Now consider two groups of workers. For a given compensating differential (a given difference in the pay between Job A and Job B) Type X workers are more likely to choose Job B. In contrast, Type Y workers are more likely to choose Job A. Type Y workers would earn more than Type X workers on average, but more Type Y than Type X workers would also have to put up with the negative characteristics of Job A. Would you argue that the wages need to be modified in order to ensure that both groups of workers earned the same wage? Maybe you would, but remember that it is the compensating differential that makes Job A worthwhile, and reducing (or eliminating) the compensating differential will increase competition for Job B. So, maybe that's not such a good idea after all.
Now what if I said that Type Y workers were men, and Type X workers were women. Would that change your answer?
That thought experiment is important. The gender wage gap is real, but it isn't all a story about discrimination. Some of it is, no doubt, but some of the gender wage gap may be due to differences in preferences for job characteristics, and only one of those characteristics is the wage. How much of the gender wage gap is due to differences in preferences between men and women? It turns out that is quite a difficult question to answer, but a recent paper by Cody Cook (Uber), Rebecca Diamond (Stanford), Jonathon Hall (Uber), John List (University of Chicago) and Paul Oyer (Stanford) provides some interesting insights. They use data from Uber, and the great thing about their data is that there is no role for discrimination because, as they put it:
Uber set its driver fares and fees through a simple, publicly available formula, which is invariant between drivers. Further, similar to many parts of the larger gig economy, on Uber there is no negotiation of earnings, earnings are not directly tied to tenure or hours worked per week, and we can demonstrate that customer-side discrimination is not materially important. These job attributes explicitly rule out the possibility of a "job-flexibility penalty".At the national level, they find a gender pay gap of around 7% when looking at hourly earnings of Uber drivers. However, in decomposing the gender wage gap, they focus on data from Chicago (although they note that their results are not sensitive to the choice of city, and in the appendix they present similar looking results for Boston, Detroit, and Houston). Their data on Chicago drivers includes 120,223 drivers (just over 30% female), and about 33 million driver-hours of observations. They find that:
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.
The residual gender earnings gap that persists after controlling for these two factors can be explained by a single 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.
We interpret these determinants of the gender pay gap—a propensity to gain more experience, choice of different locations, and higher speed—as preference-based characteristics that are correlated with gender and make drivers more productive...
First, driving speed alone can explain nearly half of the gender pay gap. Second, over a third of the gap can be explained by returns to experience, 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% of the gender pay gap can be explained by choices over where to drive.In other words, in a setting where discrimination is unlikely or impossible, the gender wage gap is entirely explained by differences between men and women in experience (about one third) and preferences (about two-thirds). Preferences turn out to be a really important component of the gender wage gap. It does leave open the question of how much of the gender wage gap in other occupations (where discrimination is possible) is due to discrimination, but we can be sure that it isn't anywhere near all of the gap. The results in terms of work experience are not gender neutral though, as men will build their job experience faster if they work more hours (and they do).
The gender wage gap is real, but we need to be careful before we pronounce it as definitive evidence of sexism (such as here).
[HT: Marginal Revolution, and then Offsetting Behaviour]