Tech-Savvy Men and Caring Women: Middle School Students’ Gender Stereotypes Predict Interest in Tech-Education
How to encourage girls' interest in tech — Newsletter No.28
Hi! This week, we will be looking at the study from Sweden which looked for answers for gender segregation in the labor market in middle school stereotypes. It was especially interesting since Sweden is the most gender-equal country in the EU (European Institute for Gender Equality, 2021).
For example, in Sweden, science programs are now balanced in gender proportions, while tech-focused and care-focused educational programs are still strongly gender skewed. And, when a sector is gender-skewed, labor shortages may appear, which is apparent in both the care sector and the tech sector.
People vs. Things gender-stereotypes
The main premise of the research was that traits associated with the occupational dimensions “people”(caregiving) versus “things” (technology) may perhaps better describe current gender stereotypes in Sweden and the Western world. Namely:
People: refers to the desire and motivation to closely relate with and connect to others and is associated with traits central to caregiving, such as warmth, caring, and helpfulness. The stereotype that women are more communal than men holds well over time.
Things: Researchers propose traits associated with occupations related to “things” may be strongly masculine-typed in Sweden and Western countries, such as technical ability, tech-interest, and behaviors associated with technology, such as choosing a tech career. Sweden prides itself as an “engineering nation” and has been called a mecca for tech startups (after Silicon Valley of course), but less than 0.05% of the recent tech startups were founded by women and only 16% of employed civil engineers are women.
What was measured?
The research was done over 873 school students ages 11-14. The gender balance was nearly even, with 51% boys ( n = 449) and 49% girls (n = 424). They also recruited 86 teachers to the study, 63% of which were women, and the majority of teachers had programming as one part of their teaching.
The researchers adapted the Implicit Association Test (IAT developed by Greenwald et al.) to capture associations of caregiving and technology with gender categories. The IAT measures associations between mental concepts indirectly and is less susceptible to social desirability concerns (such as not wanting to disclose prejudice) as compared to self-report measures.
Both groups answered the adapted IAT, which used the target categories to reflect the two major sectors in the gender-segregated labor market:
Technology: The stimulus words for this category included: program, code, debug, write code, think logically, troubleshoot, create apps, and fix a computer.
Caregiving: The stimulus words included: take care of, care for, support emotionally, comfort, listen sincerely, nurse, show empathy, tend to.
They also developed common personal names and gender-identifying roles to reflect “Boys” and “Girls”.
The IAT required participants to sort a set of stimulus words into the categories “Technology” and “Caregiving” and “Boy” and “Girl” across a total of seven blocks, measuring also the time it takes to categorize it.
Additionally, researchers measured the attitudes of the participants regarding following topics:
Gender-Skewness Awareness by asking them to answer: In some occupations, more women than men work, and in others, more men than women work. In some occupations, an equal number of men versus women work. What do you believe is the case in the following occupations in Sweden?
Ability Gender Stereotypes
of the society: “How do you think most Swedes view men’s versus women’s abilities(what they are good at)? We are not asking what you think, but what you believe that others think.”
and their personal: “What do you personally think about men’s and women’s abilities (what they are good at)?
Tech interest by asking students to rate their interest in choosing a tech-focused educational program in the future on a scale from 1 to 5.
What have they found out?
Boys and girls of all ages implicitly associate technology more with men and caregiving more with women. The effect sizes were strong for all groups except for girls ages 11-12.
Next, all students, regardless of gender and school grade, were aware that both work in the care and tech sectors are gen -der-skewed in Sweden. They were all also strongly aware of societal gender stereotypes in relation to the gender-segregated labor market. This indicates that the students believe that people in Sweden think that men have stronger technical abilities than women and that women have stronger caregiving abilities than men.
Lastly, the students also tended to report personal endorsement of these stereotypes. As expected, (possibly due to social desirability), the effect sizes were smaller for the personal stereotypes, but still significant with small to medium-sized effects.
As with the students, the teachers showed a large stereotype-consistent association. The teachers also reported strong societal gender stereotypes and small to medium-sized personal stereotypes reflecting the belief that men have stronger technical ability than women and women have better caregiving ability than men.
Although both students and teachers have implicit stereotypes, teachers had them significantly stronger than the students 11-14.
One more important thing that came out of the research is that the girl’s tech interest was weaker if they had stronger implicit gendered associations for technology and caregiving and if they perceived that society endorses the belief that men are better at technology. In contrast, the boys were more interested in tech education if they endorsed stronger implicit stereotypes, but perceptions of societal beliefs were unrelated to their interest in tech gender stereotypes.
The research shares many other interesting conclusions, so if this got you interested, be sure to check it out here.
What can we do?
It suggests that to attract women to the tech industry, we need to counteract the stereotype that men are better at technology.
One is definitely to share stories that are against the gender stereotype and encourage girls’ to engage in different activities.
Liu et al. showed in their paper (2021) that educating about gender similarity appears to be the most effective remedy for stereotype threat. Maybe we cover that next time?