Published February 22, 2022 | Version v1
Journal article Open

Hiring women into senior leadership positions is associated with a reduction in gender stereotypes in organizational language

  • 1. Duke University
  • 2. Stanford University
  • 3. University of Chicago
  • 4. Columbia University

Description

Women continue to be underrepresented in leadership positions. This underrepresentation is at least partly driven by gender stereotypes that associate men, but not women, with achievement-oriented, agentic traits (e.g., assertive and decisive). These stereotypes are expressed and perpetuated in language, with women being described in less agentic terms than men. The present research suggests that appointing women to the top tiers of management can mitigate these deep-rooted stereotypes that are expressed in language. We use natural language processing techniques to analyze over 43,000 documents containing 1.23 billion words, finding that hiring female chief executive officers and board members is associated with changes in organizations' use of language, such that the semantic meaning of being a woman becomes more similar to the semantic meaning of agency. In other words, hiring women into leadership positions helps to associate women with characteristics that are critical for leadership success. Importantly, our findings suggest that changing organizational language through increasing female representation might provide a path for women to break out of the double bind: when female leaders are appointed into positions of power, women are more strongly associated with the positive aspects of agency (e.g., independent and confident) in language but not at the cost of a reduced association with communality (e.g., kind and caring). Taken together, our findings suggest that female representation is not merely an end, but also a means to systemically change insidious gender stereotypes and overcome the trade-off between women being perceived as either competent or likeable.

Data availability

We have deposited the numeric data (cosine similarities) and code used to recover the results of Study 1 publicly in an Open Science Framework repository (https://osf.io/utz29/):
M. A. Lawson, A. E. Martin, I. Huda, S. C. Matz, Vector Distance Data. OSF. https://osf.io/7t95y/. Deposited 24 November 2020.
M. A. Lawson, A. E. Martin, I. Huda, S. C. Matz, Vector Distance Data at Word Level. OSF. https://osf.io/9ztp8/. Deposited 24 November 2020.

We do not provide the raw text data, which was recovered from Capital IQ and SeekingAlpha. We do not provide the board level data used in Study 2, but do provide our code to estimate the Panel Vector Autoregression models in the same repository. The board composition data was aggregated from Capital IQ:
Institutional Shareholder Services, Directors Data. https://www.issgovernance.com/esg/governance-data/director-data/. Accessed 30 November 2018.

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Additional details

Identifiers

DOI
10.1073/pnas.2026443119
Other
oai:uchicago.tind.io:5304

UChicago Information

Division(s)
Social Sciences Division
Center(s) or Institute(s)
TMW Center for Early Learning + Public Health