We quantify the real implications of trade-offs between firm information disclosure and long-term investment efficiency. We estimate a dynamic equilibrium model in which firm managers confront realistic incentives to misreport earnings and distort their real investment choices. The model implies...

We study how organizational boundaries affect pricing decisions using comprehensive data from a large U.S. airline. We document that the firm's advanced pricing algorithm, utilizing inputs from different organizational teams, is subject to multiple biases. To quantify the impacts of these...

We find that a firm's tendency to engage in financial misconduct increases with the misconduct rates of neighboring firms. This appears to be caused by peer effects, rather than exogenous shocks like regional variation in enforcement. Effects are stronger among firms of comparable size, and...

I examine the effects of occupational licensing on the quality of Certified Public Accountants (CPAs). I exploit the staggered adoption of the 150-hour rule, which increases the educational requirements for a CPA license. The analysis shows that the rule decreases the number of entrants into the...

We argue that earnings management and fraudulent accounting have important economic consequences. In a model where the costs of earnings management are endogenous, we show that in equilibrium, bad managers hire and invest too much in order to pool with the good managers. This behavior distorts...

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984{2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the...

The last 40 years have seen huge innovations in computing technology and data availability. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can...

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised...

A key challenge for research on many questions in the social sciences is that it is difficult to link historical records in a way that allows investigators to observe people at different points in their life or across generations. In this paper, we develop a new approach that relies on millions...