“What is the pipeline problem?” is a question that often comes up in relation to the technology sector. Codio offers some valuable insights:
It’s no secret that tech has a diversity problem. According to the U.S. Bureau of Labor Statistics, men accounted for 74% of computing professionals in 2019. Merely 9% of computing professionals were Black (compared to 13% of the population), and only 8% were Latino (compared to 19% of the population). [1, 2] To complicate this picture further, these numbers do not take into account the distribution of people of color within the field, which ranges from lower-paying roles such as associate software developers and research analysts to higher-paying management positions.
The Pipeline Problem Metaphor
This problem is often framed as a “pipeline problem”—the idea that the education system does not produce enough qualified female, BIPOC, and LGBTQ+ candidates. Now that we know more about, “What is the pipeline problem?” let’s look at the possible solutions for the same. However the answer lies in the metaphor itself. The pipeline metaphor suggests that we can create a more diverse technology workforce by starting more students earlier in STEM and computer science.
Research around the impact of stereotype threat and previous coding experience on an individual’s career path suggests that there is some truth to that idea. Stereotype threat refers to the fear that one’s performance on a task will confirm a stereotype about their social group. [3, 4] For example, a stereotype put forth by our society (in both implicit and explicit ways) is that men outperform women in math. Stereotype threat has been shown to negatively impact performance. A study conducted in 2004 revealed that women scored significantly higher on the AP calculus test when the demographic question about gender was moved to the end of the test instead of the beginning. 
The CS + STEM Pipeline
Arguments in favor of expanding K-12 computer science education suggest that early exposure to computer science can help female students, queer students, and students of color feel that they “belong in tech” before they receive years and years of stereotyped messaging. Big tech companies like Amazon, Google, and Facebook are trying their hand at resolving what is known as the pipeline problem by offering computer science courses in K-12. These companies, in addition to initiatives like CS4All and Hour of Code, tie their efforts to the pipeline narrative—insisting that K-12 computer science education is key in providing “pathways” to college and career success for underprivileged students.
For example, Amazon describes its program as a “childhood-to-career pathway”. Facebook, like the other organizations mentioned, links its programs directly to creating a more diverse tech workforce: “Building a diverse workforce starts with ensuring that students from underrepresented communities have access to educational opportunities designed to inspire and empower them to pursue a career in the tech industry.”
This work is also supported by research showing that students with previous experience will likely perform better in college-level computer science courses. 
The Problem with the Pipeline metaphor
Now that we’ve gained an understanding of the question, “What is the pipeline problem?”, hopefully, we’re better positioned to understand the problem with the pipeline metaphor itself. Despite all of the initiatives in favor of early exposure to computer science in order to increase diversity in tech, there is a growing consensus that we should stop relying so heavily on this narrative to explain the lack of diversity in tech. [7, 8] It is worth taking a closer look at the narrative that the “pipeline” offers us and contemplating the ways in which it may not tell the whole story.
In tech, a “data pipeline” is a series of steps for data processing, where the output of one step automatically becomes the input of the next step. The pipeline or “pathway” metaphor—to describe what is known as the pipeline problem—implies that once students enter the pipeline, say, through introductory coding activities in elementary school, they will be set to succeed all the way from childhood through a satisfying career in tech. The pipeline metaphor prevents us from taking a closer look at other factors that might be contributing to the long-standing diversity issue in tech. It serves to shift the focus, and the blame, onto the education system, and away from tech companies themselves.
Dr. Araba Sey, the Head of Research at the United Nations University Institute on Computing and Society, contends that “barriers, not the pipeline, prevent gender equality in tech”.  Women report much higher rates of dissatisfaction at work than men and leave positions in the field of science and technology more often. . Even when women overcome years of adversity and land a tech job, they face lower salaries, a lack of promotions, workplace harassment, and an often hyper-masculine culture—just to name a few of the barriers Dr. Sey highlights.
The Leaky Pipeline Metaphor
The pipeline narrative draws attention away from factors that may discourage women, people of color, and LGBTQ+ individuals along the way, therefore also drawing attention away from the need to address the issue from more than one angle: through the education system and early exposure for all and within the tech industry as it exists today. The Kapor Center, an organization that aims to make the tech industry more diverse and inclusive, offers a modification to the original metaphor, describing the problem as a leaky pipeline.
Should we be satisfied with the leaky pipeline analogy? If the leaky pipeline is the dominant framework through which we think about early childhood computer science education, we risk overlooking other potential benefits of exposure to computer science. The leaky pipeline metaphor continues to rely on a “childhood-to-career” trajectory, emphasizing that a career in tech is the goal. For example, the goal of Facebook’s K-12 CS initiative mentioned earlier is to encourage students to “pursue a career in the tech industry.”
However, in the 21st century, technology is embedded in nearly every industry. In a world where matters of public policy are increasingly decided by algorithms, it is necessary for all citizens to have a base understanding of computer science. Algorithms decide the news we see, who gets a loan, who is granted citizenship, and more.  Having the knowledge and language to discuss these issues is an essential 21st-century skill.
Furthermore, communicating that technology is not an isolated discipline but rather a field that intersects with many others can help change students’ perspectives of tech, and highlight that tech is part of disciplines seen as more traditionally-feminine.  Research by the DevTech Research group at Tufts University suggests that coding and other activities that encourage computational thinking can help students engage in problem-solving skills that can be applied to any discipline.  Learning to code could even help with learning to read. 
Where Does This Leave Us?
Merely understanding what the pipeline problem is and how the metaphor came to be developed is not enough. Adequate efforts should be made to increase diversity in the workforce with the help of the probable aforementioned solutions or alternatives. If our goal is to encourage more female, BIPOC, and LGBTQ+ students to engage in computer science, moving away from the “pipeline” metaphor helps us identify more ways to tackle the issue. We can hold tech companies accountable for what happens “after the pipeline,” urging them to do away with practices that discourage minorities from staying in tech once they get there, as well as ever being interested in the first place. We can focus on what’s happening at every grade level, and move beyond the idea that a career “in tech” is the only acceptable outcome. Widening our understanding of what someone might do with computer science experience can inform the ways in which we contextualize programming activities for students—making it more inclusive for all.
 U.S. Bureau of Labor Statistics. (2020a, January). Labor Force Statistics from the Current Population Survey CPS CPS Program Links. https://www.bls.gov/cps/cpsaat11.htm
 U.S. Census Bureau. (2019, July). Population estimates, July 1, 2019. https://www.census.gov/quickfacts/fact/table/US#
 Sullivan, A., & Bers, M. U. (2018). Investigating the use of robotics to increase girls’ interest in engineering during early elementary school. International Journal of Technology and Design Education, 29(5), 1033–1051. https://doi.org/10.1007/s10798-018-9483-y
 Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance. Journal of Experimental Social Psychology, 35, 4–28.
 Stricker, L. J., & Ward, W. C. (2004). Stereotype threat, inquiring about test taker’s ethnicity and gender, and standardized test performance. Journal of Applied Social Psychology, 34, 665–693.
 Lewis, C. M., Titterton, N., & Clancy, M. (2012, September). Using collaboration to overcome disparities in Java experience. In Proceedings of the ninth annual international conference on International computing education research (pp. 79–86). ACM.
 Inside Higher Ed. (2015, March 3). A Metaphor to Retire. Https://Www.Insidehighered.Com/. https://www.insidehighered.com/views/2015/03/03/essay-calls-ending-leaky-pipeline-metaphor-when-discussing-women-science
 Gregg, M. (2015, December 3). The Deficiencies of Tech’s “Pipeline” Metaphor. The Atlantic. https://www.theatlantic.com/business/archive/2015/12/pipeline-stem/418647/#:%7E:text=Tech%20industry%20leaders%20are%20constantly,want%20to%20get%20more%20young
 Sey, A. (2020, February 15). Barriers, not the pipeline, prevent gender equality in tech. Harvard Business School Digital Initiative. https://digital.hbs.edu/managing-in-the-digital-economy/barriers-not-the-pipeline-prevent-gender-equality-in-tech/
 National Center for Women & Information Technology. (2016). Women in IT: The Facts Infographic [2016 Update]. https://www.ncwit.org/resources/women-it-facts-infographic-2016-update
 Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code (1st ed.). Polity.
 Cheryan, S., Master, A., & Meltzoff, A. N. (2015). Cultural stereotypes as gatekeepers: increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology, 6, 1–8. https://doi.org/10.3389/fpsyg.2015.00049
 Bers, M. U. (2017). Coding as a Playground: Programming and Computational Thinking in the Early Childhood Classroom (1st ed.). Routledge.
 Hassenfeld, Z. R., Govind, M., de Ruiter, L., E., & Bers, M. U. (2020). If you can program you can write: Learning introductory programming across literacy levels. Journal of Information Technology Education: Research, 19, 65-85. https://doi.org/10.28945/4509