There’s a really interesting new research paper titled “ The Gender Earnings Gap in the Gig Economy: Evidence from More Than a Million Rideshare Drivers ” written by five economists — two who are employed by Uber Technologies (Cody Cook and (Jonathan Hall), two Stanford graduate business professors (Rebecca Diamond and Paul Oyer) and the chairman of the University of Chicago economics department (John List).
It was featured on February 6 as a Freakonomics podcast. As the title of the paper suggests, the researchers analyzed national ride-sharing data for nearly 1.9 million Uber drivers (27.3 percent female) who provided more than 740 million Uber trips between January 2015 and March 2017 in 196 US cities. Here’s part of the Freakonomics podcast:
STEPHEN DUBNER : So you write in the paper that unlike previous studies, you were able to, “completely explain the pay gap.” So can you unpack that just a bit?
REBECCA DIAMOND : Sure. Uber pays drivers based on a relatively simple, transparent formula that takes into account how long your ride is in miles, how long the ride takes, and potentially, a surge multiplier where sometimes there’s, excessively high demand.
JOHN LIST : So the fare itself is determined by an algorithm, which is gender-blind. The dispatch itself is gender-blind. And pay structure’s tied directly to output and not negotiated.
DIAMOND : That transparency and that simplicity of pay is what makes this environment so interesting for studying a gender pay gap.
HALL : Because we were able to work with such excellent, detailed data, we believe this is a first-of-its-kind study, insofar as it can actually fully explain the gender pay gap.
DUBNER : So let me just make sure I’m clear. You’re saying there’s no gender discrimination on the Uber side, on the supply side, because the algorithm is gender-blind and the price is the price. And you’re saying there’s no gender discrimination on the passenger side. So does that mean that discrimination accounts for zero percent of whatever pay gap you find or don’t find between male and female Uber drivers?
LIST : That’s correct.
So it’s a great natural experiment to test for any differences in earnings by gender for Uber drivers, in an environment completely free of any possible gender discrimination on either the supply/driver side (gender-blind pricing by Uber) or the demand/customer side, since the researchers found empirically that riders have no gender preferences for drivers.
What exactly did the researchers conclude about gender earning differences from this mountain of evidence from nearly 2 million Uber drivers and close to a billion ride-sharing trips?
Here’s the paper’s Abstract (emphasis added):
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 percent gender earnings gap among 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: a) experience on the platform (learning-by-doing), b) preferences over where/when to work, and c) 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.
It’s a pretty significant and important finding that has implications for the rest of the labor market — you can find a gender earnings gap even in the complete absence of gender discrimination that is explained by gender differences in preferences.
Here’s more on the three non-discrimination factors that help explain the 7 percent gender earnings gap for Uber drivers.
We can explain the entire gap with three factors.
First, 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 percent 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.
The residual gender earnings gap can be explained by a third variable: average driving speed. Increasing speed increases expected driver earnings in almost all Uber settings. Drivers are paid according to the distance and time they travel on trip and, in the vast majority of cases, the loss of per-minute pay when driving quickly is outweighed by the value of completing a trip quickly to start the next trip sooner and accumulate more per-mile pay (across all trips).
We show that men’s higher driving speed is due to preference as drivers appear insensitive to the incentive to drive faster. Men’s higher average speed and the productive value of speed for Uber and the drivers (and, presumably, the passengers) enlarges the pay gap in this labor market.
What about the relative importance of those three factors that explain the gender earnings gap for Uber drivers (see chart above)?
First, driving speed alone can explain nearly half of the gender pay gap (48 percent). Second, over a third of the gap can be explained by returns to experience (36 percent), 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 percent of the gender pay gap can be explained by choices over where to drive. Men’s willingness to supply more hours per week (enabling them to learn more) and to target the most profitable locations shows that women continue to pay a cost for working reduced hours each week.
And here’s part of the paper’s conclusion:
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. 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 can open gender earnings gaps. The preference differences that contribute to pay differences in professional markets for lawyers and MBAs 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.
And here’s the final part of the Freakonomics podcast:
DUBNER : So in summary, this is a labor ecosystem — Uber drivers — that would seem to remove all gender discrimination, and yet women earn 7 percent less for doing essentially the same work.
DIAMOND : I think they’re not doing the same. That’s what we’re showing, they’re doing different — they’re making different choices in the labor market. I think the main point is that they’re not doing the same. And once you control for the differences, they are paid the same.
LIST : That’s right. We’ve stripped away all of the factors that we thought were underlying determinants of the gender earnings gap, and we go to this new vibrant gig economy that promises worker flexibility and labor flexibility and equal pay for equal work.
When you analyze the mounds and mounds of data, it ends up that we have a 7 percent gender difference. Now, what’s interesting and intriguing is that after you unpack those differences, what you find is that there are perfectly reasonable explanations for what’s happening on the Uber platform.
We’re just several months away from the next Equal Pay Day on April 10, when we’ll hear lots of noise from the gender activists and feminists about a 20 percent gender pay gap and how that forces a typical woman to have to work until the second week of April to earn what her male counterpart earned in 2018.
An underlying assumption behind Equal Pay Day is that the 20 percent gender pay gap is mostly due to gender discrimination in the labor market, which can only be “fixed” by some type of corrective action via government policies.
The Uber study above provides strong statistical support to challenge that assumption by uncovering empirical evidence of gender earnings differences among Uber drivers that have nothing to do with gender discrimination and everything to do with gender differences in work preferences and experience.
And since the researchers suggest those gender differences in preferences are pervasive throughout the labor market, it’s likely that much of the overall 20 percent gender earnings gap nationally can be explained by those differences in gender preferences and not gender discrimination.
According to the Uber study, once you control for gender differences in preferences, men and women, at least Uber drivers (and by extension to the workforce at large), are paid exactly the same.
And that means Equal Pay Day for female Uber drivers occurs on December 31, and not April 10. But that doesn’t make a very good story…..
Mark J. Perry is a scholar at AEI and a professor of economics and finance at the University of Michigan’s Flint campus.