Publications

We study the effects of H-1B immigration on U.S. industries that employ H-1B workers and their trading partners. Using a novel cross-industry design and the 1999–2003 expansion of the H-1B visa cap for identification, we find that H-1B exposure raised incomes for natives and pre-existing immigrants, with gains concentrated in non-STEM occupations. Income gains propagate forward through supply chains to downstream industries but not backward to upstream industries, consistent with a productivity shock rather than a labor supply shock. We find no direct effect on patenting, suggesting that productivity gains arise from better task execution rather than patentable invention.
 

Health insurance lowers the out-of-pocket price of healthcare, and it is well-established that this leads to higher utilization of care. This manifestation of “moral hazard” is typ ically viewed as a social cost of insurance. Within a standard model, this paper shows that a consumer’s ability to change her behavior in response to insurance can also play a central role in the ability of insurance to protect her from risk. We provide a theoretical characterization of this channel and quantify its importance empirically. Under stan dard parameterizations and estimates in the literature, we find that insurance-induced healthcare utilization can account for more than half of the total value of risk protection derived from insurance. Preventing consumers from changing their behavior would lower healthcare spending, but also result in a major loss of risk protection, on-net reducing social welfare in some cases. Our results suggest that under-utilization of healthcare may thus be an equally important threat to welfare as over-utilization.

We provide evidence that domestic outsourcing increases young-worker entry into the formal sector. Leveraging a pair of 1993–1994 Brazilian reforms that reduced the relative cost of outsourcing security guards, a triple-differences design shows that the reforms increased formal guard employment by 4% and hiring from unemployment or informality by 7%, while reallocating formal employment from older to younger workers and leaving demographic-adjusted wages unchanged. Census data corroborate the rise in formality, driven by the youngest cohorts. The compositional shift mirrors a general pattern in Brazil’s matched employer–employee records: conditional on total firm size, employers with greater occupation-specific scale hire younger workers, paid less at entry and more likely to be entering formal employment for the first time. The evidence is most consistent with contract firms supplying at scale the capabilities needed to hire productive workers from outside the formal sector—a demand-side channel for increasing formal sector employment.

Evidence that domestic outsourcing lowers pay comes largely from on-site transfers, in which workers move to a contractor but keep the same jobs. Displacement is rarely observed: whether workers lose their jobs, where they go, how earnings evolve. In Brazil’s 1993–1994 pro-outsourcing reforms, which differentially affected security guards, such transfers were rare; firms instead used occupational layoffs, shedding their guards while keeping other workers. Displaced guards’ employment recovered within five years, but many changed occupations and wages stayed about 12% lower. Lifetime losses average 1.2 to 1.5 years of pre-layoff earnings, concentrated among workers from high-wage firms, reflecting lost premia.

The Credit Surface along the leverage dimension gives the bond spread as a function of the loan-to-value ratio. Empirically, we show that uncertainty shocks typically increase spreads and steepen the credit surface, profoundly affecting the supply of credit. Theoretically, we derive necessary and sufficient conditions for the convexity of the credit surface, and for changes in the anticipated distribution of collateral prices that lead to steepening of the credit surface. Finally, we show that the credit surface itself fully reveals the entire distribution of collateral prices, thus providing a new and vivid language with which to describe uncertainty and stochastic orders. Credit surface steepening itself is a new stochastic order that may better capture our intuitive notion of more uncertainty.

This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data stream and are well suited to streaming data or to settings with storage/privacy constraints. For semiparametric monotone index models, the averaged trajectory of the second phase lead to estimators that are automatically orthogonalized and satisfy the laws of the iterated logarithms, and policy functionals are updated along the same trajectory at negligible additional cost. The averaged trajectories satisfy functional central limit theorems, which yield fast online inference via random scaling and bypass the explicit variance estimation that complicates inference for semiparametric models. Applied to a fixed large sample, our online algorithm achieves substantial computational gains over corresponding offline procedures without sacrificing statistical performance. Monte Carlo experiments show adequate behavior. Our methods are applied to 19 million traffic-stop online records from the North Carolina State Patrol (Pierson et al. 2020) and to the international trade data of Helpman et al. (2008) with over 300 regressors. We also compare our estimator to its parametric benchmark in both empirical illustrations.

Empirical models of multi-product demand rely on low-dimensional product representations to capture substitution patterns, increasingly using proxies built from unstructured data. When proxies are imperfect, standard workflows yield biased counterfactuals and invalid inference. We develop a practical toolkit to address these issues. Our methods apply to market-level and/or individual data, require minimal additional computation, provide simple standard-error formulas, and accommodate proxies from fine-tuned models. Further, we propose diagnostics to assess proxy quality. Our methods yield meaningful improvements in predicting substitution in empirically calibrated simulations and in an application where we assess counterfactual prediction performance against a ground truth.

The optimal mechanism for selling a divisible good under convex production costs can be arbitrarily complex, yet firms overwhelmingly use two-part tariffs: a fixed fee plus a constant markup over production cost. We quantify the profit this simplicity sacrifices. For regular value distributions, a two-part tariff guarantees a fraction of the optimal profit that depends only on a lower bound m on the elasticity of the marginal cost, independent of the value distribution. The guarantee approaches 1 as m grows, and is impossible without such a bound. Beyond regularity, no two-part tariff guarantees a constant fraction, but a menu of K + 1 tariffs with a common markup does when the ironed virtual value has K ironing intervals; moreover, the menu size must scale with K. With multiple product lines, the better of separate sales and a single access fee granting purchases at production cost achieves a constant fraction of the optimal profit. These results provide a theoretical foundation for the ubiquity of simple cost-based pricing.

Divorce reshapes family life, yet little is known about one of its most consequential features: the allocation of child custody. We study the impact of joint versus sole custody on both parents and children using rich administrative data from Sweden linked to over 25 years of newly-collected court custody rulings. To address selection concerns, we exploit random assignment of custody disputes to judges who differ sharply in their propensity to grant joint custody. For fathers, joint custody substantially raises earnings and improves mental health, consistent with sustained paternal involvement enhancing labor market attachment and psychological well-being. In contrast, there are no measurable labor market or mental health effects for mothers. Turning to children, joint custody increases standardized test scores and school quality without affecting mental health outcomes. Joint custody increases fathers’ chances of remarriage, keeps separated parents in closer geographic proximity, and has no effect on intimate partner violence allegations against either partner. These findings inform longstanding debates over the role of child custody in shaping post-divorce family life.

With uncertainty about persistence, we show that forecasts necessarily become more persistent and over-react at long horizons. For these reasons, correctly specified and Bayesian forecasts may under-react at short horizons and over-react at long horizons. These results provide a unified explanation for several asset pricing and forecasting puzzles, including: (i) the excess responsiveness of long-horizon rates to short rates, (ii) the dominance of apparent term premia for long-term rates, (iii) the ex post predictability of bond yields, (iv) the excess volatility of long-horizon forward prices, (v) the excess persistence of long-horizon forecasts, and (vi) the over-reaction of long-horizon forecasts.

Conversational recommender systems powered by generative AI can enhance personalization by facilitating information elicitation through follow-up questions. However, engaging in these conversations imposes a communication cost on users. As platforms with different objectives and monetization models deploy these systems, a central question is: how does the platform’s objective and sellers’ strategic response shape the design of these systems in terms of their elicitation strategy? We develop a parsimonious model of conversational elicitation in which interaction generates noisy preference information and imposes a communication cost borne by the user. A user-welfare-maximizing platform elicits more information when accurate niche matching yields large gains, even when niche users are rare. In contrast, under a conversion objective, for the same setting, the optimal strategy is to immediately recommend the same mainstream option to all users with no or minimal preference elicitation because the incremental conversion benefit from improved matching is bounded, while communication costs are borne by all users. When prices are endogenous and the platform earns a commission, increased elicitation is again optimal because improved screening raises equilibrium prices and platform revenue; however, these price responses can counteract consumer benefits and reduce user welfare. The model also highlights that the optimal elicitation intensity increases with preference heterogeneity, helping explain why conversational systems ask more in highly differentiated categories than in low-heterogeneity ones. We complement the theory with a dataset of long-form product queries that vary in length and informational content. Using our dataset and LLM-based user simulation, we quantify how additional information impacts user decisions and demonstrate that the magnitude of this impact depends on the degree of preference heterogeneity. Additionally, this dataset provides a testbed for measuring the (incremental) value of preference elicitation and may be of independent interest.

We study efficient dynamic mechanism design with independent private values when agents do not share a common prior over the stochastic environment. Each agent privately observes the stochastic kernel governing the evolution of her own type and may hold arbitrary beliefs about the kernels of others. We extend the agents’ type space to include the kernel itself and show that the dynamic team mechanism of Athey and Segal (2013) and the dynamic pivot mechanism of Bergemann and Välimäki (2010) implement the socially efficient allocation in periodic ex-post equilibrium. We further show that kernels can be elicited only once, at the outset, and that the same mechanisms induce the efficient private acquisition of the stochastic kernels.

Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the AI Premium. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factor—high AI beta firms—earn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumption—closed-source models, paying and seasoned users, and long prompts—but not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and more negative in analytical, scientific, and operations-control skills—an occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI exposure. Additionally, we provide early evidence of the rise of the agentic economy.

The AI boom has driven the Nasdaq and the Magnificent Seven tech stocks to record highs. But how much do these new records reflect underlying value, how much is speculation, and how vulnerable are these stocks and the wider market to a major downturn? Our evidence and analyses show clear signs of bubble exuberance in most of these stocks, concentrated in a few names like Nvidia, leading to latent risks for investors who assume their index funds are safely diversified and supported by wider economic fundamentals.

Originally the author envisioned this book as an exposition of some asymptotic methods used for developing statistical and econometric theory. However, over the years the focus shifted towards the inequalities and approximations at the core of those methods. In part, the book evolved into an attempt to explain how tricks invented to solve specific problems in one area can turn into general tools applicable in many areas; it attempts to shed light on the mystery of how anyone could come up with such clever ideas. As the book tries to explain, the story often starts from a small insight that slowly gets transformed into an imposing theory where motivating ideas lie hidden behind clever definitions. (Sometimes, plain old Calculus plus a smidgen of convexity magic are at work behind the scenes.) The main topics are: exponential inequalities for both sums of independent random variables and martingales; path methods for gaussian processes; maximal inequalities, with their extension via chaining arguments to uniform bounds for large (or infinite) index sets; symmetrization and the combinatorial methods initiated by Vapnik and Chervonenkis; and concentration inequalities. The final chapters also describe some ways to handle dependence.