
The gig-work model Uber's unit economics depend on.
Kessler, a technology journalist, reported on the gig economy by following individual workers over an extended period rather than by analysing platform data.
The subjects include an Uber driver, a Manhattan-based technology worker using freelance platforms, a Kansas City resident attempting to earn through online task work, a woman doing care work through a platform, and an employee of a company that attempted to convert its contractors to employees. Following the same people over time is what distinguishes the reporting: it captures how earnings, algorithm changes, platform policy shifts and personal circumstances interact.
The findings complicate the standard accounts on both sides. The flexibility is genuine and several subjects valued it. The earnings are lower than the platforms advertise once vehicle costs, unpaid waiting time, self-employment taxes and the absence of benefits are accounted for. Algorithmic management directs work and imposes discipline without any manager, and workers cannot appeal decisions to anyone. Platform policy changes alter earnings unilaterally and without notice. The absence of employment status removes unemployment insurance, workers' compensation, sick pay and the ability to organise.
Kessler also covers the technology industry's framing of this work as entrepreneurship and the political debate over classification, including the legislative and litigation fights.
The book was published in 2018 and precedes the pandemic's expansion of delivery work, the California Proposition 22 result and the subsequent regulatory developments in Europe and elsewhere. The method, following individuals over time rather than surveying at a point, produces findings about volatility and about algorithmic control that cross-sectional research does not capture.
Operators building marketplace and platform businesses who need to see how algorithmic management and policy changes are experienced by the workers on them.
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