Cited by 41Names, Identity, and Discrimination ResearchGender Diversity and InequalityCareer Development and Diversity

Are There Gender Differences in Professional Self-Promotion? An Empirical Case Study of LinkedIn Profiles Among Recent MBA Graduates

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Altenburger, Kristen, De, Rajlakshmi, Frazier, Kaylyn, Avteniev, Nikolai, Hamilton, Jim·2017·Proceedings of the International AAAI Conference on Web and Social Media

Abstract

Women are more modest than men in expressing accomplishments, referred to as the “feminine modesty effect”. Given the importance of highlighting accomplishments and skills for professional advancement, our research revisits the classical question of equal opportunity with a modern dataset to examine how women leverage LinkedIn, a professional social networking site. We first apply propensity score matching methods to identify a subset of similarly qualified female and male U.S. users who recently graduated/will be graduating (2011-2017) from a top-ranked MBA program as indicated on their LinkedIn profile. We then analyze gender differences in online self-promotion choices, an often overlooked aspect of understanding the role of gender in the professional hiring pipeline. Among matched subsets of female and male users, we find that females are less likely relative to males to utilize data fields that require writing in free-form such as the Summary and Job Description fields. However, we find for most universities that females and males are equally likely to include more structured data fields such as Honors and Skills, and for some universities females are more likely to include at least one Skill. This work begins to quantify gender biases in user-provided data and introduces important considerations for how self-presentation choices affect professional opportunity in online hiring platforms.

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  • Self-promotion gaps related to gender have been documented in the hiring domain and beyond.

    “Self-promotion gaps related to gender have been documented in the hiring domain [5] and beyond; men tend to self-evaluate higher than women even without intrinsic incentives [87].”

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Research works printed but not linked: 3

  1. Bertrand, M., and Mullainathan, S. 2004. Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination. The American Economic Re- view 94(4):991-1013.
  2. Ho, D.; Imai, K.; King, G.; and Stuart, E. 2007. Matchit: Nonparametric preprocessing for paramet- ric causal inference. Journal of Statistical Software http://gking.harvard.edu/matchit.
  3. Wang, Y.-C.; Burke, M.; and Kraut, R. 2016. Modeling self- disclosure in social networking sites. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 74-85. ACM.

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Authors
Altenburger, K., De, R., Frazier, K., Avteniev, N., & Hamilton, J.
Affiliations
  • Stanford University (US)
  • North Carolina State University (US)
  • LinkedIn (United States) (US)
Venue
Proceedings of the International AAAI Conference on Web and Social Media
Published
2017-05-03
Volume
11
Issue
1
Pages
460-463
ISSN
  • 2162-3449 (ISSN-L)
  • 2334-0770
OpenAlex
W2620600535
Cited by
35
References
23
Citations by year
2026: 4 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 2 · 2019: 6 · 2018: 1
FWCI
7.83
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
  • AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Funders
National Defense Science and Engineering Graduate
Topics
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
  • Social Sciences › Gender Studies › Gender Diversity and Inequality
  • Social Sciences › Safety Research › Career Development and Diversity
Keywords
gender differences, self-promotion, LinkedIn profiles, MBA graduates, professional social networking, online self-presentation, gender bias in hiring, equal opportunities, career development, user-generated data

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