Why are there so few Black and Hispanic computer scientists?

This came up at /r/CSEducation today, and I thought I'd summarize the literature I've seen regarding Black/Hispanic enrolments in computer science in North America. What factors do we know to be behind the lower numbers of Black/Hispanic students in North American CS classrooms?

It's a multi-part problem: fewer Black/Hispanic students show up to begin with -- and then they're less likely to graduate with a CS major at the end of their university career. I've broken up the factors I've seen in the literature based on when in the "leaky pipeline" they most apply.

I'm aiming here to give a quick-and-dirty overview of the issues -- there's a fair bit of literature on this and the references below provide an excellent place to start on the literature.

(Sidenote: The Varma paper ([2]) also looks at Aboriginal students; my impression from the few Aboriginal CSers I know is that they parallel many of the same issues. There is unfortunately very little research on First Nations, Metis and Inuit under-representation in computer science.)

The Leaky Pipeline: Middle School

1. In middle school, Black and Hispanic youth are just as interested in computer science as their White and Asian peers. [1, 2]
2. Black and Hispanic youth are less likely to have a computer at home [1, 3].
3. For White boys, video games are where many of them first "pull back the curtain" on how computers work. But while Black boys play just as much video games as White boys, modding and cheat codes aren't part of their gaming cultures -- and don't hence "pull back" the curtain [3]. They don't have the "privilege to break things".
4. Characters in video games have a lack of racial diversity [3] -- from a young age Black and Hispanic students imagine computer scientists as "White or Asian men"; computer science does not seem relevant to them.

High School

1. Black and Hispanic students are more likely to go to disadvantaged k-12 schools [4, 5]. 
2. They're less likely to graduate from high school than their white peers, and lower expectations are placed on them [1, 4]. 
3. And for those that do succeed, they're less likely to have a high school CS class available to them. The situation has actually been getting worse with the testing movement -- disadvantaged schools are removing CS since it's an "extra", and they have a hard time recruiting/retaining qualified teachers [4].

Choosing to Study University CS

1. Encouragement is really, really important. And Black/Hispanic students are less likely to be encouraged by parents, guardians, teachers, or peers to study computer science [2, 6, 7]. Encouragement has a stronger effect on students than their ability at computer science [6] -- and has the potential to overcome differences in preparation for university CS.
2. Black and Hispanic girls are less likely than their White peers to know somebody who works in STEM, and are less likely to have parents in STEM. [2]
3. Black and Hispanic youth are more aware/worried about gender/racial discrimination in STEM than their White peers [2, 7].
4. Black and Hispanic students are motivated to study computer science because it is a prestigious, secure career, and provides social status [2, 5, 6, 8]. While they are turned on by the creative, pro-social, problem solving part of computer science -- and are more engaged when CS is taught that way -- they feel like "do what you love" is a luxury for rich White people [5].
5. Black, Hispanic and low-class White women choose universities differently than middle/upper-class White women. The latter care about things like reputation and programme detalis. The former care about tuition, scholarships, and closeness to family [5]. At my university, tuition is higher for computer science than it is for other Arts & Science majors. We're likely not doing any favours to diversity here. 

Staying in CS Majors

1. When Black and Hispanic students do show up to university CS, they are more likely than their White and Asian peers to feel underprepared. Indeed, 48% of Black, Hispanic and Aboriginal students feel not prepared "at all" [5].
2. I'm gonna repeat it since it bears repeating: Encouragement has a stronger effect on students than their ability at computer science [6] -- and has the potential to overcome differences in preparation for university CS.
3. The heavy workload in CS courses is a problem for many of these students. You need to be "unmarried, single, no kids, no job, no hobbies, no dependents" [5]. Black and Hispanic students are disproportionately likely to be "non-traditional" students (have families, mature students, etc). Many Black/Hispanic students will leave CS because of the workload [5]. One contributing factor is social habits: whereas Asian students are likely to study together as part of their social life, Black students are more likely to study in isolation and not as part of their social life [8].
4. Another major reason they leave is hostility. They find they can't be taken seriously due to their race (and gender, if a woman on top of it) [2]. And they're more likely to feel like "outsiders" in CS [1]. Though they feel like outsiders, it's worth noting that lack of identification as a geek/nerd appears not to be an issue [5].

Correspondence tests: uncovering biases against women in science

Part of the controversy surrounding affirmative action and other systems which give preferential treatment to minority groups comes from the ideal notion that people are judged on their merits -- and not their gender/race/etc [6]. In such an ideal world, for instance, a female scientist would be equally likely to be hired, given tenure, or accolades as an identical male scientist.

Science likes to bill itself as a meritocracy, in which scientists are evaluated only their work. A lot of the unease many scientists have about preferential treatment is that it goes against that ideal of meritocratic science [5]. So, it's worth asking: is a female scientist equally likely to be hired/tenured/etc as an identical male scientist?

Probably the best study design for probing this type of question are correspondence tests. These refer to studies where you describe either a female individual or a male individual to a group of participants -- keeping everything but gender (or race, ethnicity, etc) constant -- and see if participants respond differently to to the woman/man.

Correspondence tests are generally easier to run than audit studies, where you hire actors to be identical to one another except for gender/race/etc. Both types of studies are useful for identifying discrimination against particular groups. Another approach is to pair real male and female scientists with equal on-paper qualifications and see whether they are equally likely to be given tenure. This approach, however, suffers from the problem of pairing: are that female and male scientist really identical except for on-paper qualifications?

In this post, I'll be describing the results of three correspondence tests looking at discrimination against women in science. These three studies are also the only such studies that I know of to have been published since the 90s. (There's an older one from the 70s that is now a bit dated.)

Generational differences of female scientists in academia

In my last post, I described how the experiences of women in CS have changed historically. In this post, we saw that the academic side of computer science is a relatively recent thing. For this post, I'd like to focus some more on that aspect of the history. Like that last post, this post will be specifically focusing on North American CS (we've seen previously that female participation in CS is different outside the West!).

Generational differences exist between female scientists in academia. Etzkowitz et al in a 1994 paper found differences in experiences and values between the trailblazing "First Generation" of women in a field, and the subsequent "Second Generation". As the paper is now 20 years old, it's not too surprising that it feels a bit out of date -- what comes after the Second Generation? (Another dated thing about the paper is that CS is described as being as female-friendly as biology.)

The Etzkowitz et al paper studied 30 academic science departments (biology, chemistry, physics, CS, and electrical engineering). They went into the study interested in the notion of critical mass -- whether having enough women in a department would lead to a positive feedback cycle leading to gender equality. (Answer: it's not that simple.) In the process of studying critical mass, they found the women who had entered the field before it was attained (First Gen) had fundamentally different experiences than the women who entered after.

Women in CS: A Historical Perspective

Female participation in computer science in North America has varied a great deal over time. Women were the original "computers" before the days of computing machines -- and then were hired as the low-status "coders" to run those machines. Over time, coding/programming was more widely recognized to be difficult -- and it was shifted from being "women's work" to "men's work".

When computer science emerged as an academic discipline in the 70s and 80s, women were well-represented (30-40%). As enrollments in CS programmes exceeded what departments could manage, they tightly restricted the paths one could take into a CS major -- unintentionally pushing non-traditional students like women out of the field. A big lesson from that period is that non-traditional students come from non-traditional paths -- many of these women were starting in majors such as psychology or linguistics, or transferring from community colleges, and hence did not follow the "standard" path into computing careers.

Subtyping, Subgrouping, and Stereotype Change

There's been a fair bit of research finding that negative stereotypes are part of what deters women and racial minorities from computer science and STEM in general (e.g. [1]). These stereotypes make it harder for women and minorities to personally identify with computer science, and amplify some of the biases that they face in CS. So for this post, I'll be going over observed phenomena in social psychology and sociology that pertain to stereotype change.

Stereotypes

Stereotypes are really hard to change. They're reinforced from many sources (media, individuals, groups, etc). But even more than that, stereotypes are schema: they are how we mentally organize information about social groups, and how we can determine whether we are "in" or "out" of a group. Schema allow us to process information effortlessly, and are pretty deeply ingrained once they're there.

The human brain is not very good at changing schema. When we see evidence that contradicts our schema, our brains will do all sorts of mental gymnastics to avoid confronting or changing the incorrect schema. Most frequently, we forget that we saw it all. Sometimes our misconceptions even get stronger [2].

This happens with stereotypes. Betz and Sekaquaptewa did a study where they showed role models to young girls, to try to motivate the girls' interest in STEM [3]. Role models were either gender-neutral, or feminine. The result? Gender-neutral role models boosted interest -- and feminine (counterstereotypic) role models actually reduced girls' interest in science. To these girls, the feminine scientist -- a stereotype violator -- is aberrant.

Why are there more women in some STEM fields than in others?

Why is it that there are more women in biology than there are in computer science in North America? Women in the biomedical fields are now earning more than 50% of undergraduate degrees in the US [1].

Biology, like computer science, was once stereotyped as masculine. Medicine continues to be stereotyped as masculine, especially fields such as surgery. Why has biology attracted so many more women than computer science?

To answer this question, I'll be synthesizing the findings of Cheryan's "Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?" [2], Cohoon's "Women in CS and Biology" [3], and Carter's "Why students with an apparent aptitude for computer science don’t choose to major in computer science" [4].

Between these three papers, four themes emerge for why women choose one STEM field over another:

1. Exposure to the field
2. Expected value of the major
3. Lack of prejudice in the scientific culture
4. Prospects of raising a family in that scientific culture

Why Are There More Women in CS in Other Cultures?

The rates of female participation in CS -- and STEM in general -- vary wildly from culture to culture. In the US, women currently make up about 18% of undergraduate CS students [1], but over in Qatar, women make up about 70% of CS undergrads [2].

Women in STEM are better represented in countries such as Turkey, Hungary, Portugal, and the Philippines. In these countries, women make up approximately 50% of STEM undergrads [3]. Indeed, well-developed countries like Canada, the US, and the UK have some of the lowest levels of female participation in STEM.

So, what cultural factors lead to fewer or more women in STEM? Per the work of Barinaga, there are five factors [3]:

1. Recently developed science capabilities, resulting in an unentrenched scientific community
2. Perception of science as a low status career
3. Class issues that overshadow gender issues
4. Compulsory math and science education in secondary school
5. Large social support for raising families