Predictors and Controls

We use as main predictors the five categories from table 1, collapsed into a single variable along the following lines:

• • Solo Male Author (solomale) for manuscripts with one male author.

• • Solo Female Author (solofemale) for manuscripts with one female author .

• • Multi-Author Male (multimale) for coauthored manuscripts written by a team of men.

• • Multi-Author Female (multifemale) for coauthored manuscripts written by a team of women.

• • Multi-Author Mixed (multimixed) for coauthored manuscripts written by a team including at least 1 woman and 1 man.

We use solo male as the reference category. In a model including only the remaining categories, the intercept would be equivalent to the coefficient of solomale, and the coefficients on the remaining categories would indicate the likelihood that manuscripts in that category differ from the baseline.

We also include a number of potential confounding or control variables:

• • A manuscript’s methodological approach, via dummies: either quant for quantiative, qual for qualitative, or mixed for multi-method pieces.^{Footnote 4}

• • Highest academic rank (highestRank) tests for whether authors or teams of authors with greater professional experience are more likely to have papers sent out and/or accepted. This takes on values from 1–4 based on the author with the highest academic rank, as follows: 1 for graduate student, 2 for post-doctoral researcher, 3 for assistant professor or the equivalent, and 4 for tenured professor or the equivalent.

Results

We present our simplest logit models in table 6. The coefficients have been exponentiated so that we can interpret them as Odds-Ratios. Recall that the baseline category for comparison is solomale. For reference purposes and for later use (see below) we also provide estimates for identically specified models that use multimale as a baseline, in table 7.

Table 6

Logit Estimates in Odds Rations, Solo Male Baseline

*<0.1; **p<.05; ***p<.01

Table 7

Logit Estimates in Odds Ratios, Multi-Author Male Baseline

*p<0.1; **p<0.05; ***p<0.01

In table 6, in terms of whether a paper is sent out for review, model 1 suggests that the odds a paper authored by one woman is sent out for review is about 31% higher than a paper written by a man. This result is statistically significant at conventional levels. Likewise, coauthored papers (whether they include a woman or not) are more likely to move on to review than papers authored by one man. Results in model 2 suggest that the rank of the solo author or the most experienced author on a collaborative team does not change the results.

These results suggest that in terms of whether a paper is sent out for review or not, the authors that are most disadvantaged are solo male authors. This result appears again in table 7, where solo-authored male manuscripts are the least likely to move forward relative to the baseline.

In terms of whether a paper is accepted or not, model 3 in table 6 suggests that conditional on being sent out, the odds of a coauthored paper with mixed genders gaining acceptance are about 67% higher than a paper with one male author. No other differences appear and even this difference disappears entirely in model 4, when we control for highest rank.^{Footnote 5}

In short, although gender and/or collaboration seem to offer some advantage over solo male authors at the initial stage of the editorial process, author rank swamps the effects of gender at the final decision stage. We say “seems” and “appears” because we still need to control for a paper’s methodological approach.^{Footnote 6}

To discover whether gender matters controlling for method and rank, in table 8 we provide predicted probabilities of the likelihood a paper is sent out for review and (conditional on being sent out) accepted. We simulated these probabilities from the posterior distribution of our base model using Zelig.^{Footnote 7} We hold rank constant at “Tenured.” Since we know that rank is an important and significant predictor after peer review, we expect that the “accept” probabilities will be relatively high. Finally, because we are describing posterior distributions we provide means and standard deviations to assess the probability a paper moves forward or is accepted.

Table 8

Predicted Probabilities by Methodology

Echoing findings in table 6, in terms of whether a paper is sent out or not, the aggregate results (the “overall’ results at the top of table 8) imply that solo female authors have about a five point advantage over solo male authors, regardless of methodological approach. However, it’s also clear that all coauthored papers are sent out at higher rates than all solo-authored papers.

Results in the rest of the table confirm that methodological approach matters a great deal for whether a paper is sent out. When all-female teams produce quantitative papers, these tend to have the highest probability of moving forward, almost 74%, about 16 points higher than the probability for mixed-author pieces. On the other hand, the probability of a qualitative paper by all-female teams being sent out for review is only 26.2%, 13.4 percentage points lower than the probability an equivalent all-male piece moving forward. Finally, solo authored female manuscripts seem to have the overall advantage when writing mixed methodology pieces, even when compared to collaborations.

The effects of gender also show up at the acceptance stage. Notice that the overall acceptance rates all hover around 26%, which makes sense given that we have assumed the authors of these manuscripts are tenured—the model simulates probabilities for papers most likely to be accepted because of rank. In the aggregate, solo male authors have less than a one-point advantage over solo female authors at gaining acceptance, conditional on the paper being sent out.

However, again we see that success also varies depending on methodological approach. Solo male authors have slightly greater success with quantitative and mixed methods papers (by 3 and 13 percentage points respectively), while solo female authors have substantially greater success with qualitative papers (by almost 9 percentage points).

Turning to collaborations, papers with a mixed gender team have an advantage over all-male and all-female teams (by 4.3 and 6.8 percentage points respectively).

In sum, controlling for authors’ highest rank and methodological approach, gender does not exert a consistent effect on editorial decisions. For the modal submission—a quantitative paper by a tenured scholar—collaboration matters much more than gender at the initial stage, but does not consistently matter at the acceptance stage. Meanwhile, gender matters only for all-male quantitative papers, which are less likely to be published than papers by all-female or mixed-gender teams. For qualitative papers, across stages solo male authors fare the worst, but collaborations fare the best by far. All-male collaborations have some advantage at the internal review stage, but mixed-gender collaborations fare better than all other groups at the acceptance stage.