The gender wage gap: So true that it's just a textbook example?

Last week, as part of my graduate student duties, I had the pleasure of grading some undergraduate econometrics midterms. Now, I've taken a few economics/statistics classes in my life, and I've noticed that all professors have the same two favorite textbook examples of basic linear regressions that they like to put up on the board to explain how regressions work and write on their tests for students to derive: the "returns to education" equation and the "gender wage gap" equation. Following suit, the econometrics midterm that I was grading featured the latter. And maybe my brain was a little fuzzy by the 70th midterm, but I started thinking - is the fact that the "gender wage gap" equation is seen as a textbook example good because it means that people acknowledge it (and hence maybe would support efforts to push for more equal pay for women)? Or is it not so good because it just means we take it as a basic fact that's not debatable and hence it can function as a classroom example or a test problem without much controversy?



See, I understand why the "returns to education" equation makes a good textbook example. First of all, economists have been trying to estimate the relationship between an extra year of schooling and wages probably since the dawn of time. And while there are debates regarding the precise magnitudes of the effect or whether the effect is truly causal, I think we'd be hard-pressed to find people who don't believe that there is a positive correlation between education and wages. Moreover, I don't think many people would debate that there *should* be a positive relationship between education and earnings, at least in our current society.

But with the "gender wage gap" equation, which usually features a negative coefficient multiplying a variable called "Female" and is interpreted to mean that women earn __% less wages than men, things aren't quite so straightforward or uncontroversial. Firstly, not everyone believes that a gender wage gap exists. Secondly, those that do believe it exists (and not just because women work in different jobs than men, or choose to take more time off, or have different preferences about work environments, or have periods), would probably argue that it should NOT. So, maybe the fact that the "gender wage gap" equation is such a common textbook example is just a sign that economists acknowledge the existence of pay disparities between men and women. But, after reading response after response after response explaining that women CEOs are correlated with lower market value or smaller companies, and marking them as correct because they accurately explained "omitted variables bias", I started feeling like the "gender wage gap" equation is a textbook example because we take it as a given. And so, it's easy to write quick answers about it on a test, and easy to use it to explain the intuition behind linear regression.

And then I thought - we never see "race wage gap" equations as textbook examples! I think if a professor wrote an equation on the board that featured a negative number multiplying a variable called "Black", people would feel uncomfortable. It's just not politically correct. However, the earnings gap between blacks and whites is greater than that between men and women, so if anything, this example would be even more true. But, I think that something inside us tells us that this is not right, and so we tend to not use this example to explain basic regression facts. Why then is the "gender wage gap" so easy to use?

Now, I know there are bigger issues in the world than this, especially in the realm of women's rights. And like I said - I was probably overthinking given that I was grading for practically 12 hours straight. Still, the optimist in me hopes that all the students exposed to the "gender wage gap" example will be more assured of its existence and more willing to fight against gender discrimination in the workplace, rather than taking it as a given and absent-mindedly finding other "textbook example" reasons for why it exists just to get more points on a test.