Skip all navigation and go to page content.

Chapter 3. Science and Engineering Labor Force

Women and Minorities in the S&E Workforce

As researchers and policymakers increasingly emphasize the need for expanding S&E capabilities in the United States, many view demographic groups with lower rates of S&E participation as an underutilized source of human capital for S&E work. Historically, in the United States, S&E fields have had particularly low concentrations of women and members of many racial and ethnic minority groups (i.e., blacks, Hispanics, American Indians or Alaska Natives), both relative to the concentrations of these groups in other occupational or degree areas and relative to their representation in the general population. However, women and racial and ethnic minorities increasingly have been choosing a wider range of degrees and occupations over time. This section presents data on S&E participation by women and by racial and ethnic minorities. It also presents data on earnings differentials by sex and by race and ethnicity.

Women in the S&E Workforce

Historically, men have outnumbered women by wide margins with regards to both S&E employment and S&E training. Although the number of women in S&E occupations or with S&E degrees nearly doubled over the past two decades, the disparity has narrowed only modestly. The imbalance is still particularly pronounced in S&E occupations. In 2010, women constituted only 28% of workers in these occupations, even though they accounted for nearly half of the college-educated workforce. Among S&E degree holders, the disparity was smaller but nonetheless significant, with women representing 37% of employed individuals with a highest degree in S&E (figure 3-29).

Women in S&E Occupations

Although women represented only 28% of individuals in S&E occupations in 2010, women’s presence varies widely across S&E occupational fields (appendix table 3-13). The percentage of female S&E workers is lowest in engineering, where women constituted 13% of the workforce in 2010. Among engineering occupations with large numbers of workers, the disparity between men and women is greatest among mechanical engineers, with women accounting for only 7% of the workforce. Other large engineering occupations in which women account for about 11% to 12% of the workforce include electrical and computer hardware engineers and aerospace, aeronautical, and astronautical engineers.

Other disproportionately male S&E occupations include physical scientists (30% women) and computer and mathematical scientists (25% women). Within the physical sciences occupations, physicists and astronomers have the largest imbalance (18% women). Within the computer and mathematical sciences occupations, the largest component, computer and information scientists, has the smallest proportion of women (23%). The mathematical scientists component is much closer to parity (46% women).

In 2010, sex parity in S&E occupations was close among life scientists (48% women). Within the life sciences occupations, biological and medical scientists, the largest component, had reached gender parity (52% women). The field of social sciences was majority female (58%). Occupations within the social sciences, however, varied with respect to the proportion of female workers. Thus, women accounted for slightly more than one-third of economists (37%) but more than two-thirds of psychologists (70%). Psychologists, estimated at about 171,000 total workers in SESTAT (appendix table 3-13), was an example of 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 (56%) are held by women (appendix table 3-13). The largest component, health-related occupations, employed a large number of women (68% women), primarily as nurse practitioners, pharmacists, registered nurses, dietitians, therapists, physician assistants, and health technologists and technicians.

Since the early 1990s, the number of women working in each broad S&E occupational category has risen significantly. The rate of growth has been strongest among life scientists, computer and mathematical scientists, and social scientists. These three broad S&E fields together employed 80% of women in S&E occupations in 2010, compared with 59% of men in S&E occupations. Between 1993 and 2010, the number of women more than doubled among life scientists (an increase of 162%) and nearly doubled among social scientists (an increase of 87%). 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 (figure 3-30).

During the same period, the number of women in computer and mathematical sciences occupations nearly doubled (an increase of 97%). However, unlike the other broad S&E occupational categories, the rate of growth in male participation was larger (161%) than that of women, resulting in an overall decline in the proportion of women from 31% to 25%. These trends made the gender disparity among computer and mathematical scientists second only to engineers. The declining proportion of women in the computer and mathematical sciences occupations reflects increasing disparities in participation among those whose highest degree is at the bachelor’s degree level. Among computer and mathematical scientists with a doctoral degree, the proportion of women increased, from 16% in 1993 to 20% in 2010.

During the past two decades, women have also increased their proportion among workers in engineering (from 9% to 13%) and in the physical sciences (from 21% to 30%). In these two occupational categories, this increase was led by an expansion of women’s numbers in the workforce (by 67% in engineering and 60% in physical sciences) while men’s numbers barely changed between 1993 and 2010.

Women among S&E Highest Degree Holders

The sex disparity among employed S&E highest degree holders is less than the disparity among those in S&E occupations. In 2010, among individuals with a highest degree in an S&E field, women constituted 37% of those who were employed, up from 31% in 1993. 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 3-14). In 2010, 54% of S&E highest degree holders in the social sciences fields were women, as were 48% of those with a highest degree in the biological and related sciences. Men outnumbered women among computer sciences and mathematics highest degree holders (28% women) and among physical sciences highest degree holders (27% women). Disparities, however, were greatest among those with a highest degree in engineering (only 14% women). In all fields except computer and mathematical sciences, the proportion of women in the workforce with associated highest degrees has been increasing over the past two decades. In computer and mathematical sciences, this proportion has declined even as the number of women with a highest degree in the field has risen.

Sex differences are not limited to the field of degree, but also extend to the level of S&E degree. Men outnumber women among S&E highest degree holders at the bachelor’s, master’s, and doctoral levels. Moreover, the sex disparity is higher among S&E doctorate holders than among S&E bachelor’s or master’s degree holders. For example, in 2010 women accounted for 38% of those whose highest degree in S&E was at the bachelor’s or master’s level but 30% of those whose highest degree in S&E was at the doctoral level (figure 3-31). At the doctoral level, however, the proportion of women has been steadily increasing. The trend at the bachelor’s and master’s levels has been somewhat different: although the proportion of women in the workforce rose from 1993 to 2003, it remained mostly steady from 2003 to 2010 (figure 3-31).

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. However, 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 non-S&E occupations. In 2010, these two broad fields accounted for three-fourths of all employed women with S&E highest degrees, compared with 41% of all employed men with S&E highest degrees (appendix table 3-14). (See sidebar, “S&E Credentials and the Male-Female Gap in S&E Employment.”)

Across all S&E degree areas, 19% of women with an S&E highest degree are employed in the S&E field in which they earned their degree compared with 32% of men (appendix table 3-15). However, within the majority of degree areas (life sciences, social sciences, and engineering), similar proportions of men and women are employed in the S&E field in which they earned their degree. Computer and mathematical sciences fields are exceptions, where a larger proportion of men (54%) than women (43%) work in an occupation that matches their degree field and a larger proportion of women (38%) than men (27%) work in non-S&E occupations. Among those with life sciences degrees, although a similar proportion of men (23%) and women (22%) work in their degree field, a larger proportion of women (35%) than men (18%) are employed in S&E-related occupations. These sex differences in the degree fields of life sciences and computer and mathematical sciences are primarily driven by those whose highest degrees are at the bachelor’s or master’s levels.

Men and women with a highest degree in an S&E field also differ in their labor force nonparticipation rates. Compared with men, women were more likely to be out of the labor force (22% versus 14% for men). The difference in nonparticipation was particularly pronounced between the ages of 30 and 65 (figure 3-32). In 2010, 19% of the women in this age group with an S&E highest degree were out of the labor force compared with 7% of the men. Many women in this group identified family reasons as an important factor: 48% of women reported that family was a factor for their labor force nonparticipation compared with 9% 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 (41% of women versus 26% of men). Men, on the other hand, were much more likely than women to cite retirement as a reason for not working (28% of women versus 71% of men).

Minorities in the S&E Workforce

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.[21] Like the preceding section, this section draws on data from NSF’s SESTAT 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 the combined criteria used in SESTAT, the majority of scientists and engineers in the United States are non-Hispanic whites. The next largest group of scientists and engineers are Asians. On the other hand, 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 3-22).

Race and Ethnicity Trends in S&E Occupations

In 2010, among the 5.4 million workers employed in S&E occupations, 70% were white, which is similar to the proportion (68%) in the U.S. population age 21 and older (table 3-22). However, S&E participation by whites varied across the broad S&E occupational categories, from 65% of computer and mathematical scientists to 81% of social scientists (appendix table 3-16). The concentration of whites in some occupations was more pronounced: they accounted for approximately 90% of workers among forestry and conservation scientists, geologists and earth scientists, and political scientists.

Asians, with nearly a million workers in S&E occupations, accounted for 19% of S&E employment. Among the overall population age 21 and older, their proportion was much smaller (5%). Asians had a large presence in computer and engineering fields, constituting 33% of computer software engineers, 30% of software developers, 40% of computer hardware engineers, 27% of bioengineers or biomedical engineers, and 35% of postsecondary teachers in engineering (appendix table 3-16). On the other hand, the proportion of Asians in social sciences occupations was much lower both compared with their participation in other S&E fields and compared with whites. For example, Asians accounted for just 6% of workers in social sciences occupations.

The social sciences are the one S&E occupational category in which the proportions of blacks (5%) and Hispanics (6%) are similar to that of Asians (6%) (appendix table 3-16). As a result, underrepresented racial and ethnic minorities (blacks, Hispanics, and American Indians or Alaska Natives) collectively outnumber Asians among social scientists. In the other broad S&E occupational categories, Asians represent a larger segment than all underrepresented racial and ethnic minorities combined.

In general, the proportions of Hispanics across the broad S&E occupational categories were roughly similar (between 5% and 6%), whereas blacks had higher rates of participation among computer and mathematical scientists (6%) relative to life scientists (3%), physical scientists (3%), and engineers (4%) (appendix table 3-16). Hispanics had a particularly large presence among sociologists (13%); psychologists (7%); aeronautical, aerospace, and astronautical engineers (9%); and civil engineers (8%). Blacks had relatively high participation rates among computer support specialists (16%), information security analysts (14%), and sociologists (13%).

Over the past two decades, the U.S. workforce in S&E occupations has been becoming more diverse with increasing proportions of Asians, blacks, and Hispanics and a decreasing proportion of whites (table 3-23). In 1993, 84% of workers in S&E occupations reported their race as white. By 2010, this proportion declined to 70%. 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 an increase in the proportion of some other groups, particularly Hispanics.

Some of the changes by race may reflect changes to the way NSF workforce surveys collect information on this topic. After 2000, respondents were able to report two or more races rather than just one. Some of those who self-reported as white in the 1990s may have instead reported a multiracial identity after 2000 once they were given the 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 2010.

Racial and Ethnic Differences among S&E Degree Holders

Among employed S&E highest degree holders, racial and ethnic groups vary with respect to their proportions in different degree fields (table 3-24; appendix table 3-17). Differences in highest degree fields largely resemble the differences among S&E occupations. Asians have higher participation rates among engineering highest degree holders and among computer and mathematical sciences highest degree holders relative to other broad S&E degree fields. Blacks have higher participation rates in computer and mathematical sciences and in the social sciences. Hispanics have higher participation rates in engineering and in the social sciences. Whites represent a larger segment of life, physical, and social sciences highest degree holders than engineering or computer and mathematical sciences highest degree holders.

The demographic groups also differ in the level of their highest degree (table 3-25). For example, Asians account for a larger proportion of those whose highest degree is at the master’s or doctoral level compared with those whose highest degree is at the bachelor’s level. Conversely, non-Asians represent a larger proportion of those whose highest degree is at the bachelor’s and master’s degree level compared with those whose highest degree is at the doctoral level.

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 (appendix table 3-15). Among blacks, Hispanics, and whites, about one-quarter or less of S&E highest degree holders work in their same broad field of highest degree. By comparison, nearly 40% of Asians work in the same broad field in which they received their highest degree.

Salary Differences for Women and Racial and Ethnic Minorities

Women and racial and ethnic minority groups generally receive less pay than their male and white counterparts (table 3-26). In 2010, among full-time workers with a highest degree in an S&E field, the median salary for women ($53,000) was about one-third lower than that for men ($80,000). Among S&E highest degree holders who work full-time in S&E occupations, the difference in median salary between men ($85,000) and women ($69,000) was smaller (19% less) (appendix table 3-18).

Salary differences among racial and ethnic groups were somewhat smaller than salary differences between men and women (table 3-26; appendix table 3-19). Among S&E highest degree holders working full time, American Indians or Alaska Natives earned 18% less than whites, blacks earned 22% less than whites, and Hispanics earned 17% less than whites. Relative to Asians, American Indians or Alaska Natives earned 21% less, blacks earned 25% less, and Hispanics earned 20% less. These salary differences were generally more modest among those who worked in S&E occupations (appendix table 3-19).

Overall, salary differences between men and women and among racial and ethnic groups remained largely unchanged between 1995 and 2010 (table 3-26).

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 difference 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 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, type of academic institution awarding highest degree (Carnegie classification and public/private status), occupational field and sector, and other relevant characteristics that are likely to influence salaries. Data bearing on salary differences between minorities (American Indians or Alaska Natives, blacks, Hispanics, Native Hawaiians or Other Pacific Islanders, and those reporting more than one race) relative to Asians and whites are also included.

Without accounting for any factors except level of degree, women working full time whose highest degree is at the bachelor’s level in an S&E field earned 31% less than men (figure 3-33).[22] The salary difference is smaller, but nonetheless substantial, at both the master’s level (29%) and the doctoral level (22%). The salary differences for non-Asian minorities relative to whites and Asians are narrower (figure 3-34). On average, minority salary levels are 22% lower than those of whites and Asians at the bachelor’s level, 14% lower at the master’s level, and 16% lower at the doctoral level.

Effects of Education, Employment, and Experience on Salary Differences

Salaries differ across degree field, occupational field and sector, and experience. For example, median salaries in 2010 were generally higher among individuals with highest degrees in engineering ($86,000), physical sciences ($68,000), or computer and mathematical sciences ($79,000) compared with those with highest degrees in life sciences ($50,000) or social sciences ($50,000). Degree areas with lower salaries generally have higher concentrations of women and of racial and ethnic minorities. Disproportionately larger proportions of degree holders in life sciences, and particularly in the social sciences, relative to other S&E degree fields, work in occupations not categorized as S&E, where salaries are generally lower than in S&E occupations (appendix table 3-18). As a result, differences in degree and occupational fields are likely to explain much of the salary differences by sex and by race and ethnicity.

Salaries also differ across employment sector. Academic and non-profit employers typically pay less for similar skills than employers in the private sector, and government compensation falls somewhere between these two groups. These differences are salient 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 or years since completing one’s degree. Because of the rapid increase in female participation in S&E fields in recent years, female S&E highest degree holders employed full time are younger than their male counterparts (median age 40 years for women versus 44 years for men), which translates to fewer years of labor market experience for women relative to men. White S&E highest degree holders with similar characteristics are also older (44 years) compared with Asians (39 years) and most other racial and ethnic minorities (Hispanics: 39 years, blacks: 42 years, American Indians or Alaska Natives: 43 years, and Native Hawaiians or Other Pacific Islanders: 33 years).

After controlling for differences in field of highest degree, degree-granting institution, field of occupation, employment sector, and experience,[23] the estimated salary difference between men and women narrows by more than half (figure 3-33). However, among men and women in similar jobs, and with similar highest degree fields and levels of experience, women still earn 12% less than men among individuals whose highest degree is at the bachelor’s level, 10% less than men among individuals whose highest degree is at the master’s level, and 9% less than men among individuals whose highest degree is at the doctoral level.

Compared with whites and Asians, other racial and ethnic groups with their highest degree at the bachelor’s level also earn less (15%) after controlling for education, occupation, and experience (figure 3-34). Although the initial salary gap for racial and ethnic minorities is smaller than for women, less of this initial salary gap is explained by differences in education, occupation, and experience. Among those whose highest degree is at the bachelor’s level, after controlling for education, occupation, and experience, more than half of the initial salary gap among racial and ethnic minorities persists, compared to less than half of the initial salary gap persisting among women. In comparison, among those with a master’s or doctoral degree, the salary gap across racial and ethnic groups is significantly attenuated: after controlling for these factors, the salary gap is only 5% for those at the master’s degree level and only 4% for those at the doctorate level.

Effects of Demographic and Other Factors on 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, both voluntary and involuntary, including, but not limited to, factors affecting individual career- and education-related decisions, differences in how individuals balance family obligations and career aspirations, 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 measures of personal and family characteristics that may affect compensation[24] to education, occupation, and experience results in only marginal changes in the estimated salary differences between men and women compared with estimates that account for education, occupation, and experience alone. Women who are similar to men along all of these dimensions receive salaries that are 11% (among bachelor’s degree holders) to 8% (among doctoral degree holders) less than their male counterparts (figure 3-33). The salary difference among racial and ethnic groups largely disappears among advanced degree holders, but a significant amount of the difference remains among bachelor’s degree holders (figure 3-34).

The analysis of salary differences suggests that attributes related to human capital (fields of education and occupation, employment sector, and experience) are much more important than socioeconomic and demographic attributes 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.[25]

Salary Differences among Recent Graduates

Salary differences among recent S&E graduates warrant particular attention. Employment metrics of recent graduates are important indicators of current conditions in the labor market, particularly for young people considering S&E careers. Salary differences among recent S&E graduates, particularly across racial and ethnic groups, are substantially narrower than in the population of S&E degree holders as a whole. This suggests that recent cohorts of S&E highest degree holders are much closer to earnings parity than their older counterparts. For example, in 2010, among recent graduates who attained their highest degree in or after 2005, minorities working full time earned 7% (among those whose S&E highest degree was at the bachelor’s or doctorate level) to 8% (among those whose S&E highest degree was at the master’s level) less than Asians and whites. These salary differences are substantially higher, ranging from 14% to 22%, among all S&E highest degree holders (regardless of graduation year) (figure 3-34). After accounting for differences in education, occupation, and experience, the salary differences for recently graduated minorities relative to whites and Asians are almost attenuated among bachelor’s degree holders (a 3% salary gap remains) and completely attenuated among advanced degree holders. In contrast, when all S&E highest degree holders (regardless of graduation cohort) are included in the analysis, a significant amount of the salary gap remains unexplained by these human capital attributes, particularly among bachelor’s degree holders (figure 3-34).

After controlling for differences in education, employment, demographic, and socioeconomic attributes, the gender salary gap among recent graduates is not completely attenuated, but it is lower. After controlling for these factors, women earn about 5% to 9% less than men among recent graduates, compared with about 8% to 11% less among all S&E highest degree holders (regardless of graduation cohort).

Notes
[21] In this chapter, American Indian or Alaska Native, Asian, black, Native Hawaiian or Other Pacific Islander, white, and more than one race refer to individuals who are not of Hispanic origin. Hispanics may be any race.
[22] Salary differences represent estimated percentage differences in women’s reported full-time annual salary relative to men’s reported full-time annual salary as of October 2010. Coefficients are estimated in an ordinary least squares regression model using natural log of full-time annual salary as the dependent variable. This estimated percentage difference in earnings differs slightly from the observed difference in median earnings by sex because the former addresses differences in mean earnings rather than median.
[23] Included are 20 SESTAT field of degree categories (out of 21 S&E fields), 38 SESTAT occupational categories (out of 39 categories), 6 SESTAT employment sector categories (out of 7), years since highest degree, years since highest degree squared, Carnegie classification of school awarding highest degree, and private/public status of postsecondary institution awarding highest degree.
[24] In addition to the education- and employment-related variables, the following indicators are included: nativity and citizenship, marital status, disability, number of children living in the household, geographic region (classified into nine U.S. Census divisions), and whether either parent holds a bachelor’s or higher level degree. The sex regression controls for racial and ethnic minority status, and the race and ethnicity regression controls for sex.
[25] The regression analysis addresses major factors that affect differences in earnings but does not attempt to cover all possible sources of difference. For a more detailed discussion on the topic, see Blau and Kahn (2007), Mincer (1974), Polachek (2008), and Xie and Shauman (2003).
Close