By Rebecca Diamond (Assistant Professor of Economics at Stanford University), Jonathan Hall (Chief Economist at Uber) and Cody Cook (Uber)

Though the gender pay gap in the United States has narrowed considerably over the past four decades, it has not closed entirely. By one estimate, women in the U.S. earn 88 cents on the dollar when compared to similar men in similar jobs.

One oft-cited factor driving this disparity is flexibility, or the lack thereof, in traditional work arrangements, leading many to speculate that the growth of the “gig” economy and flexible work will favor women and, perhaps, contribute to a further narrowing of the economy-wide gender pay gap through greater access to work opportunities. After several conversations, we realized Uber presented a tremendous opportunity to study this important question.

The working paper we are releasing today, “The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers,” written in collaboration with Professor John A. List of the University of Chicago* and Professor Paul Oyer of Stanford University, is the product of a multi-year, first-of-its-kind exploration into the existence of a gender earnings gap on the Uber platform and its underlying causes.

Using Uber’s rich datasets, we examined a sample of more than one million drivers in the United States between January 2015 and March 2017. We find that there is a roughly 7% gender earnings gap amongst drivers. However, our unique data permit us to completely unpack the underlying causes of the gender earnings gap, which we outline and explain in further detail below. As we state in the paper:

“Beyond measuring the gender earnings gap and unpacking it completely in an important labor market, our simple analysis provides insights into the roots of the gender earnings gap and… the share of the gap that can be explained by each factor.”

Prior to an examination of the gap’s underlying causes, it is worth considering the following. This result is somewhat surprising because 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.

So what’s driving the gap? Three factors: experience, speed and preferences for where to drive.

Returns to experience. We conclude that gender differences in experience account for approximately ⅓ of the earnings gap. As is the case with many professions, there is a learning curve to driving with Uber, which is especially steep at the outset. Drivers who have taken over 2,500 trips earn an average of $3 more per hour than those with less than 100 trips. Men, on average, accumulate more experience — and, hence, earn more — by working more hours each week and being less likely to stop driving with Uber.

Returns to speed. Roughly ½ of the earnings gap is explained by differences in driving speed. We find that, on average, men drive 2.2% faster than women, and there is a positive expected return to driving faster (though we also note returns may turn negative at excessive speeds). The difference in driving speed is not, however, a unique phenomenon to Uber. Data gathered from the National Highway Travel Survey (NHTS) indicates that a gender gap in driving speed exists in the wider population as well.

Returns to driving location. The remainder of the gap in earnings — approximately ⅙ — is explained by differences in where people choose to drive, with men, on average, driving in locations with higher surge and lower wait times.

Flexible work arrangements are substantially different from 9 to 5 jobs: individuals retain greater control over their earnings and hours and, in the case of Uber, earn according to defined rates for time and distance. Despite these differences, our research indicates that, much like traditional work settings, earnings gaps based on gender-based preferences** persist; yet unlike many previous studies, we are able to completely unpack the underlying determinants of the gap.

Understanding the causes of the gender earnings gap is only a first, albeit important, step. It is a complex issue with no quick fixes. In the immediate term, one area for further exploration — in the context of ridesharing — may include product improvements that provide drivers with more contextual information to help them move up the learning curve more quickly, regardless of how much or how long they drive.

Over the long term, additional research on the topic by academics in partnership with companies and industries can help advance the conversation in ways that bring us closer toward real and lasting solutions. Uber, for its part, hopes to contribute further to this important topic area through both a product design and research lens. We, as research collaborators, strongly encourage others to do the same.

*Professor List is a Chief Economist at Uber Technologies, Inc.

**For the purposes of this paper, we use ’preferences’ to refer to an individual’s optimal choices given his/her constraints. Naturally, men and women may face different constraints that will impact these choices.