What Uber tells us about the gender pay gap

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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 yesterday 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% 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

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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.

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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 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.

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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% 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.

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