Week of October 26, 2026
We study how firms choose among incentive contracts and how accurately managers predict their effects. Using a survey of managerial beliefs and a large-scale field experiment, we randomly assign agents at the market level to several widely used, expenditure-equivalent incentive schemes. In the field, the best-performing contract raises weekly firm profit by over 55% relative to the status quo, despite being ranked lower by managers, whereas the worst-performing contract performs as predicted. Managers reliably avoid poorly performing contracts but systematically underestimate top-performing ones. Standard contract theory does better as a forecaster here: an unbiased textbook principal orders the contracts correctly, or very nearly so, correctly identifying the profit leader where managers do not; where it does miss, the error is similar to managers’ and for a similar reason. We show that the performance differences across contracts reflect a mix of labor-supply and risk responses, and that managerial predictability varies with contract complexity and managerial hierarchy. Our results highlight the importance of contract design for firm performance and reveal systematic limits to managerial cognition in shaping incentives.
Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of settings. Yet existing evaluations cover few surveys and outcomes, and do not study how households adjust to changing economic conditions. We introduce a new evaluation, HouseholdBench, which unites 6 U.S. household surveys and 32 prediction tasks spanning numeric, categorical and probabilistic outcomes, related to consumption, income, labor, expectations, and housing. Using past behavior, demographics and macroeconomic conditions, the tasks test whether LLMs predict behavior, including how households adjust to changes in various policies. We evaluate 13 proprietary and open-weight LLMs against a no-change baseline and a gradient boosted tree model. Most LLMs outperform the no-change baseline, including for policy response tasks—with the best model lowering error for numeric outcomes by 12.2%. Across most tasks, gradient-boosted trees rank first; leading proprietary LLMs approach their performance, but open-weight models lag. LLMs exhibit systematic over- and underprediction across different tasks. We identify methods that enable a 4 billion parameter open-weight model to match proprietary models’ performance: fine-tuning and aggregating 16 predictions per observation. Improvements generalize to policy-response tasks, which are excluded from fine-tuning. We release our datasets, code, and leaderboard on our website.
We examine optimal non-cooperative monetary policy in a two-country New Keynesian setting with complete financial markets and pricing to market for goods. In contrast to the case in which the law of one price holds for goods, currency misalignments are an important consideration even for self-oriented policymakers. Financial markets are critical in the analysis. Under “full” commitment, the optimal policy considers the effects on asset values and hence wealth distribution across countries. Optimal policy under full commitment differs from the “partial” commitment case in which the policy commitment is not made until after asset markets clear. Surprisingly, the optimal targeting rule under strategic policy with full commitment is the same as under cooperative policy. In this setting, changes in state-contingent claim prices offset the wealth a self-oriented policymaker might seek through terms-of-trade manipulation, and we show that there are no gains from cooperation. Although a policy chosen under partial commitment may be desirable from a country’s perspective, the resulting Nash equilibrium yields lower welfare.
Some occupations pay higher wages than others, even conditional on worker characteristics, implying that occupations can be ranked on a ladder. We develop a simple decomposition to study the role this occupational ladder plays in average wage growth and in the fanning out of wages over the life cycle. We present three main findings. First, the distribution of employment shifts towards higher-paying occupations as workers gain experience. This contributes modestly to average life-cycle wage growth, with greatest impact in early career. Second, this upward movement in the occupational employment distribution is not accompanied by increasing dispersion; thus, the occupational ladder does not directly contribute to the fanning out of wages. Third, early-career workers who are well-positioned on the occupational ladder tend to earn wages far below the occupational average. However, by mid-career, workers’ relative wages within occupations (their positions on the job ladder) no longer depend on their occupation. This change in the covariance between individuals’ positions on the job and occupational ladders is a major contributor to the fanning out of wages over workers’ careers.
Over the past four decades, the earnings gap between college graduates and non-graduates has widened substantially, prompting interest in policies aimed at promoting higher education among low-income students. A key challenge for these policies is the high college dropout rate. This paper develops a dynamic model of college enrollment and completion with rich individual heterogeneity to assess how various factors influence college attendance, dropout, and graduation. We estimate the model using data from longitudinal surveys, results of quasi-experimental studies on grant aid, and large-scale randomized controlled trials. We use the model to simulate the effects of tuition subsidies, college loans, financial incentives, and increased pre-college skills on attendance and completion. We find moderate effects of most unconditional financial policies on attendance, with small effects on completion conditional on attendance. By contrast, some merit-based incentives, such as forgiving student loans upon receipt of a bachelor’s degree, have substantial effects. However, this type of policy and other financial subsidies accrue even more than the others to students from relatively wealthy backgrounds. Overall, it appears infeasible to design a financial aid policy that is simultaneously effective in raising attainment and fiscally progressive. By comparison, we find that the greatest potential for increasing attainment could come from better preparing students for college.
Synthetic control methods hold the promise of identifying a credible counterfactual by closely reproducing pretreatment outcome paths. Their reliability, however, depends in part on how effectively they constrain researchers’ less-visible degrees of freedom. We explore these methods’ sensitivity by constructing 1440 plausibly defensible specifications to estimate the employment effect of California’s $20 fast-food minimum wage. While over 96 percent of the estimated employment effects are negative, the range is vast, illustrating that synthetic control methods are sensitive to specification choice. Estimates from simpler and far more transparent difference-in-differences methods lie near the centers of the comparable distributions of synthetic control estimates.
This paper shows that insuring landlords against non-payment risk can improve housing access and upward geographic mobility for fragile tenants. We study Visale, a large-scale publicly funded rent guarantee insurance policy in France, free for eligible tenants and landlords. Using administrative household and claim payout data and quasi-experimental eligibility variation by exact age, we show the guarantee increased access to private-sector rental housing, especially for immigrants, newly formed households, and low-income renters. The scheme eased long-distance mobility toward expensive, high-wage neighborhoods at a moderate fiscal cost per marginal move, though potentially displacing comparable ineligible tenants through higher rents.
This paper studies whether governments can change social norms that constrain women’s education and work, and through which mechanism. Albania’s 1967 religion ban, which created the world’s first atheist state, provides an extreme natural experiment: religious practice was outlawed nationwide, but a 1948 classification of historic monuments determined which religious buildings remained standing. We find larger increases in female employment and schooling where no monument survived. The public visibility of the old religious order appears to be the mechanism: effects are largest where no monument remained, smaller with rural-only monuments, and smallest with urban monuments; they also decline as monument count rises, regardless of denomination. We establish how social norms can outlive formal authority.
This paper examines the role of government capacity to collect taxes in driving economic development. We first draw on a new macro-historical database to document that tax collections relative to tax administration expenditures rise sharply with GDP per capita. We then interpret this finding in a neoclassical growth model with a tax administration sector in which labor inputs are required to raise tax revenues. Countries differ in their levels of tax productivity, capturing how effectively labor is used to raise taxes, as well as their levels of productivity in creating consumption and investment goods. Governments use tax revenues to provide public capital that complements private factors in production and to make transfers. Quantitatively, our model predicts that increases in tax productivity in low-income economies can raise GDP per capita substantially more than suggested by Hulten’s theorem.
This paper studies policy interventions intended to eliminate gender differences in labor market outcomes, principally wages. Because gender is an individual attribute rather than a manipulable treatment, the ideal experiment of randomly assigning gender to evaluate its effects is infeasible. Policies that remove gender labels from the employment process are the closest feasible counterpart. We quantify both the intended and unintended consequences of the policies through simulated interventions of an estimated equilibrium model of statistical discrimination with dynamic choices of education and labor market participation. In the short run the policy that requires gender free applications in the market for skilled labor eliminates wage gaps but once education choices respond the effects are attenuated. A more comprehensive policy that enforces gender free compensation reverses the wage gap in skilled markets and eliminates it in unskilled labor markets, even once education adjusts. These interventions impact college rates, the college premium gap and participation rates as well. The final section studies these interventions using a difference-in-differences (DID) approach to analyze the simulated data. With education fixed, the DID method recovers the policy effects but this is not the case once education adjusts, reflecting the effects of the intervention on both treated and control groups through equilibrium adjustments in beliefs.
During research and development, firms must decide which types of individuals to engage in developing and testing their products. We investigate how lowering the cost of including one group shapes its representation, subsequent innovation, and product adoption. In 2000, Medicare expanded coverage of clinical trial costs for elderly trial participants, a historically underrepresented group in drug development. This policy increases representation: trial sponsors expand trial eligibility to include elderly participants and increase the share of elderly participants. We find an immediate and large increase in the number of clinical trials for diseases common among the elderly, with the largest increase in drugs not yet approved and in approved drugs tested in new diseases. We document an increase in drug utilization of newly tested drugs among elderly individuals and analyze trial results, adverse events, and drug approvals. Our findings show how financial incentives can increase representation in product development and shift the rate, direction, and adoption of innovation to potentially benefit underserved groups.
Health care utilization per capita varies widely across the United States, yet higher utilization areas have no better patient health. In the late 2000s, this pattern was widely interpreted as evidence that health care costs could be cut substantially without harming patients, which in turn shaped the 2010 Affordable Care Act. We describe how our understanding has since evolved, emphasizing three points. First, new evidence indicates quantitatively meaningful roles for both supply-side and demand-side factors in driving regional variation in health care and in health outcomes. Second, health care productivity varies substantially across providers; improving it requires better understanding of why productivity differs. Third, we know even less about the sources of area-level productivity differences. We suggest several ways in which greater engagement with the productivity literature outside of health care could yield valuable insights.
Optimal tax policy frequently prescribes different tax treatments for activities that are difficult in practice for tax administrators to distinguish, such as expenditures undertaken for business versus personal purposes. We study the optimal design of income tax deductions for such expenditures under the assumption that a common deduction rate must be applied to both forms of the expenditure. The Pareto optimal common deduction turns on the relative compensated spending response for each margin, weighted by the income tax wedge. We also characterize the welfare gain from distinguishing between the two expenditure forms as a harmonic mean of the compensated responses. We illustrate both results with an application to air travel expenses.
We use Luxembourg Income Study and International Social Survey Programme microdata on 27 countries over the 1986-2019 period, augmented by policy and labor market information, to estimate the impact of family-friendly policies on gender gaps in employment, wages, earnings, and part-time work, as well as child penalties in employment and earnings. We find that longer duration of parental leave generally lowers gender gaps in wages, employment, earnings, and child penalties in employment and earnings, except for high levels of this entitlement, where further increases in leave raise gender gaps. We also find that the right to request a part-time work schedule raises relative female part-time employment but lowers employment and earnings gender gaps. Higher government expenditures for childcare lower employment, earnings, and part-time employment gender gaps as well as child employment and earnings penalties. These results are generally stronger for the less educated than for the highly educated, possibly reflecting higher costs of taking time off for highly educated women. Finally, expansion of family-friendly policies over this period can account for 24-33% of our sample’s reduction in gender gaps in employment and earnings and 46-76% of the reduction in child penalties in these outcomes. The slower expansion of such policies in the United States than elsewhere accounts for 6-15% of the slower reduction in US gender employment and earnings gaps and 20% of the slower fall in the US child employment penalty.
Whether to engage in sex and with whom are extremely consequential decisions for young women worldwide, and particularly in sub-Saharan Africa, where HIV rates are high and adolescent pregnancies are common. Yet little is known about the extent to which these decisions are financially motivated. We combine a randomized controlled trial of monthly cash assistance to female students at the University of Zambia with detailed, high-frequency data on sexual behavior and partner characteristics. We find that this temporary increase in income significantly changes women's sexual behavior. Treated participants are 5.1 percentage points less likely to have sex during the treatment period, and the rate of condomless sex falls by 30%. Among women who were not sexually active at baseline, the likelihood of having sex falls by 43%, and partner quality conditional on sex greatly improves. Financial assistance also reduces financial precarity, increasing food security, mental health, and cognitive capacity. These results can be rationalized with a dynamic model of partner search in which women can choose to search for boyfriends or high-income partners who provide financial support and bargain over both transfers and condom use. We conclude that a substantial share of sexual activity is financially motivated in this setting, even in a population not typically regarded as vulnerable. Increasing young women's income may be a useful tool for reducing the negative externalities of risky sex and improving match quality.
In 2024, 315,000 H-2A temporary seasonal work visas were issued to agricultural workers in the US. The H-2A program is controversial: earnings are high, but critics argue that workers face poor working and living conditions and that families bear the costs of separation. We randomize H-2A seasonal visas for Mexican workers. During a single US season, migrants earn more than USD 16,000 and send 70% of these earnings to origin households. Living standards in Mexico rise: food security increases, the probability of living in poverty falls by 10 percentage points, and families invest in home improvements, durable assets, and debt reduction, with little investment in businesses or productive assets. Migration has spillovers onto the household: the mental health of household respondents worsens and they avoid higher-risk situations such as walking at night, and intra-household dynamics change. Migrants generally report satisfaction with H-2A jobs and contracts, all report that they would migrate again, and 85% do so the following season.
We study how regional specialization patterns and welfare are affected by uncertainty and economies of scale in an open economy. We use a spatial equilibrium model with sectoral economies of scale, aggregate uncertainty, and irreversible mobility decisions made by heterogenous workers. We analytically characterize how the interactions between comparative advantage, both of workers and firms, economies of scale, and uncertainty shape specialization patterns and, hence, the gains from trade. We find empirical support for the model predictions by documenting the impact of aggregate changes in volatility of sectoral productivity on U.S. regional economies. We calibrate the model using detailed data on U.S. commuting zones and international trade, and extending hat-algebra methods to accommodate uncertainty. Quantitatively, we find that uncertainty shifts employment away from riskier sectors and locations, relative to a deterministic benchmark, lowering the U.S. aggregate gains from trade by around 20 percent. The interaction between uncertainty and scale economies brings down the benefits of specialization. For regions that would otherwise lose from trade, the presence of agglomeration forces and a more elastic labor supply mitigates those losses.
Immigrant founders of US venture-backed startups exit successfully more often than native founders. Does this hold worldwide? Using career histories for 913,226 founder-firm pairs in 213 countries (1990–2026), we compare founders who build abroad (expats) or return home after foreign experience (returnees) with comparable local founders in the same country. Both expats and returnees raise early funding more often globally. Expats' success premium, however, concentrates in the US (18 percent) and UK (26 percent), especially among those entering the US through graduate school, work, or direct founding. Returnees rarely show such a premium. Capital follows mobile founders across borders, but success follows them selectively.
It has been widely documented that patients in the US who are associated with disadvantaged demographics receive care at lower quality nursing homes. To understand the role that selective admissions may play in contributing to this pattern, we conducted a nationwide audit study of the impact of patient race, payer, and medical condition on responses to about 5,500 availability inquiries sent to about 3,000 nursing homes. We find 10-15% lower availability for Medicaid patients compared to private-pay patients and for patients with Alzheimer’s disease compared to those without; these availability gaps generally increase with nursing home quality. We find no statistically significant evidence of differential availability by race. To quantify the implications of these selective admissions practices, we embed the audit study results in a model of constrained nursing home choice. We estimate that if, counterfactually, Medicaid patients had the same choice sets as their private-pay counterparts, they would attend higher quality nursing homes on average, but the private-Medicaid gap in quality would be reduced by only 5-10%. Our results suggest that supply-side availability differences by payer are widespread, but that they are unlikely to be the primary factor behind the observed gap in quality of nursing homes attended.
Household energy use accounts for approximately one third of U.S. emissions. Guided by a new survey, we develop a quantitative model in which households differ in income and their desire to reduce their emissions. Our analysis of cost-minimizing subsidies for clean equipment, such as electric vehicles and heat pumps, and residential solar yields three insights. First, solar receives the highest subsidy rate and accounts for over one third of abatement, reflecting its complementarities with clean equipment. Second, restricting subsidy eligibility to lower-income households generates only small cost savings. Third, without environmental preferences, household emissions would be over 15 percent higher.
We estimate the returns to specialization in medical oncology, in which subspecialists focus on specific cancer types. Using 2.2 million Medicare chemotherapy episodes from 2008–2020, we instrument for subspecialist access with differential distance to cancer-type-specific subspecialists versus general oncologists. Access to a relevant subspecialist reduces three-year mortality by 4.5 percentage points (10 percent), without increasing Medicare spending or measured provider fragmentation. Subspecialists also increase the use of newer therapies and diagnosis-specific clinical trials. These findings show that specialization raises productivity when expertise is closely matched to the task.
We introduce an LLM workflow for functional form sensitivity analysis. A researcher estimates the value of an object of interest by maximizing the fit of a working model to some data. The working model assumes an arbitrary form for some function. We define sensitivity by how much it is possible to change the estimate of the object of interest, without compromising fit, by selecting a different functional form. The workflow first looks for an analytic solution. If it fails to find one, it approximates the solution via numerical methods that respect the fit constraint at each step of a solver path. The workflow yields a verifiable lower bound on the in-sample sensitivity of the researcher’s estimate, and is fast in many practical situations. We illustrate with an application to the welfare cost of adverse selection in health insurance.
Variance decompositions from regressions with worker and firm fixed effects are widely used to measure heterogeneity and sorting in linked data. Variances computed directly from estimated effects overstate heterogeneity because they include estimation noise, and the bias can be large when few workers move between firms. This article introduces fevc, a Stata command that implements the leave-out bias correction of Kline, Saggio, and Sølvsten (2020, “Leave-Out Estimation of Variance Components,” Econometrica 88: 1859–1898). fevc reports corrected variances of worker and firm effects, their covariance, and the variance of their sum. Researchers choose the unit left out, the treatment of controls, and the weights that define the population being described. The command also reports standard errors for projections of fixed effects on observed characteristics. It has two backends: a source-only Mata implementation that runs wherever Stata runs, and a parallel Rust plugin for large datasets. Calculations based on random projections report Monte Carlo standard errors that measure their numerical precision. In a public extract of Italian social-security records, the correction halves the estimated variance of firm effects and reverses the sign of the worker–firm covariance. In synthetic benchmarks with 6.4 million observations and 28 threads, the Rust backend is 1.3 to 3.5 times as fast as the Matlab package of Kline, Saggio, and Sølvsten. Under match deletion, its measured peak memory is about 30 percent of the Matlab package’s.
Using generative AI, we extract managers’ economic expectations from over 120,000 corporate conference call transcripts. The resulting AI Economy Score predicts GDP growth, production, employment, and wages five or more quarters ahead, beyond survey forecasts and other existing measures. Industry- and firm-level scores capture sector- and firm-specific activity, and a composite of firm, industry, and macroeconomic expectations further improves forecasting power and horizon. Predictability concentrates among historically more accurate, less biased managers, consistent with an information rather than a noise channel. Managerial expectations thus offer incremental insights into economic activity, with implications for macro- and microeconomic decision-making.
Firms ask workers to train AI systems that will replace them. We model this with a worker who can shirk, cooperate with training, or obstruct training while maintaining productivity. Because training ends the job but only cooperation completes training, dismissal becomes a weaker penalty for shirking. The firm must raise the worker’s rent, and the worker gains from their replacement. AI can create jobs paying more than they produce and eliminate jobs it never automates. Firms paying only wages may slow training. A credible completion bonus equal to the worker’s rent without AI removes both the gain and the slowdown.
We develop a tractable framework for analyzing geopolitics as a network of coordination and strategic influence. Countries choose geopolitical actions that trade off domestic interests against incentives to align with connected countries. They can exert costly bilateral influence to affect one another’s actions. We characterize equilibrium actions and bilateral influence in closed form. Cross-country differences in domestic interests decompose into latent geopolitical cleavages, each of which the network transmits into geopolitical actions independently of the others. The cross-country covariance matrices of domestic interests and of geopolitical actions identify the cleavages and their importance, from which we recover the network. We implement the framework using data on United Nations voting and democratic orientation. The inferred network is well approximated by a few dimensions, aligns with external measures of geopolitical ties, and recovers well-known historical cleavages. We undertake counterfactuals for domestic interests and network connections and quantify three notions of geopolitical power
We build a model of two hegemons that have valuable trading relationships with each other and at the same time compete in exerting geoeconomic power over countries in the rest of the world. When the hegemons trade with each other, their optimal policy is shaped both by classic economic considerations - the profitability of that specific trade - and by geoeconomic competition - how the trade affects the power of each hegemon vis-a-vis the rest of the world. We show that containment, a policy mix in which a hegemon limits sales of its inputs to the rival hegemon and uses its power to demand that the rest of the world shift away from sourcing from the rival, arises when the two hegemons offer relatively substitutable exports, are less interdependent with each other, and have more direct geopolitical rivalry. Countries in the rest of the world benefit, all else equal, from hegemons containing each other since they partially squander their power with offsetting demands. However, containment policy can also worsen the efficiency of global production, leaving the rest of the world worse off.
How competitive will the market for generative AI be as it matures? Using novel transaction level credit and debit card data covering 174 million consumers and 114 billion transactions, we study the adoption of paid services and the demand-side foundations of competition in this market. As of April 2026, approximately 2.9% of U.S. consumers pay for at least one major generative AI service, with adoption highest among younger and higher-income consumers. Nearly all of these paid users subscribe to basic plans that cost $30 or less per month. Three patterns suggest limited differentiation and low switching costs. First, although multihoming is common, it has declined as providers’ capabilities have expanded. Second, annual customer retention rates range from 20% to 45% across major AI providers, lower than those for many other subscription products. Third, switching rises around major model releases. Motivated by these findings, we develop and estimate a structural model of AI service demand and supply that reveals strong competitive pressure. Consumers are highly price sensitive, have much weaker preferences for particular providers than for AI overall, and view AI services as increasingly close substitutes. Our results suggest that, absent a substantial increase in AI model quality differentiation, growing substitutability among services will constrain providers’ pricing power and allow consumers to capture a large share of the surplus generated by these services.
We study institutional cash management by constructing a novel dataset that records the specific money market funds (MMFs) held by each mutual fund in each month from 2010 to 2024. We document remarkably high inertia: more than half of mutual funds never switched their MMF choice throughout the sample period, despite non-negligible yield dispersion. To decompose the sources of this inertia, we develop a two-stage structural model that separately identifies the determinants of portfolio managers’ attentiveness (“waking up”) and their conditional preferences once attentive. We estimate the model using a methodology that combines BLP-style contraction mapping with maximum likelihood. We establish three main findings. First, MMFs with less attentive investor bases engage in greater liquidity transformation while offering lower yields. As a result, they earn higher profits and exhibit significantly higher survival rates, especially during periods of low interest rates. Second, we find that aggregate flows appear more yield-sensitive during low interest rate periods (the well-documented “reaching for yield” phenomenon) not because individual investors become more yield-seeking, but because a larger fraction of investors becomes attentive—a composition effect rather than a preference shift. Third, counterfactual analysis reveals a potential tension between market efficiency and financial stability if AI-driven automation reduces investor inattention. In the absence of inattention, fees fall by approximately half, yield dispersion collapses to near zero, and market concentration declines by roughly 20%, but monthly fund flows increase by about three times and the incidence of fund-months in which redemptions exceed a fund’s weekly liquid assets rises by about six times, raising the risk of fire sales and MMF runs.
In 2017, the federal government ended payments for cost-sharing reduction (CSR) subsidies for the Health Insurance Marketplaces created by the Affordable Care Act. Rather than uniformly increase Marketplace plans’ premiums to offset the loss of these payments, over 40 states responded by silver loading—only increasing the premiums of silver plans to which CSR subsidies apply and premium tax credits are tied. Silver loading was intended to increase the affordability of Marketplace coverage by increasing premium tax credits and thereby enrollment, though the literature lacks causal estimates. Furthermore, Congress is considering reinstating CSR subsidy funding, effectively undoing silver loading. We use panel data on 2015-2021 Marketplace health plan premiums and enrollment in 12 states from the Centers for Medicare and Medicaid Services to estimate silver loading’s effects using contiguous county-pair models. We find that silver loading functioned as intended, lowering the cost of Marketplace coverage and increasing enrollment by 8-14% among subsidized Marketplace enrollees with incomes between 250% and 400% of the poverty level. We project that Congress ending silver loading would decrease Marketplace enrollment by at least 330,000 enrollees in 2027.
We look at the effects of debt and primary fiscal balances on sovereign credit ratings through the lens of a simple model. We find that the ratings differ from the implications of the model in three important ways. They give much more weight to debt relative to forecast primary balances. They understate the effects of the difference between the interest rate and the growth rate. They give a very large role to country effects. For the same level of debt and forecast primary balances, they imply extremely different ratings across countries, and imply extremely different levels of debt needed to reach a given rating.
Using a panel of U.S. imports and customs duties at the HS 6-digit level, we document that in a typical quarter about 20 percent of tariffs rise, 20 percent fall, and 60 percent remain unchanged. Motivated by these facts, we develop a model of staggered tariff setting and embed it in a dynamic open-economy framework. Staggering changes the dynamics of the cross-sectional mean tariff, generating hump-shaped macroeconomic responses to tariff shocks. Staggering also overturns the Razin–Svensson trade- balance neutrality result for permanent tariff increases, because the gradual adjustment of the mean tariff creates an anticipation effect that induces agents to front-load net imports. Inherited tariff dispersion has first-order macroeconomic effects, allowing tariff harmonization to be studied with linear approximation methods. We find that eliminating tariff dispersion while holding the mean tariff unchanged has effects akin to a transitory tariff cut in an economy without staggering.
Governments invoke national security to regulate foreign acquisitions of domestic firms. Why is ownership regulation a distinct policy margin beyond standard tools, like production subsidies and trade taxes? We show that contractual frictions make multinational firm boundaries central to geoeconomic policy. Within firms, ownership allocates residual control that affects outcomes through non-contractible decisions. Between governments, the absence of enforceable agreements makes the economic costs of conflict shape geopolitical bargaining. Foreign ownership depends on private returns to foreign control, but also determines national exposure to geopolitical conflict. It can increase foreign dependence, technology transfer, or sabotage scope. If such margins were contractible, the government could regulate them directly. When they are not, ownership regulation is the second-best tool that changes control rights and, thus, geopolitical outcomes. Consistent with our mechanism, U.S. regulation emphasizes the national-security risks of foreign control over firm decisions, has tightened with rising geopolitical tensions, and is tighter in sectors with higher exposure to defense demand and contractual frictions.
Behavioral finance seeks to improve our understanding of markets, investors, and firms by adopting a psychologically realistic description of how people think. While behavioral finance ideas have been part of human discourse for decades if not centuries, the modern research effort in the field began in the 1980s. Forty years later, behavioral finance is a vibrant area of financial economics. In this article, I explain why by reviewing some of its recent progress. I focus on three topics – beliefs about future earnings, beliefs about future returns, and prospect theory – and on their applications to understanding asset prices and investor behavior. I close by commenting on some new directions in the field – cognitive foundations, non-traditional decision processes, and artificial intelligence.
Stakeholder theory asks who is in, who is out, and who gets what. Existing work illuminates important dimensions of firm–stakeholder relationships but does not supply a common, transaction-level rule connecting these questions. We use the Coase theorem to develop such a rule. A Coasian stakeholder is an actor with shutdown power, defined as the de facto capacity, within the relevant time horizon, to prevent, materially delay, or substantially reduce the expected joint value of a focal value-creating transaction. If all such actors are identified and bargaining and enforcement are costless, value-maximizing transactions proceed, although rights and bargaining positions may affect who captures value. When transaction costs are positive, however, identification, bargaining, coalition formation, and enforcement can delay, distort, or suppress those transactions. Across six propositions, we explain how shutdown power is identified and acquired, how complementarity and bargaining conditions shape value capture, how firms reshape stakeholder sets through coalition costs and governance design, and why consolidated opposition can sometimes preserve transactions that fragmented claims would suppress.