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Mapping Inequalities Across the On-Demand Economy

Narratives around the on-demand economy have often elided key differences in how the same policies, technologies, and practices can have contrasting effects on different workers’ experiences on the ground, both within and across sectors. As on-demand business models bring practices like algorithmic management and on-demand scheduling into new areas of work, these same practices may both empower and adversely affect workers in unanticipated ways.

This project will identify questions and surface areas of inquiry in the field such as:

  • How do features of gig economy labor platforms affect existing social and economic inequalities within worker populations?
  • What is the role of workers’ social positions in their experiences of work in the gig economy?
  • How do features such as unpredictable work scheduling, rating systems, and indirect management affect the ways that workers strategize everyday life decisions and plan for the future?
  • How does on-demand work affect stability, emotional health, and well-being of communities?
  • What are the social support systems that workers rely on to sustain and carry out their work?

Through multi-sited ethnographic research including interviews with workers, clients, and industry actors, this project will map social stratification within these new forms of work, and will serve to build a foundation for engagement with a wide variety of stakeholders in future research and to project viable pathways of policy intervention.

The fieldwork for this project targets what is at stake for low-wage workers across two segments of the on-demand economy:

  • Care and cleaning industry. Led by postdoctoral scholar Julia Ticona and research analyst Alexandra Mateescu.
  • Driver services (Uber, Lyft). Led by researcher Alex Rosenblat.

Our primary goal is to contribute a more refined understanding of the ways that on-demand work shapes workers’ lives, by producing a detailed map of the spectrum of workers, as well as the business models affecting them.

Output:

This project is funded by the Robert Wood Johnson Foundation and the W.K Kellogg Foundation.

Julia Ticona
@JuliaTicona1  
Alexandra Mateescu
@cariatidaa  
Alex Rosenblat
@mawnikr  

For more information about this initiative, email info@datasociety.net.