This document discusses women in IT and highlights several key facts: 1) Companies with more gender diverse teams experience greater innovation, higher profits, and stronger business performance. 2) Despite growing technology jobs, women's participation in these fields is declining. Only 18% of computer science degrees are earned by women. 3) Over half of technical women leave their jobs at the mid-level point, which is twice the quit rate of men, representing a costly loss of talent. Unconscious bias, lack of support, and work-life challenges are some of the main reasons women leave technical roles.

1. WOMEN IN IT: THE FACTS!

2. Companies Benefit from Diversity in
Innovation!
è Increased sales revenue,
more customers, bigger
market share"
è Higher-than-average
profitability"
è Greater return on equity
and return to shareholders "
è Greater potential for
creativity, sharing of
knowledge, task fulfillment"
"
Sources: Workplace Diversity Pays, American Sociological Review (2009), Capitalizing on Thought Diversity, Research-
Technology Management (2009), The Difference, Scott Page (2007), Innovative Potential, London Business School
(2007), The Bottom Line, Catalyst (2004) "

3. As Technology Jobs Increase,
Women’s Participation Declines!

4. Current Trends in the Computing
Education Pipeline Also Troubling!
• If current graduation trends continue, the
industry will only be able to fill 30% of the
1.4 million tech jobs added by 2020. "
• Only 18% of computer and information
science degrees were earned by women in
2010.!
Source: U.S. Dept of Labor, Employment Projections, 2010-2020

5. Failing Not Just to Attract but to
Retain Technical Talent!
è 74% of technical women say they love their
work"
"
è Yet 56% leave at the “mid-level”"
"
è This is twice the quit rate for men"
Source: Athena Factor, Center for Work-Life Policy, 2008 "

6. A Costly Time to Leave;!
A Crucial Point for Intervention!
!
Average turnover cost for a technical
employee"
Percent of women who will continue working
after they leave"
Number of women who might have
remained if this attrition were reduced"
Sources: Capturing Turnover Costs, Joins, 2000; TalentKeepers, 2010; Athena Factor, 2008"

7. WHY Are They Leaving? The Key
Reasons !
è UNCONSCIOUS
BIAS
è Lack
of
Mentors
&
Professional
Development
è Supervisory
RelaAonships
è Bias
in
Performance
Reviews
and
PromoAon
è Lack
of
Support
for
CompeAng
Life
ResponsibiliAes
Source: Women in IT:The Facts, NCWIT"

8. Why Are They Leaving? !
Unconscious Bias!
We know that unconscious biases are
particularly salient in organizations or
professions dominated by a single group (e.g.,
tech, elementary teaching)!
!
"

9. What is Unconscious Bias?!
è Unconscious bias results from “schemas” "
è Schemas are necessary; everyone has them"
è They let us pay attention to only select
information"
è We need them to live … but …"
è They also lead to implicit or unconscious bias"
"
Sources: Banaji & Hardin, 1996; Biernat, Manis & Nelson, 1991; Bertrand & Mullainathan, 2004. "

10. What do you see?!
"
"

11. Count How Many Passes!
http://www.youtube.com/watch?v=nkn3wRyb9Bk"
"

12. A Technical Example: Heidi Roizen, A
Case Study!
» BA and MBA from Stanford
» Co-founder of T/Maker Company (1983
software company, acquired in 1994) and
SkinnySongs
» VP at Apple Computer
» Venture capitalist: Managing Director of
Mobius VC

13. A Technical Example: Heidi & Howard,
Columbia Business School Study!

14. A Technical Example: Heidi & Howard!

15. A Technical Example: Heidi & Howard!

16. Subtle Dynamics: Stereotype Threat!
• Fear that our performance will confirm
negative stereotypes
• Raises anxiety, reduces confidence and
risk-taking, lowers performance

17. Subtle Instances Add Up: !
Stereotype Threat!
Example: White male
engineering students
score lower when told in
advance that Asians
typically score higher on
math tests"
Source: Aronson, et al., 1999; Steele & Aronson, 1998"

18. How Unconscious Bias Affects the
Workplace!

19. CREATE AN ECOSYSTEM.!

20. Call to Action: Individuals!
• Educate yourself and examine decisions, beliefs,
actions for hidden biases
• Examine your actions for self-limiting behaviors or
beliefs
• Question interpretations such as “so and so is just not
a risk taker” – consider more complex interpretations
and encourage others to do so
• Provide encouragement as appropriate

21. Call to Action: Supervisors & Managers!
• Advance women into senior technical roles and
provide encouragement as appropriate
• Assure healthy debate in team sessions
• “Sponsor” underrepresented groups on the
technical career path – note that this is not the
same as mentoring
• Examine your task assignment and
performance reviews for bias

22. Call to Action: NCWIT Resources to
Help!
"
è Supervisory Program-in-a-Box Series: 5-part series
helping supervisors address unconscious biases"
è Mentoring Technical Women Program-in-a-Box:
Ready-made tools for implementing and evaluating a
research-based mentoring program!
è Industry Strategic Planning Guide for Increasing
Women’s Participation in Computing: Blueprint for
planning efforts to address all areas of the “ecosystem”"

23. Bias Impedes Technology Innovation:
What Can Your Company Do?!
Percent of IT patents Percent of open source
held by women contributed by women
Percent of corporate officer positions in Fortune 500
technology companies held by women
Percent of board positions in Fortune 500 technology
companies held by women
Percent of senior management positions in technical/R&D
departments held by women, in a Silicon Valley study
Percent of senior management positions in non-technical
departments held by women, in a Silicon Valley study
Source: Women & IT: The Facts, NCWIT."