As already explained, a number of different factors might influence researchers’ decisions to coauthor: preferences for individual or team work, for same-sex or gender-mixed teams; the availability of (compatible) coauthors, that can improve the quality of the research output; finally, the potential returns of each publication to the researchers’ own career advancement.
If coauthorship decisions were only based on the potential returns to each publication, women might engage in different behaviours depending on the likelihood of being fairly attributed merit for their coauthored work.
Empirical strategy
I analyse gender differences in authorship patterns by field, using information from the IRIS dataset. I use the adoption approach to see whether there are systematic gender differences that are consistent with women anticipating penalty for coauthorship, as found in section 5. Specifically, I explore whether women opt more than men for solo-authoring and for coauthoring with their female colleagues. Additionally, I analyse whether women coauthor more than men with their university colleagues. Engagement in interuniversity collaborations might reflect how active the researcher is in the academic community, expanding their networks also outside of the home university.24
At this stage, I do not control for individuals’ characteristics and I do not have information neither on the quality of publications, nor on the author position in each publication. Therefore, the observed differences in coauthorship patterns are to be interpreted with caution, as they do not take into account potential quality differences.
I exploit fields’ heterogeneity to investigate whether gender differences in coauthorship patterns are more marked in fields where potential information asymmetries are larger, namely in larger fields and where authors are listed in alphabetical order rather than in order of contribution. In fact, in smaller subfields, the academic community might be more tightly connected and it may be more likely that researchers and their publications are better known within the circle, in comparison with very large subfields.
If the tradition in the field is to list publications’ authors alphabetically, it is not possible to signal one’s input into each publication. In these fields, women might refrain from coauthoring; on the
24 Collaboration with colleagues from other universities might be linked with personal preferences or with other constraints, such as family, which might affect women more than men.
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other hand, in fields where the lead author is signalled by being listed as the first author, women might not need to strategically engage in less coauthoring.
Moreover, I explore whether female researchers engage in different coauthorship behaviour in STEMM fields and in subfields where women are less represented, where stereotypes against women might be larger and there might be imperfect information with regards to their quality. In fact, potentially, differential attribution of merit to male and female authors in the presence of information asymmetries might depend on how women are regarded in the field.
Using OLS method, I regress four different measures of authorship on gender, controlling for academic fields:
𝑌𝑖 = 𝛽0+ 𝛽1𝐹𝑒𝑚𝑎𝑙𝑒𝑖+ 𝛽2𝑋𝑡+ 𝜖𝑖 (3) where Yie is a continuous variable that represents: a) the share of coauthored publications; b) the
number of coauthors per publications; c) the share of coauthors from the same university; and d) the share of women among coauthors, of individual i. All variables are measured in annual terms to take into account different career lengths of researchers. Female is a dummy variable that indicates the gender of the researcher. Xt is a vector that represents area, subfield and department
dummies.
I estimate equation (3) multiple times, with more narrow controls each time, because gender differences in authorship patterns at the aggregate level might overlook gender segregation within fields and subfields. In fact, academic fields differ in authorship traditions, as well as in their degree of feminisation. For example, there are more women in humanities compared to science, and coauthorship is more common in science than humanities. Therefore, by only looking at the aggregate, it might appear that women coauthor less than men.
However, there is a drawback in adding field controls. On the one hand, it allows to understand whether field differences in terms of authorship traditions explain the observed overall gender differences in authorship patterns. On the other hand, it confounds potential sorting of women into fields and departments that differ in their characteristics, for example in terms of potential information asymmetries and stereotypes.
Another factor that could affect women’s coauthorship decisions is the availability of potential coauthors within the department. Despite the new possibilities of cooperation outside of one’s university brought about by the internet and the increased internationalisation of academia, the
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department still represents an important source of potential coauthors. If there are few female colleagues, women might have a lower chance to coauthor with other women or to coauthor at all.
Main results
When no controls are included, women appear to coauthor less often than men (Table 8, Panel A) and to have 1.55 fewer coauthors per publication (Panel B). However, as already explained, these results overlook the variation across different academic fields. In Table 4, we saw that, overall, women are more present in fields where coauthoring is relatively less common. In fact, as soon as field controls are included, women seem to coauthor relatively more than men: the share of coauthored publications of women’s is 1.2 p.p. higher than men’s (Panel A). As for the number of coauthors, controlling for fields, women have 0.32 fewer coauthors than men, but the difference is not statistically significant (Panel B).
When I also control for narrow academic subfields (SSD), the gender difference in the share of coauthored publications is reduced further to 0.8 p.p. and women still seem to be involved in smaller teams, with 0.35 fewer coauthors.
I control for departments as well, because it is possible that women self-select into departments that have different ways of doing research, implying different coauthorship practices. I define department colleagues as the authors from the same university that pertain to the same subfield. The differences in the tendency to coauthor and the number of coauthors between male and female department colleagues are comparable to the gender differences between researchers from the same subfield.
Panel C of Table 8 shows the gender differences in the share of internal coauthors in coauthored publications. With and without area, subfield and department controls, the share of internal coauthors in women’s publications is around 2.5 p.p. higher than in men’s, suggesting that they are less involved in inter-university projects.
In Panel D, we see that women coauthor more with their female university colleagues (recall that only the gender of internal coauthors is observed). However, when field and subfield controls are included, the difference decreases from 13.5 p.p. to 8.5 p.p., and further to 4.4 p.p. Adding department controls, surprisingly, the share of female internal coauthors in women’s publications is 3.6 p.p. lower than in their male department colleagues’. However, at this level of disaggregation, it is important to take into account the availability of female coauthors. In fact, because on average there are more men in universities, for a given number of women in a department, each has a smaller set of potential female coauthors in comparison with their male
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colleagues, and vice versa. Therefore, I compare departments with the same number of researchers and the same gender composition. However, although the difference gets smaller (1.6 p.p.), the share of female coauthors in women’s publications is still lower than in male’s publications.
Table 8 – Gender differences in coauthorship patterns
Panel A: % coauthored publications
Female -0.055*** 0.012** 0.008*** 0.009*** (0.001) (0.004) (0.002) (0.002) Constant 0.727*** 0.704*** 0.705*** 0.705***
(0.001) (0.001) (0.001) (0.001)
Controls:
Field: Area (16 cat.) - Yes - - Subfield: SSD (370 cat.) - - Yes - Department (10241 cat.) - - - Yes Observations 471,033 471,033 471,033 471,033 R-squared 0.004 0.660 0.688 0.729
Panel B: # coauthors per publication
Female -1.550*** -0.320 -0.354** -0.325* (0.104) (0.360) (0.157) (0.188) Constant 5.367*** 4.945*** 4.957*** 4.947***
(0.061) (0.124) (0.054) (0.065)
Controls:
Field: Area (16 cat.) - Yes - - Subfield: SSD (370 cat.) - - Yes - Department (10241 cat.) - - - Yes Observations 471,033 471,033 471,033 471,033 R-squared 0.000 0.044 0.082 0.220
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Panel C: % internal coauthors per publication
Female 0.028*** 0.031*** 0.028*** 0.025*** (0.001) (0.003) (0.003) (0.002) Constant 0.452*** 0.451*** 0.452*** 0.453***
(0.001) (0.001) (0.001) (0.001)
Controls:
Field: Area (16 cat.) - Yes - - Subfield: SSD (370 cat.) - - Yes - Department (9339 cat.) - - - Yes Observations 334,294 334,294 334,294 334,294 R-squared 0.002 0.049 0.074 0.279
Panel D: % female coauthors
Female 0.135*** 0.085*** 0.044*** -0.036*** -0.016*** (0.001) (0.009) (0.005) (0.004) (0.004) Constant 0.321*** 0.338*** 0.351*** 0.378*** 0.371*** (0.001) (0.003) (0.001) (0.001) (0.002)
Controls:
Field: Area (16 cat.) - Yes - - - Subfield: SSD (368 cat.) - - Yes - - Department (8462 cat.) - - - Yes - Gender composition of the
department (9226 cat.) - - - - Yes Observations 310,354 310,354 310,354 310,354 310,354 R-squared 0.034 0.107 0.160 0.316 0.207
Notes: OLS estimates. The sample includes authors for which information on field is available (i.e. researchers,
associate and full professors). Each observation corresponds to author-times-year. Each Panel corresponds to a different dependent variable. Each is a continuous variable that measure: a) the share of coauthored publications of a given year; b) the average number of coauthors out of all publications of a given year; c) the share of coauthors from the same university in coauthored publications of a given year; d) the share of female out of coauthors from the same university in coauthored publications of a given year. Each coefficient corresponds to an independent regression. Gender composition of the department groups together departments with the same number of men and women. Field, Subfield and Department (Subfield x University) dummies are included as indicated, and standard errors are clustered at the corresponding level.
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To understand whether the observed differences are driven by specific fields, I compare men and women’s publications in each academic area with two-tailed t-tests (Table 9). Women single- author less than men in almost all areas, apart from in civil engineering, history and sociology. As for the number of coauthors, in most fields there are no differences. The exceptions are in geosciences, medicine and sociology, where women have more coauthors, and history, law and physics, where they have fewer. Physics seems to drive the differences observed overall. With fourteen more coauthors per publication than women, male physicists appear to participate in much larger projects. In fact, when the regressions shown in Panel B of Table 8 are estimated again excluding physics, women have 0.1 fewer coauthors than their male subfield colleagues, and there are no statistically significant differences at the department level (see Table A.2. in the Appendix).
Women generally coauthor more with university colleagues, except for geosciences. In arts and languages, law, sociology and civil engineering there are no gender differences.
As for the share of female internal coauthors per publication, women consistently coauthor more with other female colleagues than men do, except for in civil engineering and geosciences, where the difference is not statistically significant.
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Table 9 – Gender differences in coauthorship patterns by area
% of coauthored publications # of coauthors
Area Mean SD men women p-value Mean SD men women p-value Architecture 0.36 0.37 0.36 0.38 0.00 0.92 1.56 0.91 0.94 0.27 Arts and Languages 0.16 0.29 0.16 0.17 0.00 0.44 1.61 0.44 0.44 1.00 Biology 0.97 0.13 0.96 0.97 0.00 5.94 10.09 5.90 5.99 0.34 Chemistry 0.98 0.11 0.97 0.98 0.00 5.26 10.72 5.27 5.25 0.90 Civil Engineering 0.90 0.23 0.90 0.91 0.19 2.54 3.22 2.54 2.53 0.91 Economics and Business 0.67 0.40 0.65 0.70 0.00 1.58 9.89 1.61 1.51 0.38 Geosciences 0.95 0.16 0.94 0.96 0.00 4.27 3.51 4.22 4.40 0.02 History 0.18 0.30 0.18 0.18 0.90 0.59 6.28 0.65 0.50 0.06 Industrial Engineering 0.95 0.16 0.95 0.96 0.00 3.69 15.84 3.74 3.40 0.12 Law 0.12 0.26 0.13 0.11 0.00 0.42 4.96 0.45 0.35 0.08 Mathematics 0.85 0.30 0.85 0.86 0.00 2.14 8.18 2.20 2.02 0.11 Medicine 0.96 0.15 0.96 0.97 0.00 7.18 10.22 7.09 7.39 0.00 Physics 0.95 0.17 0.95 0.96 0.00 36.36 154.00 38.39 26.98 0.00 Psycology 0.84 0.28 0.81 0.86 0.00 2.72 4.63 2.67 2.76 0.33 Sociology 0.25 0.35 0.25 0.25 0.40 0.58 1.71 0.56 0.62 0.04 Veterinary 0.93 0.20 0.92 0.94 0.00 4.25 13.38 4.25 4.25 0.98
% of internal coauthors % of female coauthors
Area Mean SD men women p-value Mean SD men women p-value Architecture 0.58 0.38 0.56 0.62 0.00 0.42 0.39 0.40 0.45 0.00 Arts and Languages 0.40 0.41 0.39 0.40 0.23 0.50 0.43 0.45 0.54 0.00 Biology 0.47 0.28 0.45 0.49 0.00 0.51 0.32 0.48 0.55 0.00 Chemistry 0.52 0.26 0.51 0.54 0.00 0.43 0.31 0.41 0.47 0.00 Civil Engineering 0.54 0.33 0.54 0.55 0.12 0.25 0.32 0.25 0.26 0.10 Economics and Business 0.48 0.39 0.47 0.50 0.00 0.38 0.40 0.34 0.45 0.00 Geosciences 0.39 0.28 0.40 0.38 0.01 0.30 0.32 0.29 0.30 0.14 History 0.46 0.41 0.44 0.49 0.00 0.44 0.42 0.38 0.53 0.00 Industrial Engineering 0.55 0.30 0.55 0.56 0.08 0.18 0.27 0.17 0.25 0.00 Law 0.49 0.42 0.48 0.50 0.12 0.33 0.41 0.30 0.39 0.00 Mathematics 0.41 0.37 0.39 0.45 0.00 0.32 0.39 0.28 0.40 0.00 Medicine 0.40 0.26 0.39 0.42 0.00 0.36 0.31 0.33 0.44 0.00 Physics 0.33 0.28 0.32 0.36 0.00 0.23 0.28 0.22 0.27 0.00 Psychology 0.45 0.32 0.43 0.47 0.00 0.58 0.37 0.51 0.63 0.00 Sociology 0.48 0.41 0.47 0.48 0.69 0.39 0.42 0.33 0.49 0.00 Veterinary 0.54 0.28 0.53 0.56 0.00 0.41 0.32 0.39 0.46 0.00
Notes: the table shows information on annual publications by authors in different fields, and the p-value of a t-test
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Heterogeneity analysis
As already explained, if authorship decisions are made strategically, we can expect to see different patterns depending on potential information asymmetries and gender stereotypes. I compare how coauthorship patterns differ depending on the size of the subfield, the norms of listing authors, the degree of feminization of the type of discipline (STEMM or humanities and social sciences).
The median number of scholars in each subfield (SSD) is twenty and the median share of women in each subfield is 29%. I define subfields with fewer than twenty scholars small, and those with more than twenty large. Analogously, I define more and less feminised fields as having a share of women above or below 29%, respectively. For norms of listing authors, I follow the same classification explained in section 5.3.1., and I focus on STEMM fields.
Gender differences in coauthorship patterns in smaller and larger subfields are consistent with expectations (Panel A of Table 10). In smaller subfields, there are no gender differences in the tendency to coauthor and in team size, whereas in larger ones women tend to coauthor more, but select into smaller teams then men. The gender difference in the share of female coauthors is also more marked in larger fields than in smaller ones.
In Panel B, I compare STEMM fields where authors are more commonly listed in alphabetical order with STEMM fields where order of contribution is the norm. If it is not possible to signal one’s input into each publication, we would expect women to coauthor less than men, or at least opt for coauthoring more with their female colleagues. As for the tendency to coauthor, if ever, women in “contribution fields” coauthor more than men by a mere 0.8 p.p., whereas in “alphabetical fields” this difference is 2.4 p.p. On the other hand, women in “alphabetical fields” have 0.09 fewer coauthors than their male colleagues, whereas “in contribution fields” there are no statistically significant differences in team size. Consistent with expectations is women’s choice with regards to their coauthors’ gender. In fact, in “contribution fields”, the share of female coauthors in women’s publications is 3.3 p.p. higher than in men’s publications (8% difference), whereas this difference is as high as 9.7 p.p. in “alphabetical fields” (34%).
The results are thus consistent with the possibility that information asymmetries negatively affect female researchers’ willingness to join large teams and to coauthor with their male colleagues.
In Panel C, it can be seen that, dividing subfields by their degree of feminisation, women coauthor less with their female colleagues in less feminised subfields. This, assuming that the feminisation of the field is a good proxy for the level of stereotypes against women, is not consistent with expectations. However, it is to be pointed out that fewer women also imply fewer potential females
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to coauthor with. There are no gender differences for what concerns the share of coauthored publications. In line with expectations, women in less feminised fields have fewer coauthors than men, whereas there are no gender differences in the number of coauthors in more feminised fields.
Women in STEMM fields have more coauthored publications than their male peers, but significantly fewer coauthors. On the other hand, there are no gender differences in the propensity to coauthor, and there is a small and slightly significant difference in team size in humanities and social sciences (Panel D). As for the share of female coauthors, the evidence goes against expectations. In both types of fields, women coauthor with their female colleagues more than men, but this difference is larger in humanities than in scientific fields. However, the issue of availability of female colleagues applies in this case as well. There are more female potential coauthors in humanities than there are in STEMM fields, and this might affect women’s choices.
The results are in line with the expectation that gender stereotypes influence female researchers’ decision to join larger teams. However, they are not consistent with the hypothesis that gender stereotypes affect female researchers’ decisions to coauthor with men.
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Table 10 – Heterogeneity analysis
Panel A: Subfield size
% of coauthored publications # of coauthors % of female internal coauthors
Small subfields Large subfields Small subfields Large subfields Small subfields Large subfields
Female -0.001 0.010*** -0.103 -0.417** 0.023*** 0.050*** (0.003) (0.002) (0.094) (0.195) (0.009) (0.005) Constant 0.704*** 0.706*** 3.430*** 5.342*** 0.363*** 0.348***
(0.001) (0.001) (0.034) (0.066) (0.003) (0.002) R-squared 0.746 0.672 0.063 0.082 0.162 0.159
Panel B: Norms of listing authors in STEMM fields
% of coauthored publications # of coauthors % of female internal coauthors
by Contribution Mixed Alphabetical by Contribution Mixed Alphabetical by Contribution Mixed Alphabetical
Female 0.008*** 0.021*** 0.024* -0.088 -1.811* -0.085** 0.033*** 0.031*** 0.097*** (0.001) (0.005) (0.005) (0.070) (0.921) (0.027) (0.005) (0.009) (0.016) Constant 0.958*** 0.926*** 0.843*** 6.200*** 10.540*** 2.170*** 0.407*** 0.242*** 0.285***
(0.001) (0.001) (0.002) (0.026) (0.194) (0.009) (0.002) (0.002) (0.005) R-squared 0.077 0.070 0.073 0.025 0.074 0.008 0.129 0.179 0.042
Panel C: Feminisation of subfield
% of coauthored publications # of coauthors % of female internal coauthors
Less feminised More feminised Less feminised More feminised Less feminised More feminised
Female 0.008** 0.008*** -0.574* -0.178 0.033*** 0.054*** (0.003) (0.002) (0.322) (0.116) (0.006) (0.006) Constant 0.752*** 0.654*** 6.249*** 3.519*** 0.272*** 0.448***
(0.001) (0.001) (0.076) (0.053) (0.001) (0.003) R-squared 0.655 0.709 0.080 0.087 0.101 0.078
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Panel D: Discipline
% of coauthored publications # of coauthors % of female internal coauthors
SSH STEMM SSH STEMM SSH STEMM
Female -0.0002 0.013*** -0.108* -0.519** 0.077*** 0.037*** (0.004) (0.002) (0.063) (0.257) (0.011) (0.005) Constant 0.292*** 0.938*** 0.806*** 7.292*** 0.375*** 0.347***
(0.002) (0.001) (0.025) (0.081) (0.004) (0.002) R-squared 0.357 0.1 0.018 0.075 0.064 0.186
Notes: OLS estimates. The sample includes authors for which information on field is available (i.e. researchers, associate and full professors). Each observation
corresponds to author-times-year. Each Panel corresponds to a different split between fields. Panel A according to the subfield size in terms of number of scholars: small is below the median of 20, large is above it. Panel B according to the norms of listing authors in STEMM fields: Mixed means that less than two-thirds of the publications followed either norm. Alphabetical include: Mathematics and Informatics. By Contribution include: Biology, Vet, Chemistry and Medicine. Panel C according to the share of women in the subfield: less feminised is below the median of 0.29, more feminised is above it. Panel D according to the discipline: STEMM is science, technology, engineering, mathematics, medicine and psychology. SSH is humanities and social sciences, including economics.
Each pair of columns corresponds to a different continuous variable: the share of coauthored publications of a given year; the average number of coauthors out of all publications of a given year; the share of female out of coauthors from the same university in coauthored publications of a given year.
Each coefficient corresponds to an independent regression. Subfield dummies are included and standard errors are clustered at the corresponding level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
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7. Conclusions
A recent contribution to the literature exploring reasons for the observed lower promotion rates of women in academia was made by Sarsons (2017a; 2017b), who found evidence that female economists with coauthored papers are relatively less likely to be promoted to tenure, especially if they coauthor with men. Because in economics authors are listed in alphabetical order and not according to contribution, information asymmetry seemed to play a role in the unequal attribution of credit for coauthored research.
I test Sarsons’s hypothesis using data on around 60,000 evaluations of aspiring associate and full professors in all academic disciplines in Italy. The advantage of this setup is that candidates are evaluated by a randomly selected committee and exclusively on the basis of their research output. Moreover, I add several quality measures of the publications listed in the academic CV of the applicants.
Overall, there is evidence of coauthorship penalties for women, as found by Sarsons. Specifically, I find last- and middle-authorship penalties, whereas I find no gender differences in the returns to single- and first-authored publications.
The results are consistent with the possibility that women are evaluated differently from men in the presence of information asymmetries and stereotypes. Heterogeneity analysis provides further evidence in support of this idea. In fact, whereas there are no coauthorship penalties for female applicants to full professorship, in evaluations to associate professorship there are significant gender differences in the returns to different types of coauthored publications. With regards to norms of listing authors, there are gender differences in the returns to middle-authored publications in scientific fields where authors are listed according to contribution. However, a counterintuitive result is that no gender differences exist in the returns to all types of publications in scientific fields with alphabetical norms. Moreover, stereotypes in STEMM fields seem to penalise women when they undertake leadership roles as heads of labs, as women appear to suffer a last-authorship penalty.
Additionally, following the adoption approach, I explore whether observed coauthorship patterns are consistent with the possibility that women anticipate a coauthorship disadvantage, using data on all publications in the Italian academia from the past twenty years.
I find that, overall, women coauthor less and have more female coauthors than men. However, these gender differences are largely explained by gender segregation across fields. Strikingly,