Take a look at this widely cited ‘fact’, which you’ve probably heard a version of at one time or another:
Men will happily apply for a job they are only 60% qualified for, whilst women tend to wait until they are 100% qualified.
This statistic is bitesized and memorable, which probably explains why it’s quoted so often. It seems to help explain why fewer women reach senior positions – we want to believe it because it corresponds to data we already have, which makes it susceptible to confirmation bias. But where does the statistic actually come from, and how good is the data?
Tracking down the source
A little digging revealed a chain of sources and citation, which Curt Rice has written about in the Huffington Post:
- Rice found the stat in Sheryl Sandburg’s book Lean In, about women and the workplace …
- Sandburg apparently found it in an article in The McKinsey Quarterly (which isn’t in the public domain) …
- And the authors of that article attribute it to “internal research at Hewlett-Packard”.
Rice continued to follow the chain, trying to establish what this Hewlett-Packard ‘research’ consisted of. Eventually, an author of the McKinsey Quarterly paper told him:
“This came from interviews with senior executives, which we conducted with many companies, including [Hewlett-Packard]. I do not have access to the internal HP document, nor do I have the actual contact, as this was a series of confidential interviews.”
As Rice points out, it starts to seem most likely that the 100%/60% statistic came from an off-the-cuff generalisation by one of the senior executives at HP.
No study, no data set, no science.
Looking for better evidence
Before we write off the statistic completely, we can look at Tara Mohr’s study for the Harvard Business Review, which aims to add a bit of nuance to this topic. Mohr surveyed more than a thousand people about the reasons they might decide not to apply for jobs they weren’t fully qualified for. She splits the results into men and women and compares the distribution of their answers in the graphic below.
To be sure, this study doesn’t seem exactly rigorous either – Mohr doesn’t give much detail about how she selected her interviewees (“predominantly American professionals”), and some of the possible answers seem to overlap, or are ambiguously worded.
But Mohr does at least recognise that deciding not to apply for a job is a more complex process than just asking yourself, ‘Am I qualified enough?’ Mohr’s results suggest that women might be more worried about ‘failure’, and that they place more importance on following recruiters’ guidelines. And certainly, in a workplace culture where ‘following the rules’ might be seen as weak or unimaginative (or ‘feminine’), it’s easy to see how a more careful approach to job applications might disadvantage you, even if you think you’re being courteous to the recruiter.
How we tackle this at UEA
When we analysed promotions data in BIO, we noticed that over a recent five-year period, female faculty members applying for promotion had a higher success rate (75%) than male faculty (52%). Although this is positive in one sense, it could also suggest that women are waiting longer than they need to – and longer than men – before applying.
We’ve started to address this in several ways. The first is to make BIO promotions benchmarking data available across the school. We’ve based these data on anonymised successful applications in BIO from the last five years, and included data such as research income, research publishing benchmarks, and teaching data using our workload survey. This information is circulated to all staff when we invite promotions applications, and aims to give everyone additional information, relevant to our School, about readiness for promotion.
We’ve also worked with our Human Resources team to try to ensure that promotions are being discussed regularly during faculty appraisals. This gives each faculty member a chance to raise questions and for the Head of School and other appraisers to encourage faculty to apply as soon as they meet the criteria.
With such a small sample size, data from any individual year might not tell us much, but we’ll be plotting the trends over the next few years and hope to see the impact.