Workforce Scheduling with Heterogeneous Time Preferences: Effective Wages and Workers Supply

Abstract

Problem definition. Motivated by the debate around workers' welfare in the gig economy, we propose a framework to evaluate current practices and possible alternatives. We study a setting in which customers seek service from workers and a platform facilitates such matches over the course of the day. The platform allocates time slots to workers using an allocation policy, and the workers are strategic agents who maximize their expected utility that depends on their preferred times to work, the allocated slots, and the total availability time. The platform seeks to ensure that a sufficient number of workers are available to satisfy demand, whereas the workers aim to maximize their wage-driven utility.

Methodology and results. We evaluate policies on two dimensions critical to any firm: the supply of workers across the day, and the effective wages of workers. We illustrate that several families of currently deployed policies have serious limitations. We find these limitations exist because the policies do not let workers fully express their preferences and/or cannot account for heterogeneity in such preferences. We propose a new allocation policy and establish strong performance guarantees with respect to both the workers supply and effective wages. The policy is simple and fully leverages the market information to reach better market outcomes. We supplement our theory with numerical experiments in the context of ride-hailing calibrated on various New York City datasets that illustrate performance across a range of markets.

Managerial implications. The paper highlights a fundamental inefficiency of policies currently deployed that limit workers' ability to express their preferences. By allowing workers to express their temporal preferences, and using the priority-based allocation principles we devise to accommodate heterogeneity in such preferences, it is possible to obtain a potentially significant Pareto improvement, maintaining (or even increasing) workers supply while also increasing workers' effective wages.

Keywords: on-demand transportation, scheduling, driver supply, gig-economy wages