When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity

We analyze a large, detailed operational data set from a restaurant chain to shed new light on how workload (defined as the hourly average number of diners assigned to a server) affects servers’ performance (measured as hourly sales). We use an exogenous shock implementation of a labor scheduling software to disentangle the endogeneity between demand and supply in this setting. We find that when the overall workload is low, an increase in workload leads to higher server performance. However, there is a saturation point after which any further increase in the workload leads to a decline in performance. In the focal restaurant chain we find that this saturation point is generally not reached and, counter-intuitively, the chain can reduce the staffing level and achieve both significantly higher sales (an estimated 35% increase) and lower labor costs (an estimated 20% decrease).