Data interrogation, not headlines, will close the gender pay gap

23 May 2019 By Ken Charman

Ken Charman explains why we need to look beyond the top-line numbers to truly get to the bottom of the pay gap

The gender pay gap in the UK is a big problem. At least, that’s what the headlines tell us.

However it shouldn’t be forgotten that statutory reporting requirements are there to demonstrate whether pay gaps exist and in which pay range any gap is widest. They are not there to explain how or why such pay gaps have come about. 

Companies that really want to understand what’s going on need to interrogate the unique data that they hold within their organisation. 

As Goodhart’s Law states: “When a measure becomes a target, it ceases to be a good measure.” In this situation, people’s behaviour changes to work specifically towards a set target and the outcome of this change may be damaging to an organisation or contrary to its objectives. 

For this reason, the gender pay gap is not always a good measure of gender discrimination as it may improve for reasons that have nothing to do with improving the lot of women. In fact, I would argue that the widening pay gap within some companies could come about as a result of a corporate strategy that should be encouraged rather than criticised. 

For example, if a company decides to employ a large number of female apprentices one year in order to, over the next 10 years, rectify an existing gender split within its workforce, this could result in a widening gender pay gap year-on-year. As a result, the year-on-year figures for the gender pay gap look bad, even though they have come about as part of a 10-year strategy that should be applauded. 

This example demonstrates why headline figures simply aren’t good enough. Unfortunately though, most companies are still missing the granular data they need – as well as the ability to interrogate it – to identify what really matters to them.

If companies are relying on crude, high-level generalisations in the news headlines to make decisions about bias within their corporate structure, it is more than likely because informative and unique data about reward is spread across many disconnected systems. 

This is a major problem, especially because even those companies that do have rich data at their fingertips fail to employ the data-savvy, intellectually curious individuals needed to make the most of it.

At present, companies have to submit nine metrics to the government website on gender pay reporting. If companies had this information available in real time, they could review it regularly, hold people to account for gaps, assess where improvements had been made, correct anomalies and improve working practices going forward.

Whatever you think about the usefulness of gender pay gap headlines, the issue does raise important questions about talent management. 

If talent is normally distributed across gender, you would expect reward to reflect the same distribution. If it does not, your organisation is undervaluing some talent and overvaluing others. That’s a poor use of human capital. 

This logic shouldn’t be confined to gender either. It also applies to many other ways of classifying humans into groups, such as race, religion, class or even height, weight and age. 

As humans, we are all wired to act automatically on biases without giving them the slow consideration they need. But with data, we can fix our errors, shaking ourselves out of our assumptions and making better business decisions based on considered interpretation of the facts.

Ken Charman is CEO of uFlexReward

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