Chapter 3 | Science and Engineering Labor Force

Women and Minorities in the S&E Workforce

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Historically, men have outnumbered women by wide margins in both S&E employment and S&E training. Although the number of women in S&E occupations or with S&E degrees has doubled over the past two decades, the disparity has narrowed only modestly. This imbalance is still particularly pronounced in S&E occupations. In 2015, women constituted only 28% of workers in these occupations, although they accounted for half of the college-educated workforce overall. Among S&E degree holders, the disparity was smaller but nonetheless significant, with women representing 40% of employed individuals with a highest degree in S&E (Figure 3-26).

Although women represented only 28% of individuals in S&E occupations in 2015, women’s participation varies widely across S&E occupational fields (Figure 3-27; Appendix Table 3-12). The percentage of female S&E workers continues to be lowest in engineering, where women constituted 15% of the workforce in 2015. Among engineering occupations with large numbers of workers, women accounted for only 9% of the workforce of mechanical engineers and about 10% to 13% of the workforce of electrical and computer hardware engineers and of aerospace, aeronautical, and astronautical engineers. However, among civil engineers, women make up about one-fifth of the workers (Appendix Table 3-12).

Other disproportionately male S&E occupations include physical scientists (28% women) and computer and mathematical scientists (26% women). Within computer and mathematical sciences occupations, the largest component, computer and information scientists, has a smaller proportion of women (24%) compared with the mathematical scientists component, which is closer to parity (43% women).

In 2015, sex parity in S&E occupations was close among life scientists (48% women). The largest component of life sciences, biological and medical scientists, had reached gender parity (53% women). The field of social sciences was majority female (60%). Occupations within social sciences, however, varied widely: women accounted for only 38% of economists but for 73% of psychologists. Psychologists, estimated at about 213,000 total workers (Appendix Table 3-12), are a large S&E occupation with substantially more women than men.

In contrast to jobs in S&E occupations, a majority of jobs in S&E-related occupations (58%) are held by women (Appendix Table 3-12). The largest component, health-related occupations, has a large share of women (70%) whose jobs are primarily as nurse practitioners, pharmacists, registered nurses, dietitians, therapists, physician assistants, and health technologists and technicians; women represented the majority of workers in these particular health occupations. In contrast, among health occupations such as diagnosing and treating practitioners, women accounted for a much smaller proportion (42%).

Since the early 1990s, the number of women working in each broad S&E occupational category has risen significantly (Figure 3-27). The rate of growth has been strong among life scientists, computer and mathematical scientists, and social scientists. These three broad S&E fields together employed 81% of women in S&E occupations in 2015, compared with 63% of men in S&E occupations (Appendix Table 3-12). Between 1993 and 2015, the number of women nearly tripled among life scientists (an increase of 175%) and more than doubled among social scientists (an increase of 112%). The number of men also grew, but the rate of growth for women was greater than that for men, resulting in an increase in the proportion of female life scientists and female social scientists.

During the same period, the number of women in computer and mathematical sciences occupations also nearly tripled (an increase of 173%). However, this new, rapidly growing and changing field attracted relatively more men than women (male participation grew 239%). The result has been an overall decline in the proportion of women, from 31% to 26%. These trends make the gender disparity among computer and mathematical scientists second only to the gender disparity among engineers. However, the declining proportion of women in computer and mathematical sciences occupations does not extend to doctorate-level workers: Among those with a doctorate, the proportion of women increased, from 16% in 1993 to 26% in 2015.

During the past two decades, the proportion of women also increased among workers in engineering (from 9% to 15%) and in physical sciences (from 21% to 28%). In these two occupational categories, this increase was led by an expansion of women’s numbers in the workforce (by 108% in engineering and 53% in physical sciences), while men’s numbers barely changed between 1993 and 2015.

The sex disparity among employed S&E highest degree holders is less than the disparity among those in S&E occupations. In 2015, among individuals with a highest degree in an S&E field, women constituted 40% of those who were employed, up from 30% in 1993 (Figure 3-26). The pattern of variation in the proportion of men and women among degree fields echoes the pattern of variation among occupations associated with those fields (Appendix Table 3-13). In 2015, 57% of S&E highest degree holders in social sciences fields were women, as were 51% of those with a highest degree in the biological and related sciences. Men outnumbered women among computer and mathematical sciences highest degree holders (28% women) and among physical sciences highest degree holders (34% women). Disparities, however, were greatest among those with a highest degree in engineering (15% women).

In all broad S&E fields except computer and mathematical sciences, the proportion of women in the workforce with associated highest degrees has been increasing since 1993. In computer and mathematical sciences, this proportion has declined as the number of women with a highest degree in the field has risen, but women’s numbers have increased less than those of men in this new and rapidly growing field.

Sex differences are not limited to the field of degree but also extend to the level of S&E degree. Overall, men outnumber women among S&E highest degree holders at the bachelor’s, master’s, and doctoral degree levels. The sex disparity is more severe among S&E doctorate holders than among S&E bachelor’s or master’s degree holders. For example, in 2015, women accounted for 41% and 40% of those whose highest degree in S&E was at the bachelor’s or master’s degree level, respectively, but for 31% of those whose highest degree in S&E was at the doctoral level. Engineering was an exception: in this field, women represented similar proportions of highest degree holders at the bachelor’s (15%) and doctorate degree levels (13%). However, for S&E fields overall at all three degree levels, the proportion of women has risen in the past two decades (Figure 3-28).

Working men and women with S&E highest degrees also differ in the extent to which they are employed in the same field as their S&E highest degree. This disparity is largely the result of women having a high concentration in the two degree areas—social sciences and life sciences—where degree holders most often work in an occupation outside of S&E. In 2015, these two broad fields accounted for nearly three-fourths (74%) of all employed women with S&E highest degrees, compared with 40% of all employed men with S&E highest degrees (Appendix Table 3-13).

Across all S&E degree areas, 18% of women with an S&E highest degree are employed in the S&E field in which they earned their highest degree, compared with 33% of men (Appendix Table 3-14). However, the pattern varies by degree fields. Among life sciences and engineering degree holders, similar proportions of men and women are employed in the broad S&E field in which they earned their degree. Computer and mathematical sciences fields represent an exception in which a larger proportion of men (59%) than women (43%) work in an occupation that matches their broad degree field and a larger proportion of women (37%) than men (25%) work in non-S&E occupations. The majority of social sciences degree holders work in non-S&E occupations, and this pattern is observed among both male (78%) and female (81%) degree holders.

Men and women with a highest degree in an S&E field also differ in their labor force nonparticipation rates. Compared with men, women are more likely to be out of the labor force (22% versus 16% for men). The difference in nonparticipation was particularly pronounced between the ages of 30 and 65 (Figure 3-29). In 2015, 19% of the women in this age group with an S&E highest degree were out of the labor force, compared with 8% of the men. Many women in this group identified family reasons as an important factor: 44% of women reported that family was a factor for their labor force nonparticipation, compared with 12% of men. Within this age range, women were also much more likely than men to report that they did not need to work or did not want to work (29% of women versus 17% of men). Men, on the other hand, were much more likely than women to cite retirement as a reason for not working (24% of women versus 50% of men).

The participation of underrepresented racial and ethnic minorities in the S&E workforce has been a concern of policymakers who are interested in the development and employment of diverse human capital to maintain the United States’ global competitiveness in S&E. This section addresses the level of diversity in S&E by race and Hispanic ethnicity.^​ Like the preceding section, this section draws on data from NSF’s surveys to report on levels of S&E participation, first across occupations and then across the overall workforce with S&E degrees.

Whether defined by occupation, S&E degree, or a combination of the two, the majority of scientists and engineers in the United States are non-Hispanic whites. The next largest group of scientists and engineers are Asians. Several racial and ethnic minority groups, including blacks, Hispanics, and American Indians or Alaska Natives, have low levels of participation in S&E fields both compared with other groups and compared with their proportion in the population (Table 3-19).

In 2015, among the 6.4 million workers employed in S&E occupations, 67% were white, which is close to the proportion (66%) in the U.S. population age 21 and older (Table 3-19). However, S&E participation by whites varied across the broad S&E occupational categories, from 62% of computer and mathematical scientists to 70% or more among the remaining broad S&E occupational fields of biological and life scientists, physical and related scientists, social scientists, and engineers (Appendix Table 3-15). The concentration of whites in some occupations was more pronounced: they accounted for at least 90% of workers among forestry and conservation scientists and geologists and earth scientists.

Asians, with 1.3 million workers in S&E occupations, accounted for 21% of S&E employment, much higher than their share of the U.S. population age 21 and older (6%). Asians had a large presence in computer and engineering occupations, constituting 38% of computer software engineers, 34% of software developers, 34% of computer hardware engineers, 36% of computer and information research scientists, and 30% of postsecondary teachers in engineering (Appendix Table 3-15). On the contrary, the proportion of Asians in social sciences occupations was much lower than their proportions in other S&E fields.

Overall, Hispanics accounted for 6% of employment in S&E occupations, which is lower than their share of the U.S. population age 21 and older (15%) (Table 3-19). Hispanics had a particularly large presence among psychologists (13%) and economists (13%); aerospace, aeronautical, or astronautical engineers (12%); and industrial engineers (9%). Blacks accounted for 5% of S&E employment, which is lower than their share of the U.S. population age 21 and older (12%). Blacks had relatively high participation rates among computer systems analysts (8%), computer support specialists (10%), information security analysts (13%), psychologists (9%), and industrial engineers (9%).

Over the past two decades, the U.S. workforce in S&E occupations has become more diverse, with increasing proportions of Asians, blacks, and Hispanics and a decreasing proportion of whites (Table 3-20). In 1993, 84% of workers in S&E occupations reported their race as white. By 2015, this proportion had declined to 67%. Most of the decline in the proportion of whites during this period was offset by an increase in the proportion of Asians and, to a lesser degree, by increases in the proportion of other groups, particularly Hispanics.

Some of the changes by race over time may reflect changes in the way that NSF and other federal agencies collect information on this topic as well as changes in racial composition of the general population over time. After 2000, respondents to NSF surveys were able to report more than one race. Some of those who self-reported as white in the 1990s may have instead reported a multiracial identity after 2000 once they were given this option, which would decrease the estimated numbers of whites. However, because less than 2% of S&E workers reported a multiracial identity in years when that option was available, it is unlikely that this change contributed much to the decline in the proportion of whites between 1993 and 2015.

Among those in the workforce whose highest degree is in S&E, the shares of racial and ethnic groups vary similarly across degree fields, as they do in occupations (Table 3-21; Appendix Table 3-16). Compared to most other broad S&E fields, Asians have higher participation rates among those with degrees in engineering and in computer and mathematical sciences; blacks have higher participation rates among those with degrees in computer and mathematical sciences and in social sciences; Hispanics have slightly lower participation rates among those with degrees in computer and mathematical sciences and in physical sciences. Whites represent smaller segments of degree holders in engineering and computer and mathematical sciences than in life, physical, and social sciences.

The demographic groups also differ in the level of their S&E highest degree (Table 3-22), with Asians accounting for larger proportions of those whose highest degree is at the master’s or doctoral level, relative to their counterparts with a highest degree at the bachelor’s level. Conversely, blacks, Hispanics, and whites all represent larger proportions of those whose highest degree is at the bachelor’s degree level, relative to those with a doctorate as their highest degree.

Asian S&E highest degree holders are more likely than those in other racial and ethnic groups to work in S&E occupations and to work in the area in which they earned their degree. Among black, Hispanic, and white S&E degree holders, between 20% and 26% work in their same broad field, compared to 37% among Asian S&E degree holders (Appendix Table 3-14).

The rise in female participation in S&E over the past two decades was the result of increasing participation by all race and ethnic groups, although the growth among Asian and Hispanic women was particularly strong. Among workers in S&E occupations, the number of women who identified themselves as Asian or Hispanic increased sixfold between 1995 and 2015. As a result, both the Asian share and the Hispanic share of female workers in S&E occupations rose during this period (Table 3-23). The number of women employed in S&E occupations who reported themselves as black more than doubled (rising by 159%) between 1995 and 2015. In comparison, although the number of female workers who identified themselves as being white and not of Hispanic origin rose substantially (97%), their participation did not grow as steeply as members of other race and ethnic groups, resulting in an overall decline in the share of white female S&E workers over time (Table 3-23). A broadly similar pattern is observed among female S&E highest degree holders.

Women and racial and ethnic minority groups generally receive less pay than their male and white counterparts (Table 3-24). However, salary differences between men and women were somewhat larger than salary differences among racial and ethnic groups (Table 3-24; Appendix Table 3-17 and Appendix Table 3-18).

Salaries differ across degree field, occupational field and sector, and experience. Such differences in degree and occupational fields account for a portion of the salary differences by sex and by race and ethnicity. Median salaries in 2015 were generally higher among full-time workers with a highest degree in engineering ($92,000), computer and mathematical sciences ($97,000), or physical sciences ($78,000) than for those with a highest degree in life sciences ($62,000) or social sciences ($69,000). Degree areas with lower salaries generally have higher concentrations of women and of racial and ethnic minorities. Disproportionately larger shares of degree holders in life sciences, and particularly in social sciences, compared with other S&E degree fields, work in occupations not categorized as S&E, and the salaries for these occupations are generally lower than for S&E occupations (Appendix Table 3-17).

Salaries also differ across employment sectors. Academic and nonprofit employers typically pay less for similar skills than employers in the private sector, and government compensation generally falls somewhere between these two groups. These differences are important for understanding salary variations by sex and by race and ethnicity because men, Asians, and whites are more highly concentrated in the private, for-profit sector.

Salaries also vary by indicators of experience, such as age and years since completing one’s degree. Because of the rapid increase in female participation in S&E fields in recent years, women with S&E degrees who are employed full time generally have fewer years of labor market experience than their male counterparts: the median number of years since highest degree is 14 years for women versus 17 years for men; the median age is 39 years for women versus 43 years for men. Whites with S&E degrees who are employed full time also generally have more years of labor market experience than other racial and ethnic groups: the median number of years since highest degree is 18 years for whites, 14 years for Asians, 11 years for Hispanics, and 12 years for blacks.

Differences in average age, work experience, academic training, sector and occupation of employment, and other characteristics can make direct comparison of salary statistics misleading. Statistical models can estimate the size of the salary difference between men and women, or the salary differences between racial and ethnic groups, when various salary-related factors are taken into account. Estimates of these differences vary somewhat depending on the assumptions that underlie the statistical model used. The analyses presented in this section show that statistical models used to control for effects of education, experience, and other factors on salaries tend to reduce, but not fully eliminate, the disparities. The remainder of this section presents estimated salary differences between men and women among individuals who are otherwise similar in age, work experience, field of highest degree, occupational field and sector, number of children, and other relevant characteristics that are likely to influence salaries. Data related to salary differences between minorities (American Indians or Alaska Natives, blacks, Hispanics, Native Hawaiians or Other Pacific Islanders, and those reporting more than one race) and Asians and whites are also included.

Accounting only for level of degree, women working full time whose highest S&E degree is at the bachelor’s level earned 30% less than men (Figure 3-30).^​ The salary difference is smaller but substantial at both the master’s level (28%) and the doctoral level (21%). The salary differences for non-Asian minorities relative to whites and Asians are narrower (Figure 3-31). On average, minority salary levels are 24% lower than those of whites and Asians at the bachelor’s level, 18% lower at the master’s level, and 14% lower at the doctoral level.

Controlling for the effects of differences in field of highest degree, degree-granting institution, field of occupation, employment sector, and experience,^​ the estimated salary difference between men and women narrows by more than half (Figure 3-30). However, women still earn 9% less than men among individuals whose highest degree is at the bachelor’s level, and 8% less than men among individuals whose highest degree is at the master’s or doctoral level. The pattern by degree level is similar among racial and ethnic groups: compared with whites and Asians, S&E highest degree holders in other racial and ethnic groups working full time earn 9% and 5% less for the bachelor’s and doctoral degree levels, respectively (Figure 3-31). ^​

The analysis of salary differences suggests that attributes related to human capital (fields of education and occupation, employment sector, and experience) rather than socioeconomic and demographic attributes have a greater influence in explaining the salary differences observed among S&E highest degree holders by sex and across racial and ethnic groups. Nonetheless, the analysis also shows that measurable differences in human capital do not entirely explain income differences between demographic groups.^​

Readers should keep in mind that the interaction between demographic attributes and those related to human capital are complicated. For instance, among scientists and engineers who are not working, women are more likely than men to report family reasons for not working, and this pattern is quite robust across race and ethnic groups (it holds for Asians, whites, and underrepresented minorities). Furthermore, women who remain in the workforce may choose labor-force pathways that are more amenable to having a family. For example, among scientists and engineers who work part time, women are more likely than men to cite family reasons for working part time.^​ These factors are likely to affect labor market outcomes for women and thus complicate the analysis involving human capital, demographic attributes, and salary differences.

Salaries vary by factors beyond education, occupation, and experience. For example, marital status, the presence of children, parental education, and other personal characteristics are often associated with salary differences. These differences reflect a wide range of issues, including (but not limited to) factors affecting individual career- and education-related decisions, differences in how individuals balance family obligations and career aspirations, and productivity and human capital differences among workers that surveys do not measure, and possible effects of employer prejudice or discrimination. Salaries also differ across regions, partly reflecting differences in the cost of living across geographic areas.

However, adding such measures of personal and family characteristics to education, occupation, and experience results in only marginal changes in the estimated salary differences between men and women, and among racial and ethnic groups,^​ compared with estimates that account for education, occupation, and experience alone. Women’s adjusted salary differentials are 8% among S&E doctorates—7% among S&E bachelor’s degree and 6% among master’s degree holders (Figure 3-30). Adjusted salary differences among racial and ethnic groups are approximately 8% and 9% among bachelor’s degree and doctorate holders, respectively (Figure 3-31).^​