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Historical comparison of gender inequality in scientiﬁc

careers across countries and disciplines

Junming Huang

a,b,c,1

, Alexander J. Gates

a,1

, Roberta Sinatra

d,e

, and Albert-L

aszl

o Barab

asi

a,f,g,h,2

Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115;

CompleX Lab, School of Computer Science and

Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;

Paul and Marcia Wythes Center on Contemporary China,

Princeton University, Princeton, NJ 08540;

Department of Computer Science, IT University of Copenhagen, 2300 Copenhagen, Denmark;

ISI Foundation,

10126 Turin, Italy;

Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115;

Department of

Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115; and

Department of Network and Data Science, Central European

University, 1051 Budapest, Hungary

Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved January 22, 2020 (received for review August 15, 2019)

There is extensive, yet fragmented, evidence of gender differ-

ences in academia suggesting that women are underrepresented

in most scientiﬁc disciplines and publish fewer articles through-

out a career, and their work acquires fewer citations. Here, we

offer a comprehensive picture of longitudinal gender differences in

performance through a bibliometric analysis of academic publish-

ing careers by reconstructing the complete publication history of

over 1.5 million gender-identiﬁed authors whose publishing career

ended between 1955 and 2010, covering 83 countries and 13 dis-

ciplines. We ﬁnd that, paradoxically, the increase of participation

of women in science over the past 60 years was accompanied by

an increase of gender differences in both productivity and impact.

Most surprisingly, though, we uncover two gender invariants, ﬁnd-

ing that men and women publish at a comparable annual rate

and have equivalent career-wise impact for the same size body of

work. Finally, we demonstrate that differences in publishing career

lengths and dropout rates explain a large portion of the reported

career-wise differences in productivity and impact, although pro-

ductivity differences still remain. This comprehensive picture of

gender inequality in academia can help rephrase the conversation

around the sustainability of women’s careers in academia, with

important consequences for institutions and policy makers.

gender inequality | science of science | STEM | scientiﬁc careers

ender differences in academia, captured by disparities in

the number of female and male authors, their productiv-

ity, citations, recognition, and salary, are well documented across

all disciplines and countries (1–8). The epitome of gender differ-

ence is the “productivity puzzle” (9–13)—the persistent evidence

that men publish more than women over the course of their

career, which has inspired a plethora of possible explanations

(14–16), from differences in family responsibilities (17–19), to

career absences (20), resource allocation (21), the role of peer

review (22), collaboration (23, 24), role stereotypes (25), aca-

demic rank (26), specialization (27), and work climate (28).

The persistence of these gender differences could perpetuate

the naive interpretation that the research programs of female

and male scientists are not equivalent. However, such simplistic

reading of the data dismisses increasing evidence that systemic

barriers impede the female academic. Indeed, the deep interre-

latedness of these factors has limited our ability to differentiate

the causes from the consequences of the productivity puzzle,

complicating the scientiﬁc community’s ability to enact effective

policies to address it.

A key methodological obstacle has been the difﬁculty to recon-

struct full publishing careers for scientists of both genders across

the diverse academic population. Consequently, much of the

available evidence on gender differences is based on case stud-

ies limited to subsets of active scientists in speciﬁc countries,

disciplines, or institutions, making it difﬁcult to compare and

generalize the ﬁnding to all of science. A further complication

arises from the heavy-tailed nature of academia: a dispropor-

tionately small number of authors produce a large fraction of

the publications and receive the majority of the citations (29),

an effect that is exacerbated in small sample sizes (30). To truly

understand the roots of the gender inequality, we need to survey

the whole longitudinal, disciplinary, and geographical landscape,

which is possible only if we capture complete publishing careers

for all scientists across disciplinary and national boundaries.

Here, we reconstructed the full publishing career of 7,863,861

scientists from their publication record in the Web of Sci-

ence (WoS) database between 1900 and 2016. By deploying a

state-of-the-art method for gender identiﬁcation (SI Appendix,

section S2.E), we identiﬁed the gender of over 3 million authors

(856,889 female and 2,146,926 male) spanning 83 countries

and 13 major disciplines (SI Appendix, section S2). We then

focused on 1,523,002 scientists (412,808 female and 1,110,194

male) whose publishing careers ended between 1955 and 2010

(SI Appendix, sections S1 and S2.H), allowing us to systemati-

cally compare complete male and female careers. This extensive

sample covers 33% of all papers published between 1955 and

2010 but due to methodological limitations, systematically lacks

Signiﬁcance

Empirical evidence suggests signiﬁcant gender differences in

the total productivity and impact of academic careers across

science, technology, engineering, and mathematics (STEM)

ﬁelds. Paradoxically, the increase in the number of women

academics over the past 60 years has increased these gen-

der differences. Yet, we ﬁnd that men and women publish a

comparable number of papers per year and have equivalent

career-wise impact for the same total number of publications.

This suggests the productivity and impact of gender differ-

ences are explained by different publishing career lengths

and dropout rates. This comprehensive picture of gender

inequality in academic publishing can help rephrase the con-

versation around the sustainability of women’s careers in

academia, with important consequences for institutions and

policy makers.

Author contributions: J.H., A.J.G., R.S., and A.-L.B. designed research; J.H. and A.J.G. per-

formed research; J.H. and A.J.G. analyzed data; and J.H., A.J.G., R.S., and A.-L.B. wrote

the paper.y

Competing interest statement: A.-L.B. is founder of Nomix, Foodome, and Scipher

Medicine, companies that explore the role of networks in health. All other authors

declare no competing interest.y

This article is a PNAS Direct Submission.y

This open access article is distributed under Creative Commons Attribution License 4.0

(CC BY).y

See online for related content such as Commentaries.y

J.H. and A.J.G. contributed equally to this work.y

To whom correspondence may be addressed. Email: A.Barabasi@northeastern.edu.y

This article contains supporting information online at https://www.pnas.org/lookup/suppl/

doi:10.1073/pnas.1914221117/-/DCSupplemental.y

First published February 18, 2020.

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Fig. 1. Gender imbalance since 1955. (A) The number of active female (orange) and male (blue) authors over time and the total proportions of authors

(Inset). (B and C) The proportion of female authors in several disciplines (B) and countries (C); for the full list, see SI Appendix, Tables S3 and S4. (D) The

academic publishing career of a scientist is characterized by his or her temporal publication record. For each publication, we identify the date (gold dot) and

number of citations after 10 years c

(gold line, lower). The aggregation by year provides the yearly productivity (light gold bars), while the aggregation

over the entire career yields the total productivity (solid yellow bar, right) and total impact (solid yellow bar, right). Career length is calculated as the time

between the ﬁrst and last publication, and the annual productivity (dashed gold line) represents the average yearly productivity. Authors drop out from our

data when they published their last article.

authors from China, Japan, Korea, Brazil, Malaysia, and Singa-

pore (SI Appendix, section S2). To demonstrate the robustness

of our ﬁndings to database bias and author disambiguation

errors, we independently replicated our results in two addi-

tional datasets: the Microsoft Academic Graph (MAG) (31)

and the Digital Bibliography & Library Project (DBLP), each

using different criteria for publication inclusion and methodolo-

gies for career reconstruction (SI Appendix, sections S1 and S6).

Our focus on bibliometric data limits our analysis to publishing

careers and is unable to capture the career dynamics of teaching,

administrative, industrial, or government related research activ-

ities. Nevertheless, our efforts constitute an extensive attempt

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SOCIAL SCIENCES

to quantify gender inequality in science, technology, engineer-

ing, and mathematics (STEM) publications and citations, offer-

ing a longitudinal, career-wise perspective across national and

disciplinary boundaries.

The Increasing and Persistent Gender Gap

Across all years and disciplines, women account for 27% of

authors, a number that hides important trends: while in 1955

women represented only 12% of all active authors, that frac-

tion steadily increased over the last century, reaching 35% by

2005 (Fig. 1A). Yet, these aggregate numbers hide considerable

disciplinary differences, as the fraction of women is as low as

15% in math, physics, and computer science and reaches 33%

in psychology (Fig. 1B). We also observe signiﬁcant variations by

country, ﬁnding that the proportion of female scientists can be as

low as 28% in Germany and reaches parity with 50% in Russia

(Fig. 1C).

The low proportion of women actively publishing in STEM

captures only one aspect of gender inequality. Equally important

are the persistent productivity and impact differences between

the genders (Fig. 1D). We ﬁnd that while, on average, male sci-

entists publish 13.2 papers during their career, female authors

publish only 9.6, resulting in a 27% gender gap in total pro-

ductivity (Fig. 2A). The difference is particularly pronounced

among productive authors, as male authors in the top 20% pro-

ductivity bracket publish 37% more papers than female authors

(Fig. 2A). Interestingly, the gender gap disappears for median

productive authors (middle 20%) and reverses for the authors

in the bottom 20%. The gender gap in total productivity per-

sists for all disciplines and almost all countries (Fig. 2 B and

C). We also observe a large gender gap in total productivity for

the highest-ranked afﬁliations (Fig. 2D) (determined from the

2019 Times Higher Education World University Rankings; SI

Appendix, section S2.D).

AB C DE

FG H IJ

Fig. 2. Gender gap in scientiﬁc publishing careers. The gender gap is quantiﬁed by the relative difference between the mean for male (blue) and female

(orange) authors. In all cases the, relative gender differences are statistically signiﬁcant, as established by the two-sided t test, with P values < 10

−4

, unless

otherwise stated (see SI Appendix, section S4.A for test statistics). (A–E) Total productivity broken down by percentile (A), discipline (B), country (C), afﬁliation

rank (D), and decade (E). The gender gap in productivity has been increasing from the 1950s to the 2000s. (F–J) Total impact subdivided by percentile (F),

discipline (G), country (H), afﬁliation rank (I), and decade (J). (K–O) Annual productivity is nearly identical for male and female authors when subdivided by

percentile (K), discipline (L), country (M), afﬁliation rank (N), and decade (O). (P–T) Career length broken down by percentile (P), discipline (Q), country (R),

afﬁliation rank (S), and decade (T).

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Fig. 3. Controlling for career length. (A and B) The gender gap in career length strongly correlates with the gender gap in productivity across disciplines

(Pearson correlation, 0.80) (A) and countries (Pearson correlation, 0.56) (B). A gender gap of 0.0% indicates gender equality, while negative gaps indicate the

career length or productivity is greater for male careers, and positive gaps indicate the feature is greater for female careers. (C) In a matching experiment,

equal samples are constructed by matching every female author with a male author having an identical discipline, country, and career length. (D) The

average productivity provided by the matching experiment for career length compared to the population; the gender gap is reduced from 27.4% in the

population to 7.8% in the matched samples. (E) The average impact provided by the matching experiment for career length compared to the original

unmatched sample. Where visible, error bars denote 1 SD.

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We measure the total impact during an academic career by

the number of citations accrued 10 years after publication (c

)

by each paper published during a career (Fig. 1D), after remov-

ing self-citations and rescaling to account for citation inﬂation

(32–34) (SI Appendix, section S2.F). We ﬁnd that male scientists

receive 30% more citations for their publications than female

scientists (Fig. 2F). Once again, the total impact difference is

the largest for high-impact authors and reverses for median-

and low-impact authors: male authors in the top 20% in career

impact receive 36% more citations than their female counter-

parts. The disparity in impact persists in almost all countries

and all disciplines (Fig. 1 G and H), and can be found, to a

lesser extent, across all afﬁliations regardless of afﬁliation rank

(Fig. 1I).

Paradoxically, the gradual increase in the fraction of women

in science (5) (Fig. 1A) is accompanied by a steady increase in

both the productivity and impact gender gaps (Fig. 2 E and J).

The gender gap in total productivity rose from near 10% in the

1950s to a strong bias toward male productivity (35% gap) in

the 2000s. The gender gap in total impact actually switches from

slightly more female impact in the 1950s to a 34% gap favoring

male authors in the same time frame. These observations dis-

rupt the conventional wisdom that academia can achieve gender

equality simply by increasing the number of participating female

authors.

In summary, despite recent attempts to level the playing ﬁeld,

men continue to outnumber women 2 to 1 in the scientiﬁc

workforce and, on average, have more productive careers and

accumulate more impact. These results conﬁrm, using a uniﬁed

methodology spanning most of science, previous observations in

speciﬁc disciplines and countries (2, 9, 11, 12, 16, 35–38) and sup-

port in a quantitative manner the perception that global gender

differences in academia is a universal phenomenon persisting in

every STEM discipline and in most geographic regions. More-

over, we ﬁnd that the gender gaps in productivity and impact

have increased signiﬁcantly over the last 60 years. The universal-

ity of the phenomenon prompts us to ask: What characteristics

of academic careers drive the observed gender-based differences

in total productivity and impact?

Annual Productivity and Career Length

As total productivity and impact over a career represent a con-

volution of annual productivity and publishing career length, to

identify the roots of the gender gap, we must separate these two

factors. Traditionally, the difﬁculty of reconstructing full pub-

lishing careers has limited the study of annual productivity to

a small subset of authors or to career patterns observable dur-

ing a ﬁxed time frame (39–46). Access to the full publishing

career data allows us to decompose each author’s total productiv-

ity into his or her annual productivity and career length, deﬁned

as the time span between a scientist’s ﬁrst and last publication

(Fig. 1D and SI Appendix, section S3). We ﬁnd that the annual

productivity differences between men and women are negligi-

ble: female authors publish, on average, 1.33 papers per year,

while male authors publish, on average, 1.32 papers per year,

a difference, that while statistically signiﬁcant, is considerably

smaller than other gender disparities (0.9%, P value < 10

−9

;

Fig. 2K). This result is observed in all countries and disciplines

(Fig. 2 L and M), and we replicated it in all three datasets (SI

Appendix, section S6). The gender difference in annual produc-

tivity is small even among the most productive authors (4% for

the top 20%) and is reversed for authors of median and low

productivity.

The average annual productivity of scientists has slightly

decreased over time; yet, there is consistently no fundamental

difference between the genders (Fig. 2O). In other words, when

it comes to the number of publications per year, female and male

authors are largely indistinguishable, representing the ﬁrst gen-

der invariant quantity in performance metrics. As we show next,

this invariant, our key result, helps us probe the possible roots of

the observed gender gaps.

The comparable annual productivity of male and female scien-

tists suggests that the large gender gap in total career productivity

is determined by differences in career length. To test if this is

the case, we measured the career length (Fig. 1D) of each sci-

entist in the database, ﬁnding that, on average, male authors

reach an academic age of 11.0 years before ceasing to pub-

lish, while the average terminal academic age of female authors

is only 9.3 years (Fig. 2P). This gap persists when authors

are grouped by either discipline, country, or afﬁliation (Fig. 2

Q, R, and S) and has been increasing over the past 60 years

(Fig. 2T). Taken together, Fig. 2 K and T suggests that a sig-

niﬁcant fraction of the variation in total productivity is rooted

in variations in career lengths. This conclusion is supported by a

strong correlation between the career-length gap and the career-

wise productivity gap when we subdivide scientists by discipline

(Fig. 3A; Pearson correlation, 0.80) and country (Fig. 3B; Pear-

son correlation, 0.58). In other words, this strong correlation

implies that disciplines or countries with a large gender differ-

ence in the career length also have a large gender difference

in total productivity, while those disciplines or countries with

small gender differences in the career length also have a small

gender difference in total productivity. For example, the gen-

der gap in career length is smallest in applied physics (2.6%),

as so is the gender gap in total productivity (7.8%). In con-

trast, in biology and chemistry, men have 19.2% longer careers

on average, resulting in a total productivity gender gap that

exceeds 35.1%.

Given the largely indistinguishable annual productivity pat-

terns, we next ask how much of the total productivity and the

total impact gender gaps observed above (Fig. 2 A and F) could

be explained by the variation in career length. For this, we per-

form a matching experiment designed to eliminate the gender

gaps in career length. In the ﬁrst population, for each female

scientist, we select a male scientist from the same discipline

(Fig. 3C and SI Appendix, section S4.B). We then constructed

a second matched population, as a subset of the ﬁrst, in which

each female scientist is matched to a male scientist from the

same discipline and with exactly the same career length. In these

career length-matched samples, the gender gap in total produc-

tivity reduces from 31.0 to 7.8% (Fig. 3D). Furthermore, the

gender gap in the total impact is also reduced from 38.4 to 12.0%

(Fig. 3E). By matching pairs of authors based on observable

confounding variables, such as their discipline, we mitigate the

inﬂuence of these variables on the gender gaps. More strenuous

matching criteria controlling for country and afﬁliation rank do

not greatly affect these results, although they limit us to much

smaller matched populations (SI Appendix, section S4.B and Fig.

S1). While matching cannot rule out that gender differences are

inﬂuenced by unmatched variables that are unobserved here,

the signiﬁcant decrease in the productivity and impact gender

gaps when we control for career length suggests that publication

career length is a signiﬁcant correlate of gender differences in

academia.

To address the factors governing the end of a publishing

career, we calculated the dropout rate, deﬁned as the yearly

fraction of authors in the population who have just published

their last paper (42, 47). We ﬁnd that, on average, 9.0% of

active male scientists stop publishing each year, while the yearly

dropout rate for women is nearly 10.8% (Fig. 4A). In other

words, each year, women scientists have a 19.5% higher risk

to leave academia than male scientists, giving male authors a

major cumulative advantage over time. Moreover, this observa-

tion demonstrates that the dropout gap is not limited to junior

researchers but persists at similar rates throughout scientiﬁc

careers.

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Fig. 4. Author’s age-dependent dropout rate. (A) Dropout rate for male (blue) and female (orange) authors over their academic ages. (B) The cumulative

survival rate for male and female authors over their academic ages. (C and D) The effect of controlling for the age-dependent dropout rate on the gender

gaps in total productivity (C) and impact (D). (E) The total impact gap is eliminated in the matched sample based on total productivity. (F) The gender gap

in the total number of collaborators is eliminated in the matched sample based on total productivity.

The average causal effect of this differential attrition is

demonstrated through a counterfactual experiment in which we

shorten the careers of male authors to simulate dropout rates

matching their female counterparts at the same career stage

(Fig. 4 C and D and SI Appendix, section S4.F). We ﬁnd that

under similar dropout rates, the differences in total productiv-

ity and total impact reduce by roughly two-thirds, namely from

27.4 to 9.0% and from 30.5 to 12.1%, respectively. This result,

combined with our previous matching experiment (Fig. 3 D and

E), suggests that the difference in dropout rates is a key fac-

tor in the observed total productivity and impact differences,

accounting for about 67% of the productivity and impact gaps.

Yet, the differential dropout rates do not account for the whole

effect, suggesting that auxiliary disruptive effects, from percep-

tion of talent to resource allocation (15, 21), may also play a

potential role.

The reduction of the gender gaps in both total productiv-

ity and total impact by similar amounts suggests that total

impact, being the summation over individual articles, may be

primarily dependent on productivity (15). To test this hypoth-

esis, we conducted another matching experiment in which we

selected a male author from the same discipline and with exactly

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SOCIAL SCIENCES

the same number of total publications as each female author

(SI Appendix, section S4.D). In these matched samples, the

gender gap in the total impact is completely eliminated, drop-

ping from 38.4% in favor of male authors to 0.8% in favor

of female authors (Fig. 4E). This reveals a second gender-

invariant quantity—there is no discernible difference in impact

between male and female scientists for the same size body

of work. This second gender invariant reinforces our main

ﬁnding that it is career-length differences that drive the total

productivity gap, which consequently drives the impact gen-

der gap in academia. Interestingly, controlling for productivity

similarly ﬂips the gender gap in the total number of collabora-

tors throughout a career, from 13.3% in favor of male authors

to 16% in favor of female authors (Fig. 4F and SI Appendix,

section S4.E).

Summary and Discussion

The reconstruction of full publishing careers of scientists allowed

us to conﬁrm the differences in total productivity and impact

between female and male scientists across disciplines and coun-

tries since 1955. We showed that the gradual increase in the

fraction of women in STEM was accompanied by an increase in

the gender disparities in productivity and impact. It is particularly

troubling that the gender gap is the most pronounced among the

highly productive authors—those who train the new generations

of scientists and serve as role models for them. Yet, we also

found two gender invariants, revealing that active female and

male scientists have largely indistinguishable yearly performance

and receive a comparable number of citations for the same size

body of work. These gender-invariant quantities allowed us to

show that a large portion of the observed gender gaps are rooted

in gender-speciﬁc dropout rates and the subsequent gender gaps

in publishing career length and total productivity. This ﬁnding

suggests that we must rephrase the conversation about gen-

der inequality around the sustainability of woman’s careers in

academia, with important administrative and policy implications

(16, 37, 48–53).

It is often argued that in order to reduce the gender gap, the

scientiﬁc community must make efforts to nurture junior female

researchers. We ﬁnd, however, that the academic system is losing

women at a higher rate at every stage of their careers, suggesting

that focusing on junior scientists alone may not be sufﬁcient to

reduce the observed career-wise gender imbalance. The cumula-

tive impact of this career-wide effect dramatically increases the

gender disparity for senior mentors in academia, perpetuating

the cycle of lower retention and advancement of female faculty

(10, 53–55).

Our focus on closed careers limited our study to careers that

ended by 2010, eliminating currently active careers. Therefore,

further work is needed to detect the impact of recent efforts

by many institutions and funding agencies to support the par-

ticipation of women and minorities (41, 56). Our analysis of

all careers and the factors that dominate the gender gap could

offer a baseline for such experimental studies in the future.

Due to the reliability of gender disambiguation, we were also

unable to assess author gender for China, Japan, Korea, Brazil,

Malaysia, and Singapore, whose inclusion would provide a more

comprehensive global perspective of gender differences in sci-

ence. Since scientists from these countries signiﬁcantly increased

their contributions to the global scientiﬁc discourse, there is a

pressing need for future work to develop more accurate gen-

der identiﬁcation methodologies. Despite these limitations, our

work suggests the importance of temporal controls for study-

ing academic careers and, in particular, gender inequality in

academia.

It is important to emphasize that the end of a publishing

career does not always imply an end of an academic career;

authors who stopped publishing often retain teaching or admin-

istrative duties or conduct productive research in industry or

governmental positions, with less pressure to communicate their

ﬁndings through research publications. Scientiﬁc publications

represent only one of the possible academic outputs; in some

academic disciplines, books and patents are equally important,

and all three of our data sources (WoS, MAG, and DBLP)

tend to overrepresent STEM and English language publications

(57), thereby possibly biasing our analysis. Furthermore, our

bibliometric approach can draw deep insight into the large-scale

statistical patterns reﬂecting gender differences, and yet we can-

not observe and test potential variation in the organizational

context and resources available to individual researchers (13,

58). However, our results do suggest important consequences

for the organizational structures within academic departments.

Namely, we ﬁnd that a key component of the gender gaps

in productivity and impact may not be rooted in gender-

speciﬁc processes through which academics conduct research

and contribute publications but by the gender-speciﬁc sustain-

ability of that effort over the course of an entire academic

career.

Data and Code Availability. The DBLP and MAG are publicly

available from their source websites (SI Appendix). Other related

and relevant data and code are available from the corresponding

author upon request.

ACKNOWLEDGMENTS. We thank Alice Grishchenko for help with the visu-

alizations. We also thank the wonderful research community at the Center

for Complex Network Research, and in particular Yasamin Khorramzadeh,

for helpful discussions, and Kathrin Zippel at Northeastern University for

valuable suggestions. A.J.G. and A.-L.B. were supported in part by Temple-

ton Foundation Contract 61066 and Air Force Ofﬁce of Scientiﬁc Research

Award FA9550-19-1-0354. J.H. and A.-L.B. were supported in part by

Defense Advanced Research Projects Agency Contract DARPA-BAA-15-39.

R.S. acknowledges support from Air Force Ofﬁce of Scientiﬁc Research

Grants FA9550-15-1-0077 and FA9550-15-1-0364.

1. T. J. Ley, B. H. Hamilton, The gender gap in NIH grant applications. Science 322, 1472–

1474 (2008).

2. V. Lariviere, C. Ni, Y. Gingras, B. Cronin, C. R. Sugimoto, Global gender disparities in

science. Nature 504, 211–213 (2013).

3. H. Shen, Inequality quantiﬁed: Mind the gender gap. Nature News 495, 22–24

(2013).

4. A. E. Lincoln, S. Pincus, J. B. Koster, P. S. Leboy, The Matilda effect in science:

Awards and prizes in the US, 1990s and 2000s. Soc. Stud. Sci. 42, 307–320

(2012).

5. L. Holman, D. Stuart-Fox, C. E. Hauser, The gender gap in science: How long until

women are equally represented? PLoS Biol. 16, e2004956 (2018).

6. K. Zippel, Women in Global Science: Advancing Academic Careers Through

International Collaboration (Stanford University Press, Stanford, CA, 2017).

7. “Gender in the global research landscape” (Tech. Rep., Elsevier, 2017; https://www.

elsevier.com/research-intelligence/campaigns/gender-17).

8. National Academy of Sciences, National Academy of Engineering, and Institute

of Medicine, Beyond Bias and Barriers: Fulﬁlling the Potential of Women in Aca-

demic Science and Engineering (The National Academies Press, Washington, DC,

2007).

9. J. R. Cole, H. Zuckerman, The productivity puzzle: Persistence and changes in pat-

terns of publication of men and women scientists. Adv. Motivation Achiev. 2, 217–256

(1984).

10. J. S. Long, The origins of sex differences in science. Soc. Forces 68, 1297–1316 (1990).

11. J. S. Long, Measures of sex differences in scientiﬁc productivity. Soc. Forces 71, 159–

178 (1992).

12. Y. Xie, K. A. Shauman, Sex differences in research productivity: New evidence about

an old puzzle. Am. Socio. Rev. 63, 847–870 (1998).

13. M. F. Fox, K. Whittington, M. Linkova, “Gender,(in) equity, and the scientiﬁc work-

force” in Handbook of Science and Technology Studies (MIT Press, Cambridge, MA,

2017).

14. G. Abramo, C. A. D’Angelo, A. Caprasecca, Gender differences in research productiv-

ity: A bibliometric analysis of the Italian academic system. Scientometrics 79, 517–539

(2009).

15. V. Lariviere, E. Vignola-Gagne, C. Villeneuve, P. Gelinas, Y. Gingras, Sex differences

in research funding, productivity and impact: An analysis of Qu

ebec university

professors. Scientometrics 87, 483–498 (2011).

16. Y. Xie, K. A. Shauman, Women in Science: Career Processes and Outcomes (Harvard

University Press, 2003).

Huang et al. PNAS | March 3, 2020 | vol. 117 | no. 9 | 4615

Downloaded by guest on May 11, 2020

17. P. L. Carr, Relation of family responsibilities and gender to the productivity and career

satisfaction of medical faculty. Ann. Intern. Med. 129, 532–538 (1998).

18. S. Stack, Gender, children and research productivity. Res. High. Educ. 45, 891–920

(2004).

19. M. F. Fox, Gender, family characteristics, and publication productivity among

scientists. Soc. Stud. Sci. 35, 131–150 (2005).

20. E. Z. Cameron, A. M. White, M. E. Gray, Solving the productivity and impact puzzle:

Do men outperform women, or are metrics biased? BioScience 66, 245–252 (2016).

21. J. Duch et al., The possible role of resource requirements and academic career-choice

risk on gender differences in publication rate and impact. PLoS One 7, e51332 (2012).

22. R. M. Borsuk et al., To name or not to name: The effect of changing author gender

on peer review. BioScience 59, 985–989 (2009).

23. M. Jadidi, F. Karimi, H. Lietz, C. Wagner, Gender disparities in science? Dropout, pro-

ductivity, collaborations and success of male and female computer scientists. Adv.

Complex Syst. 21, 1750011 (2017).

24. K. M. Uhly, L. M. Visser, K. S. Zippel, Gendered patterns in international research

collaborations in academia. Stud. High Educ. 42, 760–782 (2015).

25. A. H. Eagly, C. Nater, D. I. Miller, M. Kaufmann, S. Sczesny, Gender stereotypes have

changed: A cross-temporal meta-analysis of U.S. public opinion polls from 1946 to

2018. Am. Psychol., 10.1037/amp0000494 (2019).

26. P. van den Besselaar, U. Sandstr

om, Vicious circles of gender bias, lower positions, and

lower performance: Gender differences in scholarly productivity and impact. PLoS

One 12, e0183301 (2017).

27. E. Leahey, Gender differences in productivity: Research specialization as a missing

link. Gend. Soc. 20, 754–780 (2006).

28. P. Bronstein, L. Farnsworth, Gender differences in faculty experiences of inter-

personal climate and processes for advancement. Res. High. Educ. 39, 557–585

(1998).

29. S. Fortunato et al., Science of science. Science 359, eaao0185 (2018).

30. A. Clauset, C. R. Shalizi, M. E. J. Newman, Power-law distributions in empirical data.

SIAM Rev. 51, 661–703 (2009).

31. A. Sinha et al., “An overview of Microsoft Academic Service (MAS) and Applica-

tions” in Proceedings of the 24th International Conference on World Wide Web

(International World Wide Web Conference Committee, 2015), pp. 243–246.

32. R. Sinatra, D. Wang, P. Deville, C. Song, A.-L. Barab

asi, Quantifying the evolution of

individual scientiﬁc impact. Science 354, aaf5239 (2016).

33. D. Wang, C. Song, A.-L. Barab

asi, Quantifying long-term scientiﬁc impact. Science 342,

127–132 (2013).

34. F. Radicchi, S. Fortunato, C. Castellano, Universality of citation distributions: Toward

an objective measure of scientiﬁc impact. Proc. Natl. Acad. Sci. U.S.A. 105, 17268–

17272 (2008).

35. I. E. Broder, Professional achievements and gender differences among academic

economists. Econ. Inq. 31, 116–127 (1993).

36. G. Abramo, C. A. D’Angelo, A. Caprasecca, The contribution of star scientists to overall

sex differences in research productivity. Scientometrics 81, 81–137 (2009).

37. D. Maliniak, R. Powers, B. F. Walter, The gender citation gap in international relations.

Int. Organ. 67, 889–922 (2013).

38. J. D. West, J. Jacquet, M. M. King, S. J. Correll, C. T. Bergstrom, The role of gender in

scholarly authorship. PLoS One 8, e66212 (2013).

39. K. Rorstad, D. W. Aksnes, Publication rate expressed by age, gender and academic

position – a large-scale analysis of Norwegian academic staff. J. Informetrics 9, 317–

333 (2015).

40. M. R. E. Symonds, N. J. Gemmell, T. L. Braisher, K. L. Gorringe, M. A. Elgar, Gender dif-

ferences in publication output: Towards an unbiased metric of research performance.

PLoS One 1, e127 (2006).

41. P. van Arensbergen, I. van der Weijden, P. van den Besselaar, Gender differences in

scientiﬁc productivity: A persisting phenomenon? Scientometrics 93, 857–868 (2012).

42. D. Kaminski, C. Geisler, Survival analysis of faculty retention in science and

engineering by gender. Science 335, 864–866 (2012).

43. J. M. Box-Steffensmeier et al., Survival analysis of faculty retention and promotion in

the social sciences by gender. PLoS One 10, e0143093 (2015).

44. S. F. Way, D. B. Larremore, A. Clauset, “Gender, productivity, and prestige in computer

science faculty hiring networks” in Proceedings of the 25th International Confer-

ence on World Wide Web (International World Wide Web Conferences Steering

Committee, 2016), pp. 1169–1179.

45. S. F. Way, A. C. Morgan, A. Clauset, D. B. Larremore, The misleading narrative of the

canonical faculty productivity trajectory. Proc. Natl. Acad. Sci. U.S.A. 114, E9216–E9223

(2017).

46. L. A. Hechtman et al., NIH funding longevity by gender. Proc. Natl. Acad. Sci. U.S.A.

115, 7943–7948 (2018).

47. S. Milojevi

c, F. Radicchi, J. P. Walsh, Changing demographics of scientiﬁc careers:

The rise of the temporary workforce. Proc. Natl. Acad. Sci. U.S.A. 115, 12616–12623

(2018).

48. H. Etzkowitz, C. Kemelgor, B. Uzzi, Athena Unbound: The Advancement of Women

in Science and Technology (Cambridge University Press, 2000).

49. S. J. Ceci, W. M. Williams, Understanding current causes of women’s underrepresen-

tation in science. Proc. Natl. Acad. Sci. U.S.A. 108, 3157–3162 (2011).

50. J. M. Sheltzer, J. C. Smith, Elite male faculty in the life sciences employ fewer women.

Proc. Natl. Acad. Sci. U.S.A. 111, 10107–10112 (2014).

51. W. M. Williams, S. J. Ceci, National hiring experiments reveal 2:1 faculty prefer-

ence for women on stem tenure track. Proc. Natl. Acad. Sci. U.S.A. 112, 5360–5365

(2015).

52. M. W. Nielsen et al., Opinion: Gender diversity leads to better science. Proc. Natl.

Acad. Sci. U.S.A. 114, 1740–1742 (2017).

53. E. A. Cech, M. Blair-Loy, The changing career trajectories of new parents in stem. Proc.

Natl. Acad. Sci. U.S.A. 116, 4182–4187 (2019).

54. National Research Council, Gender Differences at Critical Transitions in the Careers

of Science, Engineering, and Mathematics Faculty (The National Academies Press,

2010).

55. L. R. Martinez, K. R. O’Brien, M. R. Hebl, Fleeing the ivory tower: Gender differ-

ences in the turnover experiences of women faculty. J. Wom. Health 26, 580–586

(2017).

56. A. J. Stewart, V. Valian, An Inclusive Academy: Achieving Diversity and Excellence (MIT

Press, 2018).

57. P. Mongeon, A. Paul-Hus, The journal coverage of Web of Science and Scopus: A

comparative analysis. Scientometrics 106, 213–228 (2016).

58. C. Wennerc, A. Wold, Nepotism and sexism in peer-review. Nature 387, 341–343

(1997).

4616 | www.pnas.org/cgi/doi/10.1073/pnas.1914221117 Huang et al.

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An important finding from this paper, "This suggests the productivity and impact of gender differences are explained by different publishing career lengths and dropout rates." Always be careful when evaluating proportions without controlling for disciplinary differences! Just take it from Simpson... https://en.wikipedia.org/wiki/Simpson%27s_paradox Albert-Laszlo Barabasi is a physicist and network scientist who introduced the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems. He is the director of the Center for Network Science at Northeastern University. Here is a link to the "Emergence of Scaling in Random Networks": https://science.sciencemag.org/content/286/5439/509.full Source: https://en.wikipedia.org/wiki/Albert-L%C3%A1szl%C3%B3_Barab%C3%A1si This access to the full publishing career data was key to their discovery of the different career lengths of male and female researchers.

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