Socio-Economic Equity in Tech

Dated Oct 28, 2021; last modified on Sat, 06 Nov 2021

When it comes to STEM diversity goals, Asian American and Pacific Islander (AAPI) tends to be excluded from the URMs under discussion, e.g. .

However, AAPI as a blanket term obscures the struggles of member groups, e.g. \(62\%\) of AAPI adults aged 24 and older have an associate’s degree or higher, compared to 28% of Native Hawaiians and Pacific Islanders of the same age. AA comprises \(\approx 50\) ethnic groups, while PI has \(\approx 20\).

The term “Asian American” was introduced in 1968 to unite different communities of Asian decent to create a more formidable protest bloc, inspired by the Black Power movement . Beyond some threshold, umbrella terms outlive their original purpose. Figuring out this threshold, and re-educating the masses on the new boundaries is a hard problem.

The Leaky STEM Pipeline

There is a repeated mention of “the leaky pipeline problem”, in that there is a failure to retain minorities in STEM. Consequently, there is active research on how to fix these leaks.

addresses STEM in general, and not specifically computer science. One of their key qualifications is drawing their recommendations from 40+ years of federal and private funding and tracking of programs aimed at narrowing URM educational achievement gaps.

Increase institutional accountability by publishing incoming student interest, declared major, and program graduation rates, and all three by student ethnicity, gender, socioeconomic status, and first-generation status.

Institutions should create strategic partnerships with programs that create lift for highly talented and motivated URM students, who may or not be prepared.

Curricular changes. For example, Harvey Mudd increased women in CS from 10% to 40% over a 5-year period through a restructured (more creative problem solving) intro to CS course, early exposure to research, and regular intro to women CS professionals . Course-based undergraduate research experiences infuse entry-level classes with hands-on research opportunities.

Address student resource disparities as URM students are overrepresented in low socioeconomic status categories. Lack of resources hinders the ability to engage fully in studies. Recommendations: institutional financial commitments, federal & private funding agencies, political actions, etc.

Encourage creativity in URMs by linking work done in their STEM fields to personal and culturally valued outcomes . A person’s value of a task is a stronger predictor of task motivation and creativity than expectancies for success . URM students are especially motivated by commununal/altruistic goals than by individual success .

observed this in children; believe it generalizes to university students.

had modest findings on the effect of mentorship on self-efficacy for URM students. approached this question from a social influence model perspective, and found:

  • Two semesters of research uniquely predict overall science self-efficacy, identity, and values.
  • Quality mentorship uniquely predicted overall science self-efficacy, identity, and values, but did not predict growth in science efficacy.
  • Efficacy and values significantly related to, but did not uniquely predict career choice 4 years after graduation. However, science identity uniquely predicted career choice.

defines “self-efficacy” as one’s self-assessment as capable to doing scientific work, “identity” as in science is an important aspect of their identity, and membership in the scientific community, and “values” as in internalizing the values of the scientific community.

At the Workplace

This section is based on . Find different voices.

Systemic issues: biased hiring practices; non-inclusive work environment causing churn.

Companies have been pumping $$$ into unconscious bias training, anonymizing resumes, etc., but the numbers have barely moved.

founded interviewing.io, which facilitates employers to conduct anonymous technical interviews on the plaform. interviewing.io presents candidates based on past live technical interviews, as opposed to resumes. Their value prop is high conversions, short time loop, and reduced hiring bias.

Reported % women engineers is inflated because ‘technical roles’ are defined broadly. Furthermore, global numbers may mask unimpressive local numbers, e.g. India’s 35% may hide the US’s 16%.

In the last 15 years, \(\approx 20\%\) of CS graduates have been women. The hiring rate at tech for women is \(\approx 20\%\) too. We can’t get to parity, even if we’re unbiased. Companies should expand hiring pipelines beyond top schools and top companies.

  1. We ran the numbers, and there really is a pipeline problem in eng hiring. Aline Lerner. http://blog.interviewing.io/we-ran-the-numbers-and-there-really-is-a-pipeline-problem-in-eng-hiring/ . https://news.ycombinator.com/item?id=21694348 . Dec 3, 2019.
  2. Improving Underrepresented Minority Student Persistence in STEM. Mica Estrada; Myra Burnett; Andrew G. Campbell; Patricia B. Campbell; Wilfred F. Denetclaw; Carlos G. Gutiérrez; Sylvia Hurtado; Gilbert H. John; John Matsui; Richard McGee; Camellia Moses Okpodu; T. Joan Robinson; Michael F. Summers; Maggie Werner-Washburne; MariaElena Zavala. CBE - Life Sciences Education, Vol. 15, No. 3. https://doi.org/10.1187/cbe.16-01-0038 . 2016.
  3. The term 'Asian American' doesn’t serve everyone it covers. Li Zhou. https://www.vox.com/identities/22380197/asian-american-pacific-islander-aapi-heritage-anti-asian-hate-attacks . May 5, 2021. Accessed Oct 28, 2021.
  4. Solving the Equation: The Variables for Women's Success in Engineering and Computing. Corbett, Christianne; Catherine Hill. 2015.
  5. Handbook of Motivation at School. Wentzel, Kathryn R; David B. Miele. 2009.
  6. Talking about leaving: factors contributing to high attrition rates among science, mathematics & engineering undergraduate majors: final report to the Alfred P. Sloan Foundation on an ethnographic inquiry at seven institutions. Elaine Seymour. 1994.
  7. A desire to help others: Goals of high-achieving female science undergraduates. Miller, Patricia H.; Sue V. Rosser; Joann P. Benigno; Mireille L. Zieseniss. Women's Studies Quarterly, 28.1/2 (2000): 128-142.. 2000.
  8. Giving back or giving up: Native American student experiences in science and engineering. Smith, Jessi L.; Erin Cech; Anneke Metz; Meghan Huntoon; Christina Moyer. Cultural Diversity and Ethnic Minority Psychology, 20(3), 413–429. 2014.
  9. Collectivism and individualism as cultural syndromes. Triandis, Harry C. Cross-cultural Research, Vol. 27, No. 3-4: 155-180. 1993.
  10. Academic Self-Efficacy and Performance of Underrepresented STEM Majors: Gender, Ethnic, and Social Class Patterns. David MacPhee; Samantha Farro; Silvia Sara Canetto. Analyses of Social Issues and Public Policy, Vol. 13, No. 1, 2013, pp. 347-369. https://www.researchgate.net/profile/Catherine-Cohan/publication/229803010_Special_Reviewers/links/5efe903f92851c52d6136fc1/Special-Reviewers.pdf .
  11. A Longitudinal Study of How Quality Mentorship and Research Experience Integrate Underrepresented Minorities into STEM Careers. Estrada, Mica; Paul R. Hernandez; P. Wesley Schultz. CBE - Life Sciences Education, Vol 17, No. 1. https://doi.org/10.1187/cbe.17-04-0066 . 2018.
  12. Toward a Model of Social Influence that Explains Minority Student Integration into the Scientific Community. Estrada, Mica; Anna Woodcock; Paul R. Hernandez; P. Wesley Schultz. Journal of Educational Psychology, Vol. 103, No. 1. https://dx.doi.org/10.1037%2Fa0020743 . 2011.