Labour Control in Delivery and Care Platforms: A Comparative Study from Chile

Research on digital platforms has been strongly shaped by studies of delivery work, where labour control is commonly associated with algorithmic management, that is, automated real-time surveillance and coordination. Much less attention has been paid to how labour control operates in platform-mediated care work and to the differences between platform sectors. This paper argues that comparing delivery and care platforms helps reveal both the diversity of labour control arrangements within platform capitalism and the limits of approaches centred exclusively on algorithmic management.

Understanding how platforms control work is not only a theoretical issue, but also a central policy concern. The degree to which platforms determine workers' labour processes has been crucial in legal and political debates over employment classification, labour protections, and platform responsibility for occupational risks. However, when platform control is interpreted mainly through the experience of delivery platforms, more diffuse and less visible forms of control operating in other sectors — and generating different kinds of vulnerabilities for workers — risk remaining unrecognised. 

The paper compares food delivery and paid care platforms in Chile, focusing on Uber Eats and Babysits. The analysis draws on 40 interviews with platform workers conducted within the Data4Workers project, complemented by platform data obtained through data access requests, and publicly available information from the platforms' websites. Chile offers a particularly relevant setting for this comparison due to the rapid expansion of platform labour in a context marked by informality, traditionally hierarchical labour relations, and comparatively weak labour protections.

The comparison shows important differences in how platforms organise, monitor, and discipline labour. In delivery platforms, algorithmic management is more intense and extensive: control is closely tied to the real-time coordination of workflow through geolocation, task allocation, incentives, and temporal pressure. Workers describe an environment characterised by constant connectivity, opaque metrics, and strong pressure to remain continuously available. In care platforms, by contrast, algorithmic control is more limited and less centred on the minute-by-minute direction of tasks. Instead, control focuses more strongly on governing access to work, visibility, reputation, and trust. Profiles, ratings, and verification systems become central mechanisms through which workers compete for opportunities and adapt to clients' expectations.

We argue, however, that these differences do not mean that care platform workers enjoy greater autonomy, but rather that platform control cannot be reduced to the direct action of algorithms alone. To understand these dynamics, the paper draws on Maffie's concept of “laundering control”, which refers to the way platforms conceal or diffuse managerial authority by distributing control functions to customers and other actors. The comparison suggests that laundering control is central to the labour process in both sectors, although it becomes particularly important in care platforms. Since labour takes place largely beyond the platforms' direct observational capacity, the platform substitutes direct monitoring with the delegation of disciplinary power to the client as a “proxy employer”. Under these conditions, care platforms rely more heavily on one-way reputational systems, client evaluations, and self-management to coordinate labour while simultaneously blurring the boundaries of the employment relationship.