Incentivizing Flexible Workers in the Gig Economy: The Case of Ride-Hailing
通过空间模型分析网约车平台不同佣金和定价策略对司机分布、留存及顾客需求的影响，发现灵活佣金比固定佣金更有效，且对公平敏感的顾客市场宜采用固定定价加灵活佣金。
On-demand platforms such as ride-sharing services rely heavily on economic incentives to attract, retain, and manage independent workers who have significant discretion over whether and where to work. Using an analytically tractable spatial model, we explore the impact of different pricing and commission strategies on customer demand, driver entry and retention, and their location choices. Our model yields several unique results and actionable insights. We find that flexible commission policies are more effective than fixed commission policies in allocating drivers efficiently across locations, reducing bottlenecks, and improving driver retention. We also show that commission-based interventions are more effective than price interventions in responding to labor market changes, as they directly affect driver incentives without distorting customer demand. Finally, if fairness-sensitive customers are prevalent in the market, then fixed pricing, combined with flexible commissions, becomes the optimal rule. Simulations based on actual ride patterns from New York City and Los Angeles confirm our insights.