Ride-Hail Drivers, Taxi Drivers and Multiple Jobholders: Who Takes the Most Risks and Why?

Alexandra D Lefcoe, Catherine E Connelly and Ian R Gellatly
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Alexandra D Lefcoe: McMaster University, Canada
Catherine E Connelly: McMaster University, Canada
Ian R Gellatly: University of Alberta, Canada

Work, Employment & Society, 2024, vol. 38, issue 5, 1307-1332

Abstract: Little is known about how the use of ride-hail apps (e.g. Uber, Lyft) affects drivers’ propensity to engage in risky behaviours. Drawing on labour process theory, this study examines how algorithmic control of ride-hail drivers encourages risky driving (i.e. violating road safety rules, carrying weapons). Furthermore, the theory of work precarity is used to explain why multiple jobholders (MJHers), who work for ride-hail companies, drive taxis and hold other jobs, may be more likely to take risks while driving due to income insecurity and erratic work hours. The hypotheses are tested in a sample ( N = 191) of ride-hail drivers, taxi drivers and MJHers. The results suggest that MJHers are more likely to engage in risky driving in comparison to ride-hail and taxi drivers. Theoretical, practical and policy implications are discussed.

Keywords: algorithmic management; gig workers; multiple jobholding; precarity; risk; safety (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:woemps:v:38:y:2024:i:5:p:1307-1332

DOI: 10.1177/09500170231185212

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