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DIVERSITY IN HIGH TECH

Executive Summary

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.

Technology Industry Occupations 1997 & 2012
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.
Figure 2: Percent of U.S. women who report...
Source: Joan C Williams, Katherine W. Phillips, and Erika V. Hall from HBR.ORG
Figure 2
Figure 3: Set Women ar Driven but Often Feel Stalled
Source: Center for Talent Innovation from HBR.ORG
Figure 3 [14]
Figure 4: SET Women Perceive Bias at Their Companies
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.

TABLE 1: INDUSTRIES USED TO DEFINE HIGH TECH

4-Digit Code

INDUSTRY LABEL

3254

Pharmaceutical and Medicine Manufacturing

3333

Commercial and Service Industry Machinery Manufacturing

3341

Computer and Peripheral Equipment Manufacturing

3342

Communications Equipment Manufacturing

3343

Audio and Video Equipment Manufacturing

3344

Semiconductor and Other Electronic Component Manufacturing

3345

Navigational, Measuring, Electrometrical, and Control Instruments Manufacturing

3346

Manufacturing and Reproducing Magnetic and Optical Media

3364

Aerospace Product and Parts Manufacturing

3391

Medical Equipment and Supplies Manufacturing

5112

Software Publishers

5179

Other Telecommunications

5191

Other Information Services

5413

Architectural, Engineering, and Related Services

5415

Computer Systems Design and Related Services

5417

Scientific Research and Development Services

5419

Other Professional, Scientific, and Technical Services

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).

In our discussion below, we will use national high tech sector figures as well as figures from two geographic areas that we believe encompass the heart of what is known as Silicon Valley: San Francisco-Oakland-Fremont[17], in California (CA) and Santa Clara County, CA. Other high tech corridors in the U.S. were also identified for potential future research in Appendix Table I.

Summary of Findings Compared with all industries reported in the 2014 EEO-1 private sector survey, overall participation rates of whites, Asian Americans, and males in U.S high tech industries were disproportionally higher, especially in the Silicon Valley geographic area.

African Americans and Hispanics were under-represented nationwide in the high tech sector when compared with the overall private industries, (see Figure 5); African Americans and Hispanics were especially under-represented in the high tech sector in the Silicon Valley geographic area.

Whites and men dominated high tech leadership positions as Executive/Senior Level Officials and Managers (Executives) and First/Mid-Level Officials and Managers (Managers) nationwide, and dominated even more strongly in the Silicon Valley geographic area.

Women lagged behind men in leadership positions and in technology jobs, as Technicians and Professionals, in the high tech sector. These gender differences were particularly pronounced in high tech sector of Santa Clara County.

African Americans and Hispanics were disproportionately fewer in leadership positions and in technology jobs in the high tech sector nationwide. These groups had negligible employment representation in high tech industries in the San Francisco Bay Area.

Asian Americans were represented in management and executive positions at a markedly lower rate than their representation in Professional occupations in the high tech industry both nationally and in Silicon Valley.

INDUSTRY PARTICIPATION BY GENDER SEX AND RACE GROUPS
HIGH TECH VS. ALL PRIVATE INDUSTRIES
Chart: Data in following table
 

High Tech Industries Only
(percent)

All Private Industries
(percent)

White

68.53

63.47

Black

7.4

14.38

Hispanic

7.97

13.86

Asian American

14.04

5.77

Am. Indian

0.42

0.56

Hawaiian (NHOPI)

0.34

0.43

Two or more races

1.3

1.53

Women

35.68

48.16

Total Employment (N)

5,341,599

57,399,178

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
Chart: Data in following table
 

High Tech Industries Only
(percent)

All Private Industries
(percent)

Executives, Senior Officials and Managers

2.61

1.58

First/Mid Officials and Managers

14.25

9.51

Professionals

43.47

19.76

Technicians

9.22

5.66

Sale Workers

6.39

12.32

Clerical Workers

9.83

12.84

Craft Workers

4.39

5.61

Operatives

7.62

10.09

Laborers

1.48

7.07

Service Workers

0.73

15.5

Total Employment ( percent)

100.00

100.00

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
Chart: Data in following table
 

EEO-1 Professionals
( percent)

EEO-1 Technicians
( percent)

White

68.03

68.58

Black

5.27

9.01

Hispanic

5.28

10.23

Asian American

19.49

9.68

Total Employment (N)

2,321,969

452,359

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.

TABLE 2: LEADERSHIP POSITIONS BY RACE AND ETHNICITY IN HIGH TECH

  

Executives
(percent)

Managers
(percent)

White

83.31

76.53

Black

1.92

4.12

Hispanic

3.11

4.91

Asian American

10.5

12.98

Totals (N)

139,575

761,380

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: SELECT JOB CATEGORIES BY RACE AND ETHNICITY IN HIGH TECH v. ALL PRIVATE INDUSTRY

High Tech

WHITE

BLACK

HISPANIC

ASIAN AMERICAN

Total Employment (N)

Executives, Senior Officials and Managers

83.31%

1.92%

3.11%

10.55%

139,575

First/Mid Officials & Managers

76.53%

4.12%

4.91%

12.98%

761,380

Professionals

68.03%

5.27%

5.28%

19.49%

2,321,969

Technicians

68.58%

9.01%

10.23%

9.68%

452,359

All Private Industry

WHITE

BLACK

HISPANIC

ASIAN AMERICAN

Total Employment (N)

Executives, Senior Officials & Managers

86.97%

3.13%

3.87%

4.88%

833,367

First/Mid Officials & Managers

77.53%

7.12%

7.43%

6.31%

4,766,041

Professionals

72.89%

7.64%

5.79%

11.74%

10,534,689

Technicians

67.17%

13.79%

10.09%

6.56%

2,870,353

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
Chart: Data in following table
 

Women
(percent)

Men
(percent)

Executives, Senior Officials & Managers

20.44

79.56

First/Mid Officials & Managers

30.10

69.90

Professionals

31.89

68.11

Technicians

23.74

76.26

Total Employment

1,846,801

3,494,798

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: SELECT JOB CATEGORIES BY SEX IN HIGH TECH v. ALL PRIVATE INDUSTRY
 

High Tech

All Private Industry

Women
(percent)

Men
(percent)

Women
(percent)

Men
(percent)

Executives, Senior Officials and Managers

20.44

79.56

28.81

71.19

First/Mid Officials & Managers

30.1

69.9

38.96

61.04

Professionals

31.89

68.11

53.42

46.58

Technicians

23.74

76.26

50.12

49.88

Total Employment

1,846,801

3,494,798

24,422,889

26,728,926

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
Chart: Data in following table Chart: Data in following table
 

San Francisco-Oakland-Fremont

Santa Clara County

White

54.86

44.11

Black

3.35

2.08

Hispanic

6.66

5.93

Asian American

32.07

45.65

Am. Indian

0.28

0.22

Hawaiian (NHOPI)

0.71

0.5

TOMR

2.07

1.5

Women

36.68

28.91

Total Employment

198,275

257,342

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
Chart: Data in following table Chart: Data in following table
 

San Francisco-Oakland-Fremont, CBSA

CA Santa Clara County

 

Women

(percent)

Men

( percent)

Women

(percent)

Men

(percent)

Executives, Senior Officials and Managers

21.82

78.18

17.93

82.07

First/Mid Officials and Managers

34.31

65.69

27.55

72.45

Professionals

35.95

64.05

27.4

72.6

Technicians

29.04

70.96

26.31

73.69

Total Employment (N)

72,730

125,538

74,403

182,939

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
Chart: Data in following table Chart: Data in following table

San Francisco-Oakland-Fremont, CBSA

WHITE

BLACK

HISPANIC

ASIAN AMERICAN

Executive, Senior Officials and Managers

76.41

1.16

2.79

17.86

First/Mid Officer and Manager

62.43

2.31

4.69

28.25

Professionals

52.59

2.45

4.99

37.2

Technicians

40.08

6.59

12.38

36.54

Total Employment (N)

108,782

6,635

13,215

63,593

Santa Clara County, CA

WHITE

BLACK

HISPANIC

ASIAN AMERICAN

Executive, Senior Officials and Managers

61.9

0.86

3.14

32.92

First/Mid Officials and Managers

53.7

1.48

4.52

38.49

Professionals

39.32

1.52

3.97

51.15

Technicians

42.03

7.82

11.91

34.69

Total Employment (N)

113,501

5,352

15,272

117,482

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.


TABLE 5: LIST OF CITY NAMES - VARIABLE IN EEO-1 DATABASE USED IN SILICON VALLEY REPORTING (CBSA 41860 and 41940)

ALAMEDA

BERKELEY

BRISBANE

BURLINGAME

CAMPBELL

CONCORD

CORTE MADERA

CUPERTINO

EMERYVILLE

FOSTER CITY

FREMONT

HAYWARD

HERCULES

LIVERMORE

LOS GATOS

MENLO PARK

MILPITAS

MOUNTAIN VIEW

NEWARK

OAKLAND

PALO ALTO

PLEASANTON

REDWOOD CITY

RICHMOND

SAN BRUNO

SAN FRANCISCO

SAN JOSE

SAN MATEO

SAN RAFAEL

SANTA CLARA

STANFORD

SUNNYVALE

WALNUT CREEK

N=33


TABLE 6: 2014 EEO-1 DATA FOR TOP RANKED 75 SILICON VALLEY TECH FIRMS AGGREGATED

Total Employed

209,089

100%

Women

62,960

30%

Men

146,129

70%

Asian American

86,340

41%

Black

5,720

3.%

Hispanic

12,824

6.%

White

99,222

47%

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.

TABLE 7: 2014 EEO-1 DATA FOR ALL OTHER (NON-TECH) SILICON VALLEY FIRMS
AGGREGATED

Total Employed

770,290

100%

Women

375,026

49%

Men

395,264

51%

Asian American

186,493

24%

Black

62,789

8.%

Hispanic

168,873

22%

White

312,627

41%

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.

TABLE 8: 2014 EEO-1 DATA FOR TOP RANKED 75 SILICON VALLEY TECH FIRMS AGGREGATED (EEO-1 job groups as a percent of total employment)

Total Employment

Professionals

Sales

Technicians

Executives & Managers Combined

All Other EEO-1 Occupations

100%

58%

8.%

6.%

21%

6%

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.

TABLE 9: 2014 EEO-1 DATA FOR SELECT TOP RANKED 75 SILICON VALLEY TECH FIRMS AGGREGATED
(Women/Men and Non-Whites/Whites in EEO occupations)
 

Professionals

Sales

Technicians

Executives & Managers Combined

Women

30%

25%

23%

28%

Men

70%

75%

77%

72%

Total

100

100

100

100

Asian American

50%

11%

23%

36%

Black

2%

3%

11%

Less than 1 percent

Hispanic

4%

6%

12%

1.6%

White

41%

77%

50.%

57%

All other

3%

3%

4%

5%

Total

100

100

100

100

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 10: 2014 EEO-1 DATA FOR ALL OTHER (NON-TECH) FIRMS IN SILICON VALLEY AGGREGATED
(EEO occupations as a percent of total employment)

Total

Prof

Sales

Tech

Blue Collar

Executive-Manager

Service

Clerical

100%

24%

12%

5.%

16%

13%

18%

12%

Table 11 shows the occupational composition by sex and race.

TABLE 11: 2014 EEO-1 DATA FOR ALL OTHER (NON-TECH) FIRMS IN SILICON VALLEY AGGREGATED
(Women/Men and Non-Whites/Whites in EEO occupations)

Total

Prof

Sales

Tech

Blue Collar*

Executive-Manager

Service

Clerical

Percent of Employment

24%

12%

5.%

16%

13%

18%

12%

Women

56%

54%

49%

16%

43%

50%

73%

Men

44%

46%

51%

84%

57%

50%

27%

Total

100

100

100

100

100

100

100

Asian American

32%

20%

35%

16%

20%

24%

25%

Black

5%

9%

8%

10%

5%

12%

10%

Hispanic

7.5%

25%

15%

40%

10%

34%

20%

White

52%

40%

37%

30%

62%

23%

38%

All Other

3.5%

6%

5%

4%

3%

7%

7%

Total

100

100

100

100

100

100

100

*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
STEM Occupations, by occupational group
APPENDIX TABLE 1: TOP HIGH TECH GEOGRAPHIC AREAS IDENTIFIED FOR POTENTIAL FUTURE RESEARCH

CBSA TITLE

REPORTING UNITS (N)

TOTAL HIGH TECH EMPLOYMENT (N)

New York-Newark-Jersey City, NY-NJ-PA

2,405

363,444

Los Angeles-Long Beach-Anaheim, CA

1,912

269,452

Washington-Arlington-Alexandria, DC-VA-MD-WV

3,561

266,378

San Jose-Sunnyvale-Santa Clara, CA

890

257,349

Boston-Cambridge-Newton, MA-NH

1,443

224,533

Seattle-Tacoma-Bellevue, WA

867

197,046

Dallas-Fort Worth-Arlington, TX

1,217

189,615

Chicago-Naperville-Elgin, IL-IN-WI

1,462

181,721

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

1,039

130,582

Atlanta-Sandy Springs-Roswell, GA

1,042

128,296

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.


APPENDIX TABLE 2: NAICS-CODE BASED DEFINITION OF HIGH TECH INDUSTRIES

4-DIGIT CODE

INDUSTRY LABEL

3254

Pharmaceutical and Medicine Manufacturing

3333

Commercial and Service Industry Machinery Manufacturing

3341

Computer and Peripheral Equipment Manufacturing

3342

Communications Equipment Manufacturing

3343

Audio and Video Equipment Manufacturing

3344

Semiconductor and Other Electronic Component Manufacturing

3345

Navigational, Measuring, Electrometrical, and Control Instruments Manufacturing

3346

Manufacturing and Reproducing Magnetic and Optical Media

3364

Aerospace Product and Parts Manufacturing

3391

Medical Equipment and Supplies Manufacturing

5112

Software Publishers

5179

Other Telecommunications

5191

Other Information Services

5413

Architectural, Engineering, and Related Services

5415

Computer Systems Design and Related Services

5417

Scientific Research and Development Services

5419

Other Professional, Scientific, and Technical Services


ANNOTATED BIBLIOGRAPHY

1. " Are There High-Tech Industries or Only High-Tech Firms? Evidence From New Technology-Based Firms" John R. Baldwin and Guy Gellatly. Microeconomics Division, Statistics Canada December 1998

Far from producing definitive classifications, existing measures of technological advancement are found to be wanting. Classification schemes that rely on a single-measure of technological prowess, as many do, may incorrectly rank industries and/or classify sectors. Second, firms that possess the advanced competencies that contribute to technological prowess are found in many industries, and are not as sector-specific as previous attempts at classification suggest. Simply stated, low-tech industries 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 may prove dubious. The competency-based approach represents a considerable advance over previous efforts: it formally recognizes the multidimensional nature of technological prowess.

Firms that we identify as advanced in this study have the characteristics associated with new technology-based firms. They are innovative; 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. Industries that might be classified as low-tech on the basis of indices are not devoid of high tech firms-on average, they contain half as many high tech firms as can be found in high tech industries. It should not be claimed that high-knowledge, high tech firms are confined exclusively to these more visible industries.

2. "How U.S. tech-sector jobs have grown, changed in 15 years" by Drew DeSilver Pew Research Center, March 2014

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, 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.) The chart below shows just how different the structure of 2012's technology industry is from that of 15 years earlier. Some occupations, such as web developers and information security analysts, simply didn't exist back then (at least not under those names). Others have dramatically grown (programmers/software developers, support specialists) or shrunk (computer operators).

3. "The Joys of Urban Tech: Goodbye, office parks. Drawn by amenities and talent, tech firms are opting for cities" By Richard Florida Wall Street Journal, Aug. 31, 2012

A generation or so ago, high tech companies were more like factories. They developed proprietary software systems, designed and manufactured chips, built computers, they deployed big engineering teams and created the infrastructure that made the Internet possible and they needed big suburban campuses to house them.

The changing nature of technology-cloud-based applications in particular-enable new start-ups to succeed more quickly, with smaller teams and much smaller footprints. High tech products and industries are more multidisciplinary than they used to be so success often requires excellence in more than one field of technology and in other lines of business. The companies that succeed are the ones that stay in the closest contact with their end-users and first adopters. Design is central to successful new hardware products Design talent is overwhelmingly concentrated in big cities, with their leading design schools and multiple industries that draw upon such skills. Other areas of high tech are premised less on breakthrough innovations and more on the application of technology to massive new markets in retailing, advertising, media, financial services, education, publishing, communications, fashion and music. Tech companies are dispersing to areas where access to their need for diverse talent can be accommodated.

4. "STEM 101: Intro to Tomorrow's Jobs" Dennis Vilorio. Occupational Outlook Quarterly; Spring 2014 www.bls.org/ooq

There is no universally agreed-upon definition of STEM. STEM workers use their knowledge of science, technology, engineering, or math to try to understand how the world works and to solve problems. A list of 100 STEM occupations (excluding healthcare) was compiled by several federal agencies; see Appendix Figure I for this list. The BLS projects overall STEM employment to grow about 13 percent 2012-2022, somewhat faster than the 11 percent projected for all occupations. The largest numbers of professional and technical jobs (not fastest growing) are expected to be in software development and applications, computer systems analysis and user support. Software development and systems analysis jobs generally require a Bachelor's Degree while user support requires "some college, no degree (See Appendix TABLE I A for lists of 15 rapidly growing occupations and occupations with the largest number of jobs.)

5. "Want A Tech Career? LinkedIn Finds 12 Eye-Catching Paths" by George Anders. Forbes, Tech (August 25, 2015)

LinkedIn data scientist Alice Ma has crunched the numbers. In a new blog post, she highlights 12 eye-catching ways that non-technical strivers can be welcomed into the coders' lair. From 2010 to 2013, hiring of liberal-arts majors in tech companies actually grew 10 percent faster than the rate of job offers to computer-science and engineering majors.

BIBLIOGRAPHY FIGURE I

Jobs Held by Liberal Arts Majors at Tech Companies

Jobs Held by Liberal Arts Majors at Tech Companies

6. "Gender Segregation in Fields of Study at Community Colleges and Implications for Future Earnings" Layla Moughari, Rhiana Gunn-Wright, and Barbara Gault, Ph.D. Institute for Women's Policy Research IPWR#C395 (May 2012)

While men out earn women regardless of occupation, occupational field contributes substantially to the pay gap. Women outnumber men in community colleges, receiving 56.8 percent of associate degrees but men comprise at least seventy percent of graduates in engineering, mathematics, and computer science while women dominate in the lower paying fields.

7. "Closing the STEM Skills Gap" by STEM Education Coalition www.stemedcoalition.org

The STEM Coalition meets with legislators, legislative staff, and community leaders to discuss STEM policy and education. The Coalition works with U.S. House STEM Education Caucus. The Coalition recommends "robust and targeted investments" preparing and training elementary and secondary school teachers in "STEM-specific pedagogical knowledge" enabling them to excite students and foster strong student learning in STEM subjects through a strong emphasis on hands-on, inquiry-based learning activities for students from an early age. We should encourage learning through working directly with STEM professionals in internships, and participating in field experiences and STEM-related competitions. Informal education such as museums, maker-spaces, or after school groups - are valuable and essential partners for STEM education improvement

There are almost twice as many job postings in STEM fields as there are qualified applicants to fill them. Half of STEM jobs do not require a traditional four-year degree and pay on average 10 per cent higher than non-STEM jobs.[25] Public/private partnerships are recommended to create a suitable workforce.

8. Science, Technology, Engineering, and Mathematics (STEM) Education: A Primer by Heather B. Gonzalez and Jeffrey J. Kuenzi. Congressional Research Service, 11-15-2012

Graduate enrollments in science and engineering (S&E) grew 35 percent over the last decade.

S&E enrollments grew for groups generally under-represented in S&E, increase by demographic group:

  • Hispanic/Latino, 65 percent
  • American Indian/Alaska Native, 55 percent
  • African American students 50 percent

Analysts have identified between 105 and 252 STEM education programs or activities at 13 to 15 federal agencies.

According to the U.S. Census Bureau, the percentage of U.S. bachelor's degree holders with undergraduate degrees in science and engineering (S&E) was 36.4 percent in 2009 (approximately 20 million people).

The NSF estimates that the percentage of bachelor's degrees in S&E fields has held relatively constant-at between approximately 30 percent and 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 S&E bachelor's degrees awarded annually more than doubled between 1966 and 2008 (from 184,313 to 494,627). 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 2008.33 Graduate enrollments show similar upward trends.

In the decade between 2000 and 2010, graduate enrollments in S&E fields grew by 35 percent. Further, among U.S. citizens and permanent residents, S&E graduate enrollments among Hispanic/Latino, American Indian/Alaska Native, and black/African American students grew at a higher rate than that of whites (not of Hispanic origin) and Asian Americanss.39 While 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)38 they accounted for 77.1 percent of the psychology degrees and 58.3 percent of the biological and agricultural sciences degrees in 2008,[26]

Foreign students earn roughly one-third of all U.S. S&E doctoral degrees and earn half (or more) of U.S. doctoral degrees in the specific fields of engineering, physics, computer sciences, and economics. In 2009, there were 611,629 graduate students in science and engineering fields in the United States. Of these 168,850 (27.6 percent) were temporary residents.[27]

9. "How tech companies compare in employee diversity" FORTUNE August 29, 2014

At least 14 high tech companies have released data on their gender, racial, and ethnic diversity. Fortune ranked them in individual categories (leadership team, technical workers) and overall diversity. These graphs are shown in Appendix Figure II. Here's how they stacked up, overall by Fortune's measure:

  • LinkedIn
  • Apple
  • EBay
  • Indiegogo & Yahoo (tied)
  • Pinterest
  • Pandora
  • Facebook
  • Intel & Google (tied)
  • Twitter
  • Cisco
  • Hewlett-Packard
  • Microsoft

10. "Does the Tech Industry Even Deserve Women?" By Cecilia D'Anastasio

https://broadly.vice.com/en_us/article/does-the-tech-industry-even-deserve-women September 6, 2015

Women and minorities in tech have a special responsibility; in addition to their jobs, minorities in tech are employed as demographic icons. In that capacity, they often must defend their identity against a culturally-sanctioned exclusivity. That job never pays. Feminists weigh being tolerant of abuse or out of a job. Harassment happens, startlingly often and unprovoked, and it can feel it comes with the territory of tech jobs.

The problem isn't necessarily that women don't care about programming, or that women in tech aren't measuring up, according to Lean Out contributors, the problem is that internalized misogyny and financially-reinforced tokenism runs through the veins of tech. Women in tech are the canary in the coal mine. When the canary starts dying you know the environment is toxic. Instead, the tech industry is looking at the canary, wondering why it can't breathe, saying 'Lean in!

11. Athena Factor 2.0: Accelerating Female Talent in Science, Engineering & Technology by Sylvia Ann Hewlett and Laura Sherbin with Fabiola Dieudonné, Christina Fargnoli, and Catherine Fredman TalentInnovation.org, 2014

In 2008, when we published The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology, our data showed that while the female talent pipeline in SET was surprisingly robust, women were dropping out of the field in droves. Over time, fully 52 percent of highly qualified women working for SET companies quit their jobs. While 80 percent of U.S., 87 percent of Brazilian, 90 percent of Chinese and 93 percent of Indian SET women say they love their work. However, a sizable proportion say they feel stalled and say they are likely to quit their jobs within a year. Women who say they are likely to quit within a year: 32 percent U.S.; 22 percent Brazil; 30 percent China; 20 percent India.

Looking at the barriers to SET women's advancement through a lens refined by our recent we see promising levers for change. The most obvious solution: sponsorship. Sponsors help their protégés crack the unwritten code of executive presence, improving their chances of being perceived as leadership material. Most important to the companies employing them, sponsors help women get their ideas heard.

Our research shows that when SET women are fully engaged, and when leadership creates the speak-up culture wherein their ideas might be heard, companies enjoy a "diversity dividend" that translates into increased market share and entry into altogether new markets.

12. Why So Few? Women in Science, Technology, Engineering, and Math. Catherine Hill, Ph.D. Christianne Corbett Andresse St. Rose, Ed.D. AAUW 2010, updated 2015 in Solving the Equation and reported The Stats On Women In Tech Are Actually Getting Worse byEmily Peck, Executive Editor, Business and Technology Huffington Post, Updated Mar 27, 2015

In 2013, just 26 percent of computing jobs in the U.S. were held by women, down from 35 percent in 1990, according to the study released Thursday by the American Association of University Women. In 2013, more than half of the biological scientists in the U.S. were women, compared to 42 percent in 1990.

Prejudices tend to make their way into the hiring process. .Men are twice as likely as women to be hired for a job in mathematics when the only difference between candidates is gender, (Proceedings of the National Academy of Sciences March 10, 2014).

At Google, women make up 30 percent of the company's overall workforce, but hold only 17 percent of the company's tech jobs. At Facebook, 15 percent of tech roles are staffed by women. At Twitter, it's a laughable 10 percent. For non-technical jobs at Twitter (think marketing, HR, sales), the gender split is 50-50.

Diversity needs to be made a clear priority at companies. That happens only when diversity moves out of workshops and becomes factored into the hiring managers' bottom lines.

13. "How stereotypes impair women's careers in science" by Ernesto Reubena, Paola Sapienzab, and Luigi ZingalescProceedings of the National Academy of Sciences, January 31, 2014

Without provision of information about candidates other than their appearance, men are twice more likely to be hired for a mathematical task than women. If ability is self-reported, women still are discriminated against, because employers do not fully account for men's tendency to boast about performance. Providing full information about candidates' past performance reduces discrimination but does not eliminate it. Implicit stereotypes (as measured by the Implicit Association Test) predict not only the initial bias in beliefs but also the suboptimal updating of gender-related expectations when performance-related information comes from the subjects themselves.

14. "Why are women leaving the tech industry in droves?" by Tracey Lien Los Angeles Times Feb.22, 2015

Reasons include a "hostile" male culture, a sense of isolation and lack of a clear career path. The attitudes holding them back are subtle, and hence more difficult to challenge.

"The continuous pattern of all these people treating me like I didn't know what was going on, or excluding me from conversations and not trusting my assertions, all these things added up and it felt like there was an undercurrent of sexism," Tracy Chou said.

That's one difficulty in tackling the problem, said Alaina Percival of Women Who Code "They're [things that are] so small you'd never even complain about them," Percival said. "But they happen day after day. They're the kind of things that separate and exclude you from the team…". So far, no company has found a solution for retaining women.

15. "Stopping the Exodus of Women in Science" by Sylvia Ann Hewlett, Carolyn Buck Luce, Lisa J. Servon. Harvard Business Review June 2008

Fifty-two percent of female scientists, engineers, and technologists abandon their careers! Business leaders decry the shortage and lobby for more H-1B visas although the talent they seek is available. 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. Five reasons appear to account for the loss: workplace hostility, isolation, conflict between women's preferred work rhythms and the "firefighting" work style generally rewarded, long hours and travel schedules conflict with women's heavy household management workload, and women's lack of advancement in the professions and corporate ladders. If corporate initiatives to stem the brain drain reduce attrition by 25 percent there would be 220 thousand additional highly qualified female STEM workers.

16. "Why Women Quit Science" on line title "She Wanted to Do Her Research. He Wanted to Talk 'Feelings.'" by A. Hope Jahren. New York Times (March 4, 2016)

Women are no longer a race and ethnic within higher education; women's enrollment in graduate education in the United States has been greater than men's for each of the last 30 years; as of 2012, there were 13 women enrolled for every 10 men. Yet, in physical sciences, seven B.S. degrees are granted to women for every 10 granted to men; three M.S. degrees granted to women for every five granted to men; one Ph.D. degree granted to a woman for every two granted to men. The absence of women is progressive and persistent - despite more than 20 years of programs intended to encourage the participation of girls and women.

Women reported both isolation and intimidation as barriers blocking their scholarly path; and while 23 percent of freshmen reported not having experienced these barriers, only 3 percent of seniors did. Few studies exist, but in a survey of 191 female fellowship recipients, 12 percent indicated that they had been sexually harassed as a student or early professional. Sexual harassment is very rarely publicly punished when reported, and then only after a pattern of relatively egregious offenses. And, it never stops.

17. "The 5 Biases Pushing Women Out of STEM" by Joan C. Williams Harvard Business Review (March 24, 2015)

Bias, not pipeline issues or personal choices pushes women out of science. Bias functions differently depending on race and ethnicity. Based on a survey and in-depth interviews of female scientists (557 and 60 respectively):

  • 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".
  • Isolation is a problem: 42 percent of Black women, 38percent of Latinas, 37 percent of Asian women and 32 percent of White women agreed that socializing with colleagues negatively affect perceptions of their competence.

18. "What's Holding Women Back in Science and Technology Industries"Center for Talent Innovation and Hewlett Consulting Partners LLCHarvard Business Review, September 2015

New research from the Center for Talent Innovation shows that U.S. women working in SET fields are 45 percent more likely than their male peers to leave the industry within the year. Over 80 percent of U.S. women love what they do; in Brazil, China, and India, the numbers are close to 90 percent. Over three-quarters (76 percent) of U.S. women consider themselves "very ambitious," as do 92 percent of Chinese and 89 percent of Indian SET women. Yet, they feel stalled, blocked from contributing to their full potential, and stymied by bias and a double standard. They feel marginalized by the environment of "arrogant nerds" and "hard hat culture". Thirty-two percent of U.S. women say they are likely to leave within a year, as do 22 percent of Brazilian women, 30 percent of women in China, and 20 percent in India.

19. "The Hiring Dilemma for High-tech Firms: 'Make vs. Buy'" Knowledge @ Wharton http://knowledge.wharton.upenn.edu/article/the-hiring-dilemma-for-high-tech-firms-make-vs-buy/ (Nov 02, 2005)

The article reports research findings and recommendations. HR strategy complements technology strategy; in a fast-paced industry, product life cycles are growing shorter. Firms are facing more opportunities for change and more adjustments to the workforce. When skills need to be adjusted, 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.

A classic example of this phenomenon is Hewlett Packard over the last 20 years. They had such a reputation for use of internal labor markets, where they hired employees at an early stage and then developed them throughout their careers. But now they are operating more on the spot market. In order to keep pace with other technology firms, they hire on the outside.[28]

Technology firms in short product life markets, and thus with high R&D spending, must have a mix of engineers dominated by the new skills required for the new technology with a small emphasis on engineers with experience on the last generation of technology. High tech firms need to balance the two strategies; experienced workers have firm-specific knowledge that can't be replaced on the outside market, but when you are not investing a lot in developing the skills of a work force, employees will leave.

20. "Immigration and America's high tech industry: The jobs machine"The Economist April 13, 2013

A bunch of other Silicon Valley types are planning to launch a well-funded political-advocacy group to lobby for more visas for skilled immigrants. Applications for this year's quota of 65,000 "H-1B" visas for such workers began on April 1st. In less than a week they were oversubscribed. The proportion of start-ups in Silicon Valley founded by immigrants has fallen from 52 percent to 44 percent since 2005.

High tech employment growing fastest in places you might not associate with bits and bytes. Some are being created by start-ups local to the area. Other companies in tech hubs have opened faraway offices to tap new pools of skilled labor. Logistics matter, too. Bloom Energy decided to open a factory in Delaware to make it easier to get its fuel cells, which are the size of a small car, to customers on the east coast. And View, another immigrant-founded Californian start-up, has opened its only factory in Mississippi, because it is a good place from which to ship stuff to the rest of America.

High tech jobs matter not just to software engineers, scientists and the folk working in factories, estimates indicate that for every job created in the high tech sector, another 4.3 jobs emerge over time in the local economy. That is more than three times the local "multiplier" for manufacturing jobs.[29]

21. "The STEM Workforce: An Overview" Fact Sheet 2014, AFL-CIO Department for Professional Employees.

This fact sheet outlines the employment and earning trends in STEM occupations; unionization in STEM fields; the location of STEM jobs; gender, race, and ethnicity in STEM; and the challenges offshoring and U.S. guest worker visa programs pose for U.S. STEM workers. Data is drawn from the U.S. Census, American Community Survey, Bureau of Labor Statistics and other public sources.

22. "About Face: Most Companies say they want to attract a diverse workforce, but few deliver." by Claire Cain Miller. New York Times Magazine, The Work Issue, Feb. 28,2010

GapJumpers was formed to recruit tech workers in Silicon Valley based on applicant performance in challenges that mimic job tasks. The goal was to increase diversity by eliminating the effect of elite colleges in the hiring process. But, companies still received applicant names and photos in addition to test results. It wasn't until the company adopted the practice used by symphony orchestras, anonymity for all candidates and selection based on test results alone, that non-White applicants increased from 20 to 60 percent of those chosen for an interview. The tech industry is well suited to this approach as jobs require the ability to produce something that can be evaluated by peers.

There is some truth to the "pipeline" theory attributing lack of employment diversity in tech industries to lack of applicant diversity and self-selection of minorities and women away from STEM fields. Yet, nearly 9 percent of graduates from the top 25 computer science programs are Black, Latino, or Native American while only 5 percent of the large tech firms are from one of these groups. There are "a handful" of Silicon Valley start-ups like Gild and Textio working on technological fixes to increase diversity in hiring.


BIBLIOGRAPHY TABLE I A:
Selected STEM occupations with many job openings, projected 2012-22

Occupation

Job openings, projected 2012 - 22

Employment

Median annual wage, May 2013

Typical entry-level education 1

2012

Projected 2022

Software developers, applications

218,500

613,000

752,900

$92,660

Bachelor's degree

Computer systems analysts

209,600

520,600

648,400

81,190

Bachelor's degree

Computer user support specialists2

196,900

547,700

658,500

46,620

Some college, no degree

Software developers, systems software

134,700

405,000

487,800

101,410

Bachelor's degree

Civil engineers

120,100

272,900

326,600

80,770

Bachelor's degree

Computer programmers

118,100

343,700

372,100

76,140

Bachelor's degree

Sales representatives, wholesale and manufacturing, technical and scientific products2

111,800

382,300

419,500

74,520

Bachelor's degree

Network and computer systems administrators

100,500

366,400

409,400

74,000

Bachelor's degree

Mechanical engineers

99,700

258,100

269,700

82,100

Bachelor's degree

Computer and information systems managers3

97,100

332,700

383,600

123,950

Bachelor's degree

Industrial engineers

75,400

223,300

233,400

80,300

Bachelor's degree

Architectural and engineering managers3

60,600

193,800

206,900

128,170

Bachelor's degree

Web developers

50,700

141,400

169,900

63,160

Associate's degree

Electrical engineers

44,100

166,100

174,000

89,180

Bachelor's degree

Computer network architects3

43,500

143,400

164,300

95,380

Bachelor's degree

1 Unless otherwise specified, occupations typically require neither work experience in a related occupation nor on-the-job training to obtain competency.

2 In addition to the education specified, this occupation typically requires moderate-term on-the-job training for workers to obtain competency.

3 In addition to the education specified, this occupation typically requires 5 years or more of work experience in a related occupation.

Source: U.S. Bureau of Labor Statistics, Employment Projections program (employment, projections, and education data) and Occupational Employment Statistics survey (wage data).

BIBLIOGRAPHY TABLE I B:
Selected STEM occupations with fast employment growth, projected 2012-22

Occupation

Employment growth, projected 2012 - 22 (percent)

Employment

Median annual wage, May 2013

Typical entry-level education 1

2012

Projected 2022

Information security analysts2

37 percent

75,100

102,500

$88,590

Bachelor's degree

Operations research analysts

27

73,200

92,700

74,630

Bachelor's degree

Statisticians

27

27,600

34,900

79,290

Master's degree

Biomedical engineers

27

19,400

24,600

88,670

Bachelor's degree

Actuaries3

26

24,300

30,600

94,340

Bachelor's degree

Petroleum engineers

26

38,500

48,400

132,320

Bachelor's degree

Computer systems analysts

25

520,600

648,400

81,190

Bachelor's degree

Software developers, applications

23

613,000

752,900

92,660

Bachelor's degree

Mathematicians

23

3,500

4,300

102,440

Master's degree

Software developers, systems software

20

405,000

487,800

101,410

Bachelor's degree

Computer user support specialists4

20

547,700

658,500

46,620

Some college,

no degree

Web developers

20

141,400

169,900

63,160

Associate's degree

Civil engineers

20

272,900

326,600

80,770

Bachelor's degree

Biological science teachers, postsecondary

20

61,400

73,400

75,740

Doctoral or professional degree

Environmental science and protection technicians, including health

19

32,800

38,900

41,700

Associate's degree

1 Unless otherwise specified, occupations typically require neither work experience in a related occupation nor on-the-job training to obtain competency.

2 In addition to the education specified, this occupation typically requires less than 5 years of work experience in a related occupation.

3 In addition to the education specified, this occupation typically requires long-term on-the-job training for workers to obtain competency.

4 In addition to the education specified, this occupation typically requires moderate-term on-the-job training for workers to obtain competency.

Source: U.S. Bureau of Labor Statistics, Employment Projections program (employment, projections, and education data) and Occupational Employment Statistics survey (wage data).



[1] Beginning in 1966 all employers with 100 or more employees (lower thresholds apply to federal contractors) have been required by law to file the Employer Information Report EEO-1 with the EEOC. In FY 2013 approximately 70,000 employers filed an EEO-1. These forms indicate the composition of an employer's workforces by sex and by race/ethnic category. The EEO-1 form collects data on ten major job categories.

[2] Occupational Employment Projections to 2022, Bureau of Labor Statistics Monthly Labor Review: www.bls.gov/opub/mlr/2013/article/occupational-employment-projections-to-2022.htm .

[3] EEO-1 reports filed by employers with more than 100 employees provide data based on race, color, sex and national origin, but do not report data on age or disability. We are aware that both groups are underrepresented in the tech workforce, suggesting the need for research to understand the causes and potential solutions.

[4] This classification is now dated as tech companies want educational institutions to bear the training cost, including software-specific training. See "The Hiring Dilemma for High Tech Firms: Make vs. Buy" " Knowledge @ Wharton

[5] Science, Engineering, and Technology (SET).

[6] according to Education Department data analyzed by Maya A. Beasley, associate professor of sociology at the University of Connecticut, quoted in Gonzalez and Kuenzi, 2012.

[7] Lowell, B. Lindsay, and Hal Salzman. The Urban Institute. Into the Eye of the Storm: Assessing the Evidence on

Science and Engineering Education, Quality, and Workforce Demand. The Urban Institute, 2007.

[8] National Science Foundation, cited in Gonzalez and Kuenzi 2012

[9] Bachelor's, master's, and doctor's degrees conferred by postsecondary institutions, by field of study: Selected years, 1970-71-2011-12. Available at: http://nces.ed.gov/programs/digest/2013menu_tables.asp

[10] U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, Household Data Annual

Average 2012, Table 39. (Cited in AFL-CIO, 2014)

[11] U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, Household Data Annual

Average 2013, Table 11. (Cited in AFL-CIO, 2014)

[12] U.S. Census Bureau, DataFerrett, American Community Survey, Public Use Microdata, 2012. (Cited in AFL-CIO, 2014)

[13] Women in science, technology, engineering, or math.

[14] SET Science, Engineering, and Technology

[15] Beginning in 1966 all employers with 100 or more employees (lower thresholds apply to federal contractors) have been required by law to file the Employer Information Report EEO-1 with the EEOC. In FY 2014 approximately 70,000 employers filed an EEO-1. These forms indicate the composition of an employer's workforces by sex and by race/ethnic category. More information about the EEO-1 survey and the associated reports can be found at www.eeoc.gov. Employment totals and subgroup aggregates were generated from four types of reports: single establishment report (Type 1 Report), headquarters report (Type 3 Report), multiple establishment report with at least 50 workers (Type 4 report), and multiple establishment report with fewer than 50 workers (Type 8 Report). This inclusion criterion is different from our typical EEO-1 aggregates which we release annually to the public on our website.

[16] 1) Executives, Senior Level Officials and Managers; 2) First/Mid-Level Officials and Managers; 3) Professionals; 4) Technicians; 5) Sales Workers; 6) Administrative Support Workers; 7) Craft Workers; 8) Operatives; 9) Laborers and Helpers; and 10) Service Workers. For examples of job titles and descriptions see https://www.eeoc.gov/employers/eeo1survey/jobclassguide.cfm

[17] This is a core-based statistical area, which is defined by Office of Management and Budget as an area that consists of one or more counties anchored by a large urban center, including at least 10,000 people. Adjacent counties are included if they are socioeconomically tied to the urban center.

[18] Another possibility is that CEOs and other top Executives may be more likely to be business management professionals and have a business management background as opposed to a tech or STEM background.

[20] The article did not describe its ranking methodology.

[21] The NAICS reported in this section are from the Top Ranked 75 Firms in Silicon Valley and do not completely match the definition of high tech industries used elsewhere in this report.

[22] The "Silicon Valley" is generally understood to include the southern half of the San Francisco Peninsula, sections of the East Bay and all of the Santa Clara Valley. This includes parts of the Santa Clara County, San Mateo County and Alameda County. In the prior section the area is defined in terms of two metropolitan areas. For this section, we construct Silicon Valley as the physical location (cities, counties) of the top 75 ranked tech firms and their establishments. This is done in order to get a better fit with the ranking produced by the San Jose Mercury News (April 2015).

[23] As another matter of interest, we searched for federal contractor status for all 75 tech firms used in this section. Contractor status is a reporting item on the EEO-1 form. We found that more than half, 57 percent, had at least one current federal contract in 2014. Because federal contractors are now obligated to collect self-reported disability status, data on the employment of people with disabilities in High Tech firms will be available for future study (this is not a data point collected on EEOC surveys).

[24] After eliminating all firms with a technology industry NAIC, there are 2,939 firms (e.g., unique parent headquarter ID numbers) with a total of 9,278 establishments in the Silicon Valley.

[25] Note from RT: This is disputed in other articles cited here. The focus on elite degrees and hostile atmosphere may contribute to the shortage. The perceived shortage in turn may motivate the higher salaries, absent which salaries would drop.

[26] Data from the National Science Foundation, National Center for Science and Engineering Statistics

[27] National Science Board, Science and Engineering Indicators: 2012, NSB 12-01, National Science Foundation, January 13, 2012, p. 2-28.

[28] There is substantial pressure for educators to train students in specific skills rather than focus on developing fundamental abilities, and has been noted for decades. The tendency for companies to externalize the cost of firm specific training is also a cost-cutting strategy. These workers are shed with the next product cycle.

[29] This would make the multiplier for manufacturing very low; The key is in how the "local" economy is defined and the wage level of the manufacturing (vs. tech) jobs. It may be the case that the multiplier effect is geographically larger in manufacturing in high tech.