Abstract
The landscape of gender in education and the workforce has shifted over the past decades: women have made gains in representation, equitable pay, and recognition through awards, grants, and publications. Despite overall change, differences persist in the fields of science, technology, engineering, and mathematics (STEM). This Viewpoints article on gender disparities in STEM offers an overarching perspective by addressing what the issues are, why the issues may emerge, and how the issues may be solved. In Part 1, recent data on gaps in representation, compensation, and recognition (awards, grants, publications) are reviewed, highlighting differences across subfields (e.g., computer science vs biology) and across career trajectories (e.g., bachelor’s degrees vs senior faculty). In Part 2, evidence on leading explanations for these gaps, including explanations centered on abilities, preferences, and explicit and implicit bias, is presented. Particular attention is paid to implicit bias: mental processes that exist largely outside of conscious awareness and control in both male and female perceivers and female targets themselves. Given its prevalence and persistence, implicit bias warrants a central focus for research and application. Finally, in Part 3, the current knowledge is presented on interventions to change individuals’ beliefs and behaviors, as well as organizational culture and practices. The moral issues surrounding equal access aside, understanding and addressing the complex issues surrounding gender in STEM are important because of the possible benefits to STEM and society that will be realized only when full participation of all capable and qualified individuals is guaranteed.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 7228-7243 |
| Number of pages | 16 |
| Journal | Journal of Neuroscience |
| Volume | 39 |
| Issue number | 37 |
| DOIs | |
| State | Published - 2019 |
Funding
Unlike the data on gender differences in representation and compensation, gender gaps in overall grant success rates now appear small to nonexistent. While early studies of funding patterns suggested that women were less likely to receive grants than men (e.g., in Sweden; Wenneras & Wold, 1997), this no longer appears to be the case among many U.S. funding agencies. Across the National Science Foundation (NSF), United States Department of Agriculture, and the National Institutes of Health (NIH), the percentage of female applicants receiving grants is now approximately equivalent to the percentage of male applicants receiving grants (Hosek et al., 2005; Pohlhaus et al., 2011; U.S. Government Accountability Office, 2015). This progress toward granting par- ity is likely the result of the conscious efforts of governmental funding agencies to collect the necessary data and conduct formal reviews of their own evaluation processes and possible biases (e.g., through the NSF Authorization Act of 2002) (Hosek et al., 2005). In addition to such observational data showing similar success rates for men and women, recent experimental studies also indicate similar granting rates for identical male and female grant applicants (Forscher et al., 2019). Received March 23, 2019; revised June 21, 2019; accepted July 27, 2019. This work was supported by Harvard Dean’s Competitive Fund for Promising Scholarship to M.R.B. All original data and analysis scripts reported in this paper are available through OSF: https://osf.io/n9jca/?view_only= 35d16807663d4cdbab34c0a64f43a999 (currently available as an anonymized view-only link that will be made publicfollowingpeer-review).WethankSapnaCheryan,AmandaDiekman,AliceEagly,DanielStorage,andMartin Chalfie for comments on earlier versions of this manuscript. The authors declare no competing financial interests. CorrespondenceshouldbeaddressedtoTessaE.S.Charlesworthattet371@g.harvard.eduorMahzarinR.Banaji at [email protected]. https://doi.org/10.1523/JNEUROSCI.0475-18.2019 Copyright © 2019 the authors
Keywords
- Explicit bias
- Gender
- Implicit bias
- STEM
ASJC Scopus subject areas
- General Neuroscience
