We measure the behavior of 1,114 CEOs in six countries parsing granular CEO diary data through an unsupervised machine learning algorithm. The algorithm uncovers two distinct behavioral types: “leaders” and “managers”. Leaders focus on multi-function, high-level meetings, while managers...

Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers...

The pandemic catalyzed an enduring shift to remote work. To measure and characterize this shift, we examine more than 250 million job vacancy postings across five English-speaking countries. Our measurements rely on a state-of-the-art language-processing framework that we fit, test, and refine...

We exploit a unique combination of administrative sources and survey data to study the match between firms and managers. The data includes manager characteristics, such as risk aversion and talent; firm characteristics, such as ownership; detailed measures of managerial practices relative to...

We design a field experiment to study how the allocation of authority between frontline procurement officers and their monitors affects performance both directly and through the response to incentives. In collaboration with the government of Punjab, Pakistan, we shift authority from monitors to...

We present evidence on the labor supply of CEOs, and on whether family and professional CEOs differ on this dimension. We do so through a new survey instrument that allows us to codify CEOs' diaries in a detailed and comparable fashion, and to build a bottom-up measure of CEO labor supply. The...