Unraveling the Implications of Silent Labor Time in the Gig Economy (Revision 1 )

Problem definition: The gig economy is driven by labor flexibility for companies and self-scheduling for workers. However, it introduces additional discretion for workers, who must search for tasks during on-the-job inter-task time. This uncompensated task-seeking interval, “Silent Labor Time (SLT),” necessitates balancing effort between searching and execution, affecting performance.The authors study how effort allocated during SLT influences workers’ performance and earnings, identify moderators, and explain what drives effort allocation decisions. Methodology/results: The authors collaborate with a food delivery platform, where the distance drivers travel to find the next order (“relocation distance”) represents the effort allocated during SLT. Using instrumental variable regression, they find that each additional kilometer of relocation reduces order allocation by 5.4%, average on-task speed by 2.7%, and earnings by 14.8% in the hour after relocation. These declines align with the conservation of resources theory, which posits that SLT search depletes resources that would otherwise be available for execution. Relocations not towards familiar clusters and those that increase the supply-demand imbalance are most detrimental to performance. Although relocation is effort-intensive and hurts both drivers and platforms, affinity to an area motivates drivers to relocate to preferred locations, trading relocation costs for the perceived benefits, such as a sense of belonging. To assess affinity’s impact, we employ two steps: (i) testing whether relocation depends on the current location’s affinity using a fixed effects linear regression; and (ii) estimating a choice model to evaluate how affinity shapes the destination cluster. Managerial implications: These findings offer actionable insights for drivers and platforms to optimize SLT. Informed allocation of effort during SLT, for example, relocating toward familiar clusters when local demand exceeds supply, increases earnings. For platforms, understanding and leveraging drivers’ cluster affinity and heterogeneous amenability to relocation enables targeted guidance that rebalances supply across space and time, enhancing driver satisfaction and efficiency.