DATA BRIEF: Women and Girls of Color in Computing

TECHNOLOGY AND THE GLOBAL DATA HIGHLIGHTS ECONOMY • Women of color currently constitute 39% of the female- identified population in the United States, and will Technology is a significant driver of economic growth and comprise the majority by 2060. development across the globe (Dutta, Geiger, & Lanvin, 2015). Technology plays a critical role in the United States • Just 4% of all high school students taking AP Computer economy and workforce, with nearly one-quarter of the Science in 2017 were Latinx girls, 2% were Black girls, country’s total economic output produced by high-tech and <1% were Native American/Alaskan Native girls. industries (Bureau of Labor Statistics [BLS], 2016, 2017) and • Women of color make up less than 10% of all Bachelor’s nearly 1 million job openings projected in computer and degrees earned in computing, and Latinx women information technology over the next 10 years (BLS, 2017). are most underrepresented in computing Bachelor’s In addition to being among the fastest-growing, computing degree completion rates relative to their population in postsecondary education. occupations are also among the most economically lucrative, with median salaries more than twice the median • Women earn 21% of all doctorates in computing, wage for all other occupations (BLS, 2015b) and significant however, less than 5% are awarded to Black, Latinx, wealth being generated by technology creators and Native American/Alaskan Native, or Native Hawaiian/ investors (CB Insights, 2017). Yet, the technology workforce Pacific Islander women. is not representative of the diversity of the United • Among all women employed in computer and States population, with the vast majority of individuals information science occupations, only 12% are Black employed in computer and mathematical occupations or Latinx women; In 177 Silicon Valley firms, less than being White (63%) and male (75%; BLS, 2015). To ensure 2% of all workers are Black, Latinx, or Native American/ Alaskan Native women. the future economic growth and prosperity of the United States, developing a robust, skilled, and diverse national • While White women and Asian women participate in workforce will be essential. Simultaneously, increasing roughly equal rates in the overall workforce, Asian equity in economic opportunity and decreasing inequality women are significantlyless likely to be in leadership will be directly linked to the preparation of individuals positions. from marginalized and underrepresented communities to • Less than 1% of Silicon Valley tech leadership positions participate in the rapidly evolving technology economy. are held by Latinx women, and <0.5% are held by Black Thus, the current and pervasive lack of racial/ethnic and women. gender diversity in the technology ecosystem presents a • Women of color account for 80% of the new female-led significant national challenge. small businesses, but in tech, Black women account for less than 4% of female-led startups. WOMEN AND GIRLS OF COLOR IN THE vastly underrepresented across the technology pipeline and UNITED STATES many of the strategies, interventions and investments to diversify the technology ecosystem have focused on race or Women of color currently constitute 18% of the overall gender, overlooking the intersection of race and gender (and U.S. population and 39% of the roughly 163 million female- other marginalized sexual, cultural, economic, religious, and identified population in the United States (Census, 2014; linguistic identities). Figure 1). Nearly half of female students in K-12 education are girls of color (U.S. Department of Education, 2013) and by 2060, the census projects that women of color will INTERSECTIONALITY IN COMPUTING comprise the majority of the female population in the United Foundational scholarship on Intersectionality theory States (Census, 2014, Catalyst, 2017; Figure 2). articulates the ways in which racism and sexism affect educational, social and occupational outcomes of women FIGURE 1 of color in ways that FIGUREcannot 2be fully captured by examining US Female Population by Race, Ethnicity experiencesUS Female Populationby race or Growthgender byseparately Race, Ethnicity (Crenshaw, 1989, (2015-2060) .5% 1991). Intersectionality describes the complex interactions 1% 120 108% ■ White between multiple identities and120% dynamics of power, 6% ■ Latinx 100 ■ Black racism, sexism, and oppression (Hill Collins and Bilge 2016; 14% ■ Native American/ 80 Crenshaw, 1991). Intersectionality theory encompasses62% Alaskan Native not60 only the intersection between marginalized racial and ■ Asian 41% 40% 17% ■ Native Hawaiian/ gender40 identities, but also additional marginalized identities 61% Pacific Islander including20 sexual orientation, socioeconomic status, religion, age, ability,-9% and linguistic background, among others. Within 0 Latinx Black Asian Native Amer/ Native science, technology, engineering and math (STEM) fields, the -20 White Alaskan Hawaiian/ unique combined and cumulative challenges Native Pacific ofIslander racism and

Source: U.S. Bureau of Census (2014) sexism experiencedSource: U.S. Bureauby women of Census of (2014)color has been described as the “double-bind” (Malcom et al., 1975; Ong et al., 2011; FIGURE 1 FIGURE 2 Williams et al., 2014), which leads to disparities throughout US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) the STEM pipeline. In the technology pipeline, specifically, .5% 1% 120 the double-bind for women and girls of color begins early ■ White 108% FIGURE 3120% FIGURE 4 6% ■ Latinx 100AP CS Participation, by Gender and Race/Ethnicity inK access – 12 Female and participation Enrollment vs. in APcomputing CS Participation education, persists ■ Black throughout post-secondary education, and culminates in 14% ■ Native American/ 80.05% 1% 1% ■ Male (All Races)62% Alaskan Native disparities2% in participation at all levels■ White of the technology 60.05% ■ Black/African American ■ Asian 8% ■ Latinx 10% 41% Female40% workforce, technology entrepreneurship, and venture capital 17% ■ Native Hawaiian/ ■ Black 40 ■ Latinx/Hispanic Female 61% Pacific Islander 8% (Scott et12% al., 2018). ■ Asian 4%20 ■ Asian Female 0.50% ■ Native American/ -9% ■ Native American/ 2% 8% Alaskan Native 0 Latinx Black AsianAlaskan Native Amer/ Native Native ■ Native Hawaiian/ 2% White Alaskan Hawaiian/ 24% 4% -20 74% Pacific Native Islander Pacific Female Islander ■ White Female 10% Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014) ■ Other Female US K – 12 EnrollmentIntersectionality AP CS Participation describes Further, recent data has also shown that women of color, (Female) (Female) the complex interactions between specificallySource: College Black Board and (2017); Latinx Includes women, AP CS Aare and the AP fastestCS Principles. growing Source: College Board (2017); U.S. Department of Education, O ce for group of entrepreneurs in the United States, creating over Civil Rights (2014);multiple Data for Pacific identities Islander and Nativeand Hawaiiandynamics student enrollment unavailable. FIGURE 3 80% of the new women-ledFIGURE small 4businesses and nearly of power, racism, sexism, AP CS Participation, by Gender and Race/Ethnicity doublingK – the 12 Femalenumber Enrollment of businesses vs. AP started CS Participation by women FIGURE 5 and oppression .05% 1% of color since1% 2007 (AMEX, 2016; MacBride, 2015). Both ■ Male (All Races) Female2% AP CS Participation, by Race/Ethnicity■ White .05% ■ Black/African American the rapidly increasing size and entrepreneurial■ Latinx activity of 10% 8%.10% Female .2% ■ Black this population indicates significant■ earningWhite and spending 8% ■ Latinx/Hispanic Female ■ Asian 12% 6% ■ Black/African American 4% ■ Asian Female potential. Yet despite the size0.50% and projected■ Native growth American/ of this ■ Native American/ ■ Latinx/Hispanic 2% important segment of the population,8% womenAlaskan of Nativecolor remain Alaskan Native ■ Asian ■ Native Hawaiian/ 2% ■ Native American/ 32%24% 38% 4% 74% Pacific Islander Female Alaskan Native Kapor Center/ASU CGEST | 2 ■ White Female 10% ■ Native Hawaiian/ ■ Other Female Pacific Islander US K – 12 Enrollment AP CS Participation■ Other (Female)16% 7% (Female)

Source: College Board (2017); Includes AP CS A and AP CS Principles. Source: College Board (2017); U.S. Department of Education, O ce for Civil Rights (2014); Data for Pacific Islander and Native Hawaiian student enrollment unavailable. Source: College Board (2017); Includes AP CS A and AP CS Principles

FIGURE 5 Female AP CS Participation, by Race/Ethnicity FIGURE 6 Bachelor’s Degree Completion in Computer Sciences, .2% .10% ■ White by Gender and Race/Ethnicity 6% ■ Black/African American 2% .1% ■ Latinx/Hispanic 2% ■ Male (All Races) ■ Asian 3% ■ White Female ■ Native American/ 3% ■ Black/African American 32% 38% Alaskan Native 8% Female ■ Native Hawaiian/ ■ Latinx/Hispanic Female Pacific Islander ■ Asian Female ■ Other ■ Native American/ 16% 7% Alaskan Native Female 82% ■ Native Hawaiian/ Pacific Islander Female ■ Other

Source: College Board (2017); Includes AP CS A and AP CS Principles

Source: National Science Foundation (2016a).

FIGURE 6 Bachelor’s Degree Completion in Computer Sciences, by Gender and Race/Ethnicity .1% FIGURE 7 FIGURE 8 2% ■ Male (All Races) 2% Post-Secondary Enrollment vs. Bachelor’s Degree PhD Completion in Computer Sciences, by Gender ■ White Female Page 4, Figure 8: the 3% Completion in Computing and Race/Ethnicity 3% ■ Black/African American 4% should be Other/- 8% Female 2% 4% <1% ■ White ■ Male (All Races) Multiple Races and ■ Latinx/Hispanic Female <0.5%8% 4% ■ Asian Female 3% 8% ■ Latinx ■ Latinx/Hispanic Female the <1% should be 8% ■ Native American/ 12% ■ Black 2% ■ Black/African American 2% Native American/Alas- Alaskan Native Female 10% ■ Asian 1% 10% Female 82% ■ Native Hawaiian/ 4% ■ Native American/ ■ White Female kan Native. Pacific Islander Female Alaskan Native ■ Asian Female ■ Other 24%31% 2% <0.5% ■ Native Hawaiian/ ■ Native American/ 3% 10% Pacific Islander Alaskan Native Female 79% 2% 8% ■ Other/Multiple Races Female Source: National Science Foundation (2016a). Post-Secondary Enrollment Bachelor’s Degree (Female) Completion in Computing (Female)

Source: National Center for Education Statistics (2013). Percentages Source: National Science Foundation (2016c) reflect the percentage of the total population. FIGURE 7 FIGURE 8 Post-Secondary Enrollment vs. Bachelor’s Degree PhD Completion in Computer Sciences, by Gender Page 4, Figure 8: the Completion in Computing and Race/Ethnicity 4% should be Other/- 2% 4% <1% ■ White ■ Male (All Races) Multiple Races and <0.5%8% 4% 3% 8% ■ Latinx ■ Latinx/Hispanic Female the <1% should be 8% 12% ■ Black 2% ■ Black/African American 2% Native American/Alas- 10% ■ Asian 1% 10% Female 4% ■ Native American/ ■ White Female kan Native. Alaskan Native ■ Asian Female 24%31% 2% <0.5% ■ Native Hawaiian/ ■ Native American/ 3% 10% Pacific Islander Alaskan Native Female 79% 2% 8% ■ Other/Multiple Races Female Post-Secondary Enrollment Bachelor’s Degree (Female) Completion in Computing (Female)

Source: National Center for Education Statistics (2013). Percentages Source: National Science Foundation (2016c) reflect the percentage of the total population. FIGURE 1 FIGURE 2 US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) .5% 1% 120 ■ White 108% 120% 6% ■ Latinx 100 ■ Black 14% ■ Native American/ 80 62% Alaskan Native 60 ■ Asian 41% 40% 17% ■ Native Hawaiian/ 40 61% Pacific Islander 20 -9% 0 Latinx Black Asian Native Amer/ Native -20 White Alaskan Hawaiian/ Research has documented a multitude of structural predictor of pursuing computing in college, disparities in Native Pacific Islander and social/psychological barriers facing women and computing course-takingSource: U.S. and Bureau availability of Census (2014) place girls of color Source: U.S. Bureau of Census (2014) underrepresented people of color throughout the pipeline, at a significant disadvantage for future computing education including lack of access to rigorous STEM and computer and careers (Mattern, Shaw, and Ewing, 2011). FIGURE 1 FIGURE 21 FIGURE 2 science coursework,US Female lack of Populationdiverse peers by Race, and roleEthnicity models, US FemaleUS Female Population Population Growth by Race, by Race, Ethnicity Ethnicity US Female Population Growth by Race, Ethnicity unwelcoming classroom and workplace environments, (2015-2060) (2015-2060) .5% 120 .5% FIGURE 3 FIGURE 4 1% 1% 108% 120% 120 stereotype threat, bias in recruiting, hiring,■ andWhite promotion, AP CS Participation, by Gender■ andWhite Race/Ethnicity K – 12 Female108% Enrollment vs.120% AP CS Participation inequitable pay, lack 6%of access to influential■ Latinx social networks 100 6% ■ Latinx 100 ■ Black .05% 1% ■ Black 1% 80 ■ Male (All Races) ■ White and bias in venture14% capital investment (Scott■ Native et al., American/ 2018). 14% ■ Native American/ 80 2% .05% ■ Black/African American62% ■ Latinx 62% While much of this research identifies the Alaskanbarriers Native facing 60 10% Alaskan Native 8% ■ Asian Female 60 ■ Black 41% ■ Asian40% 41% 40% women and people17% of color, there is a dearth■ Native of research Hawaiian/ 40 8% ■ Latinx/Hispanic Female 12% ■ Asian 4% 17% ■ AsianNative Female Hawaiian/ 40 on the specific experiences61% and outcomes Pacificof women Islander of 61% 0.50% ■ Native American/ 20 ■ NativePacific American/Islander 2% 20 8% Alaskan Native color along the computing pipeline. Without identifying and -9% Alaskan Native -9% 0 Latinx Black Asian■ Native Native Hawaiian/ Amer/ Native 0 2% understanding the specific challenges facing women of color 24% Latinx Black4% Asian Native Amer/ Native -20 White 74% Pacific Alaskan Islander Hawaiian/ Female White Alaskan Hawaiian/ in the computing and technology pipeline, interventions will ■ White Native Female Pacific Islander -20 10% Native Pacific Islander

Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census■ Other (2014) Female be exclusionary, insufficient, and ineffective. Source: U.S. Bureau of Census (2014) US K – 12 EnrollmentSource: U.S. AP CS Bureau Participation of Census (2014) (Female) (Female)

Source: College Board (2017); Includes AP CS A and AP CS Principles. Source: College Board (2017); U.S. Department of Education, O ce for Civil Rights (2014); Data for Pacific Islander and Native Hawaiian student There is a dearth of research enrollment unavailable. FIGURE 3 FIGURE 4 on the specific experiences FIGURE 3 FIGURE 4 AP CS Participation, by Gender and Race/Ethnicity APK – CS 12 Participation,Female Enrollment FIGUREby Gender vs. 5 AP and CS Race/Ethnicity Participation K – 12 Female Enrollment vs. AP CS Participation .05% 1% Female1% AP CS Participation, by Race/Ethnicity and outcomes of women■ Maleof color (All Races) .05% 1% 1% 2% ■ Male■ White(All Races) ■ White .05% ■ Black/African American .10% 2% .05%.2%8% ■ Black/African■ Latinx American ■ Latinx along the10% computing pipeline.Female 10% ■ White 8% Female■ Black ■ Black ■ Latinx/Hispanic Female 6% ■ Black/African American 8% 8%12% ■ Latinx/Hispanic■ Asian Female ■ Asian 4% ■ Asian Female ■ Latinx/Hispanic 12% 4% 0.50% ■ Asian■ Native Female American/ 0.50% ■ Native American/ ■ Native American/ ■ Asian 2% 8% ■ NativeAlaskan American/ Native Alaskan Native Alaskan Native 2% ■ Native American/ 8% 32% 38% Alaskan Native 2% Alaskan Native GIRLS AND WOMEN OF COLOR■ Native ACROSS Hawaiian/ 24% ■ Native Hawaiian/ 2% 4% 24% 4% 74% Pacific Islander Female 74% ■ NativePacific Hawaiian/Islander Female THE COMPUTING PIPELINE 10% ■ White Female ■ PacificWhite FemaleIslander 10% ■ Other Female ■ Other PreK-12 Education 16% 7% ■ Other Female US K – 12 Enrollment AP CS Participation US K – 12 Enrollment AP CS Participation Although 50% of the school-aged population is female and (Female) (Female) (Female) (Female)

42% are Black, Latinx, or Native American (NCES, 2017), just Source: College Board (2017); Includes AP CS A and AP CS Principles. Source:Source: CollegeCollege BoardBoard (2017); U.S.Includes Department AP CS A of and Education, AP CS Principles. O ce for Source: College Board (2017); U.S. Department of Education, O ce for Civil Rights (2014); Data for Pacific Islander and Native Hawaiian student 23% of all students taking AP Computer Science in 2017 Source: College Board (2017); Includes AP CS A and AP CS Principles Civil Rights (2014); Data for Pacific Islander and Native Hawaiian student enrollment unavailable. enrollment unavailable. were female and just 20% were Black, Latinx, or Native American (College Board, 2017). Among all AP Computer FIGURE 5 Science test takers, just 4% were Latinx girls, 2% were Black FIGUREFIGURE 65 Female AP CS Participation, by Race/Ethnicity Female AP CS Participation, by Race/Ethnicity girls, and <1% were Native American/Alaskan Native girls, Bachelor’s Degree Completion in Computer Sciences, .10% by Gender and Race/Ethnicity with Asian and White.2% girls each constituting 8% and 10%, .2% .10% ■ White .1% ■ White 2% ■ Male (All Races) respectively (College 6%Board, 2017; Figure■ 3Black/African and Figure American 4). 2% 6% ■ Black/African American ■ White Female ■ Latinx/Hispanic 3% ■ Latinx/Hispanic Among the female students taking AP Computer Science, ■ Black/African American ■ Asian 3% ■ Asian Female just 23% were Black, Latinx or Native American,■ Native withAmerican/ the 8% ■ Native American/ 32% 38% 32% 38% ■ Latinx/Hispanic Female Alaskan Native Alaskan Native vast majority being White (38%) or Asian (32%; College ■ Asian Female ■ Native Hawaiian/ ■ Native Hawaiian/ ■ Native American/ Board, 2017; Figure 5). Among the studentsPacific of color Islander who Pacific Islander Alaskan Native Female took AP Computer Science, less than one-third■ Other (29%) were ■ Other 16% 7% 16% 82%7% ■ Native Hawaiian/ female, demonstrating both within-gender and within-race Pacific Islander Female disparities. In 13 states, there were no Latinx or Black female ■ Other students who participated in AP CS A (Ericson, 2017). Since Source: College Board (2017); Includes AP CS A and AP CS Principles Source: College Board (2017); Includes AP CS A and AP CS Principles taking AP Computer Science in high school is a significant Source: National Science Foundation (2016a).

FIGURE 6 FIGURE 6 Kapor Center/ASU CGEST | 3 Bachelor’s Degree Completion in Computer Sciences, Bachelor’s Degree Completion in Computer Sciences, by Gender and Race/Ethnicity by GenderFIGURE and Race/Ethnicity 7 FIGURE 8 .1% 2% Post-Secondary2% .1% Enrollment vs. Bachelor’s Degree PhD Completion in Computer Sciences, by Gender 2% ■ Male (All Races) ■ Male (All Races) Page 4, Figure 8: the 2% Completion in Computing and Race/Ethnicity 3% ■ White Female ■ White Female 4% should be Other/- 3% ■ Black/African American 3% 2%3% ■ Black/African American 4% <1% Multiple Races and 8% Female <0.5%8% Female■ White ■ Male (All Races) 8% 8% 4% ■ Latinx/Hispanic Female 3% ■ Latinx/Hispanic■ Latinx Female ■ Latinx/Hispanic Female the <1% should be 8% ■ Asian Female 12% ■ Asian■ Black Female 2% ■ Black/African American ■ Native American/ 2% Native American/Alas- 10% ■ Native■ Asian American/ 1% 10% Female 4% Alaskan Native Female Alaskan■ Native Native American/ Female ■ White Female kan Native. 82% ■ Native Hawaiian/ 82% ■ NativeAlaskan Hawaiian/ Native ■ Asian Female Pacific Islander Female 24%31% 2% <0.5% Pacific■ Native Islander Hawaiian/ Female ■ Native American/ ■ Other 3% 10% Pacific Islander Alaskan Native Female ■ Other 79% 2% 8% ■ Other/Multiple Races Female Post-Secondary Enrollment Bachelor’s Degree (Female) Completion in Computing (Female) Source: National Science Foundation (2016a). Source: National Science Foundation (2016a).

Source: National Center for Education Statistics (2013). Percentages Source: National Science Foundation (2016c) reflect the percentage of the total population.

FIGURE 7 FIGUREFIGURE 78 FIGURE 8 Post-Secondary Enrollment vs. Bachelor’s Degree Post-SecondaryPhD Completion Enrollment in Computer vs. Sciences, Bachelor’s by Degree Gender PhD CompletionPage 4, Figure in Computer 8: the Sciences, by Gender Completion in Computing and Race/Ethnicity Page 4, Figure 8: the Completion in Computing 4% shouldand be Race/Ethnicity Other/- 4% should be Other/- 2% 4%2%<1% <1% <0.5%8% ■ White ■ Male (All Races) 4%Multiple Races and Multiple Races and 8% <0.5%8% 4% ■ White ■ Male (All Races) 3% ■ Latinx 3% 8% ■ Latinx/Hispanic Female the <1%4% should be 8% ■ Latinx ■ Latinx/Hispanic Female the <1% should be 12% ■ Black 2% 8% ■ Black/African American 2% 12% ■ Black 2% Native American/Alas■ -Black/African American Native American/Alas- 10% ■ Asian 1% 10%10% 2% Female 4% ■ Asian 1% 10%kan Native. Female ■ Native American/ 4% ■ White■ FemaleNative American/ ■ White Female kan Native. Alaskan Native ■ Asian FemaleAlaskan Native ■ Asian Female 31% 2% ■ Native Hawaiian/ ■ Native American/ 24% <0.5% 24%31% 2% <0.5% ■ Native Hawaiian/ ■ Native American/ 3% 10% Pacific Islander 10% Alaskan Native Female 3%79% Pacific Islander Alaskan Native Female 2% ■ Other/Multiple Races 79% 8% 2% 8% ■ Other/Multiple Races Female Female Post-Secondary Enrollment Bachelor’s Degree Post-Secondary Enrollment Bachelor’s Degree (Female) Completion in Computing (Female) (Female) Completion in Computing (Female)

Source: National Center for Education Statistics (2013). Percentages Source: NationalSource: Center National for Education Science Foundation Statistics (2013). (2016c) Percentages Source: National Science Foundation (2016c) reflect the percentage of the total population. reflect the percentage of the total population. FIGURE 1 FIGURE 2 US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) .5% FIGURE 1 FIGURE 2 1% 120 US Female Population by■ Race, White Ethnicity US Female Population108% Growth120% by Race, Ethnicity 6% ■ Latinx 100 (2015-2060) .5% 1% ■ Black 120 ■ White 108% 120% 14% ■ Native American/ 80 6% 62% ■ LatinxAlaskan Native 100 ■ Black 60 ■ Asian 40% 14% ■ Native American/ 80 41% 17% ■ Native Hawaiian/ 40 62% 61% AlaskanPacific Islander Native 60 ■ Asian 20 41% 40% 17% ■ Native Hawaiian/ 40 -9% 61% Pacific Islander 0 Latinx Black Asian Native Amer/ Native 20 White Alaskan Hawaiian/ -20 -9% Native Pacific Islander 0 Latinx Black Asian Native Amer/ Native Source: U.S. Bureau of Census (2014) White Source: U.S. Bureau of Census (2014) Alaskan Hawaiian/ -20 Native Pacific Islander

Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014) FIGURE 1 FIGURE 2 US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) .5% FIGURE 3 FIGURE 4 1% 120 ■ White AP CS Participation,108% by Gender120% and Race/Ethnicity K – 12 Female Enrollment vs. AP CS Participation 6% ■ Latinx 100 FIGURE 3 FIGURE 4 ■ Black .05% 1% 1% AP CS Participation, by Gender■ and Male Race/Ethnicity (All Races) K – 122% Female Enrollment vs. AP CS■ ParticipationWhite 14% ■ Native American/ 80.05% ■ Black/African American62% ■ Latinx Alaskan Native 10% 8% 60.05% 1% Female 1% ■ Black ■ Asian 41% ■■ MaleLatinx/Hispanic (All40% Races) Female 2% ■ White .05%8% ■ Black/African American 12% ■ Asian 17% ■ Native Hawaiian/ 4%40 ■ Asian Female 8% ■ Latinx 61% 10% 0.50% ■ Native American/ Pacific Islander ■ FemaleNative American/ ■ Black 2%20 8% Alaskan Native 8% ■ Latinx/HispanicAlaskan Native Female 12% ■ Asian -9% ■ Asian Female 4%0 ■ Native Hawaiian/ 0.50%2% ■ Native American/ Latinx Black Asian■ Native Native American/Amer/ Native 24% 4% 2% 74% Pacific Islander Female 8% Alaskan Native White Alaskan Alaskan Native Hawaiian/ -20 ■ White Native Female Pacific Islander 10% ■ Native Hawaiian/ 2% ■ Other Female 24% 4% Source: U.S. Bureau of Census (2014) Source:74% U.S. Bureau of CensusPacific (2014) Islander Female US K – 12 Enrollment AP CS Participation ■ White Female (Female) (Female)10% ■ Other Female Source: College Board (2017); Includes AP CS A and AP CS Principles. Source: US K College– 12 Enrollment Board (2017); AP CS U.S. Participation Department of Education, O ce for Civil Rights(Female) (2014); Data for Pacific (Female) Islander and Native Hawaiian student enrollment unavailable. Source: College Board (2017); Includes AP CS A and AP CS Principles. Source: College Board (2017); U.S. Department of Education, O ce for FIGURE 3 FIGURE 4 Civil Rights (2014); Data for Pacific Islander and Native Hawaiian student enrollment unavailable. AP CS Participation, by Gender and Race/Ethnicity K – 12 Female EnrollmentFIGURE vs. 5 AP CS Participation Female AP CS Participation, by Race/Ethnicity .05% 1% 1% ■ Male (All Races) 2%.10% FIGURE 5 ■ White .05% ■ Black/African American .2% Female8% AP CS Participation,■ by White Race/Ethnicity■ Latinx 10% Female 6% ■ Black/African■ Black American ■ Latinx/Hispanic Female 8% .2%12%.10% ■ Latinx/Hispanic■ Asian 4% ■ Asian Female 0.50% ■■ WhiteAsian■ Native American/ ■ Native American/ ■ Black/African American 2% 6% 8% ■ NativeAlaskan American/ Native Alaskan Native 32% 38% ■ Latinx/HispanicAlaskan Native ■ Native Hawaiian/ 2% 24% 4% ■■ AsianNative Hawaiian/ 74% Pacific Islander Female ■ NativePacific American/Islander ■ White Female 32% 38% 10% ■ AlaskanOther Native ■ Other Female 16% 7% ■ Native Hawaiian/ US K – 12 Enrollment AP CS Participation Pacific Islander (Female) (Female) ■ Other 16% 7% Source: College Board (2017); Includes AP CS A and AP CS Principles. Source: College Board (2017); U.S. Department of Education, O ce for CivilSource: Rights College (2014); DataBoard for (2017); Pacific Includes Islander AP and CS NativeA and APHawaiian CS Principles student enrollment unavailable.

Source: College Board (2017); Includes AP CS A and AP CS Principles FIGURE 5 FIGURE 6 Post-SecondaryFemale Education AP CS Participation, by Race/Ethnicity Bachelor’s Degree Completion in Computer Sciences, Despite comprising half of the college-aged population, by Gender and Race/Ethnicity .2% .10% FIGURE 6 ■ White .1% women earn just 18% of Bachelor’s degrees in the computer Bachelor’s2% Degree Completion in■ Computer Male (All Races) Sciences, ■ Black/African American 2% 6% by Gender and Race/Ethnicity■ White Female sciences. Women of color combined make■ Latinx/Hispanic up less than 3% 3% .1% ■ Black/African American ■ Asian 2% ■ Male (All Races) 10% of all Bachelor’s degrees earned in computing, with 2%8% Female ■ Native American/ ■ White Female 32% 38% 3% ■ Latinx/Hispanic Female Black women constituting 3% of all computingAlaskan degree Native 3% ■■ Black/AfricanAsian Female American ■ Native Hawaiian/ earners, and Latinx and Asian women comprising 2% each 8% ■ Female Native American/ Pacific Islander (Figure 6 and 7; NSF, 2016a). Among all women who earn ■ Latinx/HispanicAlaskan Native FemaleFemale ■ Other ■ Asian Female computing Bachelor’s16% degrees,7% the majority are White 82% ■ Native Hawaiian/ ■ NativePacific American/Islander Female women (50%), followed by Black women (15%), Asian ■ AlaskanOther Native Female 82% ■ Native Hawaiian/ women (12%), Latinx women (11%) and Native American/ Pacific Islander Female Alaskan NativeSource: womenCollege Board (<1%; (2017); NSF, Includes 2016a). AP CS All A andwomen AP CS arePrinciples ■ Other underrepresented in Bachelor’s degree completion rates Source: National Science Foundation (2016a). relative to their population in postsecondary education, with Source: National Science Foundation (2016a). Latinx women most vastly underrepresentedFIGURE 6 (Figure 7). In graduate computingBachelor’s Degreeeducation, Completion women in comprise Computer a Sciences,higher by Gender and Race/Ethnicity FIGURE 7 FIGURE 8 percentage of the population.1% earning Master’s degrees in Post-Secondary Enrollment vs. Bachelor’s Degree PhD Completion in Computer Sciences, by Gender 2% ■ Male (All Races) Page 4, Figure 8: the computing (27%),2% but significantly fewer are Black, Latinx, CompletionFIGURE in Computing 7 and FIGURERace/Ethnicity 8 3% ■ White Female 4% should be Other/- or Native American/Alaskan3% Native, or Native■ Black/African Hawaiian/ American Post-Secondary2% Enrollment vs. Bachelor’s Degree PhD4% Completion<1% in Computer Sciences, by Gender Page 4, Figure 8: the ■ White ■ Male (All Races) Multiple Races and 8% Female <0.5%8% Completion in Computing 4% and Race/Ethnicity Pacific Islander ( NSF, 2016b). Similarly, at the doctoral level, 3% 8% ■ Latinx ■ Latinx/Hispanic Female 4%the should<1% should be Other/ be - ■ Latinx/Hispanic Female 8% <1% 12%2% ■ Black 4% ■ Black/African American women earn 21% of all doctorates in computer■ Asian Femalescience, ■ White 2% ■ Male (All Races) MultipleNative American/Alas Races and - <0.5%8%10% 2% ■ Asian 10% 4% Female ■ Native American/ 3% 8% 1% ■ Latinx/Hispanic Female however just one-fifth of these are awarded to Black, Latinx, 4% ■■ LatinxNative American/ ■ White Female thekan <1% Native. should be Alaskan Native Female 8% ■ Black/African American Native American/Alaskan Native, or Native Hawaiian/ 12% ■ BlackAlaskan Native 2% ■ Asian Female Native American/Alas- 82% ■ Native Hawaiian/ 10% 2% ■ Asian 10% Female 24%31% 2% <0.5% ■ Native Hawaiian/ 1% ■ Native American/ Pacific Islander women (NSF, 2016c; FigurePacific 8). IslanderThe lack Female 4% ■ White Female kan Native. 3% 10% ■ NativePacific American/Islander Alaskan Native Female ■ Other 79% of women of color completing degrees in the computer 2% Alaskan Native ■■ AsianOther/Multiple Female Races 31% 2% 8% 24% <0.5% ■ Native Hawaiian/ ■ NativeFemale American/ sciences limits access to lucrative careers in technology Post-Secondary Enrollment3% Bachelor’s10% Degree Pacific Islander Alaskan Native Female (Female) Completion2% in Computing (Female) 79% (and affiliated opportunitiesSource: National to Science pursue Foundation tech entrepreneurship (2016a). 8% ■ Other/Multiple Races Female and investments careers) and limits the number of women Post-SecondarySource: National Enrollment Center for Bachelor’sEducation Degree Statistics (2013). Percentages Source: National Science Foundation (2016c) (Female)reflect the Completion percentage in Computing of the total (Female) population. of color in academia. Without women of color in computing at the higher education stage, the lack of social scaffolding Source: National Center for Education Statistics (2013). Percentages Source: National Science Foundation (2016c) (role models, sponsorship, andFIGURE support) 7 for girls and reflect the percentageFIGURE of the 8 total population. Post-Secondary Enrollment vs. Bachelor’s Degree PhD Completion in Computer Sciences, by Gender women of color will continue to constrain efforts to broaden Page 4, Figure 8: the Completion in Computing and Race/Ethnicity participation in computing. 4% should be Other/- 2% 4% <1% ■ White ■ Male (All Races) Multiple Races and <0.5%8% 4% 3% 8% ■ Latinx ■ Latinx/Hispanic Female the <1% should be 8% 12% ■ Black 2% ■ Black/African American 2% Native American/Alas- 10% ■ Asian 1% 10% Female 4% ■ Native American/ ■ White Female kan Native. Alaskan Native ■ Asian Female 24%31% 2% <0.5% ■ Native Hawaiian/ ■ Native American/ 3% 10% Pacific Islander Alaskan Native Female 79% 2% 8% ■ Other/Multiple Races Female Post-Secondary Enrollment Bachelor’s Degree (Female) Completion in Computing (Female)

Source: National Center for Education Statistics (2013). Percentages Source: National Science Foundation (2016c) reflect the percentage of the total population.

Kapor Center/ASU CGEST | 4 FIGURE x FIGURE x US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) .5% 1% 120 ■ White 108% 120% 6% ■ Latinx 100 ■ Black 80 14% ■ Native American/ 62% Alaskan Native 60 ■ Asian 41% 40% 17% ■ Native Hawaiian/ 40 61% Pacific Islander 20 -9% 0 Latinx Black Asian Native Amer/ Native -20 white Alaskan Hawaiian/ Native Pacific Islander

Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014)

FIGURE x FIGURE x Technology Workforce FIGURE x FIGURE x US Female Population by Race, Ethnicity US Female PopulationFIGURE Growth 9 by Race, Ethnicity FIGURE 10 Over the past several decades, the proportionUS of Female women Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity Disparities between(2015-2060) CS Degree Earners and the Silicon Valley Tech Workforce Participation by in the technology workforce.5% has decreased substantially. (2015-2060) 1% .5% 120 Computing Workforce by Race and Gender Gender and Race/Ethnicity ■ White1% 108% 120%120 While women currently comprise roughly half of the overall ■ White 108% 120% <1% 6% ■ Latinx 100 13% <1% <1% 6% ■ Latinx 100 2% workforce, they make up just 35% of the■ technologyBlack 32% ■ Male (All Races) ■ 80Black17% 14% ■ Native American/ 80 ■ White Female workforce (EEOC, 2016). Among all women employed14% in ■ Native American/ 62% Alaskan Native 11% 7% ■ White 12% 62% ■ Asian Female 60Alaskan Native 5% computer and information science occupations,■ Asian 56% are 60 ■ Latinx ■ Latinx/Hispanic Female ■ Asian 41% 40% 17% ■ Native Hawaiian/ 40 ■ Black 41% 40% ■ Black/African white women, 32% are Asian women, 7% are17% Black women ■ Native Hawaiian/ 40 14% 61% Pacific Islander 55% ■ Asian American Female 61% 20Pacific Islander 56% and 5% are Latinx women (Figure 9; NSF, 2015a, 2016a). 20 ■ Native American/ -9% There are significant disparities between the percentage -9% 70% Alaskan Native Female 0 Latinx Black Asian Native Amer/ Native 0 Latinx Black Asian Native Amer/ Native ■ Native Hawaiian/ of Black and Latinx women earning computer science -20 white Alaskan Hawaiian/ Pacific Islander Female Bachelor’s Degrees Employed Computer-20 and Native white Pacific Islander Alaskan Hawaiian/ degrees and their percentage in the nationwide computing Earned in CS (Female) Information Scientists (Female) Native Pacific Islander Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014) and information sciences workforce, demonstratingSource: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014) Source: NSF (2015a). Table 9-7; NSF (2016a). Table 5-4. Native American/ Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC particular challenges with recruiting and retaining women Alaskan Native and Native Hawaiian populations were too small to report. category of “professionals.” The percentages for Other/Multiple Races not reported. of color (Figure 9; NSF, 2015a, 2016a). In Silicon Valley, men constitute 70% of the workforce, Asian and White women FIGURE 11 comprise a combined 26% of the professional workforce, FIGURE 9 FIGURE 10 Silicon Valley Tech Leadership by FIGURE 9 FIGURE 10 while Black, Latinx,Disparities and betweenNative American/Alaskan CS Degree Earners Native and the Silicon Valley Tech Workforce ParticipationGender by and Race/Ethnicity Disparities between CS Degree Earners and the Silicon Valley Tech Workforce Participation by Computing Workforce by Race and Gender Gender and Race/Ethnicity women each constitute 2% or less (Figure 10;Computing EEOC, 2016; Workforce by Race and Gender <1%Gender<1% and Race/Ethnicity <1% Hongsdusit & Rangarajan,13% 2018). These race and gender <1% <1% <1% 13% 2% 5%<1% <1% ■ Male (All2% Races) disparities are even more dramatic when32% examining the ■ Male (All Races) 17% 32% ■ White Female ■ Male (All Races) 17% 15% ■ White Female demographic composition11% of the leadership7% of Silicon■ White 12% ■ Asian Female ■ White Female 11% 7% ■ White 12% ■ Asian Female 5% ■ Latinx ■ Latinx/Hispanic Female ■ Asian Female Valley technology companies, where males account for 5% ■ Latinx ■ Latinx/Hispanic Female ■ Black ■ Black/African ■ Latinx/Hispanic Female 14% ■ Black ■ Black/African 80% of all tech leaders. While white women and Asian■ Asian American14% Female ■ Black/African 55% ■ Asian 80% American Female 56% 55% ■ Native American/ American Female women participate in roughly equal rates in the overall 56% ■ Native American/ 70% Alaskan Native Female 70% Alaskan Native Female workforce, Asian women are significantly less likely to be ■ Native Hawaiian/ ■ Native Hawaiian/ in leadership positions (Figure 11; EEOC, 2016; Gee & Peck, Pacific Islander Female Bachelor’s Degrees Employed Computer and Pacific Islander Female Earned in CS (Female) Information Scientists Bachelor’s (Female) Degrees Employed Computer and 2017; Hongsdusit & Rangarajan, 2018). Less Earned than in CS 1% (Female) of Information Scientists (Female) Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC category of “Executives, Senior O—cials & Managers”; The figures for Silicon ValleySource: tech NSF (2015a). leadership Table 9-7; positions NSF (2016a). are Table held 5-4. by Native Latinx American/ Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC Source: NSF (2015a). Table 9-7; NSF (2016a). Table 5-4. Native American/ NativeSource: American/Alaskan EEOC, 2016; Hongsdusit Native/Native & Rangarajan Hawaiian (2018); females Includes in leadership EEOC Alaskan Native and Native Hawaiian populations were too small to report. category of “professionals.” women, and less than 0.5% are held byAlaskan Black Native women. and Native The Hawaiian populations were too small to report. positionscategory are of too “professionals.” small to report. The percentages for Other/Multiple Races not reported. lack of representation of women of color in professionalThe percentages andfor Other/Multiple Races not reported. leadership positions limits access to high-wage, high-growth FIGURE 12 FIGURE 13 FIGURE 11 TechnologyFIGURE Startup 11Founders by Technology Startup Founders by Gender occupations, increases inequality in wealth distribution,Silicon Valley Tech Leadership by Silicon ValleyRace/Ethnicity Tech Leadership by and limits the many benefits and contributions of a Genderdiverse and Race/Ethnicity 1% Gender<.5% and Race/Ethnicity <1% <1% workforce (Dahlberg & Intel, 2016; Hunt et al., 2018; Lorenzo <1% <1% et al., 2018). These disparities also limit access to 5%networks 17% 12%5% of social and financial capital and critical experiences ■ Male (All Races) 15% ■■ Male White (All Races) ■ White15% Female ■ Male necessary to launch or invest in venture-backed technology ■■ WhiteAsian Female ■ Asian Female ■ Female companies. ■■ AsianBlack Female 83% ■ Latinx/Hispanic Female 87% ■■ Latinx/HispanicLatinx Female ■ Black/African 80% ■ Black/African American Female80% American Female

Source: Teare (2017) Source: CB Insights (2010) Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC category of “Executives, Senior O—cials & Managers”; The figures for category of “Executives, Senior O—cials & Managers”; The figures for Native American/Alaskan Native/Native Hawaiian females in leadership Native American/Alaskan Native/Native Hawaiian females in leadership positions are too small to report. positions are too small to report. FIGURE 14 FIGURE 15 Technology Investors by Race/Ethnicity Technology Investors by Gender FIGURE 12 FIGURE 13 FIGURE 12 FIGURE 13 Technology Startup Founders by Technology1% Startup Founders by Gender Technology Startup Founders by1% 0% ■ WhiteTechnology Male Startup Founders by Gender Race/Ethnicity 2% 1% Race/Ethnicity ■ Asian Male <.5% 1% 11% <.5% 6% ■ Black Male Kapor■ Latinx Center/ASU Male CGEST | 5 17% ■ White Female 12% 17% 12% 20% ■ Asian Female ■ Male ■ White ■ Black Female 89% ■ Female ■ White ■ Male ■ Asian 58% ■ Latinx Female ■ Male ■ Asian ■ Female ■ Black 11% 83% ■ Female ■ Black 83% 87% ■ Latinx 87% ■ Latinx

Source: Kerby (2018) Source: Kerby (2016)

Source: Teare (2017) Source: CB Insights (2010) Source: Teare (2017) Source: CB Insights (2010)

FIGURE 14 FIGURE 15 FIGURE 14 FIGURE 15 Technology Investors by Race/Ethnicity Technology Investors by Gender Technology Investors by Race/Ethnicity Technology Investors by Gender 1% 1% 0% ■ White Male1% 2% 1% 0% ■ White Male ■ Asian2% Male ■ Asian Male ■ Black Male 11% 6% ■ Black Male 11% ■ Latinx6% Male ■ Latinx Male ■ White Female ■ White Female 20% ■ Asian Female ■ Male 20% ■ Asian Female ■ Male ■ Black Female ■ Female ■ Black Female89% ■ Female 58% ■ Latinx Female 89% 58% ■ Latinx Female 11% 11%

Source: Kerby (2018) Source: Kerby (2016) Source: Kerby (2018) Source: Kerby (2016) FIGURE x FIGURE x US Female Population by Race, Ethnicity US Female Population Growth by Race, Ethnicity (2015-2060) .5% 1% 120 ■ White 108% 120% 6% ■ Latinx 100 ■ Black 80 14% ■ Native American/ 62% Alaskan Native 60 ■ Asian 41% 40% 17% ■ Native Hawaiian/ 40 61% Pacific Islander 20 -9% 0 Latinx Black Asian Native Amer/ Native -20 white Alaskan Hawaiian/ Native Pacific Islander

Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014)

FIGURE x FIGURE x US Female Population by Race, Ethnicity US Female PopulationFIGURE Growth x by Race, Ethnicity FIGURE x US Female Population(2015-2060)FIGURE by 9 Race, Ethnicity US Female PopulationFIGURE Growth 10 by Race, Ethnicity .5% 120Disparities between CS Degree Earners and the Silicon Valley Tech(2015-2060) Workforce Participation by 1% .5%108% 120% ■ White Computing1% Workforce by Race and Gender 120 Gender108% and Race/Ethnicity 100 ■ White 120% 6% ■ Latinx <1% ■ Black 13%6% ■ Latinx 100 <1% <1% 80 ■ Black 2% 14% ■ Native American/ 32% 80 ■ Male (All Races) 14%17% ■ Native American/62% Alaskan Native 60 ■ White Female 62% ■ Asian 11% 7% Alaskan Native■ White 60 12% ■ Asian Female 41% 5% 40% 17% ■ Native Hawaiian/ 40 ■ Asian ■ Latinx 41% ■ Latinx/Hispanic40% Female 61% Pacific Islander 17% ■ Native Hawaiian/■ Black 40 ■ Black/African 61% 14% 20 55% Pacific Islander■ Asian American Female -9% 56% 20 0 -9% ■ Native American/ Latinx Black Asian Native Amer/ Native 70% Alaskan Native Female 0 Latinx Black Asian Native Amer/ Native -20 white Alaskan Hawaiian/ ■ Native Hawaiian/ Native Pacific Islander white Alaskan Hawaiian/ -20 Pacific Islander Female Bachelor’s Degrees Employed Computer and Native Pacific Islander Source: U.S. Bureau of Census (2014) Earned in CS (Female)Source: U.S. Information Bureau of Scientists Census (Female) (2014) Source: U.S. Bureau of Census (2014) Source: U.S. Bureau of Census (2014) Source: NSF (2015a). Table 9-7; NSF (2016a). Table 5-4. Native American/ Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC Alaskan Native and Native Hawaiian populations were too small to report. category of “professionals.” The percentages for Other/Multiple Races not reported.

FIGURE 9 FIGURE 10 FIGURE 11 Disparities between CS Degree Earners and the Silicon Valley TechFIGURE Workforce 9 ParticipationSilicon by Valley Tech Leadership by FIGURE 10 Computing Workforce by Race and Gender DisparitiesGender between and CS Race/Ethnicity Degree Earners andGender the and Race/EthnicitySilicon Valley Tech Workforce Participation by Computing Workforce by Race and Gender Gender and Race/Ethnicity <1% <1% <1% 13% <1% <1% <1% 13%2% <1% <1% 32% ■ Male (All Races) 2% 17% 32% 5% ■ Male (All Races) 17% ■ White Female 12% ■ White Female 11% 7% ■ White ■ Asian15% Female ■ Male (All Races) 5% 11% 7% ■ White 12% ■ Asian Female ■ Latinx 5%■ Latinx/Hispanic Female ■ White Female ■ Black ■ Black/African■ Latinx ■ Asian Female ■ Latinx/Hispanic Female 14% ■ Black/African 55% ■ Asian American Female■ Black ■ Latinx/Hispanic14% Female 56% 55% ■ Native American/■ Asian ■ Black/African American Female 56% 80% 70% Alaskan Native Female American Female ■ Native American/ ■ Native Hawaiian/ 70% Alaskan Native Female ■ Native Hawaiian/ Bachelor’s Degrees Employed Computer and Pacific Islander Female Pacific Islander Female Earned in CS (Female) Information Scientists (Female) Bachelor’s Degrees Employed Computer and Earned in CS (Female) Information Scientists (Female) Source: NSF (2015a). Table 9-7; NSF (2016a). Table 5-4. Native American/ Source: EEOC, 2016; Hongsdusit & RangarajanSource: EEOC,(2018); 2016;Includes Hongsdusit EEOC & Rangarajan (2018); Includes EEOC Alaskan Native and Native Hawaiian populations were too small to report. Source: NSF (2015a). Tablecategory 9-7; NSFof “professionals.” (2016a).category Table of5-4. “Executives, Native American/ Senior O—cialsSource: & Managers”; EEOC, The2016; figures Hongsdusit for & Rangarajan (2018); Includes EEOC The percentages for Other/Multiple Races not reported. Alaskan Native and Native Hawaiian populationsNative American/Alaskan were too small to Native/Nativereport. Hawaiian females in leadershipcategory of “professionals.” The percentages for Other/Multiple Races not reported.positions are too small to report. Tech Entrepreneurship and Venture Capital FIGURE 11 The most recent landscape data on tech entrepreneurs FIGURE 12 FIGURE 13 Silicon Valley Tech Leadership by FIGURE 11 Technology Startup Founders by Technology Startup Founders by Gender indicates that 83% of startup founders are male, and Genderjust and Race/Ethnicity Silicon Valley Tech Leadership by Race/Ethnicity 1% Gender and Race/Ethnicity 17% are female (Figure 13; CB Insights, 2010). Eighty-seven<1% <1% <.5% <1% <1% percent of technology startup founders are White, 12% are 5% Asian, and just 1% are Black (with fewer than 1% Latinx or 5% 17% ■ Male (All 12%Races) 15% ■ Male (All Races) Native American/Alaskan Native women; Figure 12; Teare, ■ White Female ■ White15% ■ White Female ■ Asian Female ■ Asian ■ Male 2017). While there is currently very limited intersectional ■ Asian Female ■ Latinx/Hispanic Female ■ Black ■ Female data on women of color in entrepreneurship, estimates ■ Latinx/Hispanic Female83% ■ Black/African 87% ■ Latinx 80% ■ Black/African from one study indicate that among the over 6,700 female American Female 80% American Female tech startup founders, Black women account for less than 4% of female-led tech startups (Project Diane, 2018).

Large discrepancies are also seen in theSource: amount EEOC, of 2016; money Hongsdusit & Rangarajan (2018); Includes EEOCSource: Teare (2017) Source: CB Insights (2010) invested in companies, by founder race,category ethnicity, of “Executives, and Senior O—cials & Managers”; The figures for Source: EEOC, 2016; Hongsdusit & Rangarajan (2018); Includes EEOC Native American/Alaskan Native/Native Hawaiian females in leadership category of “Executives, Senior O—cials & Managers”; The figures for gender, with women-led companies raising one-tenthpositions of are too small to report. Native American/Alaskan Native/Native Hawaiian females in leadership the amount of capital as male-led companies ($10B versus positions are too small to report. FIGURE 14 FIGURE 15 $90B in 2016) and all-Asian founding teams raising more FIGURE 12 Technology InvestorsFIGURE by 13 Race/Ethnicity Technology Investors by Gender Technology Startup Founders by Technology StartupFIGURE Founders 12 by Gender FIGURE 13 than 3 times the amount that all-Black founding teams Technology Startup Founders by Technology Startup Founders by Gender Race/Ethnicity 1% 1% 0% raise ($4M versus $1.3M;1% Teare, 2017). An analysis of Black 2% Race/Ethnicity ■ White Male <.5% 1% <.5% ■ Asian Male female founders estimated that the average raised by 11% 6% ■ Black Male Black female founders was $42,000, over $1M less than the 17% ■ Latinx Male 12% 17% amount raised in the average seed round (Project Diane, 12% ■ White Female ■ White 20% ■ Asian Female ■ Male ■ WhiteMale 2018). Just 11% of all technology investors■ areAsian female, and ■ Black Female 89% ■ Female ■ AsianFemale ■ Male fewer than 25% are Asian, Black or Latinx■ professionalsBlack 58%83% ■ Latinx Female ■ Female 87% ■ Latinx 11% ■ Black 83% (Figure 14). A recent study of roughly 1,500 investors found 87% ■ Latinx that just 1% of venture capitalists are Black women (and 0% are Latinx women), while White women comprise 11% and Source: Kerby (2018) Source: Kerby (2016) Asian women comprise 6% (FigureSource: Teare 14; Kerby,(2017) 2018). There is Source: CB Insights (2010) currently no available intersectional data on the percentage Source: Teare (2017) Source: CB Insights (2010) of Asian, Latinx or Native American women who launch technology startups, how much funding they secure, and to what extent all women of color FIGUREinvest in 14 technology startups. FIGURE 15 Technology Investors by Race/Ethnicity TechnologyFIGURE Investors 14 by Gender FIGURE 15 Without the participation of women of color in the creation Technology Investors by Race/Ethnicity Technology Investors by Gender 1% 1% 0% of new technology2% enterprises, products and■ solutions, White Male 1% 1% 0% ■ Asian Male 2% ■ White Male women of color will be excluded from opportunities to 11% ■ Asian Male 6% ■ Black Male 11% develop and invest in products intended to ■solve Latinx pressing Male 6% ■ Black Male ■ Latinx Male challenges facing diverse communities, develop■ White wealth Female 20% ■ Asian Female ■ Male■ White Female 20% ■ Asian Female in salary, equity, and investment returns, and■ decreaseBlack Female 89% ■ Female ■ Male widening inequality. 58% ■ Latinx Female ■ Black Female 89% ■ Female 11% 58% ■ Latinx Female 11%

Source: Kerby (2018) Source: Kerby (2016) Source: Kerby (2018) Source: Kerby (2016)

Kapor Center/ASU CGEST | 6 THE WOMEN OF COLOR IN COMPUTING Participation and Retention of Women of Color in the RESEARCHER/PRACTITIONER Technology Workforce: Including intersectional data COLLABORATIVE trends on workforce participation across all levels and company types; strategies to recruit and hire women of color and reduce bias in the hiring process; strategies to retain and promote women of color (e.g., Employee Without identifying and understanding Resource Groups, pay/leave/incentives, sponsorship/ the specific challenges facing women of mentorship, referral and hiring bonuses, and diverse teams and leaders). color in the computing and technology Participation of Women of Color Across the pipeline, any interventions made will be Entrepreneurship and Venture Capital Ecosystem: exclusionary, insufficient, Including intersectional baseline data trends on startup and venture capital participation and capital received/ and ineffective. invested; social, financial, and psychological barriers specific to women of color in launching startups and entering investing; strategies to increase participation of Transformation across all levels of the tech ecosystem is women of color in entrepreneurship (e.g., WOC-specific needed in order to identify and address obstacles specific funds, incubators/accelerators, pitch competitions, small to women of color and increase participation, persistence, business loans/grants, mentorship). and success of underrepresented women of color in By developing and disseminating research on trends, computing. A critical first step towards this transformation barriers, and solutions to increasing participation and will be to build upon existing theory, research, and data persistence in computing among women of color, to develop a robust body of literature on women of this collaborative can both increase awareness of color in computing. The Women of Color in Computing the challenges facing women of color and mobilize Researcher/Practitioner Collaborative is a new initiative stakeholders with strategies and solutions to effectively which aims to develop foundational landscape data on increase their participation across the computing pipeline. the participation of women of color across the computing pipeline, identify obstacles and barriers unique to women of color in computing, and explore the efficacy of interventions to improve the outcomes for women of color in computing. This project will fund and disseminate research on the following priority topic areas: Entry, Persistence, and Degree Completion in Computing in Higher Education: Including intersectional and longitudinal data trends on entrance into computing majors and degree completion; strategies to leverage or increase social support networks, including at Minority- Serving Institutions; the efficacy of non-traditional pathways into 4-year colleges and the computing workforce; effective and innovative CS curriculum and pedagogy to increase engagement and persistence.

Kapor Center/ASU CGEST | 7 The Women of Color in Computing Researcher/Practitioner Collaborative is a new initiative which aims to develop foundational landscape data on the participation and pathways to success of women of color across the computing pipeline, identify obstacles and barriers unique to women of color in computing, and explore the efficacy of various interventions to improve the outcomes for women of color in computing and technology.

To stay informed about research and interventions for women and girls of color in computing, please join the Women of Color in Computing Researcher/Practitioner Collaborative by visiting: wocincomputing.org.

ABOUT US Kapor Center: The Kapor Center aims to enhance diversity and inclusion in the technology and entrepreneurship ecosystem through increasing access to tech and STEM education programs, conducting research on access and opportunity in computing, investing in community organizations and gap-closing social ventures, and increasing access to capital among diverse entrepreneurs.

ASU Center for Gender Equity in Science and Technology: As a unique research unit, a diverse and interdisciplinary community of scholars, students, policy makers and practitioners unite to establish best practices for culturally responsive programs for girls/ women of color. CGEST hosts three branches: Advocacy, Capacity Building, and Knowledge.

Pivotal Ventures is an investment and incubation company created by Melinda Gates. We partner with organizations and individuals who share our urgency for social progress in the United States. Together, we grow understanding, expand participation, encourage cooperation and fuel new approaches that substantially improve people’s lives. Pivotal Ventures believes women as drivers of tech innovation will pave the way to a brighter future. We therefore invest in creating new pathways into tech for women and girls; fostering supportive tech environments for retention, progression of women leaders; and enabling women entrepreneurs as innovators.

Suggested citation: McAlear, F., Scott, A., Scott, K. & Weiss, S. (2018). Data Brief: Women of Color in Computing.

Kapor Center/ASU CGEST | 8 APPENDIX Underrepresented Women of Color: Underrepresented women of color are distinguished in this project as women from racial/ TERMINOLOGY & DEFINITIONS ethnic groups who are traditionally underrepresented in Gender: Understanding that gender is a socially constructed computing education, degree completion, the tech workforce, identity and that there is great diversity within gender identity and and entrepreneurship/VC, in comparison to their representation expression, this project focuses on individuals who at least partially in the U.S. population and representation among potential pools identify with an identity of “female,” “woman,” “girl,” “feminine,” of candidates (e.g., the total labor force, CS degree-earners). “womxn” or similar descriptors and identities. Additionally, Underrepresentation varies across computing contexts and it is we intentionally include transgender women, and non-binary necessary to specify the domain in which underrepresentation individuals within the gender group of focus in this project, as is present when categorizing underrepresented women of we are most interested in the non-majority gender group within color. Specifically, Black, Latinx, American Indian/Alaskan Native computing. Within a broader focus on intersectionality, we will aim women are underrepresented across the pipeline, in participation to identify trends and experiences at the intersection of gender in computing education, completion of computing degrees, identity and racial identity within computing and acknowledge the participation in entrepreneurship and venture capital, and Asian diversity of gender identities within the scope of work focused on women as a whole are overrepresented in K-12 computing women and girls of color. education and within the tech workforce, but underrepresented in Race/Ethnicity: This project uses the Office of Management leadership positions within the technology workforce. and Budget (OMB) definitions of race/ethnicity categories for Computing and Computer Science: Computing is a broad term Black/African American, American Indian/Alaskan Native, Asian, defined by the Association for Computing Machinery as “any goal- Native Hawaiian/Pacific Islander, White, and Latino/Hispanic. This oriented activity requiring, benefiting from, or creating computers... project includes “people of color” or individuals who identify as a including five sub-disciplines of computer science, computer racial/ethnic group other than white. Most data sources contain engineering, information systems, information technology and only broad race/ethnicity categories, which limits the ability to software engineering.” Computer science is defined by the disaggregate by important subgroups which demonstrate varying Association for Computing Machinery as the “study of computers levels of educational attainment and participation in computing and algorithmic processes, including their principles, their (e.g., South Asian, East Asian, Southeast Asian). hardware and software designs, their implementation, and their Women of Color: The term “women of color” requires careful impact on society.” Computing and computer science are used definition and contextualization for proper use, both in terms interchangeably throughout this project. of who is included in the definition and why the term is needed Technology Ecosystem: The technology or computing ecosystem to distinguish women of color as a particular subset of the is used to describe the environment which prepares students population. The categories of “women” and “people of color” for technology careers, produces and utilizes technology and are numerically marginalized gender and racial/ethnic groups in technology-driven products, and creates and invests in tech computing (and oftentimes face social disparities and inequity companies. in broader American society). Intersectionality theory describes women of color as experiencing both intersecting identities Computing/Technology Pipeline: The term pipeline is used to which exist within structures of power and privilege to compound provide a structure for understanding barriers at various stages marginalization by both race/ethnicity and gender. To specifically and points in time as participants enter, proceed through, and examine this category, a broad definition is used to define “women exit, and is a popular metaphor for researchers studying inclusion of color” as individuals at least partially identifying as women and exclusion in STEM education and careers. The stages are not AND identifying as a member of a racial/ethnic group other than intended to solely be linear, and our use of the pipeline metaphor white (specifically, Black/African-American, Latinx/Hispanic, Asian, is not intended to suggest that all students and professionals Native American/Alaskan Native, and Native Hawaiian/Pacific follow a linear trajectory from preschool through entrepreneurship. Islander). This category is extremely broad, and experiences of While some follow a traditional pathway, there are many points of women of color can be vastly different based on context and entry to the tech workforce and entrepreneurship, and also many additional intersectional identities including, socioeconomic status, points of exit and re-entry. physical ability, sexual orientation, age, religion, immigration status, schooling background, parenting/caregiving status, linguistic background, nation of origin, etc. Where possible, this project will examine and identify experiences of women of color based on intersections with other identities. This project focuses on women of color within the social, cultural, and historical context of the United States, while understanding significant differences exist in definitions, histories, and experiences of women of color across the globe.

Kapor Center/ASU CGEST | 9 LIMITATIONS Several limitations are important to note in this data brief, specifically related to the lack of rigorous and comprehensive data across all racial/ethnic and gender groups. In many cases the available intersectional data does not contain all racial/ ethnic groups included in the definition of women of color (e.g., Native American/Alaskan Native in the tech workforce and entrepreneurship; Latinx and Asian women in entrepreneurship and venture capital). The lack of data on non-binary and transgender individuals does not allow us to report on these gender subgroups. While this project utilizes intersectionality as a framework the lack of available data does not allow for intersectional analyses of women of color by other demographic variables, including SES, sexual orientation, physical ability, etc. This focus on existing data is not intended to overlook the importance of examining other intersectional identities and experiences of women of color in computing, and instead provides a preliminary baseline of the participation rates of women of color across the computing pipeline.

REFERENCES American Express (AMEX) (2016). State of Women Owned Businesses Report. Retrieved from: http://about.americanexpress. com/news/docs/2017-State-of-Women-Owned-Businesses-Report.pdf. Bureau of Labor Statistics (2015a). “Table 3.4 Civilian Labor Force by Age, Sex, Race, and Ethnicity, 1994, 2004, 2014, and Projected 2024,” Employment Projections (2015). Bureau of Labor Statistics (2015b). Occupational Outlook Handbook: Computer and Information Technology Occupations. Retrieved from: https://www.bls.gov/ooh/computer-and-information-technology/home.htm Bureau of Labor Statistics (2016). High-Tech Industry: What it is and why it matters to our economic future. Retrieved from: https://www.bls.gov/opub/btn/volume-5/pdf/the-high-tech-industry-what-is-it-and-why-it-Matters-to-our-economic-future.pdf Bureau of Labor Statistics (2017). Occupational Employment Projections: 2016-26. Retrieved from: https://www.bls.gov/news. release/ecopro.nr0.htm CB Insights (2010). Venture Capital Human Capital Report. CB Insights (2017). Tech IPO Report. Retrieved from: https://www.cbinsights.com/research-tech-ipo-2017. Catalyst (2017). Quick Take: Women of Color in the United States. Retrieved from: http://www.catalyst.org/knowledge/women- color-united-states-0 College Board (2017). AP Program Participation and Performance Data, 2017. Hill Collins, P. & Bilge, S. (2016). Intersectionality. Cambridge, UK; Malden, MA; Polity Press. Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics. University of Chicago Legal Forum 1989(1). Retrieved from: https://philpapers.org/ archive/CREDTI.pdf Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review, 43(6), 1241–1299. https://doi.org/10.2307/1229039 Dalberg & Intel (2016). Decoding Diversity: The Financial and Economic Returns in Tech. Dutta, S., Geiger, T., & Lanvin, B. (2015). The global information technology report 2015. ICTs for inclusive growth. Geneva: World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_Global_IT_Report_2015.pdf Equal Employment Opportunity Commission (EEOC) (2016). Diversity in High Tech. Ericson, B. (2017). AP Data for the United States: 1998-2017. Retrieved from: http://home.cc.gatech.edu/ice-gt/599. Gee, B. & Peck, D. (2017). The Illusion of Asian Success: Scant Progress for Minorities in Cracking the Glass Ceiling from 2007–2015. Hongsdusit, G. & Rangarajan, S. (2018, June 25). Here’s the clearest picture of Silicon Valley’s diversity yet: It’s bad. But some companies are doing less bad. Retrieved from: https://www.revealnews.org/article/heres-the-clearest-picture-of-silicon-valleys- diversity-yet/ Hunt, V., Yee, L., Prince, S. & Dixon-Fyle, S. (2018). Delivering through diversity. Retrieved from: https://www.mckinsey.com/ business-functions/organization/our-insights/delivering-through-diversity?cid=other-eml-alt-mip-mck-oth-1801&hlkid=7136f77cb5 de4b4e9f1de817d75a042d&hctky=2607504&hdpid=fdef79b6-4f45-4162-ac58-14db7193e4c6 Kerby, R. (2018, July 30) Where Did You Go to School? [Blog post]. Retrieved from: https://blog.usejournal.com/where-did-you- go-to-school-bde54d846188 Lorenzo, R., Voigt, N., Tsusaka, M., Krentz, M., & Abuzahr, K. (2018, January 23). How Diverse Leadership Teams Boost

Kapor Center/ASU CGEST | 10 Innovation. Retrieved from: https://www.bcg.com/publications/2018/how-diverse-leadership-teams-boost-innovation.aspx MacBride, E. (2015, November 11). Latino-Owned Business Survey Shows $1.4 Trillion Left on the Table. Retrieved from: https:// www.gsb.stanford.edu/insights/latino-owned-business-survey-shows-12-trillion-left-table Malcom, S., Hall, P., & Brown, J. (1975). The double bind: The price of being a minority woman in science. Washington, DC: American Association for the Advancement of Science. Martin, A., McAlear, F. & Scott, A. (2015). Path Not Found: Disparities in Access to Computer Science Courses in California High Schools. Accessed at: http://www.lpfi.org/pnf. Mattern, K. D., Shaw, E.J. & Ewing, M. (2011). Is AP Exam Participation and Performance Related to Choice of College Major? New York: The College Board. Retrieved from: http://research.collegeboard.org/sites/default/files/info2go/2012/8/infotogo-2011- 6-ap- participationperformance-major- choice.pdf National Center for Education Statistics (2013). Enrollment and percentage distribution of enrollment in public elementary and secondary schools by race/ethnicity and region: Selected years, fall 1995 through fall 2023 (Table 203.50). National Center for Education Statistics. (2017). Racial/Ethnic Enrollment in Public Schools. Accessed at: https://nces.ed.gov/ programs/coe/indicator_cge.asp National Science Foundation (2016a). National Center for Science and Engineering Statistics, special tabulations of U.S. Department of Education, National Center for Education Statistics, Integrated Postsecondary Education Data System, Completions Survey. Bachelor’s degrees awarded to women by field, citizenship, ethnicity, and race. Table: 5-4. National Science Foundation (2016b). National Center for Science and Engineering Statistics, Master’s degrees awarded by field, citizenship, race and ethnicity; Table 6-3 and 6-4. National Science Foundation (2016c). National Center for Science and Engineering Statistics, S&E Doctorates Awarded to U.S. Citizens and Permanent Residents, by field, sex, ethnicity, and race. Table 7-7. Ong, M., Wright, C., Espinosa, L., Orfield, G (2011). Inside the Double Bind: A Synthesis of Empirical Research on Undergraduate and Graduate Women of Color in Science, Technology, Engineering, and Mathematics. Harvard Educational Review, 81(2), 172- 209. Project Diane (2018). The State of Black Women Founders, 2018. Retrieved from: http://www.projectdiane.digitalundivided.com/ Scott, A., Klein, F.K., McAlear, F., Martin, A. & Koshy, S. (2018). The Leaky Tech Pipeline: A Comprehensive Framework for Understanding and Addressing the Lack of Diversity Across the Technology Ecosystem. Retrieved from: http://www. leakytechpipeline.com/wp-content/themes/kapor/pdf/KC18001_report_v6.pdf Teare, G. (2017) In 2017, Only 17% of Startups Have A Female Founder. TechCrunch (April 19, 2017). Retrieved from: https:// techcrunch.com/2017/04/19/in-2017-only-17-of-startups-have-a-female-founder/ U.S. Bureau of the Census (2014). National Population Projections Tables. Table 10: Projections of the Population by Sex, Hispanic Origin, and Race for the United States: 2015 to 2060. Retrieved from: https://www.census.gov/data/tables/2014/demo/ popproj/2014-summary-tables.html U.S. Department of Education. (2014). Office for Civil Rights 2013-2014. Public school female students overall and by race/ ethnicity, students with disabilities served under IDEA and those served solely under Section 504, and students who are English language learners, by state: School Year 2013-14. Retrieved from: https://ocrdata.ed.gov/StateNationalEstimations/ Estimations_2013_14 Williams, J. C., Phillips, K. W., & Hall, E. V. (2014). Double Jeopardy? Gender Bias Against Women of Color in Science. UC Hastings College of the Law. Retrieved from www.worklifelaw.org

PHOTO CREDITS Pages 1 & 8: Image courtesy of #WOCinTech Chat - Creative Commons Attribution - License. Page 7: Photo by SMASH Program, www.smash.org.

Kapor Center/ASU CGEST | 11