Week of March 02, 2026
We study how decisions made by organ procurement organizations (OPOs)—non-profits that coordinate organ recovery from deceased donors—affect the availability of organs for transplant in the United States. We develop a structural econometric model of a pivotal OPO decision: whether to approach a potential donor’s family to request authorization for donation. Our model conceptualizes this decision in two parts. The OPO first estimates the probabilities of two downstream outcomes: authorization (i.e., family consent) and transplant (i.e., whether the donated organs would be successfully transplanted). It then applies a cost-benefit decision rule that maps these estimates to an approach decision. Our model separately identifies the OPO’s beliefs (i.e., probability estimates), its preferences (i.e., costs / benefits), and the true probabilities of authorization and transplant. We apply our model to a dataset of 35,856 potential donors referred to four OPOs between 2016 and 2021. We find that OPOs missed a substantial number of donation opportunities, recovering organs from only 39% of transplantable donors. Of these missed opportunities, an estimated 16% resulted from conservative preferences, 12% from miscalibrated beliefs, and the remaining 72% from family declines. We conduct a detailed counterfactual policy evaluation to identify impactful and actionable changes to OPO decisions. We find that drastically increasing approach rates would allow OPOs to recover organs from 43% more donors, creating over $100 million in additional annual societal benefit.
Ample empirical evidence documents deviations from the canonical consumption-savings model; yet, it remains difficult to assess the roles of different underlying distortions, such as financial constraints and behavioral preferences. We develop a sufficient-statistics approach that measures individual-level wedges between observed and counterfactual “frictionless” consumption. Since different distortions imply different wedge properties, wedges provide a diagnostic to distinguish between models. We measure wedges using administrative transactions data linked to surveyed expectations for a population of middle-income, low-liquidity US consumers. The expectations data allow us to distinguish wedges attributable to frictions and behavioral preferences from wedges driven by deviations from full-information rational expectations (FIRE). We find that consumption wedges are large and heterogeneous: the median wedge is 40% of frictionless consumption in absolute value, with 49% of consumers under-consuming (negative wedges) and 51% over-consuming (positive wedges). Borrowing constraints cannot rationalize this pattern because they only generate negative wedges. Models combining present bias with borrowing constraints, or featuring consumption adjustment costs, best account for the wedge properties we document.
This paper investigates financial stability risks arising from banks' interest rate exposure and uninsured deposit funding. We develop a model of heterogeneous banks featuring endogenous run risk to jointly analyze portfolio and funding choices. The model replicates key empirical patterns, including the concentration of uninsured deposits in larger banks. We analyze the impact of monetary policy rate hikes and evaluate the capacity of microprudential tools to mitigate bank fragility. Results demonstrate that tightening capital requirements significantly lowers run risk. Higher liquidity requirements targeting uninsured deposits efficiently reduce run risk, provided they are met exclusively with reserves.
Do industrial policies that promote clean energy offer a “ray of hope”, increasing a country’s growth and welfare, whilst simultaneously reducing carbon emissions? We study the impact of Chinese solar subsidies whose implementation by city-regions went alongside massive expansion of the sector and a dramatic fall in global solar prices. We construct new city and firm panel data on solar policies, patenting and output. Using synthetic-difference-in-differences 2004-2020, we find production and innovation subsidies were more effective than demand-side (installation) subsidies in generating large and persistent increases in local innovation, net entry, production and exports. Demand policies did, however, reduce local pollution. To examine aggregate effects, we build and structurally estimate a quantitative spatial model with endogenous innovation and heterogeneous productivity across firms and cities, which accounts for business stealing and knowledge spillovers. Counterfactual analysis shows that: (i) local effects remain substantial at the macro level explaining 40%-50% of the aggregate changes in solar innovation, prices and revenues; (ii) social benefits to Chinese citizens exceed subsidy costs by 65% (and double this when environmental benefits are included); and (iii) although all subsidy types increase welfare, innovation subsidies are the most cost-effective.
As the economy emerges from a crisis, macroeconomic policy confronts a dilemma: a protracted stimulus can foster a more inclusive labor market recovery, yet risks igniting inflation that ultimately undermines workers’ welfare through real income erosion. This tension amplifies in the presence of the ZLB and aggregate capacity constraints. We embed this insight into a quantitative model of the US economy. We study how monetary and fiscal policies managed this inflation-inclusion trade-off after the pandemic, contrasting actual outcomes with counterfactual scenarios. Our experiments yield five findings: (i) the trade-off was unusually difficult because policy was squeezed between these two constraints; (ii) inflationary pressures arose from the joint deployment of prolonged monetary and fiscal stimulus; either policy alone would have produced milder price dynamics; (iii) either inclusive fiscal policy or inclusive monetary policy in isolation would have been sufficient to contain the negative labor market hysteresis at the bottom of the distribution; (iv) inclusive fiscal policy combined with a more traditionally inflation-focused central bank would have achieved higher welfare for the vast majority of households; (v) welfare effects reflect mostly corrections of incomplete-market inefficiencies rather than gains from aggregate stabilization.
This paper studies how minimum wage policy affects firms’ adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992–2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean.
This paper addresses two fundamental macroeconomics questions. First, are macro shocks large enough to alter the course of the economy? Second, are they large enough to materially impact economic welfare? Lucas and many others have addressed these issues, but do so primarily in the context of representative agent models. We study these questions using a large-scale, general equilibrium, stochastic, overlapping generations model. We consider 80 generations overlapping in an economy buffeted by realistically calibrated total factor productivity and capital depreciation shocks. The model is solved using Marcet’s projection method taking explicit account of the full state space, which comprises 81 variables. Our findings, some recapitulated from prior studies by Hasanhodzic and Kotlikoff, suggest macro shocks are second order both with respect to their impact on aggregate variables and individual welfare. Specifically, the probability that the stochastic economy’s long-run aggregates materially deviate from their deterministic counterparts is less than one percent. Furthermore, the realized (simulated) lifetime utility of generations born in the long run rarely differs from deterministic long-run utility levels by more than 1 percent, measured as consumption-compensating differentials. These findings are supported by the model’s small equity premium. Moreover, they are essentially indifferent to the presence of a bond market. Both results suggest agents are minimally concerned with precautionary savings against these shocks. Our RBC-in-OLG findings suggest that what really moves the macroeconomy and demands attention is policy, not shocks.
High technology products are characterized by the rapid introduction of new models and the corresponding disappearance of older models. The paper addresses the problems associated with the construction of price indexes for these products. Several methods for the quality adjustment of product prices are considered: hedonic regressions that use either product characteristics (Time Dummy Characteristics regressions) or the product itself as the ultimate characteristic (Time Product Dummy regressions). The paper also considered regressions where the economic importance of products is taken into account (weighted versus unweighted regressions). The indexes which were generated by the hedonic regressions were compared to traditional index numbers that did not make any special adjustments for quality change. The Expanding Window variant of a Weighted Time Product Dummy regression was used to address the chain drift problem. Finally, the estimation of systems of inverse demand functions was also used to generate various price indexes. The alternative approaches were implemented using Japanese price and quantity data on laptop sales in Japan for the 24 months over the years 2020-2021.
Researchers generally acknowledge that statistical tests must be adjusted when hundreds of factors and trading strategies have been examined. But how should these adjustments be made? Existing methods are often misunderstood or misapplied. We show that proper inference requires accounting for dependence across tests, correctly specifying the null distribution, and mitigating sample-selection bias. We develop a simple framework that avoids assumptions about the total number of tests run and yields a lower bound on valid significance thresholds - implying that researchers should employ a t-statistic cutoff of at least 3.0. In addition, we advocate using the local False Discovery Rate, which provides the probability that the null hypothesis is true for a given test-statistic realization - information that a conventional p-value cannot supply.
We use security-level data from the Investors Monthly Manual (IMM) to construct capital-weighted return indexes for the London Stock Exchange over the period 1870–1929. We find a significant and persistent equity risk premium of 3.7% over commercial paper and 4.5% over long-term government bonds, with significant co-movement with GDP growth. Returns decline monotonically with claim seniority: common stocks earn more than preferred shares, which earn more than corporate bonds. Both equity risk premia are highly significant, and the rolling 10-year return spread for stocks minus bonds is positive for every interval in the 60-year sample period.
The Vietnam draft conscripted hundreds of thousands of young Americans into an integrated military. I combine near-random draft lottery variation with administrative voter data to study the long-run racial integration effects of coerced national service. Black and Native American veterans became more likely to marry white spouses, identify as Republicans, and live in more-integrated neighborhoods. Improved economic standing may partly mediate these effects. Effects are larger for Southerners and are precisely null for white veterans. Coerced military service generates substantial but asymmetric cross-racial political convergence and racial integration: Vietnam-era service caused about 20 percent of affected cohorts' interracial marriages.
Transaction-level quantity discounts are a pervasive feature of US trade, shaping both price variation and tariff incidence. Using administrative microdata, we show that these discounts reflect transaction-level scale economies rather than market power. Accounting for these micro-level economies resolves a key puzzle: while observed import prices rose one-for-one with 2018-2019 US tariffs, we show this was driven by the loss of scale economies as transaction sizes collapsed. Controlling for this scale effect, the strategic pass-through of tariffs to scale-free prices falls to 60 percent, implying foreign exporters absorbed a significant share of the burden through reduced markups.
Agricultural soils represent one of the largest underutilized opportunities for climate mitigation through land-based carbon sequestration. This study analyzes how farmers make long-term decisions about adopting soil conservation practices, such as no-till and reduced tillage, when soil organic carbon (SOC) accumulation generates additional payments, while explicitly considering risk associated with SOC sequestration variability. Using an infinite-horizon dynamic optimization model, the study quantifies the carbon payment levels required to incentivize adoption across different soil types. Results show that the required payments vary widely, from $8/ton C/year on well-drained soils to $32/ton C/year on poorly drained soils, highlighting the need for spatially targeted carbon incentives. The analysis demonstrates that risk in the SOC sequestration amount affects farmer choices: higher uncertainty increases the payments needed and can lead farmers to prefer lower-risk, lower-reward practices. For farmers who value intertemporal consumption smoothing, compensation requirements rise with the elasticity of intertemporal substitution. These findings underscore the importance of accounting for soil heterogeneity, outcome variability, and intertemporal preferences when designing effective carbon payment programs to promote long-term soil carbon sequestration.
We examine the historical frequency of stock market booms, crashes, and bubbles in the United States from 1792 to 2024 using aggregate market data and industry-level portfolios. We define a bubble as a large boom followed by a crash that reverses the market’s prior gains. Bubbles are extremely rare. We extend the industry-level analysis of Greenwood, Shleifer, and You (2019) through 2024 and replicate their findings out of sample using Cowles Commission industry data from 1871 to 1938. Booms do not reliably predict crashes, but they do predict higher subsequent volatility, increasing the likelihood of both large gains and large losses.
We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain model that does not require the user to take a stand on the order of integration appear reasonably well calibrated for forecast horizons of 10 and 25 years. For plausibly non-stationary variables, a random walk model appears reasonably well calibrated for forecast horizons of 10 and 25 years.
On April 20, 2020, the crude oil benchmark in North America, the West Texas Intermediate (WTI) futures contract for delivery in Cushing, Oklahoma, settled below zero for the first time in history. We combine new empirical evidence with a stylized theoretical model to show that a key catalyst was the accumulation of unusually large long positions in the expiring contract held by uninformed financial traders unable to take physical delivery. These positions distorted the demand signal in the futures market, intensifying pressure on the limited storage capacity and precipitating a sharp price dislocation. We then document that this dislocation significantly influenced oil production decisions through contractual exposure to WTI-based pricing. Even oil producers far from Cushing that were not directly impacted by the storage constraints responded with sharp output curtailments in the face of heightened benchmark risk. Our findings highlight how transitory futures price dislocations due to noise trader demand can have real economic consequences.
While finance theory distinguishes the roles of equity and debt in supporting firm growth, their differential impacts on international trade remain underexplored. This study provides the first empirical analysis of how access to equity financing affects firm exports. We leverage the unique institutional setting in China, where initial public offerings (IPOs) require stringent regulatory approval, ensuring that only qualified firms advance to the final review stage. Our empirical strategy compares the export performance of successful IPO applicants with that of “near misses"—applicants rejected at the final review meetings. To sharpen identification, we utilize meeting records to exclude rejections citing concerns about future revenue growth or profitability risks, as these may entail unobserved shocks to export performance. Our cohort based difference-in-differences analysis reveals that IPO approval leads to a significant annualized increase of more than 6% in firm exports over the subsequent six years. Distinct from previous findings on debt financing, IPO approval primarily affects the extensive margin, enabling firms to expand into more destination-product markets. Mechanism tests suggest that IPOs enhance exports by financing intangible investments and fostering risk-taking activities.
This paper decomposes welfare measures of policy reforms into parts attributable to redistribution and parts due to efficiency. We further decompose efficiency into subcomponents such as gains from better insurance against idiosyncratic and aggregate risk. Our decomposition of welfare measures associated with alternative feasible allocations is cast in terms of a coordinate system that uses generalized Pareto–Negishi weights to capture inequality and production and consumption wedges to capture distortions. Our decomposition has several desirable properties. It attributes welfare changes from movements along a Pareto frontier to redistribution; it attributes negative efficiency changes to movements away from the Pareto frontier; and it produces subcomponent shares of welfare changes that are numeraire-invariant and symmetric with respect to the direction of the reform. Our decomposition can be explained in terms of an implicit tax-and-transfer system in which redistribution captures real income changes, efficiency captures deadweight losses, and output costs.
We study geoeconomic competition and capital reallocation in global financial markets, using the foreign exchange (FX) funding market as our empirical setting. FX funding, obtained by borrowing one currency while pledging another through FX swaps, is instrumental to cross-border investment and provides high-frequency measures of capital reallocation. Countries compete for FX funding through policy actions that shift investment returns or funding costs, thereby inducing global portfolio rebalancing by private investors. We quantify this competition by measuring how one country’s inflow responds to another country’s actions, which we call “reallocation exposure.” Because observed funding flows reflect common shocks and strategic interactions across countries, bilateral influence is difficult to identify. We resolve this challenge by identifying “funding fronts,” the independent margins of portfolio adjustment that enable systematic estimation of reallocation exposure. Applying our framework to a proprietary dataset, we find that FX funding competition is concentrated in a small number of funding fronts, with a dominant U.S. dollar front accounting for most capital reallocation. Consequently, changes in U.S. conditions generate disproportionately large reallocations elsewhere. We use reallocation exposure to construct time-varying measures of geoeconomic power and show that variations systematically track major monetary, fiscal, and geopolitical events. Finally, we characterize the network of financial competition and cooperation and show that strategic responses implied by reallocation exposure align with cross-country movements in policy rates.
In turbulent times, political labels become increasingly uninformative about politicians’ true policy preferences or their ability to withstand the influence of special interest groups. We offer a model in which politicians use campaign rhetoric to signal their political preferences in multiple dimensions. In equilibrium, the less popular types try to pool with the more popular ones, whereas the more popular types seek to separate themselves. The ability of voters to process information shapes politicians’ campaign rhetoric. If the signals on the cultural dimension are more precise, politicians signal more there, even if the economy is more important to voters. The unpopular type benefits from increased conformity, which bridges the candidates’ rhetoric and makes it more difficult for voters to make an informed decision.
We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We build a dynamic model of learning and decision-making in which successful decisions require combining shared, community-level general knowledge with individual-level, context-specific knowledge; these two inputs are complements. Learning exhibits economies of scope: costly human effort jointly produces a private signal about their own context and a “thin” public signal that accumulates into the community’s stock of general knowledge, generating a learning externality. Agentic AI delivers context-specific recommendations that substitute for human effort. By contrast, a richer stock of general knowledge complements human effort by raising its marginal return. The model highlights a sharp dynamic tension: while agentic AI can improve contemporaneous decision quality, it can also erode learning incentives that sustain long-run collective knowledge. When human effort is sufficiently elastic and agentic recommendations exceed an accuracy threshold, the economy can tip into a knowledge-collapse steady state in which general knowledge vanishes ultimately, despite high-quality personalized advice. Welfare is generally non-monotone in agentic accuracy, implying an interior, welfare-maximizing level of agentic precision and motivating information-design regulations. In contrast, greater aggregation capacity for general knowledge—meaning more effective sharing and pooling of human-generated general knowledge—unambiguously raises welfare and increases resilience to knowledge collapse.
This paper estimates how rate cuts increase consumption, via debt and asset prices. Using administrative UK data on mortgages and consumption, we exploit the expiry of fixed-rate mortgages to construct six million household-level natural experiments. A 1pp reduction in mortgage rates raises consumption by 3% in the following 6 months. Using plausibly exogenous variation in how house prices respond to rate cuts, we show that consumption increases mostly because households borrow against higher house prices; lower debt service after rate cuts matters less. These results suggest that in large part, monetary policy affects consumption through asset prices and borrowing
We design an online platform to connect unemployed job seekers with ‘buddies’: former job seekers who recently found employment. We focus on job seekers who search in occupations with poor prospects and buddies who successfully switched occupations. In a randomized controlled trial, we evaluate the impact of access to the platform on labor market outcomes. We find sizable effects. Thirteen to 18 months after getting access, initially unemployed job seekers are 6 percentage points (11%) more likely to be employed and earn e 226 more per month than those without access. The positive impact is concentrated among the long-term unemployed.
Improving education and labor market outcomes for low-income students is critical for advancing socioeconomic mobility in the United States. We use longitudinal data on five cohorts of 9th grade students to explore how Massachusetts public high schools affect the longer-term outcomes of students, with a special focus on students from low-income families. Using detailed administrative and student survey data, we estimate school value-added impacts on college outcomes and earnings. Observationally similar students who attend a school at the 80th percentile of the value-added distribution instead of a school at the 20th percentile are 11% more likely to enroll in college, are 31% more likely to graduate from a four-year college, and earn 25% (or $10,500) more annually at age 30. On average, schools that improve students’ longer-run outcomes the most are those that improve their 10th grade test scores and increase their college plans the most.
The large increase in remote work since 2020 has prompted concerns about adverse effects on population loneliness and mental health. We show that any such adverse effects were small, in a UK context. We use data from UKHLS and differences-in-differences estimators that flexibly control for a rich set of covariates to compare changes in key variables amongst two groups: those who worked in teleworkable occupations in 2019, and those who worked in non-teleworkable occupations in 2019. While the former experience large and persistent increases in their probability of working remotely compared to the latter, any relative changes in self-reported loneliness or adverse mental health symptoms are transitory and disappear by the year 2023.
A central insight from neoclassical economics is that international trade operates like an improvement in production technology. It generates mutual aggregate welfare gains for countries as a whole, but creates winners and losers within countries. Tariffs are a tax on this trading technology and distort the prices faced by domestic consumers and producers. Large countries can use tariffs to improve their terms of trade on world markets. But if all countries try to do so, they can end up with lower welfare than if they cooperated to liberalize trade. Tariffs can be used to redistribute income between the winners and losers from trade within countries. But there can be other more efficient ways to achieve redistribution. Policies to promote economic activity in critical industries can be rationalized based on externalities or national security. But these arguments typically rationalize targeted policies towards those industries and tariffs can be dominated by other policy interventions. Empirical findings from the recent waves of U.S. tariffs suggest that most of the incidence of these tariffs has been borne by U.S. importers, wholesalers, retailers and consumers rather than by foreign exporters. These tariffs have led to a large-scale reorganization of U.S. supply chains away from China to third countries. Although this reorganization has substantially reduced China's share of U.S. imports, the U.S. remains indirectly exposed to China through the imports of these third countries.
Climate disasters threaten intergenerational equity by exposing future generations to rising risks. We develop a model in which a government learns about disaster risk and enforces a sustainability criterion requiring expected social welfare to be non-decreasing over time. This criterion—similar to the principle underlying the UN Sustainable Development Goals—can be decentralized through state-contingent fiscal instruments: when perceived disaster risk is high, the constraint binds and government raises a consumption tax to finance investment subsidies for resilience. Such a fiscal rule leads to intergenerational-welfare smoothing and improves asset valuations despite adverse climate news due to commitments to future resilience. Compared with a government that adopts a social discount rate lower than households’, the sustainability-constraint rule responds to disaster risk and is better aligned with observed consumption-based climate-resilience taxes, such as those implemented in Greece and Spain.