Only five years have passed since gender pay gap reporting was first made mandatory in the UK and a lot has gone on since 2017. Before the pandemic, we were only just starting to scratch the surface in terms of drawing patterns and tracing trends, and furlough blew a lot of that data out of the window by opening up short-term pay gaps which are only now starting to recede back. The data and data-gathering in this whole area is still very immature and could certainly benefit from more introspection, checks and balances in several key areas.
Addressing anomalies in report data
There are several areas where ambiguity can currently creep in because of the way pay data is recorded.
- Data is currently taken on pay after and not before salary sacrifice, even though SS is an individual choice. This means a man and a woman with identical jobs and salaries could appear unequally paid if one puts more into their pension and the other into childcare vouchers.
- Too many exclusions can mar the data. Company partners, for example, are not paid through a ‘normal’ salary. They often receive a different amount each month and for that reason are often excluded from reporting. They are often men and among the highest paid, so excluding them from the calculations can cloud the picture.
- While base pay is annualised to bring part-time and full-time workers onto a level playing field, the data around bonuses is misleading. With more women working part-time roles than men, their actual bonuses across the board appear smaller in the reporting data, but the lower bonus is actually just a reflection of their part-time hours.
- Data currently compares larger bonus-type payments like-for-like with smaller recognition schemes prevalent lower down a company’s food chain. Again, on paper, this creates the illusion of a massive gap, but the numbers reflect gender distribution rather than unfair pay.
- The big new elephant in the room is the news this week that the UK government - as of 3 October 2022 - has changed the reporting threshold from 250 employees to 500 employees, ultimately removing the need for 40,000 businesses to report gender pay gaps. We would urge them to continue doing so.
A better understanding of the distribution and what it means to the data
To understand your pay gap and know what's driving it, having a rock-solid grasp of your employee distribution – gender, ethnicity, disability, age across all quartiles – is essential. Calculating headline figures is easy but the devil is in the detail.
Having a greater distribution of men at the top and lower-paid women at the bottom tends to be the root cause of most pay gaps we see, and yet it doesn't mean that pay is unfair. The three lower-paid quartiles in an organisation might be made up of 75% of female employees because they are predominantly part-time admin roles that are better suited to a mother’s work-life balance. Those women might be being paid the same rate as the men at the same level, but the company’s headline figures might suggest a gender pay gap. The system needs to better understand and measure these gaps in distribution. If there is distribution disparity, and if that is an issue, then that is a separate concern for a company to deal with through a combination of time, succession planning, recruitment and talent pool management.
Ultimately, you will always still want to recruit the best person for the job, so you're not going to turn down the best candidate based on gender, and a better understanding of distribution will help companies avoid bad decisions.
Increasing the scope of reporting
In gender reporting, ‘male’ and ‘female’ are currently the only two metrics reported, and it remains to be seen whether a greater degree of gender diversity is brought in over time. In terms of ethnicity reporting, the data isn't yet deep enough to enable meaningful analysis. There has been talk of this being made mandatory – and movements like Business in the Community and the Race at Work Charter are pushing for change - but it is complicated and sensitive. The ONS has 17 different ethnic categories for people to choose from, which shows how complex it would be to harvest data and then analyse it. Without 100% accurate data, results could be ambiguous.
In terms of disability pay gap reporting, we are starting to see this too but there are challenges to overcome in terms of how a person identifies. Some disabilities are visible, some are not. Some people might hide a disability, while others consider themselves disabled without any obvious signs. Small data samples for ‘disabled’ individuals also means it is currently difficult to analyse at a granular level.
Establishing ground zero for policy and process
This whole area of reporting is so young that we are even seeing huge disparity in the way each country manages it. In Australia and Italy, for example, companies don't calculate their own gender pay gap, the government does, which has pros and cons. In the UK, since the government’s latest announcement, 40,000 fewer organisations are now required to publish reports, while in other countries that threshold was already much lower. Only time will tell who is right.
Overall, it is right to constantly assess the transparency, effectiveness and inclusivity of our policies and processes, notably around promotion and uplifts in salary but also starting before that with recruitment: how we advertise, interview, make an offer, manage salary negotiations (given men often consider themselves in a stronger negotiating position that women do). Does the framework we’re working within ensure fair practice for both genders? When it does, many of the issues we’re seeing with data and pay gaps will heal themselves organically, over time.
Meaningful analysis
Lots of consultancies are offering gender pay gap reports but most are basic calculators leading to basic reports. The time-consuming piece, where Innecto comes in, is the qualitative analysis across quartiles and factoring in distribution, function, level and market forces. Who is included? Who is excluded? What is the full-time/part-time impact on the annual bonus pay gap? Have long-term sickness and maternity cases been factored in? Have non-executive directors and partners been excluded? Only if the data going in is accurate, will the analysis coming out be of any real value.
