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My understanding is that the correlations in question persist, and are not small, when those other things are either controlled for or taken out of the picture. For example, here is an informal writeup of a PNAS article finding evidence of bias favouring male over female job applicants when everything about the applications was exactly the same apart from the name.
Does that include e.g. the likelihood of the applicant going on maternity leave in the near future?
Obviously not. Equally obviously, said likelihood has no bearing on the applicant's competence, which was rated substantially and significantly lower by the faculty in the study when the application bore a female rather than a male name.
(Good statistics on this seem hard to come by, but it looks like the average age at first birth for college graduates in the US is about 30 nowadays; I'd say the probability of an imminent maternity leave for a 22-year-old with a new job as a lab manager in a university is pretty damn small, even if she happens to be called Jennifer rather than John.)
Competence in research might mean: "Likelihood that this person has the chance of making a valuable contribution to their scientific field."
I don't think that there anything wrong when a science faculty defines competence that way.
I'm too lazy to search for data on education-based cohorts, but only 57.5% of US women are childless by the age of 25.
The source I found showed a really drastic difference between college-educated and not-college-educated women.
There is a really nifty way to solve this, by the way. Do what the Norwegians do. Half of maternity leave accrue to the other parent and is non-transferable.
That way career impact of child birth becomes gender neutral - for anyone married, anyways. And like all the best of feminist ideas, it is irreversible policy because it benefits both genders.
Men get time of to spend some time with their kid, and women don't have to worry about potential employers shunning them out of fear of having them go on leave because potential employers cannot hire anyone without that risk attached. Well, post menopausal women, I suppose. Doesn't seem likely to become a dominant hiring strategy.
Of course, maternity leave isn't the only way in which women can chose family over career. Also, this kind of policy amounts to valuing "equality" for its own sake above everything else, like productivity.
And single men.
Because an unspoken condition of employment that prospective employees must stay single is a management technique made of win.
Errh.. Not. Good lord. would you want to manage a team made up of 100% celibate men? This is not a weakspot in the law, because it's not a runaround anyone sane enough to not already be bankrupt would attempt.
It might on the margin inspire people to hire more people in their forties and fifties, - people who have had any children they are likely to have, but from the point of view of the government, that's also not a flaw, but more of a "Secondary benefit free with just legislation".
They make awesome startups. Redirected sexual energy is powerful :-)
Erm ... there's this guy in Rome who tried that ... I think they had some problems.
Well the institution in question is the oldest continuously operating institution around today so they certainly have something going for them.
With chastity pledge as a part of the job contract.
There are even clearer examples of gender bias on the unconscious level. The fact that women are hired at equal rates as men by orchestras if, and only if, the audition is behind a curtain and everyone enters barefoot so the hiring committee cant tell gender by footstep sounds is the most damning I can think of right now. Because that is a straight up test of competence at the only skill relevant for the job, and applicant genitalia still sway supposed experts unless extreme measures are taken to blind them to that factor. Basically, at this point there is such a huge pile of evidence that human beings are just completely incompetent at screening out utterly irrelevant factors that I would judge it sensible hiring policy in any field to have the job interview behind a curtain and a vocoder.
... Fuck it, I'm using that in a story. It fits right into a certain culture I'm building. ;)
I would not recommend conducting hiring tests for an orchestra behind a vocoder :-).
(Other than that: yes, I agree, except that actually conducting hiring interviews that way would probably actually lose more signal than it eliminated noise, at least in the fields I'm familiar with interviewing in. Alas.)
Just curious... What fields are those?
Software development, engineering, mathematics (in industry rather than academia).
The loss of signal could probably be eliminated in all of these, with some effort. The sort of thing I'm thinking of where signal would be lost by default is where you ask the interview candidate to design something, write a bit of code, sketch a system they worked on in the past, etc., on paper or whiteboard. If the candidate has to be behind a curtain, that's difficult to do and probably involves irksome extra latency (e.g., a system where they write or sketch whatever they want to and then step aside, and only then does the interviewer get to see what they did).
You could work around this with computerized whiteboards -- the candidate sits in one room and the interviewers in another, both rooms have electronic whiteboards, and they are coupled so that anything written on one shows up on the other too.
(Or by using something other than whiteboards that's easier to decouple in this way. For instance, for a coding task some kind of collaborative text editor may do better.)
I see. (I had guessed you were talking of people who have to directly¹ interact with perspective customers, so you have to know what they look and sound like in order to know what first impression perspective customers might get.)
That's not necessarily irrational in general. The other information on the resume does not prevent the name from also providing potentially relevant information.
I'd suggest you look up "screening off" in any text on Bayesian inference. The explanation on the wiki is not really the greatest.
But when you have information that is closer and more specific to the property you're trying to predict, you should expect to increasingly disregard information that is further from it. Even if your prior asserts that sex predicts competence, when you have more direct measures of competence of a particular candidate, they should screen off the less-direct one in your prior.
If there evidence that the effect size of discrimination stays the same regardless how much information an application provides?
I know what screening off is - I was saying that not all the information is screened off here. There are still other issues given the premise that names taken alone predict competence to some extent. For example, one resume may be more likely to be honest than another, and even if the resume is completely honest, reversion to the mean is likely to be larger in one case than another.
So: take a look at the paper, or at the informal summary of it to which I also linked, and then tell us whether you consider that -- given all the information provided to the faculty in the application -- knowing whether the candidate is male or female gives anywhere near enough further information to justify the differences in rated competence found by the researchers.
It seems to me that for that to be so, there would need to be absolutely huge differences between men and women, so big that no one with any brain and any integrity would deny that men are much much much better scientists than women. Do you think that's the case?
I think that regardless of the actual facts, assuming the difference is counterfactually that large, it's still very plausible that almost everyone would still deny any difference exists, due to political and cultural forces.
While I don't think there is such a large difference, I don't accept the argument from "people wouldn't pretend a big difference doesn't exist".
I wasn't merely arguing that if there were such a large difference everyone would admit it. I was also arguing that if there were such a large difference we'd all know it. Obviously this argument will be more persuasive to people who (like me) think it's clear from observation that there isn't so huge a difference between men and women, than to people who don't.
Just by way of reminder: we'd be looking for a difference large enough that, knowing
the difference between male and female suffices to make a difference to their estimated competence of 0.7 points on a 5-point scale. That would have to be either a really really enormous difference between men and women, or a really weird difference -- weird in that whatever it is somehow manages to make a big difference in competence without having any effect on academic performance, test scores, or reported faculty opinions. Which presumably would require it to be quite narrow in scope but, again, really really enormous in size.
And it seems about as obvious to me that there isn't such a difference as that (say) there isn't a difference of 20cm in typical heights between men and women. Not just because if there were then it would be widely admitted (maybe it would, maybe not) but because it would be obvious.
Now, of course I could be wrong. There could be such an enormous difference and I could be somehow blind to it for some weird cultural-political reason or something. But is it really too much to suggest that when
it's reasonable to take that as strong evidence for bias in favour of men over women that isn't simply a proportionate response to actual differences in competence? I mean, it's just Bayes' theorem. How likely is that outcome if people do have such bias? How likely is it if they don't? (Not "is it possible if they don't?". The answer to that sort of question is almost always yes, regardless of what's true.)
It's not entirely clear that these are two different things. Admitting a highly politically incorrect opinion publicly and admitting it to oneself or one's friends aren't really completely separate. People tend to believe what they profess, and what they hear others profess.
I suspect one source of the disagreement between us may be that you're assigning a high predictive ability to academic performance, while I don't even assign it a very high correlation. This may be because my intuition is trained on different academic fields. I don't have any experience with scientific lab managers (the job the study's resumes applied for). I do have experience with programmers and other related fields, mostly below the doctoral level.
When I first read that I thought: but there is about a 20cm difference in the average heights of men and women! Is gjm arguing the opposite point from what I thought, or maybe being sarcastic?
So I checked the average height differences between the sexes, and the male:female ratio is typically between 1.07-1.09. This translates to 8-15 cm of difference. So while it's not as much as 20cm, it's "only" a 2x difference from my prediction. Maybe I'm just bad at translating what I see into centimeters and this difference is much more obvious to you than it is to me.
I don't disagree with this. I just think the cultural power of "politically correct" thinking is strong enough to make people ignore truths of the magnitude of this being counterfactually wrong and stick to accepted explanations.
Maybe gjm's System 1 automatically compensates for the difference -- I know that unless I'm deliberately paying attentìon to people's height I'm much less likely to notice it if a man is six feet tall than if a woman is six feet tall, and for all we know the same might apply to gjm.
I'm guessing this effect doesn't just apply to height.
Academic performance is one of the things known to the faculty (and the same between the "male" and "female" conditions); it is not the only one. The relevant question is: How much predictive power does the totality of the information provided have, and conditioned on that how much predictive power does the sex of the applicant have? It looks to me as if the answers, on any account of sex differences that I find credible, are "quite a bit" and "scarcely any".
By "academic performance" I was referring to all of these bullet points:
Which (from your summary) I understand is pretty much all of the information in the application letter.
I'm not claiming that sex differences have predictive power; I'm claiming that academic performance doesn't have as much power as we'd like and recruiters have to look for more info.
No. If the argument is more clear because you think that it supports the outcome that you prefer you are engaging in motivated cognition. It's an error in reasoning.
But I didn't say, and I don't think it's true, that the argument is clearer "because [I] think it supports the outcome [I] prefer". I "prefer" that outcome, in part, because it seems clear from observation that there isn't that sort of huge difference between men and women. That is not a reasoning error, it's straightforward inference.
So your theory is that all observed larger number of men at the upper end of any bell curve is due to sexism? And the larger number of men at the lower end of most bell curve, e.g., more men in prison is due to..something?
Most of the data I've seen suggests women have lower variance, here Robin Hansen discusses some of the implications about variance in test scores.
It quite easy to make wrong arguments in favor of positions that are true. If you think that an argument is good just because you think it's conclusion is true it's time to pause and reflect and look at a situation where the same structure of the argument would lead to a conclusion that's false.
Even if men and woman are on average equally qualified that doesn't mean that a specific subset is. For a hiring manager it's not important whether there's causation. Correlation in the data set is enough.
And/or if the argument is less clear to other people because they think it supports the outcome that they don't like they are engaging in motivated cognition.
By the same logic you could say that someone who hires people with high SRT scores engages in SRT bias. Someone who hires based on SRT scores could simply reasonably believe that people with high SRT scores are more competent.
Google's HR department has a variety of factors on which it judges candidates. A few years afterwards they reevaluate their hiring decisions. They run a regression analysis and see which factors predict job performance at Google. They learn from that analysis and switch their hiring decision to hiring people which score highly on the factors that the regression analysis found predictive.
That's how making rational hiring decisions looks like. In the process they found that college marks aren't very relevant for predicting job performance. Being good at Fermi estimates unfortunately isn't as well, so those LW people who train Fermi estimates don't get benefits anymore when they want to get a job at Google.
Given current laws Google is not allowed to put values such as gender into the mix they use to make hiring decisions. That means that Google can't make the hiring decisions that maximize predicted job performance.
The politics of the issue also make it pretty bad PR for them to publish results about the effects of a model that includes gender if the correct value in the regression analysis would mean worse chances for woman getting a job. It's good PR for them if the correct value would mean to favor woman. No big company that does regression analysis on job performance published data that favoring in gender would mean hiring more woman. Factoring in gender into a regression analysis would mean that any bias against woman in subjective competence evaluations in interviews would be canceled by that factor.
Just imagine if a big company would find that by putting gender into their regression analysis they would hiring more women and get better average job performance as a result. Don't you think those companies would lobby Washington to allow them to put gender into hiring decisions? The silence on the issue speaks.
It could be that the silencing of feminists who want to prevent "privileged" from talking about the issue is strong enough that rational companies don't dare to speak about their need to change their hiring practices to hire more woman via making data driven arguments. If that's the case that says a lot about the concept of privilege and it's problem in shutting down rational arguments.
Imagine that academic performance has a really low value for predicting job performance. People that spend a lot of time preparing for tests get better academic marks. Woman spent more time than men preparing for academic tests. That means a woman of equal competence scores higher because she puts in more work. The test isn't anymore a strict measure of competence but a measure of effort at scoring highly of the test. In that scenario it makes sense to infer that a woman with the same test score as a man is likely less competent as the man as long as you are hiring for "competence" and not for "putting in effort to game the test".
If you write down the math you see that it depends on your priors for the effect size of how gender correlates with job performance.
Sure. It is possible to construct possible worlds in which the behaviour of the academic faculty investigated in this study is rational and unbiased and sensible and good. The question is: How credible is it that our world is one of them?
If you think it is at all credible, then I invite you to show me the numbers. Tell me what you think the actual relationship is between gender, academic performance, job performance, etc. Tell me why you think the numbers you've suggested are credible, and why they lead to the sort of results found in this study. Because my prediction is that to get the sort of results found in this study you will need to assume numbers that are really implausible. I could, of course, be wrong; in which case, show me. But I don't think anything is achieved by reiterating that it's possible for the results of this study to be consistent with good and unbiased (more precisely: "biased" only in the sense of recognizing genuine relevant correlations) decisions by the faculty. We all (I hope) know that already. "Possible" is waaaaay too low a bar.
Making wrong arguments isn't good even if it leads to a true conclusion. I haven't argued that the world happens to be shaped a certain way. I argue that your arguments are wrong. LessWrong is primarily a forum for rational debate. If you arguing for a position that I believe to be true but make arguments that are flawed I will object. That's because arguments aren't soldiers.
On the matter of the extend of gender discrimination I don't have a fixed opinion. My uncertainty interval is pretty large. Not having a small uncertainty interval because you fall for flawed arguments matters. The fact that humans are by default overconfident is well replicated.
But if we become back to grades as a predictor: Google did find that academic performance is no good predictor for job performance at Google.
Of course Google won't give you the relevant data as an academic does, but Google is a company that wants to make money. It actually has a stake in hiring high performing individuals.
While we are at it, you argue as if scientific studies nearly always replicate. We don't live in a world where that's true. Political debates tend to make people overconfident.
I'm not really commenting on the object-level issue, just on the dubious logic of claiming that the name can't matter if everything else is equal. In practice I'd guess it's likely that the difference in rating is larger than justified.
I'm not sure anyone's quite claiming that.
Just that if you asked, ahead of time, a question like "So, what would it take to convince you beyond reasonable doubt that there's bias favouring men over women in the academic employment market?", the answer you'd get would likely be pretty much exactly what this study found.
Of course, there are always loopholes, just like a sufficiently ingenious creationist can always find contrived explanations for why some scientific finding is compatible with creationism. The speed of light is changing! The aftermath of Noah's flood just happened to deposit corpses in the layers we find in the fossil record! The world was created with the appearance of great age! Similarly, we can find contrived explanations for why an identical-looking application gets such different assessments depending on whether it's thought to come from a man or a woman. They might be really worried about maternity leave, and choose to define taking maternity leave as a variety of incompetence! There might be differences in competence between men and women that make a big difference to scientific productivity but are completely undetectable by academic testing and unmentionable by faculty! There might be really big differences that everyone conspires not to admit to the existence of! Sure, there might. And the earth might be 6000 years old.
If this is indeed the case than why isn't the system approaching an equilibrium similar to the one the system reached for Asians, Irish, and Scottish Highlanders?
I don't think I can usefully attempt to answer the question, because it isn't perfectly clear to me (1) what sort of "equilibrium" you have in mind or (2) why you think I should "if this is indeed the case" expect the system to approach such an equilibrium. The linked article, consisting mostly of several pages of Macaulay, doesn't do much to make either of those things clear to me.
Would you care to be more explicit?
The point is that Asians, Irish, and Scottish Highlanders were able to overcome negative stereotypes and "microagressions", and whatever other epicycles the SJW crowd feels like inventing, towards them. Why not blacks and women? You know maybe there really are innate differences involved here.
What it would take to show that there is bias favoring men over women would involve showing that men are more likely to be hred than women and that this imbalance in hiring rate is not justified.
If you apply a high cutoff the difference is pretty big.
In the present case -- as you would see if you looked at the study in question, which I therefore guess you haven't -- the level of ability we're looking at (for "male" and "female" candidates) is not super-high, and in particular isn't high enough for the sort of variance difference you have in mind to make a big difference.
These are candidates with a bachelor's degree only, GPA of 3.2, and all the information in the application designed to make them look like decent but not stellar candidates for the job. We're not talking about the extreme tails of the ability distribution here; the tails have already been cut off.
All the listed information is actually remarkable little. Like I said below the most "objective" thing on your list is the GRE score and even standardized test scores have high variance.
Would that "everything" include things like college degrees, remember affirmative action is a thing in college admissions. Also, an applicant's sex conveys information, are you sure the other information was enough to completely screen that out? The other thing to take into account is that if I hire a women and she doesn't work out, I risk getting hit with a wrongful termination suite if I fire her.
They took the exact same application, sometimes with male-looking names and sometimes with female-looking names, and asked faculty for their opinions about them. The female versions were rated substantially (and significantly at the 0.001 level) worse for "competence", "hireability" and "willingness to mentor this student". The gap in estimated competence was about the same in size and significance as the gaps in the other metrics, which to me seems to indicate that differences in fear of a wrongful termination suit didn't contribute much if at all. (On looking at the relevant bit of the paper, the authors agree and have some statistical analysis that allegedly supports this view.)
When asked roughly what starting salary they'd offer the applicants, the "female" applications attracted ~12% lower figures.
(The details are all there at the other end of the link I gave.)
I'm not completely sure of anything, ever. But: The information included: age, degree granted and university that granted it, GPA, GRE scores, extracts from application letter and faculty letters of recommendation, etc. If there's any residual information to speak of in knowing whether the applicant was male or female, I'd be rather surprised; if there's enough to justify the differences found in the study, I'd be flabbergasted.
[EDITED to add: While affirmative action may be "a thing in college admissions", to the best of my knowledge it is not "a thing" in the awarding of college degrees, the calculation of GPAs, etc.]
If women are more likely to use maternity leave or otherwise devote more resources to family and less to the job than men are, and if they are more likely to sue for sexual harassment than men, then most of these assessments could be correct; seeing a female name actually does give information.
As I have said a few times already in this thread, the numbers make it look very much as if the dominating factor was an assessment that the "female" candidates were less competent than the "male" ones. Lack of commitment and increased lawsuit risk don't seem to me like matters of competence and I would expect the faculty surveyed to share that opinion.
Do you have a rough estimate of (1) how much more likely women would have to be than men to do those things, in order to justify a difference in evaluation of the magnitude found by this study, and (2) how much more likely women actually are to do those things?
(Two remarks in regard to sexual harassment lawsuits. 1: I think the relevant figure isn't how much more likely women are to file such suits but how much more likely they are to file them when no harassment has really occurred. But perhaps not: suppose women are more likely to be victims of sexual harassment sufficient to justify a lawsuit, and therefore more likely to file such lawsuits; then one possible position would be to consider women less desirable employees on those grounds and rate them as less competent. Personally, I think that would be odious, but I can imagine that some people might disagree. 2: My understanding is that actually such lawsuits are really rather rare, much too rare for rational consideration of their risk to yield the reported difference in evaluation even if (a) all such lawsuits are assumed groundless but successful and (b) the resulting losses in productivity and collegiality are assigned to lack of "competence" by the person filing the lawsuit. However, I don't have extensive statistics on this and will be happy to be corrected if wrong.)
The problem is that "whether sexual harassment has occurred" isn't all that well-defined. You can of course define "sexual harassment" however you want but then you have to establish you it's a bad thing. For example, from a briefing at the company I work at the examples of "sexual harassment" was:
1) a woman goes to work in somewhat provocative/revealing clothing and a male coworker complements her on her appearance.
2) a manager used the phrase "guys and gals".
Frankly if these examples are typical of "sexual harassment", I'd say sexual harassment isn't a problem.
Did either of these examples result in lawsuits?
I don't know, the presenter didn't say. Although the fact that these were presented as examples of behaviors not to engage in, is telling. Also even if they don't bring a lawsuit, the fact that they make an issue out of these kinds of things is not conducive to a good work environment.
Of course I wasn't there. But it occurs to me that there are several reasons why "marginal" examples might actually be the most useful:
I'd put the examples you give in the category of (not typical examples of sexual harassment, but) things that are frequently harmless but (1) might cause easily-avoided annoyance or upset in some cases and so should maybe be avoided and (2) in some cases might indicate, or be thought to indicate, an underlying bad attitude (women in the workplace being seen primarily as eye candy; women being seen as lower-status and akin to children).
I repeat: of course I wasn't there and don't know exactly what your presenter said about these examples. If s/he said "these things are definitely harassment and you could get in serious trouble for doing them" then I'd regard that as unreasonable; if s/he said "these things may seem harmless, and often they are, but you should still avoid them", I'd agree.
Anyway, I mention all this just in the interests of mutual understanding; it's all kinda irrelevant to the question of whether "greater risk of sexual harassment lawsuits" is a good justification for rating an identically-described person as substantially more "competent" if they have a male name than a female name. Do you really think it is?
The problem is that it causes people to treat it as an archetypical example.
I fail to see why it should be policy to cater to people who are clearly being unreasonable.
While lawsuits may be rare, they are expensive, and people are risk-averse.
Also, the range of behavior that has to be avoided to avoid an unjustified lawsuit is much wider than the range of behavior that has to be avoided to avoid a justified lawsuit, and since even unjustified lawsuits are expensive, the former category is what really matters.
For a lot of colleges the hard part is getting in, and getting the degree isn't that hard conditional on getting in.
That would be why the application also included the applicant's GPA. And also both GRE scores. And a bunch of other things.
GPA is meaningless without knowing how hard the classes the applicant took were.
So is your claim that the scores on a single GRE test completely capture everything about an applicant relevant to job performance?
I find your question absolutely bewildering, given that the very sentence I wrote that mentioned GRE scores mentioned them only as part of a list of things.
Yes and when I pointed out the problems with all the other things in the list your reply basically amounted to "you haven't made any objections to GRE scores".
You didn't point out the problems with all the other things in the list, you made a claim about one thing in the list. My reply did not (as I have already pointed out) amount to "you haven't made any objections to GRE scores".
Regardless, no metric is perfect, and no one has been claiming otherwise. Accordingly, it is possible in principle (as I have already said more than once in this discussion) that there might be male/female differences that are either really huge, or highly relevant to scientific competence but startlingly uncorrelated with all the information provided to the faculty in this study, and that render the assessments they made rational given the information they had.
However, it seems pretty unlikely to me.
What do you think? Is the best explanation for these very different assessment of identical applications from differently-named candidates, in your opinion, that the faculty making the assessment are aware of such huge differences between men and women and have weighed them roughly correctly (not necessarily consciously and explicitly) to arrive at the results that have? If so, could you sketch for me roughly what these differences are and how they lead to that result? Because I'm having real trouble thinking of any hypothesis of this sort that's consistent with what I think I've observed of the relative abilities of men and women.
Let's see: there are numerous ones the most relevant are: women have less variation in intelligence then men and so there fewer unusually smart women. Women are worse at taking criticism. There is also a lot of stuff about the kind of hierarchies men and women tend to form.
Have you actually been observing the relative abilities between men and women, or is your reaction whenever you notice a woman doing something badly or acting emotionally to hit yourself for having a "sexist" thought?
Do I believe that Americans are generally more intelligent than Europeans? No, I don't. At the same time in the LW census the average American has somthing like a 10 point higher IQ. In the data set there a strong correlation.
I think the intuition that the factor of the name should be zero is wrong even if there no causal effect because gender simply interacts in complex ways with many other things. I'm not sure in what direction the factor is going to correct, which might also be different in different situations but assuming that it contains no information at all doesn't seem to be right.