DIVERSITY IN HIGH TECH

The high tech sector has become a major source of economic growth fueling the U.S. economy. As an innovation leader, the high tech sector has impacted how we communicate and access information, distribute products and services, and address critical societal problems. Because this sector is the source of an increasing number of jobs, it is particularly important that the U.S. Equal Employment Opportunity Commission (EEOC) and its stakeholders understand the emerging trends in this industry. Ensuring a sufficient supply of workers with the appropriate skills and credentials and addressing the lack of diversity among high tech workers have become central public policy concerns. This report seeks to shed more light on employment patterns in the high tech industry by providing an overview of literature as a backdrop to understanding high tech employment, and analyzing corresponding summary data from the Employer Information EEO-1 Report (EEO-1)[1] collected in 2014.

Employment in computer science and engineering is growing at twice the rate of the national average.[2] These jobs tend to provide higher pay and better benefits, and they have been more resilient to economic downturns than other private sector industries over the past decade. In addition, jobs in the high tech industry have a strong potential for growth. These jobs are important to companies in all industries that require workers with technology skills. Employment trends in the high tech sector are therefore important to the national economic and employment outlook.

The industries and occupations associated with “high tech” are rapidly evolving. There is no single high tech industry-rather, new technology has transformed industries like telecommunications and manufacturing and the functions of numerous occupations. Sections I and II of this report define the high tech industry, or the “high tech sector,” as industries that employ a high concentration of employees in science, technology, engineering and mathematics (STEM) occupations and the production of goods and services advancing the use of electronic and computer-based production methods. This sector requires a substantial professional labor force and employs about a quarter of U.S. professionals and about 5-6 percent of the total labor force. Section III of this report examines the top 75 high tech firms in the Silicon Valley area based on a ranking by the San Jose Mercury News that looked at revenue, profitability and other criteria to identify leading “Silicon Valley tech firms.”

This report aims to add to the public policy discussion by exploring employment trends in the high tech sector in three ways: Section I provides a brief overview of some of the literature addressing high tech employment; Section II analyzes EEO-1 data from the high tech sector both nationwide and in the geographic area generally referred to as Silicon Valley; and Section III reviews employment statistics derived from a group of leading Silicon Valley firms. Although growth in the high-tech sector has increasingly occurred in a wide range of geographic areas, this analysis provides a national picture along with a more focused examination on the well-established tech industry in Silicon Valley. The report also identifies geographic areas with high concentrations of high tech jobs that may benefit from future study. Additionally, important areas for further study include employment for older workers and individuals with disabilities.

Section I briefly reviews the literature addressing high tech employment, which has tended to focus on two issues: 1) the supply of labor with appropriate skills and 2) the reasons behind the underrepresentation of women and minority workers in the relevant labor force. One body of literature emphasizes the challenges for the U.S. education system to produce appropriately skilled workers and the factors that influence the prevalence of women and minorities in particular career paths and occupations. Another body of literature focuses on the attrition of women and minorities as students and as employees. This literature cites research and personal experience indicating that bias impedes the full and equal participation of women and minorities in STEM fields.

Section II examines employment trends in the high tech sector through an analysis of the available 2014 EEO-1 data. By using nationwide 2014 EEO-1 data to examine the participation of women and minorities in overall private sector employment compared to that of the high tech sector, we identified several concerning trends:

• Compared to overall private industry, the high tech sector employed a larger share of whites (63.5 percent to 68.5 percent), Asian Americans (5.8 percent to 14 percent) and men (52 percent to 64 percent), and a smaller share of African Americans (14.4 percent to 7.4 percent), Hispanics (13.9 percent to 8 percent), and women (48 percent to 36 percent).
• In the tech sector nationwide, whites are represented at a higher rate in the Executives category (83.3 percent), which typically encompasses the highest level jobs in the organization. This is roughly over 15 percentage points higher than their representation in the Professionals category (68 percent), which includes jobs such as computer programming. However, other groups are represented at significantly lower rates in the Executives category than in the Professionals category; African Americans (2 percent to 5.3 percent), Hispanics (3.1 percent to 5.3 percent), and Asian Americans (10.6 percent to 19.5 percent).
• Of those in the Executives category in high tech, about 80 percent are men and 20 percent are women. Within the overall private sector, 71 percent of Executive positions are men and about 29 percent are women.

Additionally, we examined 2014 EEO-1 data from a geographic area associated with Silicon Valley. This includes the San Francisco-Oakland-Fremont core-based statistical area (CBSA) and Santa Clara County. The labor force in these areas has notably different demographics from that of the U.S. as a whole. By using EEO-1 data specific to the Silicon Valley area, we can see how its tech workforce differs demographically from the tech workforce nationwide.

Finally, Section III, as the third avenue to examine the nature of employment in high tech industries, uses 2014 EEO-1 data to examine the labor force participation rate at select leading “Silicon Valley tech firms,” identified by a San Jose Mercury News analysis. Below are some observations:

• Among Executives, 57 percent of employees were white, 36 percent were Asian American, 1.6 percent were Hispanic and less than 1 percent were African American.
• These firms had a notable contrast in the demographics of professional as compared to management jobs (executives and managers combined). Asian Americans make up 50 percent of professional jobs among these firms while comprising 36 percent of management positions. This is roughly a negative gap of 14 percentage points. White employees make up 41 percent of professional jobs and 57 percent of management jobs. This is roughly a positive difference of about 16 percentage points.
• In Silicon Valley, employment of women and men in non-technology firms is at about parity with 49 percent women and 51 percent men. This compares to the 30 percent participation rate for women at 75 select leading Silicon Valley tech firms.
• When the Executives and Managers job categories are combined, African American workers are less than 1 percent of this group at these select leading Silicon Valley firms, and Hispanic workers are 1.6 percent.

DIVERSITY IN HIGH TECH

This report examines demographic diversity in the “high tech” sector. This is a timely and relevant topic for the Commission due to the growth of this sector, the quality of the jobs it provides, and the influence that this work has on other industries and on society in general.

This report is divided into three major sections. The first section provides a brief, introductory literature review to introduce the relevant issues and provide a backdrop for the data points that follow. The second section examines employment trends in the high tech sector using 2014 EEO-1 data[3] by comparing tech and overall private industry nationwide and within the Silicon Valley geographic area. The final section uses 2014 EEO-1 data to focus on the leading “Silicon Valley tech firms” as recently identified by a popular news source local to the area.

I. LITERATURE REVIEW

HIGH TECH: EVOLUTION OF THE INDUSTRY

Development of a high tech workforce has long been a source of concern; it is a major growth sector that requires workers with specific skills often perceived to be in relatively short supply among U.S. workers. The available work in this industry is considered to be highly sought after, as the jobs tend to pay well and offer attractive benefits. At the same time, lack of diversity in employment has led to under-utilization of available talent and under-recruitment of potentially valuable employees. When examining the pipeline for high tech jobs, a mixed story develops. The literature indicates some increase in employment of women and non-white workers in these occupations, accompanied by a steady exodus of these same workers, particularly women, from tech jobs.

The industries and occupations associated with “high tech” are rapidly evolving. There is no single high tech industry; rather, new technology has transformed industries like telecommunications and manufacturing and the functions of numerous occupations, from clerical work to scientific research. Occupations unknown a decade earlier have become common (Baldwin and Gellatly, 1998; DeSilver, 2014). Classification schemes that rely on a single-measure of technological expertise, as many do, may incorrectly rank industries and/or classify sectors.

Companies utilizing advanced technological processes, requiring a labor force with cutting-edge technical competencies to develop innovative products, are found in many industries, not only high tech. Industries perceived as low-tech are not devoid of high tech firms, nor are high tech industries comprised exclusively of high tech firms. Consequently, broad generalizations at the industry-level are imprecise. On average, industries that may be classified as low-tech by some indices contain half as many high tech firms as can be found in high tech industries. Consequently, it should not be claimed that high-knowledge, high tech firms are confined exclusively to these more visible high tech industries (Baldwin and Gellatly (1998). Research on this project revealed that “typical,” well-known high tech companies were in such industries as auto manufacturing (NAICS 3361), retail stores (NAICS 4539), information services (NAICS 5191), consumer goods rental (NAICS 5322) and office administrative services (NAICS 5611).

Baldwin and Gellatly (1998) classify high tech firms as those producing innovative technology; they introduce new products and processes; they place great emphasis on technology; they appreciate the importance of a skilled workforce, and they train their workers.[4] This competency-based approach represented a considerable advance over previous efforts: it formally recognized the multidimensional nature of technological expertise.

DeSilver (2014) notes that based on data collected from November 2009 to May 2012, about 3.9 million workers – roughly 3 percent of the nation’s payroll workforce (Occupational Employment Statistics, Bureau of Labor Statistics (BLS)) – work in what we might think of as “core” tech occupations – not people who simply use computing technology in their jobs, but whose jobs involve making that technology work for the rest of us. Occupations involving the installation and repair of telecommunications lines and equipment, as well as computer repairers were excluded.

Figure 1 shows just how different the structure of the technology industry was in 2012 compared to 15 years earlier.

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Figure 1

Some 2012 occupations, such as web developers and information security analysts, simply did not exist in 1997, while others have dramatically grown (programmers and software developers, computer and network support specialists) or shrunk (computer operators). Computers have become ubiquitous in the workplace; their use is no longer confined to a specialist. Use of computers is a general skill expected of most office, technical, and professional employees.

HIGH TECH GEOGRAPHY: DISPERSING

The location of high tech industries has also changed substantially. From its early establishment in large compounds in suburban office parks of Silicon Valley, CA and Route 128 in Boston, the industries dispersed to urban areas across the US and around the world (Florida, 2012). High tech companies, like their products, have become an integral part of the production of goods and services. They have moved from a niche economic product dependent on highly specialized expertise to become a major source of economic vitality.

The remarkable growth and dispersion of high tech products and companies has been accompanied by anxiety over the ability of the US educational system to supply an adequate workforce to support its rapid expansion and development of new products. Appendix Table I-A shows employment growth in selected science, technology, engineering and mathematics (STEM) occupations. It has been noted that there are almost twice as many job postings in STEM fields as there are qualified applicants to fill them. Further, when ranked against other developed countries in the area of problem solving with technology the U.S. came in absolute last. Groups such as the STEM Education Coalition urge that additional resources be allocated to the computer sciences, and higher educational standards for math and science education starting in elementary school to prepare the future workforce. Modern manufacturing requires a computer literate worker capable of dealing with highly specialized machines and tools that require advanced skills (STEM Education Coalition).

However, other sources note that stereotyping and bias, often implicit and unconscious, has led to underutilization of the available workforce. The result is an overwhelming dominance of white men and scant participation of African Americans and other racial minorities, Hispanics, and women in STEM and high tech related occupations. The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology, published data in 2008 showing that while the female talent pipeline in SET[5] was surprisingly robust, women were dropping out of the field large numbers. Other accounts emphasize the importance of stereotypes and implicit bias in limiting the perceived labor pool (see discussion below).

Moughari et al., 2012 noted that men comprise at least 70 percent of graduates in engineering, mathematics, and computer science, while women dominate in the lower paying fields. Others point out that in this is not uniformly the case in all science and math occupations and that, while underrepresented among those educated for the industry, women and minorities are more underrepresented among those actually employed in the industry. It has been shown, for example, that men are twice as likely as women to be hired for a job in mathematics when the only difference between candidates is gender (Ernesto Reubena et al. 2014).

LABOR DIVERSITY: SUPPLY vs. DEMAND

Attributing lack of employment diversity in high tech industries to lack of applicant diversity and self-selection of minorities and women away from STEM fields focuses on only part of the industries’ hiring and retention situation. While there is some truth to the “pipeline” theory and anxiety over the ability of the US educational system to provide a sufficiently large, well trained, and diverse labor pool, there are additional factors at play. For example, about nine percent of graduates from the nation’s top computer science programs are from under-represented minority groups. However, only five percent of the large tech firm employees are from one of these groups.[6] This presents the unlikely scenarios that either major employers in the field are unable to attract four out of nine under-represented minority graduates from top schools or almost half of the minority graduates of top schools do not qualify for the positions for which they were educated.

Citing The Urban Institute[7], “labor market indicators do not demonstrate a supply shortage. The United States’ education system produces a supply of qualified [science and engineering] graduates in much greater numbers than the jobs available.” Estimates indicate that close to 50 percent of STEM graduates in the U.S. are not hired in STEM-related fields (Lindsay & Salzman, 2007).

Sources are largely consistent that the number of people receiving undergraduate degrees in science and engineering has increased markedly over the past decade. According to the U.S. Census Bureau, the percentage of U.S. college graduates with bachelor’s degrees in science and engineering (S&E) was 36.4 percent in 2009 (approximately 20 million people). National Science Foundation[8] estimates are similar: the percentage of bachelor’s degrees in S&E fields has been approximately 30 to 35 percent of all bachelor’s degrees for the past four decades. However, because the U.S. college-age population grew during these years, the total number of science and engineering (S&E) bachelor’s degrees awarded annually more than doubled between 1966 and 2008 (from 184,313 to 494,627).

Women account for relatively small percentages of degree recipients in certain STEM fields: only 18.5 percent of bachelor’s degrees in engineering went to women in 2008. (Williams, 2015) Women accounted for 77.1 percent of the psychology degrees and 58.3 percent of the biological and agricultural sciences degrees in 2008 (Data from the National Science Foundation, National Center for Science and Engineering Statistics[9]).

Gonzalez and Kuenzi, 2012 make the following observations:

Graduate enrollments in science and engineering grew 35 percent over the last decade. Notably, science and engineering enrollments grew more for racial and ethnic groups generally under-represented in science and engineering.

• Hispanic/Latino enrollment increased by 65 percent
• American Indian/Alaska Native enrollment increased by 55 percent
• African American enrollment increased by 50 percent

Since 1966, the percentage of doctorates in S&E fields has ranged between approximately 56 percent and 67 percent of all graduate degrees (where a field of study has been reported). The total number of doctoral degrees in S&E fields has nearly tripled, growing from 11,570 in 1966 to 32,827 in 2008 (Peck, 2015). Graduate enrollments show similar upward trends.

The AFL-CIO reported that, based on Bureau of Labor Statistics data, the median weekly earnings for women (2012) were 11 to 25 percent lower than they were for men in every STEM occupation for which there is available data. But this may be less of a difference than in other professional fields, as in 2013, on average, men employed in professional and related occupations earned 27 percent more than women.[10]

Additionally, black professionals represented 9.3 percent of the professional workforce and Hispanic professionals 8.2 percent.

• In computer and mathematical occupations, 8.3 percent of workers were black or African American, 6.3 were Hispanic or Latino.
• In the life, physical, and social sciences, black professionals were under-represented, making up 5.6 percent of the workforce, and in architecture and engineering occupations, Black professionals were just 5.5 percent of the workforce in 2013.
• Workers of Hispanic origin comprised 7.5 percent of the architecture and engineering field and 7.9 percent of life, physical, and social scientists.[11]

Based on data from the American Community Survey, there is a racial and ethnic pay gap as well: Asian Americans reported the highest average earnings in STEM occupations, while non-Hispanic whites also had above average earnings; black and Hispanic professionals earned below average wages in 2012.[12]

EXITING TECH & RELATED FIELDS

Over time, over half of highly qualified women working in science, engineering and technology companies quit their jobs (Hewlett et al., 2008). In 2013, just 26 percent of computing jobs in the U.S. were held by women, down from 35 percent in 1990, according to a study by the American Association of University Women. Although 80 percent of U.S. women working in STEM fields say they love their work, 32 percent also say they feel stalled and are likely to quit within a year. Research by The Center for Work-Life Policy shows that 41 percent of qualified scientists, engineers and technologists are women at the lower rungs of corporate ladders but more than half quit their jobs.

This loss appears attributable to the following: 1) inhospitable work cultures; 2) isolation; 3) conflict between women’s preferred work rhythms and the “firefighting” work style generally rewarded; 4) long hours and travel schedules conflict with women’s heavy household management workload; and 5) women’s lack of advancement in the professions and corporate ladders. If corporate initiatives to stem the brain drain reduced attrition by just 25 percent, there would be 220,000 additional highly qualified female STEM workers (Hewlett et al., 2008).

Williams (2015) posits that it is bias that pushes women out of STEM jobs, rather than pipeline issues or personal choice accounting for their absence. Based on a survey and in-depth interviews of female scientists[13] (557 survey participants and 60 interviewees), Williams makes the following observations:

• Two-thirds of women report having to prove themselves over and over; their success discounted and their expertise questioned.
  • Three-fourths of Black women reported this phenomenon.
• Thirty-four percent reported pressure to play a traditionally feminine role, including 41 percent of Asian women.
  • Fifty-three percent reported backlash from speaking their minds directly or being outspoken or decisive.
  • Women, particularly Black and Latina women, are seen as angry when they fail to conform to female stereotypes
• Almost two thirds of women with children say their commitment and competence were questioned and opportunities decreased after having children.
• Three fourths of women surveyed said that women in their workplace supported each other; one fifth said they felt as if they were competing with women colleagues for “the woman spot.”
• Bias functions differently depending on race and ethnicity. Isolation is a problem: 42 percent of Black women, 38 percent of Latinas, 37 percent of Asian women and 32 percent of white women agreed that socializing with colleagues negatively affect perceptions of their competence.

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Source: Joan C Williams, Katherine W. Phillips, and Erika V. Hall from HBR.ORG
Figure 2

Source: Center for Talent Innovation from HBR.ORG
Figure 3 [14]

Source: Center for Talent Innovation from HBR.ORG
Figure 4

Exit from the Educational Pipeline

The impact of the “exits” discussed above is perhaps most problematic in the educational pipeline. Women are no longer a minority within higher education-in fact, women’s enrollment in graduate education in the United States has been greater than men’s for the past three decades. As of 2012, there were 13 women enrolled for every 10 men. However, a greater number of male students seem to graduate with science degrees, as compared to their female classmates. In the physical sciences for example, seven B.S. degrees are granted to women for every 10 granted to men; three M.S. degrees are granted to women for every five granted to men; one Ph.D. degree granted to a woman for every two granted to men (Jahren, 2016).

Women who leave science report both isolation and intimidation as barriers to their success. While 23 percent of freshmen reported not having experienced these barriers, only three percent of seniors did, suggesting that this reaction to women in science education is a lesson learned by female students over time (Jahren, 2016). In a survey of 191 female fellowship recipients, 12 percent indicated that they had been sexually harassed as a student or early professional (Jahren, 2016).

SUMMARY AND CONCLUSION

Despite rapid transformation in the field, the overwhelming dominance of white men in the industries and occupations associated with technology has remained. This tendency includes occupations requiring less education than a four-year bachelor’s degree (Fortune, 2014).

Discussion of the lack of gender, racial and ethnic diversity in the high tech industries generally divides into two themes: the “pipeline” problem-STEM occupations attracting white men-and the inhospitable culture in relevant industries and occupations forcing women and minorities to tolerate the environment or leave the field.

The literature summarized below represents both themes. The “pipeline problem” is represented by Moughari et al. (2012) and Gonzalez and Kuenzi (2012). The second theme is documented through numerous published analyses, mostly addressing the challenges faced by women (D’Anastasio, 2015; Hewlett et al., 2014; Peck, 2015; Reubena et al., 2014; Lien, 2015; Hewlett et al., 2008). Evidence of dissatisfaction among minority groups is more likely to be found in the comments sections following “pipeline” articles. Attrition of women mid-career is described as a substantial contributor to the paucity of women in STEM professions and high tech industries (Jahren, 2016).

The reluctance of high tech companies to train new employees could be contributing to the lack of diversity. Williams (2015) provides a technological argument for this trend. The Harvard Business Review (2015) addresses the issue of “guest workers” on H-1B visas; immigration and jobs in high tech (Knowledge 2005). A high tech recruiter points to the mystique of elite colleges and advocates job candidate anonymity to increase diversity in hiring (The Economist, 2013). There are notable alternative efforts to spread high tech skills and introduce women and minorities to the joys of technology based work. A few of the many available examples are Black Girls Code, Hack the Hood, Lesbians Who Tech, Code 2040, #YesWeCode, and the Center for Talent Innovation.

The fast-changing nature of the high tech industry may contribute to the exit of new employees such as women and non-whites. A study by the Wharton School reports research findings and recommendations. They note that Human Resources strategy complements technology strategy; in a fast-paced industry, product life cycles are growing shorter. Firms are facing more opportunities for change, requiring more adjustments to the workforce. When skills need to be adjusted, firms may find that it pays to buy the skills instead of developing them.

The opposite is true for slower moving industries operating in marketplaces with less change -these findings could be significant for human resource management strategies. As the pace of technological change has quickened, and as global competition has shortened product life cycles, firms have had to rethink their technology investment strategies and their human resource management practices in order to remain competitive.

See the Annotated Bibliography for supplemental tables and graphs.

II. EXAMINATION OF NATIONWIDE AND SILICON VALLEY EEO-1 DATA

EMPLOYMENT DIVERSITY IN THE HIGH TECH SECTOR

Explanation of Data

This section focuses on sex, race, and ethnicity diversity in the U.S. high tech sector. The definition of “high tech sector” that we use is the group of industries, based on the four-digit code of North American Industry Classification System (NAICS), listed in Table 1. An industry is considered high tech if “technology-oriented workers” within an industry, as identified by occupations of the staff, account for at least 25 percent of the total jobs within the listed industries.

The data utilized for this section comes from the 2014 EEO-1 reports from US private sector employers.[15] The EEO-1 form collects data on ten major job categories.[16]

Because more than half of the high tech employment was made up of Professionals (44 percent) and Technicians (10.7 percent, see Figure 7), these job groups received separate analysis, along with the management job groups (Executives, Senior Level Officials & Managers, and First/Mid-Level Officials and Managers).

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INDUSTRY PARTICIPATION BY GENDER SEX AND RACE GROUPS
HIGH TECH VS. ALL PRIVATE INDUSTRIES

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Figure 5

Source: Equal Employment Opportunity Commission, Employer Information Numbers may not add up to totals due to rounding.

As shown in Figure 5, compared to all industries in the U.S. private sector, high tech had a relatively larger share of whites (68.5 percent vs. 63.5 percent), and a larger share of Asian Americans (14 vs. 5.8 percent). Other groups were less represented by a significant margin in the tech sector compared to all private industry, including African Americans (7.4 vs. 14.3 percent) and Hispanics (8 vs. 13.9 percent). There was a 12-percentage-point difference between female participation in high tech versus all private industries (35.7 vs. 48.2 percent).

OCCUPATIONAL DISTRIBUTION
HIGH TECH VS. ALL PRIVATE INDUSTRIES

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Figure 6

Source: Equal Employment Opportunity Commission, Employer Information Reports Numbers may not add up to totals due to rounding.

Figure 6 shows that two occupational categories-Professionals and Technicians-are represented at higher rates in the tech sector than in other industries. Together they accounted for approximately 54 percent of the total high tech employment, compared to the 25.4 percent of all industries combined nationally, meriting further examination. Technology workers in high tech industries, defined in this analysis as Professionals and Technicians, include significant numbers of engineers, software developers and programmers, life scientists and mathematicians.

PROFESSIONALS AND TECHNICIANS IN HIGH TECH BY RACE AND ETHNICITY

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Figure 7

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

Figure 7 examines employment figures in the Professional and the Technical occupational categories in the high tech sector. Examples of Professional occupations in this sector include computer programmers, software developers, web developers, and database administrators. Examples of technical occupations in this sector include electrical and electronics engineering technicians, electro-mechanical technicians, and medical records and health information technicians.

Whites made up the largest share of Professionals (68.03 percent) with Asian Americans holding the second largest share at 19.5 percent. As a contrast, African Americans made up 5.27 percent and Hispanics 5.28 percent. Whites held a dominant share of the Technicians job group as well (68.6 percent). African Americans, Hispanics, and Asian Americans each represented approximately 9-10 percent of Technicians.

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

Table 2 shows that of leadership positions in high tech, over four-in-five, or 83.3 percent, of Executives were white compared to 10.5 percent for Asian Americans, 1.9 percent for African Americans and 3.1 percent for Hispanics. Executives in the high tech sector would likely include the chief executive officer, and the chief technology officer, as well as Executives found in other industries such as the chief human capital officer. Managers in the high tech industry would include occupations like computer and information systems managers. Note that Asian Americans make up around 19.5 percent of Professionals in the high tech industry but only 10.5 percent of its Executives, in this analysis of the data.

Table 3 examines select occupational categories by race and ethnicity in high tech and overall private industry. If we assume there is a path of advancement from the ranks of Professional into the Executives, Senior Officials and Managers category, we would expect that racial groups would be similar between the two job categories.[18] However, whites are represented at a larger rate in the Executives, Senior Officials and Managers category. African Americans and Asian Americans are represented at about half the rate within Executives, Senior Officials and Managers than in the Professionals job category. Hispanics are also less represented in Executives, Senior Officials and Managers than in Professionals.

WOMEN IN LEADERSHIP POSITIONS AND TECHNOLOGY JOBS IN U.S. HIGH TECH INDUSTRIES

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Figure 8

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

Figure 8 shows female employment in leadership positions in high tech industries. For every one female Executive, Senior Official and Manager there were four males in the same ranking position (79.6 percent vs. 20.4 percent). Female high tech workers, in contrast to their male counterparts, were also significantly outnumbered in technology jobs as Professionals (31.9 percent vs. 68.1 percent) and Technicians (23.7 percent vs. 76.3 percent).

Table 4 presents select occupational categories by sex comparing the high tech sector with overall private industry. As you can see above, women comprise a smaller percentage (20 percent) of Executives, Senior Officials and Managers in the high tech industry than they do in the overall workforce (29 percent). Moreover, women are represented at lower rates in all high tech job categories as compared to overall private industry. The differences in the Professional (roughly a 21 percentage point difference) and Technician categories (roughly a 26 percentage point difference) are particularly striking.

HIGH TECH PARTICIPATION OF WOMEN AND MINORITIES IN SAN FRANCISCO BAY AREA: 2014

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Figure 9

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

In Figure 9 we examine demographics of employment in the high tech sector in the Silicon Valley area specifically, defined by the geographic region including San Francisco-Oakland-Fremont and one county to the south, Santa Clara. These results show that in high tech in the San Francisco-Oakland-Fremont area, over half of the high tech employment was white (54.9 percent). African Americans and Hispanics were 3.3 and 6.6 percent, respectively. Women comprised 36.7 percent of the total high tech employment.

In Santa Clara County, where many of the top high tech firms are headquartered, whites and Asian Americans each comprised around 45 percent of the total high tech workforce, totaling about 90 percent. That means, on average, of one-hundred workers, only two were African American and fewer than six were Hispanic. Women made up less than one-third of the county’s high tech workforce (28.9 percent). Taken together, these results show under-representation of Black and Hispanic employees in Silicon Valley, and in the heart of Silicon Valley (Santa Clara County) in particular. The same pattern is observed for women.

WOMEN IN LEADERSHIP POSITIONS AND PROFESSIONAL JOBS IN HIGH TECH INDUSTRIES IN SAN FRANCISCO BAY AREA: 2014

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Figure 10

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

Figure 10 illustrates that in San Francisco-Oakland-Fremont area, women made up 21.8 percent of the total Executives, Senior Officials and Managers and 34.3 percent of the total First/Mid Officials and Managers in high tech industries. Over one-in-three, or 35.95 percent, of the total Professionals were female and about 29.2 percent of the Technicians were women, both lower than their male counterparts.

LEADERSHIP POSITIONS AND TECHNOLOGY JOBS IN HIGH TECH INDUSTRIES
BY RACE AND ETHNICITY IN SAN FRANCISCO BAY AREA: 2014

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Figure 11

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.

In Santa Clara County, women were 17.9 percent of the Executive, Senior Officials and Managers and 27.6 percent of the First/Mid Officials and Managers. About 27.6 percent of the Professionals were female and about 26.3 percent of the Technicians were women in the county’s high tech industries.

In high tech for San Francisco-Oakland-Fremont area, whites make up over three-quarter of the Executive, Senior Officials and Managers (76.4 percent) and Asian Americans around 17.8 percent. African Americans were 2.8 percent and Hispanics were 7.7 percent. For every hundred Professionals, there were 1.5 African Americans and fewer than four Hispanics.

A similar picture was found in high tech in Santa Clara County. The majority of the Executive, Senior Officials and Managers positions were held by either whites (61.9 percent) or Asian Americans (32.9 percent). Over half of the Professional jobs reported in the EEO-1 were staffed by Asian Americans (51.2 percent) and about 40 percent by whites (39.3 percent). African Americans and Hispanics were less represented in both Executive, Senior Officials and Managers positions (0.86 percent and 3.14 percent, respectively) and in Professional jobs (1.52 percent and 3.97 percent, respectively).

Note that while Asian Americans made up large percentages of Professional employees in the the San Francisco metro area (37.2%), and especially in Santa Clara county (51.15%), representation of this demographic group in Executive, Senior Officials and Managers was markedly lower (17.86% and 32.92%, respectively). This preliminary finding may suggest something of a ‘glass ceiling’ for Asian Americans working in Silicon Valley, one that seems especially pronounced in what we consider to be the heart of the region, Santa Clara County.

III. EXAMINATION OF LEADING HIGH TECH EMPLOYERS IN SILICON VALLEY

The firms analyzed in this section come from a 2015 San Jose Mercury news article, “Silicon Valley’s Top 150 Companies.”[19] The article produced a ranking of high tech firms in the Silicon Valley area based on revenue, profitability and other criteria.[20] To provide a more focused window on diversity in high tech employment, we examined the workforce composition of those tech companies regarded by industry insiders as leaders in the field. From the published list, we selected the first 75 rank-ordered firms that had an EEO-1 on file for 2014, which is the latest year available for EEO1 data at the time of this report.[21] In the case where a firm did not have an EEO-1 report on file, we moved to the next firm on the list.

We then created a data set containing the 2014 EEO-1 report data for the 75 firms and all of their establishments located within Silicon Valley. We defined Silicon Valley as all cites within the CBSAs of San Francisco-Oakland-Fremont and of San Jose-Sunnyvale-Santa Clara. A list of these cities included in these two CBSAs is included in Table 5.[22] We examined a total of 230 establishments belonging to the Top 75 Tech Firms.

Workforce Composition[23]

In Table 6 we show in frequency and percent the workforce composition of the top 75 ranked firms in Silicon Valley by sex and race-ethnicity. Data come from 2014 EEO-1 reports for the firms and their establishments physically located in the Silicon Valley. In 2014, total employment for these firms aggregated was 209,089.

N=33

N=230 establishments

What is striking in this table is the degree of sex and race segregation. Women comprise just 30 percent of total employment and Asian Americans and Whites comprise 88 percent of all employment.

In Table 6, we see that composition of the select top ranked 75 Silicon Valley tech firms is strongly characterized by sex and race segregation; or, in another words, there is little diversity. But as a point of comparison, what does the workforce composition of the non-tech firms in Silicon Valley look like by sex and race?

Table 7 shows, in frequency and percent, the aggregated workforce composition for all other (non-tech) firms and their establishments also in Silicon Valley.[24] Based on 2014 EEO-1 reports for firms and their establishments, total employment for these firms was 770,290.

N=9,278 establishments

For these non-high tech firms, employment of women and men is at about parity with 49 percent women and 51 percent men. Whites make up less than half of total employment at 41 percent. Of the remainder, Asian Americans comprise 24 percent, Hispanics 22 percent and African Americans 8 percent.

In Table 8, we examine the distribution of occupations. We specifically examine the ten EEO occupations employers use to report employees’ job duties for EEO-1 reporting purposes.

Two occupational types dominate, Professionals at 58 percent and Executives, Senior Officials and Managers combined with First/Mid Officials and Managers at 21 percent. In Table 9, we take the same view but examine the distribution of women and men, whites and non-whites for the four most populous EEO occupations, Professionals, Sales, Technicians and Executives, Senior Officials and Managers combined with First/Mid Officials and Managers.

Note that Asian Americans again make up a large percentage of Professional employees working at these firms (50%), but a smaller percentage of the management teams (36%). At the same time, African Americans and Hispanics make up a very small percentage of both employment groups (Professionals and Executives and Managers combined). Contrasting again with our aggregated pool of non-high tech firms in Silicon Valley, we see in Table 10, more diversity of occupational types—which we would expect.

Table 11 shows the occupational composition by sex and race.

*This combines the EEO occupations Operatives, Laborers & Helpers and Craft Workers.

There is very little occupational segregation (unequal distribution among job groups) by gender within these occupations except for two: Blue-Collar and Clerical. For the remainder there is almost parity for the other EEO-1 occupations. Additionally, there is more race-ethnicity diversity than within the high tech firms examined in the previous table.

APPENDIX FIGURE 1: STEM OCCUPATIONS

Source: Equal Employment Opportunity Commission, Employer Information Reports (EEO-1 Single, Headquarters, and Establishment Reports, 2014). Numbers may not add up to totals due to rounding.