Estimates of the pay differential between men and women vary by sector, and range from between 6.8 and 26.1 per cent. The gap is influenced by a number of factors including:
· discrimination and bias in hiring and pay decisions
· women and men working in different industries and different jobs, with female-dominated industries and jobs attracting lower wages
· women’s disproportionate share of unpaid caring and domestic work
· lack of workplace flexibility to accommodate caring and other responsibilities, especially in senior roles
· women’s greater time out of the workforce impacting career progression and opportunities.
One of the latest pieces of research on this issue draws on an analysis of nearly 2 million Uber drivers and three quarters of a billion rides.
Putting the big data aspects of this study aside, the really interesting part of this study is that Uber uses a gender-blind algorithm to set driver fares and fees. Uber’s publicly available formula is invariant between drivers. It is not directly tied to tenure or hours worked per week. Earnings are not the subject of negotiation. And customers do not have the opportunity to discriminate between drivers.
Given all this, you’d expect that hourly earnings for male and female drivers to be about the same.
Ubernomonists however, have found that female drivers earn about 7 per cent less then male drivers.
There are three main reasons for this.
The first is that male drivers, on average, drive faster than female drivers. And because they drive faster, they complete more trips per hour, which is rewarded by the algorithm. This explains about 48 per cent of the difference.
The second reason has to do with when and where drivers are driving. Earnings vary predictably during the course of the week, and some routes are more lucrative than others. Male drivers are typically driving more lucrative rides (such as airport trips) than female drivers. That’s another 16 per cent.
The final 36 per cent is the result of differences in productivity. Uber drivers learn-as-they-do. With experience, drivers learn where to drive, when to drive, and how to strategically cancel and accept trips so as to maximise their returns on the platform. A driver with more than 2,500 lifetime trips will earns 14 per cent more per hour (about $US 3) than a driver who has completed fewer than 100 trips.
Gender comes into play here because men are much more likely to have spent more time on the platform. Three quarters of new female drivers will quit the platform within six months, compared to 63 per cent of male drivers. Male drivers typically drive about 50 per cent more trips per week as well.
Indeed, when you control for driver experience, the gender earnings gap shrinks to just 1.4 per cent — showing there’s no inherent differences in productivity between the two groups.
By no means do these results excuse the presence of a gender pay differential in the broader economy, nor discrimination in the workplace generally.
Rather, they illustrate the importance of on-the-job-training and experience and the impact that investments in worker productivity can have on outcomes. There are few tasks simpler than driving someone from point A to point B. With Uber, it’s made even easier because the more complicated parts of the task — finding the fare, exchanging monies and following directions — are all taken care of by an app. Yet even with this most simple task, productivity explains more than a third of the difference in driver rewards! The learnings in this example are organic, unstructured and on-the-job. Imagine the impact that purposeful investment in productivity could make for tasks of higher complexity.
Post script: International Women’s Day is on Thursday, March 8. You can join the department’s Women’s Network by contacting womensnetwork.
TALES FROM THE LONG TAIL 