It is well-known that female employees in the United States earn less than their male counterparts, with most studies finding that females earn somewhere between 80 and 90 percent of what males earn. Various factors are cited to explain the differential in earnings, including prior employment and earnings history, differences in industry and occupation, time spent in the workforce, and biases against working mothers (among other factors). The gap in earnings does, however, diminish substantially when salary data is controlled for individual job functions at the same level and among employees of the employer.
But how do female and male earnings compare in the gig economy? Specifically, how do female and male earnings compare where the worker’s sex/gender is unknown to the person hiring the labor? What about where compensation is paid without regard to the worker’s prior earnings history, industry experience, or willingness to work long hours?
A recent study performed by researchers from Stanford University and the University of Chicago sought to answer some of these questions by examining data from over one million Uber drivers. Notably, Uber’s rates – and by extension, the amounts drivers earn by giving rides through Uber’s app – are based on a fixed, non-negotiable formula. Drivers earn a base fare plus minute and distance rates for the time and distance from pickup to drop-off. Uber does not pay higher rates to drivers with more tenure, nor does Uber penalize drivers for working fewer hours or working at different times of the day. Uber’s rate-setting algorithm is seemingly devoid of the factors that could, on their face, create a disparity in pay based on gender.
Nonetheless, the study concluded that female Uber drivers earned, on average, seven percent less than their male counterparts. The researchers found three main factors accounting for the difference in pay: (1) male drivers tend to live and drive in more lucrative locations and earn additional compensation by driving, more frequently than females, in areas with higher crime rates and more drinking establishments; (2) male drivers are typically more experienced than female drivers (having completed more rides for Uber, on average), and more experienced drivers are more adept at using the Uber application to maximize their earnings; and (3) male drivers drive faster than female drivers and therefore complete more trips in less time. Researchers also concluded that customer-driven discrimination, such as by canceling trips upon learning the hired driver is female, did not materially impact driver earnings.
This study is educational inasmuch as it found a statistically significant gender pay gap in connection with labor for which compensation is not based on the factors that have traditionally explained the gender pay gap—e.g., prior earnings history and bias against working mothers. The pay gap is especially significant given that female and male drivers are performing the exact same type of work, through the exact same platform, under non-negotiable rates. The study serves to demonstrate that a thorough analysis of pay equality requires holistic examination of the many factors that may contribute to the gender pay disparity, including both the traditional pay inequality factors and factors that are facially neutral.
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