Uber’s Shift-y Work
Uber promotes the idea that drivers enjoy total freedom from working flexible schedules. Their advertisements boast that “freedom pays weekly” and you can “be your own boss” and “choose your hours.”
But, Uber’s heat maps, real-time and predictive messages, and hourly guarantees all indicate that drivers should work at particular places at particular times. Surge pricing communicates this message through nudges and messaging, but hourly guarantees are more explicit tools for labor logistics management. Select drivers are invited to opt-in to guaranteed hourly fares if they meet stricter working conditions.
Example 1: the guarantee requires drivers to work specific shifts, from 6am-9am, and 4pm-7pm; to accept 90% of ride requests; complete 1 trip her hour; and be online 50 minutes of every hour. Although it is not stated in this texted message alert, hourly guarantees usually require drivers to receive high ratings on all trips. That information isn’t always included in shorter messages, like texts.
Example 2: Uber offered what is effectively shift work to drivers in Austin if they work 6am-10am and 4pm-8pm Monday to Thursday, and 7am-12pm and 6pm-3am, and meet a series of conditions, which includes maintaining a rating of 4.7/5 stars during the guaranteed period.
If drivers have to be online and available for work for 50 minutes of every hour, and they have to accept nearly all ride requests, they are effectively prohibited from working for multiple other services, like Lyft, but they’re not explicitly instructed to avoid it.
When Uber sets low rates for routine work, incentive-based pay steers drivers into working under much stricter and less flexible conditions.*
For example, Uber unilaterally implemented rate cuts that affected 100 US and Canadian cities after New Year’s 2016. Some drivers report that with rate cuts, it’s no longer worthwhile for them to drive unless there is an added incentive, such as surge pricing.
Thus, Uber leverages control over drivers’ schedules while simultaneously sustaining the idea that drivers enjoy total freedom from working flexible schedules.
The regular occurrence of surge pricing along with heat maps of passenger activity and affective messaging all work as behavioral engagement tools that impact how drivers schedule their work, and their effects are amplified when low base rates result in unreliable income, undercutting the “freedom” that drivers have to login and log-out at-will.
Surge pricing is displayed to drivers through a type of heat map visualization, where the algorithmic assessment of supply and demand will temporarily raise fares for a particular geographic location.
Visible to both riders and drivers, the creation of such surge pricing zones is billed by Uber as a means to ensure positive customer experience by enticing new supply to an area of high demand. Surge pricing, however, is unreliable: notably, pricing is based on what a passenger sees on screen in their location, not a driver’s position. Drivers travel to surge pricing zones in search of fares advertised at a given rate, but they can and do receive ride requests from passengers in other, adjacent areas. A driver may enter a zone that is surging at 3.5x, but receive ride requests at a lower surge rate, such as 1.5 based on the passenger’s (not the driver’s) location.
In forums and in interviews, some drivers describe this as a type of wage theft: they are advertised one rate of pay through heat maps, but given another.
Others offer the company rhetoric, which is that surge pricing is subject to dynamic change and that the rate they see for their area may not reflect the rate at which passengers request them. Some drivers report that passengers are gaming the system by placing their pick-up location pin outside a surge zone, and then calling drivers to redirect them to their actual pick-up location. Drivers also noted that they would sometimes converge en masse at a surging area, and find that supply was no longer too low — the surge would disappear. Some drivers reported experimenting with trying to game these algorithms, and many developed responses to surge pricing based on their experience with its duration, reliability, and potential reward in their respective locations.
As various drivers become familiar with the features and functions of the app, they have begun to advise each other and to ask about surge; “don’t chase the surge,” is offered in online Uber driver forums as guiding advice to new drivers.
Uber’s heat maps and messages indicate that drivers will make more money if they drive at a particular time or in a particular place: drivers must weigh the costs/benefits of this in relation to how much more competition they anticipate and how much they trust Uber’s incentives. Many drivers expressed frustration, and enthusiasm alike for surge pricing because its very dynamism is characteristically fickle and opaque, a finding supported by researchers Min Kyung Lee and her colleagues at Carnegie Mellon.
This frustration stems partly from the implicit, technophilic promise of accuracy and fairness, but these notions are blurred by the idea that the governing responsibility for pay resides with algorithms, which acts as a disclaimer against company responsibility for shifting pay rates.
Through an appeal to the concept of algorithms, Uber can generate and co-ordinate clusters of labor in response to dynamic market conditions without explaining the reliability of its cluster incentives or guaranteeing the validity, accuracy, or error rates of its labor deployments.
This rhetorical appeal to algorithmic certainty also appears in the affective messaging that Uber sends to its drivers at key moments — such as when they’re about to log off. Rather than an appeal from Uber’s position as employer — we’d like for you to keep working — these messages cite the (presumably algorithmically derived) idea that demand is high in that driver’s location at that exact time.
All told, surge pricing and guaranteed hourly fares with stricter working conditions direct drivers away from the idea of total freedom and flexibility, and ushers them into working when a) surge is predicted to happen, b) when surge is happening, and c) when hourly rate guarantees produce specific shifts for drivers to work in.
I find the gamic elements of labor logistics fascinating.
Update (Feb. 12, 2016): Uber limits drivers in NYC to 12-hour shifts to prevent driver fatigue (although drivers could just switch to other apps, like Lyft or Gett…).
*Part of this blog is excerpted from our research draft, co-authored w/ Luke Stark (NYU): http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2686227