This paper develops a complete-markets model to analyze the determinants of endogenous trade imbalances across countries. We introduce a framework where countries can trade in Arrow-Debreu securities to insure against different states of the world, which enables them to run deficits in some states and surpluses in others. The model allows for counterfactual analysis of various trade policy scenarios, such as unilateral tariff impositions. We derive the conditions under which trade deficits arise endogenously and discuss implications for welfare and trade policy analysis.
We present a model of digital advertising with three key features: (1) advertisers can reach consumers on and off a platform, (2) additional data enhances the value of advertiser–consumer matches, and (3) the allocation of advertisements follows an auction-like mechanism. We contrast data-augmented auctions, which leverage the platform’s data advantage to improve match quality, with managed-campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions the on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficiently high off-platform product prices. It attains the vertical integration profit for the platform and the advertisers, and it increases off-platform product prices while decreasing consumer surplus, relative to data-augmented auctions.
This chapter argues that parenting choices are a central force in the joint evolution of culture and economic outcomes. We present a framework in which parents-motivated by both their children’s future success and their own normative beliefs-choose parenting styles and transmit cultural traits responding to economic incentives. Values such as work ethic, patience, and religiosity are more likely to be instilled when their anticipated returns, economic or otherwise, are high. The interaction between parenting and economic conditions gives rise to endogenous cultural and economic stratification. We extend the model to include residential sorting and social interactions, showing how neighborhood choice reinforces disparities in trust and human capital. Empirical evidence from the World Values Survey supports the model’s key predictions. We conclude by highlighting open questions at the intersection of parenting, culture, and inequality.
We use the tools of mechanism design combined with the theory of risk measures to analyze how a cash-constrained owner of an asset with known, stochastic returns raises capital from a population of investors who differ in their risk aversion and budget constraints. The issuer partitions the asset's cash flow into several asset-backed securities, one for each type of investor. The optimal partition conforms to the commonly observed practice of tranching into senior debt, junior debt, and equity. Tranching arises endogenously due to the differences in risk appetites among agents and in the budget constraints they face.
This paper examines how high school specialization shapes college investment decisions and their subsequent returns through dynamic complementarities. Using Swedish administrative data, we estimate a dynamic Roy model that accounts for selection on multidimensional skills, family background, prior investments, and unobserved heterogeneity. We identify the model using rich skill measures and quasi-experimental variation in program popularity. For marginal students, STEM specialization in high school increases wages by 9%, with more than half this return attributed to dynamic complementarities that enhance the productivity of subsequent college investments. Consequently, we find that counterfactual policies encouraging high school STEM specialization generate twice the returns of equivalent college-level interventions. These findings demonstrate how the timing of specialized human capital investments matters during adolescence, with important implications for education policies that encourage or restrict specialization.
Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers — proxy means-testing, nominations from community members, and an algorithmic approach using machine learning to predict poverty using mobile phone usage behavior — and study how their cost-effectiveness varies with the scale and scope of the program. We collect mobile phone records from all major telecom operators in Bangladesh and conduct community-based wealth rankings and detailed consumption surveys of 5,000 households, to select the 22,000 poorest households for $300 transfers from 106,000 listed households. While proxy-means testing is most accurate, algorithmic targeting becomes more cost-effective for national-scale programs where large numbers of households have to be screened. We explore the external validity of these insights using survey data and mobile phone records data from Togo, and cross-country information on benefit transfer programs from the World Bank.
We develop a tractable framework to explore how beliefs about long-term economic growth shape macroeconomic and financial stability. By modeling belief distortions among productive capital users, we provide an analytical characterization of a novel phenomenon termed the “net worth trap”, wherein overly optimistic or pessimistic beliefs among productive agents prevent them from rebuilding wealth, causing permanent inefficiencies. A procyclical swing in beliefs reduces or exacerbates the instability, indicating that the type of belief when the economy is vulnerable has important consequences on financial stability and macroeconomic dynamics.
In digital advertising, auctions determine the allocation of sponsored search, sponsored product, or display advertisements. The bids in these auctions for attention are largely generated by auto-bidding algorithms that are driven by platform-provided data.
We analyze the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms. First, we consider the equilibrium bidding behavior of an individual advertiser who controls the auto-bidding algorithm through the choice of their budget. Second, we examine the interaction when all bidders use budget-controlled bidding algorithms. Finally, we derive the bidding algorithm that maximizes the platform revenue while ensuring that all advertisers continue to participate.
A monopolist offers personalized prices to consumers with unit demand, heterogeneous values, and idiosyncratic costs, who differ in a protected characteristic, such as race or gender. The seller is subject to a non-discrimination constraint: consumers with the same cost, but different characteristics must face identical prices. Such constraints arise in regulated markets like credit or insurance. The setting reduces to an optimal transport, and we characterize the optimal pricing rule. Under this rule, consumers may retain surplus, and either group may benefit. Strengthening the constraint to cover transaction prices redistributes surplus, harming the low-value group and benefiting the high-value group.
Using newly-linked administrative and commercial data from Virginia spanning 25 years, we study the consequences of incarceration. While previous research has examined labor market outcomes and recidivism, we focus on two of the primary channels through which low-income households build wealth: asset ownership (homes and cars) and human capital formation. To identify causal effects, we use a matched differencein-differences design. In line with much of the literature on the impact of incarceration in the U.S., we find no evidence of scarring effects on labor market outcomes or changes in recidivism beyond the incapacitation period. However, we find that incarceration leads to a persistent reduction in asset accumulation: seven years after sentencing, homeownership has declined by 1.1 percentage points (12.1%) and car ownership by 2.7 percentage points (18.1%). Incarceration also lowers human capital formation, reducing college enrollment by 1.4 percentage points (15.1%).
Market-based environmental regulations are seldom used in low-income countries, where pollution is highest but state capacity is often low. We collaborated with the Gujarat Pollution Control Board (GPCB) to design and experimentally evaluate the world’s first particulate-matter emissions market, which covered industrial plants in a large Indian city. There are three main findings. First, the market functioned well. Treatment plants, randomly assigned to the emissions market, traded permits to become significant net sellers or buyers. After trading, treatment plants held enough permits to cover their emissions 99% of the time, compared with just 66% compliance with standards under the command-and-control status quo. Second, treatment plants reduced pollution emissions, relative to control plants, by 20%–30%. Third, the market reduced abatement costs by an estimated 11%, holding constant emissions. This cost-savings estimate is based on plant-specific marginal cost curves that we estimate from the universe of bids to buy and sell permits in the market. The combination of pollution reductions and low costs imply that the emissions market has mortality benefits that exceed its costs by at least 25 times.
Leading AI firms claim to prioritize social welfare. How should firms with a social mandate price and deploy AI? We derive pricing formulas that depart from profit maximization by incorporating incentives to enhance welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A welfarist firm that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A conservative firm focused on labormarket stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks.
I propose a model in which workers experience fatigue over time and can restore productivity by taking breaks. Optimal schedules feature evenly spaced, full-recovery breaks; when breaks are costless, they should occur frequently, but switching costs make the optimal number finite. The model is embedded in a principal-agent framework with contractual frictions. When employers control the schedule, workers overwork; when workers self-manage, they overrest. Both lead to inefficiencies. These results shed light on the trade-offs in remote work arrangements, especially following COVID-19. The analysis highlights how control rights, incentive design, and recovery constraints interact—and why neither rigid supervision nor full autonomy guarantees efficiency.
The classic tariff formula states that the optimal unilateral tariff equals the inverse of the foreign export supply elasticity. We generalize this result and show that an intertemporal tariff formula characterizes the efficient tariff in a large class of dynamic heterogeneous agent (HA) economies with multiple goods. Intertemporal export supply elasticities and relative tariff revenue weights are sufficient statistics for the optimal tariff that decentralizes the efficient allocation. We also develop a general theory of second-best optimal tariffs. In dynamic HA incomplete markets economies, Ramsey optimal tariffs trade off intertemporal terms of trade manipulation against production efficiency, risk-sharing, and redistribution. Intertemporal export supply elasticities and relative tariff revenue weights remain sufficient statistics for the intertemporal terms of trade manipulation motive of second-best optimal tariffs. We apply our results to a quantitative heterogeneous agent New Keynesian (HANK) model with trade.
We examine the effects of international trade in the presence of a set of domestic distortions giving rise to informality, a prevalent phenomenon in developing countries. In our quantitative model, the informal sector arises from burdensome taxes and regulations that are imperfectly enforced by the government. In equilibrium, smaller, less productive firms face fewer distortions than larger, more productive ones, potentially leading to substantial misallocation. We show that in settings with a large informal sector, the gains from trade are significantly amplified, as reductions in trade barriers imply a reallocation of resources from initially less distorted to more distorted firms. We confirm findings from earlier reduced-form studies that the informal sector mitigates the impact of negative labor demand shocks on unemployment. Nonetheless, the informal sector can exacerbate the adverse real income effects of economic downturns, amplifying misallocation. Last, our research sheds light on the relationship between trade openness and cross-firm wage inequality.