disparaties in stem employment by sex, race, and hispanic origin

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 LandivarIssued September 2013ACS-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,” improv-ing the U.S. economy and standard of living.1 In 2007, Congress passed the America COMPETES Act, reautho-rized 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 engi-neering major at the start of their college experience, and less likely to remain in these majors by its con-clusion.4 Because most STEM workers have a science or engineering college degree, underrepresentation among science and engineering majors could contrib-ute to the underrepresentation of women, Blacks, and Hispanics in STEM employment.5

This report details the historical demographic com-position 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 non-Hispanic 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 occu-pations, 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 per-cent 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 revi-sion. The SOC primarily classifies workers based on the type of work performed, rather than the educa-tion 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, tech-nology, engineering, and mathematics occupations. This includes computer and mathematical occupations, engineers, engineering techni-cians, 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 major-ity 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 occupa-tions, 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 importantactivities or duties? (For example: patient care,directing hiring policies, supervising order clerks,typing and filing, reconciling financial records)

11

13191119

§.4,4¤

d. Social Security or Railroad Retirement.Person 1 (continued)

manufacturing?

wholesale trade?

retail trade?

other (agriculture, construction, service,government, etc.)?

Yes ➔

NoTOTAL AMOUNT for past

12 months

$ .00,

Yes ➔

No

e. Supplemental Security Income (SSI).

TOTAL AMOUNT for past12 months

$ .00,

Yes ➔

No

f. Any public assistance or welfare paymentsfrom the state or local welfare office.


$ .00,

Yes ➔

No

$ .00,

g. Retirement, survivor, or disability pensions.Do NOT include Social Security.


44 Is this mainly – Mark (X) ONE box.

48 What was this person’s total income during thePAST 12 MONTHS? Add entries in questions 47ato 47h; subtract any losses. If net income was a loss,enter the amount and mark (X) the "Loss" box next tothe dollar amount.

h. Any other sources of income received regularly such as Veterans’ (VA) payments,unemployment compensation, child supportor alimony. Do NOT include lump sum paymentssuch as money from an inheritance or the sale of ahome.

Yes ➔

No

$ .00,


$ .00, ,

None OR

LossTOTAL AMOUNT for past

12 months

➜ Continue with the questions for Person 2 onthe next page. If no one is listed as person 2 onpage 2, SKIP to page 28 for mailing instructions.

Yes ➔

No

Mark (X) the "Yes" box for each type of income thisperson received, and give your best estimate of theTOTAL AMOUNT during the PAST 12 MONTHS. (NOTE: The "past 12 months" is the period fromtoday’s date one year ago up through today.)

Mark (X) the "No" box to show types of incomeNOT received.

If net income was a loss, mark the "Loss" box tothe right of the dollar amount.

For income received jointly, report the appropriateshare 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 beforedeductions for taxes, bonds, dues, or other items.


Yes ➔

No

$ .00,

b. Self-employment income from own nonfarmbusinesses or farm businesses, includingproprietorships and partnerships. Report NET income after business expenses.


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, mailorder house, auto engine manufacturing, bank)

Answer questions 41 – 46 if this personworked in the past 5 years. Otherwise, SKIP to question 47.

L

41 – 46 CURRENT OR MOST RECENT JOB ACTIVITY. Describe clearly this person’s chiefjob activity or business last week. If this personhad more than one job, describe the one at which this person worked the most hours. If thisperson had no job or business last week, giveinformation for his/her last job or business.

Was this person – Mark (X) ONE box.

41

an employee of a PRIVATE FOR-PROFITcompany or business, or of an individual, forwages, 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 INCORPORATEDbusiness, professional practice, or farm?

SELF-EMPLOYED in own INCORPORATEDbusiness, professional practice, or farm?

working WITHOUT PAY in family businessor farm?

If now on active duty inthe Armed Forces, mark (X) this box ➔ and print the branch of the Armed Forces.

Name of company, business, or other employer

For whom did this person work?42

c. Interest, dividends, net rental income,royalty income, or income from estatesand trusts. Report even small amounts creditedto an account.

Yes ➔

No

$ .00,


Loss

U.S. Census Bureau 3

STEM Occupation Classification

There has been a lack of consen-sus on who qualifies as a STEM worker.10 To enhance comparabil-ity 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, math-ematics, and information technol-ogy occupations; (2) science- and engineering-related occupations; and (3) nonscience and engi-neering 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 fol-lows 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., post-secondary teachers are not sepa-rated 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 informa-tion systems managers, architectural and engineering managers, and natural science managers)

Business and financial operations Non-STEMComputer, 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-STEMProtective service Non-STEMFood preparation and serving Non-STEMBuilding and grounds cleaning Non-STEMPersonal 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-STEMConstruction and extraction Non-STEMInstallation, maintenance, and repair Non-STEM

Production, transportation, and material moving Production Non-STEMTransportation Non-STEMMaterial 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/>.

http://www.census.gov/people/io/methodology/

http://www.census.gov/people/io/methodology/


STEM occupations consist primar-ily 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 tech-nicians. Non-STEM occupations are all other occupations not classified in STEM or STEM-related occupa-tions (Table 1). According to the Census Bureau occupation code list, there are 63 specific STEM occupations, 35 STEM-related occu-pations, and 437 non-STEM occupa-tions (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 engi-neering; science- and engineering-related; business; education; and arts, humanities, and other. Data on field of degree are not available for vocational, graduate, or profes-sional 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 engineer-ing 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 popula-tion. 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

8

Yes

No ➔ SKIP to question 15a

Please copy the name of Person 1 from page 2,then continue answering questions below.Last Name

First Name

Where was this person born?

In the United States – Print name of state.

Yes, born in the United States ➔ SKIP to 10a

Outside the United States – Print name offoreign country, or Puerto Rico, Guam, etc.

Yes, born in Puerto Rico, Guam, theU.S. Virgin Islands, or Northern Marianas

Yes, born abroad of U.S. citizen parent or parents

No, not a U.S. citizen

Yes, U.S. citizen by naturalization – Print yearof naturalization

Is this person a citizen of the United States?

When did this person come to live in theUnited States? Print numbers in boxes.

MI

Year

No, has not attended in the last 3 months ➔ SKIP to question 11

Yes, public school, public college

Yes, private school, private college, home school

Nursery school, preschool

Kindergarten

Grade 1 through 12 – Specifygrade 1 – 12

College undergraduate years (freshman tosenior)Graduate or professional school beyond abachelor’s degree (for example: MA or PhDprogram, or medical or law school)

What is the highest degree or level of schoolthis person has COMPLETED? Mark (X) ONE box.If currently enrolled, mark the previous grade orhighest degree received.

No schooling completed

Regular high school diploma

Some college credit, but less than 1 year ofcollege 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 ormunicipio 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 orPuerto Rico

10

7

➜

8

9

§.4+v¤

13191085

Person 1

a. At any time IN THE LAST 3 MONTHS, has thisperson attended school or college? Includeonly nursery or preschool, kindergarten, elementary school, home school, and schoolingwhich leads to a high school diploma or a collegedegree.

b. What grade or level was this person attending?Mark (X) ONE box.

NURSERY OR PRESCHOOL THROUGH GRADE 12

Nursery school

Kindergarten

Grade 1 through 11 – Specifygrade 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 apartment1 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 thanEnglish at home?

15

NO SCHOOLING COMPLETED

14

Answer question 12 if this person has abachelor’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 con�dentiality protection, sampling error, nonsampling error, and de�nitions, see www.census.gov/acs/www/)

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

Mathematicaloccupations

Social scienceoccupations

Life and physicalscience occupations

Engineeringoccupations

Computeroccupations 50

32

12

4

3

100


life and physical scientists (12 per-cent), social scientists (4 percent), and workers employed in math-ematical 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 occupa-tions, occupations that make up more than 80 percent of all STEM employment (Table 3). In fact, women’s representation in com-puter 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 inter-nal 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 sci-ence occupations since the 1970s, growth has tapered off in engineer-ing since 1990. In 2011, women were 13 percent of engineers, 27 percent of computer professionals, 41 percent of life and physical sci-entists, 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 statisticsBiological, agricultural, and environmental sciencesPhysical and related sciencePsychologySocial sciencesEngineeringMultidisciplinary 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 languagesLiberal arts and historyVisual and performing artsCommunicationsOther (e.g., criminal justice, social work)


Table 3 provides estimates of STEM employment by detailed STEM occupational categories. Among the occupations with the largest rep-resentation of women is psycholo-gist (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 aver-age 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 employ-ment 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)

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

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netw

ork

arch

itect

s ..

....

....

....

....

.91

,677

6,92

21.

30.

111

.42.

571

.03.

213

.11.

87.

21.

46.

92.

1

Com

pute

r oc

cupa

tions

, all

othe

r ..

....

....

....

..32

6,32

210

,125

4.5

0.1

24.1

1.4

66.9

1.7

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1.1

10.6

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7.9

1.1

Mat

hem

atic

al o

ccu

pat

ion

s20

2,66

77,

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

A

ctua

ries

...

....

....

....

....

....

....

....

...

22,0

692,

387

0.3

0.1

36.2

5.3

79.1

4.2

14.9

3.3

2.4

1.5

2.7

2.4

M

athe

mat

icia

ns .

....

....

....

....

....

....

....

2,45

095

50.

00.

123

.314

.364

.218

.419

.816

.816

.014

.90.

06.

1

Ope

ratio

ns r

esea

rch

anal

ysts

...

....

....

....

...

133,

100

6,57

11.

80.

148

.12.

571

.42.

28.

71.

410

.81.

46.

91.

2

Sta

tistic

ians

...

....

....

....

....

....

....

....

.42

,358

3,92

00.

60.

150

.24.

862

.54.

019

.93.

78.

23.

36.

22.

2

Mis

cella

neou

s m

athe

mat

ical

sci

ence

occ

upat

ions

..

2,69

086

80.

00.

151

.814

.070

.215

.819

.614

.40.

05.

62.

92.

9

En

gin

eeri

ng

occ

up

atio

ns

. . . .

. . . .

. . . .

. . . .

. . . .

. .2,

305,

215

26,3

7031

.90 .

313

.20 .

475

.20 .

611

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34 .

90 .

37 .

10 .

4

Arc

hite

ctur

al a

nd e

ngin

eerin

g m

anag

ers

....

....

..13

0,20

75,

603

1.8

0.1

10.2

1.3

82.4

1.9

8.4

1.3

2.2

0.7

5.3

1.2

S

urve

yors

, car

togr

aphe

rs, a

nd p

hoto

gram

met

rists

..

35,1

903,

191

0.5

0.1

19.9

3.4

90.0

2.9

2.3

1.0

2.9

1.8

3.7

2.0

A

eros

pace

eng

inee

rs .

....

....

....

....

....

....

124,

902

5,64

91.

70.

111

.31.

577

.81.

910

.11.

33.

21.

06.

81.

4

Agr

icul

tura

l eng

inee

rs .

....

....

....

....

....

...

2,38

989

10.

00.

18.

19.

187

.910

.80.

06.

33.

05.

18.

49.

4

Bio

med

ical

eng

inee

rs ..

....

....

....

....

....

...

13,3

831,

851

0.2

0.1

15.7

5.6

74.2

5.1

15.8

4.3

3.9

3.1

3.5

2.4

C

hem

ical

eng

inee

rs .

....

....

....

....

....

....

.47

,214

3,20

30.

70.

112

.62.

977

.24.

19.

82.

74.

51.

87.

42.

7

Civ

il en

gine

ers

....

....

....

....

....

....

....

..26

2,06

69,

443

3.6

0.1

13.1

1.3

75.6

1.6

11.6

1.2

3.8

0.7

7.2

1.0

C

ompu

ter

hard

war

e en

gine

ers

...

....

....

....

..58

,517

4,84

60.

80.

117

.03.

363

.63.

923

.13.

55.

82.

16.

51.

9

Ele

ctric

al a

nd e

lect

roni

cs e

ngin

eers

...

....

....

..20

3,53

88,

039

2.8

0.1

8.8

1.1

67.8

2.0

19.2

1.4

5.6

1.2

6.4

1.1

E

nviro

nmen

tal e

ngin

eers

...

....

....

....

....

...

24,1

632,

364

0.3

0.1

20.2

4.0

76.3

3.7

10.2

2.9

9.7

3.0

3.3

1.8

In

dust

rial e

ngin

eers

, inc

ludi

ng h

ealth

and

saf

ety

....

156,

517

6,96

92.

20.

118

.41.

976

.82.

19.

81.

24.

41.

08.

11.

6

Mar

ine

engi

neer

s an

d na

val a

rchi

tect

s .

....

....

..10

,005

1,31

80.

10.

19.

35.

481

.37.

612

.86.

85.

03.

71.

51.

5

Mat

eria

ls e

ngin

eers

...

....

....

....

....

....

...

29,4

452,

525

0.4

0.1

11.6

3.1

78.1

4.5

12.6

3.7

3.7

1.8

4.0

1.7

M

echa

nica

l eng

inee

rs .

....

....

....

....

....

...

189,

241

6,77

22.

60.

16.

30.

879

.31.

59.

51.

24.

61.

05.

41.

0

Min

ing

and

geol

ogic

al e

ngin

eers

...

....

....

....

.7,

889

1,24

00.

10.

16.

73.

284

.96.

74.

54.

31.

12.

07.

54.

1

See

not

es a

t end

of t

able

.


Table

3.

Em

plo

ym

en

t in

ST

EM

Occu

pati

on

s: 2

01

1—

Con

.(C

ivili

an e

mplo

yed a

ged

25

to 6

4)

Occ

upat

ions

Num

ber

MO

E1

Per

cent

of

ST

EM

w

ork-

forc

eM

OE

1

Per

cent

fe

mal

eM

OE

1

Per

cent

W

hite

al

one,

no

t H

ispa

nic

or L

atin

oM

OE

1

Per

cent

A

sian

al

one

MO

E1

Per

cent

B

lack

or

Afr

ican

A

mer

i-ca

n al

one

MO

E1

Per

cent

H

ispa

nic

MO

E1

En

gin

eeri

ng

occ

up

atio

ns—

Con

.

Nuc

lear

eng

inee

rs .

....

....

....

....

....

....

...

5,68

11,

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

P

etro

leum

eng

inee

rs .

....

....

....

....

....

....

.23

,522

3,05

90.

30.

113

.54.

773

.06.

07.

53.

39.

94.

48.

83.

9

Eng

inee

rs, a

ll ot

her

....

....

....

....

....

....

...

396,

704

11,7

875.

50.

212

.80.

872

.71.

316

.11.

14.

00.

75.

30.

7

Dra

fters

...

....

....

....

....

....

....

....

....

.14

6,62

26,

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

E

ngin

eerin

g te

chni

cian

s ...

....

....

....

....

....

.35

2,70

78,

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

S

urve

ying

and

map

ping

tech

nici

ans

...

....

....

...

56,1

694,

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

S

ales

eng

inee

rs .

....

....

....

....

....

....

....

.29

,144

3,00

30.

40.

18.

62.

385

.13.

66.

11.

81.

21.

06.

93.

0

Lif

e an

d p

hys

ical

sci

ence

occ

up

atio

ns

. . . .

. . . .

. . . .

848,

514

16,3

1511

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240

.90 .

968

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916

.90 .

75 .

90 .

56 .

40 .

5

Nat

ural

sci

ence

s m

anag

ers

....

....

....

....

....

.22

,536

1,99

10.

30.

144

.14.

877

.84.

69.

33.

04.

82.

86.

82.

6

Agr

icul

tura

l and

food

sci

entis

ts .

....

....

....

....

.25

,509

2,18

40.

40.

123

.23.

685

.43.

86.

42.

12.

62.

63.

51.

4

Bio

logi

cal s

cien

tists

...

....

....

....

....

....

....

72,8

044,

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

C

onse

rvat

ion

scie

ntis

ts a

nd fo

rest

ers

...

....

....

..23

,764

2,38

20.

30.

120

.33.

488

.63.

40.

50.

63.

11.

94.

21.

8

Med

ical

and

life

sci

entis

ts .

....

....

....

....

....

.12

2,74

86,

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

A

stro

nom

ers

and

phys

icis

ts .

....

....

....

....

....

11,3

311,

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

A

gric

ultu

ral a

nd fo

od s

cien

ce te

chni

cian

s .

....

....

.26

,166

2,93

10.

40.

141

.55.

469

.94.

85.

92.

77.

62.

814

.83.

9

Bio

logi

cal t

echn

icia

ns ..

....

....

....

....

....

....

19,0

542,

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

C

hem

ical

tech

nici

ans

....

....

....

....

....

....

..61

,175

3,88

90.

80.

133

.63.

370

.03.

94.

91.

614

.53.

68.

52.

1

Geo

logi

cal a

nd p

etro

leum

tech

nici

ans

....

....

....

.14

,888

2,32

40.

20.

130

.47.

175

.65.

44.

32.

78.

13.

610

.14.

3

Nuc

lear

tech

nici

ans

....

....

....

....

....

....

...

3,22

986

80.

00.

128

.711

.991

.75.

40.

04.

78.

35.

40.

04.

7

Atm

osph

eric

and

spa

ce s

cien

tists

...

....

....

....

.8,

407

1,40

50.

10.

116

.66.

582

.96.

94.

43.

32.

73.

08.

74.

6

Che

mis

ts a

nd m

ater

ials

sci

entis

ts .

....

....

....

...

76,3

394,

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

E

nviro

nmen

tal s

cien

tists

and

geo

scie

ntis

ts .

....

....

66,5

024,

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

P

hysi

cal s

cien

tists

, all

othe

r ...

....

....

....

....

..18

0,33

26,

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

M

isce

llane

ous

life,

phy

sica

l, an

d so

cial

sci

ence

tec

hnic

ians

...

....

....

....

....

....

....

....

..11

3,73

05,

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

So

cial

sci

ence

occ

up

atio

ns

. . . .

. . . .

. . . .

. . . .

. . . .

.25

7,17

87,

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

E

cono

mis

ts .

....

....

....

....

....

....

....

....

24,4

602,

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

S

urve

y re

sear

cher

s ..

....

....

....

....

....

....

.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

P

sych

olog

ists

...

....

....

....

....

....

....

....

.16

4,51

66,

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

S

ocio

logi

sts

....

....

....

....

....

....

....

....

.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

U

rban

and

reg

iona

l pla

nner

s ..

....

....

....

....

..18

,442

2,21

00.

30.

141

.64.

874

.05.

57.

73.

09.

23.

58.

84.

3

Mis

cella

neou

s so

cial

sci

entis

ts a

nd r

elat

ed w

orke

rs .

.41

,600

3,26

60.

60.

152

.63.

577

.83.

65.

62.

16.

52.

36.

22.

0

Soc

ial s

cien

ce r

esea

rch

assi

stan

ts .

....

....

....

..3,

362

1,06

20.

00.

138

.714

.164

.813

.56.

17.

93.

83.

224

.012

.7

X N

ot a

pplic

able

.1

Dat

a ar

e ba

sed

on a

sam

ple

and

are

subj

ect t

o sa

mpl

ing

varia

bilit

y. A

mar

gin

of e

rror

is a

mea

sure

of a

n es

timat

e’s

varia

bilit

y. T

he la

rger

the

mar

gin

of e

rror

in r

elat

ion

to th

e si

ze o

f the

est

imat

es, t

he

less

rel

iabl

e th

e es

timat

e. W

hen

adde

d to

and

sub

trac

ted

from

the

estim

ate,

the

mar

gin

of e

rror

form

s th

e 90

per

cent

con

fiden

ce in

terv

al.

Not

e: E

stim

ates

for

the

Am

eric

an In

dian

and

Ala

ska

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ive,

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ive

Haw

aiia

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d O

ther

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ific

Isla

nder

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ther

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wo

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ore

Rac

es p

opul

atio

ns a

re n

ot s

how

n be

caus

e of

a s

mal

l num

ber

of s

ampl

e ob

serv

atio

ns.

Sou

rce:

U.S

. Cen

sus

Bur

eau,

201

1 A

mer

ican

Com

mun

ity S

urve

y. F

or m

ore

info

rmat

ion,

see

<w

ww

.acs

.cen

sus.

gov/

acs>

.



Percent female

Social science research assistantsMisc. social scientists and related workers

Urban and regional plannersSociologists

PsychologistsSurvey researchers

EconomistsSocial science occupations

Misc. life, physical, and social science techniciansAll other physical scientists

Environmental scientists and geoscientistsChemists and materials scientistsAtmospheric and space scientists

Nuclear techniciansGeological and petroleum technicians

Chemical techniciansBiological technicians

Agricultural and food science techniciansAstronomers and physicists

Medical and life scientistsConservation scientists and foresters

Biological scientistsAgricultural and food scientists

Natural sciences managersLife and physical science occupations

Sales engineersSurveying and mapping technicians

Engineering techniciansDrafters

All other engineersPetroleum engineers

Nuclear engineersMining and geological engineers

Mechanical engineersMaterials engineers

Marine engineers and naval architectsIndustrial engineers, including health and safety

Environmental engineersElectrical and electronics engineers

Computer hardware engineersCivil engineers

Chemical engineersBiomedical engineers

Agricultural engineersAerospace engineers

Surveyors, cartographers, and photogrammetristsArchitectural and engineering managers

Engineering occupationsMiscellaneous mathematical science occupations

StatisticiansOperations research analysts

MathematiciansActuaries

Mathematical occupationsAll other computer occupations

Computer network architectsNetwork and computer systems administrators

Database administratorsComputer support specialists

Web developersSoftware developers

Computer programmersInformation security analysts

Computer systems analystsComputer and information research scientistsComputer and information systems managers

Computer occupationsTotal 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)


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 work-ers between the ages of 45 and 54 make up the largest share of non-STEM employment (Table 4). Figure 6 shows that the STEM workforce is becoming older and more age-diverse, 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)


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

19801970

6055504540353025


Table

4.

Sele

cte

d C

hara

cte

risti

cs b

y E

mp

loym

en

t in

ST

EM

Occu

pati

on

s: 2

01

1—

Con

.(C

ivili

an e

mplo

yed a

ged

25

to 6

4)

Cha

ract

eris

tics

Tota

l S

TE

M o

ccup

atio

nsS

TE

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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.

Sele

cte

d C

hara

cte

risti

cs b

y E

mp

loym

en

t in

ST

EM

Occu

pati

on

s: 2

01

1—

Con

.(C

ivili

an e

mplo

yed a

ged

25

to 6

4)

Cha

ract

eris

tics

Tota

l S

TE

M o

ccup

atio

nsS

TE

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elat

ed o

ccup

atio

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on-S

TE

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ccup

atio

ns

Num

ber

MO

E1

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-ce

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umbe

rM

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1

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-ce

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rM

OE

1

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OE

1N

umbe

rM

OE

1

Per

-ce

ntM

OE

1

Ow

n C

hild

ren

Un

der

18—

Con

.

Fem

ale:

In fa

mily

with

ow

n ch

ildre

n un

der

18

year

s ..

....

....

....

....

....

.

Und

er 6

yea

rs o

nly

....

....

....

..

6 to

17

year

s on

ly .

....

....

....

..

Und

er 6

yea

rs a

nd 6

to 1

7 .

....

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own

child

ren

pres

ent .

....

....

...

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th in

th

e la

st 1

2 m

on

ths2

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th in

the

last

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in th

e la

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e H

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ian

or

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er P

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c Is

land

er a

lone

3 ...

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e R

aces

...

....

....

....

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pani

c or

Lat

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(of a

ny r

ace)

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29,6

224,

102,

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13,5

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66.9

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0.6

4.6

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14.9

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0.1

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186,

108

399,

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2,46

1,82

454

2,82

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473,

865

445,

135

3,32

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5

233,

410

3,77

7,58

9

6,07

6,67

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689,

948

792,

668

678,

210

33,7

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122,

703

125,

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531,

509

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16,5

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25.5 7.7

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18,4

66,7

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373,

874

11,7

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663,

370,

537

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103,

196

82,6

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35,0

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48,3

6332

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49,7

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38.8 7.1

24.7 7.1

61.2 4.4

95.6

76.6

66.2

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0.7

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1.8

16.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

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X N

ot a

pplic

able

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Dat

a ar

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sam

ple

and

are

subj

ect t

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mpl

ing

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mar

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rror

is a

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varia

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he la

rger

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rror

in r

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to th

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est

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ss r

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the

estim

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Whe

n ad

ded

to a

nd s

ubtr

acte

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e es

timat

e, th

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argi

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err

or fo

rms

the

90 p

erce

nt c

onfid

ence

inte

rval

. 2

App

lies

to w

omen

bet

wee

n th

e ag

es o

f 25

and

50.

3 N

ativ

e H

awai

ian

and

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er P

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c Is

land

er a

lone

was

com

bine

d w

ith S

ome

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er R

ace

beca

use

of a

sm

all n

umbe

r of

sam

ple

obse

rvat

ions

.S

ourc

e: U

.S. C

ensu

s B

urea

u, 2

011

Am

eric

an C

omm

unity

Sur

vey.

For

mor

e in

form

atio

n, s

ee <

ww

w.a

cs.c

ensu

s.go

v/ac

s>.


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 employ-ment were the least likely to have children at home. About 43 percent of women in STEM-related employ-ment had children at home, com-pared with 39 percent of women

in non-STEM occupations, and 38 percent of women in STEM occupa-tions. Women in STEM-related occu-pations, 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 non-STEM workers.

Figure 7. Women’s Share of the STEM Workforce by Age: 1970 to 2011(Data based on sample. For information on con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)


0

5

10

15

20

25

30

35

6055504540353025

2011

20001990

1980

1970

Age

Percent women


STEM Employment by Race and Hispanic Origin

The non-Hispanic White and Asian populations were overrep-resented 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions,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

6055504540353025

Percentage point change

Age


underrepresented in STEM occu-pations. 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 occupa-tion. Although the average racial and ethnic distribution of the STEM workforce is 71 percent non-Hispanic White, 15 percent Asian, 6 percent Black, and 7 percent Hispanic, the distribution varies

in any given STEM occupation.25 Using software developer, the larg-est STEM occupation, as an exam-ple, 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 race-alone 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 com-parison 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>.


non-Hispanic Whites, Blacks, and Hispanics are underrepresented. About 30 percent of software developers are Asian, while Asians make up 15 percent of STEM occu-pations. About 59 percent of soft-ware 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 grow-ing 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 non-Hispanic White share of the work-force. 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 over-represented in STEM occupations. In 1970, Asians were 1 percent of the workforce, but 2 percent of the STEM workforce. In 2011, Asians remained significantly overrepre-sented, 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions, see www.census.gov/acs/www/)

Note: Native Hawaiian or Other Paci�c 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 workforceSTEM

Hispanicor Latino

(of any race)

Two orMore Races

Some Other Race and

NativeHawaiian orOther Paci�c

Islander alone

American Indianand Alaska Native

alone

Asian aloneBlack orAfrican

American alone

White alone,not Hispanic

or Latino

66.970.8

10.86.4 5.5

14.5

0.6 0.41.91.81.4

4.6

14.9

6.5


Figure 10. Racial and Ethnic Representation in Detailed STEM Occupations(Data based on sample. For information on con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)


Percent0 20 40 60 80 100

Hispanic Black Asian White non-Hispanic

Social science research assistantsMisc. social scientists and related workers

Urban and regional plannersSociologists

PsychologistsSurvey researchers

EconomistsSocial science occupations

Misc. life, physical, and social science techniciansAll other physical scientists

Environmental scientists and geoscientistsChemists and materials scientistsAtmospheric and space scientists

Nuclear techniciansGeological and petroleum technicians

Chemical techniciansBiological technicians

Agricultural and food science techniciansAstronomers and physicists

Medical and life scientistsConservation scientists and foresters

Biological scientistsAgricultural and food scientists

Natural sciences managersLife and physical science occupations

Sales engineersSurveying and mapping technicians

Engineering techniciansDrafters

All other engineersPetroleum engineers

Nuclear engineersMining and geological engineers

Mechanical engineersMaterials engineers

Marine engineers and naval architectsIndustrial engineers, including health and safety

Environmental engineersElectrical and electronics engineers

Computer hardware engineersCivil engineers

Chemical engineersBiomedical engineers

Agricultural engineersAerospace engineers

Surveyors, cartographers, and photogrammetristsArchitectural and engineering managers

Engineering occupationsMiscellaneous mathematical science occupations

StatisticiansOperations research analysts

MathematiciansActuaries

Mathematical occupationsAll other computer occupations

Computer network architectsNetwork and computer systems administrators

Database administratorsComputer support specialists

Web developersSoftware developers

Computer programmersInformation security analysts

Computer systems analystsComputer and information research scientistsComputer and information systems managers

Computer occupationsTotal STEM occupations

Total employed


Figure 11. Employment in STEM Occupations by Race and Hispanic Origin: 1970 to 2011(Data based on sample. For information on con�dentiality protection, sampling error, nonsampling error, and de�nitions,see www.census.gov/acs/www/)

Note: Estimates for the American Indian and Alaska Native, Native Hawaiian and Other Paci�c 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

201120001990198019700

20

40

60

80

100

STEMTotal employed

20112000199019801970

0

20

40

60

80

100

STEM Total employed

201120001990198019700

20

40

60

80

100

STEM Total employed

20112000199019801970

Percent

White alone, not Hispanic or Latino Asian alone

Black or African American alone Hispanic or Latino


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 gradu-ates.26 Of science and engineering fields of study, women are most likely to be found in multidisci-plinary science studies (71 percent

26 Based on first listed field of bachelor’s degree.

women) and psychology (70 per-cent 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 else-where classified, such as nutrition sciences, combined majors including a science and engineering major, such as accounting and computer science, and nonspecified multidis-ciplinary 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

Per-cent

female MOE1

Per-cent

White alone,

not His-

panic or

Latino MOE1

Per-cent

Asian alone MOE1

Per-cent

Black or

African Ameri-

can alone MOE1

Per-cent His-

panic 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>.


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 grad-uates, 83.3 percent are employed,

28 The estimates for Blacks (7.1 percent) and Hispanics (6.8 percent) round to 7 per-cent 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 consid-ered 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 engi-neering 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 engi-neering 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, com-pared with fewer than 1 in 10 male science and engineering gradu-ates. 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 con�dentiality protection, sampling error, nonsampling error, andde�nitions, 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 Paci�c Islander alone. Source: U.S. Census Bureau, 2011 American Community Survey.

UnemployedEmployed 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


Table

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1

Ag

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to 3

4 ye

ars

....

....

....

....

....

....

....

5,01

4,12

243

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4,25

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84.8

0.3

210,

410

9,40

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20.

255

2,35

512

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11.0

0.2

35 to

44

year

s ..

....

....

....

....

....

....

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814,

958

41,7

734,

184,

416

40,5

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316

0,38

38,

028

3.3

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470,

159

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80.

345

to 5

4 ye

ars

....

....

....

....

....

....

....

4,47

9,41

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3,87

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86.6

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171,

380

6,06

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142

9,32

312

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9.6

0.3

55 to

64

year

s ..

....

....

....

....

....

....

..3,

864,

797

33,5

962,

824,

627

25,4

9273

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316

1,99

96,

813

4.2

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878,

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15,2

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3

Sex

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e ..

....

....

....

....

....

....

....

....

..10

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63,5

779,

270,

325

56,9

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239

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906,

250

17,0

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....

....

....

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....

....

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7,59

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5,86

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77.2

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306,

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423,

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18,0

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....

....

....

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....

....

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13,8

11,7

9669

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11,5

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8761

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83.8

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485,

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13,7

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11,

753,

609

24,4

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Whi

te a

lone

, not

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pani

c or

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....

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61,0

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55,5

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244

3,85

412

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3.4

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1,96

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59,8

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1,82

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ome

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ific

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....

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271,

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812,

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535

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or M

ore

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....

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344,

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192,

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pani

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(of a

ny r

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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 self-identify 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.


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 engineer-ing 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 XSTEM-related 10 .7 0 .2

Non-STEM 64 .3 0 .2Age25 to 34 years . . . . . . . . . . . . . . . . . . . . . . . . . 28.1 0.2 STEM 26.6 0.4 29.9 0.5

STEM-related 10.6 0.3Non-STEM 62.9 0.5

35 to 44 years . . . . . . . . . . . . . . . . . . . . . . . . . 27.6 0.2 STEM 26.6 0.5 29.5 0.5STEM-related 10.7 0.3

Non-STEM 62.6 0.545 to 54 years . . . . . . . . . . . . . . . . . . . . . . . . . 25.6 0.2 STEM 25.5 0.3 26.2 0.4


55 to 64 years . . . . . . . . . . . . . . . . . . . . . . . . . 18.7 0.2 STEM 19.2 0.3 14.4 0.3STEM-related 11.5 0.3

Non-STEM 69.3 0.4SexMale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.2 0.2 STEM 31.1 0.3 76.3 0.3


Female . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.8 0.2 STEM 15.3 0.2 23.7 0.3STEM-related 13.8 0.2

Non-STEM 71.0 0.3Race and Hispanic OriginWhite alone . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.4 0.2 STEM 23.0 0.2 70.6 0.4


White alone, not Hispanic or Latino . . . . . . . 71.5 0.2 STEM 23.3 0.2 66.9 0.4STEM-related 10.9 0.2

Non-STEM 65.8 0.3Black or African American alone . . . . . . . . . . . 7.0 0.1 STEM 17.4 0.8 4.9 0.2


Asian alone . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.0 0.1 STEM 41.4 0.6 21.5 0.4STEM-related 11.4 0.4

Non-STEM 47.3 0.6American Indian and Alaska Native alone . . . . 0.3 0.1 STEM 16.4 2.6 0.2 0.1


Some Other Race and Native Hawaiian or Other Pacific Islander alone3 . . . . . . . . . . . . .

1.5 0.1 STEM 18.0 1.5 1.1 0.1STEM-related 9.4 1.3

Non-STEM 72.6 1.8Two or More Races . . . . . . . . . . . . . . . . . . . . . 1.8 0.1 STEM 23.5 1.6 1.7 0.1


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


Science and engineering gradu-ates earn more per year when they are employed in STEM occupa-tions (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 engineer-ing graduates employed in a

STEM occupation, indicating that STEM employment boosts earn-ings 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 pro-vides a larger earnings gain among Blacks, Hispanics, and those who report Some Other Race, increas-ing 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 statis-tically 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 con�dentiality protection, sampling error, nonsampling error, and de�nitions, see www.census.gov/acs/www/)

*Native Hawaiian and Other Paci�c 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

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17.4

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Age

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Sex


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.

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graduates

Total workforce

24

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Gender gap (in percentage point)

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4

SOURCE OF THE ESTIMATES

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely demo-graphic, social, economic and hous-ing data for congressional districts, counties, places, and other locali-ties 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 informa-tion 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 inter-viewed in January 2011 through December 2011. The estimates based on this sample describe the actual average value of char-acteristics for the household and group quarter populations over this period of collection. Sampling error is the difference between an esti-mate 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 com-parative statements in this report have undergone statistical testing,

and comparisons are significant at the 90 percent level unless other-wise noted. In addition to sampling error, nonsampling error may be introduced during any of the opera-tions used to collect and process survey data such as editing, review-ing, or keying data from question-naires. 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>.


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MORE INFORMATION

For questions or comments, con-tact the author of this report:

Liana Christin Landivar, Ph.D. [email protected] 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.

disparaties in stem employment by sex, race, and hispanic origin

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