PreprintPDF Available

Higher Research Productivity = More pay? Gender Pay-for-Productivity Inequity Across Disciplines

Authors:
Preprints and early-stage research may not have been peer reviewed yet.

Figures

Content may be subject to copyright.
Higher Research Productivity = More pay? Gender Pay-for-Productivity Inequity Across
Disciplines
Charissa Samaniego1, Bobbie A. Dirr2, Maryam A. Kazmi1, Peggy Lindner3, Dejun Tony Kong4,
Evonzia Jeff-Eke1, Christiane Spitzmueller*1
1 Department of Psychology, 126 Heyne Building, 1500 Cullen Blvd, Houston, 77204
2 Strategic Research & Assessment Branch, HQ Air Force Personnel Center, 550 C St W,
Randolph AFB, TX 78150-4747. The views expressed in this article are those of the authors and
are not necessarily those of the U.S. Government, Department of Defense, or the U.S. Air Force.
3 Department of Information and Logistics Technology, 387 College of Technology Building,
4450 University Dr., Houston, TX 77204
4 Leeds School of Business, University of Colorado Boulder, 995 Regent Drive, Boulder, CO
80309-0419, United States
*Corresponding author
Christiane Spitzmueller,
Email: cspitzmu@central.uh.edu
Keywords: Academia, Gender, h-index, Compensation
Manuscript
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 2
Abstract
Gender pay equity for academics continues to be elusive. Adding to scholarship around structural
barriers to gender equity in academic settings, we investigate the link between scholarly
performance and compensation. We expect high research productivity to be differentially
associated with compensation outcomes for men and women. Building on social role theory, we
hypothesize that these relationships are contingent upon whether researchers are inside or outside
of Science, Technology, Engineering, and Mathematics (STEM). Using the h-index,
compensation, and researcher demographics for 3,033 STEM and social and behavioral sciences
(SBS) researchers from 17 R1 universities, we applied multilevel modeling techniques and
showed that cumulative research productivity was more strongly related to compensation for
men versus women researchers. However, these effects only held in STEM disciplines but not in
SBS disciplines. Based on these results, we recommend that institutions consider changing how
pay analyses are conducted and advocate for adding explicit modeling of scientific performance-
compensation links as part of routine pay equity analyses.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
Running head: HIGHER RESEARCH PRODUCIVITY = MORE PAY? 1
Higher Research Productivity = More pay? Gender Pay-for-Productivity Inequity Across
Disciplines
The pervasive under-representation of women researchers, specifically in tenured and
tenure-earning faculty positions in Science, Technology, Engineering and Mathematics (STEM)
(Bilen-Green et al., 2008; Lariviere et al., 2013; Shen, 2013), along with various challenges
women face in their academic career progression (Bedi et al., 2012; Clauset et al., 2015;
Edmunds et al., 2016; Handelsman et al., 2005; Moss-Racusin et al., 2012; Quadlin, 2018), calls
for continued research on gender equity in academic settings. One important form of gender
inequity is pay inequity. Academic researchers are expected to be paid equitably based on their
research productivity (i.e., pay-for-productivity). Nonetheless, are men and women really paid
equally for the same level of research productivity? Or is pay-for-productivity just a myth for
women in tenured and tenure track faculty positions? If gender inequity of pay-for-productivity
exists, women are likely discouraged to continue their careers in academia, which may help
explain the “leaky pipeline” (Blickenstaff, 2005) problem seen in STEM as compared to Social
and Behavioral Sciences (SBS) disciplines. To date, many studies only examine gender
differences in academic salary while controlling for productivity (Bellas, 1997; Euwals & Ward,
2005; Ginther, Donna K. & Hayes, Kathy J., 2003; Umbach, 2007) and the results are mixed,
leaving gender differences in the strength of the pay-for-productivity relationship unexamined. In
other words, it is unclear if the gender pay gap depends on a faculty member’s productivity level.
Drawing from theory and research on social roles, we further examine gender differences in pay-
for-productivity in STEM and SBS disciplines.
In the present research, we aim to address three questions regarding pay-for-productivity
in academic settings: (1) whether, and if so, how strongly, research productivity is positively
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 2
related to researcher pay (i.e., the intensity of pay-for-productivity), (2) whether productivity is
more strongly tied to pay for men than for women (i.e., interaction of gender and pay-for-
productivity), and (3) whether gender inequity of pay-for-productivity, if any, is more severe in
the STEM disciplines than in the SBS disciplines (i.e., disciplinary difference in gender inequity
of pay-for-productivity).
Pay-for-Productivity
Pay-for-productivity, from a work motivation perspective, is deemed fair by many
workers and motivates them to achieve desired results (Lawler, 1971; Maier, 1955). Meta-
analytic studies suggest performance-contingent pay is among the best methods for boosting
performance levels (Rynes et al., 2004, 2005). In academic institutions classified as R1 by the
Carnegie Classification of Institutions of Higher Education, research constitutes the most
important job responsibility and is a significant factor determining tenure success, promotions,
and pay raises across a host of academic disciplines (Fairweather, 2005). Thus, besides their
intrinsic motivation, academic researchers’ extrinsic motivation to produce research is, to some
degree, driven by the extent to which their research productivity is linked to their pay. The
University of Arkansas for Medical Sciences introduced a performance-based incentive plan for
its College of Medicine in 2005 (Reece et al., 2008). With faculty pay directly linked to
productivity, performance increased drastically, leading to a total compensation increase of about
20%, in addition to increases in external funding and researchers’ morale and satisfaction (Reece
et al., 2008).
Some previous studies focused on whether men and women researchers receive equal pay
while controlling for factors such as academic ranks, leadership positions (Jagsi et al., 2012), and
raises (Lindley et al., 1992) as proxies for research productivity. Others have controlled
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 3
productivity by controlling for the number of publications (e.g., number or articles or books;
Bellas, 1997; Euwals & Ward, 2005; Ginther et al., 2003; Levin & Stephan, 1998; Umbach,
2007), without any measure of quality of the publications. In contrast, we explicitly measure
research productivity with h-index and investigate whether higher research productivity (and
quality) translates into higher pay to the same extent for men and women in academia (i.e., pay-
for-productivity). A researcher’s h-index has become one of the most widely used and common
metrics to quantify scholarly productivity. Introduced 15 years ago by Hirsch, it refers to the
number of publications (h) that have received at least h citations each (Hirsch, 2005). For
example, a researcher who has 10 publications with at least 10 citations (with all other
publications having less than 10 citations each), would have an h-index of 10. Although the
popularity of this index has skyrocketed, researchers have acknowledged its’ shortcomings
including: the susceptibility of inflation due to self-citations (Bartneck & Kokkelmans, 2011;
Zhivotovsky & Krutovsky, 2008), favoring more established researchers (Hirsch, 2005), no
adjustment for multiple-authorship or order of authors, and no normalization of differential
citation practices between disciplines (Alonso et al., 2009). Regardless of these drawbacks, the
h-index is a single, easily calculable number that incorporates both a measure of quantity in the
number of publications, and a proxy for quality in terms of number of citations, and is widely
used as a decision-making tool within higher education for hiring and tenure (Barnes, 2017;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 4
Scruggs et al., 2019). Therefore, its effect on compensation should be examined to determine the
full utility of this metric.
Hypothesis 1: Research productivity is positively related to researcher salary in STEM and SBS
disciplines.
Gender Differences in Pay-for-Productivity
Researchers who identify as men earn around 20% more than their women peers (Carlin
et al., 2013; Jagsi et al., 2012; Lindley et al., 1992). Despite shifts in the distribution of men and
women researchers in faculty rank, the gender pay gap has not diminished in the last 10 years. In
2020, on average across all disciplines, assistant professors who identify as women make $7,605
less than their peers who identify as men, and this difference more than doubles at the full
professor level, with women full professors making $19,030 less than full professors who are
men (The Annual Report on the Economic Status of the Profession, 201920, 2020). Disparities
between disciplines may partly explain these gender differences as higher paying disciplines
(i.e., biological sciences, engineering, and mathematics) tend to have more researchers who are
men versus lower paying disciplines (i.e., English, sociology, and gender studies) with more
women researchers(Shulman et al., 2017). However, even in disciplines with a high proportion
of women, there is still gender pay inequity and thus differences in average discipline pay cannot
entirely explain gender pay inequity. One study reported men in disciplines one standard
deviation above the mean in representation of women will earn approximately $75,0000 versus
women earning $69,000 (Umbach, 2007).
Another partial explanation for gender pay inequity has focused on the “productivity
puzzle” of women having lower average productivity levels (Cole & Zuckerman, 1984; West et
al., 2013; Xie & Shauman, 1998). A plethora of contributing factors have been examined to
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 5
possibly explain women’s lower productivity levels including family responsibilities (Ceci &
Williams, 2011; Fox, 2005; Hunter & Leahey, 2010), resource allocations (Duch et al., 2012),
and research specialization (Leahey, 2006). However, recent analyses of archival data suggest no
gender differences in journal acceptance of publications, nor in productivity levels when
controlling for structural differences, implying that when given equal resources, men and women
publish equally well (Ceci & Williams, 2011; Huang et al., 2020). While investigating gender
differences in productivity levels is an important research topic, in the current study we are not
examining why differences may occur, but instead if men and women are paid equitably for their
individual productivity level. Research on whether the gender salary gap in academia disappears
after controlling for productivity is mixed (Bellas, 1997; Euwals & Ward, 2005; Ginther et al.,
2003; Umbach, 2007). Only one study to date has examined gender differences in pay-per-
performance relationship in specific STEM disciplines (physics, earth science and physiology),
and found women were paid more per publication than men, but only for physics (Levin &
Stephan, 1998). In addition to the data being from the 1970’s, the authors only examined the
change in salary in a two-year period, likely missing crucial overall salary differences.
Gender differences in pay-for-productivity can manifest in two ways. First, women are
stereotyped as less productive and competent and perceived as having lower status than men
(England, 1992; Heilman, 2001); therefore, women are not paid as much as men when they
perform well. Second, although women are encouraged to negotiate their salary and other
employment terms, compared to men, women researchers’ salary negotiations or requests for
salary adjustments are less likely to succeed (Leibbrant & List, 2015). Women tend to anticipate
backlash for their salary negotiation/request attempts; therefore, they may either opt to not
initiate their salary negotiations/requests or lower their aspirations if they decide to do so
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 6
(Amanatullah & Morris, 2010; Amanatullah & Tinsley, 2013). Women’s salary negotiation
attempts are sometimes viewed as aggressive acts, and frequently invite hostile reactions from
others (Rudman et al., 2012). Because of gender bias in salary negotiations disfavoring women,
we argue that research productivity does not translate into women researchers’ pay as much as
men researchers’ pay.
In the current study we focus on research productivity in STEM and SBS fields and
examine the gender differences in the strength of pay-per-productivity, that is look at gender
differences in the relationship between h-index and salary (not just changes in salary). Looking
at gender differences in pay-per-productivity, allows us to examine if gender pay inequity differs
across levels of productivity. If women are paid according to stereotypes, then women who have
low productivity will be paid the correct amount, but high producing women will be underpaid
because they are assumed to be underproductive (i.e., perceived productivity mismatches actual
productivity). Thus, we expect that there will be gender salary differences at high performance
levels and not at low performance levels.
Hypothesis 2: The link between research productivity and researcher salary is stronger among
men researchers than among women researchers. Such that, men are paid more per h-index and
gender pay inequity is larger at higher levels of productivity.
STEM vs SBS
Our final inquiry pertains to the disciplinary difference in gender inequity of pay-for-
productivity. If this inequity does exist, does it vary across academic disciplines? Specifically, is
the hypothesized inequity more severe in disciplines where women are traditionally under-
represented than in other disciplines? Women are less likely to enter STEM, feel less welcomed
in these disciplines, and are less likely to stay in tenure or tenure-earning positions in these
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 7
disciplines (Clauset et al., 2015; Edmunds et al., 2016; Handelsman et al., 2005). Furthermore,
some evidence suggests that the gender pay gap is larger in STEM disciplines (Umbach, 2007;
Xu, 2015) than in other disciplines, even when researchers control for gender differences in
productivity. We postulate women having difficulty to effectively negotiate compensation to be
more pronounced in STEM disciplines than in other disciplines such as social and behavioral
sciences (SBS) where we expect this gender inequity to be less severe.
In support of our expectations, social role theory (Eagly, 1987) suggests that gender roles
prescribe what men and women should be like and provide gendered rules and norms based on
which behaviors are judged and rewarded or socially sanctioned. Men are expected to be
achievement-oriented, competitive, and analytic, whereas women are expected to be warm,
considerate, and accommodating (Eagly & Karau, 2002; Heilman, 2001). Women are not
expected to pursue STEM; instead, they are more expected to pursue SBS such as psychology,
communication, sociology, etc. (Clark Blickenstaff, 2005; Handelsman et al., 2005). Women in
STEM disciplines violate such gender role expectations and thus face unfavorable evaluations
and other social sanctions. In contrast, women researchers in SBS disciplines are less likely to
violate gender role expectations and thus may face fewer negative consequences. Such gender
role expectations are particularly strong in fields dominated by men such as STEM disciplines as
the norms are shaped by men. Women researchers who are achievement-oriented, competitive,
and analytic inevitably violate gender role expectations and thus face social sanctions including
unfavorable evaluations and social exclusion. These gender role expectations coupled with
stereotypes of women as low performers could result in lower female salaries relative to male
salaries, but only for high performing women in STEM disciplines, as women with lower
productivity are meeting prescriptive gender stereotypes. Thus, we would expect stereotyping of
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 8
productivity and gender differences in negotiation tactics to affect the salaries of highly
productive women in academic STEM disciplines.
Hypothesis 3: The gender difference in the link between research productivity and researcher
salary is larger in STEM versus SBS disciplines.
Materials and Methods
We collected research productivity and salary data of 3,033 tenured and tenure-earning
faculty members from 17 universities across the United States. Department chairs were excluded
from the analyses. Our criteria for the university selection were based on a study conducted for a
National Science Foundation ADVANCE institutional transformation project. The selected data
collection sites were large public universities in urban settings that were classified as R1
institutions (i.e., highest research activity by the Carnegie Classification of Institutions of Higher
Education). Among these universities, we selected those that made salary data publicly available.
In the first step, coders manually searched department websites of all 17 universities, and created
a database combining researchers’ gender and discipline information and their demographic
information retrieved from their publicly available CVs. In the second step, we used an
automated approach to scrape each researcher’s research productivity information (h-index) from
Google Scholar, and collected salary data from websites reporting current 9-month faculty
salaries.
Measures
Gender.
The coders utilized a combination of photographs available on departmental websites and
names to code each researcher’s gender (1 = woman, 0 = man).
Research Productivity.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 9
Research productivity was indicated by the h-index in 2019 (Hirsch, 2005), which was
scraped from each tenured and tenure-earning faculty member’s Google Scholar website. The h-
index is the most used metric for research productivity, with h being the number of papers a
researcher has authored or co-authored that has accumulated at least h citations (Hirsch, 2005).
Salary.
We collected the 9-month faculty salary data from various websites containing
university-published current faculty salaries, as noted earlier.
Controls.
We controlled for the number of years since the attainment of Ph.D. (i.e., post-Ph.D.
years) at the individual level and the following department level controls by utilizing group-
mean centering in our multilevel models: proportion of women in department, average
department years since the attainment of Ph.D. (i.e., post-Ph.D. department tenure); and mean of
h-indices within each department. Our random intercepts multilevel model inherently controlled
for the average salary level of the department. We controlled for post-Ph.D. years to ensure that
salary increases were attributed to increases in research productivity rather than just researchers’
tenure in their discipline. Our multilevel controls ensured we controlled for university and
discipline differences because department averages will be affected by both.
Results
Descriptive Statistics
Table 1 presents descriptive statistics and correlations among post-Ph.D. years, the h-
index, and salary. Correlations are presented separately for men and women researchers. The
average amounts of men and women researchers’ salary were $133,092.40 and $118,459.20,
respectively. Women, on average, made 89 cents for every dollar made by men. With 95%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 10
confidence, the average salary for men was $10,850.63 to $18,415.71 more than that of women
researchers (i.e., 9.16% to 15.55% more than the average salary for women). Gender difference
in the h-index may partially explain this gender gap of salary. With 95% confidence, we found
that men’s average h-index was 5.32 to 8.33 higher than that of women. The gender difference in
the h-index could partially be explained by the gender difference in post-Ph.D. years. Also, with
95% confidence, we found that men had 3.80 to 5.51 more post-Ph.D. years than women.
Multilevel Regression Analyses
We tested our hypotheses by conducting multilevel regression analyses, given that our
data were nested within academic departments (e.g., Psychology department at the University of
Houston). We centered gender, post-PhD years, and h-index by their respective group
(department) means (Enders & Tofighi, 2007) (mean of gender is a proportion). In all reported
models, for the sake of parsimony, we did not enter the department means of gender, post-Ph.D.
years, and the h-index as predictors because (a) we did not hypothesize the effects of these
department means, and (b) inclusion or exclusion of these department means did not change the
result patterns, presumably because we group-mean centered. The ICC of salary estimate of
22.47% (i.e., 22.47% of the variance in salary could be explained by cross-department
differences) further justified our use of multi-level regression analyses. Department-level salary
variability can be explained by both university and discipline differences. Table 2 presents the
results of the multi-level regression analyses, with profile confidence intervals being reported in
the main text. The baseline model included two control variables: post-Ph.D. years and gender (1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 11
= woman, 0 = man), with the former being a significant predictor of salary (B = 2,186.66, t =
35.22, p < .01).
In line with Hypothesis 1, researchers’ h-index, indicative of their research productivity,
was positively related to their salary level (see Model 1, Table 2). On average, a one-point
increase in the h-index translated into a salary increase of $1,000.46 (t = 22.17, p <.01), with its
95% confidence interval [$912.01, $1,088.90]. We did not find support for Hypothesis 2.
Specifically, the interaction between gender and the h-index was not significant (Model 2: B = -
120.70, t = -1.17, p = 0.24). In other words, pay-for-productivity did not differ significantly
between men and women researchers when examining both STEM and SBS discipline
simultaneously. Finally, we found support for Hypothesis 3 regarding gender inequity of pay-for-
productivity in STEM versus SBS disciplines; the three-way interaction among gender, the h-
index, and academic discipline dummy (STEM vs. SBS) was negatively related to researchers’
salary level (Model 3: B = -397.75, t = -1.86, p = .063).
We then probed the two-way interaction between gender and the h-index separately for
STEM and SBS disciplines. For the latter, gender inequity of pay-for-productivity was not
significant (B = 141.80, t = 0.76, p = 0.45). However, for the former, pay-for-productivity was
unfavorable to women versus men (B = -266.66, t = -2.13, p = .03). On average, in STEM
disciplines, men were paid $266.66 (95% confidence interval [$20.95, $512.61]) more than
women for each one-point increment in h-index. Figure 1 shows the interaction between gender
and the h-index for both STEM (Figure 1a) and SBS (Figure 1b) disciplines using group mean
centered variables. As demonstrated, for STEM disciplines, as h-index increases, predicted
salary for men is higher than for women.
Discussion
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 12
The present research reveals gender inequity of pay-for-productivity in STEM
disciplines. Consistent with work motivation theories (Rynes et al., 2004, 2005), we did find that
researchers’ salary is coupled with their research productivity as intended, but this pay-
productivity coupling was more favorable to men versus women, particularly in STEM
disciplines. It is interesting to note that previous research demonstrated high performing women
in STEM may need to overcompensate (i.e., build more relationships, acquire more knowledge,
or put in more research hours) to achieve the same level of productivity indicators as their male
colleagues (Aguinis et al., 2018). Thus, not only is the road to becoming a “star” performer more
difficult for women, they may not also see the same returns in compensation for their research
investments. Women researchers in STEM with a h-index of 49 (one standard deviation above
the mean) made around six thousand dollars less than men researchers in STEM with the same h-
index. Our study did not follow researchers longitudinally, but we can tentatively extrapolate
how a six-thousand-dollar salary gap can add up over the years (i.e., over a ten-year-period this
difference would add up to sixty-thousand-dollars). Depending on how their h-index develops
over one’s career, a highly productive woman researcher in STEM could experience even more
pay inequity.
Our finding renders support for funding agencies’ (i.e., National Science Foundation)
efforts for reducing gender inequity in STEM disciplines (Ceci & Williams, 2011) and yet
reveals the lingering challenge inherent in these efforts. Given that our analyses relied on
archival data, we could not accurately code the race/ethnicity of researchers and thus did not
include this demographic factor in our analyses. However, we speculate that pay-for-productivity
may further disadvantage those with intersectional identities, such as women of color in STEM
disciplines. Given that our focus was on determining whether there is a gender inequity of pay-
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 13
for-productivity across disciplines, we offer some plausible explanations without testing these
explanatory mechanisms. Future research should hence shed light on these possible mechanisms
to ultimately identify ways to close gaps. For example, why, when, and how pay-for-productivity
relationships are weaker for women in STEM may be a result of fewer women attempting to
continuously renegotiate their salary. Alternatively, men may be more likely to seek offers from
other institutions and their salary may benefit as a result. Last, it may be possible that women’s
attempts to renegotiate their salary based on incremental performance results in negative
reactions from administrators at the departmental, college, and university levels.
In our analyses we used the h-index as an indicator of research productivity. We
encourage future researchers aiming the productivity-pay link to use broader or supplemental
indices of productivity, such as external funding records and total citations. Even though the h-
index is a widely known metric for research productivity and is used as a decision-making tool, it
is not without weaknesses. For instance, intentional manipulation of the h-index by researchers
through self-citations or inclusion of work authored by others may render the metric problematic
for exclusive use as a research productivity indicator.
We further urge universities to regularly conduct internal analyses to adjust potential
gender inequity of pay-for-productivity. Likewise, professional associations in STEM disciplines
should regularly conduct such analyses to reduce the more limited pay-for-performance
relationships we observed for women in our study. Notably, we do not intend to assert that the h-
index should be treated as the benchmark for research productivity, as it is not problem- or
concern-free. However, the h-index is to the measurement of scholarly productivity what
democracy is to forms of government: the least problematic. We also urge universities to
continuously assess whether high levels of research productivity translate into high pay at similar
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 14
rates for men and women the alternative may be to continue to lose women scientists despite
high productivity levels and potential. The dearth of women, especially in senior
academic/faculty positions in STEM, continues to pose a significant challenge for the science
and technology workforce in the 21st century. To attract more women to enter STEM disciplines
and help them be more engaged and thrive in these disciplines and their organizations,
universities should, first and foremost, effectively address the ostensibly “sticky” problem of
gender inequity of pay-for-productivity.
Declarations
Funding
We received funding from the National Science Foundation in support of this research
(ADVANCE Institutional Transformation Grant #1409928, 2014-2019). Any opinions, findings,
and conclusions or recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the National Science Foundation.
Data
All data used in this analysis can be found at:
https://osf.io/6drsp/?view_only=d92db8841232491baaa107d0bd96c873.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 15
References
Aguinis, H., Ji, Y. H., & Joo, H. (2018). Gender productivity gap among star performers in
STEM and other scientific fields. Journal of Applied Psychology, 103(12), 12831306.
https://doi.org/10.1037/apl0000331
Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2009). h-Index: A review
focused in its variants, computation and standardization for different scientific fields.
Journal of Informetrics, 3(4), 273289. https://doi.org/10.1016/j.joi.2009.04.001
Amanatullah, E. T., & Morris, M. W. (2010). Negotiating gender roles: Gender differences in
assertive negotiating are mediated by women’s fear of backlash and attenuated when
negotiating on behalf of others. Journal of Personality and Social Psychology, 98(2),
256267. https://doi.org/10.1037/a0017094
Amanatullah, E. T., & Tinsley, C. H. (2013). Punishing female negotiators for asserting too
much…or not enough: Exploring why advocacy moderates backlash against assertive
female negotiators. Organizational Behavior and Human Decision Processes, 120(1),
110122. https://doi.org/10.1016/j.obhdp.2012.03.006
Barnes, C. (2017). The h -index Debate: An Introduction for Librarians. The Journal of
Academic Librarianship, 43(6), 487494. https://doi.org/10.1016/j.acalib.2017.08.013
Bartneck, C., & Kokkelmans, S. (2011). Detecting h-index manipulation through self-citation
analysis. Scientometrics, 87(1), 8598. https://doi.org/10.1007/s11192-010-0306-5
Bedi, G., Van Dam, N. T., & Munafo, M. (2012). Gender inequality in awarded research grants.
The Lancet, 380(9840), 474. https://doi.org/10.1016/S0140-6736(12)61292-6
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 16
Bellas, M. L. (1997). Disciplinary Differences in Faculty Salaries: Does Gender Bias Play a
Role? The Journal of Higher Education, 68(3), 299. https://doi.org/10.2307/2960043
Bilen-Green, C., Froelich, K. A., & Jacobson, S. W. (2008). The Prevalence of Women in
Academic Leadership Positions, and Potential Impact on Prevalence of Women in the
Professorial Ranks. 11.
Carlin, P. S., Kidd, M. P., Rooney, P. M., & Denton, B. (2013). Academic Wage Structure by
Gender: The Roles of Peer Review, Performance, and Market Forces. Southern Economic
Journal, 80(1), 127146. https://doi.org/10.4284/0038-4038-2010.267
Ceci, S. J., & Williams, W. M. (2011). Understanding current causes of women’s
underrepresentation in science. Proceedings of the National Academy of Sciences, 108(8),
31573162. https://doi.org/10.1073/pnas.1014871108
Clark Blickenstaff, J. (2005). Women and science careers: Leaky pipeline or gender filter?
Gender and Education, 17(4), 369386. https://doi.org/10.1080/09540250500145072
Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in
faculty hiring networks. Science Advances, 1(1), e1400005.
https://doi.org/10.1126/sciadv.1400005
Cole, J. R., & Zuckerman, H. (1984). The Productivity Puzzle: Persistence and Change in
Patterns of Publication of Men and Women Scientists. Advances in Motivation and
Achievements, 2, 17256.
Duch, J., Zeng, X. H. T., Sales-Pardo, M., Radicchi, F., Otis, S., Woodruff, T. K., & Nunes
Amaral, L. A. (2012). The Possible Role of Resource Requirements and Academic
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 17
Career-Choice Risk on Gender Differences in Publication Rate and Impact. PLoS ONE,
7(12), e51332. https://doi.org/10.1371/journal.pone.0051332
Eagly, A. H. (1987). Sex differences in social behavior: A social-role interpretation. L. Erlbaum
Associates.
Eagly, A. H., & Karau, S. J. (2002). Role congruity theory of prejudice toward female leaders.
Psychological Review, 109(3), 573598. https://doi.org/10.1037/0033-295X.109.3.573
Edmunds, L. D., Ovseiko, P. V., Shepperd, S., Greenhalgh, T., Frith, P., Roberts, N. W., Pololi,
L. H., & Buchan, A. M. (2016). Why do women choose or reject careers in academic
medicine? A narrative review of empirical evidence. The Lancet, 388(10062), 2948
2958. https://doi.org/10.1016/S0140-6736(15)01091-0
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel
models: A new look at an old issue. Psychological Methods, 12(2), 121138.
https://doi.org/10.1037/1082-989X.12.2.121
England, P. (1992). Comparable worth: Theories and evidence. Aldine de Gruyter.
Euwals, R., & Ward, M. E. (2005). What matters most: Teaching or research? Empirical
evidence on the remuneration of British academics. Applied Economics, 37(14), 1655
1672. https://doi.org/10.1080/00036840500181620
Fairweather, J. S. (2005). Beyond the Rhetoric: Trends in the Relative Value of Teaching and
Research in Faculty Salaries. The Journal of Higher Education, 76(4), 401422.
https://doi.org/10.1353/jhe.2005.0027
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 18
Fox, M. F. (2005). Gender, Family Characteristics, and Publication Productivity among
Scientists. Social Studies of Science, 35(1), 131150.
https://doi.org/10.1177/0306312705046630
Ginther, Donna K. & Hayes, Kathy J. (2003). Gender Differences in Salary and Promotion for
Faculty in the Humanitites 1977-95. The Journal of Human Resources, 38(1), 3473.
Handelsman, J., Cantor, N., Carnes, M., Denton, D., Fine, E., Grosz, B., Hinshaw, V., Marrett,
C., Rosser, S., Shalala, D., & Sheridan, J. (2005). More Women in Science. Science, 309,
11901191. https://doi.org/10.1126/science.1113252
Heilman, M. E. (2001). Description and Prescription: How Gender Stereotypes Prevent
Women’s Ascent Up the Organizational Ladder. Journal of Social Issues, 57(4), 657
674. https://doi.org/10.1111/0022-4537.00234
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings
of the National Academy of Sciences, 102(46), 1656916572.
https://doi.org/10.1073/pnas.0507655102
Huang, J., Gates, A. J., Sinatra, R., & Barabási, A.-L. (2020). Historical comparison of gender
inequality in scientific careers across countries and disciplines. Proceedings of the
National Academy of Sciences, 117(9), 46094616.
https://doi.org/10.1073/pnas.1914221117
Hunter, L. A., & Leahey, E. (2010). Parenting and research productivity: New evidence and
methods. Social Studies of Science, 40(3), 433451.
https://doi.org/10.1177/0306312709358472
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 19
Jagsi, R., Griffith, K. A., Stewart, A., Sambuco, D., DeCastro, R., & Ubel, P. A. (2012). Gender
Differences in the Salaries of Physician Researchers. JAMA, 307(22).
https://doi.org/10.1001/jama.2012.6183
Lariviere, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Global gender
disparities in science. Nature News, 504(7479), 11. https://doi.org/10.1038/504211a
Lawler, E. E. (1971). Pay and organizational effectiveness: A psychological view. McGraw-Hill.
Leahey, E. (2006). Gender Differences in Productivity: Research Specialization as a Missing
Link. Gender & Society, 20(6), 754780. https://doi.org/10.1177/0891243206293030
Leibbrant, A & List, JA. (2015). Do women avoid salary negotiations? Evidence from a large-
scale natural field experiment. Management Science, 61(9), 20162024.
Levin, S. G., & Stephan, P. E. (1998). Gender Differences in the Rewards to Publishing in
Academe: Science in the 1970s. Sex Roles, 38, 10491064.
Lindley, James T., Fish, Mary, & Jasckson, John. (1992). Gender Differences in Salaries: An
Application to Academe. Southern Economic Journal, 241259.
Maier, N. R. F. (1955). Psychology in industry, 2nd ed. Houghton Mifflin.
Moss-Racusin, C. A., Dovidio, .John F., Brescoll, V. L., Graham, M. J., & Handelsman, J.
(2012). Science faculty’s subtle gender biases favor male students. Proceedings of the
National Academy of Sciences, 109(41), 1647416479.
https://doi.org/10.1073/pnas.1211286109
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 20
Quadlin, N. (2018). The Mark of a Woman’s Record: Gender and Academic Performance in
Hiring. American Sociological Review, 83(2), 331360.
https://doi.org/10.1177/0003122418762291
Reece, E. A., Nugent, O., Wheeler, R. P., Smith, C. W., Hough, A. J., & Winter, C. (2008).
Adapting Industry-Style Business Model to Academia in a System of Performance-Based
Incentive Compensation: Academic Medicine, 83(1), 7684.
https://doi.org/10.1097/ACM.0b013e31815c6508
Rudman, L. A., Moss-Racusin, C. A., Glick, P., & Phelan, J. E. (2012). Reactions to Vanguards.
In Advances in Experimental Social Psychology (Vol. 45, pp. 167227). Elsevier.
https://doi.org/10.1016/B978-0-12-394286-9.00004-4
Rynes, S. L., Gerhart, B., & Minette, K. A. (2004). The importance of pay in employee
motivation: Discrepancies between what people say and what they do. Human Resource
Management, 43(4), 381394. https://doi.org/10.1002/hrm.20031
Rynes, S. L., Gerhart, B., & Parks, L. (2005). Personnel Psychology: Performance Evaluation
and Pay for Performance. Annual Review of Psychology, 56(1), 571600.
https://doi.org/10.1146/annurev.psych.56.091103.070254
Scruggs, R., McDermott, P. A., & Qiao, X. (2019). A Nationwide Study of Research Publication
Impact of Faculty in U.S. Higher Education Doctoral Programs. Innovative Higher
Education, 44(1), 3751. https://doi.org/10.1007/s10755-018-9447-x
Shen, H. (2013). Inequality Quantified: Mind the Gender Gap. Nature News, 495, 2224.
https://doi.org/10.1038/495022a
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
HIGHER RESEARCH PRODUCIVITY = MORE PAY? 21
Shulman, S., Hopkins, B., Kelchen, R., Persky, J., Yaya, M., Barnshaw, J., & Dunietz, S. J.
(2017). Visualizing Change: The Annual Report on the Economic Status of the
Profession, 2016- 17. Academe, 103(2), 4.
The Annual Report on the Economic Status of the Profession, 201920 (p. 30). (2020). American
Association of University Professors. https://www.aaup.org/report/annual-report-
economic-status-profession-2019-20
Umbach, P. D. (2007). Gender equity in the academic labor market: An analysis of academic
disciplines. Research in Higher Education, 48(2), 169192.
https://doi.org/10.1007/s11162-006-9043-2
West, J. D., Jacquet, J., King, M. M., Correll, S. J., & Bergstrom, C. T. (2013). The Role of
Gender in Scholarly Authorship. PLoS ONE, 8(7), e66212.
https://doi.org/10.1371/journal.pone.0066212
Xie, Y., & Shauman, K. A. (1998). Sex Differences in Research Productivity: New Evidence
about an Old Puzzle. American Sociological Review, 63(6), 847.
https://doi.org/10.2307/2657505
Xu, Y. (2015). Focusing on Women in STEM: A Longitudinal Examination of Gender-Based
Earning Gap of College Graduates. The Journal of Higher Education, 86(4), 489523.
https://doi.org/10.1353/jhe.2015.0020
Zhivotovsky, L. A., & Krutovsky, K. V. (2008). Self-citation can inflate h-index. Scientometrics,
77(2), 373375. https://doi.org/10.1007/s11192-006-1716-2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
Figure 1.
Note. Plots were generated using group mean centering for h-index and gender. Ranges for both
axes have been fixed to allow for comparison.
Figure 1
Table 1.
Descriptive Statistics and Correlations
Variable
1. Years Since PhD
2. H-index
3. Salary
1
20.01 (11.53)
0.56
0.49
2
0.63
28.66 (19.52)
0.55
3
0.56
0.56
129,734.50
(49,972.62)
Men Mean (SD)
21.08 (11.87)
30.23 (19.93)
133,092.40
(51,553.54)
Women Mean
(SD)
16.42 (9.50)
23.40 (17.08)
118,459.20
(42,380.92)
CI of Difference
between Men and
Women
3.80, 5.51
5.32, 8.33
10,850.63, 18,415.71
STEM
20.39 (11.54)
29.86 (19.32)
129,464.50
(46,431.08)
SBS
18.77 (11.42)
24.74 (19.68)
130,617.60
(60,149.14)
CI of Difference
between
Disciplines
0.65, 2.58
3.47, 6.76
3,663.30, 5,969.36
Note. Correlations for women are below the diagonal and correlations for men are above the
diagonal. All correlations are significant at p < .01. Overall means and standard deviations (across
genders) are on the diagonal. STEM: Science, Technology, Engineering, and Mathematics. SBS:
Social and Behavioral Sciences.
Table 1
1
Table 2. Multi-level Regression Analysis for Hypotheses 1-3
DV: Base
Salary
Baseline
Model 1
Model 2
Model 3
STEM
SBS
Intercept
125521.15
(69.99) **
125406.14
(69.79) **
125306.58
(69.71) **
125259.56
(69.72) **
126835.83
(70.54) **
120026.55
(25.31) **
Gender (1
= Woman,
0 = Man)
-1910.49
(1.12)
448.79
(0.29)
50.24
(0.03)
1027.30
(0.37)
-751.11
(-0.39)
585.87
(0.19)
Post-
Ph.D.
Years
2186.66
(35.22)**
1256.48
(17.70)**
1259.63
(17.73) **
1239.42
(17.48)**
1306.65
(16.95) **
974.21
(5.67) **
H-index
1000.46
(22.17) **
993.84
(21.85)**
1326.97
(15.71) **
884.37
(18.09) **
1443.87
(12.63) **
Woman
˟H-index
-120.70
(-1.17)
134.58
(0.79)
-266.66
(-2.13) *
141.8
(0.76)
Woman
˟STEM
Only
-1912.38
(-0.56)
H-index
˟STEM
Only
-418.35
(-4.71) **
Woman
˟H-index
˟STEM
Only
-397.75
(-1.86) ^
Note. Significant at ** p < .01; * p < .05; ^p = .06. Engineering and NSM n= 2,323;
SBS n= 710. Standard deviations are reported in the parentheses.
Table 2
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
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 gender 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 differences are explained by different publishing career lengths and dropout rates. This comprehensive picture of gender inequality in academic publishing can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.
Article
Full-text available
Research impact is very important in academia. This study explored the research impact of faculty in doctoral higher education programs through the use of Hirsch’s h index as measured by Google Scholar results. Characteristics of the h index in this field are discussed, and norms are offered for professors of different ranks. We also explore relationships between gender, experience, and U.S. News and World Report ranking and the index. We find that gender has no significant relationship to faculty index in this field, but faculty experience and school rankings do have a relationship. Our findings support the use of the h index in assessing research impact in the higher education field, and they may be of interest to persons beyond this field as we consider the manner in which we assess faculty research.
Book
This volume provides a detailed description of the situation of women in employment in the early 1990s and considers how sociological and economic theories of labor markets illuminate the gap in pay between the sexes.
Article
A role congruity theory of prejudice toward female leaders proposes that perceived incongruity between the female gender role and leadership roles leads to 2 forms of prejudice: (a) perceiving women less favorably than men as potential occupants of leadership roles and (b) evaluating behavior that fulfills the prescriptions of a leader role less favorably when it is enacted by a woman. One consequence is that attitudes are less positive toward female than male leaders and potential leaders. Other consequences are that it is more difficult for women to become leaders and to achieve success in leadership roles. Evidence from varied research paradigms substantiates that these consequences occur, especially in situations that heighten perceptions of incongruity between the female gender role and leadership roles.
Article
We examined the gender productivity gap in STEM and other scientific fields specifically among star performers. Study 1 included 3,853 researchers who published 3,161 articles in mathematics. Study 2 included 45,007 researchers who published 7,746 articles in genetics. Study 3 included 4,081 researchers who published 2,807 articles in applied psychology and 6,337 researchers who published 3,796 articles in mathematical psychology. Results showed that (a) the power law with exponential cutoff is the best-fitting distribution of research productivity across fields and gender groups, and (b) there is a considerable gender productivity gap among stars in favor of men across fields. Specifically, the underrepresentation of women is more extreme as we consider more elite ranges of performance (i.e., top 10%, 5%, and 1% of performers). Conceptually, results suggest that individuals vary in research productivity predominantly due to the generative mechanism of incremental differentiation, which is the mechanism that produces power laws with exponential cutoffs. Also, results suggest that incremental differentiation occurs to a greater degree among men and certain forms of discrimination may disproportionately constrain women’s output increments. Practically, results suggest that women may have to accumulate more scientific knowledge, resources, and social capital to achieve the same level of increase in total outputs as their male counterparts. Finally, we offer recommendations on interventions aimed at reducing constraints for incremental differentiation among women that could be useful for narrowing the gender productivity gap specifically among star performers.
Article
Women earn better grades than men across levels of education—but to what end? This article assesses whether men and women receive equal returns to academic performance in hiring. I conducted an audit study by submitting 2,106 job applications that experimentally manipulated applicants’ GPA, gender, and college major. Although GPA matters little for men, women benefit from moderate achievement but not high achievement. As a result, high-achieving men are called back significantly more often than high-achieving women—at a rate of nearly 2-to-1. I further find that high-achieving women are most readily penalized when they major in math: high-achieving men math majors are called back three times as often as their women counterparts. A survey experiment conducted with 261 hiring decision-makers suggests that these patterns are due to employers’ gendered standards for applicants. Employers value competence and commitment among men applicants, but instead privilege women applicants who are perceived as likeable. This standard helps moderate-achieving women, who are often described as sociable and outgoing, but hurts high-achieving women, whose personalities are viewed with more skepticism. These findings suggest that achievement invokes gendered stereotypes that penalize women for having good grades, creating unequal returns to academic performance at labor market entry.
Article
This article reviews the debate within bibliometrics regarding the h-index. Despite its popularity as a decision-making tool within higher education, the h-index has become increasingly controversial among specialists. Fundamental questions remain regarding the extent to which the h-index actually measures what it sets out to measure. Unfortunately, many aspects of this debate are confined to highly technical discussions in specialised journals. This article explains in simple terms exactly why a growing number of bibliometricians are sceptical that the h-index is a useful tool for evaluating researchers. It concludes that librarians should be cautious in their recommendations regarding this metric, at least until better evidence becomes available.
Article
Women are under-represented in academic medicine. We reviewed the empirical evidence focusing on the reasons for women's choice or rejection of careers in academic medicine. Using a systematic search, we identified 52 studies published between 1985, and 2015. More than half had methodological limitations and most were from North America. Eight main themes were explored in these studies. There was consistent evidence for four of these themes: women are interested in teaching more than in research; participation in research can encourage women into academic medicine; women lack adequate mentors and role models; and women experience gender discrimination and bias. The evidence was conflicting on four themes: women are less interested in research than men; women lose commitment to research as their education and training progress; women are deterred from academic careers by financial considerations; and women are deterred by concerns about work–life balance. Inconsistency of findings across studies suggests significant opportunities to overcome barriers by providing a more enabling environment. We identified substantial gaps in the scientific literature that could form the focus of future research, including shifting the focus from individuals' career choices to the societal and organisational contexts and cultures within which those choices are made; extending the evidence base to include a wider range of countries and settings; and testing the efficacy of interventions.
Article
This study investigates the underrepresentation of women in science, technology, engineering, and mathematics (STEM) occupations from the aspect of earning differentials. Using a national data source that tracked college graduates’ work experiences over a ten-year time frame post-bachelor’s degree, this study examines longitudinally the gender-based earning gaps of college graduates in STEM fields, and compares the earning differentials between STEM and non-STEM occupations. The findings indicate a significant departure between the earning profiles of men and women within the first ten years of employment. Further, findings indicate that women in STEM occupations experienced multiple earning penalties concurrent with their growing family obligations. To increase the representation of women in STEM fields, interventions are called for to encourage a family-friendly workplace that is open to and supportive of women managing a home and career. Also, incentives are needed to support women’s continuation to graduate education as a means to increase their human capital and to level their earning power.