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Hmmmm . . . I think that, by “males and females,” they mean “boys and girls.”
Anyway, I was interested in this paper (by David Lubinski, Camilla Benbow, and Harrison Kell) because . . . I’m one of the kids in the study. I was 11 years old at the time.
What’s happened since then? According to the abstract of the paper:
Across the two cohorts, 4.1% had earned tenure at a major research university, 2.3% were top executives at “name brand” or Fortune 500 companies, and 2.4% were attorneys at major firms or organizations; participants had published 85 books and 7,572 refereed articles, secured 681 patents, and amassed $358 million in grants. . . .
Wow, we’ve really cost the taxpayer a lot of money!
Seriously, though, this surprised me:
That’s me in cohort 2! I think the categories are intended to be mutually exclusive, so I don’t know if they classified me under “Science, Technology, Engineering, & Mathematics” or “Education: Post-secondary Faculty.”
The thing that really stunned me, though, was that 15% of us were chief executives. Whoa! Although maybe that includes self-employed people who have their own business. It’s really not so clear. “Chief executive” sounds like a bit deal to me but maybe I’m missing something.
One thing that seems particularly time-bound is the high rate of people working in law, medicine, and finance. I don’t see a strong connection between those fields and mathematical precocity (ok, maybe in some aspects of finance), but they all pay well, and law and medicine were, at least for kids of my generation, natural fields to go into for kids who were good at taking tests.
Recall that the “mathematically precocious youths” were identified by scoring high on the SAT. So it could well be labeled a study of “youths who were talented at standardized tests.” (But it’s not quite as bad as it sounds. Back in the 1970s, we didn’t see standardized tests very often, so we were taking the SAT cold. It’s not like we were sitting there in elementary school taking practice tests every year.)
Given all these high-powered occupations, I was surprised that the median salary for the married men in my group was only $150,000. And was the top centile only $400,000? Hard to believe, with all those doctors and chief executives in the sample. I can’t quite figure this one out. I mean, sure, $400,000 is a lot of money, but “top centile”—that’s the 99th percentile of income, right? There aren’t 3 guys in this group of 249 married men who are making more than $400K? Interesting if true. I guess these aren’t the highest-paid sort of doctors, lawyers, finance professionals, and CEO’s.
My biggest disappointment with the paper is near the end, when they talk about responses to attitude questions. For example, what percentage of (adult) men and women in our group agree with the statement, “Society should invest in my ideas because they are more important than those of other people”? I’d like to know. But all we are told (it’s in figure 6) is that men are about 0.3 standard deviations more likely than women to answer yes to that question. Sex differences are interesting but I’d like to see the averages too. And they’d be so easy to display!
Where they do display means, in figure 7, the results (and the displays themselves) are pretty boring. The authors have a lot of good stuff here; I’d just like to see more. And of course it’s pretty exhausting to see everything written in psychology-journal style with those p-values all over the place.
P.S. I’m disappointed to see that they reported results to fractional percentage points. “23.6%,” indeed. Couldn’t they have run this by me for comments? This sort of hyper-precision is just embarrassing.
P.P.S. Data-detective Weakliem also is suspicious of the top percentile number. It seems like the authors did make a mistake somewhere. Weakliem writes, “they must have measured income by categories and then applied some rule to translate them into the numbers shown.”
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