Works in Progress

Hemming and Hawing over Hawthorne: Work Complexity and the Divergent Effects of Monitoring on Productivity

with Aruna Ranganathan


Does monitoring workers improve or impair their productivity? Existing studies offer conflicting predictions. Using personnel and operational data from an Indian garment manufacturing plant, we examine how an RFID monitoring intervention on three of the plant's twelve production lines affected productivity. We find that the effect of monitoring varied by the complexity of the work performed. Using variation in work complexity both across and within production lines, we find that monitoring significantly increased productivity for simple work but significantly decreased productivity for complex work. We contribute to research on monitoring and productivity by demonstrating how key job characteristics that make work meaningful, such as complexity, can moderate the effect of monitoring on productivity by affecting workers' intrinsic motivation. Results also suggest that not only does the Hawthorne effect exist, but its direction can be positive, negative, or neutral depending on work complexity.

Promotions and the Peter Principle

with Danielle Li and Kelly Shue


The best worker isn't always the best candidate for manager. In these cases, do firms promote the best potential manager or the best worker in their current job? Using microdata on the performance of sales workers at 214 firms, we find evidence consistent with the Peter Principle: firms prioritize current job performance when making promotion decisions at the expense of other observable characteristics that better predict managerial quality.  We estimate that the costs of managerial mismatch are substantial, suggesting that firms make inefficient promotion decisions or the incentive benefits of emphasizing current performance is also high.

Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market

with Aaron Sojourner and Akhmed Umyarov

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In three experiments, we examine the effects of employer reputation in an online labor market (Amazon Mechanical Turk) in which employers may decline to pay workers while keeping their work product. These three experiments test the value of the employer reputation system for workers, employers, and the market. Specifically, in audit study of employers by a blinded worker, we find that working only for good employers yields 40% higher wages. Second, in an experiment that varies reputation, we find that good-reputation employers attract work of the same quality but at twice the rate as bad-reputation employers. Lastly, we exploit the natural experiment of instances when the reputation system servers are down, and find that the reputation system is serving the market by attracting work to small, good employers who appear to rely on the system to attract workers, and apparently away from the largest and best-known among good employers. This is the first clean, field evidence that employer reputation serves as a collateral against opportunism in the absence of contract enforcement in online markets.

Strength from Within: Individual and Store-Level Evidence that Transfers Outperform Hires

with Ben Rissing

Do jobs filled by transfers tend to have higher subsequent performance than those filled by hires? Using data from a major U.S. retailer with 113,749 commissioned salespeople, 1,275,127 total employees, and 2,161 establishments (stores), we evaluate the relative performance of transfers versus hires. Our evaluation improves upon prior work by offering instrumental variables, objective and subjective assessments of performance, establishment-level analyses, and much greater statistical power. We find evidence that sales performance is greater when jobs are filled by transfers rather than hires, and that stores with a higher ratio of transfers to hires tend to outperform those with low ratios. Our results are most consistent with human capital and internal labor market theories.

Incentives in Recessions

with Danielle Li and Kelly Shue

(Abstract TBA)