Publications

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.

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.

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.

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.

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.

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.

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.

While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice-based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life-years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post-transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.