The gender pay gap is a tricky concept for proponents of “evidence‐based policy”. The Government has introduced a legal requirement for businesses with more than 250 employees to publish average gender pay statistics. The rationale is for “transparency”, apparently, though it does not take Nostradamus to forecast how these will be used in public debate. The demands for action will follow.
Yet what is rarely mentioned is that there is a large amount of empirical literature in economics that explains why apparent headline pay gaps exist. Controlling for occupation types, time in the labour force, education levels, and market rewards for unpleasant or unpopular work and more, the remaining “gap” between genders almost entirely disappears. The evidence for discrimination by sex is weak to non‐existent. Judging whether the somewhat different demand for “equal pay for equal work” is achieved is likewise heavily dependent on how you define “equal work”, given the nature of different jobs.
Many would argue, with some reason, that societal expectations of women shape occupation choice, tasks assigned, the nature of pay negotiations, and decisions on how to care for children. If all male advantages in these areas were eliminated, so is implied, the sexes would be equally represented across jobs. Pay gaps for the same work would likewise be eliminated.
But is a 50–50 occupation split and pay gap elimination likely? There were already reasons to be severely doubtful. In Scandinavian countries with extensive pro‐family policies, preferences for child caring and careers still seem to result in higher pay for men.
In the UK, male models are unlikely, on average, ever to be paid as much as female. An innovative new study sheds further light, showing pay gaps still exist even in markets where contractors do the same job and workers set their own hours.
A new paper on ride‐sharing app Uber shows male drivers, on average, earn 7pc more per hour than their female counterparts in the USA. This is remarkable. Drivers have full flexibility to work when they like, pay rates are fixed and transparent, there are no earnings negotiations and customers do not appear to care about the gender of their driver. Almost all the explanations about the patriarchal nature of business environments and how non‐women friendly the working hours are therefore fall away. And yet there is still a pay gap.
Why? The study’s authors Cody Cook, Rebecca Diamond, Jonathan Hall, John List and Paul Oyer drilled down into what was happening in Chicago, and found three factors fully explain the phenomenon.
First, men were likely to have more experience using the app, and so make more efficient decisions to boost earnings. Though men and women experienced very similar learning curves for performance, men tended to have delivered more rides, both because they stayed using the app for longer and completed more rides per hour. This “experience” effect explains over a third of the average.
Second, men tended to drive faster than women, accounting for almost 50pc of the gap. Faster driving increases the number of rides completed per hour. This is not just an experience effect either. There is no evidence that women, on average, drove faster after completing more journeys. They seem to just have a preference for driving more slowly no matter how many trips they have completed.
Third, and finally, men were more likely to drive in the lucrative locations. As a result, men tended to have shorter trips to the rider, longer overall trips, and higher returns through incentives such as surge pricing. This explains about a quarter of the gap.
Might this be due to women feeling constrained about when they can drive, due to family responsibilities? No. The authors show that the times workers drive actually tend to boost women’s pay relative to men. It simply seems to be that with free choice, women Uber drivers in Chicago, on average, act differently from their male counterparts.
Politicians would do well to bear this in mind next time they wade in on some pay gap statistic for a certain industry. Yes, policies, work environments, and social norms affect career paths and earnings. But free choice in occupation and at work, including attitudes to risk and personal priorities, can drive apparently inequitable aggregated statistics too.
It is easy to perceive how this could affect other industries. Might any pay gap in the financial sector in part be explained by different attitudes to risk? Could it not be the case that warehouse staff doing ostensibly the same job as those in stores for retailers are paid more because their workplace locations are less desirable?
There are lots of potential explanations for differential pay by industry, let alone nationwide. Indeed, if campaigners and politicians really want to “eliminate the gender pay gap”, as they say, then their to‐do list is quite extensive. It includes: enforcing complete educational parity for men and women, in the same disciplines, imposing the same preferences on work‐life balance, ensuring the same time is spent in exactly the same careers, making sure men and women have the same risk‐appetite and preferences, the same levels of productivity, share domestic tasks exactly equally, have the same attributes, the same career paths and working lifetimes in the same occupations in the same sized firms.
Yet, the Uber example shows that when men and women are genuinely free to choose, they tend to choose differently. A more just goal is surely to facilitate people pursuing their own dreams.