Why So Few Women in Science? Meg Urry Yale University

Why So Few Women in Science?

Meg Urry Yale University

More women are earning science and engineering doctorates

0

10

20

30

40

50

60

Year

Per

cent

Wom

en P

hDs

Social Sciences

Life Sciences

Physical Sciences

Engineering

Per

cen

t W

om

en P

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s

But higher attrition for women between B.S. and Ph.D. degrees

0%

10%

20%

30%

40%

50%

60%

70%

% w

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e r

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Non S&EBachelor's

Non S&EPhDs

S&EBachelor's

S&E PhDs

SOURCE: NSF, Women, Minorities and Persons With Disabilities in Science and Engineering-2004

In most fields, degrees are increasingly awarded to women. Biology & medicine now ~50%.

Bachelor’s degrees in science

Attrition between B.S. and Ph.D. degrees

54% 42% All fields47% 26% Math16% 12% Physics

Women in Physics

Ivie & Ray 2005

Women in Astronomy

Ivie & Ray 2005

Career Disparities

Long 2001Sonnert & Holton 1996 Synthetic cohorts, e.g., NSF fellows –

career advancement of women slower

Salary Disparities

Egan & Bendick 1994 – factors that affect salary

Tesch et al. 1995 – resource allocation in academic medicine appointments

Reasons for Disparities?

Not family (Mason & Goulden 2002 “Do Babies Matter?”)

Xie & Shauman 2003 – interest not correlated with ability in science

Why worry?

Excellence of scienceFairness/justiceIt’s a great life!

taxpayers support science, so should benefit equally

Health of science profession more scientifically literate public more public support of science

Workforce issues …

What’s going on?

Not conscious discrimination or overt prejudice

Not differences in innate abilityKey issue: tilted playing field

Wenneras & Wold 1997 Nature Double-blind refereeing

Common Myths

Paludi & Bauer 1983, psychology paper sent to 180 referees (men & women)

John T. McKay

Joan T. McKay

J. T. McKay

Men 1.9 3.0 2.7

Women 2.3 3.0 2.6

(1=excellent, 5=bad)(1=excellent, 5=bad)

Author Author

Referee Referee

Women aren’t as good as men at science…

Women lack math ability …Stereotype threat: performing below

ability because of expectationsExample: “hard” math test

Men: 25/100 Women: 10/100 Gender gap in math ???

“This test has been designed to be gender neutral”

Women: 20/100 Men: 20/100

Also important for minorities

They prefer “caring” fields like medicine

Women don’t like physics…

Women in academic medicine are equally far behind

Hypothesis: More elite, competitive culture fewer women

There aren’t any good women to hire …

Jane DoeJohn DoeKeisha DoeJamal Doe

Women can be friendly or competent, not both

(Research shows name strongly affects success of resume, even among psychologists who are well aware of gender schemas.)

Women choose family over career…

• Women w/o children not more successful

• Many women in other demanding fields • Countries w strong support systems

(e.g., Scandinavia) have few women in physics

• Academic careers flexible: become a professor, have a family!

Biernat, Manis & Nelson 1991Porter & Geis 1981Butler & Geis 1990

Scientists are completely objective …

size matters…size matters…

blind audition…

Works for orchestras, writers, abstracts, resumes …… but not for job talks!

See story of Munich Philharmonic trombonist (Abby Conant)

Job searches are gender-blind …

Tony DeCicco, women’s soccer coachBoston Globe, June 18, 1999

Coaching (Mentoring)

If you need mentoring, you’re not good enough …

Women in Astronomy I - Baltimore, MD 1992

Women in Astronomy II – Pasadena, CA 2003

What’s going on? What’s going on? “Gender “Gender Schemas”Schemas”

Lower expectations for womenUneven evaluation

Accumulation of disadvantage

Virginia Valian Why So Slow? The Advancement of Women

Uneven Evaluation

Heilman et al. 2004 – rating asst. VPs

Norton, Vandello & Darley 2004 – rating resumes for construction job

Uhlman & Cohen 2005 – shifting criteria and (non)objectivity

Trix & Penska 2003 – letters of recommendation

Valian annotated bibliography: www.hunter.cuny.edu/genderequity/ equityMaterials/Feb2008/annobib.pdf

Sanbonmatsu, Akimoto & Gibson 1994 (Evaluation of failing students)

What’s going on? What’s going on? “Gender “Gender Schemas”Schemas”

Lower expectations for womenUneven evaluation

Accumulation of disadvantage Martell, Lane & Emrich 1996 – 1%

bias, 8 levels 65% male top management

Most of us are biasedVirginia Valian Why So Slow? The Advancement of Women

www.hunter.cuny.edu/genderequity/equityMaterials/Feb2008/annobib.pdf

Mahzarin Banaji implicit.harvard.edu

What to do?

Remedies

Women and men: educate yourselves Recognize uneven playing field Nix “lower standards”

Young women: Find the right back burner Be prepared

Leaders: lead Pressure Training (e.g., how to hire, Denton/UWa) Accountability

NAS Study: “Beyond Bias and NAS Study: “Beyond Bias and Barriers: Fulfilling the Potential of Barriers: Fulfilling the Potential of Women in Academic Science and Women in Academic Science and

Engineering”Engineering”

Statistics Learning and performance intrinsic difference? Persistence and Attrition Evaluation of success implicit bias Strategies that work Undergraduate Carnegie Mellon

Hiring faculty U. Washington toolkit Training women faculty CoaCH ADVANCE CRLT players

Institutional structures, career paths Recommendations

Change is within reach …

… but it requires action

50% women scientists unmarried

(in developed countries)Women marry

scientists/professionals