Last year, 5 economists published a research paper called “The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers.” The authors are 2 economists employed by Uber; 2 professors at Stanford University; and the chairman of the University of Chicago’s economics department.
The findings were widely discussed, including on Steven Levitt and Stephen Duber’s Freakonomics podcast (Episode 317). What’s interesting about Uber’s ridesharing data is that their compensation algorithm is believed to be entirely gender-blind.
The formula is pretty simple. It takes into account distance, time, and sometimes a surge multiplier when demand is spiking. Gender does not factor. And the same goes for the actual dispatching of rides. The software doesn’t know who is male and who is female.
What they discovered is that on average male Uber drives earn about 7% more per hour compared to females. And that 50% of this wage gap can be (apparently) explained by one variable: Men tend to drive a little faster than women. So they complete more rides per hour.
It’s also worth noting that across the US, only about 27% of Uber drivers are female (at least at the time the report was published). Women also have a higher 6-month attrition rate; 76% compared to 63% for men. In other words, more female drivers drop off the platform.
If you’re interested in this topic, you should probably have a listen to the Freakonomics podcast. They deliberate on the above in a lot more detail. You can also download a full copy of the research paper, here.