Date of Graduation
Project/Capstone - Global access
Master of Arts in Urban and Public Affairs
College of Arts and Sciences
Leo T. McCarthy Center for Public Service and the Common Good
This research analyzes the ongoing effort by Uber’s executives to prevent the reclassification of the company’s drivers from independent contractors to employees. Through rhetorical appeals made to customers, regulatory bodies, and drivers themselves, Uber’s executives are attempting to cultivate a corporate identity that portrays the company’s labor practices in a way that adheres to California’s labor laws, namely the “ABC” test for worker classification codified in Assembly Bill 5, while maintaining the company’s ill-gotten reputation as a bastion of Silicon Valley innovation. The success of this posturing hinges on attempts to conflate Uber’s labor practices with equitable social outcomes, publicize narratives that overemphasize and mischaracterize the benefits of flexible work schedules, and co-opt consumerist terminology in its description of drivers’ relation to the company. This piece embarks upon a critical analysis of these strategies, comparing the claims made in public-facing corporate rhetoric with the actual power dynamics that exist between the company and its drivers. If these strategies ultimately prove successful, they may provide a blueprint for future anti-reclassification campaigns waged by Uber throughout the United States. Regardless of whether AB5’s "ABC" test finds Uber’s drivers to be employees or independent contractors, the decision will be reached with incomplete knowledge of the algorithms that govern driver workflows, which are shielded from the public and regulators alike by intellectual property law. To remedy this uncertainty, I argue for the empowerment of municipal governments to regulate Uber’s operations within their jurisdictions and for regulatory oversight over algorithms that administer systems of labor.
Peterson, Benjamin, "Driving Disruption: Assessing Uber's Corporate Identity in the Battle over Driver Classification" (2020). Master's Projects and Capstones. 1102.
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