QUESTIONS FOR Iris Bohnet
A leading behavioural economist talks about removing workplace biases with ‘behavioural design’.
There is some disagreement about the ‘business case’ for gender equality. What is your take on it?
The disagreement is justified. The focus to date has largely been on the diversity of corporate boards and senior management teams, and the problem is, we don’t have the data required to make solid conclusions. Even when we find a correlation between gender diversity on a board and a company’s performance, we have no way of proving that there is a causal relationship going on.
Recently, a meta-analysis came out, summarizing abou t 120 studies, and it did find a small positive correlation between gender diversity and overall firm performance. But again, this was a correlation, not causation. If we want to establish causality, we will have to create teams randomly and measure whether the more diverse teams outperform the homogeneous teams. Some of the best work in this area has been done in the realm of ‘collective intelligence’ (i.e. the intelligence of groups). This research has found a strong causal relationship between gender diversity and team performance across many different tasks.
As a result, I believe we have enough evidence at the micro level that a business case exists. However, I’d love to see us move this discussion beyond a numbers game, and start to think more about fostering inclusive behaviour.
How do you define ‘behavioural design’ ?
The research shows that we can’t help but put people into categories, and behavioural design builds upon this element of how our minds work. Basically, it uses behavioural insights to de-bias organizational practices and procedures, rather than focusing on changing mindsets. Within an individual mind, biases tend to occur automatically and unconsciously, and it’s really hard to change that. It’s much easier to take steps to de-bias an organization.
Do diversity training programs work?
We don’t really know, because most organizations don’t measure the results — and the few that do have generally found that they don’t work. We have some correlational data looking at whether or not a company has a diversity training program and the actual diversity of its workforce, and in short, that correlation does not exist. So the picture is not optimistic.
A few companies are trying innovative approaches — from implicit bias training to programs aimed at specific inequalities. Carnegie Mellon’s Linda Babcock and George Loewenstein have researched the effectiveness of various de-biasing techniques. One intervention they studied is ‘perspective taking’, which simply means trying to walk in your counterpart’s shoes, take their perspective and understand where they are coming from. For example, ‘walking in an elderly person’s shoes’ by writing an essay from their perspective was shown to reduce stereotypes about the elderly.
Babcock and Loewenstein also experimented with a ‘consider the opposite’ strategy, which involves being your own devil’s advocate and questioning your assumptions — actually coming up with arguments for why your thinking might be wrong. This has been shown to work — but it requires a lot of maturity and self-awareness to be able to question yourself. It’s easier if someone else does the ‘heavy lifting’ for you.
Given all the evidence, I would urge companies to focus their training programs on capacity building and adopt the ‘unfreeze-change-refreeze’ framework — a method borrowed from my Harvard colleague, Max Bazerman. Successful ‘unfreezing’ happens when people start to question their current strategies and become curious about alternatives. Once ‘unfrozen’, you spend some time on what your organization is currently doing, and what could change. Finally, you think of ways to ‘refreeze’ the new insights gained and the new behaviours learned. In the end, the pathway to behavioural change may not be a change in individual beliefs, but instead a change in socially-shared definitions of ‘appropriate behaviour’.
One of the more recent applications of Big Data in the workplace is ‘people analytics’. Please describe how it works.
This basically entails bringing the rigour of your finance or marketing department to HR, arguing that data can help us better predict, for example, the future performance of a particular job candidate than the best interview ever could. It involves moving away from intuition and building on data.
The question is, What kind of data? Organizations can use all sorts of data points, but one powerful example is ‘looking backwards’: You can use data and machine learning to basically learn from the past. For example, you could take a close look at the data points for ‘individuals who have been highly successful’ in your organization: What are their shared characteristics? You might look at which universities they went to, and find that it’s a good thing not to come from an Ivy League school — or maybe that it’s better to have an Engineering background than a Math background.
I’d love to see us move this discussion beyond a numbers game, and start to think more about fostering inclusive behaviour.