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Bottom-up strategies, platform worker power and local action: Learning from ridehailing drivers

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  • Jonathan Woodside
  • Tara Vinodrai
  • Markus Moos

Abstract

In the digital gig economy, workers generally have limited power and are disadvantaged compared to platform operators, who are usually large technology firms. Workers are often independent contractors rather than employees in this emerging form of work. While beneficial to platform companies, these arrangements place considerable risk on workers. Moreover, the structure of the gig economy presents challenges to traditional labor organizing strategies. To identify strategies used by ridehailing drivers to improve their working conditions and highlight points of intervention for policy makers and labor organizers, we draw upon an analysis of interviews and videos posted by YouTube diarists working for Uber. We find that ridehailing drivers improve their working conditions through business planning, leveraging competition between platforms, building solidarity through social media, and using technology to manage the workplace. We find that drivers favor individualistic strategies and often lack the institutional support and knowledge to benefit more fully from these strategies. We argue that local governments and labor market intermediaries offer the potential to empower ridehailing drivers and reinvigorate interest in collective action through workforce development tools if they build on the strategies these gig workers already use.

Suggested Citation

  • Jonathan Woodside & Tara Vinodrai & Markus Moos, 2021. "Bottom-up strategies, platform worker power and local action: Learning from ridehailing drivers," Local Economy, London South Bank University, vol. 36(4), pages 325-343, June.
  • Handle: RePEc:sae:loceco:v:36:y:2021:i:4:p:325-343
    DOI: 10.1177/02690942211040170
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    Cited by:

    1. Kaveri Medappa, 2023. "Rethinking Mutual Aid Through the Lens of Social Reproduction: How Platform Drivers Ride Out Work and Life in Bengaluru, India," Journal of South Asian Development, , vol. 18(3), pages 383-408, December.
    2. Won, Jongho & Lee, Daeho & Lee, Junmin, 2023. "Understanding experiences of food-delivery-platform workers under algorithmic management using topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

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