Implications of Worker Classification in On-Demand Economy

Abstract

How should workers in the on-demand economy be classified? As contractors, employees, or somewhere in between? We study this policy question focusing primarily on the welfare of long-term (LT) workers, who have worked as much as full-time employees but have been treated as contractors. We develop a game-theoretic queueing model with a service platform and two types of workers: LT workers who commit to gig jobs ex ante according to the long-run earning rate, and ad hoc (AH) workers who participate ex post based on real-time payoffs for doing gigs. We identify two issues associated with uniform classifications: when all workers previously treated as contractors are reclassified as employees according to rulings such as the 2019 Assembly Bill No. 5 (AB5) in California, the profit-maximizing company may undercut (i.e., underpay or underhire) workers and thus LT workers' average welfare can decrease; when all are reclassified as "contractors+," a UK practice and an intermediate status between contractor and employee that provides incomplete employee benefits but allows workers to self-join, workers can overjoin such that LT workers' utilization rate will remain low and their average welfare will not be effectively enhanced. In light of these issues, we consider a discriminatory scheme that classifies only LT workers as employees while leaving AH workers as contractors. This hybrid mode still suffers from undercutting but curbs overjoining. In addition, it can do less harm to consumers and the platform operator than uniform classifications. As a companion, we also study a discriminatory dispatch policy that prioritizes LT workers over AH workers. We demonstrate the potential of this operational approach to simultaneously counteract both undercutting and overjoining. Finally, we empirically calibrate the model and apply our insights to the ride-hailing market in California, where AB5 attempted to reclassify gig workers as employees.

Keywords: On-Demand Economy, Worker Classification, Queueing Games