1. Why So Few Women in Science? Meg Urry Yale University

2. More women are earning science and engineering doctorates Percent Women PhDs

3. 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

4. In most fields, degrees are increasingly awarded to women. Biology & medicine now ~50%. Bachelor’s degrees in science

5. Attrition between B.S. and Ph.D. degrees 54%  42% All fields 47%  26% Math16%  12% Physics

6. Women in Physics Ivie & Ray 2005

7. Women in Astronomy Ivie & Ray 2005

8. Career Disparities §Long 2001 §Sonnert & Holton 1996 Synthetic cohorts, e.g., NSF fellows – career advancement of women slower

9. Salary Disparities §Egan & Bendick 1994 – factors that affect salary §Tesch et al. 1995 – resource allocation in academic medicine appointments

10. Reasons for Disparities? §Not family (Mason & Goulden 2002 “Do Babies Matter?”) §Xie & Shauman 2003 – interest not correlated with ability in science

11. Why worry? §Excellence of science §Fairness/justice §It’s a great life! l taxpayers support science, so should benefit equally §Health of science profession l more scientifically literate public  more public support of science  Workforce issues …

12. What’s going on? § Not conscious discrimination or overt prejudice § Not differences in innate ability § Key issue: tilted playing field l Wenneras & Wold 1997 Nature l Double - blind refereeing

13. Common Myths

14. Paludi & Bauer 1983, psychology paper sent to 180 referees (men & women) John T. McKay Joan T. McKay J. T. McKay Men1.93.02.7 Women2.33.02.6 (1=excellent, 5=bad) Author  Referee  Women aren’t as good as men at science…

15. Women lack math ability … §Stereotype threat: performing below ability because of expectations §Example: “hard” math test l Men: 25/100 l Women: 10/100 l Gender gap in math ??? §“This test has been designed to be gender neutral” l Women: 20/100 l Men: 20/100 §Also important for minorities

16. 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

17. 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.)

18. 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!

19. Biernat, Manis & Nelson 1991 Porter & Geis 1981 Butler & Geis 1990 Scientists are completely objective … size matters…

20. 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 …

21. Tony DeCicco, women’s soccer coach Boston Globe, June 18, 1999 Coaching (Mentoring)

22. If you need mentoring, you’re not good enough … Women in Astronomy I - Baltimore, MD 1992 Women in Astronomy II – Pasadena, CA 2003

23. What’s going on? “Gender Schemas” §Lower expectations for women §Uneven evaluation §Accumulation of disadvantage Virginia Valian Why So Slow? The Advancement of Women

24. 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

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

26. What’s going on? “Gender Schemas” §Lower expectations for women §Uneven evaluation §Accumulation of disadvantage l 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

27. What to do?

28. Remedies §Women and men: educate yourselves l Recognize uneven playing field l Nix “lower standards” §Young women: l Find the right back burner l Be prepared §Leaders: lead l Pressure l Training (e.g., how to hire, Denton/UWa) l Accountability

29. 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

30. Change is within reach … … but it requires action

31.  50% women scientists unmarried (in developed countries) Women marry scientists/professionals