The growth of the “gig” economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7% gender earnings gap amongst drivers. The uniqueness of our data–knowing exactly the production and compensation functions–permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors: experience on the platform (learning-by-doing), preferences over where/when to work, and preferences for driving speed. This suggests that, as the gig economy grows and brings more flexibility in employment, women’s relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination.

This is the abstract of Cody Cook, Rebecca Diamond, Jonathan Hall, John A. List, and Paul Oyer, “The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers,” January 2018.

Cook and Hall are at Uber Technologies, Inc. Diamond and Oyer are at Stanford University’s Graduate School of Business and NBER. List is at the University of Chicago, NBER, and Uber Technologies, Inc.

Here’s what they did and why the data were so good for seeing if there is a systematic pay gap in the absence of discrimination:

In this paper, we make use of a sample of over a million drivers to quantify the determinants of the gender earnings gap in one of the largest gig economy platforms: Uber’s platform for connecting riders and drivers. Uber set its driver fares and fees through a simple, publicly available formula, which is invariant between drivers. Further, similar to many parts of the larger gig economy, on Uber there is no negotiation of earnings, earnings are not directly tied to tenure or hours worked per week, and we can demonstrate that customer-side discrimination is not materially important. These job attributes explicitly rule out the possibility of a “job-flexibility penalty.” We use granular data on drivers and their behaviors in a given hour of the week to precisely measure driver productivity and returns to experience.

Here’s the gap and here’s why it exists:

We find that men earn roughly 7% more per hour than women on average, which is in line with prior estimates of gender earnings gaps within specifically defined jobs (Bayard et al. (2003), Barth et al. (2017)). We can explain the entire gap with three factors. First, through the logic of compensating differentials, hourly earnings on Uber vary predictably by location and time of week, and men tend to drive in more lucrative locations. The second factor is work experience. Even in the relatively simple production of a passenger’s ride, past experience is valuable for drivers. 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 Uber. Because of these returns to experience and because the typical male Uber driver has more experience than the typical female–putting them higher on the learning curve–men earn more money per hour.

And their conclusion for the economy at large:

Gig economy work is often substantially differentiated from traditional jobs: individuals have more flexibility, are often paid according to a fixed contract, and retain greater control over their earnings. Despite these differences, we show that–much like with traditional jobs–there is a gender pay gap. However, unlike earlier studies, we are able to completely explain the pay gap with three main factors related to driver preferences and learning: returns to experience, a pay premium for faster driving, and preferences for where to drive. Indeed, the contribution of the return to experience to gender earnings gaps has not gotten much attention in previous empirical literature, as it is often quite difficult to measure in traditional work settings. We find that even tracking the number of weeks worked–a common proxy for experience in the literature–does not accurately quantify experience, as men work more hours per week than women and thus accumulate experience more quickly. These results suggest that the role of on-the-job learning may contribute to the gender earnings gap more broadly in the economy than previously thought.

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.

I came across this study in a Freakonomics interview of three of the authors: List, Diamond, and Hall.

One segment of the interview at best puzzled me and at worst disturbed me:

DUBNER : So a 7 percent gap, how does that compare to the best research in other occupations?
DIAMOND: So there’s been some previous work that has looked at within-firm gender pay gaps. And seven percent is not very different than the overall average we see across all firms, even in the traditional labor market.
LIST: Sadly so.

It’s List’s “Sadly so” response. Why is it sad? I wondered if he was being ironic or humorous and so I went to that part of the interview and listened for tone. It seemed to be real sadness.

But why? Indeed, I had the exact opposite reaction. I don’t favor discrimination against women just because they’re women. I want people to paid according to their productivity. And the data show that they are. That shouldn’t be grounds for sadness. Moreover, the authors, in the conclusion I quote above, say that maybe the wage differentials in the overall labor market have little to do with discrimination. Again, grounds for happiness, not sadness.