"Skill-biased remote work and incentives" with Fabio Cerina and Luca Deidda.
Abstract:
We document that performance pay has become an increasingly common element of the compensation schemes, alongside remote work, especially in high-skill occupations. To explain these findings, we develop a firm-worker model with moral hazard to highlight how skill levels interact with organizational practices. Low-skilled workers face greater uncertainty due to the low chance of high performance. If risk-averse, they require large monetary premiums so firms adopt performance pay only if the worker is sufficiently skilled, and fixed pay with monitoring otherwise.
The unforeseen pandemic shock, which forced firms to implement remote work, reduces monitoring effectiveness thereby increasing reliance on performance pay. Post-pandemic, firms persist with remote work when workers are sufficiently skilled for performance pay to be cost-effective, while maintaining remote work only if remote monitoring is effective enough.
Our model predicts that reduced efficacy in remote monitoring disproportionately limits remote work opportunities for less-skilled workers. To empirically test this prediction, we exploit temporal variation in remote-monitoring legislation in New York State using a Difference-in-Differences analysis. The empirical results strongly support the model's predictions. Our findings suggest that pandemic policies and regulations have significantly influenced organizations' decisions regarding incentive structures and work arrangements.