Dependence and Heterogeneity in the Platform Labour Force

The emergence of platform-based gig work has generated enormous attention from scholars (Schüßler et al. 2021), even out of proportion to its relatively small prevalence in the labour force (Collins et al. 2019). One reason is likely the early rapid growth of this type of work, especially in driving and delivery, as well as the opportunity that platform-based arrangements offer to employers to convert standard employment into what is essentially a piece rate model (Dubal 2020). To date, discussions of labor conditions in the platform economy have had a strong normative dimension. Proponents hail flexibility and the “end of employment” (Sundararajan 2016) as well as the enhanced opportunities for self-employment that matching and search algorithms and crowdsourced reputational data provide (Einav, Farronato, and Levin 2016). Critics focus on the risk shift onto workers (Ravenelle 2019; Vallas 2019), management control via technology (Rosenblat and Stark 2016), and threats to stable employment (V. B. Dubal 2017). In this paper, Juliet Schor shifts attention to key analytic dimensions of this type of work and their implications for worker experiences. More specifically, she argues that the combination of the technological structure of gig work (nearly automatic, open-access employment, algorithm-driven work process) plus workers’ ability to choose schedules and hours yields an unusually heterogeneous labor force on a range of dimensions, especially patterns of work in other jobs and portfolios of household incomes. As a result, worker experiences are also more heterogeneous than in conventional workplaces. One implication is that the nexus of management control cannot be reduced to algorithmic control, as some accounts have it, but rests in significant part on the role that market discipline plays. For workers who are highly dependent on platform earnings, the fear of job loss (Bowles 1985; Schor and Bowles 1987), is an important disciplinary device that enhances technological control. By contrast, for those workers who have other jobs, pensions, and family incomes, algorithmic control and fear of de-activation are less powerful. They are able to carve out more autonomy and satisfaction in platform work. This helps to distinguish platform-based gig labor from other forms of labor relations, and clarify its novelty.

Juliet B. Schor is an economist and sociologist at Boston College. Schor’s research focuses on work, consumption, and climate change.

Juliet B. Schor (2021). Dependence and Heterogeneity in the Platform Labor Force, Governing Work in the Digital Age.

Photo: CC Ben Wicks, Source: Unsplash