> He was not saying that women who work at Google are at a biological disadvantage, in any way, and that is a perverse misreading. He was saying that on the whole there's a biological reason when you roll the dice enough that more males are suitable for that work.
I don't understand what you said there, can you elaborate? What is the difference between males being more biologically suitable and females being at a disadvantage? From my perspective, you just contradicted yourself, can you help me understand why it's not a contradiction?
What the memo proposed is that it's "possible" there are fewer women in tech right now because of the biological differences. He may not have claimed it as fact, but he implied it. The problem I have with the implication is that it's obvious that evolutionary forces are not the primary causes of the current distribution, because the distribution of women in tech has changed drastically in the last 50 years faster than evolution's say in the matter. It's not possible that the current distribution is primarily caused by biological differences, and it's exceedingly likely that it is caused by social issues. But he suggested it is possible, and followed that by suggesting we should stop treating it like a social issue because it's possible.
And all of this so far is ignoring that the memo unironically takes the opposite stance on the minority group of conservatives.
Also Americans on average are more overweight than Japanese. This does not mean there are no overweight Japanese or thin Americans, or that either are less capable of a specific sport.
Does 60% of Americans being overweight today mean that it's likely that 60% of people are naturally and biologically incapable of maintaining a healthy weight?
There are genetic differences among underweight and overweight populations, so it is "possible" that the distribution of healthy weights to overweight people is natural a result of those genetic traits, and not the result of advertising and availability of high calorie foods.
We should stop treating obesity as though it's a problem, right?
I think you misunderstood me. I was not making a biological correlation, but a statistical one; namely that group averages doesn't say anything about an individual. The nature/nurture debate of overweight people is besides the point.
Then I think you misunderstood the memo. The memo is making a biological correlation. It suggests that the current distributions might accurately reflect differences in biology.
Nature vs nurture is completely the point here, Damore argued that nature is the primary force, not nurture, and therefore we should stop nurturing women in tech.
Maybe I should have been clearer, you stated to the parent reply that:
>> I don't understand what you said there, can you elaborate? What is the difference between males being more biologically suitable and females being at a disadvantage? From my perspective, you just contradicted yourself, can you help me understand why it's not a contradiction?
This was in response to the parent that said Damore had not singled out any female google employes. The overweight example was an attempt to clarify that even though statistical averages say something about a group, it does not say something about the individual, i.e the google females should not feel singled out by statistical averages.
As for the nature/nuture point in the memo: yes the memo is making a biological claim backed by sources. It does not suggest that current distributions are correct. No, the memo is not saying that nature is the primary force, only that it might play a part [1]:
"Differences in distributions of traits between men and women may in part explain why we don’t have 50% representation of women in tech and leadership."
> The overweight example was an attempt to clarify that even though statistical averages say something about a group, it does not say something about the individual, i.e the google females should not feel singled out by statistical averages.
Right. And what I've been trying to say is that the statistical averages aren't the offensive part. That's a straw man.
Google women don't feel offended when they're told they're a minority, they already know that; but they sure might reasonably be concerned when someone suggests they're a minority "in part" "possibly" because women aren't biologically as able to engineer as men.
> No, the memo is not saying that nature is the primary force, only that it might play a part [1]:
Sure, the memo didn't say it explicitly, but it did imply that. Everyone keeps defending the exact wording as if implication and misleading statements don't exist. Suggesting it's a "part" suggests it's a measurable and large part, comparable to social causes. Pointing out that women are more neurotic (which is a clinical term with very negative popular connotation, so extremely easily misunderstood) might be a part of why Google has so few women is leading the reader to conclude it's a major factor.
This argument is cherry-picking the science in favor, and completely ignoring the contrary evidence that suggests that social issues are much larger than anything we could possibly measure about innate biological ability. For example, that different countries have very different distributions of women in engineering, or that the distributions have changed wildly in the last 50 years.
You intentional misinterpret the discussion. Well hopefully intentional, because otherwise it would reflect very poorly on you.
Let me use another example: The NFL has no rules against women playing. None. Yet 100% of players are male, because biologically the exceptionally large and athletic tend to be male. I'm male, so does that mean that I could be an NFL player? Of course not, and I in no universe am in that realm.
That is a more extreme example, but patronizingly suggesting that it's all just social is utterly laughable and just outright ignorant.
"This argument is cherry-picking the science in favor, and completely ignoring the contrary evidence that suggests that social issues are much larger"
At this point you've reached utter lol territory. You are outright being dense about actual science, and then casually waving your hands and claiming that is more authoritative.
Where is the evidence that any of these biological differences actually do cause women to choose tech careers less? Sure there are links to studies about the differences themselves, but that's it. And in fact, if you look at the changing makeup of Com Sci majors and programmers over the last 60 years, it seems to be a slight possibility at best and disingenuous at the worst.
> Right. And what I've been trying to say is that the statistical averages aren't the offensive part. That's a straw man.
Well you did pose the question, I just answered it, so it was not to erect a straw man, and I was not really trying to contradict the rest of your claim by that example, maybe I should have been clearer on that.
> Sure, the memo didn't say it explicitly, but it did imply that. Everyone keeps defending the exact wording as if implication and misleading statements don't exist. Suggesting it's a "part" suggests it's a measurable and large part, comparable to social causes.
Yes, he certainly does imply that biological causes has a measurable effect, and a large enough effect that it should be taken into consideration for measures (that he also suggests) in order to change work practices so as they might better fit females and thus increase diversity.
> Pointing out that women are more neurotic (which is a clinical term with very negative popular connotation, so extremely easily misunderstood) might be a part of why Google has so few women is leading the reader to conclude it's a major factor.
I agree that it is unfortunate that neurotic is easily misunderstood, but if he didn't use the correct clinical term he would be critized for not being scientific enough, which you are already critizing him for.
> This argument is cherry-picking the science in favor, and completely ignoring the contrary evidence that suggests that social issues are much larger than anything we could possibly measure about innate biological ability. For example, that different countries have very different distributions of women in engineering, or that the distributions have changed wildly in the last 50 years.
I don't agree with you that science has concluded that biological factors don't play a role in what professions people go into. I saw an interesting Harvard debate between Steven Pinker and Elisabeth Spelke on this [1]. The two examples you present does not explicitly contradict that it might be part biological reasons [2], the provided link has a fascinating discussion in the comment section that gives you both sides of the discussion.
I quite appreciate your measured response, thank you for that.
> I don't agree with you that science has concluded that biological factors don't play a role in what professions people go into.
I don't recall saying nature isn't a factor at all, and if I did I take it back. But I do personally believe that right now nurture, which includes social and historical gender issues plus all forms of implicit and explicit social biases and discrimination, is the biggest factor. And enough bigger that it doesn't make any sense to talk about nature yet.
I didn't really intend to contradict the possibility of any biological factor, what I'm saying is that social issues appear to me to be a far larger influence than, say, any discernible difference in IQ. The memo either disagrees or ignores that.
Given that social factors were >99% of the distribution discrepancy less than a century ago, and that we're still working through huge social issues, and that workforce distributions of women both locally and globally are far from settled, I find it pretty hard to accept the idea that we should look at anything other than social factors.
It is possible that biological differences explain some of the workforce distributions. It's also possible that nature's effect on the current sex distribution of women in US tech is not even large enough to be measurable. It's possible that should we eradicate social gender inequality globally, biology's role will even out to a 0.0001% distribution discrepancy. It's also possible that Sabine Hossenfelder is right, and that once we have equal opportunity, "the higher ranks in science and politics would be dominated by women". https://news.ycombinator.com/item?id=14976028
I will check out the videos, thank you for the links.
Social issues is quite a clearly a problem, I just guess we disagree on the degree of biological influence, although there are some interesting points you bring up.
This whole memo thing has certainly led me down the rabbit hole. Not being an actual scientist with insight into both biological and social factors, I find it hard to be to sure of where I stand on the issue, and the current political climate is certainly helping to muddy the waters.
As long as an individual person is the sum of both nature and nurture, then regardless of the exact ratio of influence, nature will continue to make a difference.
> Damore argued that nature is the primary force, not nurture, and therefore we should stop nurturing women in tech.
No, he argued that nature in part, may explain the gender mismatch. That really cant be denied. It was also specifically talking about interests rather than capabilities of working in tech. Saying you might not be interested in programming software does not mean you're not able to.
The memo also never said we should stop nurturing women but that we should use different tactics that are more of match to their common on average biological traits and avoid any other discrimination based on race/gender, instead treating everyone as individuals.
Actually, if you read it closely, this is not true. True, he does suggest a lot of potential actions that could create a more beneficial tech culture for women, but then he goes on to suggest that Google needs to determine if these types of changes would impact Google's productivity in a negative way.
This is a pretty heavy implication that these programs do not in fact have any value and that diversity is not valuable. I can agree that a perfect 50/50 split is unattainable and the wrong goal, but there are studies that show diversity confers an advantage. And as Yonatan points out, I think the author has a misunderstanding of what is valuable from an engineering culture perspective.
I didn't see that clause about productivity, can you quote that piece of the memo? https://diversitymemo.com/
Yonatan's response was an emotional tirade, refusing to debate any of the cited research and using a different context of empathy. Nobody is debating the value of empathy in engineering (and it's good to have for any job and life in general, nothing specific to tech), but empathy in setting policy is completely different.
Rules should be set based on rational analysis instead of feelings. We argue for science/math-based policies in government so asking for the same in such a large corporation with regards to such a sensitive subject seems perfectly logical.
Here is the section from the memo that I'm referencing:
> Philosophically, I don’t think we should do arbitrary social engineering of tech just to make it appealing to equal portions of both men and women. For each of these changes, we need principled reasons for why it helps Google; that is, we should be optimizing for Google—with Google’s diversity being a component of that. For example currently those trying to work extra hours or take extra stress will inevitably get ahead and if we try to change that too much, it may have disastrous consequences. Also, when considering the costs and benefits, we should keep in mind that Google’s funding is finite so its allocation is more zero-sum than is generally acknowledged.
In general I agree that a rational analysis and science based approach is valuable. In fact, I think research into diversity actually does support the idea that it provides benefits. But it's not simply as black and white as science good, empathy/moralizing bad.
For instance, it may be objectively better for a company to do a lot of things that we consider immoral or harmful to society. For instance, how much does maternity and paternity leave cost a company in productivity, time, and the expense of providing a replacement worker? In fact, it might just be more profitable not to hire women of childbearing age at all, which actually was socially accepted for a long time. But these days our society has come to the general conclusion that this is actually morally unacceptable, and the benefits to parents and society outweigh the costs to the company. A very similar argument could be employed regarding the hiring of employees in the reserves or national guard.
I think you have to look at things holistically, as an entire system, and yes, morally.
From my perspective, you just contradicted yourself, can you help me understand why it's not a contradiction?
The IQ distribution of men and women is slightly different, and this is essentially settled science (it really is, however much we might pontificate -- our genetic past rolls the dice more with males). The male curve is slightly fatter, yielding larger numbers of exceptionally high and exceptionally low members. This means absolutely nothing if you have a male with an IQ of 140 and a female with an IQ of 140, however. Nor does it mean a 100 IQ male should be working at Google because there are slightly more high IQ males born.
We are smart enough to understand the difference between set probabilities and individual traits. Right?
because the distribution of women in tech has changed drastically in the last 50 years faster than evolution's say in the matter
Obviously there are social factors. That is indisputable. But at a point the gains in leveling the sexes for some domains become harder to get because there are confounding factors. Women in engineering has stayed virtually constant for several decades now.
> The IQ distribution of men and women is slightly different, and this is essentially settled science
How different? Can you source this claim? Are the means & medians at different places? How far apart are they? Are they far enough part to justify a male/female ratio in the tech workforce of 4x? I'm not arguing with you, but you are contradicting the article at hand.
"the mainstream view is that male and female abilities are the same across the vast majority of domains — I.Q., the ability to do math, etc."
> Women in engineering has stayed virtually constant for several decades now.
Which decades are you talking about? Which countries are you talking about? Please source this wildly inaccurate claim.
"According to studies by the National Science Foundation, the percentage of BA/BS degrees in engineering awarded to women in the U.S. increased steadily from 0.4 percent in 1966 to a peak of 20.9 percent in 2002"
That's a factor of 40x in 40 years. That doesn't sound super constant to me. How fast does evolution work again?
"Only 9.6% of engineers in Australia are women"
Interesting. Does that mean it's likely that Australian women are biologically only half as engineering capable as American women?
The difference between male and female IQ curves is easily found, and is scientifically settled. I don't particularly care if I'm contradicting the article at hand -- I'm not trying to vouch for it, but am saying that it's a rational discussion.
>Please source this wildly inaccurate claim.
I said for several decades. You cite the change for over five decades.
From 1990 to today -- closing on three decades -- women in engineering has stayed virtually unchanged in the US.
You seem to be taking the shotgun approach, and seem wholly ingenuine in discussing this rationally, so I would say this discussion is done.
> The difference between male and female IQ curves is easily found, and is scientifically settled.
Can you either source this or summarize, assuming that I genuinely want to know? How big is the difference in mean & median? Do you believe the difference is primarily responsible for the difference in distribution?
> I said for several decades. You cite the change for over five decades.
You're going to nitpick over 3 vs 5? Are you saying that the distribution of women wasn't in a steady state in the 1970's but it reached steady state in the 1990's, and that now the distributions are primarily reflective of innate biology and not social causes?
The distribution of women in computer science is quite different than the distribution of women in engineering - very roughly 2x as I understand. Do you think that computer science is significantly and measurably more prone to being affected by our biological differences than engineering?
I'm think I'm bringing up reasonable points, is it really a stretch to ask about different countries and different disciplines? The memo's reasoning should reasonably apply to all women in all businesses in all countries, not just engineering or tech. He even cited gender discrepancies that are cross-cultural, this is absolutely fair game.
> You seem to be taking the shotgun approach, and seem wholly ingenuine in discussing this rationally
I'm sorry that it's getting tough for you. I'm very genuine and very serious. I disagree that I'm being irrational, but you are entitled to your opinion.
I'm just hearing defensiveness about the claims stated as fact being true. I willingly accept that there are biological differences between men and women. What I don't see clearly is a rational justification for ignoring cultural sexism.
Thank you. So, tell me if I'm summarizing correctly. The currently accepted state of things is there's no difference in average IQ between men and women, but there is a known difference in variance which is symmetric. The variance discrepancy may be associated with specific subjects.
So if we assume that the US tech sector hires only above-average people, the difference in variance could lead to some difference in mean IQ, for certain subjects, in theory.
The question of what social factors bias IQ measurements is ongoing, but the top-line summary is that general intelligence is believed by many to have no sex difference.
Is that accurate?
I am cherry picking this one, but it's one of the most directly relevant studies cited here, and I find it interesting:
"Stoet and Geary concluded that sex differences in educational achievement are not reliably linked to gender equality."
The lack of concrete numbers has been bugging me, so I decided to hunt down the variance differences and calculate the expected percentage of women in tech.
I found a paper (http://www.ams.org/notices/201201/rtx120100010p.pdf) that reports a variance ratio of 1.15 for the maths scores of boys vs girls. Interestingly, they showed a negative correlation between variance ratio and gender gap for students in different countries. (In countries where boys have higher variance of maths scores, they tend to do worse on average than girls.)
However, the hypothesis that gender ratios in CS are influenced by the higher variance additionally involves a highly selective environment. So I decided to compute how the ratio changes when a minimum score is imposed. Python code below.
To fully explain 80% men in a profession using only higher variance as the criterion, you need a threshold of at least 4 standard deviations above the norm. In terms of IQ, that is at least 160. I don't think programmers have to be quite that smart.
Nice work!! I wish I was that quick with scipy. I too was very curious to get a better sense of what kind of absolute numbers it might take before the effects are noticeable.
Would you be willing to walk me through the steps? Specifically (I'm sure this is a dumb question) what is gender ratio to population ratio?
And in the male_female_ratio_above_threshold function, why is the female part scaled to 1/1.15? Shouldn't that value be 1.0? You'd have to skip the ratio_above_threshold function and factor in a norm.cdf call directly, but I don't see why it makes sense to compare normal distributions of 1.15 and 1/1.15, it should be comparing a 1.15 times normal to a 1.0 times normal, right?
If I replace the 1/1.15 with 1.0, I get that the 80/20 split happens at an IQ of just over 195 (6.44 std devs). If I'm right, this would mean that for IQ to explain the 80/20 split completely, tech people would have to be in the top ~0.000000006% of the general population. BTW, just for perspective there are 45 such people in the entire world.
> I don't think programmers have to be quite that smart.
So, correct me if I'm wrong, but programmers would have to be that smart, it would still have to be absolute IQ of 165 or above (or maybe >195). If we assume the average programmer has an IQ of 125, the 165 threshold is ~2.6 std devs above the norm, rather than ~4.3 std devs above the norm, right? We'd be talking about the top 0.5% of the programmer population, rather than the top 0.001% of the general population.
Relatively speaking, the threshold isn't as far above the average programmer as it is above the average person, but the 80/20 split depends on the absolute threshold, yes?
> To fully explain 80% men in a profession using only higher variance as the criterion
And we'd better qualify that this is assuming 1.15 is the right number globally, and that 1.15 is valid for subjects other than math, and that using only the higher variance means that percent of women in tech ratio is 100% dependent on IQ, which in turn means that hiring and job performance and ability all correlate 100% with IQ and nothing else, and finally that IQ itself is free of social biases.
Right? Even with a better understanding of what it might take, we're still pretty far into an imaginary land built on a long series of assumptions, some of which we know to be iffy at best. Not to mention that if any other factors are involved (and we know there are) then the IQ threshold is even higher.
You got me there. After I did the calculation I got too lazy to explain it in detail, so I hoped dumping it would be enough.
The variance ratio was calculated in the paper as (variance of the boy's scores)/(variance of the girl's scores), and the most natural way of computing the effect of a threshold is (men above threshold)/(women above threshold). But we are usually more interested in the relation of individuals to the overall population. So I needed a helper function to change the male/female ratio into a male/(male + female) ratio.
However, when you directly apply this function to the variance ratio, you get the part of the overall variance that is caused by the men. For the distribution, I actually wanted the standard deviation in multiples of the average deviation. Luckily, male/(average person) = male/((male+female)/2) = 2 * male/(male+female). Take the square root to get the standard deviation from the variance.
As mentioned above, (men above threshold)/(women above threshold) gets you the male/female ratio of people above the threshold. The cumulative density function gives you the probability someone is below the threshold, but because the normal distribution is symmetric, you can just flip the sign.
Because I'm expressing the variances of both sub-populations in relation to the total population, the variance for men comes from the male/female variance ratio 1.15, while the variance for women comes from the female/male variance ratio 1/1.15. If you use sqrt(1.15) and sqrt(1.0) as standard deviations instead, your threshold is in units of female standard deviation, which is slightly lower than that of the overall population.
I think that should have answered some of your questions, but not all of them.
> Relatively speaking, the threshold isn't as far above the average programmer as it is above the average person, but the 80/20 split depends on the absolute threshold, yes?
I actually hadn't thought to put it in relation to the population of programmers, but what you are saying makes sense (with the caveat that the standard deviation of programmers might differ from the general population). However, it seems that the 80/20 split isn't just at Google, but also close to the industry average and to enrollment in CS majors, which definitely don't just include the top programmers.
> And we'd better qualify that this is assuming 1.15 is the right number globally,
Actually, 1.15 isn't the right number globally: in different countries, the variance ratio was as low as 0.9 and as high as 1.5, so it isn't actually very stable. In the US, you have the choice between 1.19, 1.11 and 1.08, depending on the test and the year of testing. 1.15 is just the average over all countries and tests.
> and that 1.15 is valid for subjects other than math,
It probably isn't, but if you have no numbers, you just take what seems closest and run with it. Another caveat is that this number was for elementary/middle school students, and it could be different for adults, either higher, because the students were not fully developed yet, or lower, because the students were in different stages of development.
> and that using only the higher variance means that percent of women in tech ratio is 100% dependent on IQ, which in turn means that hiring and job performance and ability all correlate 100% with IQ and nothing else,
If there were some other normally distributed property, e.g. "programming ability", you could use that to make a similar argument, but it would have to fulfill these requirements to be the only explanation. Modeling the hiring process as a binary threshold is also quite simplistic, but I don't think I could compute it for anything more realistic.
> and finally that IQ itself is free of social biases.
You only have to assume that if you want to make IQ (or something else) the only explanation and claim that social bias is not involved. It would absolve Google of immediate responsibility if they simply administered an IQ test to candidates, but there would still be ways bias can influence the outcome.
> Not to mention that if any other factors are involved (and we know there are) then the IQ threshold is even higher.
Don't you mean lower? There are a whole bunch of other factors that make women less likely to go into CS, stay as programmers, apply to Google (like social bias) that would lower the remaining difference the "higher variance" hypothesis would have to explain. Of course that would leave it with little overall influence, but it might be the case that all individual factors are only able to explain a small part, and it's their interaction that causes the huge difference.
Thank you, thank you, for taking the time to explain!! This makes sense to me, and this analysis is fantastic.
I see more clearly the issue with 1/1.15 that I was worried about, and what I missed was the gener_ratio_to_population_ratio inside the variance_ratio_to_standard_deviation. I was worried the variance ratio was being double counted, but I see now that it's not, you just made it symmetric.
> It would absolve Google of immediate responsibility if they simply administered an IQ test to candidates
Isn't there a very high probability that this would implicate Google rather than absolve them? I'd be really pretty extremely surprised if Google had managed to hire tens of thousands of people that are all at least 165 IQ. If they prove the average IQ is 130, they then potentially have to take responsibility for the remainder of the discrepancy.
> Don't you mean lower? There are a whole bunch of other factors that make women less likely to go into CS
I did mean higher, but you're absolutely right to call BS on that. Any factors that alone would result in a lower female ratio than 20/80 would push the IQ threshold lower. Factors that alone would result in anything higher than 20/80 would raise the IQ threshold. My assumption was that, being at a very extreme end of the spectrum more than 4(!) standard deviations from the general population, almost all other factors would be closer to 50/50 than to 0/100, and would thus raise the IQ threshold. But that is my assumption and belief, not any established fact.
Since the actual ratio is 20/80, and I believe that IQ is at best a small factor, then for my hypothesis to be right, some other actual factor must be closer to 0/100 than 50/50. That means I'd better accept your suggestion that what I really meant to say is other factors would push the IQ threshold lower not higher, because there's evidence for that. You've done me a favor. ;)
I don't understand what you said there, can you elaborate? What is the difference between males being more biologically suitable and females being at a disadvantage? From my perspective, you just contradicted yourself, can you help me understand why it's not a contradiction?
What the memo proposed is that it's "possible" there are fewer women in tech right now because of the biological differences. He may not have claimed it as fact, but he implied it. The problem I have with the implication is that it's obvious that evolutionary forces are not the primary causes of the current distribution, because the distribution of women in tech has changed drastically in the last 50 years faster than evolution's say in the matter. It's not possible that the current distribution is primarily caused by biological differences, and it's exceedingly likely that it is caused by social issues. But he suggested it is possible, and followed that by suggesting we should stop treating it like a social issue because it's possible.
And all of this so far is ignoring that the memo unironically takes the opposite stance on the minority group of conservatives.
So what is the root part that I'm missing?