Jinci Liu

Managers give feedback, but does how they say it affect worker productivity beyond what they say? Using GitHub and LinkedIn data, I analyze over 200 million code-review messages and use large language models to distinguish feedback tone (toxicity and positivity) from informational content (constructiveness). Exploiting quasi-random reviewer assignment, I find that toxic feedback reduces workers' subsequent code output by 43% and lowers its quality, whereas nontoxic criticism has no such detrimental effects. Positive feedback improves code quality and retention, with benefits spilling over to coworkers. Constructive feedback instead reallocates effort from new code production to revision. Feedback style varies persistently across reviewers, and these differences predict 22% of the variation in reviewer value-added. These differences may partly reflect prior experience: reviewers previously exposed to toxic feedback are more likely to provide it themselves.

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.