Gender in Science, Technology, Engineering, and Mathematics: Issues, Causes, Solutions

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.

Keywords: STEM; explicit bias; gender; implicit bias.

Figure 1.

Gender gap in intent to major in STEM and non-STEM fields among U.S. college entrants: a, female; b, male. Data from National Center for Education Statistics High School Longitudinal Study (Radford et al., 2018, their Table 10). For compiled raw data and code, see https://osf.io/n9jca/.

Figure 2.

Proportion of degree earners that are females across postsecondary education (2000–2015) overall and in STEM subfields. Proportions of students in each field and degree that identify as female in (a) all science and engineering (S&E) fields, including social and behavioral science (SBS), (b) traditional S&E fields (excluding social and behavioral sciences), (c) all non-S&E fields, as well as STEM subfields of (d) computer science, (e) mathematics, (f) engineering, (g) physics, and (h) biology. Data from National Science Foundation (2018). For compiled raw data and code, see https://osf.io/n9jca/.

Figure 3.

Implicit men = science/women = arts stereotypes across the lifespan, by gender. Data from the Project Implicit Demonstration website. For compiled raw data and code, see https://osf.io/n9jca/.

Figure 4.

Change over time in implicit men = science/women = arts stereotype, by gender (2005–2017). Weighted monthly means (weighting to control for sample change over time) are plotted in thin gray (for men) and black lines (for women). Decomposed trend lines (removing seasonality and random noise) are plotted in thick gray (for men) and black lines (for women). Data from the Project Implicit Demonstration website. For compiled raw data and code, see https://osf.io/n9jca/. For further details on analysis method, including controls for alternative explanations, such as sample change over time, see Charlesworth and Banaji (2019b).