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 full-time workers, who have worked as much as employees but have been treated as contractors. We develop a game-theoretic queueing model with a service platform and two types of workers: full-timers who may choose gig jobs as primary income sources and commit to a high availability for the platform, and part-timers who do gigs for supplemental incomes and have only limited availability. We show that in the status quo of contractor mode, a company would efficiently differentiate workers’ earnings in peak and off-peak periods and make full-timers commit upfront (temporal incentive pooling). While part-timers serve as a useful capacity recourse on the spot, to incentivize their participation the company may trade off the efficiency brought by the temporal incentive pooling, yet this in turn can create full-timers a positive surplus. As such, when all gig workers are reclassified as employees (according to, e.g., the California Assembly Bill No. 5) and part-timers exit the market, full-timers can be undercut (underpaid or underhired) by the profit-maximizing company and end up with a lower welfare. When all are reclassified as “contractors+,” a UK practice that provides incomplete employee benefits but allows workers to self-join, workers may overjoin such that full-timers' utilization rate can remain low and their welfare not effectively enhanced. In light of these issues, we consider a differentiated scheme that classifies only full-timers as employees. This hybrid mode still suffers from undercutting but curbs overjoining; it may also do less harm to consumers and the platform operator than uniform classifications. We also study a differentiated dispatch policy that prioritizes full-timers over part-timers. We demonstrate the potential of this operational approach to counteract both undercutting and overjoining. Finally, we calibrate the model and apply our insights to the ride-hailing market in California.

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