Women in STEM: 2017 Update

 

 

 

 

By 

Ryan Noonan 

ESA Issue Brief 

#06-17 

November 13, 2017 

U.S. Department of Commerce 

Economics and Statistics Administration 

Office of the Chief Economist 

Women in STEM: 2017 Update
 

Executive Summary 

In March, the Office of the Chief Economist (OCE) released the first in a 

series of reports updating and expanding our previous work examining 

the science, technology, engineering, and math (STEM) workforce. That 

first report, “STEM Jobs. 2017 Update,” provided an overview of STEM 

workers and their earning power. This second report provides a more 

detailed look at the gender dynamics of the STEM economy. 

!merica’s STEM workforce is crucial for generating new ideas, receiving 

and commercializing patents, and providing the flexibility and critical 

thinking required in the modern economy. While women continue to 

make gains across the broader economy, they remain underrepresented 

in STEM jobs and among STEM degree holders. Crafting policy to tap into 

the potential of women to contribute further in this vital sector requires 

an understanding of how gender is currently related to participation and 

success in STEM jobs. 

Key findings in this report, which are consistent with previous research, 

including research done by OCE, are the following. 

	 Women filled 47 percent of all U.S. jobs in 2015 but held only 24 

percent of STEM jobs. Likewise, women constitute slightly more than 

half of college educated workers but make up only 25 percent of 

college educated STEM workers. 

	 Women with STEM jobs earned 35 percent more than comparable 

women in non-STEM jobs — even higher than the 30 percent STEM 

premium for men. As a result, the gender wage gap is smaller in 

STEM jobs than in non-STEM jobs. Women with STEM jobs also 

earned 40 percent more than men with non-STEM jobs. 

	 While nearly as many women hold undergraduate degrees as men 

overall, they make up only about 30 percent of all STEM degree 

holders. Women make up a disproportionately low share of degree 

holders in all STEM fields, particularly engineering. 

	 Women with STEM degrees are less likely than their male 

counterparts to work in a STEM occupation; they are more likely to 

work in education or healthcare. 

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Women in STEM: 2017 Update 

What is STEM?
	

The acronym STEM — science, technology, engineering, and math — is defined more or less broadly 

depending on the source of the definition. There is a strong consensus that STEM, or science, 

technology, engineering, and math, workers should be defined to include core occupations in the hard 

sciences, engineering, and mathematics. However, there is often less consensus about whether to 

include other positions such as educators, managers, technicians, health-care professionals, or social 

scientists. 

Following our March 2017 report, our definition of STEM includes, in addition to the core occupations, 

professional and technical support occupations in the fields of computer science and mathematics, 

engineering, and life and physical sciences, as well as three management occupations with direct 

connections to STEM.1 Our STEM list contains 56 specific occupation codes as classified by the Standard 

Occupational Classification (SOC) (see Appendix Table 1).2,3 Because of data limitations, we could not 

include STEM educators.4 We also elected not to include social scientists.5 

In 2015, there were 8.6 million workers in STEM jobs, or 5.7 percent of the workforce.6 To put the 8.6 

million STEM workers into context, we divide STEM occupations into four categories: computer and 

math, engineering and surveying, physical and life sciences, and STEM managerial occupations.7 Across 

all levels of educational attainment, the largest group of STEM jobs is within the computer and math 

fields, which account for close to half (49 percent) of all STEM employment. Second are engineering and 

surveying occupations with 30 percent of all STEM employment, while 12 percent are in the physical and 

life sciences and 9 percent in STEM management jobs. 

Women are Underrepresented in STEM Jobs 

Data from the ensus ureau’s !merican ommunity Survey (!S) shows that women comprise 47 

percent of all workers but just 24 percent of STEM workers.8 In other words, a STEM worker is about half 

as likely to be a woman as a member of the overall workforce (see Figure 1).9 

1 These occupations are computer and information systems managers, engineering managers, and natural sciences managers. 

2 This is an increase from 50 in our 2011 report. With one exception, this increase merely reflects a change in the amount of 

occupation detail available in our primary data sources. However, we have added actuaries to the list of STEM jobs for this 

report despite their exclusion in previous work. Their inclusion does not substantially change our findings. 

3 The 2010 Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into 

occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of 

840 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are 

combined to form 461 broad occupations, 97 minor groups, and 23 major groups. Detailed occupations in the SOC with similar 

job duties, and in some cases skills, education, and/or training, are grouped together. For more information, see: 

https://www.bls.gov/soc/. 4 !lthough our principal data source, the 2015 !merican ommunity Survey (!S), collects detailed information on workers’ 

occupations, it does not break out educators by their specific field. As a result, it is not possible to distinguish math and science 

teachers and professors from other teachers and professors/ Data from the ureau of Labor Statistics’ Occupational 

Employment Statistics program show that there are roughly 200,000 postsecondary teachers in STEM fields, and their exclusion 

is unlikely to materially affect our results. 

5 The ensus ureau and the National Science Foundation count social scientists among “science and engineering jobs/” 

Following Xie and Killewald, we have opted to exclude social scientists from the definition of STEM. As the authors point out, 

undergraduate degrees in social science are not closely linked to careers in social science. Using data from the American 

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Women in STEM: 2017 Update 

The proportions of women in the overall workforce and in the STEM workforce are little changed from 

our previous report on women in STEM.10 As shown in Table 1, the share of women in the overall 

workforce has fallen from 48 percent to 47 percent, while the share of women in the STEM workforce 

has held constant. In addition, women now make up a slight majority of workers with at least a 

Community Survey, we find that, in 2015, over 97 percent of workers with social science degrees worked in non-social science 

jobs, compared to 65 percent of workers with STEM degrees working in non-STEM jobs. In addition, recipients of such degrees 

are less likely to pursue graduate education in science (including social science), and universities produce a very large number 

of social science degrees that skew any analysis of STEM education or the STEM workforce. For more on this topic, see Xie, Yu, 

and Alexandra Killewald. Is American Science in Decline? Cambridge, MA: Harvard University Press, 2012. 6 For purposes of this report, we use the primary job identified by respondents to the American Community Survey to 

determine whether a worker is in a STEM field. In order to produce better time series analysis, our previous report used data 

from the Current Population Survey, which showed that there were 9.0 million STEM workers in 2015, accounting for 6.1 

percent of the overall workforce. 

7 The distinction between “scientists” and “science occupations” is more than just semantic/ “Science occupations” covers not 

just scientists but also science technicians. Likewise, engineering and surveying occupations include engineering technicians and
 

drafters, and computer occupations range from computer support specialists to computer software engineers.
 8 For more information about the American Community Survey, see: https://www.census.gov/programs-surveys/acs/.
 9 Note that in this section we are focusing on persons with STEM jobs; while there is a lot of overlap between persons with
 

STEM jobs and persons with STEM undergraduate majors, the two sets are not identical. Later we talk about persons with STEM 

degrees and the jobs they hold.
 10 For our previous report, “Women in STEM. ! Gender Gap to Innovation,” see. 

http://esa.doc.gov/reports/women-stem-gender-gap-innovation. 

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Women in STEM: 2017 Update 

bachelor’s degree- their share in the college-educated STEM workforce has ticked up slightly, from 24 

percent to 25 percent. 

Table 1. Total and STEM Employment by Gender and Educational Attainment, 2009 and 2015 

(thousands of workers) 

Male Female Percent Female 

2009 2015 2009 2015 2009 2015 

All workers 73,580 79,067 67,058 71,506 48% 47%
 

College-educated 22,167 24,991 21,433 25,431 49% 50%
 

STEM workers 5,640 6,520 1,790 2,100 24% 24%
 

College-educated 3,259 4,469 1,199 1,497 24% 25%
 

Source: OCE calculations from Census 2009 and 2015 American Community Survey public-use microdata. 

Note: Estimates are for employed persons age 16 and over. College-educated workers are those with at least a 

bachelor’s degree/ 

However, as shown in Table 2, the concentration of women varies across the types of STEM 

occupations. The largest share of women is in the physical and life sciences and this share rose from 40 

percent to 43 percent. For computer science and math jobs, the share of women decreased slightly from 

27 percent in 2009 to 26 percent in 2015. The share of women in engineering (at 14 percent) and STEM 

management jobs (at 25 percent) held steady over this period. 

Table 2. Employment in STEM Occupations, 2009 and 2015 

(thousands of workers) 

Male Female Percent Female 

2009 2015 2009 2015 2009 2015 

STEM total 5,640 6,520 1,790 2,100 24% 24% 

Computer science and math 2,534 3,162 929 1,101 27% 26% 

Engineering 2,079 2,195 330 364 14% 14% 

Physical and life sciences 553 595 374 448 40% 43% 

STEM managers 474 568 157 187 25% 25% 

Source: OCE calculations from Census 2009 and 2015 American Community Survey public-use microdata.
 

Note: Estimates are for employed persons age 16 and over.
 

Higher levels of educational attainment are associated with higher likelihood of having a STEM job for 

both men and women, but Figure 2 shows that women are less likely than men to have a STEM job 

across all levels of attainment. While the gap lessens somewhat at the doctoral level, women with PhD 

degrees remain about half as likely as men with similar levels of educational attainment to hold a STEM 

job.11 

11 Few persons with professional degrees work in STEM jobs because such persons typically have jobs that we do 

not characterize as STEM (e.g., dentists, physicians, and lawyers). 

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Women in STEM: 2017 Update 

STEM Worker Earnings and Gender 

There is robust evidence across the economic literature of a gender wage gap—women earn less than 

men, even after controlling for a wide range of characteristics such as education and age. In our recent 

report, “STEM Jobs. 2017 Update,” we showed that STEM workers earn a significant premium over their 

non-STEM counterparts.12 Figure 3 illustrates the intersection of these two findings by showing the 

average hourly earnings of full-time, year-round workers in STEM and non-STEM jobs. On average, men 

and women earn $37.69 and $31.59 per hour, respectively, in STEM jobs—higher than the $22.56 and 

$18.17 that they earn, on average, in non-STEM occupations. For every dollar earned by a man in STEM, 

a woman in STEM earns 84 cents, a gender wage gap of 16 percent, slightly larger than the 14 percent 

wage gap we found using 2009 data but smaller than the 19 percent gender wage gap in non-STEM jobs. 

This STEM/non-STEM difference has narrowed from 7 percentage points in 2009 to 3 percentage points 

in 2015 because, while the gender gap in STEM jobs grew, the gender gap in non-STEM jobs shrank from 

21 percent to 19 percent. 

This simple comparison of average earnings can disguise other factors that affect worker earnings, 

especially age and educational attainment. We use a regression analysis to control for several 

demographic and geographic characteristics and attempt to get a more precise measure of the gender 

12 For our previous report, see: http://esa.doc.gov/reports/stem-jobs-2017-update. 

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Women in STEM: 2017 Update 

earnings gap in STEM.13 The results from these regressions clarify the magnitude of both the STEM 

earnings premium and the gender earnings gap (see Appendix Table 3).14 

We find that, all else being equal, women in STEM earn 35 percent more than their female peers in nonSTEM jobs, while the STEM premium for men is 30 percent. Both of these premiums are larger than 

those we found in 2009 (33 percent for women and 25 percent for men), suggesting a growing wage gap 

between STEM and non-STEM earnings. 

Given that the STEM premium for women is higher than for men, we would expect women in STEM to 

face a smaller gender earnings gap than women in non-STEM occupations, consistent with the data in 

Figure 2. However, because the difference between the female and male STEM premiums has declined 

13 The earnings regressions measure the natural log of wages as a function of age (up to a fourth degree 

polynomial of age), gender, marital status, race and Hispanic origin, nativity and citizenship, education, veteran 

status, region, major industry, and whether a worker is in a STEM occupation. This specification was run for the 

overall population of private sector full-time year-round wage and salary workers age 16 and over as well as for 

men and women separately. Additionally, the specification was run for college-educated workers age 25 and over 

in each of the four major STEM occupational groups. The exponential function was used to convert the results for 

presentation. Additionally, a second set of regressions was run to estimate the impact of having children at home 

on the gender wage gap, but it did not affect the overall results of the regressions shown here. 14 Because these regressions use American Community Survey microdata, they are not strictly comparable to the 

results in our previous report, which used Current Population Survey microdata. 

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Women in STEM: 2017 Update 

since 2009, we would also expect the gender earnings gap has become more similar across STEM and 

non-STEM occupations, which is also consistent with our findings. At the same time that STEM jobs have 

become higher-paying with respect to non-STEM jobs, the gender earnings gap has become more 

consistent across both kinds of jobs. 

Looking at more specific groups of STEM workers allows us to make more precise comparisons of male 

and female wages within the sector (see Appendix Table 4). Among college-educated STEM workers, the 

gender wage gap shrinks to 14 percent (compared to 12 percent in 2009), and it is even smaller within 

some of the major STEM categories (see Figure 4). The most male-dominated STEM occupational group 

— engineers— has the smallest gender earnings gap (8 percent), while the most gender-balanced group 

— physical and life sciences — has the second smallest (9 percent). Across all groups, the gender 

earnings gap has increased at least somewhat since 2009, though STEM managers have seen a much 

larger increase, from 9 percent to 14 percent. 

STEM Degrees and Fields of Study by Gender 

Parallel to our list of STEM occupations, we also identify a set of STEM undergraduate degree fields that 

span computer science and mathematics, engineering, and life and physical sciences (see Appendix 

Table 2). We define STEM degree holders as persons whose primary or secondary undergraduate major 

was in a STEM field. Following similar logic to that used in our occupation selection, we exclude 

business, healthcare, and social science majors. 

Because STEM degrees are a common path to a STEM job, and because policy conversations around 

STEM often focus on education, it is useful to examine the educational choices made by women and 

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Women in STEM: 2017 Update 

men in STEM. In our March 2017 report, we found that there are 6.0 million STEM workers with at least 

a four-year college degree, and 69 percent of these workers have a degree in a STEM field (though often 

not the same field as their job). 

Data from the 2015 ACS shows that women now make up half of all employed college graduates age 25 

and over; there were 25.0 million women and 25.0 million men who were employed and had bachelor’s 

degrees. However, in 2015, there were 3.4 million college educated working women with STEM degrees, 

about 30 percent of all working STEM-degree holders, and 7.9 million men. 

31%

59%

46%

19%

5%

14%

17%

8%

Physical and life 

sciences degree

Engineering degree

Math degree

Computer degree

0%

25%

50%

75%

100%

Men Women

Figure 5. College-educated Workers with a STEM Degree by Gender and 

STEM Degree Field, 2015

Source: OCE calculations usingAmerican Community Survey public-use microdata.

Note: Estimates are for employed persons age 25 and over. The shares for men do not add to 100% due to rounding.

7.9 million workers 3.4 million workers

Among STEM majors, the distribution of men and women differs significantly. As shown in Figure 5, 

more than half (59 percent) of all women who major in STEM fields choose a degree in the physical and 

life sciences, compared to less than one-third (31 percent) of men. Women are also more likely than 

men (14 percent versus 5 percent) to choose a math major. Engineering is the most popular STEM field 

of study for working men (46 percent), more than double the share of STEM working women with 

engineering degrees. Computer science, which had roughly equal shares of men and women in 2009 (15 

percent and 14 percent, respectively), has become far more male-dominated over the past six years, 

with employed men more than twice as likely as women to have computer degrees. 

STEM Degrees and Careers by Gender 

We have seen that college educated women are much less likely than men to major in STEM fields, but 

even women who choose such majors have very different career paths from their male counterparts. 

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Women in STEM: 2017 Update 

Overall, women with STEM degrees make up about 20 percent of all STEM-degree holders working in 

STEM jobs. About 40 percent (3.1 million) of men with STEM degrees work in STEM jobs, whereas only 

23 percent (0.8 million) of women with STEM degrees work in STEM fields (see Figure 6). While the 

share of such men has held steady since 2009, the share of women with STEM degrees who choose 

STEM occupations has fallen from 26 percent. 

39%

23%

9%

19%

6% 13%

16% 13%

3% 8%

26% 24%

STEM

Healthcare

Education

Non-STEM 

management

Office/admin

Other occupations

0%

25%

50%

75%

100%

Men Women

FIgure 6. College-educated Workers with a STEM Degree by Gender and 

STEM Occupation, 2015

Source: OCE calculations usingAmerican Community Survey public-use microdata.

Note: Estimates are for employed persons age 25 and over. The shares for men do not add to 100% due to rounding.

7.9 million workers 3.4 million workers

Men with STEM degrees are somewhat more likely to have non-STEM management jobs than women, 

16 percent versus 13 percent. This represents a narrowing of the gap we found in 2009, when only 11 

percent of women with STEM degrees were in such occupations. By contrast, STEM-educated women 

are substantially more likely than STEM-educated men to work in healthcare or education. Nearly one in 

five (19 percent) women with STEM degrees works in healthcare, compared to about one in ten (9 

percent) men. Likewise, 13 percent of female STEM majors and 6 percent of male STEM majors work in 

education. 

STEM Degree Premiums and Gender 

We have already seen that workers in STEM jobs enjoy an earnings premium over workers in non-STEM 

jobs, regardless of gender. Figure 7 shows the results of extending that analysis to understand the 

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Women in STEM: 2017 Update 

earnings premium enjoyed by men and women of having either a STEM job, a STEM degree, or both (see 

Appendix Table 5). 

Specifically, when we control for whether or not women have STEM degrees, we find that collegeeducated women (regardless of choice of undergraduate major) earn 23 percent more in STEM jobs 

than elsewhere. This is nearly double the 13 percent premium that college-educated men realize 

working in STEM. On the other hand, female STEM degree holders earn 11 percent more than women 

with other degrees, regardless of job, while the STEM degree premium for men is slightly larger at 13 

percent. 

The largest STEM-related earnings premiums go to men and women who both major in a STEM field and 

choose a STEM job. A notable finding of our analysis is that men receive an additional 5 percent 

premium for having both a STEM degree and a STEM job, while women see a small but slightly negative 

“interaction” effect/15 Despite the lack of an “interaction” benefit, the STEM degree/STEM job career 

15 The wage premium benefits associated with having either a STEM job or a STEM degree are statistically 

significant at the 99 percent confidence level for both men and women. The interaction effect significant for men 

at the 99 percent level, though it is only significant at the 90 percent level for women. 

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Women in STEM: 2017 Update 

path nets women 34 percent higher hourly earnings, on average, than those who have neither a STEM 

degree nor a STEM job, a few points higher than the corresponding 31 percent premium for men. 

Conclusion 

This report demonstrates that women are underrepresented both in STEM jobs and among those with 

undergraduate STEM degrees. Moreover, we find that there has been little overall change in this 

underrepresentation since our last report using 2009 data. The relatively few women who receive STEM 

degrees are concentrated in the physical and life sciences, while men with STEM degrees are 

concentrated in engineering. Perhaps reflecting this difference in concentration, women who receive 

STEM degrees are less likely than their male counterparts to work in STEM jobs, instead opting for more 

careers in the healthcare and education sectors. For those women who do work in STEM, we find a 

somewhat smaller gender earnings gap than for the overall labor force. 

While this report does not — and cannot — explain why gender differences in STEM exist, its aim is to 

provide data and insight to help guide more informed policymaking. STEM jobs are crucial to the 

competitiveness, innovation, and flexibility of the modern economy. The findings of this report provide 

clear evidence that there is an opportunity to expand the number of women in STEM, even as the 

gender balance of the overall labor force nears equality. 

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Women in STEM: 2017 Update 

Appendix Table 1. Detailed STEM occupations and Standard Occupational Classification (SOC) codes 

Occupation SOC code Occupation SOC code 

Computer and math occupations 

 Network systems and data  Computer scientists and systems analysts 15-10XX 15-1081 communications analysts 

 Computer programmers 15-1021  Actuaries 15-2011 

 Computer software engineers 15-1030  Mathematicians 15-2021 

 Computer support specialists 15-1041  Operations research analysts 15-2031 

 Database administrators 15-1061  Statisticians 15-2041 

 Network and computer systems  Miscellaneous mathematical science 15-1071 15-2090 administrators occupations 

Engineering and surveying occupations 

 Surveyors, cartographers, and 

photogrammetrists 17-1020  Materials engineers 17-2131 

 Aerospace engineers 17-2011  Mechanical engineers 17-2141 

 Mining and geological engineers, 17-2151 

 Agricultural engineers 17-2021 including mining safety engineers 

 Biomedical engineers 17-2031  Nuclear engineers 17-2161 

 Chemical engineers 17-2041  Petroleum engineers 17-2171 

 Civil engineers 17-2051  Engineers, all other 17-2199 

 Computer hardware engineers 17-2061  Drafters 17-3010 

 Engineering technicians, except 

 Electrical and electronic engineers 17-2070 drafters 17-3020 

 Environmental engineers 17-2081  Surveying and mapping technicians 17-3031 

 Industrial engineers, including health and 

safety 17-2110  Sales engineers 41-9031 

 Marine engineers and naval architects 17-2121 

Physical and life sciences occupations 

 Agricultural and food scientists 19-1010  Physical scientists, all other 19-2099 

 Agricultural and food science 

 Biological scientists 19-1020 technicians 19-4011 

 Conservation scientists and foresters 19-1030  Biological technicians 19-4021 

 Medical scientists 19-1040  Chemical technicians 19-4031 

 Astronomers and physicists 19-2010  Geological and petroleum technicians 19-4041 

 Atmospheric and space scientists 19-2021  Nuclear technicians 19-4051 

 Other life, physical, and social science 

 Chemists and materials scientists 19-2030 technicians 19-40XX 

 Environmental scientists and geoscientists 19-2040 

STEM managerial occupations 

 Computer and information systems 

managers 11-3021  Natural sciences managers 11-9121 

 Engineering managers 11-9041 

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Women in STEM: 2017 Update 

Appendix Table 2. Detailed STEM undergraduate majors 

Computer majors 

 Computer and information	  Computer administration  Computer science systems management and security 

 Computer programming and  Computer networking and  Information sciences data processing	 telecommunications 

Math majors 

 Mathematics and computer  Mathematics	  Statistics and decision science science 

 Applied mathematics 

Engineering majors 

 General engineering	  Environmental engineering  Petroleum engineering 

 Geological and geophysical  Aerospace engineering	  Miscellaneous engineering engineering 

 Industrial and manufacturing  Biological engineering	  Engineering technologies engineering 

 Materials engineering and  Engineering and industrial  Architectural engineering materials science management 

 Biomedical engineering  Mechanical engineering  Electrical engineering technology 

 Industrial production  Chemical engineering	  Metallurgical engineering technologies 

 Mining and mineral  Mechanical engineering related  Civil engineering engineering technologies 

 Naval architecture and marine  Miscellaneous engineering  Computer engineering engineering technologies 

 Electrical engineering  Nuclear engineering  Military technologies 

 Engineering mechanics physics 

and science 

Physical and life sciences majors 

 Animal sciences  Genetics  Physical sciences 

 Food science  Microbiology  Astronomy and astrophysics 

 Atmospheric sciences and  Plant science and agronomy	  Pharmacology meteorology 

 Soil science  Physiology  Chemistry 

 Environmental science  Zoology  Geology and earth science 

 Biology  Miscellaneous biology  Geosciences 

 Biochemical sciences  Nutrition sciences  Oceanography 

 Botany  Neuroscience  Physics 

 Cognitive science and	  Nuclear, industrial radiology, and  Molecular biology biopsychology biological technologies 

 Ecology 

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Women in STEM: 2017 Update 

Appendix Table 3. Regression Results (Overall) 

All Workers Women Only Men Only 

VARIABLES Description ln_hourly_earnings ln_hourly_earnings ln_hourly_earnings 

1.stem_job STEM job 0.310*** 0.303*** 0.262*** 

(0.00276) (0.00543) (0.00331) 

AGEP Age 0.0960*** 0.154*** 0.0514*** 

(0.00537) (0.00815) (0.00722) 

c.AGEP#c.AGEP Age (squared) -0.00144*** -0.00336*** 7.40e-05 

(0.000183) (0.000278) (0.000247) 

c.AGEP#c.AGEP Age (cubed) 6.52e-06** 3.27e-05*** -1.42e-05*** 

#c.AGEP (2.65e-06) (4.02e-06) (3.58e-06) 

c.AGEP#c.AGEP Age (4th power) 2.11e-09 -1.21e-07*** 9.99e-08*** 

#c.AGEP#c.AGEP (1.38e-08) (2.10e-08) (1.88e-08) 

1.married Married 0.156*** 0.0896*** 0.184*** 

(0.00174) (0.00244) (0.00245) 

1.race_eth Black non-Hispanic -0.147*** -0.0972*** -0.181*** 

(0.00288) (0.00385) (0.00428) 

2.race_eth Asian non-Hispanic -0.0378*** 0.0204*** -0.0675*** 

(0.00440) (0.00636) (0.00600) 

3.race_eth Other race non-Hispanic -0.0819*** -0.0571*** -0.0947*** 

(0.00561) (0.00814) (0.00762) 

4.race_eth Hispanic -0.130*** -0.103*** -0.150*** 

(0.00279) (0.00405) (0.00376) 

1.foreignborn Foreign-born citizen -0.0363*** -0.0426*** -0.0364*** 

(0.00353) (0.00497) (0.00487) 

2.foreignborn Non-citizen -0.160*** -0.186*** -0.163*** 

(0.00363) (0.00578) (0.00464) 

1.educ High school diploma 0.159*** 0.178*** 0.166*** 

(0.00330) (0.00547) (0.00407) 

2.educ Some college 0.305*** 0.362*** 0.303*** 

(0.00333) (0.00543) (0.00418) 

3.educ achelor’s degree 0.663*** 0.705*** 0.668*** 

(0.00364) (0.00575) (0.00471) 

4.educ Graduate degree 0.930*** 0.945*** 0.952*** 

(0.00417) (0.00635) (0.00552) 

1.veteran Veteran 0.0371*** -0.0173* -0.0258*** 

(0.00337) (0.00969) (0.00369) 

2.DIVISION Middle Atlantic -0.0211*** -0.0244*** -0.0216*** 

(0.00414) (0.00581) (0.00568) 

3.DIVISION East North Central -0.134*** -0.152*** -0.119*** 

(0.00397) (0.00562) (0.00542) 

4.DIVISION West North Central -0.163*** -0.177*** -0.145*** 

(0.00459) (0.00645) (0.00629) 

5.DIVISION South Atlantic -0.125*** -0.131*** -0.119*** 

(0.00391) (0.00550) (0.00537) 

6.DIVISION East South Central -0.222*** -0.241*** -0.205*** 

(0.00481) (0.00679) (0.00656) 

7.DIVISION West South Central -0.102*** -0.144*** -0.0718*** 

(0.00425) (0.00601) (0.00579) 

8.DIVISION Mountain -0.104*** -0.113*** -0.101*** 

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Women in STEM: 2017 Update 

9.DIVISION Pacific 

(0.00460) 

0.0121*** 

(0.00651) 

0.00919 

(0.00627) 

0.0101* 

1.secnum 

2.secnum 

Agriculture, mining, 

and construction 

Manufacturing 

(0.00407) 

0.185*** 

(0.00287) 

0.111*** 

(0.00579) 

0.105*** 

(0.00709) 

0.0750*** 

(0.00553) 

0.125*** 

(0.00325) 

0.0756*** 

Constant 

(0.00208) 

0.704*** 

(0.00358) 

0.0234 

(0.00257) 

1.212*** 

(0.0562) (0.0853) (0.0754) 

Observations 738,167 317,837 420,330 

R-squared 0.373 0.351 0.396 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

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Women in STEM: 2017 Update 

Appendix Table 4. Regression Results (Occupation-specific) 

Computer and Physical and life 

All STEM workers math Engineering sciences STEM managerial 

VARIABLES Description ln_hourly_earnings ln_hourly_earnings ln_hourly_earnings ln_hourly_earnings ln_hourly_earnings 

1.female Female -0.147*** -0.139*** -0.0812*** -0.0978*** -0.148*** 

(0.00689) (0.00955) (0.0136) (0.0188) (0.0200) 

AGEP Age 0.0633* 0.0921 -0.0418 0.195 0.264*** 

(0.0333) (0.0749) (0.0479) (0.131) (0.0946) 

c.AGEP#c.AGEP Age (squared) -0.000350 -0.00132 0.00252* -0.00452 -0.00592** 

(0.00107) (0.00255) (0.00152) (0.00433) (0.00282) 

c.AGEP#c.AGEP Age (cubed) -6.24e-06 6.22e-06 -4.04e-05** 5.44e-05 6.19e-05* 

#c.AGEP (1.47e-05) (3.75e-05) (2.06e-05) (6.17e-05) (3.60e-05) 

c.AGEP#c.AGEP Age (fourth 4.81e-08 -7.90e-09 2.00e-07** -2.75e-07 -2.67e-07 

#c.AGEP#c.AGEP power) (7.35e-08) (2.01e-07) (1.01e-07) (3.20e-07) (1.65e-07) 

1.married Married 0.110*** 0.0862*** 0.121*** 0.0818*** 0.109*** 

(0.00680) (0.00947) (0.0119) (0.0212) (0.0219) 

1.race_eth Black non- -0.127*** -0.152*** -0.120*** 0.0205 -0.189*** 

Hispanic (0.0163) (0.0208) (0.0339) (0.0534) (0.0468) 

2.race_eth Asian non- 0.0146 0.000548 0.00132 0.0429 -0.0508 

Hispanic (0.0101) (0.0140) (0.0177) (0.0285) (0.0336) 

3.race_eth Other race -0.0524*** -0.0733** -0.0177 -0.0282 -0.103 

non-Hispanic (0.0203) (0.0287) (0.0318) (0.0571) (0.0725) 

4.race_eth Hispanic -0.123*** -0.141*** -0.0790*** -0.156*** -0.116*** 

(0.0136) (0.0201) (0.0206) (0.0482) (0.0338) 

1.foreignborn Foreign-born 0.0247** 0.0578*** -0.0220 -0.0101 0.102*** 

citizen (0.0104) (0.0144) (0.0189) (0.0315) (0.0299) 

2.foreignborn Non-citizen 0.0238** 0.0779*** 0.00128 -0.179*** 0.0681* 

(0.0115) (0.0156) (0.0215) (0.0321) (0.0376) 

1.grad_degree Graduate 0.122*** 0.126*** 0.150*** 0.230*** 0.110*** 

degree (0.00600) (0.00868) (0.0101) (0.0201) (0.0163) 

1.veteran Veteran -0.0558*** -0.0638*** -0.0582*** 0.0193 -0.0357 

(0.0122) (0.0176) (0.0197) (0.0651) (0.0312) 

2.DIVISION Middle 0.00282 -0.0229 -0.0146 0.0301 0.0110 

Atlantic (0.0130) (0.0183) (0.0231) (0.0348) (0.0401) 

3.DIVISION -0.103*** -0.124*** -0.0924*** -0.0900** -0.108*** 

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Women in STEM: 2017 Update 

East North 

Central (0.0124) (0.0172) (0.0225) (0.0362) (0.0382) 

4.DIVISION West North -0.113*** -0.131*** -0.0947*** -0.158*** -0.0904** 

Central (0.0152) (0.0210) (0.0268) (0.0474) (0.0440) 

5.DIVISION South Atlantic -0.0327*** -0.0352** -0.0521** -0.0276 -0.0934** 

(0.0124) (0.0169) (0.0229) (0.0352) (0.0383) 

6.DIVISION East South -0.184*** -0.233*** -0.109*** -0.239*** -0.249*** 

Central (0.0178) (0.0268) (0.0295) (0.0474) (0.0541) 

7.DIVISION West South 0.0337** -0.0891*** 0.149*** 0.0747 0.0401 

Central (0.0142) (0.0196) (0.0248) (0.0469) (0.0412) 

8.DIVISION Mountain -0.0298** -0.0589*** -0.0121 0.0236 -0.0736 

(0.0142) (0.0198) (0.0251) (0.0450) (0.0452) 

9.DIVISION Pacific 0.129*** 0.133*** 0.120*** 0.103*** 0.0788** 

(0.0123) (0.0172) (0.0220) (0.0326) (0.0398) 

1.secnum Agriculture, 0.0899*** 0.0524** 0.0562*** 0.315*** 0.0888* 

mining, and 

construction (0.0141) (0.0233) (0.0193) (0.0459) (0.0461) 

2.secnum Manufacturing 0.0397*** 0.0538*** 0.00311 0.206*** 0.0631*** 

(0.00601) (0.0111) (0.0101) (0.0199) (0.0177) 

Constant 1.925*** 1.703** 3.290*** -0.0107 -0.567 

(0.374) (0.797) (0.540) (1.428) (1.143) 

Observations 40,874 19,714 12,154 4,330 4,676 

R-squared 0.211 0.195 0.221 0.352 0.205 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

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Women in STEM: 2017 Update 

Appendix Table 5. Regression Results (STEM Job and Degree Interaction Effects) 

Women Only Men Only 

VARIABLES Description ln_hourly_earnings ln_hourly_earnings 

1.stem_job STEM job 0.206*** 0.126*** 

(0.00929) (0.00724) 

1.stem_degree STEM degree 0.107*** 0.119*** 

(0.00714) (0.00612) 

1.stem_interaction STEM job and STEM degree -0.0248* 0.0262*** 

(0.0140) (0.00964) 

AGEP Age 0.179*** -0.000884 

(0.0224) (0.0251) 

c.AGEP#c.AGEP Age (squared) -0.00365*** 0.00233*** 

(0.000720) (0.000810) 

c.AGEP#c.AGEP# Age (cubed) 3.16e-05*** -4.95e-05*** 

c.AGEP (9.91e-06) (1.12e-05) 

c.AGEP#c.AGEP# Age (fourth power) -1.05e-07** 2.82e-07*** 

c.AGEP#c.AGEP (4.94e-08) (5.65e-08) 

1.married Married 0.111*** 0.225*** 

(0.00439) (0.00513) 

1.race_eth Black non-Hispanic -0.125*** -0.277*** 

(0.00779) (0.0105) 

2.race_eth Asian non-Hispanic 0.0459*** -0.0414*** 

(0.00937) (0.00920) 

3.race_eth Other race non-Hispanic -0.0724*** -0.131*** 

(0.0150) (0.0156) 

4.race_eth Hispanic -0.156*** -0.218*** 

(0.00840) (0.00924) 

1.foreignborn Foreign-born citizen -0.0564*** -0.0582*** 

(0.00850) (0.00904) 

2.foreignborn Non-citizen -0.188*** -0.131*** 

(0.0115) (0.0104) 

1.grad_degree Graduate degree 0.219*** 0.241*** 

(0.00445) (0.00466) 

1.veteran Veteran -0.0508*** -0.0721*** 

(0.0172) (0.00784) 

2.DIVISION Middle Atlantic 0.0207** 0.0177* 

(0.00918) (0.00958) 

3.DIVISION East North Central -0.140*** -0.134*** 

(0.00907) (0.00934) 

4.DIVISION West North Central -0.175*** -0.164*** 

(0.0108) (0.0110) 

5.DIVISION South Atlantic -0.104*** -0.0860*** 

(0.00883) (0.00905) 

6.DIVISION East South Central -0.236*** -0.234*** 

(0.0125) (0.0125) 

7.DIVISION West South Central -0.102*** -0.0403*** 

(0.0102) (0.0103) 

8.DIVISION Mountain -0.135*** -0.141*** 

(0.0110) (0.0110) 

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Women in STEM: 2017 Update 

9.DIVISION Pacific 0.0468*** 0.0484*** 

(0.00923) (0.00928) 

1.secnum Agriculture, mining, 0.0843*** 0.0102 

and construction (0.0134) (0.00896) 

2.secnum Manufacturing 0.141*** 0.0428*** 

(0.00723) (0.00523) 

Constant 0.197 2.158*** 

(0.250) (0.281) 

Observations 113,659 140,002 

R-squared 0.164 0.202 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05,* p<0.1 

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Women in STEM: 2017 Update 

Acknowledgments 

The author would like to thank the following persons who provided comments, 

suggestions, and other contributions to this report. Any errors in the report are solely 

the authors’ responsibility/ 

Robert Rubinovitz, Deputy Chief Economist 

David Langdon, Economist 

David Beede, Economist 

Jessica Nicholson, Economist 

Office of the Chief Economist  Economics and Statistics Administration  Page 20 
 

Women in STEM: 2017 Update 

Suggested citation:
 

Noonan, Ryan. Office of the Chief Economist, Economics and Statistics Administration, U.S. Department 

of Commerce. (November 13, 2017). Women in STEM: 2017 Update (ESA Issue Brief #06-17). Retrieved 

from https://www.esa.gov/reports/women-stem-2017-update.
 

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Technical inquiries:
 

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