Using data on over one million Uber drivers and millions of trips, Cody Cook, Rebecca Diamond, Jonathan Hall, John A. List, and Paul Oyer show that female Uber drivers earn 7% less than male drivers. What makes this paper new, however, is that UBER’s extensive data lets the authors understand in great detail why the pay gap exists. It’s not discrimination:
Uber uses a gender-blind algorithm and drivers earn according to a transparent formula based on the time and distance of trips. There are no negotiated pay rates or convex returns to long hours worked, factors that have been shown to open a gender earnings gap in other settings. Our research also finds that both average rider ratings of drivers and cancellation rates are roughly equivalent between genders and we find no evidence that outright discrimination, either by the app or by riders, is driving the gender earnings gap.
The authors find that three factors explain the gap; driving speed, experience, and choices about where to drive.
First, driving speed alone can explain nearly half of the gender pay gap. Second, over a third of the gap
can be explained by returns to experience, a factor which is often almost impossible to evaluate
in other contexts that lack high frequency data on pay, labor supply, and output. The remaining
∼20% of the gender pay gap can be explained by choices over where to drive.
Male Uber drivers, like other males, drive a bit faster than female drivers, about 2.2% faster after controlling for experience and location. Since Uber pays by time as well as by distance the returns to speed are not very high and the difference in speed is small but overall this results in an increase in pay for males of about 50 cents an hour.
Drivers learn by doing and more men than women have driven for Uber for years:
A driver with more than 2,500 lifetime trips completed earns 14% more per hour than a driver who
has completed fewer than 100 trips in her time on the platform, in part because she learn where
to drive, when to drive, and how to strategically cancel and accept trips. Male drivers accumulate
more experience than women by driving more each week and being less likely to stop driving with
Overall, female and male Uber drivers behave remarkably similarly but small differences aggregated over large samples produce a small but systematic gender gap in wages of about 7%. The gap, however, is an artifact, a social construct that has no implications for “social justice,” drivers are treated equally.
The author’s conclude:
Overall, our results suggest that, even in the gender-blind, transactional, flexible environment
of the gig economy, gender-based preferences (especially the value of time not spent at paid work
and, for drivers, preferences for driving speed) can open gender earnings gaps. The preference
differences that contribute to pay differences in professional markets for lawyers and MBA’s also
lead to earnings gaps for drivers on Uber, suggesting they are pervasive across the skill distribution
and whether in the traditional or gig workplace.