The authors have declared that no competing interests exist.
Conceived and designed the experiments: JD MS-P FR TKW LANA. Performed the experiments: JD XHTZ MS-P FR SO. Analyzed the data: JD XHTZ MS-P FR TKW LANA. Contributed reagents/materials/analysis tools: JD FR. Wrote the paper: JD XHTZ MS-P TKW LANA. Prepared the figures: XHTZ.
Many studies demonstrate that there is still a significant gender bias, especially at higher career levels, in many areas including science, technology, engineering, and mathematics (STEM). We investigated field-dependent, gender-specific effects of the selective pressures individuals experience as they pursue a career in academia within seven STEM disciplines. We built a unique database that comprises 437,787 publications authored by 4,292 faculty members at top United States research universities. Our analyses reveal that gender differences in publication rate and impact are discipline-specific. Our results also support two hypotheses. First, the widely-reported lower publication rates of female faculty are correlated with the amount of research resources typically needed in the discipline considered, and thus may be explained by the lower level of institutional support historically received by females. Second, in disciplines where pursuing an academic position incurs greater career risk, female faculty tend to have a greater fraction of higher impact publications than males. Our findings have significant, field-specific, policy implications for achieving diversity at the faculty level within the STEM disciplines.
The proportion of women faculty members in many STEM fields has been steadily increasing, but at the level of associate and full professor, men continue to far outnumber women
In contrast, to these concerns, Etzkowitz and Ranga
To determine how and why gender may affect the professional practices and scientific production of researchers, we investigated for seven STEM fields in a quantitative manner the gender-specific and discipline-specific effects of (i) research resource requirements and (ii) relative risk in pursuing an academic career. We explicitly separated the researchers in our database along disciplinary lines in order to more carefully investigate the mechanisms potentially responsible for the observed differences. In contrast to most studies concerned with this matter, we did not conduct surveys but instead systematically analyzed the complete publication records of faculty at a large number of departments in selected research universities in the United States (
Percentage awarded to females of the total number of bachelor (green lines), master (blue lines) and doctoral (purple lines) degrees in the period 1966–2008. We obtained these data from
Discipline | Departments | Female | Male | ||
Authors | Publications | Authors | Publications | ||
Chemical Engineering | 31 | 98 | 6,392 | 567 | 66,328 |
Chemistry | 35 | 198 | 13,790 | 1,020 | 137,723 |
Ecology | 15 | 106 | 3,976 | 328 | 22,425 |
Industrial Engineering | 15 | 51 | 1,498 | 261 | 11,509 |
Material Science | 26 | 98 | 9,538 | 473 | 75,373 |
Molecular Biology | 11 | 168 | 9,882 | 474 | 51,234 |
Psychology | 10 | 171 | 7,143 | 279 | 20,976 |
|
890 | 52,219 | 3,402 | 385,568 |
We collected data on the 2010 faculty rosters of selected top research institutions in the U.S. (see Supporting Information S1) in seven STEM disciplines – chemical engineering, chemistry, ecology, industrial engineering, material science, molecular biology and psychology – and measured scientific productivity and impact during the various phases of each faculty member’s academic career
Career length distribution of female (red) and male (blue) current faculty members for a selected set of U.S. universities (
Discipline | Avg. annual expeditureper PI [M$] | Median of salary [K$] | Salary premiumof non-academiccareers, |
Time to career independence, |
Frac. of graduates pursuing acad. careers, |
|
Acad. | Non-acad. | |||||
Chemical Engineering | 0.490 | 77.2 | 107.2 | 0.39 | 5.4 | 0.21 |
Chemistry | 0.515 | 64.6 | 104.2 | 0.61 | 6.2 | 0.32 |
Ecology | – | 69.0 | 95.0 | 0.38 | 8.2 | 0.71 |
Industrial Engineering | 0.094 | 74.0 | 104.0 | 0.41 | 6.1 | 0.56 |
Material Science | 0.612 | 74.5 | 102.5 | 0.38 | 6.6 | 0.20 |
Molecular Biology | 1.897 | 62.2 | 100.9 | 0.62 | 7.3 | 0.57 |
Psychology | 0.256 | 65.6 | 96.2 | 0.47 | 8.2 | 0.42 |
We first focus on research resource requirements. As mentioned earlier, the typical annual research expenditures per faculty member differ substantially across the seven disciplines. For example, industrial engineering faculty tend, for the most part, to train a small number of students at a time. Additionally, much of the research in industrial engineering is theoretical or computational in nature. These two characteristics suggest that, for industrial engineering, researchers do not need to compete against each other for limited resources, and institutional support may not be as important a factor in faculty productivity.
In contrast, most faculty in molecular biology conduct experimental research, and many require significant lab space and expensive specialized equipment. Moreover, faculty in molecular biology are able to compete for funding supporting the creation of large centers or the acquisition of major equipment. Thus, availability of resources, especially institutionally granted resources or institutional support for securing large grants, can be crucial components of academic success in molecular biology
Since historically female faculty members have received less institutional support and have had less access to research resources
We define the publication rate of a faculty member
Average number of publications authored by females (red) and males (blue) as a function of time. Data is smoothed using moving averaging over a
In order to account for the effect of career stage, we consider
Average z-score of number of publications for females (red) and males (blue) as a function of career stage. Shaded areas indicate the standard errors. See Fig. S10 for the statistical significance of the gender difference in publication rate.
Our analysis fully confirms our hypothesis (
Effects of the magnitude of the resource requirements on the difference in publication rates between genders. Ecology is not included as we could not obtain data for resource requirements. The difference in publication rates is measured by the average z-score of number of publications by females in each year, and the error bars indicate the standard errors. The resource requirements is defined as the average annual research expenditure per principal investigator in the departments studied (
It is important to point out that in our analysis we did not consider human and social capital such as collaboration level and leadership position, which may also have critical roles for a productive career
We next investigated gender-specific and discipline-specific effects of career relative risk profile of an academic career on publication patterns. The risk to pursue a faculty position after obtaining a Ph.D. varies across disciplines. A graduate student considering an academic career in chemistry faces a small risk if unsuccessful. Within about six years from publication of their first paper, successful individuals will move into independent positions (
Fraction of publications in which a faculty member is the last author (purple diamonds) and the fraction of publications in which a faculty member is the first author (green squares). In many disciplines, the senior author of a study is listed last. Looking at the change in the fraction of times a faculty member in our dataset is a first or last author can thus be used as a proxy for change in seniority-level of an individual in these disciplines. We order publications, excluding single-author publications, by years after first publication and aggregate within each discipline. We fit the data to generalized logistic functions (green/purple lines) and define career independence (grey shaded areas) as the mid-point of the logistic function for fraction of last-author publications (Methods,
In contrast, an individual considering an academic career in ecology faces a much more uncertain future. Instead of waiting six years post publication of the first paper to learn whether it will be possible to secure a faculty position, an ecologist has to wait an average of
These observations raise a critical question: Could the different risk profiles of STEM disciplines lead to distinct gender-specific selective pressures? Because pursuing an academic career is a risky undertaking and because propensity towards risk-taking
We further hypothesize that the higher qualification of females in high-risk disciplines will become apparent through higher impact per publication. In order to uncover gender differences in publication impact, we studied a commonly used metric of academic performance, the
An identified weakness of the
We next measured the deviations of
Dependence of the
The z-score of the
We then calculated the average z-scores of this publication adjusted
The data in
While we lack a theory for the true definition of career risk,
Even though we do not know its functional form, we can expand
Intercept |
|
|
|
|
|
Adj. |
|
−0.96** | 0.39** | 0.10** | 0.94 | 0.001 | |||
[−1.32, −0.61] | [0.25, 0.54] | [0.06, 0.14] | |||||
−1.03** | 0.40** | 0.79* | 0.88 | 0.001 | |||
[−1.57, −0.50] | [0.19, 0.61] | [0.29, 1.28] | |||||
−0.68* | 0.37 | 0.05* | 0.79 | 0.007 | |||
[−1.12, −0.17] | [−0.32, 1.06] | [0.01, 0.08] | |||||
−0.62* | 0.05** | 0.74 | 0.007 | ||||
[−1.14, −0.10] | [0.020 0.09] | ||||||
−0.17 | 0.40* | 0.65 | 0.02 | ||||
[−0.48, 0.14] | [0.11, 0.69] | ||||||
−0.64 | 0.38 | 0.42 | 0.01 | ||||
[−1.59, 0.32] | [−0.04, 0.79] |
The gender difference in publication impact is defined as the average
Risk in academic career choice and difference in publication impact. We quantify the risk of academic career choice according to Eq. (10). We show results for two alternative measures of difference in publication impact. In (
This model suggests that in disciplines where there are few non-academic career options available and the time to reach career independence is long, and where it is difficulty to recover salary loss due to unsuccessful academic career, pursuing an academic position is highly risky.
Our study reveals the possible contribution of perceived risk and resource allocation to the under-representation of women in STEM academic careers. Our results are not by themselves an empirical validation of the causal relationship between publication rate and resource requirements, and between publication impact and career risk, since we cannot conduct controlled experiments or account for other factors that could play a role in the measured outcome. However, the hypothesis that there is a causal relationship between gender differences in resource allocation and the reported gender differences in publication rates is plausible and well supported by our empirical observations, as is the hypothesis that there is a causal relationship between the relative risk associated with academic careers and the gender differences in publication impact.
The issues we identify here, together with the known socialization concerns surrounding work-life balance, may have created a “tipping point” that explains the nearly intractable problem of retaining women within STEM disciplines. It is equally important to think about the role these previously unrecognized risk factors may contribute to the number of under-represented minorities in the STEM pipeline. It is not possible to address this point using the methods we describe here, but there may be opportunity and new impetus to develop novel tools that can provide a more sophisticated insight into why some groups of people are not well represented in scientific subspecialties. More intriguingly, we wonder how the perceived or real risks associated with resource infrastructure and future opportunities can be translated into other fields (business, politics, the legal profession) where there is a paucity of women and minorities in the upper career rungs. Most importantly, now that these factors have been identified, it should be possible to create policies that provide better opportunities for all individuals with an aptitude for science, and perhaps in all kinds of careers, to ensure that our work force is diverse and can gain from the insights of all contributing members.
We obtained complete faculty rosters as of June, 2010 for several top research universities in the U.S. in the disciplines of chemical engineering, chemistry, ecology, industrial engineering, material science, molecular biology and psychology (see
Select last name and set of initials that the investigator could potentially use to sign her papers. For instance, David A. Tirrell has two potential WoS names, “Tirrell D” and “Tirrell DA.”
Set the year of publication range from four years before the Ph.D. date until the data acquisition time. If the Ph.D. year is not available, estimate the Ph.D. year from the list of publications listed in the investigator’s personal web page or from the date of hire. For David A. Tirrell, our protocol returns the publications from 1974 on for “Tirrell D” and “Tirrell DA” (1974 = 1978 - 4 and 1978 is the year Professor Tirrell was awarded a Ph.D.).
When current and previous positions are available, constrain the query to retrieve publications that include one of those institutions as one of the author’s address.
The disambiguation protocol downloads all types of publications of the authors. In the analysis we included articles, conference proceedings and reviews. At each step, we obtained the number of publications assigned to a particular author and checked for anomalies using a number of data features, the most important of which were:
The total number of publications is consistent with the current position of the investigator, the number of years doing research, and the type of research.
The number of publications in each year does not deviate “significantly” from the average of the surrounding years.
Journal titles of the publications are within the investigator’s field of expertise.
Our disambiguation protocol allows us to introduce different names or initials for each scientist. For example, for females, for whom there is evidence in the list of publications of their CVs that they change their family name after marriage, we include both names in the query. Note that the errors in the publication list introduced by name changes is small
For the analysis of the
Assume that an author with
We surmise that given the number of publications
We fitted the data in
The p-values of the linear correlations in
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We thank R. Guimerà, S. Mukherjee, R. D. Malmgren, P. McMullen, M. J. Stringer, and James A. Evans for comments and suggestions. We thank S. C. Tobin for editorial assistance.