Workload, Time Use and Efficiency

Abstract

An extensive literature exists on task assignment between workers (e.g.Bar-Isaac and Lévy (2022), Ghosh and Waldman (2010), Waldman (1990)), but there is relatively scant work on allocations "within" workers. Yet as knowledge work increases, it is crucial for workers to figure out how to allocate their time across tasks, especially when workload is high. In this paper, we study how workload affects performance and how workers adjust labor input and organize tasks in response to workload. We develop a dynamic multi-tasking model with labor-leisure and quality-quantity choices in a production environment that allows for efficiencies of scale. We find that in heterogeneous contexts, with more learning within projects than within the same step across projects, it is optimal to work sequentially, completing one project before starting the next. In homogeneous contexts, in which learning within the same step across projects is relatively stronger, it is optimal to work in batches, completing the same step across projects. Output increases with workload in both contexts, but while timeliness may decrease in heterogeneous contexts, quality and timeliness increase in homogeneous contexts because higher workload increases the efficiency of batch work. We provide empirical evidence of the theoretical predictions using detailed workload, productivity, internet and time use data of insurance claims examiners in two departments that handle heterogeneous and homogeneous claims respectively, and who face plausibly exogenous variation in workload. We show evidence consistent with examiners working in batches in the homogeneous context and sequentially in the heterogeneous context. A 1 SD increase in workload increases output by 2.8% in the heterogeneous and 9.3% in the homogeneous context, while timeliness and quality increase in the latter context only. JEL D24, J22, M11, M54