Click
here to close Hello! We notice that
you are using Internet Explorer, which is not supported by Echinobase
and may cause the site to display incorrectly. We suggest using a
current version of Chrome,
FireFox,
or Safari.
R Soc Open Sci
2019 Jun 01;66:181566. doi: 10.1098/rsos.181566.
Show Gene links
Show Anatomy links
Unravelling the gender productivity gap in science: a meta-analytical review.
Astegiano J
,
Sebastián-González E
,
Castanho CT
.
???displayArticle.abstract???
Women underrepresentation in science has frequently been associated with women being less productive than men (i.e. the gender productivity gap), which may be explained by women having lower success rates, producing science of lower impact and/or suffering gender bias. By performing global meta-analyses, we show that there is a gender productivity gap mostly supported by a larger scientific production ascribed to men. However, women and men show similar success rates when the researchers'' work is directly evaluated (i.e. publishing articles). Men''s success rate is higher only in productivity proxies involving peer recognition (e.g. evaluation committees, academic positions). Men''s articles showed a tendency to have higher global impact but only if studies include self-citations. We detected gender bias against women in research fields where women are underrepresented (i.e. those different from Psychology). Historical numerical unbalance, socio-psychological aspects and cultural factors may influence differences in success rate, science impact and gender bias. Thus, the maintenance of a women-unfriendly academic and non-academic environment may perpetuate the gender productivity gap. New policies to build a more egalitarian and heterogeneous scientific community and society are needed to close the gender gap in science.
Figure 1. PRISMA flow diagram representing the flow of information through the decision process of searching and inclusion of articles and observations in the meta-analyses (modified from Moher et al. [18]). k, number of articles; o, number of observations.
Figure 2. The gender productivity gap in science. The mean effect sizes ± 95% confidence intervals corresponding to (a) individual- and (b) group-based studies comparing productivity between men and women scientists (Pindividual = 0.0012, Pgroup < 0.0001). The number of observations included in each meta-analysis is reported within parentheses. For group-based studies, the effect sizes of gender productivity depending on the productivity proxy (p = 0.036), the research field (p = 0.189) and the time period (p = 0.951) are also shown. The vertical dashed line in each graphic indicates no difference between men and women scientists. Positive effect size values indicate higher men productivity, whereas negative effect sizes indicate higher women productivity. Asterisks denote the mean effect sizes significantly different from zero for Hedges' d and 0.5 for raw proportion (p < 0.05). Icons illustrate the type of primary response variable included in each meta-analysis.
Figure 3. Factors that have been associated with the gender productivity gap in science. The mean effect size ± 95% confidence intervals of (a) gender success rate (p = 0.0004), (b) gender science impact (p = 0.063) and (c) experimental gender bias (p = 0.315). The number of observations included in each meta-analysis is reported within parentheses. The effect size of gender depending on the research field (p = 0.697) and the productivity proxy (p < 0.0001) are shown for success rate. The effect of self-citations on science impact is also shown (p = 0.73). The effect size of gender depending on the research field (Psychology or not, p = 0.0002) is shown for experimental gender bias. The vertical dashed line in each graphic indicates no difference between men and women scientists. Effect size values in the right side of each graphic indicate higher men productivity, whereas those in the left side indicate higher women productivity. Asterisks denote the mean effect sizes significantly different from zero (p < 0.05). Icons illustrate the type of primary response variables included in each meta-analysis.
Bakker,
Tenure Track Policy Increases Representation of Women in Senior Academic Positions, but Is Insufficient to Achieve Gender Balance.
2016, Pubmed
Bakker,
Tenure Track Policy Increases Representation of Women in Senior Academic Positions, but Is Insufficient to Achieve Gender Balance.
2016,
Pubmed
Burstein,
Many paths to parity for women in science.
2015,
Pubmed
Cameron,
Equal opportunity metrics should benefit all researchers.
2013,
Pubmed
Ceci,
Understanding current causes of women's underrepresentation in science.
2011,
Pubmed
Ceci,
Women in Academic Science: A Changing Landscape.
2014,
Pubmed
Darling,
Use of double-blind peer review to increase author diversity.
2015,
Pubmed
DesRoches,
Activities, productivity, and compensation of men and women in the life sciences.
2010,
Pubmed
Duch,
The possible role of resource requirements and academic career-choice risk on gender differences in publication rate and impact.
2012,
Pubmed
Eagly,
Transformational, transactional, and laissez-faire leadership styles: a meta-analysis comparing women and men.
2003,
Pubmed
Fischer,
Academia's obsession with quantity.
2012,
Pubmed
Ford,
Gender inequity in speaking opportunities at the American Geophysical Union Fall Meeting.
2018,
Pubmed
Fox,
Work and family conflict in academic science: patterns and predictors among women and men in research universities.
2011,
Pubmed
Habeck,
Community-level impacts of white-tailed deer on understorey plants in North American forests: a meta-analysis.
2015,
Pubmed
Handelsman,
Careers in science. More women in science.
2005,
Pubmed
Holman,
The gender gap in science: How long until women are equally represented?
2018,
Pubmed
Kelly,
The h index and career assessment by numbers.
2006,
Pubmed
Lawler,
Gender issues. Universities urged to improve hiring and advancement of women.
2006,
Pubmed
McNutt,
Give women an even chance.
2015,
Pubmed
Mendoza-Denton,
Go beyond bias training.
2018,
Pubmed
Meyer,
Women are underrepresented in fields where success is believed to require brilliance.
2015,
Pubmed
Moher,
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
2009,
Pubmed
Moss-Racusin,
Science faculty's subtle gender biases favor male students.
2012,
Pubmed
Pautasso,
Focusing on publication quality would benefit all researchers.
2013,
Pubmed
Shaw,
Leaks in the pipeline: separating demographic inertia from ongoing gender differences in academia.
2012,
Pubmed
Symonds,
Gender differences in publication output: towards an unbiased metric of research performance.
2006,
Pubmed
Viechtbauer,
Outlier and influence diagnostics for meta-analysis.
2010,
Pubmed
van den Besselaar,
Vicious circles of gender bias, lower positions, and lower performance: Gender differences in scholarly productivity and impact.
2017,
Pubmed
van den Besselaar,
Gender differences in research performance and its impact on careers: a longitudinal case study.
2016,
Pubmed