How does managerial feedback affect worker productivity? Using data from GitHub and LinkedIn, I analyze 230 million feedback messages from code reviews across 1.7 million software teams, and use large language models to separate feedback tone (toxicity, positivity) from information content (constructiveness). Exploiting the random assignment of reviewers, I find that toxic feedback reduces workers' subsequent code output by 43% and lowers its quality, whereas nontoxic criticism has no such detrimental effects. The harm comes from attacking the worker, not from criticizing the work. Positive feedback raises productivity and firm retention, and spills over to coworkers. Information matters on a different margin: constructive feedback raises neither output nor quality but instead shifts effort from new production to revision. Feedback also reflects a managerial trait: differences in feedback style explain 22% of the variation in manager value-added. These results show that feedback style not only affects worker productivity but also captures a measurable component of manager quality.

Does the division of labor increase team productivity? This paper provides new evidence challenging the conventional view that specialization increases productivity. I create a panel dataset from GitHub, covering 35 million task allocations across 64,400 software development teams from 2017 to 2023. My result shows a negative relationship between team specialization and various productivity metrics, including output quality, quantity, and user issue resolution time. To identify causal effects, I exploit GitHub’s introduction of an automatic task assignment feature, which evenly distributes tasks across team members. Using a matched difference-in-differences design, I find that adoption of this feature reduces specialization and leads to significant gains in productivity: output quality rises by 4%, output quantity by 21%. Team communication also increases by 13%, suggesting that improved interaction and knowledge exchange are a key mechanism behind these productivity gains. These findings highlight a trade-off in non-routine production: while specialization increases task-specific human capital, it impedes cross-task knowledge spillovers that are essential for innovation.