# Presentation: 181810

## Why So Few Women in Science? 

- Meg Urry
- Yale University

## More women are earning science and engineering doctorates

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

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

## Bachelor’s degrees in science

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

- 54%  42%   All fields

- 47%  26%   Math

- 16%  12%   Physics

## Women in Physics

- Ivie & Ray 2005

## Women in Astronomy

- Ivie & Ray 2005

## Career Disparities

*Long 2001*
*Sonnert & 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 science
- Fairness/justice
- It’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 ability
- Key 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)*

- Author 

- Referee 

- Women aren’t as good as men at science...

## Women lack math ability ...

- Stereotype threat: performing below ability because of expectations
- Example: “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 Doe
- John Doe
- Keisha Doe
- Jamal 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!*

## Scientists are completely objective ...

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

- size matters...

## Job searches are gender-blind ...

- blind audition...

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

*See story of Munich Philharmonic trombonist (Abby Conant)*

## Coaching (Mentoring)

*Tony DeCicco, women’s soccer coach*
*Boston Globe, June 18, 1999*

## 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? “Gender Schemas”

- Lower expectations for women
- Uneven 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? “Gender Schemas”

- Lower expectations for women
- Uneven evaluation
- Accumulation of disadvantage
*Martell, Lane & Emrich 1996* – 1% bias, 8 levels  65% male top management
- Most of us are biased

- Virginia 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 Barriers: Fulfilling the Potential of Women in Academic Science and 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 

- 50% women scientists unmarried
- (in developed countries)

- Women marry scientists/professionals