My previous post on #AddMaleAuthorGate did not focus on the part of the peer review that discussed possible sex differences. However, that part of the peer review has since been characterized as harassment, so I thought that a closer look would be of value. I have placed the relevant part of the public part of the peer review below.

"...perhaps it is not so surprising that on average male doctoral students co-author one more paper than female doctoral students, just as, on average, male doctoral students can probably run a mile race a bit faster than female doctoral students.
... ...
As unappealing as this may be to consider, another possible explanation would be that on average the first-authored papers of men are published in better journals than those of women, either because of bias at the journal or because the papers are indeed of a better quality, on average ... And it might well be that on average men publish in better journals ... perhaps simply because men, perhaps, on average work more hours per week than women, due to marginally better health and stamina."

Below, I'll gloss the passage, with notes that characterize as charitably as possible what the reviewer might have been thinking when writing the passage. Here goes:

"...perhaps it is not so surprising that on average male doctoral students co-author one more paper than female doctoral students,..." = This finding from the manuscript might not be surprising.

"...just as, on average, male doctoral students can probably run a mile race a bit faster than female doctoral students." = There might be an explanation for the finding that reflects something other than bias against women. Let me use an obvious example to illustrate this: men and women are typically segregated by sex in track races, and this might not be due to bias against women. Of course, I believe that there is overlap in the distribution of running speed, so I will toss in an "on average" and a "probably" to signal that I am not one of those sexists who think that men are better than women in running a mile race on average. I'll even use the caveat "a bit faster" to soften the proposed suggestion.

"... ..." = I wrote something here, but this passage was redacted before my review was posted on Twitter. That double ellipsis is unusual.

"As unappealing as this may be to consider..." = I know that this next part of the review might come across as politically incorrect. I'm just trying to signal that this is only something to consider.

"...another possible explanation would be that..." = I'm just proposing this as a possibility.

"...on average..." = I understand the overlap in the distribution.

"...the first-authored papers of men are published in better journals than those of women..." = I understand this finding from the manuscript.

"...either because of bias at the journal..." = That finding might actually be due to journals being biased against women. I realize this possibility, and I am not excluding it as an explanation. I even mentioned this hypothesis first, so that no one will think that I am discounting the manuscript's preferred explanation.

"...or because the papers are indeed of a better quality, on average..." = This is the most reasonable alternate explanation that I can think of. I am NOT saying that every paper by a man is necessarily of a better quality, so I'll mention the "on average" part again because I understand that there is overlap in the distribution. However, if we measure the quality of papers by men and the quality of papers by women and then compare the two measures, it might be possible that the difference in means between the two measures is not 0.00. I hope that no one forgot that this sentence began with a set of caveats about how this is a possible explanation that might be unappealing.

"..." = I wrote something else here, but this passage was also redacted before my review was posted on Twitter.

"And it might well be that on average men publish in better journals..." = Just restating a finding from the manuscript. I remembered the "on average" caveat. That's my fifth  "on average" so far in this short passage, by the way. I hope that my I'm-not-a-sexist signals are working.

"..." = I wrote something else here, too, but this passage was also redacted before my review was posted on Twitter; this ellipsis is mid-sentence, which is a bit suspicious.

"..perhaps simply because men, perhaps.." = This is just a possibility. I used the word "perhaps" twice, so that no one misses the "perhaps"s that I used to signal that this is just a possibility.

"...on average work more hours per week than women..." = This is what it means when the male-female wage gap is smaller when we switch from weekly pay to hourly pay, right?

"...due to marginally better health and stamina." = I remember reading a meta-analysis that found that men score higher than women on tests of cardiovascular endurance; I'm pretty sure that's a plausible proxy for stamina. I hope that no one interprets "health" as life expectancy or risk of a heart attack because the fact that men die on average sooner than women or might be more likely to have a heart attack is probably not much of a factor in the publishing of academic articles by early-career researchers.

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In my voice again. Some caveats of my own:

I am not making the claim that the review or the reviewer is not sexist or that the reviewer would have made the equivalent review if the researchers were all men. The purpose of this exercise was to try to gloss as charitably as possible the part of the review that discussed sex differences. If you do not think that we should interpret the review as charitably as possible, I would be interested in an explanation why.

The purpose of this exercise was not to diminish the bias that women face in academia and elsewhere. This post makes no claim that it is inappropriate for the female researchers in this episode -- or anyone else -- to interpret the review as reflecting the type of sexism that has occurred and has continued to occur.

Rather, the purpose of this exercise was to propose the possibility that our interpretation of the review reflects some assumptions about the reviewer and that our interpretation is informed by our experiences, which might color the review in a certain way for some people and in a certain way for other people. These assumptions are not necessarily invalid and might accurately reflect reality; but I wanted to call attention to their status as assumptions.

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There has recently been much commentary on the peer review received by female researchers regarding their manuscript about gender bias in academic biology (see here, here, and here). The resulting Twitter hashtag #addmaleauthorgate indicates the basis for the charge of sexism. Here is the relevant part of the peer review:

It would probably also be beneficial to find one or two male biologists to work with (or at least obtain internal peer review from, but better yet as active co-authors), in order to serve as a possible check against interpretations that may sometimes be drifting too far away from empirical evidence into ideologically based assumptions.

I am interested in an explanation of what was sexist about this suggestion. At a certain level of abstraction, the peer reviewer suggested that a manuscript on gender bias written solely by authors of one sex might be improved by having authors of another sex read or contribute to the manuscript in order to provide a different perspective.

The part of the peer review that is public did not suggest that the female authors consult male authors to improve the manuscript's writing or to improve the manuscript's statistics; the part of the peer review that is public did not suggest consultation with male authors on a manuscript that had nothing to do with sex. It would be sexist to suggest that persons of one sex consult persons of another sex to help with statistics or to help interpret results from a chemical reaction. But that did not happen here: the suggestion was only that members of one sex consult members of the other sex in the particular context of helping to improve the *interpretation of data* in a manuscript *about gender bias.*

Consider this hypothetical. The main professional organization in biology decides to conduct research and draft a statement on gender bias in biology. The team selected to perform this task includes only men. The peer reviewer from this episode suggests that including women on the team would help "serve as a possible check against interpretations that may sometimes be drifting too far away from empirical evidence into ideologically based assumptions." Is that sexism, too? If not, why not? If so, then when ‒ if ever ‒ is it not sexist to suggest that gender diversity might be beneficial?

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Six notes:

1. I am not endorsing the peer review. I think that the peer review should have instead suggested having someone read the manuscript who would be expected to provide help thinking of and addressing alternate explanations; there is no reason to expect a man to necessarily provide such assistance.

2. The peer review mentioned particular sex differences as possible alternate explanations for the data. Maybe suggesting those alternate explanations reflects sexism, but I think that hypotheses should be characterized in terms such as substantiated or unsubstantiated instead of in terms such as sexist or inappropriate.

3. It is possible that the peer reviewer would not have suggested in an equivalent case that male authors consult female authors; that would be fairly characterized as sexism, but there is, as far as I know, no evidence of the result of this counterfactual; moreover, what the peer reviewer would have done in an equivalent case concerns only the sexism of the peer reviewer and not the sexism of the peer review.

4. I have no doubt that women in academia face bias in certain situations, and I can appreciate why this episode might be interpreted as additional evidence of gender bias. If the argument is that there is an asymmetry that makes it inappropriate to think about this episode in general terms, I can understand that position. But I would appreciate guidance about the nature and extent of this asymmetry.

5. Maybe writing a manuscript is an intimate endeavor, such that suggesting new coauthors is offensive in a way that suggesting new coauthors for a study by a professional organization is not. But that's an awfully nuanced position that would have been better articulated in an #addauthorgate hashtag.

6. Maybe the problem is that gender diversity works only or best in a large group. But that seems backwards, given that the expectation would be that a lone female student would have more of a positive influence in a class of 50 male students than in a class of 2 male students.

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UPDATE (May 4, 2015)

Good response here by JJ, Ph.D to my hypothetical.

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Here is Adam Davidson in the New York Times Magazine:

And yet the economic benefits of immigration may be the ­most ­settled fact in economics. A recent University of Chicago poll of leading economists could not find a single one who rejected the proposition.

For some reason, the New York Times online article did not link to that poll, so readers who do not trust the New York Times -- or readers who might be interested in characteristics of the poll, such as sample size, representativeness, and question wording -- must track down the poll themselves.

It appears that the poll cited by Adam Davidson is here and is limited to the aggregate effect of high-skilled immigrants:

The average US citizen would be better off if a larger number of highly educated foreign workers were legally allowed to immigrate to the US each year.

But concern about immigration is not limited to high-skilled immigrants and is not limited to the aggregate effect: a major concern is that low-skilled immigrants will have a negative effect on the poorest and most vulnerable Americans. There was a recent University of Chicago poll of leading economists on that concern, and that poll found more than a single economist to agree with that proposition; fifty percent, actually:

ImmigrationLowB---

Related: Here's what the New York Times did not mention about teacher grading bias

Related: Here's what the New York Times did not mention about the bus bias study

My comment at the New York Times summarizing this post, available after nine hours in moderation.

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You might have seen a Tweet or Facebook post on a recent study about sex bias in teacher grading:

Here is the relevant section from Claire Cain Miller's Upshot article in the New York Times describing the study's research design:

Beginning in 2002, the researchers studied three groups of Israeli students from sixth grade through the end of high school. The students were given two exams, one graded by outsiders who did not know their identities and another by teachers who knew their names.

In math, the girls outscored the boys in the exam graded anonymously, but the boys outscored the girls when graded by teachers who knew their names. The effect was not the same for tests on other subjects, like English and Hebrew. The researchers concluded that in math and science, the teachers overestimated the boys' abilities and underestimated the girls', and that this had long-term effects on students' attitudes toward the subjects.

The Upshot article does not mention that the study's first author had previously published another study using the same methodology, but with the other study finding a teacher grading bias against boys:

The evidence presented in this study confirms that the previous belief that schoolteachers have a grading bias against female students may indeed be incorrect. On the contrary: on the basis of a natural experiment that compared two evaluations of student performance–a blind score and a non-blind score–the difference estimated strongly suggests a bias against boys. The direction of the bias was replicated in all nine subjects of study, in humanities and science subjects alike, at various level of curriculum of study, among underperforming and best-performing students, in schools where girls outperform boys on average, and in schools where boys outperform girls on average (p. 2103).

This earlier study was not mentioned in the Upshot article and does not appear to have been mentioned in the New York Times ever. The Upshot article appeared in the print version of the New York Times, so it appears that Dr. Lavy has also conducted a natural experiment in media bias: report two studies with the same methodology but opposite conclusions, to test whether the New York Times will report on only the study that agrees with liberal sensibilities. That hypothesis has been confirmed.

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Social science correlations over 0.90 are relatively rare, at least for correlations of items that aren't trying to measure the same thing, so I thought I'd post about the 0.92 correlation that I came across in the data from the Leslie et al. 2015 Science article. Leslie et al. co-author Andrei Cimpian emailed me the data in Excel form, which made the analysis a lot easier.

Leslie et al. asked faculty, postdoctoral fellows, and graduate students in a given discipline to respond to this item: "Even though it's not politically correct to say it, men are often more suited than women to do high‐level work in [discipline]." Responses were made on a scale from 1 (strongly disagree) to 7 (strongly agree). Responses to that suitability stereotype item correlated at -0.19 (p=0.44, n=19) with the mean GRE verbal reasoning score for a discipline and at 0.92 (p<0.0001, n=19) with the mean GRE quantitative reasoning score for a discipline [source].

suitabilitystereotype

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Female representation varies across academic field, as illustrated in the figure below [source]. Females earned 46% of all doctoral degrees awarded in 2013, but represented substantially more than that percentage in education (68%) and the social sciences (59%), and substantially less than that percentage in the physical sciences (29%) and engineering (23%). The gray line indicates the percentage of all doctoral degrees that women earned in 2013 (46%).Female Representation by Sex and Field---

I began to look into this topic after reading the Leslie et al. 2015 Science article. I have since come across treatments that better cover the same ground [Scott Alexander, Ceci et al. 2014], so I'll post a few figures here with brief commentary.

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One possible explanation for the variation in female representation across academic field is sex differences in mathematics and verbal performance. The figure below reports SAT-Math scores [source] and SAT-Reading scores [source] by sex in 2009. The figure indicates cumulative numbers, so the point at 40% female representation and a 650 SAT-Math score indicates that females were 40% of all SAT test takers who scored a 650 or higher on the SAT-Math section. The 40% is not adjusted to account for the fact that more females than males took the SAT: there were 224,230 total persons who scored a 650 or higher on the 2009 SAT-Math test: 88,896 females, and 135,334 males.

SAT Scores by Sex

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Scott Alexander linked to this Hsu and Schombert article, which has this passage:

Thus, a strong interpretation of our result would be that even the most determined student is unlikely to master undergraduate Physics or Mathematics if their quantitative ability is below 85th percentile in the overall population. To have a 50 percent or greater chance of success (i.e., for a person of average conscientiousness or work ethic), one needs SAT-M well above 700, or in the top few percent of the overall population.

In the 2009 SAT data, females represented 36% of SAT-Math test takers who scored a 700 or above, 34% of SAT-Math test takers who scored a 750 or above, and 31% of SAT-Math test takers who scored an 800. So -- presuming no downstream discrimination or sex differences in interest or other non-academic factors -- we'd expect females to represent roughly 1/3 of PhDs in math-intensive fields such as engineering or the physical sciences. But females represent less than 1/3 of PhDs in math-intensive fields: in the 2013 data plotted in the first figure, females were 29% of physical sciences PhDs and 23% of engineering PhDs.

This is where the discussion must involve discrimination, bias, interests, life choices, and life constraints. It seems that the relevant question is the relative effect size of each factor and not the presence or absence of each factor. Ceci et al. 2009 and 2014 are good places to find a discussion of such factors.

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One way to look for evidence of discrimination against women in representation across academic field is to plot sex differences in verbal and quantitative abilities by field. If women with high levels of quantities abilities were being systematically dissuaded from STEM fields such as engineering and the physical sciences into non-STEM fields such as the social sciences and the humanities, then these displaced high-quantitative-ability women should inflate the mean quantitative reasoning scores of women in non-STEM fields. That does not appear to be the case, based on the figure below [source], which plots the mean GRE scores in 2013 in verbal reasoning and quantitative reasoning among male and female GRE test takers in each field. Of course, women with high quantitative abilities might have been systematically dissuaded from STEM fields to non-academic careers; there might also be discrimination upstream that causes observed differences in GRE scores; and there might be selection issues in play with regard to who takes the GRE exam.

GRE by Field By Sex

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This final figure plots female representation across academic field in 2013 [source] against mean male GRE test scores in verbal reasoning and quantitative reasoning, by broad academic field, in 2013 [source]. The GRE scores are limited to males to account for the fact that some fields such as education have a relatively high percentage of females and a relatively low mean GRE quantitative reasoning score. Patterns for quantitative reasoning scores match those of Randal Olson and Scott Alexander, with female representation falling as male GRE quantitative reasoning scores rise, but patterns for verbal reasoning scores don't match those of Randal Olson: in Olson's data, there is no correlation, but in the data below, there is a positive correlation. One large difference between Olson's data and the data in the figure below is that Olson's data were disaggregated into smaller fields.

Female Representation by Male GRE Score---

Notes:

1. [2015-01-25 edit] Corrected the spelling of Randal Olson, and revised the SAT graph to read "SAT Critical Reading" instead of "SAT Verbal."

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Here is the title and abstract to my 2015 MPSA proposal:

A Troublesome Belief? Social Inequality and Belief in Human Biological Differences

In A Troublesome Inheritance, Nicholas Wade speculated that biological differences might help explain inequality of outcomes between human groups. Reviewers suggested that Wade's speculations might encourage xenophobia, so, to understand the possible attitudinal consequences of such a belief, I develop predictions based on the expectation that belief in a biological explanation for group-level social inequalities reduces the perceived need for policies to reduce these inequalities. General Social Survey data supported predictions that this belief is correlated with lower support for policies to reduce sexual inequalities, support for greater social distance between racial groups, more support for traditional sex roles, and less support for immigration, but did not indicate a correlation with aggregate support for policies to reduce racial inequalities. I further developed and tested predictions regarding the possibility that persons who perceive biological differences to have resulted from unguided processes such as Darwinian evolution adopt more progressive attitudes toward social inequalities than persons who perceive biological differences to have resulted from guided processes such as intelligent design.

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UPDATE (Nov 5, 2014)

The "Troublesome Belief" proposal was accepted for the MPSA public opinion panel, "Using public opinion to gauge democracy and the good life."

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UPDATE (Mar 10, 2015)

Draft of the manuscript for the MPSA presentation is here. Data are here. Code is here.

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UPDATE (Mar 23, 2015)

Updated draft of the manuscript for the MPSA presentation is here. Data are here. Code is here. Thanks to Emil Ole William Kirkegaard for helpful comments.

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UPDATE (Feb 13, 2016)

Updated draft of the manuscript is here.

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