Hospitals worldwide are increasingly adopting patient-facing AI technologies to improve operational efficiency and patient experience. Yet, rigorous causal evidence on their real-world effectiveness remains limited. We conducted a large-scale randomized field experiment with a major public hospital system in China to evaluate an AI navigation system designed to guide patients through complex outpatient ...

We examine firms’ use of AI to help generate the language in their corporate disclosures. Employing state-of-the-art Large Language Models (LLMs) to detect and quantify AI-generated content in earnings conference calls during 2015-2024, we find that AI-powered disclosure is widespread and increasingly prevalent across firms and industries. AI-generated language exhibits relatively more positive sentiment and greater linguistic sophistication, yet it does not appear to help managers strategically mislead investors. Indeed, managers who have stronger incentives to opportunistically distort market perceptions tend to rely less on AI-generated text. We also find that AI-powered disclosure is associated with larger stock market responses to earnings releases, tighter bid-ask spreads, and smaller analyst forecast errors. These informational benefits are greater when the CEO has a personal linguistic style characterized by less clarity or more tonal inconsistency.

In this talk, Dr. Yang will present the latest findings from her lab that examine the impact of conflicting information on risk communication behaviors, as well as downstream behavioral outcomes such as policy support and mitigation action. In particular, she will present new data that examine whether priming the audience about the iterative nature of scientific process will serve as a guardrail of the detrimental effect of conflicting information.

We revisit the international spillover effects of US monetary policy using a new nighttime light (NTL) big data as a high-frequency, granular proxy for real economic activity. By merging this data with firm-level and transaction-level datasets on land auction and bond issuance by Chinese firms, we find that an unexpected U.S. monetary tightening significantly reduces its output via a novel construction ...

This presentation traces the origin, historical evolution, and current status of research on optimal distinctiveness. Building on this foundation, it highlights two frontier studies that advance theoretical understanding and refine measurement in this domain.

Mutual fund flows generate substantial price impact, amplify fragility, and trigger fire sales. We document a stark contrast for separate accounts—the dominant institutional investment vehicle: their flows generate essentially no price impact, fragility, or fire-sale risk. This pattern holds within mutual fund–separate account twins managed under identical strategies and is not driven by liquidity ...

Social science researchers are increasingly leveraging large-scale video data and computational methods to examine key concepts and questions. Yet much existing work relies on a narrow set of video features and focuses mainly on text or static visuals, leaving other high-dimensional features and modalities underexamined. This talk addresses the gap by presenting two empirical studies that use large ...

Analyzing millions of patents granted by the USPTO between 1976 and 2020, we find a pattern where specific patents only rise to prominence after considerable time has passed. Amongst these late-blooming influential patents, we show that there are key players (patent hunters) who consistently identify and develop them. Although initially overlooked, these late-blooming patents have significantly more ...

Can AI effectively perform complex econometric analysis traditionally requiring human expertise?  This paper evaluates AI agents' capability to master econometrics, focusing on empirical analysis performance. We develop ``MetricsAI'', an Econometrics AI Agent built on the open-source MetaGPT framework. This agent exhibits outstanding performance in: (1) planning econometric tasks strategically, (2)...

We study dynamic contests in which players vie for a prize through a sequence of battles. We examine how contest rules—mappings from outcome histories to the winner—shape dynamic incentives and govern rent dissipation. A strong discouragement effect arises in common formats such as tug-of-war and best-of-K, preventing full rent dissipation. We trace this effect to a structural property of these rules,...