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.
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.
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.
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.
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.
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%).
This paper develops and applies new asymptotic theory for estimation and inference in parametric autoregression with function valued cross section curve time series. The study provides a new approach to dynamic panel regression with high dimensional dependent cross section data. Here we deal with the stationary case and provide a full set of results extending those of standard Euclidean space autoregression, showing how function space curve cross section data raises efficiency and reduces bias in estimation and shortens confidence intervals in inference. Methods are developed for high-dimensional covariance kernel estimation that are useful for inference. The findings reveal that function space models with wide-domain and narrow-domain cross section dependence provide insights on the effects of various forms of cross section dependence in discrete dynamic panel models with fixed and interactive fixed effects. The methodology is applicable to panels of high dimensional wide datasets that are now available in many longitudinal studies. An empirical illustration is provided that sheds light on household Engel curves among ageing seniors in Singapore using the Singapore life panel longitudinal dataset.
The growing availability of big data enables firms to predict consumer search outcomes and outside options more accurately than consumers themselves. This paper examines how a firm can utilize such superior information to offer personalized buy-now discounts intended to deter consumer search. However, discounts can also serve as signals of attractive outside options, potentially encouraging rather than discouraging consumer search. We show that, despite the firm’s ability to tailor discounts across a continuum of consumer valuations, the firm-optimal equilibrium features a simple two-tier discount scheme, comprising a uniform positive discount when the consumer outside option is intermediate and no discount when the outside option is low or high. Furthermore, compared to a scenario where the firm lacks superior information, we find that the firm earns lower profits, consumers search more while their welfare remains unchanged, and total welfare declines.
We quantify how pollution affects aggregate productivity and welfare in spatial equilibrium. We show that skilled workers in China emigrate away from polluted cities. These patterns are evident under various empirical specifications, such as when instrumenting for pollution using upwind power plants, or thermal inversions. Pollution changes the spatial distribution of skilled and unskilled workers, and wage returns by location. We quantify the loss in aggregate productivity due to this re-sorting by estimating a spatial equilibrium model. Counterfactual simulations show that reducing pollution increases productivity through spatial re-sorting by approximately as much as the direct health benefits of clean air.
A soft-floor auction asks bidders to accept an opening price to participate in an ascending auction. If no bidder accepts, lower bids are considered using first-price rules. Soft floors are common despite being irrelevant with standard assumptions. When bidders regret losing, soft-floor auctions are more efficient and profitable than standard optimal auctions. Revenue increases as bidders are inclined to accept the opening price to compete in a regret-free ascending auction. Efficiency is improved since having a soft floor allows for a lower hard reserve price, reducing the frequency of no sale. Theory and experiment confirm these motivations from practice.
To safeguard economic and financial stability policymakers regularly take actions designed to increase resilience to systemic risks and curb speculative market behavior. To assess the effectiveness of such mitigation policies, we introduce a counterfactual approach tailored to accommodate the mildly explosive dynamics that occur during speculative bubbles. We derive asymptotics of the estimated treatment effect under a common factor structure that allows for explosive, I(1), and stationary factors, thereby having applicability to a wide range of prevailing economic conditions. An inferential procedure is proposed for the policy treatment effect that has asymptotic validity and demonstrates satisfactory finite sample performance. An empirical analysis examines the monetary policy of interest rate hikes implemented by the Reserve Bank of New Zealand, beginning in October 2021.This policy exerted a statistically significant cooling effect on all regional housing markets in New Zealand. Our findings show that this policy led to 20%-33% reductions in house prices in five out of six regions seven months after the enactment of the interest rate hike.
In digital advertising, the allocation of sponsored search, sponsored product, or display advertisements is mediated by auctions. The generation of bids in these auctions for attention is increasingly supported by auto-bidding algorithms and 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’s revenue while ensuring all advertisers continue to participate.
Caregiving is a service provided for children with the primary objective of taking care of them and ensuring that they are safe and have opportunities to learn and develop positive relationships with their caregivers and peers while their parents are away. Caregiving takes the forms of home-based care, centre-based care, school-based care, family child care and family, friend, and neighbour (FFN) care. The paper utilises preliminary findings on school attendance from a randomised controlled trial on the effects of a preschool intervention on child learning and women’s economic empowerment in Tharaka Nithi County in school-based care. The research sought to test whether a preschool-based intervention in a rural setting in Kenya influences child development and women’s labour market participation in a cost-effective manner. The project examines the impact of allowing three-year-old children to attend preschool versus the regular pre-primary education programming, which allows children aged 4 years and above to attend preschool. Implementation of the intervention started in January 2024 in 60 intervention schools where five three-year-old children were admitted to a playgroup (PG) in the pre-primary one (PP1) class. Twelve mentors and sixty caregivers were recruited and trained alongside sixty PP1 teachers from the sampled preschools to implement an adapted PP1 curriculum. The twelve mentors coached teachers weekly on the implementation of the curriculum in the five schools assigned to them. This paper presents preliminary findings on preschool attendance for the PG and PP1 children based on weekly attendance data from term one and term two of the 2024 school calendar year on the day the mentors visited the school. Findings reveal that school attendance was low during school openings, midterm breaks, and the last weeks before the schools closed. Public holidays, as well as extracurricular activities coupled with children being sent home for school levies, also contributed to children not attending school regularly. The findings further show that the attendance rate in term one was slightly higher than in term two.