Mapping Exclusion in the Organization

Organizational network analysis can reveal ways to bolster inclusivity.

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Annual workforce demographic reports show that despite prominent high-tech companies’ pledges to increase gender diversity, the pattern of underrepresentation of women persists.1 Compared with workers in other industries, executives and professionals in the tech sector are disproportionately male.2 Women account for only 30% of the workforce in the top 75 technology companies in Silicon Valley, even though women achieve near parity at nontechnology businesses in the region.3 As a female technical consultant said in a 2018 Pew Research report on women in STEM: “People automatically assume I am the secretary, or in a less technical role, because I am female. This makes it difficult for me to build a technical network to get my work done. People will call on my male co-workers, but not call on me.”4

One of the biggest barriers to women’s success is their exclusion from informal professional networks.5 To identify the challenges and solutions involved in developing gender-inclusive networks, we studied the organizational networks of dozens of companies, surveyed thousands of employees, and interviewed senior executives responsible for implementing their organization’s gender-related diversity, equity, and inclusion (DEI) efforts. (See “The Research.”) Our research made clear that who you know is as important — often more so — than what you know when it comes to rising through the ranks.

Networks are how people learn the unwritten rules of success, hear about job and promotion opportunities before they are posted, and — most critically — build a level of interpersonal trust and rapport with their contacts that translates into a willingness to pick up the phone and vouch for someone’s capabilities. According to one study, nearly 40% of the gender pay gap can be explained by the informal relationships that men have with their male managers.



1. A. Wynn, “Why Tech’s Approach to Fixing Its Gender Inequality Isn’t Working,” Harvard Business Review, Oct. 11, 2019,; and K. Rooney and Y. Khorma, “Tech Companies Say They Value Diversity, but Reports Show Little Change in Six Years,” CNBC, June 12, 2020,

2.Diversity in High Tech,” U.S. Equal Employment Opportunity Commission, accessed Oct. 1, 2021,

3.Diversity in High Tech,” U.S. Equal Employment Opportunity Commission.

4. C. Funk and K. Parker, “Women in STEM See More Gender Disparities at Work, Especially Those in Computer Jobs, Majority-Male Workplaces,” ch. 3 in “Women and Men in STEM Often at Odds Over Workplace Equity,” Pew Research Center, Jan. 9, 2018,

5. H. Ibarra, R.J. Ely, and D.M. Kolb, “Women Rising: The Unseen Barriers,” Harvard Business Review 91, no. 9 (September 2013): 60-67; “Connections That Count: The Informal Networks of Women of Color in the United States,” PDF file (New York: Catalyst, 2006),; and “Women and Men in U.S. Corporate Leadership: Same Workplace, Different Realities?” PDF file (New York: Catalyst, 2004),

6. Z.B. Cullen and R. Perez-Truglia, “The Old Boy’s Club: Schmoozing and the Gender Gap,” working paper 26530 National Bureau of Economic Research, Cambridge, Massachusetts, December 2019.

7. D.J. Brass, “Being in the Right Place: A Structural Analysis of Individual Influence in an Organization,” Administrative Science Quarterly 12, no. 1 (December 1984): 518-539.

8. M. McPherson, L. Smith-Lovin, and J.M. Cook, “Birds of a Feather: Homophily in Social Networks,” Annual Review of Sociology 27 (2001): 415-444.

9. R.S. Burt, “The Network Structure of Social Capital,” Research in Organizational Behavior 22 (2000): 345-423.

10. R. Cross and A. Parker, “The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations” (Boston: Harvard Business School Press, 2004).

11. H. Ibarra, N.M. Carter, and C. Silva, “Why Men Still Get More Promotions Than Women,” Harvard Business Review 88, no. 9 (September 2010): 80-85.

12. Burt, “The Network Structure of Social Capital.”

13. Ibid.

14. To measure brokerage, we calculated the effective size of each individual’s personal network. Effective size captures the extent to which an individual’s contacts are connected to each other, either directly or indirectly.

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