Gender pay gap reporting must be ‘robust’ before moving onto ethnicity

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During a webinar yesterday, Anthony Horrigan chief executive at Spktral discussed why ethnicity pay gap reporting is more complicated than gender and why the insurance industry still has a long way to go

Gender pay gap reporting must be ”robust” before ethnicity pay gap reporting is put into motion, because it is so much more complicated, according to Anthony Horrigan, chief executive at pay gap specialists and independent consultants Spktral.

Speaking during a webinar: IPS Group: Ethnicity Pay Gap Reporting yesterday, Horrigan gave the example of the financial services sector which, he said closely mirrors the insurance sector there is still the biggest gender pay gap and “too many men” – therefore jeopardising its talent pool.

So far, Zurich is the only insurer to officially publish its ethnicity pay gap data.

But Horrigan said that there is more work to be done to get gender pay gap reporting up to compliance standards as well as making it a workable format, but there are lots of easy fixes.

“With the Black Lives Matter campaign and then the petition for the enactment of the ethnicity pay gap reporting legislation, of which the petition got over 130,000 signatures a lot of people are saying let’s begin and make it the same as gender, he said.

“But fundamentally they are two different things.”

It follows the Black Lives Matter reawakening the debate around the low representation of black, Asian and minority ethnics insurance and the African Caribbean Insurance Network (ACIN) calling on insurers to “do more” with ethnicity pay gap reporting.

The government ran a consultation from October 2018 to January 2019 on the introduction of mandatory ethnicity pay reporting in the UK which closed on 5 September, the government said it analysing responses and will respond by the end of the year.

GDPR worries

When speaking to people about ethnicity pay gap reporting, Horrigan revealed he found some real worries around GDPR.

For example Horrigan has had questions around whether employers can collect or display ethnicity data, some employees have questioned whether the data is going to be used to discriminate against them, who will have access to the data and what companies are going to do this information.

He suggested clear and transparent outcomes from management so that staff are aware of what the data will be used for.

Statistical challenges

“I think that the biggest challenges between gender and ethnicity are the statistical challenges, this needs to be looked at carefully instead of just saying lets replace the word gender with ethnicity and crack on – and the first one is sample size,” he said.

He warned on the dangers of relying on statistics such as these, giving the example of a standard insurance organisation with a rough gender split of 55% men and 45% women however looking at this same sample through the lens of ethnicity Horrigan said that it changes.

The same example through the lens of ethnicity translates as a huge proportion of white men and women and a small portion of ethnic minorities.

“Just relying on those pay gap percentages is really dangerous as statistical variants means it can move slightly. When it comes to ethnicity where one category is big and a small category you can get variations going from 6% up to 33%. If the whole idea is to be transparent and put good information out there, you must be careful around the messaging around these numbers, Horrigan said.

“But there is a solution and that is to use representation so those pay quarters are a much better measure of what’s happening in the ethnicity pay gap. “

Masked pay gaps

However, he pointed to masked pay gaps, this is when raw data is put into buckets, giving an obvious ‘bucket’ being used as black, Asian and minority ethnics under the acronym, BAME.

“That is simplifying four categories under one big bucket, what people seem to forget is that inside these categories there is a huge amount of variance in each. So, you are masking all these individual stories inside the data,” Horrigan said.

But you cannot just rely on white versus BAME, instead he advocates having a broad picture as well as being able to zoom in on the data when necessary, he continued.

He recommends focusing on representation when it comes to gender and ethnicity – by this he means focusing on all male and female staff and where they sit in terms of pay ranges; and most importantly where they are going – are they moving up the ladder at the same rate as others or exiting the company after a short time period?

This he added is how organisations in the insurance industry could find a block or gap in their staff representation.