Getting the best
American business students may be presented with the case of “Howard” – a successful entrepreneur. After reading of his achievements they are asked to rate how likeable and effective they consider Howard to be, and if they would like to work with him.
Other students get Heidi’s case – exactly the same, except for gender. Both male and female students rate Howard highly. But when they rate Heidi she gets good ratings for effectiveness, but not so for likeability – and is not seen as all that great a potential work-mate.
Their reactions provide a “teaching moment” – illustrating the power of unconscious bias. In this case most folk are biased against those that are working against role type-casting – in Heidi’s case – a woman who is an entrepreneurial leader.
Economist Izy Sin from consultancy Motu has used Statistics NZ data to link employee pay with their employers. She found that women employees in New Zealand are paid less. But given women are likely to also be different in terms of productivity-related factors such as age and qualifications that does not explain a lot.
Sin’s contribution was to demonstrate that it would be expected that women would earn at 86 percent of men’s rate if earnings were related to productivity. The difference between this rate and the actual 74 percent level they are paid at equates to a 12 percent level of gender discrimination.
A recent book by behavioural economist Iris Bohnet of Harvard looks deeper into the decision-points that contribute to such gender gaps. She reports on differences in performance ratings and pay rates, differences in hiring and promotion decisions, and even differences in the wording of job ads that all contribute to the potential for “Heidi-type” unconscious biases coming into play. For example, in a US brokerage firm women were paid consistently less, not because they performed at a lower level, but because they were allocated lower quality clients.
Bohnet makes a great case for “designing” such biases out of play. For example, job ads that don’t include words such as ‘competitive’, that do emphasise the objectivity of performance
evaluations, and the flexibility of their work practices are likely to attract more women applicants.
Why bother? Why not attract, hire and then promote on the basis of ‘fit’ – isn’t it going to be more comfortable to work with people who not only share your taste and background but also present themselves in the same way? Comfortable maybe, but long-term effectiveness and sustainability – less likely.
A consistent finding demonstrated in the New Zealand context by Sin’s analysis is that businesses in more competitive market positions are more likely to hire the best candidates, based on job-relevant characteristics. If you are a monopoly, you can afford to absorb the costs involved in not getting the best. Is your business coasting comfortably for a fall?
We recently had the opportunity to support an academic institution hire new senior people to drive its transformational initiative.
Part of our approach was to use psychometric assessment. These were not the tests that are sometimes used to give the appearance of objectivity, but which give global ratings that are little better than horoscopes. These were “best in class” – the most accurate of the validated occupational assessments available.
We developed structured interviewing, including customised competency-based interview guides for all of the roles. Then we trained the panel members on how to interview so that they elicited relevant behavioural examples to form fact-based judgements of candidate’s behavioural potential. Just to make the panels accountable we moderated the interview process – asking the members to provide the behavioural evidence to justify their ratings, and to guide decisions around appointments.
Some results of this process: • The identification of articulate interview candidates who did
not have serious analytical ability. • An HR specialist who had the numerical skills (and other
competencies) to move to a GM role. • Interview panels that listened to personal pronouns – particularly “I” vs. “we” when candidates were describing their achievements – demonstrating their ownership of those achievements. • Interview panels who learned to listen for candidates’ description of outputs rather than generalisations and assertions. Some guidelines – based on the evidence of what makes for good decision-making around your people. • Minimise the opportunity for unconscious bias to come into play. Consider anonymising applicant or promotion information reviewed by your decision-makers – so that gender and other demographic factors are out of the picture. • Try to automate decision rules. For example, achieving
certain performance standards means automatic pay rises or bonuses. Managers want to exercise judgement – but the evidence is that having them involved in establishing the decision rules rather than making the individual decisions leads to better, fairer, more motivating decisions. • Collect the data. For example – the proportion of women who apply, who make it to interview, who accept job offers, who make it to three months (past the honeymoon), who make it to subsequent career steps. Unless you know these facts you are in a poor position to judge how you are doing (or, as Yogi Berra said – “if you don’t know where you’re going, you might end up somewhere else”). When you have such data you are in a strong position to start judging whether your initiatives are having the impact you intend them to.