Women at UBER: UNDER paid.

Lord have mercy. Is there no letup?

The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers (Pdf)

Abstract

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.

Why men earn on average 7% more.

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.

It pays to drive faster and to places further away.

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.

Full study here