Are Boys Really Better at Maths?
A data-driven story by Sacha Saint-Leger

Are boys born better than girls at maths? Or are the differences some of us observe a result of our different upbringings? Perhaps a bit of both?

In this data-driven story I compare and contrast country data from the maths portion of a global education test known as PISA (taken by more than half a million 15-year-olds around the world), with country data from an index used to measure the gap between women and men in education, the economy and political empowerment (formally known as the Gender Equity Index).

The results may surprise you!

Now, have a good look at the scatterplot below. And don't worry, it's pretty simple to understand. Each dot represents one country, the vertical axis represents the average PISA maths score for girls, and the horizontal axis represents the Gender Equity Index.

The closer the index is to 100, the more equally men and women are treated. Similarly, the higher the maths score the better.

So to recap, the further up a country is on the scatterplot, the better its girls are at maths, and the further right a country lies, the more gender equal it is.

As you can see above, on average, the more gender equal a country is, the better its girls tend to perform at maths.
If you remember from the intro, the Gender Equity Index measures the gap between women and men based on three factors: education, the economy, and political empowerment.

What happens if we choose to ignore the first two (education and political empowerment gaps) and focus on economic gaps between men and women?

We see a very similar picture! The vertical axis is the same as before and the horizontal axis this time represents the economic equity between men and women in a country, with 100 signifying that men and women are treated fairly on an economic level, and 0 meaning the gap in treatment could not be larger.

As you can see, the smaller the economic gap between men and women in a country, the better girls tend to perform at maths!

If you dig into the data further, it turns out that economic gaps are more important than education or political empowerment gaps in predicting maths ability of girls (but we won't dig into that here).

But what do we mean exactly when we talk about economic gaps between men and women? It turns out we can break down economic gaps into two areas : estimated income gaps and labour force gaps.

We'll talk more about labour force gaps a bit further down, but for now let's see what happens if we ignore labour force gaps and focus on income gaps between men and women.

This time the horizontal axis represents income equity between men and women in a country, with 100 signifying that men and women are compensated equally.

We see there is a correlation between higher income equality and higher maths scores for girls, but it isn't quite as strong as before.

Now, let's turn our attention to labour force gaps. The labour force equity score is based on the difference between the percentage of men in full-time work and the percentage of women in full-time work.

The closer the score is to 100, the smaller the gap.

Here we see a strong correlation between higher labour force equality in a country and higher maths scores for girls.

In plain english, countries that encourage men and women to participate equally in the labour force, are much more likely to have girls that perform better at maths!

Ok, so it's pretty clear that there is a positive correlation between how gender equal a country is and how well girls perform at maths. But what about boys? Do boys do better, worse, or the same in more gender equal countries?

This time the vertical axis represents the average PISA maths scores for boys, and the horizontal axis the Gender Equity Index.

Surprisingly it turns out that even boys do better in more gender equal countries! Gender equity benefits both sexes when it comes to maths ability.
As a final comparison, let's compare the performance of boys and girls directly. The vertical axis represents the average girl's maths score minus the average boy's maths score.

So values greater than 0 mean that girls scored better than boys on average, and values less than zero mean that boys scored better. The horizontal axis represents the Gender Equity Index.

What we see above is that there are nine countries where girls actually score the same as or higher than boys (look for the dots on or above the dashed grey line).

If we turn our attention to the circle, We also see that in the four most gender equal countries in the world (Finland, Sweden, Norway and Iceland), girls perform better than boys at maths.

The reason we see only three dots in the circle is because Iceland and Sweden have the exact same difference in average maths score between boys and girls, so their dots are drawn one on top of the other.

And there you have it. There is convincing data-driven evidence that, contrary to popular belief, the difference in mathematical ability between boys and girls is largely determined by culture and societal norms, not genes.

While the precise nature of these societal norms is beyond the scope of this story, I will leave you with a couple of scattered thoughts taken from Jo Boaler's book Mathematical Mindsets (Jo is a leading maths education researcher at Stanford) :

In an important study, Sian Beilock and colleagues found that the extent of negative emotions elementary teachers held about mathematics predicted the achievement of girls in their classes, but not boys (Beilock, Gunderson, Ramirez, & Levine, 2009)...this gender difference probably comes about because girls identify with their female teachers, particularly in elementary school. Girls quickly pick up on teachers' negative messages about maths - the sort that are often given out of kindness, such as 'I know this is really hard, but let's try and do it' or 'I was bad at math at school' or 'I never liked math.'

In another study, researchers found that when mothers told their daughters "I was no good at maths in school" their daughter's achievement immediately went down (Eccles & Jacobs, 1986).

The body of work on 'stereotype threat', led by the work of Claude Steele, shows clearly the damage caused by stereotypical ideas. Steele and his colleagues showed that when girls were given a message that a maths test resulted in gender differences, the girls underperformed, whereas girls who did not receive that message performed at the same level as boys on the same test. Subsequent experiments showed that women underachieved when they simply marked their gender in a box before taking a test.

Maths is often taught in such a dry, procedural, abstract way...unfortunately though, that dry, procedural maths particularly puts off girls and we know that girls, for whatever reason more than boys, want a connected subject. They want to see the connections not only between maths and the world and life, but they want to see the connections within maths as well. They want to see maths as this connected, conceptual subject, which is the subject it really is. But it's not taught in that way and that is a big part of the problem!


The 2012 Gender Equity Index data can be found here.

The 2015 PISA Maths Scores data can be found here.

The purple line that appears in the first five scatterplots is a simple linear regression line. In plain english, it's a line of best fit.

The analysis was limited to 41 countries as only 41 countries were included in both datasets : Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, Spain, Sweden, Switzerland, Turkey, Great Britain, United States, Brazil, Chile, Estonia, Idonesia, Israel, Russia, Slovenia, Latvia, Singapore, Columbia, Peru.

Scatterplots built using d3.js

Data cleaned up and merged using javascript and google docs.

Source code can be found here.