Managed Flexibility: Management Science and Worker Control in the On-Demand Economy

• In Event: Disrupting Algorithms: Innovating Work and Life in the Digital Economy - II

Abstract

To avoid costly employer obligations (from unemployment insurance to health benefits), firms in the “on-demand” economy classify their workers as independent contractors, not employees. This classification requires that firms respect workers’ autonomy in certain respects, such as allowing workers to make their own schedules or to reject undesirable jobs. However, this autonomy presents complex logistical challenges to on-demand firms, which operate primarily in the service sector—firms like Uber and Lyft coordinate rides, while Caviar and Postmates make deliveries of food or other goods. To mitigate the inefficiencies of worker autonomy, on-demand firms thus develop subtle techniques that aim to manipulate workers’ on-the-job decision-making. This paper examines those techniques.
Due company secrecy, however, it is extremely difficult to evaluate on-demand operations from the firm’s perspective. To supplement our understanding of worker-control techniques, this paper offers a critical review of literature from management science and micro-economics that models and simulates control strategies. Firms likely use these or very similar strategies to manage fleets of “autonomous” service providers. The paper focuses, in particular, on dynamic pricing and dynamic wages as a modulation that effects indirect influence over workers, with attention to the object of optimization (the fleet, the company, the customer) and the working subject (e.g., what constitutes a “rational” decision). The paper concludes by highlighting four targets for worker counter-strategies that can be used as leverage to demand better working conditions.

Author

• Aaron Shapiro, New York University