U.S. S&E Labor Force Profile
- Section Overview
- How Large Is the U.S. S&E Workforce?
- S&E Workforce Growth
- Salary Changes as an Indicator of Labor Market Conditions
- Salaries Over a Person’s Working Life
- How Are People With an S&E Education Employed?
- S&E Employment From Occupational Employment Statistics Survey
- Annual Earnings From OES Data
- Metropolitan Areas
- S&E Occupation Density by Industry
- Employment Sectors
- Employer Size
- Educational Distribution of S&E Workers
- Women and Minorities in S&E
This section profiles the U.S. S&E labor force, providing specific information about its size, recent growth patterns, projected labor demand, and trends in sector of employment. It also looks at workers’ use of their S&E training, educational background, and salaries.
The S&E labor force includes both individuals in S&E occupations and many others with S&E training who may use their knowledge in a variety of different jobs. Employment in S&E occupations has grown rapidly over the past two decades and is currently projected to continue to grow faster than general employment through the next decade. Although most individuals with S&E degrees do not work in occupations with formal S&E titles, most of them, even at the bachelor’s degree level, report doing work related to their degree even in mid- and late-career. The proportions of women and ethnic minorities in the S&E labor force continue to grow, but with the exception of Asians/Pacific Islanders, they remain smaller than their respective proportion of the overall population.
How Large Is the U.S. S&E Workforce?
Estimates of the size of the U.S. S&E workforce vary based on the criteria used to define who is a scientist or an engineer. Education, occupation, field of degree, and field of employment are all factors that may be considered. (See sidebar "Who Is a Scientist or an Engineer?")
Estimates of the size of the S&E workforce in 2006 ranged from approximately 5 million to more than 21 million individuals, depending on the definition and perspective used
If the labor force definition is limited to those in S&E occupations with at least a bachelor’s degree, the 2006 NSF SESTAT data estimated 5.0 million workers, whereas the Census Bureau’s 2005 American Community Survey estimated 3.9 million. Occupation-based estimates not limited to college graduates include 5.4 million in May 2006 from the Bureau of Labor Statistics (BLS) Occupational Employment Statistics Survey (OES) and 5.3 million from the 2005 American Community Survey. OES and NSF SESTAT occupational estimates include postsecondary teachers in S&E fields, but estimates from the American Community Survey, the Current Population Survey (CPS), and the decennial census have to exclude postsecondary teachers, as no information on field is collected.
Terminology referring to the technical labor force can be confusing. Sometimes a study will refer to the science and technology (S&T) or to the STEM (science, technology, engineering, and math) labor force. These terms are approximately equivalent, and as used in this chapter include all S&E occupations with the addition of technicians, programmers, technical managers, and a small number of nonhealth S&E-related occupations such as actuary and architect. In addition, some recent reports from private organizations have used the label "S&E labor force" to discuss what is labeled here as "S&T occupations." The estimate from the May 2006 OES of individuals employed in S&T occupations is 7.4 million.
A third measure, based on self-reported need for S&E knowledge, is available from the 2003 SESTAT for workers with degrees from all fields of study. An estimated 12.9 million workers reported needing at least a bachelor’s degree level of S&E knowledge, with 9.2 million reporting a need for knowledge of the natural sciences and engineering (NS&E) and 5.3 million a need for knowledge of the social sciences (1.6 million reported a need for both social science and NS&E knowledge). That the need for S&E knowledge is more than double the number in formal S&E occupations suggests the pervasiveness of the need for technical knowledge in the modern workplace.
S&E Workforce Growth
Occupation classifications allow examination of growth in at least one measure of scientists and engineers over extended periods (for a discussion of even longer time periods, see the sidebar "Scientists Since Babylon"). According to data from the decennial censuses, the number of workers in S&E occupations grew to 4.8 million, at an average annual rate between 1950 and 2000 of 6.4%, compared with a 1.6% average annual rate for the whole workforce older than age 18. By a broader definition of the S&T occupations including technicians and programmers, S&T occupations grew to 5.5 million at a 6.8% average annual rate
The growth rate of S&E employment continued to be greater than for the full workforce in the 1990s
In all broad categories of S&E fields, employment in the occupations directly associated with the category has grown faster than new degree production (see chapter 2 for a fuller discussion of S&E degrees). Average annual growth rates of employment and degree production are shown in
Using data from the monthly CPS from 1983 to 2006 to look at employment in S&E occupations across all sectors and education levels creates a very similar view, albeit with some significant differences. The 3.1% average annual growth rate in all S&E employment is almost triple the rate for the general workforce. This is reflected in the growing proportion of total jobs in S&E occupations, which increased from 2.6% in 1983 to 4.2% in 2006. Also noteworthy are the decreases in employment in S&E occupations in 1992 and again in 2002, evidence that S&E employment is not exempt from economic downturns
Projected Demand for S&E Workers
The most recent occupational projections from BLS, for 2004–14, forecast that total employment in occupations that NSF classifies as S&E will increase at nearly double the overall growth rate for all occupations
S&E occupations are projected to grow by 26% from 2004 to 2014, while employment in all occupations is projected to grow 13% over the same period (BLS 2006). However, S&E occupations may be particularly difficult to forecast. Many spending decisions on R&D by corporations and governments are difficult or impossible to anticipate. In addition, R&D money increasingly crosses borders in search of the best place to have particular research performed. (The United States may be a net recipient of these R&D funds; see discussion in chapter 4.) Finally, it may be difficult to anticipate new products and industries that may be created via the innovation processes that are most closely associated with scientists and engineers.
Approximately 73% of BLS’s projected increase in S&E jobs is in computer-related occupations
BLS also forecasts that job openings in NSF’s list of S&E occupations over the 2004–14 period will be a slightly greater proportion of current employment than for all occupations: 42% versus 38%
Salary Changes as an Indicator of Labor Market Conditions
Sometimes discussions of S&E labor markets use difficult-to-define words like "surplus" or "shortage" that imply a close matching between particular types of educational credentials or skill sets and particular jobs. As discussed previously in this chapter, individuals with a particular S&E degree may use their training in occupations nominally associated with different S&E fields or in occupations not considered S&E. They may also work in various sectors of employment such as private industry, academia, government, or K–12 education. All of this makes any "simple" comparison between projections of labor supply and market demand impossible.
One indicator of the level of labor market demand, compared with the supply of individuals with those skills, is the changes observed over time in the pay received by individuals with similar sets of skills. The changes between 1993 and 2003 in real (inflation-adjusted) mean salary for recent graduates in S&E fields are shown in
Among recent S&E master’s degree recipients, real mean salaries increased 12%, ranging from 3% in the social sciences to 24% in engineering.
Among recent doctoral degree recipients, the increase in median real salary was greatest for those in the physical sciences and mathematical and computer science (each 20%) and smallest was in the life sciences (8%). Evaluation of recent doctoral degree recipient salaries is made more difficult by the earnings differentials between academic and nonacademic employment, as well as the increasing prevalence of lower-paying postdoc positions.
Salaries Over a Person’s Working Life
Estimates of median salary at different points in a person’s working life are shown in
How Are People With an S&E Education Employed?
Although most S&E degree holders do not work in S&E occupations, this does not mean they do not use their S&E training. In 2003, of the 6.0 million individuals whose highest degree was in an S&E field and who did not work in S&E occupations, 66% indicated that they worked in a job either closely or somewhat related to the field of their highest S&E degree
One to four years after receiving their degrees, 96% of S&E doctoral degree holders say that they have jobs closely or somewhat related to the degrees they received, compared with 91% of master’s degree recipients and 73% of bachelor’s degree recipients
Even when a stricter criterion ("closely related") is used for the fit between an individual’s job and field of degree, the data indicate that many recent bachelor’s degree recipients work in jobs that use skills developed during their college S&E training
Employment in Non-S&E Occupations
About 6.0 million individuals whose highest degree is in S&E worked in non-S&E occupations in 2003. Of these, two-thirds said that their job was at least somewhat related to their degree
A more than two-decades-long view of unemployment trends in S&E occupations, regardless of education level, comes from the CPS data for 1983–2006. Unemployment of college degree holders in S&E occupations fell to 1.6% in 2006, reflecting a recovery from employment difficulties earlier in the decade. This compares to a 4.6% unemployment rate for all workers in 2006 and a 2.2% unemployment rate for other college graduates. Unemployment rates also declined in the S&E-related occupational categories of technicians and computer programmers (not limited by education level) to 3.1% and 2.8%, respectively.
During this 22-year period, the unemployment rate for all individuals in S&E occupations ranged from a low of 1.3% in 1997 and 1998 to a high of 4.0% in 2003. Overall, the S&E occupational unemployment rate was both lower and less volatile than either the rate for all U.S. workers (ranging from 3.9% to 9.9%), for all workers with a bachelor’s degree or higher (ranging from 1.8% to 7.8%), or for S&E technicians (ranging from 2.0% to 6.1%). During most of the period, computer programmers had an unemployment rate similar to that of S&E occupations, but greater volatility (ranging from 1.2% to 6.7%). The most recent recession (in 2001) appears to have had a strong effect on S&E employment, with the differential between S&E and general unemployment falling to only 1.9 percentage points in 2002, compared with 6.9 percentage points in 1983
Similarly, labor market conditions from 1999 to 2003 had a greater effect on the proportion of bachelor’s degree holders than on doctoral degree holders who said they were working involuntarily out of the field (IOF) of their highest degree
S&E Employment From Occupational Employment Statistics Survey
Estimates of employment in S&E occupations in the United States from the OES survey of employers reached 5.4 million in May 2006
Science and Technology Occupations
Discussions of the S&E labor force sometimes use broader definitions, referring to the S&T or the STEM labor force. These broader definitions usually include technicians, computer programmers, and technical managers, along with those occupations that NSF considers to be S&E. The broader aggregate may thus be thought of as S&E occupations plus individuals who directly manage S&E activities and the technical workers who support those in S&E occupations. Total employment in this broader set of S&T occupations was 7.4 million in May 2006. The distribution of employment across S&T occupations is shown in
A number of occupations may be considered related to this broader set of S&T occupations. They include healthcare occupations and a number of technical occupations such as actuary and architect. Overall, the more than 7 million people in these additional occupations increased by an average annual rate of 2.9%.
Annual Earnings From OES Data
Median annual earnings (regardless of education) in S&E occupations were $67,780, more than double the median ($30,400) for all occupations
The growth in mean earnings was slightly greater for all S&E and S&E-related occupation groups than for the total of all occupations included in OES, an average annual rate of 3.1% in S&E occupations, 2.9% in technology occupations, and 3.8% in other S&E-related occupations, compared with 2.9% for all occupations. Technicians and programmers experienced a slower than average 2.6% average annual growth in earnings.
United States metropolitan areas are ranked in
S&E Occupation Density by Industry
Individuals in S&E occupations are not just employed by "high-technology" employers. S&E knowledge is necessary in a variety of different industries, and as shown in
In general, industries with higher proportions of individuals in S&E occupations pay higher average salaries to both S&E and non-S&E workers. The average salary of workers in non-S&E occupations who are in industries with more than 40% S&E occupations is nearly double the average salary of workers in non-S&E occupations in industries with below average density of S&E occupations ($68,600 versus $34,600).
Industry is the largest provider of employment for individuals with S&E degrees
Industry also dominates employment in S&E occupations in the BLS’s OES survey
Small firms are important employers of scientists and engineers, particularly at the doctoral degree level. For individuals whose highest degree is in S&E and who are employed in business/industry, the distribution of employer size is shown in
Educational Distribution of S&E Workers
Discussions of the S&E workforce often focus on individuals who hold doctoral degrees. However, American Community Survey data on the educational achievement of individuals working in S&E occupations outside academia in 2005 indicate that only 7% had doctorates
Although technical issues of occupational classification may inflate the estimate of the size of the nonbaccalaureate S&E workforce, it is also true that many individuals who have not earned a bachelor’s degree enter the labor force with marketable technical skills from technical or vocational school training (with or without earned associate’s degrees), college courses, and on-the-job training. In information technology (IT), and to some extent in other occupations, employers frequently use certification exams, not formal degrees, to judge skills. (See sidebar "Who Performs R&D?" and discussion in chapter 2.)
A cross-sectional profile of median 2003 salaries for S&E degree holders over the course of their career is shown in
Women and Minorities in S&E
Demographic factors for women and minorities (such as age and years in the workforce, field of S&E employment, and highest degree level achieved) influence employment patterns. Demographically, men differ from women, and minorities differ from nonminorities; thus, their employment patterns also are likely to differ. For example, because larger numbers of women and minorities entered S&E fields only recently, women and minority men generally are younger than non-Hispanic white males and have fewer years of experience. Age and stage in career in turn influence such employment-related factors as salary, position, tenure, and work activity. In addition, employment patterns vary by field (see sidebar "Growth of Representation of Women and Ethnic Minorities in S&E Occupations"), and these differences influence S&E employment, unemployment, salaries, and work activities. Highest degree earned, yet another important influence, particularly affects primary work activity and salary.
Representation of Women in S&E
Women constituted more than one-fourth (26%) of the college-educated workforce in S&E occupations (and more than one-third, 37%, of those with S&E degrees) but close to half (47%) of the total U.S. college-educated labor force in 2005.
Age Distribution and Experience. Differences in age and related time spent in the workforce account for many of the differences in employment characteristics between men and women. On average, women in the S&E workforce are younger than men
S&E Occupation. Representation of men and women also differs according to field of occupation. For example, in 2003, women constituted 52% of social scientists, compared with 29% of physical scientists and 11% of engineers
Labor Force Participation, Employment, and Unemployment. Unemployment rates were somewhat higher for women in S&E occupations than for men in 2003: 3.7% of men and 4.2% of women were unemployed. By comparison, the unemployment rate in 1993 was 2.7% for men and 2.1% for women
Representation of Racial and Ethnic Minorities in S&E
With the exception of Asians/Pacific Islanders, racial and ethnic minorities represent only a small proportion of those employed in S&E occupations in the United States. Collectively, blacks, Hispanics, and other ethnic groups (the latter includes American Indians/Alaska Natives) constitute 24% of the total U.S. population, 13% of college graduates, and 10% of the college educated in S&E occupations.
Although Asians/Pacific Islanders constitute only 5% of the U.S. population, they accounted for 7% of college graduates and 14% of those employed in S&E occupations in 2003. Although 82% of Asians/Pacific Islanders in S&E occupations were foreign born, native-born Asians/Pacific Islanders are more highly represented in S&E than in the workforce as a whole.
Age Distribution. As in the case of women, underrepresented racial and ethnic minorities are much younger than non-
Hispanic whites in the same S&E occupations
S&E Occupation. Asian/Pacific Islander, black, and American Indian/Alaska Native scientists and engineers tend to work in different fields than their white and Hispanic counterparts. Fewer Asians/Pacific Islanders work in social sciences than in other fields. In 2003, they constituted 4% of social scientists but more than 11% of engineers and more than 13% of individuals working in mathematics and computer sciences. More black scientists and engineers work in social sciences and in computer sciences and mathematics than in other fields. In 2003, blacks constituted approximately 5% of social scientists, 4% of computer scientists and mathematicians, 3% of physical scientists and engineers, and 2% of life scientists. Other ethnic groups (which include American Indians/Alaska Natives) work predominantly in social and life sciences, accounting for 0.4% of social and life scientists and 0.3% or less of scientists in other fields in 2003. Hispanics appear to have a more even representation across all fields, constituting approximately 2.5%–4.5% of scientists and engineers in each field.
Trends in Median Salaries. In 2003, female scientists and engineers earned a median annual salary of $53,000, about 25% less than the median annual salary of $70,000 earned by male scientists and engineers
Between 1993 and 2003, median annual salaries for women in S&E occupations increased by 33%, compared with an increase of 40% for male median salaries
Salaries for individuals in S&E occupations also vary among the different racial and ethnic groups. In 2003 whites and Asians/Pacific Islanders in S&E occupations earned similar median annual salaries of $67,000 and $70,000, respectively, compared with $60,000 for Hispanics and $58,000 for blacks
Analysis of Salary Differentials. It is often difficult to use gross differences in the salaries of women and ethnic minorities in S&E as indicators of the progress of individuals in those groups in S&E employment. Differences in average age, work experience, fields of degree, and other characteristics can make direct comparison of salary and earnings statistics misleading. Generally, engineers earn a higher salary than social scientists, and newer employees earn less than those with more experience. One common statistical method that can be used to look simultaneously at salary and other differences is regression analysis.
Differences in mean annual salary are substantial when comparing all individuals with S&E degrees only by level of degree, with no other statistical controls: in 2003, women with S&E bachelor’s degrees had full-time mean salaries that were 34.2% less than those of men with S&E bachelor’s degrees. Blacks, Hispanics, and individuals in other underrepresented ethnic groups with S&E bachelor’s degrees had full-time salaries that were 18.8% less than those of non-Hispanic whites and Asians/Pacific Islanders with S&E bachelor’s degrees. These differentials are somewhat lower than those shown in a similar analysis using 1999 data (see Science and Engineering Indicators 2006 [NSB 2006]). These raw differences in salary are lower but still large at the doctoral level (–18.5% for women and –13.2% for underrepresented ethnic groups). Foreign-born individuals with U.S. S&E degrees have slightly lower salaries than U.S. natives (–2.7% at the bachelor’s and –1.8% at the doctoral levels), but at the master’s degree level earn 10.0% more than U.S. natives.
Effects of Age and Years Since Degree on Salary Differentials. Salary differences between men and women reflect to some extent the lower average ages of women with degrees in most S&E fields. Controlling for differences in age and years since receipt of degree reduces salary differentials for women compared with men by only about 1 percentage point at the bachelor’s (to –33.2%) and master’s (to –30.6), but by two-fifths at the doctoral level (to –11.1%). Two factors may explain why statistical controls make less difference at lower degree levels: a similar proportion of men and women with S&E degrees are in midcareer, but a larger proportion of men are at older ages where salaries begin to decline.
Similar small drops in salary differentials are found for underrepresented ethnic minorities. Such controls reduce salary differentials of underrepresented minorities compared with non-Hispanic whites and Asians/Pacific Islanders by only 1 or 2 percentage points at the bachelor’s and master’s degree levels, but by half at the doctoral level (to –6.6%).
Effects of Field of Degree on Salary Differentials. Controlling for field of degree and for age and years since degree reduces the estimated salary differentials for women with S&E degrees to –25.4% at the bachelor’s level and to –7.9% at the doctoral level. These reductions generally reflect the greater concentration of women in the lower-paying social and life sciences as opposed to engineering and computer sciences. As noted above, this identifies only one factor associated with salary differences and does not speak to why differences exist between men and women in field of degree or whether salaries are affected by the percentage of women with degrees in each field.
Field of degree is associated with significant estimated salary differentials for underrepresented ethnic groups relative to all other ethnic groups. Controlling for field of degree further reduces salary differentials to –13.6% for those individuals with S&E bachelor’s degrees and to –5.2% for those individuals with S&E doctorates. Thus, age, years since degree, and field of degree are associated with two-thirds of doctoral-level salary differentials for underrepresented ethnic groups.
Compared with natives, foreign-born individuals with advanced S&E degrees show no statistically significant salary differences when controlling for age, years since degree, and field of degree. At the bachelor’s degree level, foreign-born S&E degree holders still had a –6.3% salary differential.
Effects of Occupation and Employer Characteristics on Salary Differentials. Occupation and employer characteristics affect compensation. Academic and nonprofit employers typically pay less for the same skills than employers pay in the private sector, and government compensation falls somewhere between the two groups. Other factors affecting salary are relation of work performed to degree earned: whether the person is working in S&E or in R&D, employer size, and U.S. region. However, occupation and employer characteristics may not be determined solely by individual choice, for they may also reflect in part an individual’s career success.
When comparing women with men and underrepresented ethnic groups with non-Hispanic whites and Asians/Pacific Islanders, controlling for occupation and employer reduces salary differentials somewhat beyond what is found when controlling for age, years since degree, and field of degree. At the doctoral level, the addition of occupation leaves no statistically significant difference between the salaries of underrepresented ethnic groups, compared with whites and Asians. For the foreign born, controlling for occupational characteristics actually moves differentials in a negative direction, suggesting that the foreign born generally have better-paying occupations than natives.
Effects of Family and Personal Characteristics on Salary Differentials. Marital status, the presence of children, parental education, and other personal characteristics are often associated with differences in compensation. Although these differences may involve discrimination, they may also reflect many subtle individual differences that might affect work productivity. For example, having highly educated parents is associated with higher salaries for individuals of all ethnicities and genders, and may well be associated with greater academic achievement not directly measured in these data. However, for many individuals in many ethnic groups, historical discrimination probably affected parents’ educational opportunities and achievement.
As with occupation and employer characteristics, controlling for these characteristics changes salary differentials only slightly for each group and degree level. However, it does have enough of an effect to eliminate the rest of the estimated salary differentials for both underrepresented ethnic groups with advanced S&E degrees vis-à-vis all others, and for foreign-born individuals vis-à-vis native-born individuals.
An additional issue for the wage differentials of women, however, is that family and child variables often have different effects for men and women. Marriage is associated with higher salaries for both men and women with S&E degrees, but has a larger positive association for men. Children have a positive association with salary for men but a negative association with salary for women, except at the doctoral level, where children have no statistically significant effect. Allowing for these differences in gender effects in the model reduces the salary differential at the bachelor’s degree level by 10.4 percentage points (to –7.8%) and at the master’s level by 9.4 percentage points (to 5.8%), and leaves no statistical significant difference in earnings at the doctoral level.
 Although BLS labor force projections do a reasonable job of forecasting employment in many occupations (see Alpert and Auyer 2003), the mean absolute percentage error in the 1988 forecast of employment in detailed occupations in 2000 was 23.2%.
 Since their growth rate projection is near the overall average, engineers and physical scientists are classified as having average growth by BLS.
 Not all analyses of changes in earnings are able to control for level of skill. For example, data on average earnings within occupation over time may not be a good indicator of labor market conditions if the average experience level was to fall for workers in a rapidly growing occupation.
 Many comparisons using Census Bureau data on occupations are limited to looking at "nonacademic S&E occupations" because the occupation of "postsecondary teacher" has not been broken out into subjects in most recent census surveys.
 Specifically presented here are coefficients from linear regressions using the 2003 Scientists and Engineers Statistical Data System (SESTAT) data file of individual characteristics on the natural log of reported full-time annual salary as of October 2003.
 "Underrepresented ethnic group" as used here includes individuals who reported their race as black, American Indian/Alaska Native, or other, or who reported Hispanic ethnicity.
 In the regression equation, this is the form: age, age2, age3, age4; years since highest degree (YSD), YSD2, YSD3, YSD4.
 Included were 20 dummy variables for NSF/SRS SESTAT field-of-degree categories (out of 21 S&E fields; the excluded category in the regressions was "other social science").
 Variables added here include 34 SESTAT occupational groups (excluding "other non-S&E"), whether individuals said their jobs were closely related to their degrees, whether individuals worked in R&D, whether their employers had fewer than 100 employees, and their employers' U.S. census region.
 Variables added here include dummy variables for marriage, number of children in the household younger than 18, whether the father had a bachelor's degree, whether either parent had a graduate degree, and citizenship. Also, sex, nativity, and ethnic minority variables are included in all regression equations.