The relative pay of men and women looks deeply unfair -- especially given the long history of oppression and disenfranchisement suffered by women. However, appearances are sometimes deceiving, so it would be nice to know exactly what is going on. (In this case, appearances are not deceiving but they are also not very helpful for fixing the system!)
Lots of research has been done on the gender pay gap. Overwhelmingly, the goal of such research is to explain the gap. And explanations (or partial explanations) are diverse. This is a very difficult problem! Researchers have pointed to: job experience, career continuity, weekly hours worked, attitudes toward economic risk,wage bargaining habits, choice of education-type (e.g., college major), sector of the economy, and, of course, gender discrimination (which might be personal, institutional, or cultural).
How to explain the pay gap is still contentious, but here is my view of the literature, for what it's worth.
Some of the pay gap is explained by market forces that are not gender-biased in principle but are gender-biased in practice. All else equal, employers want to employ people who have more experience, more education, and more continuity in their careers. Motherhood (but not fatherhood) tends to work against all of those in practice (though it need not in principle). So, women are, in practice, differentially penalized for having children.
Some of the pay gap is due to implicit and explicit discrimination. For example, mothers face a wage penalty in addition to the hits they take to experience, education, and career continuity. And women also sometimes face hiring discrimination: more on this in a moment.
And finally, some of the pay gap is explained by differences in the education and employment that men and women seek. In fact, education and employment sought--especially the sector of the economy in which one works--seems to have more influence than any of the other causes of the pay gap. As it turns out, women typically earn less economically valuable degrees in college than do their male counterparts and then they work in lower-paying sectors of the economy than do their male counterparts. And so, we have the set-up for an instance of Simpson's Paradox.
When I first set out to write this blog post -- several weeks ago, after an interesting exchange with Noreen Sugrue, Helga Varden, and some others over dinner -- I was intending to simply use the gender wage gap as an excuse to look at Simpson's Paradox. The issue is too complicated to leave it at Simpson's Paradox, but I still want to get the paradox into the discussion.
So first, the simplest description of the technical problem: Simpson's Paradox occurs when a statistical association between two variables either disappears or reverses when one conditions on some other variable(s). (Sometimes people reserve the label "Simpson's Paradox" for reversals -- e.g., from positive association to negative association.)
Why does Simpson's Paradox matter in general? Well, we want to be able to make informed decisions about what personal actions we should take in order to get the best possible outcomes for ourselves, and also we want to make informed decisions about what public policies we should endorse in order to get the best possible outcomes for our societies. We want to make effective, efficient, and fair interventions. In order to do that, we need to know (or at least approximately know) the causal structure.
Ordinarily, one wants to take statistical associations as an imperfect guide to causal structure. But one problem for inferring the correct causal structure from statistical data is that the associations are not generally stable when we start conditioning on additional variables. Take a classic example.
You have cancer, and you want to go to the better out of two hospitals in your local area. You look at cancer survival rates and see the following:
Given just this information, the reasonable thing to do is to choose Hospital A. After all, Hospital A has a 6% better survival rate than Hospital B.
But now suppose that I tell you the two hospitals take difficult cancer cases at different rates. Suppose that the data breaks down as follows when we condition on whether a cancer case was hard or easy.
|Hospital A, Easy||84%|
|Hospital A, Hard||20%|
|Hospital B, Easy||87.5%|
|Hospital B, Hard||25%|
After conditioning on the difficulty of the cancer case, we see that Hospital B has a better survival rate regardless of the kind of cancer! Hospital B's treatment of cancer appears to dominate Hospital A's treatment of cancer.
With the wage gap, the problem is trickier than in the toy hospital example, but the principle is the same. And, we actually see evidence of some Simpson-like behavior. For example, consider the graph below from the 2007 AAUW report, Behind the Pay Gap.
Statistically significant wage differences are in bold. Notice that although overall, women earn significantly less than men, in many occupations, women and men are not statistically different with respect to their pay. The point I want to make here is that if we want to have an efficient, effective policy, we need to know what the wage gap looks like in detail, and we need to know why it is the way it is. Just to illustrate with a simple defective policy, we could imagine the government requiring a uniform pay raise for women across all occupations such that the aggregate pay of women comes out equal to that of men. The problem is that some women would still be worse off than men. (Not to mention that some women would be better off than men.) A better approach would be to target the occupations in which women are practically discriminated against.
In a similar vein, Laurie Morgan observes that the wage gap disappears entirely when one focuses on people with graduate education. And, as I have, Morgan points to the policy implications:
To the extent women earn similar rewards to men for college majors, changing women's distribution on college majors would be expected to produce dramatic gains in pay. This would focus our attention on women's choices of college majors, a line of research and policy interest well under way. If, however, women migrate to lower-paying jobs than similarly trained men in spite of similar educations, this focuses our attention on posteducational and labor market processes, including employer discrimination. (629)Knowing what causes the pay gap affects how we go about fixing the pay gap. Should our intervention be targeted at education and career choices? Or should it be targeted at discriminatory practices of employers? Or both to similar or different degrees or what?
Throwing a further wrench into the works is the fact that it also makes a difference exactly how one dis-aggregates. So, whereas the AAUW finds that in nearly half of the economic sectors they looked at, women have wages statistically indistinguishable from men, in this beautiful graph from the Bureau of Labor Statistics, we find a sizable gender pay gap in every sector of the economy except construction!
At first glance, one might think that Simpson-like explanations of the gender pay gap are like market forces explanations in being gender neutral -- at least in principle. However, the case is murky. One wants to know why women make the choices they make, and one wants to know why the economy rewards the professional choices that it does.
Let me illustrate the problem. We have reason to believe that some (how much?) of the selection or filtering of women into less economically rewarding degrees and careers is due to institutional and cultural biases. We can see this in hiring practices. For example, in an experimental investigation of sexual discrimination in hiring in England, researchers used resumes that differed only in the sex (gender?) of the applicant. They found that females were preferentially hired for the "female" occupation of secretary and males were preferentially hired for the "male" occupation of engineer. (For two so-called "mixed" occupations, they found a slight preference for females.)
We have reason to think that discrimination against women is due in part to institutional and cultural prejudices. And we have no reason to doubt that those same mechanisms operate with respect to recruitment and retention of women in characteristically male (and characteristically high-paying) fields, like engineering. Even at the level of choice of college major.