and business growth, thereby providing substantial opportunities for inventors. In contrast, average entrepreneurs are more influenced by their family’s entrepreneurship background. Our economic model links these dynamics to overall economic progress, highlighting how higher education influences career paths in entrepreneurship and invention. We identify talent misallocation caused by unequal education access, particularly affecting lower-income families. Our findings indicates the most effective policies strengthen the interplay between higher education, innovation, and entrepreneurship to foster transformative businesses and achieve long-run economic growth.

This paper characterizes the stationary equilibrium of a continuous-time neoclassical production economy with capital accumulation in which agents can insure against idiosyncratic income risk by trading agent-shock contingent assets, subject to limited commitment constraints that rule out selling these assets short. For a general N-state Poisson labor productivity process we characterize the optimal consumption-asset allocation, the stationary asset distribution as well as the aggregate supply of capital by the household sector. For the special case in which production is Cobb-Douglas, agent labor productivity takes two values, one of which is zero, and agents have log-utility, we solve the equilibrium interest rate, capital stock and the consumption distribution in closed form. The paper therefore provides a tractable alternative to the standard incomplete markets general equilibrium model as in Aiyagari (1994).

How do innovation and education policy affect individual career choice and aggregate productivity? This paper analyzes the various layers that connect R&D subsidies and higher education policy to productivity growth. We put the development of scarce talent and career choice at the center of a new endogenous growth framework with individual-level heterogeneity in talent, frictions, and preferences. We link the model to micro-level data from Denmark and uncover a host of facts about the links between talent, higher education, and innovation. We use these facts to calibrate the model and study counter-factual policy exercises. We find that R&D subsidies, while less effective than standard models, can be strengthened when combined with higher education policy that alleviates financial frictions for talented youth. Education and innovation policies not only alleviate different frictions, but also impact innovation at different time horizons. Education policy is also more effective in societies with high income inequality.

View the Interactive Tool

The Progress of Opulence

Prior research shows that businesses are more productive when they are clustered together, due to agglomeration spillovers: Economic benefits that firms gain from being close to each other, resulting in increased productivity. . The resulting concentration of economic activity may also cause agglomeration shadows: The negative impact on economic activity and city formation in areas surrounding large cities, where the concentration of businesses discourages growth in nearby locations. , which discourage growth in the areas surrounding large cities. Identifying these shadows is complicated, however, because cities affect each other in both directions and because of “ wave interference: The overlapping and interacting effects of economic activity in different regions, which makes it difficult to isolate the role of agglomeration shadows. ” that arises when averaging across different areas.

To overcome these challenges and identify agglomeration shadows, the authors develop simulations that build on a model from Fujita, Krugman, and Mori (1999). They then use the locations of ancient ports near the Mediterranean, which they show seeded modern cities. They collect data on population density in 2015 across 2.3 million 1km by 1km grid cells within 50km of the coast and within 200km of their nearest ancient port (detailed in their working paper) to estimate the impacts of ancient ports on modern conditions in these areas. They find the following:

Ancient port locations cast agglomeration shadows on surrounding areas, in ways consistent with the model simulations. Ancient port locations exhibit a distinct “wave” effect, where population density declines markedly out to 20km and then increases up to 40km. This estimated agglomeration shadow, roughly 10-30km from ancient port locations, is similar to the distance of a typical day’s travel for pack animals or carts in the ancient world. This wave pattern is more obscured around ancient ports that lost their harbors by the modern era, suggesting that the loss of a geographic advantage leads to more diffuse agglomeration shadows.
Agglomeration shadows reflect general economic competition between cities rather than competition between port activities. The loss of a natural harbor in neighboring ancient port locations decreases the likelihood of modern port structures in a location, but increases that location’s city activity.
The authors also find evidence of agglomeration shadows in modern city locations more generally. Across all the modern cities in the authors’ sample, they show that there are fewer large cities (> 500,000 people) whose nearest large city is within 40km, and more large cities from 40km to 60km.

The upshot is that encouraging growth in particular places can discourage growth in nearby areas. Prior research shows that the same can be true of concentration of medical services, as providers benefit from geographically concentrated production. This research has implications for the design of place-based policies that emphasize investment in a specific location, as it suggests potential negative consequences for surrounding areas.

Whom would you hire? Amy is clearly the most productive on a per unit, or ratio, basis: Her employer only has to pay for one minute’s work for 10 minutes of net production. But Sally, in an aggregate sense, is more productive: She nets her employer 60 net units every afternoon, six times more than Amy produces. It would be great to hire Amy if she could maintain the same per-unit productivity and work for 5 hours, but perhaps she is not willing to work 5 hours or her productivity plummets after working 1 minute.

Richard Hornbeck of UChicago’s Booth School of Business uses that simple story with his students to describe alternative ways of thinking about productivity. This distinction—between ratio productivity and aggregate productivity—helps in thinking about the central findings of a recent paper he coauthored with New York University’s Martin Rotemberg, “Railroads, Reallocation, and the Rise of American Manufacturing.” Hornbeck and Rotemberg show that many US counties were held back from expanding by market inefficiencies, and when these counties were encouraged to grow by the expanding railroad network, there were substantial gains in aggregate productivity that have been missed in previous analyses.

Chart

Figure 1: Waterways and Railroads, by Decade

Notes: Panel A shows the waterway network: natural waterways (including navigable rivers, lakes, and oceans) and constructed canals. Panel B adds railroads constructed by 1860, Panel C adds railroads constructed between 1860 and 1870, and Panel D adds railroads constructed beween 1870 and 1880.

Efficiency unbound

The manufacturing sector in the United States expanded substantially in the latter half of the 19th century. This increase occurred alongside the expansion of the railroad network, as coast-to-coast and regional rail lines opened large domestic markets to new areas and to new commodity resources. How important was rail in the development of US manufacturing and other sectors? While railroads were a clear technological improvement in the transportation sector, and while they brought competition to water-based transportation, previous research has revealed only a relatively modest contribution from rail lines. The direct benefits of improved transportation were clear from reducing resources spent on transportation itself, but—in the aggregate—they were small compared to the general rate of economic growth.

This new research upends this conventional wisdom by incorporating the indirect benefits of rail transportation through the expansion in manufacturing and other sectors that were inefficiently small. In effect, railroads induced increased manufacturing activity in places that were previously held back by expensive modes of transportation. Many of these new places—whether from the existence of untapped natural endowments, commodities, or labor supply—proved particularly efficient at production.

Before describing the authors’ findings in more detail, it is useful to revisit the above description of ratio vs. aggregate productivity. In the first case, economists refer to ratio productivity as technical efficiency (or total factor productivity), which is the amount of output you would expect to produce given increased inputs; in other words, the amount that is produced beyond expectations. For example, if a firm begins using more labor, capital, or land, you would expect output to increase. However, you may be surprised on the upside: that difference between expected and actual output is technical efficiency growth.

The authors estimate that absent an expanded rail network, US aggregate productivity would have been 25 percent lower in 1890, equaling about $3 billion or a 25 percent reduction in gross domestic product (GDP). Previous estimates put this loss at 3.2 or 2.7 percent of GDP.

Now imagine that this same firm, which has increased its inputs, experiences an increase in the value of its output that is greater than the increase in the costs of its inputs – but its technical efficiency was unchanged. How could this be? This reflects increases in reallocative efficiency, which stems from inefficiencies in resource allocation in the economy. When markets are efficient, and technical efficiency is unchanged, increases in input usage lead to increases in the value of output that are equal to the increases in the cost of inputs (or marginal output equals marginal cost). When there are market inefficiencies, due to firms’ inability to access enough capital or firms being able to price above marginal cost, then the value of the increased output can exceed the value of the increased inputs (marginal output is greater than marginal cost). In this case, increases in firm production contribute to an overall increase in aggregate productivity. The authors investigate how the expansion of the railroad network impacted both these forms of productivity – technical efficiency and reallocative efficiency – and find substantial impacts of the railroads through increases in reallocative efficiency.

To conduct their analysis, the authors employed US Census of Manufacturers’ data from 1860, 1870, and 1880. This 20-year period was the primary focus of their research, though they extend some of their analyses to 1890 and 1900. At the time, census takers were charged to include information from all manufacturers with more than $500 in sales, including smaller operations run, for example, out of semi-permanent structures like sheds or other outbuildings. Further, these manufacturing data included the annual value of output, the annual cost of materials, the annual cost of labor, and the value of invested capital.

To determine the contributions of an expanded rail network, the authors measure how changes in the network affected market access for various counties. This market access reflects the degree to which a county’s manufacturers had improved access to workers, consumers, and material inputs from the introduction of rail service. The authors’ findings were clear: relative increases in county market access resulted in substantial increases in manufacturing productivity (roughly 13 percent for every one standard deviation increase in county market access from 1860 to 1880). These gains were driven by the increases in market access in marginally productive areas, in other words, from increases in reallocative efficiency. Imagine, for example, a particular county that was endowed with abundant natural resources, but productive firms could not get enough capital or were pricing substantially above their costs – the overall economy benefits when those firms expand production.

With such gains at a county level, what was the impact on aggregate productivity? The authors estimate that absent an expanded rail network, US aggregate productivity would have been 25 percent lower in 1890, equaling about $3 billion or a 25 percent reduction in gross domestic product (GDP). Previous estimates put this loss at 3.2 or 2.7 percent of GDP. Further, the authors’ estimate a 43 percent annual social rate of return¹ on the $8 billion of capital invested in railroads in 1890. Only 8 percent of that social rate of return was captured by the railroads.

Much of this increase in aggregate productivity growth extends from an increase in national population. Absent railroads, had US aggregate population remained fixed in 1890, the real wages of workers would have declined by 34 percent. In addition, the gains from reallocating workers across counties would have been lost, amounting to lower aggregate productivity of about 5.3 percent.

CLOSING TAKEAWAY
Railroads allowed for increasing production in counties that were otherwise underutilizing inputs, or where the value marginal product of inputs (be they materials, capital, or labor) was greater than their marginal cost. In other words, counties that were inefficiently small experienced large gains after the arrival of rail lines. These potential gains from infrastructure are indeed largest when the economy is most inefficient, to which the authors cheekily note: “With great problems come great possibilities.”

Conclusion

The contribution of railroads to the economic growth of the United States, especially in the latter half of the 19th century, has long been debated by researchers. For many decades, due in part to the Nobel prize winning research by Robert Fogel, conventional wisdom held that while railroads delivered clear direct benefits to the economy, the aggregate effect of extending rail lines to new counties was relatively small compared with the general pace of economic growth.

This research, though, reveals how railroad lines linked domestic markets throughout the United States (as the accompanying maps reveal). Railroads allowed for increasing production in counties that were otherwise underutilizing inputs, or where the value marginal product of inputs (be they labor, capital, or materials) was greater than their marginal cost. In other words, counties that were inefficiently small experienced large gains after the arrival of rail lines.

The indirect benefits derived from the expanded economic activities that were generated by the expanded railroad network were much larger than the direct benefits derived from lower transportation costs, which is how researchers have traditionally measured the impact of railroads on the US economy in the second half of the 19th century. As noted above, US aggregate productivity would have been 25 percent lower in 1890 absent railroads, much higher than previous estimates of around 3 percent.

While the authors do not offer specific policy prescriptions based on their work, their results suggest that, at a minimum, policymakers—especially those in developing countries—should pay attention to all benefits derived (both direct and indirect) from improved transportation networks. These potential gains are indeed largest when the economy is most inefficient, to which the authors cheekily note: “With great problems come great possibilities.”

¹The social rate of return not only considers all direct benefits associated with the railroads, such as railroad profits and decreased resources spent on transportation, but also considers indirect benefits through increases in input usage (materials, labor, capital) whose marginal product was greater than their marginal cost.

View Working Paper View on SSRN View Research Brief

Trade & Political Economy

We estimate the mortality impact of local labor market exposure to the 1994 North American Free Trade Agreement (NAFTA) as well as to other local area shocks, and provide a parsimonious empirical explanation for differently-signed mortality estimates across different sources of local labor market contractions. Leveraging spatial variation in exposure to Mexican important competition from NAFTA, we find that more exposed areas experienced larger increases in mortality. In the 15 years post-NAFTA, an area with average NAFTA exposure experienced an increase in annual, age-adjusted mortality of 0.68 percent (standard error = 0.19), an increase that more than erases prior estimates of the welfare gains from NAFTA’s nationwide economic benefits. Mortality increases appear across all broad age by sex groups, but are particularly pronounced among working-age men, a demographic that also experienced disproportionate NAFTA-induced declines in (primarily manufacturing) employment. Additional evidence from other local labor market shocks reveals a systematic pattern: declines in local area manufacturing employment increase mortality, while declines in local area non-manufacturing employment decrease mortality. These findings suggest that the sign and magnitude of any mortality impacts of future economic shocks likely depends critically on the extent to which employment declines are concentrated in the manufacturing sector.

Statutory tariff rates (which can differ from actual rates) on US imports have risen dramatically to levels not seen since the 1930s, with the trade-weighted average rate at 27% in September 2025. Imports from 176 exporters, on goods accounting for more than 70% of total US imports, faced higher tariffs than at the end of 2024.

While fears of a trade war between the United States and affected countries rose along with rising tariff rates, retaliation has been limited, with China the important exception, as noted in Bullet 3 below. China responded in kind to US announcements of large and broad tariffs, with both countries pushing each other beyond 100 percent rates on nearly all traded goods. Though subsequently reducing those bilateral tariff rates, they remain historically high.

What are the effects on US producers and consumers, and for the dollar? The authors examine the effects of the tariffs during last year’s tariff hikes (as of September 2025), and during the previous episode of tariff changes during 2018-19 to find the following:

Actual tariff rates in the current period are only about half the statutory rates due to shipment lags, exemptions, trade agreement utilization, and enforcement issues. This is one reason why price impacts so far are lower than many April forecasts predicted. While shipment lags will dissipate, other factors may persist.
US importers and consumers largely bear the tariff costs, not foreign exporters. Tariff pass-through rates were high in both periods, 80% in 2018-2019 and 94% in 2025, meaning tariff-inclusive import prices rose nearly in step with the tariffs, as exporters generally did not significantly lower prices.
Chinese goods dropped from 22% of US imports in late 2017 to 8% by September 2025. Countries like India and Vietnam gained significant market share as alternative suppliers, though it’s unclear how much this reflects genuine production in India and Vietnam vs. Chinese goods routed through these countries.
Because imported inputs are crucial for US manufacturers and pass-through is high, American producers face substantial cost increases. The study calculates that for some sectors, the effective “production tariff” rate (a hypothetical tax rate on production costs that might have an equivalent impact on U.S. manufacturing as the import tariffs) exceeded 2 percentage points. Manufacturing overall saw an increase above 1 percentage point in 2025.
Finally, regarding the dollar, unlike 2018-2019 when the dollar strengthened as predicted, the dollar depreciated significantly in 2025 despite the tariffs, suggesting other economic forces are likely at play.

Questions persist regarding the impact of tariffs on the US economy, especially given the unpredictable nature, in terms of substance and timing, of tariff policy. This work offers current context and a framework for evaluating ongoing effects.

Written by Abby Hiller • Designed by Maia Rabenold

View Working Paper View on SSRN View Research Brief

We use global tariffs to reveal the weights that nations implicitly place on the welfare of their trading partners relative to their own. Our estimated welfare weights suggest that formal and informal rules of the world trading system make countries internalize the impact of their policies onto others to a substantial extent, though not fully. On average, countries place 25% less value on transfers to foreigners than transfers to their own residents. Across nations, we find that countries that put higher welfare weights on the welfare of foreigners also tend to receive higher weights from them, consistent with a general form of reciprocity among nations. Using our estimated welfare weights, we provide a first look at what countries stand to lose, or gain, from the dissolution of the world trading system as we know it.

Who bears the burden of tariffs? In light of recent tariffs on imports, policymakers, researchers, and the public have debated how tariff costs are distributed among foreign producers, domestic importers, trade intermediaries, and final consumers. While existing research suggests that import prices (inclusive of tariffs) tend to increase with tariffs, it remains unclear how these costs are transmitted to consumers. This paper resolves this disconnect by tracing tariff impacts on prices throughout the entire supply chain, from foreign producers through importers, distributors, and retailers to final consumers.

The authors examine US tariffs imposed on European wines in October 2019 as part of the Airbus-Boeing subsidy dispute. The policy levied a 25% tariff specifically on still wines with ≤14% alcohol by volume (ABV) from France, Germany, Spain, and the United Kingdom. Using confidential transaction-level data from a major wine importer matched to exporter and downstream distributor and retail prices, the researchers compare price changes for tariffed wines (still wines ≤14% ABV) against a control group of non-tariffed wines (still wines >14% ABV and sparkling wines) from producers that sold no tariffed products.

They find the following:

Consumers paid more than the tariff in dollar terms. Foreign producers lowered their prices by 5.2% following the 25% tariff, absorbing roughly one-quarter of the tariff burden. However, because the producer’s price decline was much smaller than the 25% tariff rate, the importer still faced a net cost increase—paying a lower pre-tariff price but a higher tariff-inclusive price overall. As these costs moved through the supply chain, domestic markups amplified the price increase. The importer raised prices to distributors by 5.4%, absorbing some of the tariff through lower margins but passing most of the cost downstream. Retail prices ultimately rose by 6.9%. For a wine initially priced at $5 at the border, consumers paid $1.59 more per bottle while the government collected only $1.19 in tariffs—a dollar pass-through exceeding 100%. Even accounting for statistical uncertainty across all stages, the consumer dollar cost per dollar of tariff revenue exceeded 68% with 90% confidence.
Price effects emerged gradually, taking nearly a year to reach consumers. Import prices began declining three months after tariffs took effect, as foreign suppliers adjusted their pricing. The importer’s prices to distributors increased around the same time. However, retail prices did not rise until approximately 10-12 months after the tariffs were imposed, and remained elevated well beyond when tariffs were suspended in March 2021. This lag structure reflects inventory management, contract timing, and the multiple stages goods traverse before reaching consumers.
Tariff engineering created compositional bias in trade statistics. Immediately after tariffs took effect, the share of new wine label applications for products >14% ABV from France jumped by 40 percentage points. Roughly one-quarter of this increase came from existing products switching their reported ABV from ≤14% to >14%, crossing the tariff threshold without necessarily changing the wine itself. This strategic relabeling demonstrates how firms adapt product characteristics to minimize tariff exposure and shows that standard unit value measures from customs data can produce misleading pass-through estimates when product composition shifts systematically.

These findings offer important lessons for policymakers evaluating trade policy, particularly as they interpret pass-through estimates for tariffs imposed in 2025. Conventional measures of tariff pass-through, expressed in percentage terms, can substantially understate the actual cost burden on consumers when goods pass through multiple distribution stages with significant markups. The timing of price adjustments also matters, as the nearly year-long lag before retail prices fully adjust means tariffs can influence inflation well after implementation. Finally, the systematic product reclassification to avoid tariffs demonstrates how firms strategically respond to trade policy through product adaptation rather than just pricing adjustments, potentially limiting revenue collection and distorting trade statistics.

Written by Abby Hiller • Designed by Maia Rabenold

Why do voters support protectionist policies that materially harm them? Recent evidence shows that tariffs raise consumer prices and generate retaliatory trade measures that reduce employment. Such policies remain politically popular, however, particularly among those most economically affected. In this paper, the authors offer a new explanation: support for nationalist economic policies stems from a fundamental desire for dominance, which generates preferences for excluding others from consumption opportunities.

The authors build on prior research documenting that a substantial portion of the population derives utility not just from consuming goods, but from consuming goods that others desire but cannot obtain. They incorporate this desire for dominance into a model of international trade, showing that such exclusionary preferences reduce the value of trade and generate support for restrictive policies. The model predicts that people with exclusionary preferences will support tariffs that harm both their own consumption and their trading partner’s consumption, but will show no such preference for policies that affect only domestic consumption.

To test these predictions, the researchers conduct two surveys. They begin by measuring respondents’ exclusionary preferences using an incentivized experimental method in which participants bid on a unique good under three scenarios with varying degrees of exclusion of other potential buyers. Those whose willingness to pay increased with the level of exclusion are classified as having “preferences for exclusion,” a pattern observed in roughly 40% of respondents (consistent with prior research). Respondents are then randomly assigned to evaluate tariff policies under different conditions and asked about their support for various economic policies.

The authors find the following:

Exclusionary preferences strongly predict tariff support, but only when tariffs harm trading partners. Those with exclusionary preferences are 12.3 percentage points more likely to support a 15% tariff that would raise prices domestically. When respondents are told the tariff would not harm the foreign country, support between those with and without exclusionary preferences is statistically indistinguishable.
Those with exclusionary preferences are more accepting of inflation caused by tariffs than by other policies. When comparing support for tariffs versus stimulus policies that would generate identical 15% price increases, respondents with exclusionary preferences show significantly higher support for tariffs.
Exclusionary preferences predict support for a broad range of protectionist policies that harm domestic consumers. Beyond tariffs, those with exclusionary preferences are significantly more likely to support policies explicitly designed to maintain consumption gaps between nations, even when informed these policies would raise prices for Americans. They also show higher support for restricting foreign investment, emphasizing that the US should “come out on top” in trade relations, and limiting purchases from foreign countries. These patterns held across different trading partners (China, Mexico, and Canada), suggesting the effects are not driven by hostility toward specific nations.
The relationship between exclusionary preferences and policy support is not explained by political ideology or cognitive biases. While political preferences partially mediate the relationship (Democrats are less likely to hold exclusionary preferences), the core association remains strong and statistically significant after controlling for party affiliation and zero-sum thinking (a cognitive bias where people believe gains for some come at others’ expense).

These findings have important implications for understanding the political economy of trade policy. The results suggest that voter support for protectionist measures may be driven less by misunderstanding of economic costs or by narrow self-interest than by a fundamental preference for policies that exclude foreign consumers from consumption opportunities, even at personal economic cost. This helps explain why tariffs remain politically popular despite clear evidence that they raise prices and harm employment. The findings also suggest that inflation stemming from protectionist policies may generate less political backlash than equivalent price increases from other sources, as voters with exclusionary preferences view such costs as more acceptable when they serve to limit foreign consumption.

Written by Abby Hiller • Designed by Maia Rabenold

View Working Paper View on SSRN

My research suggests that world inequality is explained by the incidence of extractive and inclusive institutions. But why do some countries have extractive institutions? I distinguish between two main reasons; first, power relations; second, the “normative order.” Normative orders provide justifications and legitimacy for institutions which may not generate prosperity, but may achieve other goals. These distinctions are critical because they create very different challenges in trying to make institutions more inclusive and create prosperity. I show how countries move from the economic periphery as a consequence of changing both. My own intellectual journey has been in the other direction, however, hence the title of the paper: I was fortunate to be born in Britain, but I have had to unlearn much of my own experience, socialization and training in order to see other societies on their own terms. That’s crucial to be able to help them, but also to learn from them.

Trade policies often create winners and losers within a society. For example, lowering tariffs on imports may benefit consumers by reducing prices, and harm domestic manufacturers by increasing competition. In this paper, the authors study trade policies to reveal the extent to which policymakers prioritize certain groups.

The authors develop a model to measure the tradeoffs between different beneficiaries of trade policies. Their model accounts for differences in exposure to international trade across regions and industries, both directly through imports and exports as well as indirectly through exposure to trade that arises from domestic supply chains. They apply their model to US trade policy in 2017 and estimate the value society places on a dollar transferred across individuals from 50 states (plus Washington, DC), and 23 industries. They find the following:

Tariff policy in 2017 suggests that society assigns different values to the income of US individuals who are working in different industries and living in different states. A hypothetical $1 received by an individual at the 99th percentile of the authors’ estimates of social value is equivalent to $1.91 received by an individual at the 10th percentile.
This variation is mostly driven by large differences between people working in different industries. Tariff policy in 2017 suggests that society values income received by individuals working in the Apparel, Textiles, and Metals industries 450% more than income received by the average US individual.
Perhaps surprisingly, differences in social values across states only play a minor role in explaining 2017 tariff policy. Society appears to value income received by residents of Idaho, the state with the highest social weight, only 8% more than that received by residents of West Virginia, the state with the lowest social weight.
Thirty percent of the variation in 2017 tariffs across different goods and trading partners appears motivated by redistribution, of which 27% of the variation appears aimed at benefitting particular industries directly, and 3% appears aimed at targeting particular states.
The monetary transfers associated with redistributive trade protection are large. Transitioning to a hypothetical US economy where everyone is valued equally would shift roughly $2,400 per worker each year from region-sector pairs at the top decile of the authors’ welfare weights to those at the bottom decile.
The sectors that exhibit the most lobbying behavior are clear winners from redistributive trade protection. They receive almost $5,000 per worker annually through tariff protections, despite spending less than $100 per worker annually on lobbying.

Trade policy is by no means the only policy tool available to governments seeking to help some of their constituents at the expense of others. Environmental policy, competition policy, and financial regulation are all areas to which the approach developed in the paper would be straightforward to apply. In all such cases, this analysis can offer a blueprint for identifying who the politically favored are and for evaluating the economic importance of the political favors they receive.

We use micro data collected at the border and at retailers to characterize the effects brought by recent changes in US trade policy – particularly the tariffs placed on imports from China – on importers, consumers, and exporters. We start by documenting that the tariffs were almost fully passed through to total prices paid by importers, suggesting the tariffs’ incidence has fallen largely on the United States. Since we estimate the response of prices to exchange rates to be far more muted, the recent depreciation of the Chinese renminbi is unlikely to alter this conclusion. Next, using product-level data from several large multi-national retailers, we demonstrate that the impact of the tariffs on retail prices is more mixed. Some affected product categories have seen sharp price increases, but the difference between affected and unaffected products is generally quite modest, suggesting that retail margins have fallen. These retailers’ imports increased after the initial announcement of possible tariffs, but before their full implementation, so the intermediate passthrough of tariffs to their prices may not persist. Finally, in contrast to the case of foreign exporters facing US tariffs, we show that US exporters lowered their prices on goods subjected to foreign retaliatory tariffs compared to exports of non-targeted goods.

Hopefully you didn’t wait too long. Following the late-2017 announcement of tariffs on all washers imported to the United States, prices increased by about 12 percent in the first half of 2018 compared to a control group of other appliances. In addition, prices for dryers—often purchased in tandem with washing machines—also rose by about 12 percent, even though dryers were not subject to a tariff.

On the one hand, these price increases were unsurprising given the tariff announcement. On the other hand, washers had been the subject of multiple import restrictions since 2012 and the price of this ubiquitous household appliance had actually declined over the ensuing years. What happened in 2018 that caused washing machine prices to spike? How did the price of washing machines continue to slide post-2012 when the industry was subjected to antidumping duties on imports from selected countries to the United States? Aaron Flaaen, Federal Reserve Board economist, Ali Hortaçsu, UChicago professor of economics, and Felix Tintelnot, UChicago assistant professor of economics, address these and other questions in their working paper, “The Production, Relocation, and Price Effects of US Trade Policy: The Case of Washing Machines.” Their work is preliminary and ongoing, but they find that the clear losers from such tariffs are the consumers of targeted products.

Chart

Figure 1 · US Washing Machine Imports and Korean Washing Machine Part Exports by Country

Global tariffs find their way to the consumer

In December 2011, an anti-dumping investigation against South Korea and Mexico was announced by the US International Trade Commission (USITC): the two countries, which were the leading exporters of washing machines to the US, were charged with dumping washing machines onto the US market with artificially low prices, and thereby harming US domestic producers. Tariffs were soon placed on washing machines imported to the US from South Korea and Mexico.

Normally, such a restriction in supply, assuming continued demand, would increase prices. However, just one month after the anti-dumping investigation was announced, imports of washing machines from South Korea and Mexico started to fall while the majority source of import imports shifted to China. Indeed, as Figure 1 illustrates, China quickly became the largest exporter of washing machines to the United States, essentially trading places with South Korea.

This shift in trade patterns demonstrates that washing machine manufacturers responded to the country-specific tariffs by simply moving production to a different country. Following this shift, China remained the leading exporter of washing machines to the US into 2016. Eventually, though, US domestic producers appealed to the USITC, and an anti-dumping investigation was announced against China. By July 2016, when an anti-dumping order was issued, imports of washers from China had fallen below Thailand and Vietnam, where new production facilities were already operating.

After this second round of “country-hopping” these tariffs, in February 2018, the US applied a world-wide tariff on imports of washing machines. Imports of washers to the U.S. spiked at the end of 2017, then fell back steeply once the tariffs were applied: evidence of strategic use of anticipatory “front-running” of tariff changes.

While the country of origin for washing machines changed quickly over the previous six years, one thing remained the same for consumers: prices continued to decline over that period. However, as described above, following the application of broad tariffs on all washer imports, the price of washing machines increased roughly 12 percent in 2018. Importantly, and somewhat surprisingly, prices also rose at the same rate for dryers, a complementary good that was not subject to tariffs. All brands (both domestic and foreign) show notable price increases following these 2018 tariffs.

One revealing finding in this work is the tight price relationship between washers and dryers, even when washers, for example, are the product that is subject to tariffs. Among the five leading manufacturers of washers, roughly three-quarters of models have matching dryers. When the authors compare only electric washers and dryers, they show that in about 85 percent of the matching sets, the washers and dryers have the same price.

Finally, one promise of increased tariffs is to drive manufacturing “home” and thus increase employment as foreign-made goods become more expensive. To this point, Samsung has opened a South Carolina plant in January 2018 with plans to hire 1,000 new workers by 2020, LG is scheduled to open a Tennessee plant offering 600 jobs by sometime in 2019, and Whirlpool has announced the addition of 200 workers to existing domestic production. While on the surface it appears that the global tariffs of 2018 were a successful policy outcome, the results of the paper show the costs for consumers associated with these new jobs. The increases in consumer prices described above translate into a total consumer cost of $1.5 billion per year, or about $820,000 per new job.

While the country of origin for washing machines changed quickly over the previous six years, one thing remained the same for consumers: prices continued to decline over that period. However, following the application of broad tariffs on all washer imports, the price of washing machines increased roughly 12 percent in 2018.

As this description of the authors’ research reveals, tariffs imposed on individual countries can result in “country-hopping” by producers, which can actually result in lower production costs and, conceivably, lower costs for consumers. However, when global tariffs are applied, production-shifting is no longer profitable. This may shift some jobs to the tariff-imposing country—in this case the US—but at a steep price that will find its way to the final product, where consumers will pay the price.

Conclusion

Tariffs increase the cost of doing business, which often leads to increased prices for intermediate goods (those used in production) and final goods (those purchased by consumers and businesses). However, tracing the impact of a tariff through the production and delivery of a particular good is difficult; the effort is often inhibited by incomplete or private data that companies hold close. The case of washing machines, though, offers a clear view on the impact of global tariffs for a particular product. Indeed, as this research reveals, complementary goods—in this case, dryers—can also be affected. However, when single-product tariffs are applied to individual countries, production may shift to another country and could actually lower production costs and, thus, prices for consumers.

Can these lessons be applied to other consumer products that are impacted, say, by other tariff increases or other restrictive measures? It may be too early to tell. The case of washing machines is special in that tariffs were placed on a particular product for which the authors were able to attain relevant data. For most products, such data are unavailable and it will likely take time for increased prices to reveal themselves in the data.

In the end, there are two lessons for policymakers, according to this research: tariffs applied to individual countries may be ineffective, and tariffs applied globally tend to result in significant costs to consumers. Possible winners are domestic producers that benefit from increased market share, as well as those domestic workers who may gain employment. Those jobs can come at a steep price, though, as the example of washing machine production shows: $820,000 per job. Consumers not only pay a higher price for finished goods from global tariffs, but they can also suffer an efficiency loss; for example, households that cannot afford new products because of price increases cannot benefit from the improved performance that such products offer. With the increased use of trade policies by the United States, the authors recommend more research into the interactions between firm-level decisions and the effects on consumers and the broader economy.

CLOSING TAKEAWAY
In the end, there are two lessons for policymakers, according to this research: tariffs applied to individual countries may be ineffective, and tariffs applied globally tend to result in significant costs to consumers. Possible winners are domestic producers that benefit from increased prices, as well as those domestic workers who may gain employment.

Government & Public Finance

Human capital: the collective skills, knowledge, and abilities of individuals that can be used to create economic value , as measured by levels of school-based education, is unevenly distributed across space. In 2000, people in the Netherlands had an average of 10.8 years of schooling, compared to 2.5 years in the Central African Republic. Comparing inhabitants of the most and least educated corners of the globe—specifically, 1° × 1° grid cells at the 90th and the 10th percentiles of educational attainment—this range goes from 11.8 to 3.4 years.

What drives these large differences in human capital across space? The authors examine how two factors shape the geography of development, both today and in the future. First, the cost of acquiring human capital varies widely across locations—in some places, access to education is relatively expensive or difficult, limiting the supply of human capital. Second, the productivity of human capital differs across locations—where human capital generates higher returns, demand for it will be greater. These forces interact with migration, trade, and innovation to determine how human capital evolves across the globe over time.

To address this question, the authors develop a dynamic spatial model of the world economy at a 1° × 1° resolution. In the model, individuals choose where to live and how much human capital to acquire, taking into account that both moving between locations and upgrading human capital are costly, and these costs vary across space. Firms in each location produce differentiated goods using labor, human capital, and land, with trade between locations subject to transport costs.

The model incorporates two key productivity forces. First, a location’s overall productivity benefits from agglomeration economies: the productivity benefits that arise when economic activity concentrates in a particular location, as firms and workers gain advantages from being near one another —as population density increases, so does productivity. Second, locations accumulate human-capital-augmenting technology over time through two channels: local innovation (which depends on the local stock of human capital) and diffusion from other locations. This creates dynamic feedback loops where human capital today boosts innovation tomorrow, attracting more people and generating further innovation.

The authors quantify the model using data on population, income, and schooling from 2000. They identify location-specific costs of acquiring human capital by matching the model to observed changes in schooling levels between 2000 and subsequent years. They then simulate the model forward for 200 years to project how human capital and economic development evolve across space.

They find the following:

The model predicts strong persistence in the geography of development. Over the span of two centuries, today’s developed regions, such as coastal Australia, Western Europe, Japan, and the United States, remain the most developed 200 years from now. The same persistence holds for population density, as locations that are dense today continue to be dense two centuries from now.
Even after 200 years, the world economy remains far from reaching a steady state where all regions grow at the same rate. This finding stands in sharp contrast to spatial models that ignore human capital, which instead predict that poor but densely populated areas will eventually catch up to wealthier regions through agglomeration effects alone.
Low education costs drive persistent advantages in developed regions. Because highly developed regions tend to have low education costs, their human capital levels tend to be high, both in the short and the long run. This advantage is magnified by dynamic feedback loops over the transition path. Current levels of human capital improve future productivity, because human capital is an input in the growth of human-capital-augmenting productivity.
The model reveals a strong negative correlation between education costs and local economic fundamentals, as places with better amenities, higher productivity, and more favorable conditions for development also tend to have cheaper access to education. As a result, the low cost of acquiring human capital in the developed world keeps these regions ahead, generating the persistence the model projects.
Reducing education costs by the same percentage across poor regions generates local gains but may cause global losses. The authors examine counterfactual policies that lower the cost of human capital acquisition while maintaining the relative differences between locations within a region. Whether implemented in sub-Saharan Africa, Latin America, or Central and South Asia, the local economy benefits: human capital levels rise, both in the short and the long run. This enhances innovation in human-capital-augmenting productivity, generating positive dynamic effects.
Higher local welfare retains a larger share of the global population in the target region, further reinforcing productivity through agglomeration economies. However, the effects on global welfare differ markedly by region. When the policy is implemented in a low-income region, like sub-Saharan Africa and Central and South Asia, the local increase in population comes at the expense of regions with better economic fundamentals, reducing global agglomeration and innovation. In contrast, when implemented in a middle-income region like Latin America, the population reallocation comes partly from regions with worse fundamentals, improving outcomes globally.
Equalizing educational costs across space may lead to unintended consequences. As an alternative policy, the authors consider setting the cost of education to the same level across all grid cells within a region, such as sub-Saharan Africa—eliminating the variation in education costs between locations. Because schooling costs are higher in less developed areas within the region, equalizing costs lowers them more dramatically in those areas.
This creates incentives for population to migrate toward locations with weaker economic fundamentals. As a result, an increasingly larger share of the region’s population resides in less productive locations, which hurts overall innovation and weakens agglomeration economies through greater geographic dispersion. These negative effects may partly or even fully offset the positive impact of cheaper access to human capital.

These findings suggest that effective development policies must account for spatial frictions, agglomeration effects, and the dynamic relationship between human capital and productivity across space. When evaluating education policies, policymakers must consider not only local benefits but also how these policies reshape the global distribution of population and economic activity. Policies that retain population in regions with weak economic fundamentals may generate local gains while producing global losses. Moreover, within-region heterogeneity matters: equalizing access to education across locations with varying economic potential can trigger population movements that undermine the policy’s intended benefits.

Written by Abby Hiller • Designed by Maia Rabenold

One of the most important—and politically contentious—US policy debates in recent decades involves income inequality, including whether the country’s fiscal (or tax) system has become less progressive and less redistributive over recent decades and, thus, exacerbates inequality. To the point: Many reporters, economists, and policymakers believe that the reduced top income and corporate tax rates initiated in the early 1980s and continued today are key to understanding the subsequent increase in income inequality.

For some researchers, these lower tax rates—especially on income for the top 1% and 0.1%, who have experienced income gains—have upended the U.S. tax system and made it less progressive: A tax system wherein the average tax rate increases as income increases. or possibly even regressive: A tax system wherein the average tax rate goes down as income increases. , a serious charge against a tax system founded on the principle that the more you earn, the more you pay. This paper challenges that emerging consensus by reassessing what and how data are measured to find that the US tax and transfer system redistributes more now than it did in the last several decades.

The authors come to this conclusion by reviewing the methodologies of recent research, and by focusing on all income levels, not just the top 1%. The authors also describe the many ways that differing measurements and definitions can result in dissimilar conclusions. Their findings are best described via the accompanying figures, which incorporate data from three recent research efforts that estimate changes to progressivity and redistribution and that make their data publicly available—Congressional Budget Office (CBO) 2022; Auten and Splinter (AS) 2024; and Piketty, Saez, and Zucman (PSZ) 2018).

The first figure compares the tax and transfer rates produced by AS and PSZ. PSZ only provide consistent rankings for the bottom 50 percent, the 50-90 percent (which they call the middle 40 percent), the top 10 percent, the top 1 percent, and smaller groupings at the top. To compare the results to PSZ, the authors use the AS data for the same groups. Data from CBO (2022) yield the same or even stronger results when the population is divided into quintiles. The figure shows the tax and transfer rates for each of these groups from 1966 (the first year of consistent data in AS) until 2019.

The key finding in the first figure is that both datasets show that the crucial change over the last 60 years has been the dramatic increase in transfers to the bottom half of the population. This increase swamps the changes in tax rates for the top half of the population.

The second figure helps us understand how the fiscal system has increased transfers to bottom income groups. The Panel A shows transfers as a share of national income. Panel B drills further down, showing just the transfers to the bottom quintile along with its income share.

The second figure illustrates that the downward distribution of income is somewhat nuanced. Although total transfers have gone up over time (from 5.2% of national income in 1966 to 15.3% of national income in 2019), transfers to the bottom quintile peaked in 1975 at 5.7% of national income and declined since then to 4.7% of national income. The increase in transfers as a share of national income has instead largely accrued to the middle quintiles.

Bottom line: Policymakers take note—there is broad agreement among researchers that net transfer rates to bottom incomes have increased, and the size of those increases swamp any changes at the top. Relatedly, the tax and transfer system has become more redistributive over the last half century, with much of that increase occurring in the last several decades.

Written by David Fettig • Designed by Maia Rabenold

View Working Paper View on SSRN

The Perry Preschool Project, the longest-running experimental study of an early childhood education program, demonstrates how such interventions can yield long-term personal, societal, and intergenerational benefits for disadvantaged populations. The evidence is clear: investments in high-quality early childhood education and parental engagement can deliver returns even 50 years later. The program’s findings remain scientifically robust, particularly when analyzed through rigorous small-sample inference methods. The program’s findings also contradict common criticisms of preschool, as, when measured correctly, treatment effects on IQ do not fadeout. This paper draws insights from both the original founders and recent empirical studies, emphasizing the critical role of parental involvement in early education. The authors advocate for a scientific agenda focused on understanding the mechanisms behind treatment effects, rather than replicating specific programs. The analysis also underscores the broader implications of early childhood interventions for social mobility and human capital formation. Analysts of early childhood education should recognize that although credentials and formal curricula contribute to successful programs, the true measure of quality lies in adult-child interactions, which play an essential role.

A famous adage says that an army marches on its stomach. Armies need money, and access to debt financing has often proved crucial to military success. For example, during the Napoleonic Wars of the early 19th century, Great Britain’s credibility with international lenders allowed for significant debt financing and delivered a military advantage over France, which relied heavily on taxation.¹ This advantage in debt financing was a key factor in Great Britain’s victory, which also established Great Britain as the militarily dominant state (or hegemon: In global relations, a hegemon is a dominant power, often defined by military strength, but also including economics and other factors. ) on the world stage and financial center of the world.

Today, the title of global hegemon goes to the United States and accords similar benefits to those of the British 200 years ago, including the ability to incur debt financing in international markets at preferential interest rates. Known as an “exorbitant privilege,” this borrowing capacity puts the United States at a distinct advantage over such rivals as, say, China, a country with global aspirations of its own. China’s recent rise, both economically and militarily, is moving the country into the position of a geopolitical rival to US dominance. More broadly, the unipolar world of the post-Cold War era with the US at its center, appears to be giving way to new military conflicts worldwide. These challenges to US military dominance raise important questions about government financing in a world with globalized debt markets, including: How does the presence of global debt markets impact the military balance between countries? How does the military balance affect global debt markets? And how do hegemonic transitions take place?

The authors of this new work address these questions by, first, documenting facts about the financial privileges extended to hegemonic states, including:

Global hegemons have historically borrowed at lower interest rates than other countries.
The interest spread for different countries relative to the global hegemon rises when geopolitical tensions increase. (See figure.)
And finally, the losers in a geopolitical conflict experience greater inflation and debt devaluation relative to victorious countries.

The authors incorporate these facts in a game-theoretic model in which military dominance is driven by internal (or endogenous: In economics, an endogenous variable is a variable in a model that is determined by its relationship with other variables in the model. Endogenous variables are also known as dependent variables. ) factors like military spending and external (or exogenous: Exogenous variables are the opposite of exogenous variables, which are independent variables that are determined outside of the model and cannot be predicted by the model. ) factors like geography and technology. Countries decide how much to spend on defense versus other goods, how much to borrow in international financial markets, and whether to default in the face of military defeat. The default rate is then reflected in a country’s financing costs, with the country expected to be more likely to suffer in a military contest facing higher borrowing rates. The model reveals the following:

When war ensues between two countries with low debt capacity, the stronger of the two (as determined by exogenous factors) is expected to win and, therefore, experiences lower funding costs. The opposite occurs for the weaker country, which is perceived to have a higher default risk and, thus, experiences increased funding costs. Over time, the funding advantage of the stronger country increases, translating into greater relative military spending and a larger probability of victory.
Things become more complicated when the debt capacity of the two countries is intermediate or high, including, for example, a scenario where the weaker country obtains funding to secure military victory. In this case, bond market participants anticipate that the exogenously weaker country will invest enough in the military to overwhelm the stronger country’s military advantage. The bond market’s belief underpins a lower funding cost for the weaker country, meaning that it is less likely to default, which in turn increases the country’s debt capacity in support of more military investment.
The model also offers insights into how transitions from one hegemon to another can occur over time. Debt capacity is again found to play an important role. When debt capacity is low or intermediate, the steady state persists, whereby the initial relative level of military power determines long-term dominance (or geopolitical hysteresis: In global politics, hysteresis can mean that a country retains its dominant position even after the factors that led to that event have been removed or otherwise run their course. Similarly, in economics, the state of an economy can persist after foundational factors have expired. ). Thus, for a new hegemon to emerge, war is necessary. However, if debt capacity is high, shifts in market expectations could favor one country over the other and determine hegemony without war (a situation of geopolitical fragility). These shifts can potentially occur repeatedly, leading to volatility in both financial markets and military power.

Does a strong hegemon ensure stability? Not necessarily. Factors that strengthen the link between geopolitical and financial dominance—such as higher debt capacity, a higher probability of war, and a higher war risk premium—also bring risks, making it possible that changing perceptions in bond markets lead to coordination on a new hegemon, and leading to repeated transitions between hegemons even in the absence of war.

This analysis not only provides insight into historical conflicts but, importantly, sheds light on current relations between the United States and China. For example, any effort to boost China’s financial capacity (such as internationalizing its currency) would likely affect US national security. Similarly, any US policy that threatens its own debt capacity or status in international financial markets—such as a failure to raise the debt ceiling, resulting in a technical default on Treasury bonds—could have significant national security implications. The link between funding capacity and potential global conflict is as real today as it was 200 years ago.

¹Bordo, Michael D., and Eugene N. White. 1991. “A Tale of Two Currencies: British and French Finance during the Napoleonic Wars.” The Journal of Economic History, 51(2): 303–316.

Combating waste is a perennial problem for public programs. The Office of Management and Budget estimates that over 7% federal spending in the United States was wasted in 2021, and by some estimates, over half of wasted federal spending goes undetected. Monitoring for waste, however, presents a challenging tradeoff: while monitoring could in principle reduce wasteful spending, it can also increase hassle costs or add to the administrative burden associated with these programs. Despite the importance of this question, there is little empirical evidence on the magnitude and nature of the tradeoffs associated with monitoring for waste in public spending. Motivated by this, this paper considers these tradeoffs in the context of Medicare, the federal health insurance program for the elderly and disabled.

The author studies this question in the context of Medicare audits. In response to growing concern about public funds being wasted spending on unnecessary short-term hospital stays, Medicare directed private auditing firms (Recovery Audit Contractors, or RACs) to monitor hospitals’ Medicare claims beginning in 2011, and reclaim payments for unnecessary inpatient admissions. The author conducts her analysis using data from these audits, which she matches to Medicare inpatient claims data. She also collects information about the hospitals in her dataset, including their administrative costs and technological investments.

Using these data, the author uses two approaches to identify the causal effect of an audit: First, she compares neighboring hospitals that are subject to differentially aggressive RACs. Second, she compares groups of patients who, because of rules that governing auditing, have visits that are subject to arbitrarily different audit likelihoods. She finds the following:

Audits reduce Medicare spending on hospital admissions substantially–every dollar that Medicare spends on monitoring hospitals recovers $24–29. Ninety percent of these savings stem from the deterrence of future spending, rather than the recovery of prior spending, as hospitals subject to more aggressive RACs tend to reduce their admissions. On average, a one percentage point (46%) increase in the share of a hospital’s admissions that are audited leads to a 2% drop in admissions. Extrapolating these effects to the author’s full hospital sample, the RAC program led to upwards of $9 billion in Medicare savings from 2011 to 2015.
Monitoring primarily deters low-value admissions. Hospitals are less likely to admit patients with higher audit risk, but these patients were no more likely to return to the hospital due to a missed diagnosis.
Audits lead hospitals to invest in compliance technology to assess whether admitting a patient is medically necessary. Hospitals subject to more audits are more likely to adopt “medical necessity checking” software, which cross-references electronic health records with payer (i.e., insurer) rules to provide guidance on the medical necessity of care in real time. Accordingly, hospital administrative costs rise: for every $1000 in Medicare savings in 2011–2015, hospitals incur $178–218 in administrative costs. But these costs are mostly concentrated as a one-time spike that occurs at the onset of the program expansion in 2011, rather than ongoing hassle costs.

The upshot is that monitoring can be a highly effective tool to combat waste in public spending and improve compliance with policy goals. Notably, policymakers only considered the recovered payments when assessing the cost-effectiveness of the RAC program, making the large deterrence effect revealed here particularly striking and underscoring the importance of incorporating measures of deterrence into cost-effectiveness evaluations.

We examine how a sovereign’s ability to borrow abroad affects the country’s growth and steady state consumption, assuming that the government is both myopic and self-interested. Surprisingly, government myopia can increase a country’s access to external borrowing. In turn, access to borrowing can extend the government’s effective horizon as the government’s ability to borrow hinges on it convincing creditors they will be repaid, which gives it a stake in incentivizing private production and savings despite its self-interest. In a high-saving country, the lengthening of the government’s effective horizon can incentivize it to tax less, resulting in a “growth boost”, with higher steady-state household consumption than if it could not borrow. However, in a country that saves little, the government may engage in more repressive policies to enhance its debt capacity and spending. This could lead to a “growth trap” where household steady-state consumption is lower than if the government had no access to external borrowing. We discuss the effectiveness of alternative debt policies, including declaring the sovereign’s debt “odious”, debt relief, and debt ceilings.

To counter the negative economic effects of the COVID pandemic on the US economy, Congress enacted six COVID-19 relief laws in 2020 and 2021 totaling about $4.6 trillion.¹ That extraordinary infusion, along with continued deficit spending that will likely extend into the foreseeable future, has raised questions about the link between fiscal spending and inflation.

For economists and policymakers who depend on models to run approximations of economic activity, test hypotheses, and make predictions, such persistent deficit spending raises thorny theoretical challenges. Many prevailing economic models feature a representative agent (RA) economy, which means that there is only one decision-maker in the model that represents all agents of a certain type, whether consumer, business, banks, and so on. These models are difficult to apply when governments run persistent deficits – like the situation in the US today. The reason is that RA households have no reason to hold government debt if the government is not generating the surpluses that allow it to pay positive real interest on the debt.

Heterogeneous agent (HA) models resolve this problem because households in these models continue to hold government debt even when real interest rates are negative; that is, households are willing to pay the government interest, which finances the deficit. Households engage in such saving behavior as a precaution—in times of persistent deficit spending, US government debt is still the safest port in a storm.

HA models are also useful because they can be used to examine cases in which deficits are delivered heterogeneously across households, as occurred during COVID when the US government made transfer payments to households based on income levels. Such targeted efforts deliver heterogeneous effects.

In order to examine the role of redistribution in shaping inflation dynamics in a time of persistent deficits, this work develops a model with three features:

a fiscal authority that issues nominal debt to finance real expenditures and transfers to households;
a monetary authority that sets the short-term nominal interest rate on government debt; and
heterogeneous agents and incomplete financial markets, so that households have a precautionary motive to save to self-insure against idiosyncratic income risk.

With their model in hand, the authors run a number of quantitative experiments that deliver several lessons for policy, including:

On the effects of permanently increasing deficits: If the government permanently increases lump sum transfers to households without raising taxes, the largest sustainable primary deficit is 4.6% of GDP, or 40% higher than current levels. However, how the government distributes funds is key. The more redistribution there is in the tax and transfer system (for example, its degree of progressivity), the less scope there is for the government to increase deficits in the future. The reason is that more social insurance precludes the need to save, which lowers household demand for government debt. Therefore, more progressive tax systems reduce fiscal space.

On the effects of a fiscal helicopter drop: When governments increase the money supply, either by printing money or through a temporary expansion or tax cut without raising taxes to pay for it, economists refer to this as a “helicopter drop.” Such drops can be targeted, that is, different households receive different amounts, or untargeted. The authors consider a helicopter drop of around 16% of annual GDP, roughly the size of the fiscal expansion in the United States over the course of the COVID-19 pandemic. When such an experiment is run with representative agent models, a 16% spike in inflation is generated. More money equals an equivalent rise in prices.

However, what happens in a heterogeneous agent world? The authors’ heterogeneous model finds an additional 30 percent increase in short-run inflation than in an RA model. Why? When a low-income household receives a $1,000 check and then loses purchasing power due to inflation, they actually end up better off – the amount of wealth that is inflated away is small relative to the check they received. But a high-income household ends up worse off because they check is small relative to their lost wealth from inflation. The low-income household is therefore inclined to increase spending, while the high-income household is inclined to cut spending. But these two effects do not “zero out.” In total, the increased spending among lower-income households would be larger than the decreased spending of wealthier households and, overall, prices would rise.

Now imagine a targeted helicopter drop, where lower-income households receive larger checks relative to wealthier households. In such a case, the higher marginal propensity to consume (MPC) among lower-income households is amplified because lower-income households receive even more funds than they would have with an untargeted drop.

On the effects of purely redistributive policy that hold both debts and deficits constant: In this world, budget neutral redistribution is also inflationary. The authors run numerical experiments in which the government levies a one-time wealth tax on households in the top percentiles of the wealth distribution, and redistributes the proceeds lump-sum to households in the bottom half of the wealth distribution. As noted above, real redistribution toward high MPC households places upward pressure on consumption, which leads to a jump in the price level. Further, and policymakers take note: If the central bank does not react accordingly, and if deficits become higher over time, then persistently higher inflation will likely follow.

The authors’ methodology also allows for an exploration of other economic phenomena. For example, recent years have seen the rise of what many call “secular stagnation,” which is a term used to describe the relatively moribund US economy (stagnation) over a long period (secular, as opposed to cyclical or short term). Characteristics of secular stagnation include a dearth of private investment, driven in part by IT efficiencies that decrease the need for capital investment. The kicker is that monetary policy is neutered in such a world; low interest rates, even those hovering at zero or that are effectively negative, are ineffective at generating economic activity. By showing how persistent deficits drive down real interest rates, this work offers a novel explanation for secular stagnation.

Bottom Line: At a policy forum sponsored by the European Central Bank in September 2021, Federal Reserve Chairman Jay Powell called the effects of supply-side constraints on inflation during COVID a “surprise,” adding: “It’s not that our inflation models are wrong, although they are certainly not perfect, but just the scope and persistence of the supply-side constraints were missed.”²

Although Powell was not talking about the link between inflation and income redistribution via a progressive tax policy, which is the subject of this paper, his comment does make one highly relatable and salient point: Models matter, and when models miss their mark, policy can suffer. This work improves upon existing models to offer a new theoretical framework that better approximates economic activity in the real world, offering novel insights into the effects of persistent fiscal deficits on inflation.

¹US Government Accountability Office (Feb. 28, 2023). “COVID-19 Relief: Funding and Spending as of Jan. 31, 2023,” (GAO-23-106647), gao.gov/products/gao-23-106647#:~:text=Six%20COVID%2D19%20relief%20laws,for%20pandemic%20response%20and%20recovery.

²Arnold, Martin and Colby Smith (Sept. 29, 2021), “Fed’s Powell warns inflationary supply chain snags may persist,” Financial Times, ft.com/content/90fc98ad-d69b-44c5-8902-b93c4f952805. See also, “ECB Forum on Central Banking 2021: Beyond the pandemic: the future of monetary policy,” ecb.europa.eu/pub/conferences/html/20210928_ecb_forum_on_central_banking.en.html.

Normally, when governments accrue deficits and increase their debt, the result is higher interest rates because the Treasury must issue more bonds, which reduces their price and raises the interest rate.

However, these are not normal times. The current phenomenon—interest rates that are consistently low and below the growth rate of the economy—has been dubbed a “free lunch” in which governments can continue to spend freely (issue debt) without having to lower future expenditures or raise taxes.

How can this be? Every Econ 101 student is told that there is no such thing as a free lunch, that someone must pay the bill. In “A Goldilocks Theory of Fiscal Deficits,” the authors address this free lunch puzzle by introducing a new factor to the interest rate and growth equation. While it is true that a free lunch is possible when nominal rates remain below nominal growth, the authors describe how persistent spending in a low interest rate environment will—if the spending is too high—increase debt relative to GDP at a rate that will negate the free lunch. Interest rates will rise, in this scenario, and the lunch tab will be paid.

Further, it is possible to devise a methodology to set parameters on deficit spending that allows governments to maintain a free lunch. This insight has important implications for policymakers, including those currently making spending decisions in a zero lower bound (ZLB) environment, and who wish to remain in a Goldilocks zone.

Chart

Figure 1: As Debt-to-GDP Rises, So Does R Minus G

Notes: This Figure shows the relationship between the R minus G (the nominal interest rate on government debt minus the nominal growth rate) for G7 countries (Canada, France, Germany, Italy, Japan, UK, and the US) from 1950 to 2019. The dots reflect the raw data, and the red line is the fitted regression line, which reveals that as government debt to GDP rises, so does R minus G.

R < G = Hey kids, free lunch, order what you like.

R < G – φ = Let’s order from the value menu.

If you had asked economists 20 years ago whether debt-to-GDP ratios like those experienced by advanced economies today were sustainable, they would have replied: Of course not. However, there is a simple equation that describes why those predictions would have been wrong: When the nominal interest rate (R) is less than the nominal growth rate of the economy (G), governments can continue to spend without worrying about reducing future expenditures or raising taxes.

Researchers have noted this phenomenon in recent years, as the authors describe, but one of the contributions of this work is to put parameters around how much governments can spend and still attain their free lunch. That is, the insight here is not only that those deficits have a ceiling, but that governments can put a number on it and set parameters around fiscal spending to avoid future cuts in spending or higher taxes.

Before describing the authors’ new equation, it is useful to briefly describe what makes government debt unique relative to other debt. How can governments get away with endless borrowing when, for example, you cannot do the same with your household debt or corporations are likewise restricted in their borrowing capacity? Essentially, government debt is unique because it provides a form of insurance: Households and corporations choose to hold a certain amount of debt and financial intermediaries are even required by regulators to hold a certain amount of government debt. If private investors are convinced that they can trust a government’s debt, for example, even in the throes of a deep recession, then that debt serves as a form of insurance. Private debt does not serve this function.

If you had asked economists 20 years ago whether debt-to-GDP ratios like those experienced by advanced economies today were sustainable, they would have replied: Of course not. However, those predictions would have been wrong.

However, this trust is not limitless. As noted above, one of the authors’ key contributions is to devise a methodology that quantifies the implicit insurance of government debt. They do this by adding a factor to the “R < G = a free lunch” equation that accounts for how much a government can spend before investors lose faith in the debt, and they label this factor with the Greek letter phi, or φ, to construct a new equation: R < G – φ = a free lunch.

In practice, this means that while governments can continue to spend freely if nominal R is less than nominal G, they must do so within parameters that keep accumulated debt below a certain level relative to GDP. In the case of the United States in 2019, for example, the authors find that the government could sustain a maximum permanent primary deficit of just over 2% of GDP at a stable debt-to-GDP ratio of 110%. Indeed, US government spending fell just under that mark.

This equation is complicated, though, in an era when interest rates are so consistently low that they hover at zero, or the zero lower bound (ZLB). A key characteristic of these economies is that aggregate demand is inordinately low and, in effect, holding down growth. This work’s second contribution shows that, under such a scenario, greater deficits may reduce rather than increase debt. This occurs because greater deficits raise aggregate demand and inflation, which translates into higher nominal growth rates. These higher growth rates then push debt down as it increases the speed at which debt is “inflated away.” Recall R < G and imagine that these increased deficits raise G and, thus, ensure that R remains below G. This indirect effect through the nominal growth rate can be sufficiently strong to overwhelm the direct effect of greater deficits on debt. The free lunch continues.

The authors also apply their framework to analyze the role of inequality and tax progressivity (nearly 70 percent of US government debt is by US households in the top 10 percent of the US wealth distribution), to find that increased inequality, modeled as a greater share of income earned by savers, increases the availability of free lunch policies outside the ZLB. This points to a potential conflict between the goal of reducing inequality (e.g., via progressive taxation) on the one hand, and funding large deficits on the other. At the ZLB, inequality reduces fiscal space as it reduces aggregate demand and nominal growth rates.

CLOSING TAKEAWAY
Under such a zero lower bound (ZLB) scenario, greater deficits may reduce rather than increase debt because greater deficits raise aggregate demand and inflation, which translates into higher nominal growth rates.

Conclusion

Bottom line: Contrary to intuition, there is a free lunch when it comes to government spending, given certain parameters. This work shows that the textbook view that raising deficits must, at some point, be reduced below their original level (through reduced spending and/or higher taxes), does not hold in all cases. Debt may not explode to unsustainable levels if R < G – φ, that is, if the increase in deficits is modest. Further, debt (or accumulated deficits) may not even rise at all if the economy is at the ZLB and the nominal growth rate is sufficiently responsive to increased deficits.

The authors stress that these insights apply to a long run “steady state” economy; that is, not necessarily to times when an economy is experiencing a negative shock like the COVID pandemic. That said, their methodology is applicable to other research efforts that could yield useful insight into short-run effects of fiscal crises, even as applied to non-steady-state economies. At some point, though, the effects of COVID will mostly pass and the economy will enter a new steady state phase wherein policymakers will again confront the possibility of free lunch deficit spending—to a point.

The U.S. Supplemental Security Income (SSI) program provides cash assistance to the families of 1.2 million low-income children with disabilities. When these children turn 18, they are reevaluated to determine whether their medical condition meets the eligibility criteria for adult SSI. About 40% of children who receive SSI just before age 18 are removed from SSI because of this reevaluation. Relative to those who stay on SSI in adulthood, these children lose nearly $10,000 annually in SSI benefits in adulthood.

Among other issues, this raises questions for policymakers and researchers about the long-term effects of providing welfare benefits to disadvantaged youth on employment and criminal justice involvement. On the one hand, cash assistance could provide a basic level of income and well-being to youth who face barriers to employment and thereby reduce their criminal justice involvement. On the other hand, welfare benefits could discourage work at a formative time and discourage the development of skills, good habits, or attachment to the labor force, potentially even increasing criminal justice involvement.

To investigate these questions, the authors build a unique dataset that allows them to measure the effect of SSI on joint employment and criminal justice outcomes, and to follow the outcomes of youth for two decades after they are removed from SSI. The first-ever descriptive statistics from this linkage indicate that nearly 40% of recent SSI cohorts are involved in the criminal justice system in adulthood, making criminal justice involvement a high-powered outcome for individuals who received SSI benefits as children.

Among other results, the authors find the following:

SSI removal at age 18 in 1996 increases the number of criminal charges by a statistically significant 20% (2.04 to 2.50 charges) over the following two decades, with concentration in activities for which income generation is a primary motivation.
“Income-generating” charges (such as burglary, theft, fraud/forgery, robbery, drug distribution, and prostitution) increase by 60%, compared to just 10% for charges not associated with income generation.
The likelihood of incarceration in each year from ages 18 to 38, averaged over the 21 years, increases from 4.7 to 7.6 percentage points, a statistically significant 60%, in the two decades following SSI removal.
Men and women respond differently to SSI removal. For men, the largest and most precise increase is for theft charges, and the annual likelihood of incarceration for men increases from 7.2 to 10.8 percentage points (50%).
The effect of SSI removal on criminal charges is even larger for women than for men, and for women is concentrated almost exclusively in activities associated with income generation. Like men, the largest effects for women are for theft charges, but unlike men, women also have large increases in prostitution charges and fraud charges. The annual likelihood of incarceration for women increases from 0.7 to 2.4 percentage points (220%).
Illegal income-generating activity leads to higher rates of incarceration, especially for groups with a high baseline incarceration rate, including Black youth and youth from the most disadvantaged families.
Broadly, this work suggests that contemporaneous SSI income during adulthood is not the primary driver of criminal justice involvement. Instead, it is more likely the loss of SSI income in early adulthood that permanently increases the propensity to commit crimes throughout adulthood.
Finally, the costs of enforcement and incarceration from SSI removal approach, and thus nearly negate, the savings from reduced SSI benefits.

This work raises key questions for future research that have important implications for policymakers, especially concerning the likely effects of new or expanded general welfare programs. For example, should we expect the broader population of disadvantaged children to respond similarly to welfare benefits compared to children receiving SSI? And are the effects of gaining and losing welfare benefits symmetric, or does losing benefits have a larger effect than gaining benefits?

This paper uses a natural field experiment to examine the effectiveness of specific nudges on tax compliance amongst firms and the self-employed in the Dominican Republic. In collaboration with the Dominican Republic’s tax authority, we designed messages for more than 28,000 self-employed workers and over 56,000 firms. Leveraging administrative tax data, we find evidence that our nudges (increasing the salience of prison sentences or public disclosure of tax evaders) have large effects on increasing tax compliance, primarily working through the channel of decreasing claimed tax exemptions. Interestingly, we find that firms are more impacted than the self-employed, and that firm size is critically linked to nudge effectiveness: larger firms are considerably more influenced by nudges than smaller firms. We find this latter result noteworthy given the paucity of evidence showing significant behavioral impacts of nudges amongst the largest players in a market. Overall, our messages increased tax revenue by $193 million (roughly 0.23% of the Dominican Republic’s GDP in 2018), with over $100 million constituting income that the government would not have received without our field experimental nudges.