Fractional Work: For some a choice for others a necessity. For all, a major distinction between what one needs from the work. Technologies. Several studies identified how only a fraction of participants in platform livelihood were full time. Some divide platform work and sales across multiple platforms. Others work a full-time job and use platforms to augment the income of a pull time job. Others work only part time on a platform while in school, or helping family. If one is building the optimal platform for individuals, or if one is regulating for optimal outcomes for individuals, which individuals are being designed for? What are the assumptions about how much individuals rely on that platform as the primary source of income, protection and benefits for them and their families?
Across the corpus of 75 studies, many researchers have made distinctions between full-time and part-time work. This split, whether by choice or by necessity, interacts with several of the 12 core elements, including Flexibility, Earnings, and Betweenness & Protection, as well as with the crosscutting element of youth.
Early studies on microwork and freelancing were among the first to discern and describe the split between full- and part-time engagement in platform livelihoods. Lehdonvirta (2016) describes microworkers in the Philippines as “precariots” pursuing several ways of earning a living. Across various country samples and microtasking platforms, others found 25% (Gupta et al. 2014), 32% (Rani and Furrer 2019), 32% (Berg et al. 2018) or 40% full-timers (Brawley and Pury 2016). This dynamic is not limited to microwork and freelancing. Budiman (2020) is a full-time personal driver who hopped on to Indonesia’s Gojek ride-hailing platform when available. In Zollmann and Wanjala’s (2020) study across several platform livelihood types, “almost no one in the study [was] doing only one thing.” In Kenya, as elsewhere, side hustles are everywhere, and have become increasingly digital (Partnership for Finance in a Digital Africa 2019). In China,
Part-time workers constitute a good part of the online labor force for both types of platforms—43% on the ride-hailing apps and 60% on the food-delivery apps, respectively…. Close to 70% of part-time drivers for ride services and 40% of part-time riders for food delivery worked less than 8 hours a day. (J. Y. Chen, Sun, and Qiu 2020, 17–18)
These fractions, regardless whether it’s 25% or 75% full-timers, create a distinction between a “core group” and a ”rotating periphery” (Berg 2016). Grey and Suri (2019, 103–104) call it a “Pareto distribution” in which
work currently organizes around a small percentage of people who turn project-driven tasks into full-time work. A slightly larger proportion of people consistently contribute a few hours here and there as their schedules allow. And the majority people come to the platforms to experiment and may find their way to intermittent or regular work, but they are just as likely to do one or two jobs and leave. All three approaches to ghost work contribute to the platform’s bottom line. Even opening an account and having it added to the platform’s “head count”—whether worker is active or not—generates value for the platform, as it gives the appearance the platform has a lot of labor on standby…. For our purposes, we labeled these three groups experimentalists, regulars, and always-on.
The overall distribution of part-time versus full-time pursuit of platform livelihoods likely differs across livelihood type and by country. For example, other studies have found that most Gojek drivers in Indonesia are full time (Nastiti 2017), while another study in China suggested that the majority of platform workers report a primary source of income from platform work (B. Chen et al. 2020). This depends also on how the questions are asked; there’s a difference between driving all of one’s income and most of one’s income from a given livelihood, and how individuals might describe one livelihood among many as their “primary” one (J. Y. Chen, Sun, and Qiu 2020). It’s for these reasons that Grey and Suri’s distinction between experimentalists, regulars, and always-on is a durable, cross-sectoral dynamic in platform livelihoods, and why it drives differences in individuals’ experiences.
Whether one is fully or fractionally engaged can determine how much participation and identity one brings to the work (Wood, Lehdonvirta, and Graham 2018). Whether one is fractionally or fully engaged can determine how likely it is that one has benefits from outside work or full-time contracts (J. Y. Chen, Sun, and Qiu 2020), and can determine how selective one wants to be in taking that next gig. After all, part-timers can afford to say no (Anwar and Graham 2019). As Ma and Hanrahan (2019, 247) note about drivers in in the Global North: “Drivers’ level of dependency on ride-sharing plays an important role in their experience of working on the platform.”
And of course, the distribution of who is a part-timer is not random. This fractionality of livelihoods may intersect with gender (women are more likely part-timers, as observed in local services [Garcia et al. 2020]) and with youth (it’s not surprising that many freelancers and microtask workers are young people, perhaps still in university, still working towards full-time employment [Melia 2020; Onkokame, Schoentgen, and Gillwald 2018]).
Finally, in some cases, part-time work is split between several distinct livelihoods. Someone can be a shopkeeper by day and a freelancer at night (Partnership for Finance in a Digital Africa 2019). In other cases, it’s the same activity, done where the platform supports a fraction of the livelihood. This kind of work, driving outside the app, working on one’s own on a project for former clients in freelancing, etc., has its own set of dynamics about the relative power shared between individuals and the platforms, and the trajectory of individual careers. We will cover this in a little bit more detail in the section on Contestation.
The overall implication of this section can be summed up as this: If one is building the optimal platform for individuals, or if one is regulating for optimal outcomes for individuals, which individuals are being designed for it? What are the assumptions about how much one uses the platform, and how much individuals rely on that platform as the primary source of income for them and their families?
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