The average North-Korean mathematician

Here are the top-fifteen countries ranked by how well their teams do at the International Math Olympiads:

When I first saw this ranking, I was surprised to see that North Koreans have such an impressive track record, especially when you factor in their relatively small population. One possible interpretation is that East Asians are just particularly good at mathematics, just like in the stereotypes, even when they live in one of the world’s worst dictatorships.

But I don’t believe that. In fact, I believe North Koreans are, on average, particularly bad at math. More than 40% of the population is undernourished. Many of the students involved in the IMOs grew up in the 1990s, during the March of Suffering, when hundreds of thousands of North Koreans died of famine. That is not exactly the best context to learn mathematics, not to mention the direct effect of nutrients on the brain. There does not seem to be a lot of famous North Korean mathematicians either1There is actually a candidate from the North Korean IMO team who managed to escape during the 2016 Olympiads in Hong-Kong. He is now living in South Korea. I wish him to become a famous mathematician.. Thus, realistically, if all 18 years-old from North Korea were to take a math test, they would probably score much worse than their South Korean neighbors. And yet, Best Korea reaches almost the same score with only half the source population. What is their secret?

This piece on the current state of mathematics in North Korea gives it away. “The entire nation suffered greatly during and after the March of Suffering, when the economy collapsed. Yet, North Korea maintained its educational system, focusing on the gifted and special schools such as the First High Schools to preserve the next generation. The limited resources were concentrated towards gifted students. Students were tested and selected at the end of elementary school.” In that second interpretation, the primary concern of the North Korean government is to produce a few very brilliant students every year, who will bring back medals from the Olympiads and make the country look good. The rest of the population’s skills at mathematics are less of a concern.

When we receive new information, we update our beliefs to keep them compatible with the new observations, doing an informal version of Bayesian updating. Before learning about the North Korean IMO team, my prior beliefs were something like “most of the country is starving and their education is mostly propaganda, there is no way they can be good at math”. After seeing the IMO results, I had to update. In the first interpretation, we update the mean – the average math skill is higher than I previously thought. In the second interpretation, we leave the mean untouched, but we make the upper tail of the distribution heavier. Most North Koreans are not particularly good at math, but a few of them are heavily nurtured for the sole purpose of winning medals at the IMO. As we will see later in this article, this problem has some pretty important consequences for how we understand society, and those who ignore it might take pretty bad policy decisions.

But first, let’s break it apart and see how it really works. There will be a few formulas, but nothing that can hurt you, I promise. Consider a probability distribution where the outcome x happens with probability p(x). For any integer n, the formula below gives what we call the nth moment of a distribution, centered on \mu.


To put it simply, moments describe how things are distributed around a center. For example, if a planet is rotating around its center of mass, you can use moments to describe how its mass is distributed around it. But here I will only talk about their use in statistics, where each moment encodes one particular characteristic of a probability distribution. Let’s sketch some plots to see what it is all about.

First moment: replace n with 1 and μ with 0 in the previous formula. We get


which is – suprise – the definition of the mean. Changing the first moment just shifts the distribution towards higher or lower values, while keeping the same shape.

Second moment: for n = 2, we get


If we set μ to be (arbitrarily, for simplicity) equal to the mean, we obtain the definition of the variance! The second moment around the mean describes how values are spread away from the average, while the mean remains constant.

Third moment (n = 3): the third moment describes how skewed (asymmetric) the distribution is, while the mean and the variance remain constant.

Fourth moment (n = 4): this describes how leptokurtic or platykurtic your distribution is, while the mean, variance and skew remain constant. These words basically describe how long the tails of your distribution are, or “how extreme the extreme values are”.

You could go on to higher n, each time bringing in more detail about what the distribution really looks like, until you end up with a perfect description of the distribution. By only mentioning the first few moments, you can describe a population with only a few numbers (rather than infinite), but it only gives a “simplified” version of the true distribution, as on the left graph below:

Say you want to describe the height of humans. As everybody knows, height follows a normal distribution, so you could just give the mean and standard deviation of human height, and get a fairly accurate description of the distribution. But there is always a wise-ass in the back of the room to point out that the normal distribution is defined over \mathbb{R}, so for a large enough population, some humans will have a negative height. The problem here is that we only gave information about the first two moments and neglected all the higher ones. As it turns out, humans are only viable within a certain range of height, below or above which people don’t survive. This erodes the tails of the distribution, effectively making it more platykurtic2If I can get even one reader to use the word platykurtic in real life, I’ll consider this article a success..

Let’s come back to the remarkable scores of North Koreans at the Math Olympiads. What these scores teach us is not that North Korean high-schoolers are really good at math, but that many of the high-schoolers who are really good at math are North Koreans. On the distribution plots, it would translate to something like this:

With North Koreans in purple and another country that does worse in the IMOs (say, France), in black. So you are looking at the tails and try to infer something about the rest of the distribution. Recall the plots above. Which one could it be?

Answer: just by looking at the extreme values, you cannot possibly tell, because any of these plots would potentially match. In Bayesian terms, each moment of the distribution has its own prior, and when you encounter new information, you could in principle update any of them to match the new data. So how can we make sure we are not updating the wrong moment? When you have a large representative sample that reflects the entire distribution, this is easy. When you only have information about the “top 10” extreme values, it is impossible. This is unfortunate because the extreme values are precisely what gets all our attention – most of what we see in the media is about the most talented athletes, the most dishonest politicians, the craziest people, the most violent criminals, and so forth. Thus, when we hear new information about extreme cases, it’s important to be careful about which moment to update.

This problem also occurs in reverse – in the same way looking at the tails doesn’t tell you anything about the average, looking at the average doesn’t tell you anything about the tails. An example: on a typical year, more Americans die from falling than from viral infections. So one could argue that we should dedicate more resources to prevent falls than viral infections. Except the number of deaths from falls is fairly stable (you will never have a pandemic of people starting to slip in their bathtubs 100 times more than usual). On the other hand, virus transmission is a multiplicative process, so most outbreaks will be mostly harmless (remember how SARS-cov-1 killed less than 1000 people, those were the days) but a few of them will be really bad. In other words, yearly deaths from falls have a higher mean than deaths from viruses, but since the latter are highly skewed and leptokurtic, they might deserve more attention. (For a detailed analysis of this, just ask Nassim Taleb.)

There are a lot of other interesting things to say about the moments of a probability distribution, like the deep connection between them and the partition function in statistical thermodynamics, or the fact that in my drawings the purple line always crosses the black like exactly n times. But these are for nerds, and it’s time to move on to the secret topic of this article. Let’s talk about SEX AND VIOLENCE.

This will not come as a surprise: most criminals are men. In the USA, men represent 93% of the prison population. Of course, discrimination in the justice system explains some part of the gap, but I doubt it accounts for the whole 9-fold difference. Accordingly, it is a solid cultural stereotypes that men use violence and women use communication. Everybody knows that. Nevertheless, having just read the previous paragraphs, you wonder: “are we really updating the right moment?”

A recent meta-analysis by Thöni et al. sheds some light on the question. Published in the journal Pyschological Science, it synthesizes 23 studies (with >8000 participants), about gender differences in cooperation. In such studies, participants play cooperation games against each other. These games are essentially a multiplayer, continuous version of the Prisoner’s Dilemma – players can choose to be more or less cooperative, with possible strategies ranging from total selfishness to total selflessness.

So, in cooperation games, we expect women to cooperate more often than men, right? After all, women are socialized to be caring, supportive and empathetic, while men are taught to be selfish and dominant, aren’t they? To find out, Thöni et al aligned all of these studies on a single cooperativeness scale, and compared the scores of men and women. Here are the averages, for three different game variants:

This is strange. On average, men and women are just equally cooperative. If society really allows men to behave selfishly, it should be visible somewhere in all these studies. I mean, where are all the criminals/rapists/politicians? It’s undeniable that most of them are men, right?

The problem with the graph above is that it only shows averages, so it misses the most important information – that men’s level of cooperation is much more variable than women’s. So if you zoom on the people who were either very selfish or very cooperative, you find a wild majority of men. If you zoom on people who kind-of cooperated but were also kind-of selfish, you find predominantly women.

As I’m sure you’ve noticed, the title of the Thöni et al paper says “evolutionary perspective”. As far as I’m concerned, I’m fairly skeptical about evolutionary psychology, since it is one of the fields with the worst track record of reproducibility ever. To be fair, a good part of evpsych is just regular psychology where the researchers added a little bit of speculative evolutionary varnish to make it look more exciting. This aside, real evpsych is apparently not so bad. But that’s not the important part of the paper – what matters is that there is increasingly strong evidence that men are indeed more variable than women in behaviors like cooperation. Whether it is due to hormones, culture, discrimination or cultural evolution is up to debate and I don’t think the current data is remotely sufficient to answer this question.

(Side note: if you must read one paper on the topic, I recommend this German study where they measure the testosterone level of fans of a football team, then have them play Prisoner’s Dilemma against fans of a rival team. I wouldn’t draw any strong conclusion from this just yet, but it’s a fun read.)

The thing is, men are not only found to be more variable in cooperation, but in tons of other things. These include aggression, exam grades, PISA scores, all kinds of cognitive tests, personality, creativity, vocational interests and even some neuroanatomical features. In the last few years, support for the greater male variability hypothesis has accumulated, so much that it is no longer possible to claim to understand gender or masculinity without taking it into account.

Alas, that’s not how stereotyping works. Instead, we see news report showing all these male criminals, and assume that our society turns men into violent and selfish creatures and call them toxic3Here is Dworkin: “Men are distinguished from women by their commitment to do violence rather than to be victimized by it. Men are rewarded for learning the practice of violence in virtually any sphere of activity by money, admiration, recognition, respect, and the genuflection of others honoring their sacred and proven masculinity.” (Remember – in the above study, the majority of “unconditional cooperators” were men.). Internet people make up a hashtag to ridicule those who complain about the generalization. We see all these male IMO medalists, and – depending on your favorite political tradition – either assume that men have an unfair advantage in maths, or that they are inherently better at it. The former worldview serves as a basis for public policy. The question of which moment to update rarely even comes up.

This makes me wonder whether this process of looking at the extremes then updating our beliefs about the mean is just the normal way we learn. If that is the case, how many other things are we missing?

Invisible privileges


Let’s review the evidence.

African-Americans are twice as likely to be stopped by the police. Police officers speak less respectfully to them. They are more likely to use violence against them. Overall, African-American men get killed by the police 2.5 times as often as White men. Then, African-Americans face discrimination at every stage of the justice system. For an identical case and history, African-American defendants have 10% higher odds of being incarcerated. When they are, they receive 10% longer sentences for the same crime.

African-Americans also face discrimination when they are the victims. Criminals receive lighter sentences when their victim is black1 However, it is not clear whether this is really due to discrimination or to other factors. In fatal traffic accidents, drivers receive a 53% shorter sentence if the person they killed happens to be black. When a black person goes missing, there is 3.1 times less media coverage than if the victim is white2This is called the Missing White Woman Syndrome. This study was conducted in 2016, before the Black Lives Matter movement gained popularity. It would be interesting to see how things have changed as a result..

Institutional discrimination also appears in the education system. Teachers systematically give better grades to students from the white majority than to ethnic minorities, for identical works3There are two kinds of methodologies to address this question. The most common is to compare the grades obtained by a student when the teacher knows her identity, with grades obtained by the same student on blind examinations. The second one is to fabricate a fake essay and ask teachers to grade it, while changing only the name of the student, and see if they are graded differently.. At school, African-American children receive harsher punishments for the same behavior as well as closer surveillance from teachers. And overall, in the US, African-Americans are 12% less likely to access higher education than white people.

Then, there is housing discrimination. When ethnic minorities apply to rent an apartment, their odds of receiving a positive response are 47% lower, everything else equal. With no surprise, African-Americans are 4.5 times as likely to be homeless, and then 45% less likely to be sheltered.

In addition, ethnic minorities generally have poorer health than white people. Black people work more dangerous jobs, making them 33% more likely than white people to be injured at work. They are 16 % more likely to die on their workplace. On average, the life of black people is 4.3 years shorter than white people’s.

Most of those results are from large studies, they are solid and have been replicated many times. Yet some people decide to completely ignore all the evidence, and still deny the existence of racist discrimination. How is it even possible? What is going on in the head of racism-deniers?


Men are 2.5 times more likely than women to be stopped by the police. Police officers are more likely to arrest men and more lenient toward women. Overall, men get killed by the police 20 times more often than women. Then, men face discrimination at every stage of the justice system. Men are more likely to be considered guilty and receive harsher sentences than women for an identical case and history4These studies are called “mock juror trials”. They use a panel of jurors who are presented with a fictional case, where only the gender or ethnicity of the defendant is changed, and asked what the sentence should be. This way, everything is exactly identical except the gender of the defendant, so any difference can be attributed to discrimination. Some studies even staged fake audiences with comedians for extra realism.. Men have 1.64 to 2.15 times higher odds of being incarcerated, depending on the study. When they are, men also receive 30% to 63% longer sentences for the same crime compared to women5These are observational studies, meaning they look at the outcomes of a large number of real-life cases, taking into account offense severity, previous offenses, whether the defendant has to take care of children, and other confounders.. The sexist bias favoring women is much larger than the racial bias – that is, black women are treated better than white men. As you might expect, justice’s double-standard against men is especially marked for sexual offenses.

Men also face discrimination when they are the victims. Criminals receive lighter sentences when their victim is a man6With the same caveat as for racial discrimination.. In fatal traffic accidents, drivers receive a 36% shorter sentence if the person they killed happens to be a man. When a man goes missing, there is 2.9 times less media coverage than if the victim is a woman.

Institutional discrimination also appears in the education system. Teachers systematically give better grades to girls than to boys, for identical works. This happens already in elementary school, continues in middle school, and again in high school, and again in college7Interestingly, these studies found that female teachers were on average more biased in favor of girls than male teachers.. This favoritism for girls has measurable effects on boys’ progress and future career orientation. Parents also invest more time teaching girls than boys and spend 25% more money on girls’ education. At school, boys receive harsher punishments for the same behavior as well as closer surveillance from teachers. And overall, in the US, men are 16% less likely to access higher education than women. Here again, the gender gap is larger than the racial gap8Moreover, unlike for ethnic minorities, there is no affirmative action attempting to correct this disparity – even when women are more likely to access higher education, affirmative action is still in favor of women..

Then, there is housing discrimination. When women apply to rent an apartment, their odds of receiving a positive response are 28% higher than men, everything else equal. With no surprise, men are 1.5 times as likely to be homeless, and then 40% less likely to be sheltered. A study in France found that 90% of the people who die in the streets are men9It should be noted that the gender gap in homelessness is more marked in France than in the USA..

In addition, men generally have poorer health than women. Men work more dangerous jobs, making them 40% more likely than women to be injured at work. They are 8 times more likely to die on their workplace. On average, the life of men is 5 years shorter than women’s10The gap in life expectancy is commonly attributed to biological factors, as a legitimizing myth. However, this study on monks and nuns (who do pretty much the exact same things throughout their lives) found that at most one year of the gap could be attributed to biological differences.. In spite of this, there is much more scientific research and US national offices dedicated to women’s health. Medical research on women’s health receives considerably more funding than men’s health, even for conditions that affect men more often11See the tables from page 56. For lung cancer, in 2016 the NIH spent $180,000,000 for women-specific research, $318,000 (!) for men-specific research, and $136,000,000 for lung cancer in general. They also spent $1,916,000 for women’s suicides, and only $156,000 for men’s suicides, despite men dying from suicide about four times as often..

Like for racism, most of those results are from large studies, they are solid and have been replicated many times. Yet some people decide to completely ignore all the evidence, and still deny the existence of discrimination privileging women. Just like racism, discrimination against men has been systematically made invisible.


I am aware that many readers will hear about discrimination against men for the first time. Perhaps you’ve heard about discrimination from the police beforehand, but did you know about the grading discrimination? Did you know about the housing discrimination? If not, why didn’t anybody tell you about it?

One thing to consider is that people can’t really tell how much discrimination they face based on their subjective experience. In their classic 1997 book Social Dominance, social psychologists Jim Sidanius and Felicia Pratto report that (in 1997) many African-Americans had no clue about how much racism they faced12See page 106 of the book.. In the 1990s, 58% of African-Americans believed they had the same housing opportunities as white people. 46% thought they had the same chances at employment, and 63% thought they had the same chances in education – despite clear evidence of the contrary13Sidanius and Pratto dedicate the third part of their book to evidence of discrimination against black people. However, they completely disregard discrimination against men – to be fair, most of the evidence that I discussed here was published after the book Social Dominance came out, so you can’t blame the authors.. This is one of the universal patterns described in Social Dominance: unfair treatment against subordinate groups is overlooked, legitimized, and actively erased by the dominant status quo, until even the discriminated population believes it is not real. It is perfectly possible to face discrimination on a daily basis and be completely unaware of it.

In addition, there is growing evidence that people (academics, the media, people in general) care very little about the issues that affect men. Most people know about manspreading, but have never heard about the teacher grading gap. People think gender balance at work is important, but only in professions where women are underrepresented. Scientific studies that find a bias against women are cited far more often than studies that find a bias against men, even when the later use larger samples. Remember the kidnapping study I mentioned above, which found that there is less media coverage when a man goes missing? This is the same process. Presumably, this attention disparity is the result of traditional gender roles, which (among many other things) say that men are not expected to complain, and will be shamed if they do so – but this is a complicated topic that deserves a future blog post on its own.

As a takeaway, there is a striking similarity between discrimination against ethnic minorities and discrimination against men. My point is not to say that minorities or men “have it harder”, nor is it that racism is exactly identical to sexism – the historical and social mechanisms are obviously entirely different. My point is that, currently, men and ethnic minorities experience a similar pattern of stereotyping and discrimination in their daily life. The strange polarization of the culture wars makes it even harder to notice: the political tribes who care about racism are sharply separated from the tribes who care about men’s issues. This is unfortunate, because both tribes share the common goal of eliminating discrimination14Of course, there are also traditionalists who just use men’s issues as an excuse to attack feminism, hoping to restore traditional gender roles. I personally believe, on the contrary, that traditional gender roles are the cause for discrimination, and that we need to step away from them. – maybe their filter bubbles only show them one side of the problem? It took decades for the majority of the population to realize that racist discrimination is real. For sexism against men, such a shift in collective consciousness has yet to happen.

If you spot any mistake or inaccuracy in this text or the supporting evidence, please let me know in the comments, so I can correct it.

Annex: what about hiring discrimination?

Hiring discrimination can be measured by sending fictional resumes to employers, only changing the ethnicity or gender of the applicant, and counting how many replies you get. As you expect, equally-qualified ethnic minorities are far less likely to be hired. Regarding gender discrimination, the evidence is much more mixed. This makes it very easy to cherry-pick studies that show discrimination against women (if you read feminist sources) or against men (if you read MRA sources). This meta-analysis found moderate discrimination against men, but only in female-dominated jobs. This systematic review lists 11 studies looking at pure gender discrimination (man vs woman). Two of them found discrimination against women, four of them found discrimination against men, and the rest found no discrimination. A recent study which tracked recruiters’ behavior on online hiring markets found that women face a 6.7% penalty in men-dominated occupations, and that men face a 12.6% penalty in women-dominated occupations. Overall, gender discrimination in hiring is much less systematic than racial discrimination. This discrepancy is probably a remnant of the traditional gender division of labor, since men were traditionally assigned to salaried jobs. In any case, the common claim that it is harder for women to find employment appears to be wrong.


30-11-2020 – According Leeth et al., 2005, the racial gaps in fatal and non-fatal workplace injuries are respectively 16% and 33%, not 20% as previously reported.

01-02-2021 – A few studies on the effect of victim gender/origins on sentencing found no evidence for discrimination after controlling for case details. Thanks Greg for pointing that out. I also moved hiring discrimination to an annex, and added the recent study by Hangartner et al.