This paper studies how a manager’s tone when giving feedback to workers affects individual productivity and output quality. We construct a novel panel dataset that links software engineers and managers to their email communications and code contributions on the largest open source software project, the Linux kernel. We identify tones used in the emails (e.g., toxic, polite, encouraging) using natural language processing and machine learning techniques. We find a strong negative relationship between manager toxicity and engineer productivity. Using an instrumental variables design to address endogeneity in a manager’s choice of tone, we find that receiving toxic feedback from a manager reduces the likelihood that an engineer completes a programming task, increases the amount of time to task completion, and decreases the likelihood that an engineer completes more tasks in the next 30 days.
Joint with Carolyn Tsao