Why do we measure?
When thinking about how to measure the potential for discrimination in the workplace, the same question arises – how do we determine if we are being discriminatory? Pay Gap measurements are one simple tool that organisations use to answer this question.
There are multiple components of compensation for work – annual leave time, expected work hours, potential for growth and career development and benefits packages such as private medical and dental care and car allowances. But, the most fundamental aspect of work compensation is how much someone gets paid, either through wages/salary or bonus. As such, measuring if there is a systematic difference in how much different groups of employees are paid is a basic way of understanding if an organisation may be discriminating at a fundamental level.
The most common form this has taken is the Gender Pay Gap (GPG), in part because the academic evidence indicated that women were being systematically paid less than men in the same jobs. The average GPG in the UK as of April 2021 for full time employees was 7.9%. In the US people often talk about how women earn 77 cents on the dollar compared to men. However, this gets even more complicated when we include race/ethnicity. The ethnicity pay gap (EPG) in the UK varies greatly by region, and has been shown to be as high as 24% in some areas. When you combine race and gender, you see that race exacerbates GPGs – in the US while white women have a pay gap of 18% compared to white men, Black women have a pay gap of 35% and for Latina women the pay gap is 42%.
The challenge with pay gap reporting
Measuring GPG isn’t quite as simple as calculating an average salary for women and men and comparing them to each other – how do we account for part-time workers or hourly workers? How do we account for people on parental or caregiving leave, long term illness or disability, or injury? Do we include those on unpaid leave, but with their usual salary?
Much of the pay discrepancy between genders can be accounted for by seniority level in an organisation and number of hours worked. That is, if we compared men and women who have the same seniority in the same organisation and have the same expected hours, the gender pay gap would likely be much lower. This would then provide answers to two key questions:
- Is there an actual gender bias in pay happening at the organisation, since we’re comparing men and women in the same roles with the same hours?
- Is the actual issue not pay discrimination but hiring or promotion discrimination at upper levels?
By answering these questions, an organisation would be able to target their interventions much more effectively and close their pay gaps much more quickly. The UK government’s regulations do not take these factors into account in their Gender Pay Gap calculation guidance. As a result, the pay gaps that are reported and published are likely skewed and less accurate than they could be and are less likely to lead to effective interventions to close those gaps.
Solving the pay gap challenge
Many organisations who have to calculate their GPGs (and soon, EPGs as well) are forced to have one or multiple employees spend a lot of time preparing the data and doing the appropriate calculations. As a result, they only go as far as the government regulations push them – there often aren’t enough resources to go further even if a company has the will to do so. This means they may be basing interventions on closing pay gaps on skewed data, leading to less sustainable or effective results.
To rectify this, Included is able to use an organisation’s human resources data to calculate more accurate pay gaps. We can follow the UK government’s guidance and calculate the pay gaps for official reporting purposes, but we can also calculate pay gaps that adjust for seniority, role, and hours (if relevant). As a result, we are able to produce a more accurate view of where pay gaps lie and what is driving them – whether it’s actually systematic difference in pay for different groups even at the same level/ role/ hours, or whether other factors like promotion or type of employment (salaried vs. hourly) are playing a bigger role. This means that organisations will have a clearer view of what is actually happening in their organisation.
Moreover, depending on what data is available in an organisation, Included is able to calculate pay gaps along any demographic lines that the organisation collects data on. That is, we can analyse pay data from gender and ethnicity perspectives, but we can also do so by disability, age, veteran status, or any other category as long as the data is captured.
Finally, if an organisation wants to understand their pay gap for a demographic group it does not currently collect data on, Included can partner with the organisation to anonymously and securely collect and hold that data from employees and perform the analysis for the organisation.
In this way, Included’s methodology will allow an organisation to have a much clearer and more accurate picture of their employees’ pay data and any gaps that may exist. This will ensure that interventions that are put into place to close those gaps are targeting the underlying issues driving those results rather than papering over the problem.