5 Comments
Jan 9, 2022Liked by Sam Atis

Interesting, the BIGI seems to be a simplified version of the GGGI, which doesn't look at as many measures. The BIGI only considers, 1) childhood educational opportunities, 2) healthy life expectancy, and 3) life satisfaction. The GGGI considers 14 different measures, weighs them, and then sums them. I figure the good way to help clarify this disagreement would be to separately measure the correlation between the gender-STEM value and each of the 14 measures.

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Great piece - alas there are few 'rational' people on the internet!

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Jan 10, 2022·edited Jan 10, 2022

Fundamentally I think that men and women are different, not better or worse but anecdotally different, so it's no surprise that when given the most opportunity to do so, they will choose different things. Anybody who went to school at a child, speaking honestly, will say that on average boys and girls tend to lean more towards different things.

Valuing men and women by their aspirations towards the traditionally male-dominated STEM fields seems like quite a masculine-dominant mindset; who's to say that going into these fields is an important metric to look at?

I don't think this is a paradox so much as a symptom of framing gender relations as a zero sum men vs women dichotomy where each side has to score points by taking from the other.

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“The argument is this: weirdly, stereotypes about men being better than women at maths seem to be stronger in countries with more gender equality. The argument that Breda et al make is that the negative correlation between gender equality metrics and the number of women in STEM goes away when you control for the stereotypes about men and women in STEM.”

This seems dubious. Let’s suppose that gender stereotypes are truly stronger in more egalitarian countries. This still leaves open the possibility that these stereotypes are stronger precisely because greater gender equality allows biological differences to manifest more fully. In other words, we would expect people in Nordic countries to notice that women disproportionately become nurses and insofar as it happens less disproportionately in less egalitarian countries then residents of those countries will “stereotype” women as likely to be nurses to a lesser degree.

Controlling the effect away by redefining these manifesting differences as stereotypes seems like bad science.

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