Whether gender bias contributes to women’s under-representation in scientific fields is still controversial. Past research is limited by relying on explicit questionnaire ratings in mock-hiring scenarios, thereby ignoring the potential role of implicit gender bias in the real world. We examine the interactive effect of explicit and implicit gender biases on promotion decisions made by scientific evaluation committees representing the whole scientific spectrum in the course of an annual nationwide competition for elite research positions. Findings reveal that committees with strong implicit gender biases promoted fewer women at year 2 (when committees were not reminded of the study) relative to year 1 (when the study was announced) if those committees did not explicitly believe that external barriers hold women back. When committees believed that women face external barriers, implicit biases did not predict selecting more men over women. This finding highlights the importance of educating evaluative committees about gender biases.
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Most of the data supporting the findings of this study at the committee level are available within the paper (and its Supplementary Information files). The entire dataset that supports the findings of this study is available from the Open Science Framework repository (https://osf.io/umf62/). Individual-level data are available on request from the authors.
The code used to perform the primary analyses of the study is available from the corresponding authors upon request.
Gibney, E. Women under-represented in world’s science academies. Nature News http://www.nature.com/news/women-under-represented-in-world-s-science-academies-1.19465 (29 February 2016).
Gender in research and innovation. Statistics and Indicators https://ec.europa.eu/research/swafs/pdf/pub_gender_equality/she_figures_2015-leaflet-web.pdf (She Figures, European Union, 2015).
Science and Engineering Indicators 2016 https://www.nsf.gov/statistics/2016/nsb20161/#/ (National Science Foundation, 2016).
Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Science faculty’s subtle gender biases favor male students. Proc. Natl Acad. Sci. USA 109, 16474–16479 (2012).
Reuben, E., Sapienza, P. & Zingales, L. How stereotypes impair women’s careers in science. Proc. Natl Acad. Sci. USA 111, 4403–4408 (2014).
Steinpreis, R., Anders, K. & Ritzke, D. The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: a national empirical study. Sex Roles 41, 509–528 (1999).
Williams, W. M. & Ceci, S. J. National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track. Proc. Natl Acad. Sci. USA 112, 5360–5365 (2015).
Breda, T. & Hillion, M. Teaching accreditation exams reveal grading biases favor women in male-dominated disciplines in France. Science 353, 474–478 (2016).
Williams, W. M. & Ceci, S. J. Academic science isn’t sexist. The New York Times https://www.nytimes.com/2014/11/02/opinion/sunday/academic-science-isnt-sexist.html (31 October 2014).
Baumeister, R. F., Vohs, K. D. & Funder, D. C. Psychology as the science of self-reports and finger movements: whatever happened to actual behavior? Perspect. Psychol. Sci. 2, 396–403 (2007).
Bernstein, R. Scientific community. No sexism in science? Not so fast, critics say. Science 346, 798 (2014).
Stewart, A. & Valian, V. An Inclusive Academy: Achieving Diversity and Excellence (MIT Press, 2018).
Devine, P. G. Stereotypes and prejudice: their automatic and controlled components. J. Pers. Soc. Psychol. 56, 5–18 (1989).
Devine, P. G. et al. A gender bias habit-breaking intervention led to increased hiring of female faculty in STEMM departments. J. Exp. Soc. Psychol. 73, 211–215 (2017).
Greenwald, A. G., McGhee, D. E. & Schwarz, J. L. K. Measuring individual differences in implicit cognition: the implicit association test. J. Pers. Soc. Psychol. 74, 1464–1480 (1998).
Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L. & Banaji, M. R. Understanding and using the implicit association test: III. Meta-analysis of predictive validity. J. Pers. Soc. Psychol. 97, 17–41 (2009).
Nosek, B. A. et al. National differences in gender–science stereotypes predict national sex differences in science and math achievement. Proc. Natl Acad. Sci. USA 106, 10593–10597 (2009).
Rudman, L. A. Implicit Measures for Social and Personality Psychology (Sage Publications, 2011).
Nosek, B. A., Banaji, M. R. & Greenwald, A. G. Math = male, me = female, therefore math not = me. J. Pers. Soc. Psychol. 83, 44–59 (2002).
Miller, D. I., Eagly, A. H. & Linn, M. C. Women’s representation in science predicts national gender–science stereotypes: evidence from 66 nations. J. Educ. Psychol. 107, 631–644 (2015).
Fazio, R. H. & Olson, M. A. in Dual-Process Theories of the Social Mind 155–172 (eds Gawronski, B., Trope, Y. & Sherman, J. W.) (Guilford Press, 2014).
Crandall, C. S. & Eshleman, A. A justification–suppression model of the expression and experience of prejudice. Psychol. Bull. 129, 414–446 (2003).
Uhlmann, E. L. & Cohen, G. L. “I think it, therefore it’s true”: effects of self-perceived objectivity on hiring discrimination. Organ. Behav. Hum. Decis. Process 104, 207–223 (2007).
Caplar, N., Tacchella, S. & Birrer, S. Quantitative evaluation of gender bias in astronomical publications from citation counts. Nat. Astron. 1, 0141 (2017).
Hayes, A. F. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach (Guilford Press, 2013).
Miller, D. I. & Halpern, D. F. The new science of cognitive sex differences. Trends Cogn. Sci. 18, 37–45 (2014).
Régner, I. et al. Individual differences in working memory moderate stereotype-threat effects. Psychol. Sci. 21, 1646–1648 (2010).
Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115, 336–356 (2008).
Valian, V. Why So Slow? The Advancement of Women (MIT Press, 1998).
Stewart, A. J., Malley, J. E. & Herzog, K. A. Increasing the representation of women faculty in STEM departments: what makes a difference? J. Women Minor. Sci. Eng. 22, 23–47 (2016).
Cox, W. T. L. Multiple Determinants of Prejudicial and Nonprejudicial Behavior. PhD thesis, Univ. Wisconsin-Madison (2015).
Sady, K. & Aamodt, M. G. in Adverse Impact Analysis: Understanding Data, Statistics, and Risk (eds Morris, S. B. and Dunleavy, E. M.) 216–238 (Taylor & Francis, 2017).
Nosek, B. A., Greenwald, A. G. & Banaji, M. R. Understanding and using the implicit association test: II. Method variables and construct validity. Pers. Soc. Psychol. Bull. 31, 166–180 (2005).
Sommers, S. R. On racial diversity and group decision making: identifying multiple effects of racial composition on jury deliberations. J. Pers. Soc. Psychol. 90, 597–612 (2006).
Klein, K. J. & Kozlowski, S. W. J. From micro to meso: critical steps in conceptualizing and conducting multilevel research. Org. Res. Methods 3, 211–236 (2000).
Chan, D. Functional relations among constructs in the same content domain at different levels of analysis: a typology of composition models. J. Appl. Psychol. 83, 234–246 (1998).
Bell, S. T. Deep-level composition variables as predictors of team performance: a meta-analysis. J. Appl. Psychol. 92, 595–615 (2007).
Bradley, B. H., Klotz, A. C., Postlethwaite, B. E. & Brown, K. G. Ready to rumble: how team personality composition and task conflict interact to improve performance. J. Appl. Psychol. 98, 385–392 (2013).
Devine, D. J. & Philips, J. L. Do smarter teams do better: a meta-analysis of cognitive ability and team performance. Small Group Res. 32, 507–532 (2001)
We thank S. Heine, E. Uhlmann, S. Spencer, W. Hall, J. Nezlek and V. Valian for providing valuable input on an earlier version of the manuscript and/or the analyses. Financial support was provided by the Mission pour la Place des Femmes au CNRS (the CNRS Mission for Women’s Integration) and the Social Sciences and Humanities Research Council (895-2017-1025; Canada). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work benefited greatly from technical support provided by the CNRS Mission for Women’s Integration. We thank A. Greenwald, N. Sriram and the other members of Project Implicit for efficient technical assistance on the IAT.
The authors declare no competing interests.
Peer review information: Primary Handling Editor: Stavroula Kousta.
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