Uber and Lyft run their whole model on assuming their “employees” are independent contractors, meaning that the companies get to rake in the profit without assuming most of the risks and costs of the operation. This is of course dubious and hopefully the courts will find in this way. Alex Rosenblat sums up her research on whether Uber is in fact an employer:
Last year, my colleague Luke Stark at NYU and I spent nine months studying how U.S. Uber drivers interact with the platform. We analyzed online driver forums, where tens of thousands of drivers share advice and compares notes on their experiences and challenges with the Uber system. We also conducted in-depth interviews with seven drivers to explore worker experiences of the on-demand economy.
We found that through Uber’s app design and deployment, the company produces what many reasonable observers would define as a managed labor force. Drivers have the freedom to log in or log out of work at will, but once they’re online, their activities on the platform are heavily monitored. The platform redistributes management functions to semiautomated and algorithmic systems, as well as to consumers.
Algorithmic management, however, can create a deal of ambiguity around what is expected of workers — and who is really in charge. Uber’s neutral branding as an intermediary between supply (drivers) and demand (passengers) belies the important employment structures and hierarchies that emerge through its software platform.
Uber sets the rates. Uber has full power to unilaterally set and change the fares passengers pay, the rates that drivers are paid, and the commission Uber takes. While Uber’s contract with its “partners” outlines (section 4.1) that the fare Uber sets is a “recommended” amount (drivers technically have the right to charge less, but not more, than the pre-arranged fare), there is no way for drivers to actually negotiate the fare within the Uber driver app.
Uber sets the performance targets. Uber’s three main performance metrics are the driver’s rating, how many rides the driver accepts, and how many times they cancel a ride. Generally, Uber requires drivers to maintain a high ride acceptance rate, such as 80% or 90%, and a low cancellation rate, such as 5% in San Francisco (as of July 2015), or they risk deactivation (temporary suspension or permanent firing) from the platform.
Uber’s system enforces blind acceptance of passengers, as drivers are not shown the passenger’s destination or how much they could earn on the fare. While this could deter destination-based discrimination, and Uber markets this as a feature of its system, whenever Uber drivers accept a ride, they effectively take a financial risk that the ride will only cost the “minimum fare,” an amount that varies by city. In Savannah, Georgia, for example, the minimum fare is $5 for uberX, which drivers perceive as unprofitable, because Uber takes a $1.60 booking fee (formerly a “safe rides” fee) off the top, plus their commission of at least 20% on the remaining $3.40. That leaves the driver with $2.72, not accounting for any of their expenses, such as gas.
There are also sections on how Uber acts as management and plays a major role in “suggesting” drivers’ schedules. The conclusion:
In many ways, automation can obscure the role of management, but as our research illustrates, algorithmic management cannot be conflated with worker autonomy. Uber’s model clearly raises new challenges for companies that aim to produce scalable, standardized services for consumers through the automation of worker-employer relationships.