U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

census.gov

Disparities in STEM Employment by Sex, Race,

and Hispanic Origin 

American Community Survey Reports

By Liana Christin Landivar

Issued September 2013

ACS-24

INTRODUCTION

Industry, government, and academic leaders cite 

increasing the science, technology, engineering, and 

mathematics (STEM) workforce as a top concern. The 

National Academy of Sciences, National Academy of 

Engineering, and the Institute of Medicine describe 

STEM as “high-quality, knowledge-intensive jobs . . . 

that lead to discovery and new technology,” improving the U.S. economy and standard of living.1 In 2007, 

Congress passed the America COMPETES Act, reauthorized in 2010, to increase funding for STEM education 

and research.2

One focus area for increasing the STEM workforce has 

been to reduce disparities in STEM employment by sex, 

race, and Hispanic origin. Historically, women, Blacks, 

and Hispanics have been underrepresented in STEM 

employment.3 Researchers find that women, Blacks, 

1 Committee on Prospering in the Global Economy of the 21st

Century: An Agenda for American Science and Technology, 2007,

“Rising Above the Gathering Storm: Energizing and Employing America 

for a Brighter Economic Future,” P.1, National Academy of Sciences, 

National Academy of Engineering, and Institute of Medicine of the 

National Academies, The National Academies Press, Washington, DC. 2 America Creating Opportunities to Meaningfully Promote

Excellence in Technology, Education, and Science Act, Public Law No: 

110-69, August 9, 2007, <www.gpo.gov/fdsys/pkg/PLAW-110publ69

/pdf/PLAW-110publ69.pdf>. 3 Federal surveys now give respondents the option of reporting 

more than one race. Therefore, two basic ways of defining a race 

group are possible. A group such as Asian may be defined as those 

who reported Asian and no other race (the race-alone or single-race 

concept) or as those who reported Asian regardless of whether they 

also reported another race (the race-alone-or-in-combination concept). 

The body of this report (text, figures, and tables) shows data using the 

first approach (race alone). Use of the single-race population does not 

imply that it is the preferred method of presenting or analyzing data. 

The Census Bureau uses a variety of approaches. This report will refer 

and Hispanics are less likely to be in a science or engineering major at the start of their college experience, 

and less likely to remain in these majors by its conclusion.4 Because most STEM workers have a science 

or engineering college degree, underrepresentation 

among science and engineering majors could contribute to the underrepresentation of women, Blacks, and 

Hispanics in STEM employment.5

This report details the historical demographic composition of STEM occupations, followed by a detailed 

examination of current STEM employment by age 

and sex, presence of children in the household, and 

race and Hispanic origin based on the 2011 American 

Community Survey (ACS). The report concludes with 

an examination of the demographic characteristics of 

science and engineering graduates who are currently 

employed in a STEM occupation. 

to the White-alone population as White, the Black-alone population as 

Black, the Asian-alone population as Asian, and the American Indian 

and Alaska Native-alone population as American Indian and Alaska 

Native. Because of a small number of sample observations, estimates 

for Native Hawaiian or Other Pacific Islander are combined with those 

who report Some Other Race. In the analyses presented here, the 

term “non-Hispanic White” refers to people who are not Hispanic and 

who reported White and no other race. The Census Bureau uses nonHispanic Whites as the comparison group for other race groups and 

Hispanics. Because Hispanics may be any race, data in this report for 

Hispanics overlap with data for racial groups. 4 Amanda L. Griffith, 2010, “Persistence of Women and Minorities

in STEM Field Majors: Is It the School That Matters?” Economics of

Education Review 29(6): 911–922. 5 For more information on the educational background of

STEM workers, see Liana Christin Landivar, 2013, “The Relationship 

Between Science and Engineering Education and Employment in

STEM Occupations,” ACS-23, U.S. Census Bureau, available at

<www.census.gov/people/io/publications/reports.html>.
2 U.S. Census Bureau

HIGHLIGHTS

• Women’s representation in STEM 

occupations has increased since 

the 1970s, but they remain 

significantly underrepresented in 

engineering and computer occupations, occupations that make 

up more than 80 percent of all 

STEM employment. Women’s 

representation in computer 

occupations has declined since 

the 1990s.

• Among science and engineering 

graduates, men are employed 

in a STEM occupation at twice 

the rate of women: 31 percent 

compared with 15 percent. 

Nearly 1 in 5 female science and 

engineering graduates are out of 

the labor force, compared with 

less than 1 in 10 male science 

and engineering graduates.

• The most recent decades show 

less growth in STEM employment 

among younger women. Most of 

the growth in women’s share of 

STEM employment among those 

under the age of 40 occurred 

between 1970 and 1990.

• About 41 percent of Asians with 

a science or engineering degree 

are currently employed in a 

STEM occupation, followed by 

individuals who self-identify as 

Two or More Races (24 percent) 

and non-Hispanic White (23 

percent).6

• Blacks and Hispanics have been 

consistently underrepresented 

in STEM employment. In 2011, 

11 percent of the workforce was 

Black, while 6 percent of STEM 

workers were Black (up from 2 

percent in 1970). Although the 

Hispanic share of the workforce 

6 The estimates for Two or More Races 

and non-Hispanic White are not statistically 

different. 

has increased significantly from 

3 percent in 1970 to 15 percent 

in 2011, Hispanics were 7 percent of the STEM workforce in 

2011. 

CLASSIFICATION

Occupational Classification

Occupation statistics are compiled 

from data that are coded based on 

the 2010 Standard Occupational 

Classification (SOC) manual.7 All 

federal statistical agencies use the 

SOC to classify workers and jobs 

into occupational categories. The 

SOC was first published in 1980 

with subsequent revisions in 2000 

and 2010. The revision process 

7 The SOC manual is available at

<www.bls.gov/soc>.

is carried out by the Standard 

Occupational Classification 

Policy Committee (SOCPC), which 

included representatives of nine 

federal agencies for the 2010 revision. The SOC primarily classifies 

workers based on the type of work 

performed, rather than the education or training required.8 Census 

Bureau occupation codes, based on 

the 2010 SOC, provide 539 specific 

occupational categories arranged 

into 23 major occupational groups.9

ACS respondents were asked to 

write descriptions of the type of 

work and activities they do on the 

job (Figure 1). These responses 

are then coded into one of the 539 

Census Bureau occupations. 

8 SOC Classification Principle 2 states, 

“Occupations are classified based on the work 

performed and, in some cases on the skills, 

education, and/or training needed to perform 

the work at a competent level.” 9 The Census Bureau has developed and 

maintained its own occupation code list since 

it started collecting data on occupation in 

1850. The Census Bureau occupation code 

list has followed the structure of the Standard 

Occupational Classification since it was 

implemented in 1980, but aggregates smaller 

categories for confidentiality and statistical 

precision.

WHAT IS STEM? 

STEM workers are those 

employed in science, technology, engineering, and 

mathematics occupations. 

This includes computer and 

mathematical occupations, 

engineers, engineering technicians, life scientists, physical 

scientists, social scientists, and 

science technicians. STEM is 

subject-matter driven. As such, 

it includes managers, teachers, 

practitioners, researchers, and 

technicians. Although the majority of the STEM workforce has 

at least a bachelor’s degree, the 

STEM workforce also includes 

those with associate’s degrees 

and high school diplomas. The 

Census Bureau occupation code 

list contains 63 STEM occupations, accounting for 6 percent 

of the total civilian workforce 

aged 25 to 64.

Figure 1. 

Reproduction of the 

Write-In Questions on 

Occupation From the 

2011 American 

Community Survey

45 What kind of work was this person doing?

(For example: registered nurse, personnel manager, 

supervisor of order department, secretary, 

accountant)

46 What were this person’s most important

activities or duties? (For example: patient care,

directing hiring policies, supervising order clerks,

typing and filing, reconciling financial records)

d. Social Security or 

Person 1 (continued)

manufacturing?

wholesale trade?

Yes ➔

No TOT

$ 

Yes ➔

No

e. Supplemental Sec

TOT

$ 

Yes ➔

No

f. Any public assist

from the state or 

TOT

$ 

Yes ➔

No

$ 

g. Retirement, survi

Do NOT include So

TO

44 Is this mainly – Mark (X) ONE box.

48 What was this perso

PAST 12 MONTHS? 

to 47h; subtract any lo

enter the amount and 

the dollar amount.

h. Any other source

regularly such as 

unemployment co

or alimony. Do NO

such as money from 

home.

Yes ➔

No

$ 

TO

$ 

None OR

TO

Yes ➔

No

Mark (X) the "Yes" box for each type of income this

person received, and give your best estimate of the

TOTAL AMOUNT during the PAST 12 MONTHS. 

(NOTE: The "past 12 months" is the period from

today’s date one year ago up through today.)

Mark (X) the "No" box to show types of income

NOT received.

If net income was a loss, mark the "Loss" box to

the right of the dollar amount. 

For income received jointly, report the appropriate

share for each person – or, if that’s not possible, 

report the whole amount for only one person and 

mark the "No" box for the other person.

$ .00 ,

a. Wages, salary, commissions, bonuses, 

or tips from all jobs. Report amount before

deductions for taxes, bonds, dues, or other items.

TOTAL AMOUNT for past 12 months

Yes ➔

No

$ .00 ,

b. Self-employment income from own nonfarm

businesses or farm businesses, including

proprietorships and partnerships. Report 

NET income after business expenses.

TOTAL AMOUNT for past 12 months

Loss

47 INCOME IN THE PAST 12 MONTHS

43 What kind of business or industry was this?

Describe the activity at the location where employed.

(For example: hospital, newspaper publishing, mail

order house, auto engine manufacturing, bank)

Answer questions 41 – 46 if this person

worked in the past 5 years. Otherwise, 

SKIP to question 47.

L

41 – 46 CURRENT OR MOST RECENT JOB 

ACTIVITY. Describe clearly this person’s chief

job activity or business last week. If this person

had more than one job, describe the one at 

which this person worked the most hours. If this

person had no job or business last week, give

information for his/her last job or business.

Was this person – 

Mark (X) ONE box.

41

an employee of a PRIVATE FOR-PROFIT

company or business, or of an individual, for

wages, salary, or commissions?

an employee of a PRIVATE NOT-FOR-PROFIT, 

tax-exempt, or charitable organization?

a local GOVERNMENT employee

(city, county, etc.)?

a state GOVERNMENT employee?

a Federal GOVERNMENT employee?

SELF-EMPLOYED in own NOT INCORPORATED

business, professional practice, or farm?

SELF-EMPLOYED in own INCORPORATED

business, professional practice, or farm?

working WITHOUT PAY in family business

or farm?

If now on active duty in

the Armed Forces, mark (X) this box ➔ 

and print the branch of the Armed Forces.

Name of company, business, or other employer

42 For whom did this person work?

c. Interest, dividends, net rental income,

royalty income, or income from estates

and trusts. Report even small amounts credited

to an account.

Yes ➔

$ .00 
U.S. Census Bureau 3

STEM Occupation Classification

There has been a lack of consensus on who qualifies as a STEM 

worker.10 To enhance comparability across statistical agencies and 

organizations studying the STEM 

workforce, the SOCPC convened 

throughout 2011 at the request 

of the Office of Management and 

Budget (OMB) to create guidelines 

for the classification of STEM 

10 David Langdon, George McKittrick, 

David Beede, Beethika Khan, and Mark

Doms, 2011, “STEM: Good Jobs Now and

for the Future,” Economics and Statistics 

Administration, Issue Brief #03-11,

<www.esa.doc.gov/sites/default/files

/reports/documents/stemfinalyjuly14_1.pdf>.

workers.11 The SOCPC identified 

three occupational domains:

(1) science, engineering, mathematics, and information technology occupations; (2) science- and 

engineering-related occupations; 

and (3) nonscience and engineering occupations. The final 

11 The SOCPC formed a STEM workgroup 

with representatives from Department

of Labor, Bureau of Labor Statistics and 

Employment Training Administration; the 

Department of Commerce, Census Bureau; 

the Department of Defense, Defense

Manpower Data Center; the Equal

Employment Opportunity Commission;

the Department of Health and Human 

Services, Health Resources and Services 

Administration; the Department of Education, 

National Center for Education Statistics; and 

the National Science Foundation, National 

Center for Science and Engineering Statistics.

recommendations issued by the 

SOCPC were reviewed by outside 

agencies and approved by the OMB 

in April 2012.12 This report follows the SOCPC recommendations. 

To apply the recommendations to 

Census Bureau occupations, some 

exceptions were necessary because 

of lack of detail to separate STEM 

and non-STEM workers (e.g., postsecondary teachers are not separated by subject matter). The final 

list of STEM occupations used in 

this report is available at

<www.census.gov/people/io

/methodology/>. 

12 The final recommendations are available 

at <www.bls.gov/soc/#crosswalks>.

Table 1.

Classification of STEM, STEM-Related, and Non-STEM Occupations

High-level occupation aggregation Occupation group STEM occupation classification

Management, business, science, and arts Management Non-STEM (exc. computer and information systems managers, architectural and 

engineering managers, and natural science 

managers)

Business and financial operations Non-STEM

Computer, math, engineering, and

science

STEM (exc. architects; incl. sales engineers, 

computer and information systems managers, 

architectural and engineering managers, and 

natural science managers)

Education, legal, community service, 

arts, and media

Non-STEM

Healthcare practitioners and technicians STEM-related (incl. architects)

Service Healthcare support Non-STEM

Protective service Non-STEM

Food preparation and serving Non-STEM

Building and grounds cleaning Non-STEM

Personal care and service Non-STEM

Sales and office Sales and related Non-STEM (exc. sales engineers)

Office and administrative support Non-STEM

Natural resources, construction, and maintenance Farming, fishing, and forestry Non-STEM

Construction and extraction Non-STEM

Installation, maintenance, and repair Non-STEM

Production, transportation, and material moving Production Non-STEM

Transportation Non-STEM

Material moving Non-STEM

Note: The full list of Census Bureau occupations used in this report and occupation-specific classification is available at

<www.census.gov/people/io/methodology/>.
4 U.S. Census Bureau

STEM occupations consist primarily of those employed in computer 

and mathematical occupations, 

engineers, life scientists, physical 

scientists, and social scientists. 

STEM-related occupations consist 

primarily of architects, healthcare 

practitioners, and healthcare technicians. Non-STEM occupations are 

all other occupations not classified 

in STEM or STEM-related occupations (Table 1). According to the 

Census Bureau occupation code 

list, there are 63 specific STEM 

occupations, 35 STEM-related occupations, and 437 non-STEM occupations (excluding military-specific 

occupations). 

Field of Degree Classification

The ACS provided statistics on 

field of bachelor’s degree for the 

first time in 2009. Respondents 

aged 25 and over who held a 

bachelor’s degree were asked to 

write in the specific field(s) of any 

bachelor’s degree earned (Figure 

2). The Census Bureau coded these 

responses into 188 majors. These 

majors were then categorized into 5 broad fields and 15 detailed 

fields (Table 2). The broad set of 

fields includes: science and engineering; science- and engineeringrelated; business; education; and 

arts, humanities, and other. Data 

on field of degree are not available 

for vocational, graduate, or professional degrees.13

13 The field of degree classification 

presented in this report is consistent with 

the field of degree classification in American 

FactFinder tables. The National Science

Foundation uses slightly different field of 

degree categories, consistent with the ACS 

Public Use Microdata Sample files at

<www.census.gov/acs/www/data

_documentation/pums_documentation/>. 

EMPLOYMENT IN STEM 

OCCUPATIONS 

In 2011, there were 7.2 million 

STEM workers aged 25 to 64, 

accounting for 6 percent of the 

workforce.14 Half of STEM workers 

worked in computer occupations 

(Figure 3). Engineers and engineering technicians were 32 percent of 

the STEM workforce, followed by 

14 The estimates in this report are based 

on responses from a sample of the population. As with all surveys, estimates may vary 

from the actual values because of sampling 

variation or other factors. All comparisons 

made in this report have undergone statistical 

testing and are significant at the 90 percent 

confidence level unless otherwise noted.

Figure 2.

Reproduction of the 

Write-In Question on 

Field of Degree From 

the 2011 American 

Community Survey

No, outside the United States and 

Puerto Rico – Print name of foreign country, or U.S. Virgin Islands, Guam, etc., below; 

then SKIP to question 16

Yes, this house ➔ SKIP to question 16

Yes

No ➔ SKIP to question 15a

e of Person 1 from page 2,

ring questions below.

n born?

es – Print name of state.

nited States ➔ SKIP to 10a

d States – Print name of 

r Puerto Rico, Guam, etc.

o Rico, Guam, the

s, or Northern Marianas

of U.S. citizen parent 

zen

y naturalization – Print year 

n of the United States?

 come to live in the

umbers in boxes.

MI

ended in the last 3 

P to question 11

ool, public college

hool, private college, 

, preschool

h 12 – Specify 

raduate years (freshman to

ofessional school beyond a

ree (for example: MA or PhD 

edical or law school)

What is the highest degree or level of school

this person has COMPLETED? Mark (X) ONE box.

If currently enrolled, mark the previous grade or

highest degree received.

No schooling completed

Regular high school diploma

Some college credit, but less than 1 year of

college credit

Master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA)

Professional degree beyond a bachelor’s degree

(for example: MD, DDS, DVM, LLB, JD)

Doctorate degree (for example: PhD, EdD)

(For example: Italian, Jamaican, African Am.,

Cambodian, Cape Verdean, Norwegian, Dominican,

French Canadian, Haitian, Korean, Lebanese, Polish,

Nigerian, Mexican, Taiwanese, Ukrainian, and so on.)

12th grade – NO DIPLOMA

1 or more years of college credit, no degree

Associate’s degree (for example: AA, AS)

Bachelor’s degree (for example: BA, BS)

Person is under 1 year old ➔ SKIP to question 16

b. Where did this person live 1 year ago?

Name of city, town, or post office

ZIP Code

Name of U.S. county or

municipio in Puerto Rico

Name of U.S. state or 

Puerto Rico

b. What is this language?

c. How well does this person speak English?

Very well

Well

Not well

Not at all

11

For example: Korean, Italian, Spanish, Vietnamese

13

No, different house in the United States or

Puerto Rico

+v¤

13191085

 LAST 3 MONTHS, has this

chool or college? Include

chool, kindergarten, 

ome school, and schooling

h school diploma or a college

l was this person attending?

NURSERY OR PRESCHOOL THROUGH GRADE 12

Nursery school

Kindergarten

Grade 1 through 11 – Specify grade 1 – 11

HIGH SCHOOL GRADUATE

GED or alternative credential

COLLEGE OR SOME COLLEGE

AFTER BACHELOR’S DEGREE a. Did this person live in this house or apartment

1 year ago?

Address (Number and street name)

What is this person’s ancestry or ethnic origin?

12

a. Does this person speak a language other than

English at home?

15

NO SCHOOLING COMPLETED

14

Answer question 12 if this person has a

bachelor’s degree or higher. Otherwise,

SKIP to question 13.

F

This question focuses on this person’s 

BACHELOR’S DEGREE. Please print below the 

specific major(s) of any BACHELOR’S DEGREES 

this person has received. (For example: chemical 

engineering, elementary teacher education, 

organizational psychology)

Figure 3. 

Occupational Distribution of STEM Workers

(In percent. Data based on sample. For information on condentiality 

protection, sampling error, nonsampling error, and denitions, see 

www.census.gov/acs/www/)

Source: U.S. Census Bureau, 2011 American Community Survey.

Mathematical

occupations

Social science

occupations

Life and physical

science occupations

Engineering

occupations

Computer

occupations 50

32

12

4

3

100
U.S. Census Bureau 5

life and physical scientists (12 percent), social scientists (4 percent), 

and workers employed in mathematical occupations (3 percent).

Men and Women in STEM 

Occupations

Although women make up nearly 

half of the working population, 

they remain underrepresented in 

STEM occupations. In 2011, 26 

percent of STEM workers were 

women and 74 percent were men. 

There has been uneven growth in 

women’s representation in STEM 

occupations since the 1970s. In 

1970, women were 3 percent of 

engineers, 14 percent of life and 

physical scientists, 15 percent 

of mathematical and computer 

workers, and 17 percent of social 

scientists (Figure 4).15

By 2011, women’s representation 

had grown in all STEM occupation 

groups. However, they remained 

significantly underrepresented in 

engineering and computer occupations, occupations that make up 

more than 80 percent of all STEM 

employment (Table 3). In fact, 

women’s representation in computer occupations has declined 

since the 1990s. This mirrors 

the decline in women’s share of 

bachelor’s degrees in computer 

15 Estimates for 1970, 1980, 1990, and 

2000 in this report were obtained using internal Census Bureau files. Estimates may differ 

from those published previously because of 

specific age restrictions (25 to 64) used in 

this report.

science awarded since the 1980s.16

Women’s underrepresentation in 

STEM is a result of their significant 

underrepresentation in engineering 

and computer occupations, rather 

than math and science occupations. 

While women’s representation has 

continued to grow in math and science occupations since the 1970s, 

growth has tapered off in engineering since 1990. In 2011, women 

were 13 percent of engineers, 27 

percent of computer professionals, 

41 percent of life and physical scientists, 47 percent of mathematical 

workers, and 61 percent of social 

scientists. 

16 National Science Foundation, Division of 

Science Resources Statistics, 2011, “Women, 

Minorities, and Persons with Disabilities 

in Science and Engineering: 2011,” Special 

Report NSF 11-309, Arlington, VA.

Table 2.

Field of Bachelor’s Degree Classification

Broad fields Detailed fields

Science and engineering Computers, mathematics, and statistics

Biological, agricultural, and environmental sciences

Physical and related science

Psychology

Social sciences

Engineering

Multidisciplinary studies

Science- and engineering-related Science- and engineering-related (e.g., nursing, architecture, mathematics teacher education)

Business Business (e.g., business management, accounting)

Education Education (e.g., elementary education, general education)

Arts, humanities, and other Literature and languages

Liberal arts and history

Visual and performing arts

Communications

Other (e.g., criminal justice, social work)
6 U.S. Census Bureau

Table 3 provides estimates of STEM 

employment by detailed STEM 

occupational categories. Among the 

occupations with the largest representation of women is psychologist (70 percent are women), while 

mechanical engineers have among 

the lowest female representation

(6 percent are women).17

Figure 5 shows that women’s 

employment in STEM is below average in most STEM occupations. If 

women were equally represented 

in STEM occupations, their share 

17 The estimate for psychologists is not 

statistically different from the estimates for 

sociologists and survey researchers.

would approximate 48 percent, 

which is the share of the workforce 

that is female. Women’s employment shares do vary significantly 

by detailed occupation, but their 

share of employment is lowest in 

engineering occupations.

Figure 4. 

Women’s Employment in STEM Occupations: 1970 to 2011

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 decennial censuses and 2011 American Community Survey.

0

10

20

30

40

50

60

70

Social scientists

Life and physical

scientists

Mathematical

workers

Computer workers

Engineers

1970 1980 1990 2000 2011

17

3

14

15

61

47

41

27

13

Percent female

0

20

40

60

Total employed

STEM

1970 1980 1990 2000 2011
U.S. Census Bureau 

7

Table 3.Employment in STEM Occupations: 2011—Con.(Civilian employed aged 25 to 64)

Occupations

Number MOE1

Percent 

of STEM 

workforce MOE1 Percent female MOE1

Percent 

White 

alone, 

not

Hispanic 

or Latino MOE1

Percent 

Asian 

alone MOE1

Percent 

Black or 

African 

American 

alone MOE1

Percent 

Hispanic MOE1

Total employed . . . . . . . . . . . . . . . . . . . . . . . . 116,445,308 106,429 X X 47.5 0.1 66.9 0.1 5.5 0.1 10.8 0.1 14.9 0.1Total STEM occupations . . . . . . . . . . . . . . . . 7,227,620 48,618 100.0 X 25.8 0.3 70.8 0.3 14.5 0.2 6.4 0.2 6.5 0.2

Computer occupations . . . . . . . . . . . . . . . . . . . . . . . 3,614,046 33,846 50.0 0.3 26.6 0.4 67.9 0.4 16.8 0.4 7.3 0.3 6.0 0.2

Computer and information systems managers. . . . . 504,161 13,167 7.0 0.2 29.5 1.0 74.6 1.2 11.6 0.7 6.1 0.8 5.6 0.6Computer and information research scientists. . . . . . 14,981 1,747 0.2 0.1 24.9 5.1 77.6 5.3 14.0 4.5 2.4 1.6 4.1 2.4Computer systems analysts. . . . . . . . . . . . . . . . . . . . 431,894 12,361 6.0 0.2 35.3 1.2 66.2 1.3 16.8 1.2 9.0 0.9 5.7 0.6Information security analysts. . . . . . . . . . . . . . . . . . . 47,196 3,987 0.7 0.1 22.9 3.0 70.9 3.7 7.6 2.0 11.5 2.7 7.5 2.4Computer programmers. . . . . . . . . . . . . . . . . . . . . . . 415,229 9,773 5.7 0.1 23.0 1.0 71.0 1.2 17.0 1.0 4.7 0.6 5.3 0.6Software developers. . . . . . . . . . . . . . . . . . . . . . . . . 851,921 16,796 11.8 0.2 22.1 0.8 59.1 1.0 30.1 1.0 5.1 0.5 3.9 0.4

Web developers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149,739 7,382 2.1 0.1 37.0 2.6 75.1 2.3 12.0 1.7 4.4 1.0 6.1 1.2

Computer support specialists. . . . . . . . . . . . . . . . . . 463,148 12,148 6.4 0.2 28.9 1.0 70.0 1.4 8.1 0.7 11.6 0.9 8.6 0.8Database administrators. . . . . . . . . . . . . . . . . . . . . . 97,415 5,637 1.3 0.1 40.1 2.4 67.1 2.7 16.5 2.1 7.6 1.3 6.4 1.3Network and computer systems administrators. . . . . 220,363 8,475 3.0 0.1 19.9 1.6 73.5 1.7 9.4 1.2 7.9 1.0 7.8 1.1Computer network architects. . . . . . . . . . . . . . . . . . . 91,677 6,922 1.3 0.1 11.4 2.5 71.0 3.2 13.1 1.8 7.2 1.4 6.9 2.1Computer occupations, all other. . . . . . . . . . . . . . . . 326,322 10,125 4.5 0.1 24.1 1.4 66.9 1.7 12.1 1.1 10.6 1.1 7.9 1.1

Mathematical occupations 202,667 7,916 2.8 0.1 47.0 2.1 70.3 1.6 12.0 1.4 9.3 1.2 6.1 1.0

Actuaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22,069 2,387 0.3 0.1 36.2 5.3 79.1 4.2 14.9 3.3 2.4 1.5 2.7 2.4Mathematicians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,450 955 0.0 0.1 23.3 14.3 64.2 18.4 19.8 16.8 16.0 14.9 0.0 6.1Operations research analysts. . . . . . . . . . . . . . . . . . 133,100 6,571 1.8 0.1 48.1 2.5 71.4 2.2 8.7 1.4 10.8 1.4 6.9 1.2Statisticians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42,358 3,920 0.6 0.1 50.2 4.8 62.5 4.0 19.9 3.7 8.2 3.3 6.2 2.2Miscellaneous mathematical science occupations. . 2,690 868 0.0 0.1 51.8 14.0 70.2 15.8 19.6 14.4 0.0 5.6 2.9 2.9

Engineering occupations . . . . . . . . . . . . . . . . . . . . . 2,305,215 26,370 31.9 0.3 13.2 0.4 75.2 0.6 11.3 0.3 4.9 0.3 7.1 0.4

Architectural and engineering managers. . . . . . . . . . 130,207 5,603 1.8 0.1 10.2 1.3 82.4 1.9 8.4 1.3 2.2 0.7 5.3 1.2Surveyors, cartographers, and photogrammetrists. . 35,190 3,191 0.5 0.1 19.9 3.4 90.0 2.9 2.3 1.0 2.9 1.8 3.7 2.0Aerospace engineers. . . . . . . . . . . . . . . . . . . . . . . . . 124,902 5,649 1.7 0.1 11.3 1.5 77.8 1.9 10.1 1.3 3.2 1.0 6.8 1.4Agricultural engineers. . . . . . . . . . . . . . . . . . . . . . . . 2,389 891 0.0 0.1 8.1 9.1 87.9 10.8 0.0 6.3 3.0 5.1 8.4 9.4Biomedical engineers. . . . . . . . . . . . . . . . . . . . . . . . . 13,383 1,851 0.2 0.1 15.7 5.6 74.2 5.1 15.8 4.3 3.9 3.1 3.5 2.4Chemical engineers. . . . . . . . . . . . . . . . . . . . . . . . . . 47,214 3,203 0.7 0.1 12.6 2.9 77.2 4.1 9.8 2.7 4.5 1.8 7.4 2.7Civil engineers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262,066 9,443 3.6 0.1 13.1 1.3 75.6 1.6 11.6 1.2 3.8 0.7 7.2 1.0Computer hardware engineers. . . . . . . . . . . . . . . . . 58,517 4,846 0.8 0.1 17.0 3.3 63.6 3.9 23.1 3.5 5.8 2.1 6.5 1.9Electrical and electronics engineers. . . . . . . . . . . . . 203,538 8,039 2.8 0.1 8.8 1.1 67.8 2.0 19.2 1.4 5.6 1.2 6.4 1.1Environmental engineers. . . . . . . . . . . . . . . . . . . . . . 24,163 2,364 0.3 0.1 20.2 4.0 76.3 3.7 10.2 2.9 9.7 3.0 3.3 1.8Industrial engineers, including health and safety. . . . 156,517 6,969 2.2 0.1 18.4 1.9 76.8 2.1 9.8 1.2 4.4 1.0 8.1 1.6Marine engineers and naval architects. . . . . . . . . . . 10,005 1,318 0.1 0.1 9.3 5.4 81.3 7.6 12.8 6.8 5.0 3.7 1.5 1.5Materials engineers. . . . . . . . . . . . . . . . . . . . . . . . . . 29,445 2,525 0.4 0.1 11.6 3.1 78.1 4.5 12.6 3.7 3.7 1.8 4.0 1.7Mechanical engineers. . . . . . . . . . . . . . . . . . . . . . . . 189,241 6,772 2.6 0.1 6.3 0.8 79.3 1.5 9.5 1.2 4.6 1.0 5.4 1.0Mining and geological engineers. . . . . . . . . . . . . . . . 7,889 1,240 0.1 0.1 6.7 3.2 84.9 6.7 4.5 4.3 1.1 2.0 7.5 4.1

See notes at end of table.
8 U.S. Census Bureau Table 3. Employment in STEM Occupations: 2011—Con. (Civilian employed aged 25 to 64) Occupations Number MOE1 Percent of STEM work- force MOE1 Percent female MOE1 Percent White alone, not Hispanic or Latino MOE1 Percent Asian alone MOE1 Percent Black or African Ameri- can alone MOE1 Percent Hispanic MOE1 Engineering occupations—Con. Nuclear engineers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,681 1,461 0.1 0.1 11.5 8.2 90.3 6.5 4.7 3.1 0.0 2.7 5.0 5.5 Petroleum engineers. . . . . . . . . . . . . . . . . . . . . . . . . . 23,522 3,059 0.3 0.1 13.5 4.7 73.0 6.0 7.5 3.3 9.9 4.4 8.8 3.9 Engineers, all other. . . . . . . . . . . . . . . . . . . . . . . . . . . 396,704 11,787 5.5 0.2 12.8 0.8 72.7 1.3 16.1 1.1 4.0 0.7 5.3 0.7 Drafters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146,622 6,217 2.0 0.1 17.5 1.8 76.5 2.3 6.0 1.0 4.5 1.2 11.7 2.0 Engineering technicians. . . . . . . . . . . . . . . . . . . . . . . . 352,707 8,425 4.9 0.1 17.4 0.9 72.0 1.5 7.9 0.8 8.3 0.9 9.8 1.1 Surveying and mapping technicians. . . . . . . . . . . . . . 56,169 4,257 0.8 0.1 7.8 1.6 82.6 2.6 1.7 0.8 4.0 1.4 8.8 2.2 Sales engineers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29,144 3,003 0.4 0.1 8.6 2.3 85.1 3.6 6.1 1.8 1.2 1.0 6.9 3.0 Life and physical science occupations . . . . . . . . . . . 848,514 16,315 11.7 0.2 40.9 0.9 68.6 0.9 16.9 0.7 5.9 0.5 6.4 0.5 Natural sciences managers. . . . . . . . . . . . . . . . . . . . . 22,536 1,991 0.3 0.1 44.1 4.8 77.8 4.6 9.3 3.0 4.8 2.8 6.8 2.6 Agricultural and food scientists. . . . . . . . . . . . . . . . . . 25,509 2,184 0.4 0.1 23.2 3.6 85.4 3.8 6.4 2.1 2.6 2.6 3.5 1.4 Biological scientists. . . . . . . . . . . . . . . . . . . . . . . . . . . 72,804 4,924 1.0 0.1 46.9 2.6 75.1 2.9 14.3 2.4 4.4 2.0 3.9 1.1 Conservation scientists and foresters. . . . . . . . . . . . . 23,764 2,382 0.3 0.1 20.3 3.4 88.6 3.4 0.5 0.6 3.1 1.9 4.2 1.8 Medical and life scientists. . . . . . . . . . . . . . . . . . . . . . 122,748 6,204 1.7 0.1 52.9 2.1 55.4 2.3 31.9 2.1 5.1 1.3 5.3 1.0 Astronomers and physicists. . . . . . . . . . . . . . . . . . . . . 11,331 1,760 0.2 0.1 19.7 6.0 71.3 7.1 15.8 5.8 2.4 2.6 6.4 5.2 Agricultural and food science technicians. . . . . . . . . . 26,166 2,931 0.4 0.1 41.5 5.4 69.9 4.8 5.9 2.7 7.6 2.8 14.8 3.9 Biological technicians. . . . . . . . . . . . . . . . . . . . . . . . . . 19,054 2,553 0.3 0.1 40.7 5.5 55.8 5.9 19.1 4.6 7.5 3.3 14.8 5.4 Chemical technicians. . . . . . . . . . . . . . . . . . . . . . . . . . 61,175 3,889 0.8 0.1 33.6 3.3 70.0 3.9 4.9 1.6 14.5 3.6 8.5 2.1 Geological and petroleum technicians. . . . . . . . . . . . . 14,888 2,324 0.2 0.1 30.4 7.1 75.6 5.4 4.3 2.7 8.1 3.6 10.1 4.3 Nuclear technicians. . . . . . . . . . . . . . . . . . . . . . . . . . . 3,229 868 0.0 0.1 28.7 11.9 91.7 5.4 0.0 4.7 8.3 5.4 0.0 4.7 Atmospheric and space scientists. . . . . . . . . . . . . . . . 8,407 1,405 0.1 0.1 16.6 6.5 82.9 6.9 4.4 3.3 2.7 3.0 8.7 4.6 Chemists and materials scientists. . . . . . . . . . . . . . . . 76,339 4,632 1.1 0.1 39.6 2.7 66.2 3.4 19.9 2.3 6.8 1.8 6.2 2.0 Environmental scientists and geoscientists. . . . . . . . . 66,502 4,444 0.9 0.1 30.4 3.2 86.0 2.2 5.0 1.6 2.7 1.1 3.9 1.2 Physical scientists, all other. . . . . . . . . . . . . . . . . . . . . 180,332 6,788 2.5 0.1 40.9 1.5 63.8 1.9 26.5 1.8 3.5 0.8 4.5 0.8 Miscellaneous life, physical, and social science technicians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113,730 5,271 1.6 0.1 48.3 2.7 65.9 2.3 11.6 1.3 9.5 1.7 10.3 1.9 Social science occupations . . . . . . . . . . . . . . . . . . . . 257,178 7,674 3.6 0.1 61.2 1.5 79.3 1.6 4.5 0.7 6.5 1.1 7.8 0.9 Economists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24,460 2,564 0.3 0.1 32.7 4.6 65.5 5.2 12.7 3.0 8.1 4.0 11.5 3.8 Survey researchers. . . . . . . . . . . . . . . . . . . . . . . . . . . 1,602 628 0.0 0.1 58.0 19.4 84.5 14.6 6.2 10.2 4.0 6.8 5.3 8.3 Psychologists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164,516 6,551 2.3 0.1 70.3 1.7 82.9 2.0 2.7 0.8 6.0 1.4 6.9 1.0 Sociologists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,196 969 0.0 0.1 61.0 13.5 67.9 18.6 2.6 4.3 5.4 8.3 22.3 15.3 Urban and regional planners. . . . . . . . . . . . . . . . . . . . 18,442 2,210 0.3 0.1 41.6 4.8 74.0 5.5 7.7 3.0 9.2 3.5 8.8 4.3 Miscellaneous social scientists and related workers. . 41,600 3,266 0.6 0.1 52.6 3.5 77.8 3.6 5.6 2.1 6.5 2.3 6.2 2.0 Social science research assistants. . . . . . . . . . . . . . . 3,362 1,062 0.0 0.1 38.7 14.1 64.8 13.5 6.1 7.9 3.8 3.2 24.0 12.7 X Not applicable. 1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimates, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval. Note: Estimates for the American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, Some Other Race, and Two or More Races populations are not shown because of a small number of sample observations. Source: U.S. Census Bureau, 2011 American Community Survey. For more information, see <www.acs.census.gov/acs>.
U.S. Census Bureau 9

Source: U.S. Census Bureau, 2011 American Community Survey. 

Percent female

Social science research assistants

Misc. social scientists and related workers

Urban and regional planners

Sociologists

Psychologists

Survey researchers

Economists

Social science occupations

Misc. life, physical, and social science technicians

All other physical scientists

Environmental scientists and geoscientists

Chemists and materials scientists

Atmospheric and space scientists

Nuclear technicians

Geological and petroleum technicians

Chemical technicians

Biological technicians

Agricultural and food science technicians

Astronomers and physicists

Medical and life scientists

Conservation scientists and foresters

Biological scientists

Agricultural and food scientists

Natural sciences managers

Life and physical science occupations

Sales engineers

Surveying and mapping technicians

Engineering technicians

Drafters

All other engineers

Petroleum engineers

Nuclear engineers

Mining and geological engineers

Mechanical engineers

Materials engineers

Marine engineers and naval architects

Industrial engineers, including health and safety

Environmental engineers

Electrical and electronics engineers

Computer hardware engineers

Civil engineers

Chemical engineers

Biomedical engineers

Agricultural engineers

Aerospace engineers

Surveyors, cartographers, and photogrammetrists

Architectural and engineering managers

Engineering occupations

Miscellaneous mathematical science occupations

Statisticians

Operations research analysts

Mathematicians

Actuaries

Mathematical occupations

All other computer occupations

Computer network architects

Network and computer systems administrators

Database administrators

Computer support specialists

Web developers

Software developers

Computer programmers

Information security analysts

Computer systems analysts

Computer and information research scientists

Computer and information systems managers

Computer occupations

Total STEM occupations

Total employed

0 10 20 30 40 50 60 70 80

Figure 5. 

Women's Employment by Detailed STEM Occupations

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)
10 U.S. Census Bureau

STEM Employment by Age 

and Sex

STEM workers are more likely to be 

in core working-age groups (aged 

25 to 54) than non-STEM workers. 

Because most STEM workers have a 

bachelor’s degree or higher level of 

education, few STEM workers are 

younger than 25.18 However, STEM 

18 For more information about STEM

workers under the age of 25, see “Selected 

Characteristics by Employment in STEM

Occupations: 2011” at <www.census.gov

/people/io/publications/reports.html>.

workers have a slightly younger 

age profile than non-STEM workers. 

Workers between the ages of 35 

and 44 make up the largest share 

of the STEM workforce, while workers between the ages of 45 and 54 

make up the largest share of nonSTEM employment (Table 4). Figure 

6 shows that the STEM workforce 

is becoming older and more agediverse, compared with 1970 to 

1990, when a larger share of STEM 

workers were in their twenties and 

thirties.19

The aging of the STEM workforce 

may have a disproportionate 

impact on women’s share of the 

STEM workforce. Some research 

indicates that younger women 

today are more likely to pursue 

training in a STEM field, and this 

may contribute to their larger share 

of employment in STEM compared 

19 The aging of the STEM workforce is 

similar to the aging of the total workforce. 

Figure 6. 

Age Distribution of STEM Workers: 1970 to 2011

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 decennial censuses and 2011 American Community Survey.

Percent distribution of STEM employment

Age

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

2011

2000

1990

1980

1970

25 30 35 40 45 50 55 60
U.S. Census Bureau 11 Table 4. Selected Characteristics by Employment in STEM Occupations: 2011—Con. (Civilian employed aged 25 to 64) Characteristics Total STEM occupations STEM-related occupations Non-STEM occupations Number MOE1 Per- cent MOE1 Number MOE1 Per- cent MOE1 Number MOE1 Per- cent MOE1 Number MOE1 Per- cent MOE1 Total . . . . . . . . . . . . . . . . . . . . . . . . 116,445,308 106,429 100.0 X 7,227,620 48,618 100.0 X 7,829,769 49,792 100.0 X 101,387,919 104,828 100.0 X Sex Male. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61,162,449 66,968 52.5 0.1 5,363,422 40,866 74.2 0.3 2,036,665 25,112 26.0 0.3 53,762,362 74,742 53.0 0.1 Female. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55,282,859 73,264 47.5 0.1 1,864,198 21,887 25.8 0.3 5,793,104 40,817 74.0 0.3 47,625,557 79,979 47.0 0.1 Age 25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . 30,158,891 52,737 25.9 0.1 1,953,956 26,903 27.0 0.3 1,976,807 26,509 25.2 0.3 26,228,128 56,076 25.9 0.1 35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . 30,744,454 51,258 26.4 0.1 2,086,259 27,879 28.9 0.3 2,105,841 26,294 26.9 0.3 26,552,354 47,754 26.2 0.1 45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . 32,998,018 56,946 28.3 0.1 2,015,184 22,689 27.9 0.3 2,163,221 25,869 27.6 0.3 28,819,613 53,566 28.4 0.1 55 to 64 years. . . . . . . . . . . . . . . . . . . . . . . 22,543,945 45,677 19.4 0.1 1,172,221 16,081 16.2 0.2 1,583,900 18,372 20.2 0.2 19,787,824 49,053 19.5 0.1 Male: 25 to 34 years. . . . . . . . . . . . . . . . . . . . . 16,004,873 35,867 26.2 0.1 1,435,260 22,647 26.8 0.3 475,169 12,222 23.3 0.5 14,094,444 43,478 26.2 0.1 35 to 44 years. . . . . . . . . . . . . . . . . . . . . 16,432,230 36,062 26.9 0.1 1,562,400 23,881 29.1 0.4 564,217 14,021 27.7 0.6 14,305,613 39,018 26.6 0.1 45 to 54 years. . . . . . . . . . . . . . . . . . . . . 17,134,230 39,021 28.0 0.1 1,486,545 19,946 27.7 0.3 542,754 12,307 26.6 0.5 15,104,931 40,771 28.1 0.1 55 to 64 years. . . . . . . . . . . . . . . . . . . . . 11,591,116 32,494 19.0 0.1 879,217 12,705 16.4 0.2 454,525 9,898 22.3 0.4 10,257,374 35,609 19.1 0.1 Female: 25 to 34 years. . . . . . . . . . . . . . . . . . . . . 14,154,018 38,780 25.6 0.1 518,696 11,429 27.8 0.5 1,501,638 21,994 25.9 0.3 12,133,684 41,782 25.5 0.1 35 to 44 years. . . . . . . . . . . . . . . . . . . . . 14,312,224 36,535 25.9 0.1 523,859 11,974 28.1 0.6 1,541,624 22,411 26.6 0.3 12,246,741 35,872 25.7 0.1 45 to 54 years. . . . . . . . . . . . . . . . . . . . . 15,863,788 39,719 28.7 0.1 528,639 11,496 28.4 0.5 1,620,467 20,997 28.0 0.3 13,714,682 40,880 28.8 0.1 55 to 64 years. . . . . . . . . . . . . . . . . . . . . 10,952,829 34,102 19.8 0.1 293,004 8,930 15.7 0.4 1,129,375 15,835 19.5 0.3 9,530,450 36,586 20.0 0.1 Own Children Under 18 In family with own children under 18 years. . . . . . . . . . . . . . . . . . . . . . . . . . . 45,855,783 112,765 39.5 0.1 2,986,479 27,335 41.3 0.3 3,336,585 31,052 42.6 0.3 39,532,719 103,589 39.1 0.1 Under 6 years only. . . . . . . . . . . . . . . . . . 9,471,459 76,011 8.2 0.1 796,053 15,334 11.0 0.2 766,158 14,738 9.8 0.2 7,909,248 65,821 7.8 0.1 6 to 17 years only. . . . . . . . . . . . . . . . . . . 27,282,894 90,065 23.5 0.1 1,642,629 20,403 22.7 0.3 1,951,277 22,617 24.9 0.2 23,688,988 82,303 23.4 0.1 Under 6 years and 6 to 17. . . . . . . . . . . . 9,101,430 59,594 7.8 0.1 547,797 13,052 7.6 0.2 619,150 14,264 7.9 0.2 7,934,483 56,114 7.8 0.1 No own children present. . . . . . . . . . . . . . . 70,332,664 115,892 60.5 0.1 4,236,837 37,731 58.7 0.3 4,489,252 35,403 57.4 0.3 61,606,575 121,269 60.9 0.1 Male: In family with own children under 18 years. . . . . . . . . . . . . . . . . . . . . . . 24,226,161 76,831 39.7 0.1 2,285,458 23,916 42.6 0.3 874,761 16,681 43.0 0.5 21,065,942 77,136 39.3 0.1 Under 6 years only. . . . . . . . . . . . . . 5,368,653 48,293 8.8 0.1 609,945 13,955 11.4 0.2 223,334 7,404 11.0 0.4 4,535,374 44,908 8.5 0.1 6 to 17 years only. . . . . . . . . . . . . . . 13,686,817 51,472 22.4 0.1 1,242,783 17,787 23.2 0.3 477,412 12,328 23.5 0.5 11,966,622 49,663 22.3 0.1 Under 6 years and 6 to 17. . . . . . . . 5,170,691 40,060 8.5 0.1 432,730 10,472 8.1 0.2 174,015 7,994 8.6 0.4 4,563,946 37,467 8.5 0.1 No own children present. . . . . . . . . . . . 36,770,383 72,984 60.3 0.1 3,075,277 31,830 57.4 0.3 1,160,427 15,561 57.0 0.5 32,534,679 74,975 60.7 0.1 See notes at end of table.
12 U.S. Census Bureau

with older women.20 This perspec-

tive would be consistent with a 

cohort effect, where we would 

expect higher shares of female 

employment in STEM in the future, 

as young women who are in STEM 

occupations age and retain STEM 

employment. On the other hand, 

these estimates could be consistent 

with an age effect. That is, when 

women are young, they are more 

likely to be employed in STEM, but 

as they age, they move out of STEM 

employment. 

In the 1970s, women’s share of 

STEM occupations was 12 percent 

when they were 25 years old, 

sharply declined when women were 

in their late-twenties, and remained 

relatively low until retirement 

(Figure 7).21 In 2011, women had 

higher shares of STEM employment 

than in the 1970s, starting out at 

27 percent at the age of 25, and 

relative to earlier decades, showed 

more stability in STEM employment 

during peak employment ages and 

into retirement.22 However, while 

women’s share of STEM employ-

ment is up since 1970, the most 

recent decades show much less 

20 Economics and Statistics

Administration, 2012, “STEM Across the 

“Gen(d)erations,” Economic Briefing,

<www.esa.doc.gov/blog/2012/04/24/>. 21 Compared to women’s share of total 

employment, women’s share of STEM employ-

ment showed little recuperation beyond 

childbearing ages in the 1970s. In the total 

workforce, women’s employment increased 

when they were in their thirties, while in 

the STEM workforce, women’s employment 

declined in their mid-twenties and remained 

low until retirement. 22 Compared to the total workforce, there 

was a steeper decline in women’s share of 

employment in STEM occupations at older 

ages in 2011. 

Table 4.Selected Characteristics by Employment in STEM Occupations: 2011—Con.(Civilian employed aged 25 to 64)

Characteristics

Total STEM occupations STEM-related occupations Non-STEM occupations

Number MOE1

Percent MOE1 Number MOE1 Per- cent MOE1 Number MOE1 Per- cent MOE1 Number MOE1 Per- cent MOE1

Own Children Under 18—Con. Female:

In family with own children under 

 18 years .......................

Under 6 years only ..............6 to 17 years only ...............

Under 6 years and 6 to 17 ........

No own children present ............

Birth in the last 12 months2

Birth in the last 12 months ..............No birth in the last 12 months ...........Race and Hispanic OriginWhite alone .........................

White alone, not Hispanic or Latino .....

Black or African American alone .........Asian alone .........................American Indian and Alaska Native alone ..Some Other Race and Native Hawaiian  or Other Pacific Islander alone3 .........

Two or More Races ...................Hispanic or Latino (of any race) ..........

21,629,622

4,102,806

13,596,077

3,930,739

33,562,281

1,725,937

36,404,50589,234,79577,920,90812,628,937

6,404,993

749,596

5,367,0872,059,900

17,368,595

63,88936,06553,18232,03975,49423,19765,73399,42777,26446,38930,11017,90448,58936,08846,082

39.2

7.4

24.6

7.1

60.8

4.5

95.576.666.910.8

5.50.64.61.8

14.9

0.10.10.10.10.10.10.10.10.10.10.10.10.10.10.1

701,021186,108399,846115,067

1,161,560

67,008

1,308,2345,454,7825,113,563

461,949

1,047,444

26,077

103,078134,290467,520

12,370

6,1578,9465,927

17,489

3,952

16,82044,90239,79013,48516,159

2,9295,4316,699

16,056

37.610.021.5

6.2

62.4

4.9

95.175.570.8

6.4

14.5

0.41.41.96.5

0.50.30.40.30.50.30.30.30.30.20.20.10.10.10.2

2,461,824

542,824

1,473,865

445,135

3,328,825

233,410

3,777,5896,076,6765,689,948

792,668678,210

33,708

122,703125,804531,509

22,53411,86816,52310,63931,069

7,829

33,59947,33644,88116,81015,331

2,7706,7896,944

15,477

42.5

9.4

25.5

7.7

57.5

5.8

94.277.672.710.1

8.70.41.61.66.8

0.30.20.30.20.30.20.20.30.30.20.20.10.10.10.2

18,466,777

3,373,874

11,722,366

3,370,537

29,071,896

1,425,519

31,318,68277,703,33767,117,39711,374,320

4,679,339

689,811

5,141,3061,799,806

16,369,566

59,64731,52350,02631,95479,20221,22971,656

103,196

82,61546,67735,00717,33948,36332,36849,709

38.8

7.1

24.7

7.1

61.2

4.4

95.676.666.211.2

4.60.75.11.8

16.1

0.10.10.10.10.10.10.10.10.10.10.10.10.10.10.1

X Not applicable.1 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimates, the less reliable 

the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval. 

2 Applies to women between the ages of 25 and 50.3 Native Hawaiian and Other Pacific Islander alone was combined with Some Other Race because of a small number of sample observations.

Source: U.S. Census Bureau, 2011 American Community Survey. For more information, see <www.acs.census.gov/acs>.
U.S. Census Bureau 13

growth in STEM among younger 

women compared with earlier 

decades (Figure 8). Most of the 

growth in women’s share of STEM 

employment among those under 

the age of 40 occurred between 

1970 and 1990.

Among STEM workers, women 

were less likely than men to have 

children at home. About 62 percent 

of women had no children at home, 

compared with 57 percent of men 

(Table 4). Compared with other 

women, women in STEM employment were the least likely to have 

children at home. About 43 percent 

of women in STEM-related employment had children at home, compared with 39 percent of women 

in non-STEM occupations, and 38 

percent of women in STEM occupations. Women in STEM-related occupations, which include architects 

and healthcare practitioners, were 

also more likely to have given birth 

in the last 12 months: 5.8 percent, 

compared with 4.9 percent of STEM 

workers and 4.4 percent of nonSTEM workers.

Figure 7. 

Women’s Share of the STEM Workforce by Age: 1970 to 2011

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 decennial censuses and 2011 American Community Survey. 

0

5

10

15

20

25

30

35

25 30 35 40 45 50 55 60

2011

2000

1990

1980

1970

Age

Percent women
14 U.S. Census Bureau

STEM Employment by Race and 

Hispanic Origin

The non-Hispanic White and 

Asian populations were overrepresented among STEM workers 

in 2011. About 67 percent of the 

total workforce was non-Hispanic 

White, but they held 71 percent 

of STEM jobs (Figure 9). Asians 

held 15 percent of the STEM jobs 

compared with 6 percent of all 

jobs. Blacks, American Indians and 

Alaska Natives, and those of Some 

Other Race were underrepresented 

in STEM.23 Blacks held 6 percent of 

STEM jobs, American Indians and 

Alaska Natives held 0.4 percent 

of STEM jobs, and those of Some 

Other Race held 1 percent of

STEM jobs. Hispanics were also 

23 Estimates for Some Other Race include 

Native Hawaiian or Other Pacific Islanders.

Figure 8. 

Percentage Point Change in Women’s Share of the STEM Workforce by Age: 

1970 to 2011

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Sources: U.S. Census Bureau, 1970 and 1990 decennial censuses and 2011 American Community Survey.

-5

0

5

10

15

20

25

Growth 1970 to 2011

Growth 1990 to 2011

Growth 1970 to 1990

25 30 35 40 45 50 55 60

Percentage point change

Age
U.S. Census Bureau 15

underrepresented in STEM occupations. Although they made up 

about 15 percent of the workforce, 

they held 7 percent of STEM jobs.24

24 The estimates for Black and Hispanic 

employment in STEM occupations are not 

statistically different. 

Racial and ethnic representation 

differs by detailed STEM occupation. Although the average racial 

and ethnic distribution of the STEM 

workforce is 71 percent nonHispanic White, 15 percent Asian, 

6 percent Black, and 7 percent 

Hispanic, the distribution varies 

in any given STEM occupation.25

Using software developer, the largest STEM occupation, as an example, Figure 10 shows that Asian 

workers are overrepresented, while 

25 The estimates for Black and Hispanic 

employment in STEM occupations are not 

statistically different.

Understanding Race and Hispanic Origin Concepts

The U.S. Census Bureau collects race and Hispanic origin information following the guidance of the U.S. Office 

of Management and Budget’s (OMB) 1997 Revisions to the Standards for the Classification of Federal Data on 

Race and Ethnicity.1 These federal standards mandate that race and Hispanic origin (ethnicity) are separate and 

distinct concepts and that when collecting these data via self-identification, two different questions must be 

used. Starting in 1997, OMB required federal agencies to use a minimum of five race categories: White, Black or 

African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander. For 

respondents unable to identify with any of these five race categories, OMB approved the inclusion of a sixth 

category—Some Other Race. Individuals who chose more than one of the six race categories are referred to as 

the Two or More Races population.

Individuals who responded to the question on race by indicating only one race are referred to as the racealone population or the group who reported only one race category. The text and figures of this report show 

estimates for the race-alone population. This report uses five of the OMB-approved categories: White alone, 

Black or African American alone, American Indian and Alaska Native alone, Asian alone, and Two or More 

Races. In this report, a sixth category is comprised of those who report Some Other Race or Native Hawaiian or 

Other Pacific Islander. Because of a small number of sample observations for Native Hawaiian or Other Pacific 

Islanders employed in a STEM occupation (fewer than 6,000 individuals nationwide), this group is included with 

Some Other Race. 

People who identify their origin as Hispanic or Latino may be any race. For each race group, data in this report 

include people who reported they were of Hispanic or Latino origin and people who reported they were not 

Hispanic or Latino. Because Hispanics may be of any race, data in this report for Hispanics overlap with data 

for race groups. In the analyses presented here, the term “non-Hispanic White” refers to people who are not 

Hispanic and who reported White and no other race. The Census Bureau uses non-Hispanic Whites as the comparison group for other race groups and Hispanics. For more information on the concepts of race and Hispanic 

origin, see K. Humes, N. Jones, and R. Ramirez, “Overview of Race and Hispanic Origin: 2010,” U.S. Census 

Bureau, 2010 Census Briefs, 2011, available at <www.census.gov/prod/cen2010/briefs/c2010br-02.pdf>.

1 The 1997 Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity, issued by OMB, is available at

<www.whitehouse.gov/omb/fedreg/1997standards.html>.
16 U.S. Census Bureau

non-Hispanic Whites, Blacks, and 

Hispanics are underrepresented. 

About 30 percent of software 

developers are Asian, while Asians 

make up 15 percent of STEM occupations. About 59 percent of software developers are non-Hispanic 

White, 5 percent are Black, and 4 

percent are Hispanic (Table 3). 

Asian and Hispanic employment in 

STEM occupations has been growing since 1970 (Figure 11), as has 

their overall workforce share. While 

the percentage of STEM workers 

who are non-Hispanic White 

declined from 94 percent in 1970 

to 71 percent in 2011, the share 

has mirrored the decline in the nonHispanic White share of the workforce. Blacks and Hispanics have 

been consistently underrepresented 

in STEM occupations since 1970. In 

2011, 11 percent of the workforce 

was Black, but their workforce 

share of STEM occupations was 

6 percent (up from 2 percent in 

1970). Although the Hispanic share 

of the workforce has increased 

significantly, from 3 percent in 

1970 to 15 percent in 2011, 

Hispanics made up 7 percent of 

the STEM workforce. The Hispanic 

share of STEM occupations has not 

kept pace with the increase in the 

Hispanic share of the workforce. 

Asians have been consistently overrepresented in STEM occupations. 

In 1970, Asians were 1 percent of 

the workforce, but 2 percent of the 

STEM workforce. In 2011, Asians 

remained significantly overrepresented, accounting for 15 percent 

of STEM workers and 6 percent of 

the total workforce. 

Figure 9. 

Racial and Ethnic Representation in the STEM Workforce

(In percent. Data based on sample. For information on condentiality protection, sampling error, nonsampling error, 

and denitions, see www.census.gov/acs/www/)

Note: Native Hawaiian or Other Pacic Islander alone was combined with Some Other Race because of a small number of 

sample observations. 

Source: U.S. Census Bureau, 2011 American Community Survey.

Total workforce

STEM

Hispanic

or Latino

(of any race)

Two or

More Races

Some Other 

Race and 

Native

Hawaiian or

Other Pacic

Islander alone

American Indian

and Alaska Native

alone

Black or Asian alone

African

American alone

White alone,

not Hispanic

or Latino

66.9

70.8

10.8

6.4 5.5

14.5

0.6 0.4 1.4 1.8 1.9 4.6

14.9

6.5
U.S. Census Bureau 17

Figure 10. 

Racial and Ethnic Representation in Detailed STEM Occupations

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Source: U.S. Census Bureau, 2011 American Community Survey. 

Percent

0 20 40 60 80 100

Hispanic Black Asian White non-Hispanic

Social science research assistants

Misc. social scientists and related workers

Urban and regional planners

Sociologists

Psychologists

Survey researchers

Economists

Social science occupations

Misc. life, physical, and social science technicians

All other physical scientists

Environmental scientists and geoscientists

Chemists and materials scientists

Atmospheric and space scientists

Nuclear technicians

Geological and petroleum technicians

Chemical technicians

Biological technicians

Agricultural and food science technicians

Astronomers and physicists

Medical and life scientists

Conservation scientists and foresters

Biological scientists

Agricultural and food scientists

Natural sciences managers

Life and physical science occupations

Sales engineers

Surveying and mapping technicians

Engineering technicians

Drafters

All other engineers

Petroleum engineers

Nuclear engineers

Mining and geological engineers

Mechanical engineers

Materials engineers

Marine engineers and naval architects

Industrial engineers, including health and safety

Environmental engineers

Electrical and electronics engineers

Computer hardware engineers

Civil engineers

Chemical engineers

Biomedical engineers

Agricultural engineers

Aerospace engineers

Surveyors, cartographers, and photogrammetrists

Architectural and engineering managers

Engineering occupations

Miscellaneous mathematical science occupations

Statisticians

Operations research analysts

Mathematicians

Actuaries

Mathematical occupations

All other computer occupations

Computer network architects

Network and computer systems administrators

Database administrators

Computer support specialists

Web developers

Software developers

Computer programmers

Information security analysts

Computer systems analysts

Computer and information research scientists

Computer and information systems managers

Computer occupations

Total STEM occupations

Total employed
18 U.S. Census Bureau

Figure 11. 

Employment in STEM Occupations by Race and Hispanic Origin: 1970 to 2011

(Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions,

see www.census.gov/acs/www/)

Note: Estimates for the American Indian and Alaska Native, Native Hawaiian and Other Pacic Islander, Some Other Race, and Two or More 

Races populations are not shown because of a small number of sample observations. 

Sources: U.S. Census Bureau, 1970, 1980, 1990, and 2000 decennial censuses and 2011 American Community Survey.

0

20

40

60

80

100

STEM

Total employed

1970 1980 1990 2000 2011

0

20

40

60

80

100

STEM

Total employed

1970 1980 1990 2000 2011

0

20

40

60

80

100

STEM Total employed

1970 1980 1990 2000 2011

0

20

40

60

80

100

STEM 

Total employed

1970 1980 1990 2000 2011

Percent

White alone, not Hispanic or Latino Asian alone

Black or African American alone Hispanic or Latino
U.S. Census Bureau 19

DEMOGRAPHIC 

CHARACTERISTICS OF 

SCIENCE AND ENGINEERING 

GRADUATES IN STEM 

EMPLOYMENT

Women and Black and Hispanic 

workers are underrepresented in 

STEM occupations. One explanation 

out of many is that these workers 

are less likely to have a science or 

engineering background that would 

facilitate STEM employment. Table 

5 shows the distribution of science 

and engineering graduates by sex, 

race, and Hispanic origin. Although 

women are 53 percent of college 

graduates, they are 41 percent of 

science and engineering graduates.26 Of science and engineering 

fields of study, women are most 

likely to be found in multidisciplinary science studies (71 percent 

26 Based on first listed field of bachelor’s 

degree.

women) and psychology (70 percent women).27 About 71 percent of 

science and engineering graduates 

are non-Hispanic White, 14 percent 

are Asian, 7 percent are Black, and 

27 The estimates for multidisciplinary 

science studies and psychology are not 

statistically different. Multidisciplinary science 

studies includes sciences that are not elsewhere classified, such as nutrition sciences, 

combined majors including a science and 

engineering major, such as accounting and 

computer science, and nonspecified multidisciplinary studies. 

Table 5.

Field of Degree for the First Listed Bachelor’s Degree: 2011

(Civilians aged 25 to 64 with a bachelor’s degree or higher level of education)

Field of degree

Number MOE1

Percent 

female MOE1

Percent 

White 

alone, 

not 

Hispanic 

or 

Latino MOE1

Percent 

Asian 

alone MOE1

Percent 

Black 

or 

African 

American 

alone MOE1

Percent 

Hispanic 

or 

Latino MOE1

 Total . . . . . . . . . . . . . . . . . . . . 49,543,828 161,133 52.9 0.1 74.1 0.1 9.4 0.1 7.9 0.1 6.9 0.1

Science and engineering . . . . . . . . 17,335,550 85,430 41.3 0.2 70.7 0.2 13.5 0.1 7.1 0.1 6.8 0.1

Computers, mathematics, and

 statistics. . . . . . . . . . . . . . . . . . . . . 2,213,820 25,361 33.9 0.5 63.9 0.5 19.8 0.5 8.6 0.4 5.8 0.3

Biological, agricultural, and

 environmental sciences. . . . . . . . . 3,163,602 32,126 45.4 0.5 74.9 0.5 11.9 0.3 5.7 0.3 5.5 0.2

Physical and related science. . . . . . 1,604,216 26,143 37.5 0.7 70.5 0.7 15.5 0.5 6.5 0.4 6.0 0.4

Psychology. . . . . . . . . . . . . . . . . . . . 2,429,801 25,241 69.5 0.6 75.5 0.5 5.7 0.3 9.2 0.3 7.7 0.3

Social sciences. . . . . . . . . . . . . . . . 3,904,676 39,907 47.6 0.4 73.8 0.4 8.4 0.2 8.6 0.3 7.2 0.2

Engineering. . . . . . . . . . . . . . . . . . . 3,703,261 35,023 16.3 0.3 64.7 0.4 21.2 0.4 4.5 0.2 7.9 0.3

Multidisciplinary science studies. . . 316,174 10,618 71.1 1.6 73.2 1.3 8.8 1.0 8.0 0.9 7.3 0.7

Science- and engineering-related . 4,520,818 40,101 71.3 0.3 71.7 0.3 11.7 0.3 8.5 0.3 6.4 0.2

Business . . . . . . . . . . . . . . . . . . . . . . 10,579,589 61,058 45.8 0.2 72.8 0.2 8.6 0.1 9.4 0.2 7.7 0.2

Education . . . . . . . . . . . . . . . . . . . . . 5,857,864 43,523 77.2 0.2 81.5 0.3 3.1 0.1 7.4 0.2 6.6 0.2

Arts, humanities, and other . . . . . . 11,250,007 64,610 57.4 0.2 77.6 0.2 6.0 0.1 7.8 0.2 6.8 0.1

Literature and languages. . . . . . . . . 2,075,751 25,872 67.8 0.5 78.9 0.5 8.0 0.3 5.2 0.3 6.1 0.3

Liberal arts and history. . . . . . . . . . 2,382,628 25,009 42.9 0.5 79.8 0.5 5.8 0.2 6.5 0.3 6.2 0.3

Visual and performing arts. . . . . . . . 2,068,872 29,156 60.9 0.6 78.9 0.5 8.1 0.3 4.7 0.3 6.3 0.3

Communications. . . . . . . . . . . . . . . 2,020,494 26,209 59.1 0.6 78.7 0.6 4.3 0.3 8.4 0.4 6.9 0.3

Other. . . . . . . . . . . . . . . . . . . . . . . . 2,702,262 27,301 58.3 0.6 73.0 0.5 4.3 0.2 12.7 0.4 8.2 0.3

1

 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error 

in relation to the size of the estimates, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent 

confidence interval. 

Note: Estimates for the American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, Some Other Race, and Two or More Races populations 

are not shown because of a small number of sample observations.

Source: U.S. Census Bureau, 2011 American Community Survey. For more information, see <www.acs.census.gov/acs>.
20 U.S. Census Bureau

7 percent are Hispanic.28 Relative 

to their share of college graduates, 

Blacks and non-Hispanic Whites 

are underrepresented, Asians are 

overrepresented, and Hispanics are 

about equally represented among 

science and engineering majors.29

Table 6 presents the current 

employment status of science

and engineering graduates by age, 

sex, and race and Hispanic origin.30

Of all science and engineering graduates, 83.3 percent are employed, 

28 The estimates for Blacks (7.1 percent) 

and Hispanics (6.8 percent) round to 7 percent but are statistically different. 29 The estimates for Hispanic bachelor’s 

degree holders and science and engineering 

graduates are not statistically different. 30 From this point forward, to be considered a science and engineering graduate, a 

person must have listed at least one science 

and engineering major for field of bachelor’s 

degree, but it does not have to be the first 

listed major.

3.9 percent are unemployed, and 

12.8 percent are out of the labor 

force. While the unemployment rate 

is lower among science and engineering graduates than in the total 

civilian labor force, unemployment 

rates vary by race and Hispanic 

origin.31 The unemployment rate 

among Black and American Indian 

and Alaska Native science and engineering graduates is 6.6 percent. 

Older science and engineering 

graduates are less likely to be in 

the labor force—nearly 23 percent 

of those aged 55 to 64 are not in 

the labor force (Figure 12). Female 

science and engineering graduates 

are also less likely to be in the 

labor force. Nearly 1 in 5 female 

31 Unemployment was 3.9 percent among 

science and engineering graduates and 6.9 

percent for the civilian labor force aged 25 

to 64.

science and engineering graduates 

are out of the labor force, compared with fewer than 1 in 10 male 

science and engineering graduates. American Indian and Alaska 

Native science and engineering 

graduates have the highest rates 

of labor force exit: 17.9 percent of 

American Indian and Alaska Native 

graduates are out of the labor 

force.

The majority of workers with a 

science or engineering degree are 

not currently employed in a STEM 

occupation. Only 1 in 4 science and 

engineering graduates are currently 

employed in a STEM occupation. 

Table 7 shows the percentage of 

science and engineering graduates 

in STEM employment by age, sex, 

and race and Hispanic origin. 

Figure 12. 

Employment Status of Science and Engineering Graduates by Age, Sex, and 

Race and Hispanic Origin

(In percent. Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and

denitions, see www.census.gov/acs/www/)

Hispanic or Latino (of any race)

Two or More Races

Some Other Race and NHOPI*

American Indian and Alaska Native alone

Asian alone

Black or African American alone

White alone, not Hispanic or Latino

Female

Male

55 to 64

45 to 54

35 to 44

25 to 34 84.8

86.9

86.6

73.1

87.7

77.2

81.9

82.0

83.8

81.9

75.5

82.7

81.0

*Native Hawaiian and Other Pacic Islander alone. 

Source: U.S. Census Bureau, 2011 American Community Survey.

Employed Unemployed Not in labor force

3.8

4.0

3.4

4.2

3.3

4.2

3.8

3.9

6.6

6.1

4.9

5.8

6.6

8.6

18.7

12.7

22.7

9.8

11.0

9.6

14.2

17.9

12.0

12.4

13.2

11.4

Age

Race and Hispanic Origin

Sex
U.S. Census Bureau 21 Table 6. Employment Status of Science and Engineering Graduates: 2011 (Civilians aged 25 to 64 with a bachelor’s degree in a science and engineering field1) Characteristics Total Employed Unemployed Not in the labor force Number MOE2 Number MOE2 Percent MOE2 Number MOE2 Percent MOE2 Number MOE2 Percent MOE2 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,173,287 89,125 15,139,107 77,331 83.3 0.2 704,172 16,098 3.9 0.1 2,330,008 25,627 12.8 0.1 Age 25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,014,122 43,306 4,251,357 39,505 84.8 0.3 210,410 9,402 4.2 0.2 552,355 12,029 11.0 0.2 35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,814,958 41,773 4,184,416 40,586 86.9 0.3 160,383 8,028 3.3 0.2 470,159 12,271 9.8 0.3 45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,479,410 39,280 3,878,707 34,928 86.6 0.3 171,380 6,065 3.8 0.1 429,323 12,371 9.6 0.3 55 to 64 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,864,797 33,596 2,824,627 25,492 73.1 0.3 161,999 6,813 4.2 0.2 878,171 15,263 22.7 0.3 Sex Male. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,574,099 63,577 9,270,325 56,962 87.7 0.2 397,524 11,094 3.8 0.1 906,250 17,076 8.6 0.1 Female. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,599,188 45,399 5,868,782 38,083 77.2 0.3 306,648 10,477 4.0 0.1 1,423,758 18,050 18.7 0.2 Race and Hispanic Origin White alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13,811,796 69,225 11,572,387 61,829 83.8 0.2 485,800 13,724 3.5 0.1 1,753,609 24,425 12.7 0.2 White alone, not Hispanic or Latino. . . . . . . . . . 12,907,927 61,004 10,821,933 55,594 83.8 0.2 443,854 12,409 3.4 0.1 1,642,140 23,557 12.7 0.2 Black or African American alone. . . . . . . . . . . . . . 1,284,256 23,835 1,052,888 21,203 82.0 0.6 85,020 5,428 6.6 0.4 146,348 6,510 11.4 0.4 Asian alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,401,191 23,306 1,966,948 19,871 81.9 0.4 92,914 4,971 3.9 0.2 341,329 9,662 14.2 0.4 American Indian and Alaska Native alone. . . . . . . 59,892 3,941 45,246 3,318 75.5 3.2 3,938 971 6.6 1.5 10,708 1,825 17.9 2.8 Some Other Race and Native Hawaiian or Other Pacific Islander alone3. . . . . . . . . . . . . . . . . . . . . 271,266 10,950 222,168 9,235 81.9 1.4 16,481 2,832 6.1 1.0 32,617 3,535 12.0 1.2 Two or More Races. . . . . . . . . . . . . . . . . . . . . . . . 344,886 11,819 279,470 10,904 81.0 1.2 20,019 2,421 5.8 0.7 45,397 3,861 13.2 1.0 Hispanic or Latino (of any race). . . . . . . . . . . . . . . 1,233,794 21,856 1,020,800 20,174 82.7 0.7 60,524 5,076 4.9 0.4 152,470 7,649 12.4 0.6 1 Includes individuals with multiple majors who report having at least one major in a science or engineering field at the bachelor’s level. 2 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the size of the estimates, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent confidence interval. 3 Native Hawaiian and Other Pacific Islander alone was combined with Some Other Race because of a small number of sample observations. Source: U.S. Census Bureau, 2011 American Community Survey. For more information, see <www.acs.census.gov/acs>.

Younger workers are more likely 

to be employed in a STEM occupa-

tion than older workers. About 27 

percent of workers under the age 

of 45 with a science or engineer-

ing degree are employed in a STEM 

occupation. Figure 13 presents 

the percentage of science and 

engineering graduates currently 

employed in a STEM occupation 

by age, sex, and race and Hispanic 

origin.

Employment in STEM occupations 

among science and engineering 

graduates also varies by race and 

Hispanic origin. Among science 

and engineering graduates, Asians 

are the most likely to be in a STEM 

occupation (Figure 13). About 41 

percent of Asians with a science 

and engineering degree are cur-

rently employed in a STEM field, 

followed by individuals who selfidentify as Two or More Races (24 

percent) and non-Hispanic White 

(23 percent).32

Men make up the majority of 

science and engineering gradu-

ates. About 61 percent of science 

and engineering graduates were 

men.33 Of these, 31 percent were 

employed in a STEM occupation 

and made up 76 percent of the 

STEM workforce (Figure 14). In con-

trast, women made up 39 percent 

of science and engineering gradu-

ates and 15 percent were employed 

in a STEM occupation, accounting 

for 24 percent of the STEM work-

force. Even among science and 

engineering graduates, men were 

employed in a STEM occupation at 

about twice the rate of women. 

32 The estimates for Two or More Races 

and non-Hispanic White are not statistically 

different. 33 Based on all listed fields of bachelor’s 

degree. 
22 U.S. Census Bureau

Table 7.

Selected Characteristics by Employment in STEM Occupations Among Science and 

Engineering Graduates: 2011

(Civilian employed aged 25 to 64 with a bachelor’s degree in a science and engineering field1)

Characteristics

Relationship between science and engineering education

and employment

STEM workforce with a

science or engineering

bachelor’s degree

Science and engineering degree holders Occupational distribution STEM workforce share

Percent MOE2 Occupation Percent MOE2 Percent MOE1

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 X STEM 24.9 0.2 100.0 X

STEM-related 10.7 0.2

Non-STEM 64.3 0.2

Age

25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . 28.1 0.2 STEM 26.6 0.4 29.9 0.5

STEM-related 10.6 0.3

Non-STEM 62.9 0.5

35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . 27.6 0.2 STEM 26.6 0.5 29.5 0.5

STEM-related 10.7 0.3

Non-STEM 62.6 0.5

45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . 25.6 0.2 STEM 25.5 0.3 26.2 0.4

STEM-related 10.4 0.3

Non-STEM 64.1 0.4

 55 to 64 years. . . . . . . . . . . . . . . . . . . . . . . . . 18.7 0.2 STEM 19.2 0.3 14.4 0.3

STEM-related 11.5 0.3

Non-STEM 69.3 0.4

Sex

Male. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.2 0.2 STEM 31.1 0.3 76.3 0.3

STEM-related 8.8 0.2

Non-STEM 60.1 0.3

Female. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.8 0.2 STEM 15.3 0.2 23.7 0.3

STEM-related 13.8 0.2

Non-STEM 71.0 0.3

Race and Hispanic Origin

White alone. . . . . . . . . . . . . . . . . . . . . . . . . . . 76.4 0.2 STEM 23.0 0.2 70.6 0.4

STEM-related 10.8 0.2

Non-STEM 66.2 0.3

White alone, not Hispanic or Latino. . . . . . . 71.5 0.2 STEM 23.3 0.2 66.9 0.4

STEM-related 10.9 0.2

Non-STEM 65.8 0.3

Black or African American alone. . . . . . . . . . . 7.0 0.1 STEM 17.4 0.8 4.9 0.2

STEM-related 9.8 0.6

Non-STEM 72.7 0.9

Asian alone. . . . . . . . . . . . . . . . . . . . . . . . . . . 13.0 0.1 STEM 41.4 0.6 21.5 0.4

STEM-related 11.4 0.4

Non-STEM 47.3 0.6

American Indian and Alaska Native alone. . . . 0.3 0.1 STEM 16.4 2.6 0.2 0.1

STEM-related 7.4 1.8

Non-STEM 76.2 3.3

Some Other Race and Native Hawaiian or

 Other Pacific Islander alone3

. . . . . . . . . . . . . 

1.5 0.1 STEM 18.0 1.5 1.1 0.1

STEM-related 9.4 1.3

Non-STEM 72.6 1.8

Two or More Races. . . . . . . . . . . . . . . . . . . . . 1.8 0.1 STEM 23.5 1.6 1.7 0.1

STEM-related 10.8 0.9

Non-STEM 65.7 1.8

Hispanic (of any race). . . . . . . . . . . . . . . . . . . 6.7 0.1 STEM 18.4 0.7 5.0 0.2

STEM-related 9.0 0.6

Non-STEM 72.6 0.8

X Not applicable.

1

 Includes individuals with multiple majors who report having at least one major in a science or engineering field at the bachelor’s level.

2

 Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error 

in relation to the size of the estimates, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent 

confidence interval. 3

 Native Hawaiian and Other Pacific Islander alone was combined with Some Other Race because of a small number of sample observations.

Source: U.S. Census Bureau, 2011 American Community Survey. For more information, see <www.acs.census.gov/acs>.
U.S. Census Bureau 23

Science and engineering graduates earn more per year when they 

are employed in STEM occupations (Figure 15). Among science 

and engineering graduates that 

worked full-time, year-round, men 

earned $85,000 per year compared 

with $58,800 among women. The 

gender earnings gap narrows when 

comparing science and engineering graduates employed in a 

STEM occupation, indicating that 

STEM employment boosts earnings among women more than 

among men. Women employed in 

STEM earn about $16,300 more 

per year compared with women 

trained in science and engineering 

but not employed in STEM. While 

Asians earned the most in the total 

workforce ($80,700), and among 

the most in the STEM workforce 

($89,500), STEM employment provides a larger earnings gain among 

Blacks, Hispanics, and those who 

report Some Other Race, increasing their earnings by $17,000, 

$18,300, and $22,500 per year, 

respectively.34

34 The median earnings for non-Hispanic 

White and Asian STEM workers are not statistically different. The earnings gain for STEM 

employment among Blacks and Hispanics and 

among Hispanics and those who report Some 

Other Race are not statistically different. 

Figure 13. 

Percentage of Science and Engineering Graduates Currently Employed in a 

STEM Occupation by Age, Sex, and Race and Hispanic Origin

(In percent. Data based on sample. For information on condentiality protection, sampling error, nonsampling error, 

and denitions, see www.census.gov/acs/www/)

*Native Hawaiian and Other Pacic Islander alone.

Source: U.S. Census Bureau, 2011 American Community Survey.

Hispanic or Latino (of any race)

Two or More Races

Some Other Race and NHOPI*

American Indian and Alaska Native alone

Asian alone

Black or African American alone

White alone, not Hispanic or Latino

Female

Male

55 to 64

45 to 54

35 to 44

25 to 34 26.6

31.1

19.2

25.5

26.6

17.4

23.3

15.3

18.0

23.5

18.4

41.4

16.4

Age

Race and Hispanic Origin

Sex
24 U.S. Census Bureau

Figure 14. 

Share of Total Employment, Science and Engineering Degrees, and STEM 

Employment by Sex

(In percent. Data based on sample. For information on confidentiality protection, sampling error, nonsampling error, 

and definitions, see www.census.gov/acs/www/)

*With a science or engineering bachelor's degree.

Source: U.S. Census Bureau, 2011 American Community Survey.

Male Female

STEM workforce*

Science and

engineering

graduates

Total workforce

24

76

39

61

48

52

Gender gap (in percentage point)

52

22

4

SOURCE OF THE ESTIMATES

The American Community Survey 

(ACS) is a nationwide survey 

designed to provide communities 

with reliable and timely demographic, social, economic and housing data for congressional districts, 

counties, places, and other localities every year. It has an annual 

sample size of about 3.5 million 

addresses across the United States 

and Puerto Rico and includes both 

housing units and group quarters 

(e.g., nursing homes and prisons). 

The ACS is conducted in every 

county throughout the nation, and 

every municipio in Puerto Rico, 

where it is called the Puerto Rico 

Community Survey. For information on the ACS sample design and 

other topics, visit <www.census

.gov/acs/www>.

ACCURACY OF THE 

ESTIMATES

The data presented in this report 

are based on the ACS sample interviewed in January 2011 through 

December 2011. The estimates 

based on this sample describe 

the actual average value of characteristics for the household and 

group quarter populations over this 

period of collection. Sampling error 

is the difference between an estimate based on a sample and the 

corresponding value that would be 

obtained if the estimate were based 

on the entire population (as from 

a census). Measures of sampling 

error are provided in the form of 

margins of error for all estimates 

included in this report. All comparative statements in this report 

have undergone statistical testing, 

and comparisons are significant at 

the 90 percent level unless otherwise noted. In addition to sampling 

error, nonsampling error may be 

introduced during any of the operations used to collect and process 

survey data such as editing, reviewing, or keying data from questionnaires. For more information on 

sampling and estimation methods, 

confidentiality protection, and 

sampling and nonsampling errors, 

please see the 2011 ACS Accuracy 

of the Data document located at

<www.census.gov/acs

/www/Downloads/data

_documentation/Accuracy/ACS

_Accuracy_of_Data_2011.pdf>.
U.S. Census Bureau 25 Figure 15. Median Earnings for Science and Engineering Graduates Who Work Full-Time, Year-Round by Age, Sex, and Race and Hispanic Origin (In dollars. Data based on sample. For information on condentiality protection, sampling error, nonsampling error, and denitions, see www.census.gov/acs/www/) Hispanic or Latino (of any race) Two or More Races Some Other Race and NHOPI* American Indian and Alaska Native alone Asian alone Black or African American alone White alone, not Hispanic or Latino Female Male 25 to 34 35 to 44 45 to 54 55 to 64 54,800 80,700 90,500 86,100 85,500 58,800 80,700 58,00076,900 54,000 60,600 59,000 65,700 67,700 75,10088,400 91,000 101,200 100,800 91,300 76,500 72,60089,500 75,000 77,300 81,000 *Native Hawaiian and Other Pacic Islander alone. Source: U.S. Census Bureau, 2011 American Community Survey. STEM Total employed Dierence 12,900 10,600 10,700 14,700 11,500 17,000 8,800 12,000 22,500 15,400 18,300 16,300 6,000 Age Race and Hispanic Origin Sex

MORE INFORMATION

For questions or comments, con-

tact the author of this report: 

Liana Christin Landivar, Ph.D.

liana.christin.landivar@census.gov

301-763-5878

Industry and Occupation Statistics

 Branch

Social, Economic, and Housing

 Statistics Division

U.S. Census Bureau

SUGGESTED CITATION

Liana Christin Landivar, 2013, 

“Disparities in STEM Employment 

by Sex, Race, and Hispanic Origin,” 

American Community Survey 

Reports, ACS-24, U.S. Census 

Bureau, Washington, DC.