Incentivizing Autonomous Workers

Workers in an important category of jobs select tasks autonomously. We study the tradeoff between monetary bonuses and non-monetary prizes as tools for guiding their choices. An optimal incentive scheme prioritizes workers for prizes in return for taking on underserved tasks, and this prioritization increases as incentives power up. Bonuses may additionally be used when incentives are sufficiently high-powered, but the optimal bonus is often non-monotone in the strength of incentives. Our results have important implications for the design of worker reward programs on freelancing platforms such as Uber and Airbnb