The gender wage gap is not misleading
The U.S. gender wage gap is the difference between what the median woman earns and what the median man earns in the United States. Since women earn consistently less than men, it’s typically quoted as the percentage that the median woman’s pay is of the median man’s pay. It’s gone up slowly over time:
You can also break it down by age, by race, by location, by percentile, or by occupation. You’ll find that the gender wage gap rises and falls depending on how you measure it and what restrictions you set.
I’m bringing up this simple statistic because I’ve noticed that recently, when it comes up in conversation, the person I’m talking to will often say that it’s “misleading.” When I ask them why, they mention that “women choose jobs that don’t pay as well.”
Well, I think this is incorrect. Or rather, I think that, taken as a whole, including socialization and how our culture values work, and so on, the simplistic statistic represented by the gender wage gap is actually pretty sophisticated. It captures a lot of the nuances of our sexist culture.
For example, it’s true that not as many women choose to become mathematicians versus, say, high school math teachers. But is this really an independently made choice that young women take? Or is it socialized choice? In other words, are women squeezed out of the mindset whereby they’d consider that path? Obviously the answer is “a bit of both.”
On the statistic side, then, it’s not enough to only consider “women who became mathematicians versus men who became mathematicians” when comparing ultimate wages. That would ignore the implicit socialization element that keeps women away from higher-paying jobs. Indeed, if you think about it, you’d really want to compare “women who might have become mathematicians if there weren’t so many barriers to doing so” with “men who might have become mathematicians if there weren’t so many barriers to doing so,” and I say it like that because of course, there are plenty of barriers for both men and women, although I’m pretty sure not as many men had their 6th grade teacher explicitly tell them not to study math because they “wouldn’t need it later in life” like I did.
The problem is, it’s hard to find those groups of people, because a good fraction of them didn’t become mathematicians or even high school math teachers. So we’re kind of left without a statistic at all for math nerds, if we are being honest. We just can’t collect the relevant data.
However, this same argument applies to basically every high-paying career. In fact it applies to every career, if you’re willing to generalize a bit and point out that some jobs are shunned by men for mostly social reasons, and they just happen to also be relatively underpaid as well.
So what we do, to be statistically correct, is we pool all the “women who might have done X” and we compare them against all the “men who might have done X,” where X varies over everything, and we get the best version of the gender wage gap that we can. And that’s actually what we’ve done when we compute the above statistic. It’s not misleading at all, in other words, when you take into account weird social rules we have around who should do what job and how much that job should be valued.
Just to give another example of how strong a signal this gender wage gap represents, imagine that we instead had separated the population into two different groups: the humans that were born during an even hour of the day versus the humans that were born during an odd hour of the day. We’d not expect to see a huge wage gap then, would we? And that’s because we don’t think they evenness or oddness of the hour of the day you were born really dictates much about your choice of work nor your ability to command a good salary.