Gender Equality and STEM Gaps

This blog post veers further away from (macro-) economics than my usual writing and I am anything but an expert on the topic. Thoughts and feedback are, hence, all the more welcome!

A friend (and chemistry teacher by trait) recently sent me the chart reproduced below, which is taken from a study authored by Professor Gijsbert Stoet and Professor David C. Geary and published in the journal Psychological Science earlier this year. The chart captures what the authors coin the educational-gender-equality paradox: “The More Gender Equality, the Fewer Women in STEM”, as the Atlantic puts it (STEM: Science, Technology, Engineering, Mathematics). To elaborate, countries with high levels of gender equality tend to have larger STEM gaps insofar as the proportion of women among STEM graduates is lower, on average. For example, Finland ranks second-highest (surpassed only by Iceland) according to the GGGI 2016 published by the World Economic Forum (for the 2017 version, see here), yet it also has one of the world’s largest gender gaps in college degrees in STEM fields: fewer than 25% of STEM graduates are women. Similar remarks apply to two other countries commonly seen to be at the vanguard of gender equality, Norway and Sweden. On the opposite end of the spectrum, in Algeria 41% of college STEM graduates are female, all the while it ranks 119th in the 2016 GGGI ranking. This may be called a paradox insofar as, according to this study, women are less likely to choose male-dominated math and science professions in countries that empower women. At the very least, the results went against my prior and the intuition-based expectation of every single person I talked to about this study. In this blog post, I explore a few potential explanations and interpretations of the correlation shown in the chart. [1] This is no rigorous analysis (e.g. testing for causality), but more of a brainstorming session combining ideas mentioned in the paper with points raised by friends and my own reflections. The reader may nevertheless find it interesting.

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Figure 3b in Stoet and Geary (2018).

Before we get to attempts at explanation, let’s take a brief look at the data first. For each of the 144 countries covered, the Global Gender Gap Index (GGGI) assesses four overall areas of inequality between females and males – economic participation and opportunity, educational attainment, political empowerment, health and survival – as measured by 14 key indicators (e.g. life expectancy, seats in parliament, earnings). The index is represented on a 0-1 scale, with 1.0 representing (either) complete parity (or males falling behind). It is worth noting that the index focuses on gaps rather than levels, thus facilitating a better cross-country comparison: it “assesses countries on how well they are dividing their resources and opportunities among their male and female populations, regardless of the overall levels of these resources and opportunities” (2008 Report, p. 24). As I briefly remark upon in footnote [3], the index is inevitably flawed, insofar as the indicators will not capture all dimensions of gender equality we may deem important. Meanwhile, the STEM graduation rates come from UNESCO graduation data labeled  “distribution of tertiary graduates.”

There are at least four potential explanations of – or factors relating to – the educational-gender-equality paradox we may consider. These come, respectively, under the headings “life quality pressures”, “comparative advantage”, “confounding factors”, and “data issues”. A first explanation emphasises “distal” (broad contextual) influences on females’ choices. In particular, women in less gender-equal countries may also face greater economic and general life risk which may make relatively high-paying STEM occupations more attractive relative to contexts with greater opportunities and lower risks. As a friend (and STEM PhD) put it: In rich countries like Finland and Norway, “women are more likely to have the luxury to choose any subject they please (regardless of employment outcome) … if you are a women getting third level education in a less wealthy country there’s probably more pressure to get a better paid job afterwards and they might see those opportunities in STEM.” In this view, greater gender equality is associated with greater freedom – and that includes the freedom to choose fields other than STEM. One observation from the study that is consistent with this hypothesis is as follows: even when girls’ ability in science in school equalled or excelled that of boys (on which more below), they nevertheless tended to register a lower interest in science subjects; according to the study, this is true in 76% of countries.  Of course, this raises the broader question where such differences in interest might come from and why, remarkably, the “interest gap” is greater in more gender-equal countries.

The second explanation is connected to the economic concept of comparative advantage: In two thirds of countries, girls performed similarly to or better than boys in science subjects in school. However, they often performed even better in reading, such that their comparative (rather than absolute) advantage may not lie in science. To add some figures to the picture: Across all countries, 24% of girls had science as their best subject, 25% of girls’ strength was math, and 51% excelled in reading. For boys, the percentages were 38 for science, 42 for math, and 20 for reading. Thus, an Atlantic article quotes Professor Janet Shibley Hyde: “some would say that the gender STEM gap occurs not because girls can’t do science, but because they have other alternatives, based on their strengths in verbal skills.” While these observations by themselves do not suffice to explain the educational-gender-equality paradox, “the magnitude of these sex differences in personal academic strengths and weaknesses was strongly related to national gender equality, with larger differences in more gender-equal nations,” Stoet and Geary find. As a result, such “proximal” factors as personal academic strengths may, at least in part, underpin the negative correlation between gender equality and the STEM gap.

The preceding explanation, centered around the idea of comparative advantage and individual rational choice, leaves a number of questions open. For instance, why is it that in more gender-equal countries, girls have a greater comparative advantage in non-STEM fields than in less gender-equal ones, on average? I find it difficult to think of a causal mechanism leading from greater gender equality to more pronounced relative advantages of women in non-STEM fields. Moreover, even if comparative advantage is part of the story, the data suggest that the number of girls whose relative advantage lies in science or math exceeds the number of women choosing STEM degrees also in more gender-equal countries, meaning that even in those countries women whose best subjects are math and science may be nudged away from careers in those professions.

It is possible that the answer to the puzzles or open questions raised above is that there may be confounding factors at work which, at a minimum, caution against reading causality into the observed correlation, or that there are data problems. The “life quality pressures” narrative could be read as saying that greater gender equality implies greater freedom of choice and that this, given measured differences in interest, leads to a greater STEM gap. One alternative explanation is that wealth and gender equality are correlated and that it is greater overall wealth, and not greater gender equality, that drives greater freedom of occupational choice. Indeed, having taken a brief look at (OECD) data, although the GGGI focuses on gaps in access rather than levels of resources, it turns out that the GGGI index is generally positively correlated with GDP per capita (correlation index across total sample: +0.23; +0.34 in the OECD sub-sample). Relatedly, cases like the Scandinavian countries cited at the outset illustrate another potential confounding factor: the level of social security provided in more gender-equal countries is typically higher (e.g. according to the OECD social spending measure; correlation index with GGGI in OECD sub-sample: +0.38). This reduces the “economic and general life risks” I cited above as a potential factor driving women to choose financially rewarding STEM careers in less gender-equal countries. Third, country-specific factors combined with path dependency may explain some of the patterns we observe. For instance, in the Scandinavian countries, the early entry of women in the labor market was crucial in creating a social democratic service state which nurtured a constituency of socio-cultural professionals – and which today offers relatively well-paid jobs with flexible working arrangements that may make it less attractive for women to choose STEM subjects than in countries without a comparable development path.

Lastly, and circling back to where this blog post began, the paradox may at least in part be an artefact of the data. For instance, some careers that are not STEM by definition and which have a majority of women nevertheless often do require STEM skills (e.g. medicine). To the extent that women with a comparative advantage and interest in STEM choose such occupations, the graduation rates used in the study may overstate the STEM gap in terms of people’s actual engagement with STEM subjects (though this point, by itself, does not address the role of gender equality).

On the whole, these reflections leave me less surprised about the pattern documented by Professors Stoet and Geary than I was at the outset, but a number of questions remain. Let me raise a few more to close. I am generally an optimist and believe that notwithstanding political setbacks, societies around the world will achieve greater gender equality, albeit at a speed that is inevitably too slow. However, it is worth noting that if – and that’s a big ‘if’ – the cross-sectional results established in this study also hold in a dynamic setting, i.e. if women’s STEM participation does not rise as we make progress towards greater gender equality (as measured by the GGGI [2]), this also means, for instance, that the technologies shaping our world and everyday life will likely continue to bear the imprint of male bias. As is by now frequently written about, the technologies we use, including such present-and-future-shaping things as algorithms or AI technology, are not normatively passive or value-neutral. [3] Potential normative ramifications of the pattern documented by Stoet and Geary such as this tend to make me believe that even if the data reflect women having greater freedom of choice in liberal societies, we need to continue to think hard about how to make STEM fields more attractive for women.

Footnotes
[1] For readers with Bayesian inclinations, having realised that my prior needs updating, I am trying to figure out in what ways/directions I should update.
[2] Of course, at least to the extent that such a lack of parity in occupational choices manifests itself in the perpetuation of biases and discrimination, as contemplated below, this itself would represent a failure to achieve feminist goals and in that sense gender equality would not be realised.
[3] I like the example the software engineer Liz Rush gives to illustrate how people’s beliefs impact the design of even seemingly innocent technologies. In the design of air conditioning and heating systems, the developers had to take into account variables such as what feels comfortable etc. Amongst others, the designers used the resting metabolic heart rate of a middle-aged white man – but since body’s resting metabolic heart rate varies from person to person, this means that office temperature optimization was designed around an (implicit) bias that men’s comfort in the office is what you should (or at least de facto do) use as the baseline standard.

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