ILPC 2026

Online Labor markets, also referred to as the ‘gig economy,’ have begun to provide on-going and secure sources of income for many people who are seeking job flexibility or may otherwise face barriers to work. However, a recent study by Pew (2016), exposes the structural divisions of this type of digital labor, based along racial, gendered, educational and social-class lines (Smith, 2016).

The Pew research also highlights that more than half of those who engage in digital labor have come to rely on these platforms as an “essential or important” source of income, with one third saying that gig work is essential to meeting their basic needs. While there has been much scholarship on the future of contingent work (Ross, 2009; Standing, 2011; Sennett, 1998; Scholz, 2013) and neoliberal exploitation of skimming the surplus value of labor (Terranova, 2000; Negri, 1991; Fuchs, 2013; Harvey, 2007) there has been little investigation into the systematic structural inequality inherent in digital labor markets which relies on contingent marginalized labor. Further, there is a growing difference between high-value, higher-skilled gig work and low-value, low-skilled gig work that often limits opportunities for career progression or economic growth. How then, do these systems of dependency play out for different groups over the long-term?

As more jobs move online, disadvantaged groups are lured by the promise of flexibility and ease of access, however, these workers often become reliant on this type of work that has little social security, and limited opportunity for career development. The iLabor Project, a research group out of the University of Oxford, has recently developed an economic indicator called the Online Labor Index, which measures the number of projects and tasks across platforms in real time. Using this metric as the dependent variable tracked over time, this paper seeks to answer two guiding research questions­: first, what is the ratio of high-value jobs held by marginalized groups, and has this ratio shifted over time? And second, how do opportunities for career progression and economic advancement differ based on racial, gendered, and class lines?

The paper uses a mixed methodology of the online labor index, world labor statistics on populations of flexible workers, tracked against longitudinal content analysis of high-value and low-value job ads that use coded language to recruit candidates based on race, gender and social-class. This paper expands on current debates about precarious and contingent labor in the digital economy using a neo-Marxian, feminist and critical race theoretical framework. The study aims to identify long-term effects of gig work on marginalized groups and highlight systematic predatory labor practices that renders already vulnerable groups reliant on this highly precarious contingent type of work with little room for growth.

The paper is in the initial stages of refining the methodology and will start collecting data over the next few months.