This paper studies how managers influence worker productivity through feedback. Using data from GitHub and LinkedIn, I analyze over 200 million pieces of feedback during code reviews across 1.7 million software teams. I apply large language models to classify feedback by tone (toxicity, positivity) and informational content (constructiveness). I exploit random reviewer assignments to estimate the causal effects of feedback on developer productivity and retention. Toxic feedback reduces future code quantity and quality and lowers developer retention within the firm, whereas non-toxic criticism has no such detrimental effects. Positive feedback increases productivity and retention and generates spillovers to coworkers. Constructive feedback does not affect future code quantity, though it lowers quality because revisions to reviewed code crowd out time spent on new code development. Finally, I show that feedback explains a sizable share of variation in manager quality, measured by value added to worker productivity. Overall, this paper shows that feedback tone and information shape worker productivity and retention, offering new insights into effective management.

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.