Freelancers or Employees? A Field Experiment with a Food Delivery Platform

Abstract

The decentralized nature of gig platforms gives rise to a new employment model with freelancers. However, recent industry debates have questioned whether the freelancer model should be the preferred model for the gig economy, and there have been regulatory attempts to restrict its use. This research aims to uncover new insights by examining the behavioral differences between freelancers and employees and their impact on gig platforms. Partnering with a food delivery platform, we conducted a field experiment by randomly assigning customer orders to freelancers or employees. Our findings reveal an efficiency-quality trade-off between the two employment models. On the one hand, freelancers exhibit longer order completion times, indicating lower system efficiency. This inefficiency stems from the decentralized nature of freelancer operations, which can lead to suboptimal order assignments and insufficient order batching. These issues can be mitigated with employees, as platforms can centrally coordinate order assignments. On the other hand, freelancers outperform employees in on-time performance. Our empirical evidence further reveals that this difference stems from freelancers' tendency to prioritize urgent orders that have a higher risk of missing the promised delivery times, even at the cost of traveling longer distances overall. In contrast, employees optimize routes to minimize travel distances, sometimes compromising service quality by neglecting customer commitments. These findings provide implications for developing tailored operational strategies to improve worker performance based on their employment type. Platforms can enhance freelancer efficiency by providing order selection recommendations, while motivating employees to prioritize customer satisfaction through additional incentives for on-time performance.

Keywords: gig economy, workforce management, food delivery platforms, worker behavior, field experiment