There are two recent studies of gender disparities in science and technology (referred to by the faintly awful acronym “STEM”) getting a lot of play over the last few days. As is often the case with social-science results, the data they have aren’t quite the data you would really like to have, and I think it’s worth poking at them a little, not to deny the validity of the results, but to point out the inherent limitations of the process.

The first is a study of lifetime earnings in various fields that includes this graph showing that women with a Ph.D. earn about the same amount as men with a BA:

i-0c230c85999f45d40200d9badf8b18f6-lifetime_earnings.png

That’s pretty damning. But also a little deceptive, because this is a plot of “lifetime earnings,” which means that they are necessarily doing a social-science analogue of astronomy to make this graph.

What I mean by that is that this is necessarily a retrospective study, looking back at how things were, in the same way that astronomers are looking at distant galaxies as they were back when the light was emitted, millions of years ago. The authors of the current study are following the procedure used by the Census Bureau for similar work, which they describe thusly:

Specifically, the Census approach looks at 5-year age groups — 25-29, 30-34, etc. — to get an average for each age group and then sums each of these 5-year averages of a particular demographic and/or educational group to estimate the average 40-year degree for that group.

What that means is that they’re taking a cross-section back through time. In order to get the average salary for people at age 40, they are looking at the average salary of people who are 40 now (well, who were 40 in 2007-2009, but you get the idea). That means that you’re looking at the salary of a 40-year-old Ph.D. who was hired somewhere in the 1995-2000 range, which is not necessarily the salary someone hired today should expect to be making when they are 40.

This is important for these kind of gap studies, because the data presented necessarily average over many years of changing conditions. To get the last few years of their hypothetical 40-year working life, they’re looking at the salaries of Ph.D.’s who were hired in 1970(-ish). And I hope everybody will agree that the working environment was significantly different in 1970 than it is today, particularly when it comes to differential treatment of gender and race.

This makes interpreting this graph a little trickier than is being presented by most of the links to it I’ve seen. That is, even in the hypothetical fantasy-world case where gender disparities in salary and promotion have magically been fixed today, you would still expect to see a significant gap in this sort of lifetime earnings calculation due to the gender disparities that existed in the past.

If you want to really figure out what’s going on with the earnings of different groups from this kind of study, you would need two such studies, preferably separated by a substantial interval in time. And, in fact, we have just that, in the 2002 Census Bureau report referenced in the current study (PDF), which includes this graph:

i-b4c17ffb8a7798cab5db91f906cb85b5-lifetime_earnings_2002.png

Aside from trivial differences in formatting, this is the same figure as above. The absolute numbers are different, but what really matters for this discussion is the size of the gaps, which you can measure in percentage terms. Defining the percentage difference as the difference between the two salaries (M-F) divided by the smaller of the two (i.e., the women’s salary), we have the following:

i-d2103671c74ab84c4fb5949cde12bed1-lifetime_earnings_table.png

In other words, in the 2002 report (based on data from the late ’90s), a man with a Ph.D. earned 52% more than a woman with a Ph.D., while in the 2011 report (based on data from the late ’00′s) the gap in lifetime earnings was 21%. Which is a very different sort of picture, suggesting that the job climate has improved substantially over the last decade (or, more likely, over the decade between 1960 and 1970).

(Interestingly, the current study’s gap for Ph.D.’s is smaller than any other category, and dramatically so. The same pattern holds for the older report, though the difference is probably not significant. I’m not sure what this means, but it’s interesting.)

Does this mean that everything’s rosy? No, not at all. There is one thing that remains the same in the two reports, namely that a woman with a Ph.D. earned about the same amount as a man with a B.A. (slightly more in the newer data, but the difference is about a third of the gap). Which means there’s still work to do, but the outlook for a new Ph.D. going forward is not nearly as dire as the top graph alone seems to suggest.

(One other note, not as directly relevant to this: the current study assumes an “ideal” trajectory with no gaps in employment. As one of the usual explanations of gender gaps in lifetime earnings is that women tend to be more likely to take time off for family reasons, this is obviously not perfectly realistic. However, as they note in their discussion, the method they have used would, if anything, tend to understate the difference for those women who do take time off. So, while a more detailed investigation would be nice (if kind of impractical), that’s not a killer flaw in their results. It is something to be aware of, though.)

Comments

  1. #1 Mausy5043
    August 10, 2011

    Surely that first graph is off by a factor of 10. I can’t imagine someone with less than High School to make 1.1 million dollars a year. Also the numbers on the x-axis are awkward.

  2. #2 Chad Orzel
    August 10, 2011

    That’s a graph of lifetimeearnings, not annual salary. Which is another persistent source of confusion. It’s not saying that a high school dropout makes 1.1 million on average, it’s saying that they make 1.1 million total over their 40-year working life.

  3. #3 CCPhysicist
    August 10, 2011

    I don’t see how they can get from census data that only record the amount earned in a particular time in the distant past to a revision that (1) determines how long women were out of the workforce on their own volition and, more importantly, (2) how much their salaries were reduced in later years by the lack of pay raises and promotion during the time they took off.

    In any case, the salaries by level of degree hide the gender differences in the types of degrees earned. How many of the masters degrees held by women in, say 1980, were in education because they were teaching K-12? Is the lifetime income of a BA in English the same as for a BS in Engineering?

    One area that might make for an interesting study would be medicine, because we are rapidly approaching the point where half of all new MDs are female, and this is quite a change from 20 or 30 years ago. Of course, even there you will find variations by specialty.

  4. #4 Chad Orzel
    August 10, 2011

    I don’t see how they can get from census data that only record the amount earned in a particular time in the distant past to a revision that (1) determines how long women were out of the workforce on their own volition and, more importantly, (2) how much their salaries were reduced in later years by the lack of pay raises and promotion during the time they took off.

    I think what they’re doing is much simpler than that. They’re taking the current average salary of 30-35 year old women with Ph.D.’s and using it for the 0-5 year salary, then the current average salary of 35-40 year old women with Ph.D.’s and using it for the 5-10 year salary, then the current average salary for 40-45 year old women with Ph.D.’s and using it for the 10-15 year salary, and so on. Which is why I say it’s averaging over the distant past– the average salaries for the older cohorts reflects lower starting salaries and slower promotion and so on.

    At least, that’s how I interpret the paragraph or two explaining the method. I can’t see what else they would be able to do with the data they collected.

  5. #5 CCPhysicist
    August 10, 2011

    What you say @4 is also what I think they are actually doing, which means they are not accounting for ANY of the major variables that might have been more significant in 1985 than today when trying to project income for people newly entering those careers.

  6. #6 becca
    August 11, 2011

    “Which means there’s still work to do, but the outlook for a new Ph.D. going forward is not nearly as dire as the top graph alone seems to suggest.”
    Not necessarily nearly as dire, you mean.

    Unlike galaxies, which to the best of my knowledge do not display such fickleness, it is in fact possible for wages for women to decrease relative to mens again. No data I’ve seen address what will happen to Ph.D.s going forward.

  7. #7 kim
    February 5, 2012

    what??????

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