This is a presentation from #LaraconEU where I was a speaker talking about gender equality. It contains data about women in the tech workforce.

1. Coding Like a Girl
How teams with women gain with the diversity.

2. About me
• Major in Digital Game
Development
• Senior Software Engineer with 7
years in the market
• Web Development
• Women in Technology Advocate
• Lego fanatic =)

3. Diversity
• Gender
• Racial
• Ethnic
• Sexual Orientation

4. Bias

5. Bias
• Project Implicit® from Harvard University
• Preferences, attitudes and memory

6. Project Implicit®

7. Project Implicit®
0% 7.5% 15% 22.5% 30%
1%
3%
6%
18%
18%
28%
26%
Strong automatic association of Male with
Science and Female with Liberal Arts
Moderate automatic association of Male with
Science and Female with Liberal Arts
Slight automatic association of Male with
Science and Female with Liberal Arts
Little to no association between
genders and academic domains
Slight automatic association of Male with
Liberal Arts and Female with Science
Moderate automatic association of Male with
Liberal Arts and Female with Science
Strong automatic association of Male with
Liberal Arts and Female with Science
Source: Project Implicit

8. IAT Scores
Male with Science
Female with Liberal Arts
Female with Science
Male with Liberal Arts
Source:ProjectImplicit

9. Like a Girl

10. “You know, you are really intelligent, for a girl.”
–Undisclosed friend

11. Like a Girl
• You drive like a girl
• You punch like a girl
• You fight like a girl
• You [verb] like a girl

12. Diversity Reports

13. All Areas
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
37%30%30%31%30%
62%
70%70%69%70%
Male Female Non Disclosed*
Source:CompaniesDiversityReports

14. Tech
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
15%10%17%15%20%
85%
90%
83%85%
80%
Male Female Non Disclosed*
Source:CompaniesDiversityReports

15. Non-tech
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
52%50%48%47%35%
47%50%52%53%
65%
Male Female Non Disclosed*
Source:CompaniesDiversityReports

16. High Level
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
23%21%21%23%28%
77%79%79%77%
72%
Male Female Non Disclosed*
Source:CompaniesDiversityReports

17. All Areas
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
37%30%30%31%30%
62%
70%70%69%70%
Male Female Non Disclosed*
Tech
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
15%10%17%15%20%
85%
90%
83%85%
80%
Male Female Non Disclosed*
Non-tech
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
52%50%48%47%35%
47%50%52%53%
65%
Male Female Non Disclosed*
Senior Level
0%
20%
40%
60%
80%
100%
Apple Facebook Google Twitter Yahoo
1%
23%21%21%23%28%
77%79%79%77%
72%
Male Female Non Disclosed*
Source:CompaniesDiversityReports

18. Portrait of the tech
workforce facts
• Men 2.7x more chance of leading positions
• Women gravitates towards other women
• Lack of role models
• Women values flexibility more than men
Source: Anita Borg Institute, Climbing the technical ladder

19. Rank Levels
Source: Anita Borg Institute, Climbing the technical ladder
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Women Men
20.2%
10.9%
55.2%
56%
24.6%
33.1%
Entry Mid High

20. Why diversity matters?

21. Diversity Increases
Group Performance

22. Group Performance
• Collective Intelligence is increased
• Diverse teams are more efficient
• Better problem solving
• More innovative solutions
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

23. Group Performance
• Three factors:
1 - Social sensitivity
2 - Numbers of speaking members
3 - Proportion of females on the group
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

24. Diversity Powers
Innovation

25. Innovation
• Competitive advantage
• Diverse groups outstanding performance
• Patents with mixed gender cited more often
Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute

26. Innovation
Source: London Business School, Anita Borg Institute
"If people think alike, then no matter how smart they are they most likely will get
stuck at the same locally optimal solutions. Innovating, requires thinking differently.
That's why diversity powers innovation."
–Scott Page, University of Michigan

27. Neil deGrasse Tyson

28. Neil Degrasse Tyson
Astrophysicist

29. –Neil deGrasse Tyson, at 2009 New York Conference - Link
“Before we start talking about genetic differences,
you got to come up with a system that is equal
opportunity. Then we can have that conversation.”

30. Thank you
• Twitter: @gabidavila
• Web: http://davila.blog.br
• Email: gabidavila@gmail.com

31. References
• ANITA BORG INSTITUTE - Climbing the Technical Ladder
• ANITA BORG INSTITUTE - The Case for Investing on Women
• ANITA BORG INSTITUTE - Women Technologists Count
• ANITA WOLLEY - Evidence for a Collective Intelligence Factor in the
Performance of Human Groups
• CATALYST, Why Diversity Matters?
• ERNST & YOUNG - Groundbreakers
• ILLUMINATE VENTURES - High Performance Entrepreneurs