All of the following are true of women’s representation in stem fields except ______.

Despite some positive changes, a gender gap in STEM persists around the world. This gap begins in education, fueled by gender stereotypes and expectations regarding “women’s work.”1 Systems of bias that push women and people of color out of STEM careers can also influence the products and services created by STEM organizations, such as artificial intelligence (AI).2 Organizational strategies to recruit, retain, and advance women in STEM occupations can help address these issues.

GLOBAL

Men Continue to Dominate the STEM Workforce Globally, Especially at the Highest Levels

When compared to other industries (including non-STEM), the representation of women among board directors in the information technology industry remains low but continues to increase, reaching 28% in 2021, up from 21% in 2020.3

Despite Improvements at the Board Level, the Technology Industry Still Lacks Women in Senior Leadership4

Out of the CS Gender 3000 companies, women globally account for only 17% of managers in the information technology industry.5

• In 2019, women were only 3% of CEOs.6
• Women were only around 21.0-22.0% of CFOs in information technology in 2021, although this is a higher percentage compared to other major industries.7

Cultural Expectations and Stereotypes Can Affect Women’s Participation in STEM

A UNESCO research review across eight Asian countries found a number of common sociocultural norms that can prevent women from pursuing STEM education or careers. These included:8

• A perception that STEM is an “inappropriate” choice for women and associated as a male subject.
• Expectations of domestic responsibilities, such as family care.
• Unreliable support structures and a lack of mobility.

CANADA

Women in Canada Are Less Likely to Enter and More Likely to Leave STEM Fields9

In 2019, women earned approximately one-third (36.4%) of all recipients of STEM postsecondary degrees in Canada.10

Among students earning bachelor’s degrees in 2019, women represented:11

• All STEM subjects: 41.8%
  • Science and science technology: 61.0%
  • Engineering and engineering technology: 21.7%
  • Mathematics and computer and information sciences: 31.4%

In 2021, women accounted for less than a quarter (23.5%) of those working in natural and applied sciences and related occupations.12

• In these occupations, women earned, on average, $0.84 to every $1.00 earned by men in annual wages, salaries, and commissions in 2020.13

EUROPE

Women in Europe Are Closing the Gender Gap in Science and Engineering14

In 2021, women made up more than a third (40.7%) of scientists and engineers in the EU-27, a slight increase from 38.7% in 2011.15

• High-tech remains male dominated. In 2021, women were just 32.8% of those employed in high-tech manufacturing and knowledge-intensive high-tech services in the EU-27.16

And Europe’s gender gap in STEM is especially wide in information and communication technologies in higher education as well:

Women’s Share of Bachelor’s Degrees in STEM Fields, 202017

Similar gaps exist among UK undergraduates. Despite making up over half of all undergraduates (56.8%) as well as 52.8% of undergraduates in all science fields in 2020-21, women are only 18.6% of students enrolled in engineering and technology and only 17.7% of students studying computing.18

Despite increases over the past decade, as of 2021 women still make up only 16.5% of engineers in the UK.19

• Women are also more likely than men to work in related professional roles, rather than “core” engineering work.20

Women Make Up a Small Share of Scientists and Engineers21

Despite accounting for around half of the employed US workforce, women in the United States made up only a third (34%) of those employed in STEM occupations in 2019.22

• Women with bachelor’s degrees and higher largely contribute to this proportion, making up 44% of the STEM workforce.23
• A substantial gender gap in engineering (16% women) and computer occupations (26% women) also contributes to women’s overall underrepresentation in STEM.24

Few science and engineering employees in the United States were women of color (11.6%) in 2019, including:25

• Asian women: 6.5%
• Black women: 1.8%
• Latinas: 2.4%
• American Indian/Alaska Native women: 0.1%

The share of STEM degrees is also small for women of color in the United States. In 2019–2020, women of color earned a small percentage (15.1%) of bachelor’s degrees across all STEM fields, including:26

• Asian women: 5.5%
• Black women: 3.0%
• Latinas: 4.8%
• American Indian/Alaska Native women: 0.1%

Women Working in STEM Earn Less Than Men Across All Racial and Ethnic Groups27

• When typical STEM earnings are compared across race/ethnicity and gender, Black women and Latinas earn the least.28

ADDITIONAL RESOURCES

Next Steps

Ask Catalyst Express: STEM Catalyst

Knowledge Burst: Retaining Women in STEM Catalyst (Supporter Exclusive)

Resources for organizations AnitaB.org

Society for Canadian Women in Science & Technology

WISE Campaign (UK)

Research

Quick Take: Women in Energy – Gas, Mining, and Oil Catalyst

Quick Take: Women in Healthcare Catalyst

The STEM gap: Women and girls in science, technology, engineering and math American Association of University Women (AAUW)

The Changing Career Trajectories of New Parents in STEM PNAS

Sponsorship of Women Drives Innovation and Improves Organizational Performance Accenture

DEFINITION: “STEM” refers to the fields of science, technology, engineering, and mathematics. There is no standard definition of a STEM occupation. For the purposes of this Quick Take, STEM incorporates professional and technical support occupations in the areas of life and physical sciences, computer science and mathematics, and engineering. Less agreement has been made on the inclusion of educators, healthcare professionals, and social scientists in STEM; therefore, these occupations are not covered here.29

How to cite this product: Women in science, technology, engineering, and mathematics (STEM): Quick take. Catalyst (2022).

1. Carlana, M. (2019). Implicit stereotypes: Evidence from teachers’ gender bias. The Quarterly Journal of Economics, 134(3), 1163-1224.
2. Daley, L.P. (2019). Trend brief: AI and gender bias. Catalyst.; Ahn, S. & Costigan, A. (2019). Trend brief: How AI reinforces gender stereotypes. Catalyst.
3. Milhomem, C. (2021). Women on boards: Progress report 2021. MSCI.
4. Kersley, R., Klerk, E., Boussie, A., Longworth, B.S., Natzkoff, J.A., & Ramji, D. (2019). The CS gender 3000 in 2019: The changing face of companies. Credit Suisse Research Institute.
5. Kersley, R., Klerk, E., Jiang, B., Weber, S.C., Natzkoff, J., Kharbanda, A., Longworth, B.S., Zumbühl, P. (2021). The CS gender 3000 in 2021: Broadening the diversity discussion. Credit Suisse Research Institute.
6. Kersley et al. (2021).
7. Milhomem (2021).; Kersley et al. (2021).
8. STEM education for girls and women: Breaking barriers and exploring gender inequality in Asia. (2020). UNESCO Office Bangkok and Regional Bureau for Education in Asia and the Pacific.
9. Frank, K. (2019). A gender analysis of the occupational pathways of STEM graduates in Canada. Statistics Canada, Analytical Studies Branch Research Paper Series..
10. Statistics Canada. (2021). Table 37-10-0164-01: Postsecondary graduates, by international standard classification of education, institution type, classification of instructional programs, STEM and BHASE groupings, status of student in Canada, age group and gender.[Data set].
11. Statistics Canada. (2021). Table 37-10-0164-01: Postsecondary graduates, by international standard classification of education, institution type, classification of instructional programs, STEM and BHASE groupings, status of student in Canada, age group and gender.
12. Statistics Canada. (2022). Table 14-10-0335-02: Proportion of women and men employed in occupations, annual. [Data set].
13. Statistics Canada. (2020). Table 14-10-0324-01: Average and median gender pay ratio in annual wages, salaries and commissions. [Data set].
14. Eurostat. (2022). HRST by category, sex and age. [Data set]. European Commission.
15. Eurostat. (2022). HRST by category, sex and age. [Data set]. European Commission.
16. Eurostat. (2020). Employment in technology and knowledge-intensive sectors at the national level, by sex (from 2008 onwards, NACE rev. 2). [Data set]. European Commission.
17. Eurostat. (2020). Graduates by education level, programme orientation, sex and field of education. [Data set]. European Commission.
18. Perrott, L. Higher education student statistics: UK, 2020/21 – Subjects studied. (2022). Higher Education Statistics Agency.
19. Women in engineering. (2022). EngineeringUK.
20. Women in engineering. (2022). EngineeringUK.
21. The state of U.S. science and engineering: U.S. S&E workforce. (2022). National Science Foundation.
22. The state of U.S. science and engineering: U.S. S&E workforce. (2022). National Science Foundation.
23. The state of U.S. science and engineering: U.S. S&E workforce. (2022). National Science Foundation.
24. The state of U.S. science and engineering: U.S. S&E workforce. (2022). National Science Foundation.
25. Catalyst calculation of women of color based on data from the National Science Foundation. This number comprises Black women, Latinas, Asian women, Native Hawaiian or Other Pacific Islander women, American Indian or Alaska Native women, and multiracial women. National Science Foundation. (2021). Table 9.7: Employed scientists and engineers, by ethnicity, race, occupation, highest degree level, and sex: 2019. [Data set]. Women, Minorities, and Persons with Disabilities in Science and Engineering, Data Tables.
26. Catalyst calculation of women of color based on data from the National Center for Education Statistics. This number comprises of Black women, Latinas, Asian women (including Pacific Islander), American Indian/Alaska Native, and multiracial women. National Center for Education Statistics. (2021). Table 318.45: Number and percentage distribution of science, technology, engineering, and mathematics (STEM) degrees/certificates conferred by postsecondary institutions, by race/ethnicity, level of degree/certificate, and sex of student: 2010-11 through 2019-20. [Data set]. Digest of Education Statistics: 2021 Tables and Figures.
27. Fry, R., Kennedy, B., & Funk, C. (2021). STEM jobs see uneven progress in increasing gender, racial and ethnic diversity. Pew Research Center.
28. Fry et al. (2021).
29. Noonan, R. (2017). Women in STEM: 2017 Update. US Department of Commerce, Economics and Statistics Administration, Office of the Chief Economist

Which of the following is true of studies examining the heritability of masculine and feminine traits?

Which of the following are true with regard to gender and aggression?

Which of the following is the best example of a status legitimizing beliefs?

Which of the following explanations of the origins of gender stereotypes is most consistent?