A Longer Shortlist Increases the Consideration of Female Candidates in Male-Dominant Domains
Extending candidate shortlists (which are typically used as an informal recruitment process in organizations) could help close the gender gap in hiring.
What barriers do women face when joining the workforce or advancing at the workplace? While some of them can be evident, such as gender discrimination and prejudices, other barriers are much more concealed.
These invisible obstacles are known as ‘second-generation gender bias’, and they refer to non-deliberate, hidden or subtle forms of gender bias. Second-generation bias creates a context in which women fail to thrive or reach their full potential, even when recruiters and bosses do not overtly intend to discriminate against women.
This research article focuses on a possible source of second-generation gender bias: candidate shortlist generation in informal recruitment processes. Many professional advancement opportunities (such as promotions, skills training, and mentorship) are filled through informal recruitment, referrals, and networking, where candidates are recruited at the suggestion of superiors, colleagues, and friends. Informal recruitment may pose a barrier to gender diversity in male-dominated professions because it benefits candidates that come to mind first, and in male-dominated professions, those candidates tend to be men.
In order to counteract the detrimental effects of informal shortlists for women, the authors conducted 10 studies ranging from classroom settings to organizational hiring contexts to test the simple intervention of making an informal shortlist longer as a means to increase the likelihood of listing female candidates.
When a participant generated an informal shortlist for a role in a male-dominant domain, more female candidates were included in an extended shortlist versus an initial shortlist.
- Across studies, making the shortlist longer increased the ratio of women to men from 1 in 5.52 in the initial list to 1 in 3.92 in the extended list (or from 15% to 20% female candidates).
- Study 5, which compared longer vs shorter shortlists, found a higher proportion of women in the longer shortlists (20%) compared with the shorter shortlists (17%).
The study suggests having a longer shortlist could be a simple, low-cost intervention to help reduce gender bias in the informal shortlist construction process. However, while evidence indicates a longer shortlist increases the inclusion of female candidates, many lists still contained a higher proportion of male candidates when the list was lengthened, which suggests that this intervention should be implemented within a multi-pronged approach. Future research could be aimed at examining the longer shortlist effect in organizations and the moderating role of organizational context factors such as job level (entry vs. senior position) or the opportunity type (new position, promotion, and skills training). Moreover, the authors underline this has yet to be examined in field settings, which is a limitation that should be addressed in future studies.
The authors conducted a series of 10 studies to find out whether the longer shortlist hypothesis held true. There were differences across studies but for all of them the main analyses were conducted through a Poisson regression given that the count data violated normality assumptions. The effect size was reported as r and the ratio of women to men in each time period to aid interpretation. The main analyses were also preregistered with repeated-measures t tests.
Studies 1a-c: Participants were 129 university students in study 1a, 87 adults recruited from Amazon’s Mechanical Turk (AMT) in study 1b, and 642 adults recruited from AMT in study 1c. Participants were asked to imagine that they were filmmakers who were tasked with generating an informal shortlist of three actors to star in their upcoming action-thriller film (time 1). Then participants were asked to expand the shortlist by adding three more names to the list (time 2).
Studies 2a and 2b: Participants were working adults with experience in the technology industry: 71 adults recruited from Prolific Academic (study 2a) and 194 adults recruited from AMT (study 2b). Participants were told about a technology startup in California that was looking for a new chief executive officer (CEO) and were asked to create an informal shortlist of three people who should be interviewed for the role (time 1). Then they expanded the list with three additional names (time 2).
Studies 3a and 3b: Participants were parents who had to list role models for their male or female child. Parents with a child five years of age or younger were recruited from AMT: 187 parents in study 3a and 485 parents in study 3b. Parents were asked to make a list of three role models for their child (time 1), and then expand this list with three additional role models (time 2).
Study 4: Participants were 702 adults recruited from AMT with work experience in the technology industry. They were randomly assigned to either generate a shortlist of technology CEOs by first listing three names and then expanding this list with three additional names (baseline condition), or to generate a single shortlist of six names (the six-name-list condition).
Studies 5a and 5b: Study 5a participants were 240 university students, and study 5b participants were 2,166 adults with technology industry experience recruited from Prolific Academic (198 participants), AMT (1,765 participants), and a university-managed participant pool (203 participants). Participants were randomly assigned to generate a three-person shortlist (list-three condition) or a six-person shortlist (list-six condition). Study 5a used the Hollywood action hero domain, and study 5b used the technology industry executive domain.