Publications

Drawing on the Significance Quest Theory (Kruglanski et al., 2022), we used the Honor Dictionary (Gelfand et al., 2015) in a word frequency textual analysis (Pennebaker et al., 2007) to investigate extreme rhetoric. We thus conducted two studies…

It remains a continuing challenge for companies worldwide to reliably assess the greenhouse gas emissions incurred in connection with their operations. Here we argue that financial accounting offers an architectural template for corporate carbon…

While companies today are less exposed to defined employee benefits than they were in the past, the lessons from historical restructuring transactions in these areas can and have been applied to other types of nondebt liability claims. These…

An agent acquires information dynamically until her belief about a binary state reaches an upper or lower threshold. She can choose any signal process subject to a constraint on the rate of entropy reduction. Strategies are ordered by “time risk…

Banks’ lending technology hinges on their handling of soft and hard information in dealing with different types of credit demand. Through assembling a novel dataset on banks’ investment in information technologies (IT), this paper provides…

We introduce a framework for calibrating machine learning models to satisfy finite-sample statistical guarantees. Our calibration algorithms work with any model and (unknown) data-generating distribution and do not require retraining. The…

Governments worldwide have recently launched policy support programs for hydrogen, where the level of support is to be tied to the carbon intensity of the hydrogen produced. Here we analyze the impact of alternative accounting rules for assessing…

We study how international environmental agreements can take advantage of domestic time-inconsistency problems. Policymakers often prefer future policies to be sustainable, but are tempted to invest less when being in office. We find the…

Background

Telemedicine is now a sustained modality of ambulatory surgical oncology care, yet its association with workforce utilization, patient volume, and visit type at high-volume academic centers remains understudied. Characterizing…

The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are trained on massive datasets before being adjusted or fine-tuned to make predictions on smaller…

We construct an international panel data set comprising three distinct yet plausible measures of government indebtedness: the debt-to-GDP, the interest-to-GDP, and the debt-to-equity ratios. Our analysis reveals that these measures yield…

Populism has been on the rise, posing a threat to liberal democracy. Existing evidence suggests that globalization, economic shocks, and concerns over cultural change increase support for populist agendas. Yet less is known about interventions…

Starting in February 2021, surging Berkshire Hathaway-A volume mystified market watchers. Averaging 375 shares for a decade, daily volume rose to over 20,000 shares, then plummeted nine-fold in June 2024. We demonstrate that the increased volume…

We show how to measure the welfare effects arising from increased data availability. When lenders have more data on prospective borrower costs, they can charge prices that are more aligned with these costs. This increases total social welfare and…

Objective

For many products, generic markets are very mature, with aggressive competition driving down the price of products for manufacturers. Under these conditions, manufacturers may not have an ability to invest in high-quality…

Economists have studied the impact of numerous state laws, from welfare rules to voting ID requirements. Yet for all this policy evaluation, what do we know about policy diffusion—how these policies are introduced and spread from state to state?…

As LLMs become the default interface for search, news, and everyday problem-solving, they may filter and frame political information before citizens ever confront it. Identifying and mitigating partisan “bias”—output with a systematic slant…

This paper provides early evidence on the integration and impact of Generative Artificial Intelligence (AI) in accounting at the accountant and task levels. Using a multi-method approach, we first identify heterogeneous adoption patterns,…

We develop a rational, non-behavioral framework in which analysts use empirical Bayesian techniques to construct their forecasts. Our approach assumes that analysts construct a single empirical prior by aggregating information across all of the…

We investigate the ability of Vision Language Models (VLMs) to perform visual perspective taking using a novel set of visual tasks inspired by established human tests. Our approach leverages carefully controlled scenes, in which a single humanoid…