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We study how market segmentation affects consumers when a monopolist can adjust both prices and product qualities across segments, engaging in second- and third-degree price discrimination simultaneously. We characterize the consumer-optimal segmentation and show that it has a striking structure: consumers with the same value receive the same quality in every segment, though prices differ. Under mild conditions, any segmentation harms consumers if and only if demand is more elastic than a cost-determined threshold. Hence, potential benefits for consumers depend critically on cost and demand elasticities. These findings have implications for regulatory policy regarding price discrimination and market segmentation.

As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers’ private costs. We model delegation and verification as the solution to a rational worker’s optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker’s objective) at the resulting optimal action. We formally characterize optimal worker workflows and show that AI induces phase transitions, where arbitrarily small differences in verification ability lead to sharply different behaviors. As a result, AI can amplify workers with strong verification reliability while degrading institutional worker quality for others who rationally over-delegate and reduce oversight, even when baseline task success improves and no behavioral biases are present. These results identify a structural mechanism by which AI reshapes institutional worker quality and amplifies quality disparities between workers with different verification reliability.

This paper studies nonparametric local (over-)identification and the semiparametric efficiency in modern causal frameworks. We develop a unified approach that begins by translating structural models with latent variables into their induced statistical models of observables and then analyzes local overidentification through conditional moment restrictions. We apply this approach to three popular classes of causal models: (1) the general treatment model under unconfoundedness; (2) the negative control model, and (3) the long-term causal inference model under unobserved confounding. The first model yields a locally just-identified statistical model, implying that all regular asymptotically linear estimators of the treatment effect have the same asymptotic variance, which equals the (trivial) semiparametric efficient variance bound. In contrast, the latter two models involve nonparametric endogeneity and are naturally locally overidentified; consequently, some doubly robust orthogonal moment estimators of the average treatment effect are inefficient. Whereas existing work typically imposes strong conditions to restore local just-identification to justify the efficiency of their doubly robust orthogonal moment estimators, we characterize the semiparametric efficient variance bounds, along with efficient estimators, for the (locally) overidentified models (2) and (3). A small real data application, along with a simulation study, illustrates the semiparametric efficiency gains in model (3)

We study how market segmentation affects consumers when a monopolist can adjust both prices and product qualities across segments, engaging in second- and third-degree price discrimination simultaneously. We characterize the consumer-optimal segmentation and show that it has a striking structure: consumers with the same value receive the same quality in every segment, though prices differ. Under mild conditions, any segmentation harms consumers if and only if demand is more elastic than a cost-determined threshold. Hence, potential benefits for consumers depend critically on cost and demand elasticities. These findings have implications for regulatory policy regarding price discrimination and market segmentation.

This paper considers confidence intervals (CIs) for the autoregressive (AR) parameter in an AR model with an AR parameter that may be close or equal to one. Existing CIs rely on the assumption of a stationary or fixed initial condition to obtain correct asymptotic coverage and good finite sample coverage. When this assumption fails, their coverage can be quite poor. In this paper, we introduce a new CI for the AR parameter whose coverage probability is completely robust to the initial condition, both asymptotically and in finite samples. This CI pays only a small price in terms of its length when the initial condition is stationary or fixed. The new CI also is robust to conditional heteroskedasticity of the errors.

This paper develops a framework in which a multiproduct ecosystem competes
with multiple single-product firms in both price and innovation. The ecosystem
can use data from one product to improve the quality of its other products.
We use the framework to study three regulatory policies aimed at leveling the
playing field. Restricting the ecosystem’s cross-product data usage, or forcing it
to share data with single-product firms, benefits those firms and induces them to
innovate more. However, these policies also dampen the ecosystem’s incentive to
collect data and innovate, potentially raising prices. Consumers are better off only
when single-product firms are sufficiently good at innovating. Facilitating data
exchange between single-product firms via a data cooperative can backfire and
harm them, because it induces the ecosystem to price more aggressively. For both
the data-sharing and data-cooperative policies, there exist data-compensation
schemes such that consumers are better off compared to no regulation.

This paper provides the first nationwide U.S. evidence on the effects of electric vehicle (EV) adoption on air quality and child health. Using county-level data from 2010–2021, we link EV registrations to air pollution, birth outcomes, and emergency department visits. Endogenous adoption is addressed using two-way fixed effects and an instrumental variables strategy exploiting the rollout of federally designated Alternative Fuel Corridors. Greater EV adoption significantly lowers nitrogen dioxide and improves infant and child health, reducing very low birth weight, prematurity, and asthma-related emergency visits. The largest health gains occur in high-pollution areas and exceed $1.2–$4.0 billion annually.

Human capital is central to efforts to promote growth, convergence, and the elimination of poverty. Drawing on seminal macroeconomic frameworks by Nelson-Phelps, Lucas, and subsequent developments, alongside macro and microeconomic evidence, the chapter examines the role of human capital in driving innovation and growth, emphasizing how different types of human capital matter at different stages of development, and discussing obstacles to accumulation and evidence from policy interventions.