PAGs are Pay Analysis Groups. Essentially, these are groups of employees who share similar characteristics like Job Level, Department or Job Role. You can analyse any group of employees using the PAG functionality. 

Under “equal pay for like work”, employees who do work that are mostly similar – such as by doing similar tasks or using the same skills as other employees – should be paid similarly. Therefore, when looking at specific PAGs, you can see whether there are pay gaps for groups of similar work. If there are unadjusted pay gaps, you can then look at employees’ Pay Determining Characteristics to see if this explains the initial unadjusted pay gap. Pay Determining Characteristics are factors that impact how much someone can be paid, namely education, performance, experience, tenure, and more.  

It is important to provide equal pay in order to comply with the law by identifying, explaining and eliminating unjustifiable pay gaps, and to contribute to a fairer society in which everyone has equal opportunities. 

By analysing PAGs, you are able to see the mean unadjusted pay gap and mean adjusted pay gap for a group or cohort of employees. 

The unadjusted pay gap is the raw difference between the average pay of your reference group and the average pay of your comparator group – this gives an overall overview of your pay gap, although outliers can impact the figure. The adjusted pay gap is the pay gap after adjusting for factors that influence the pay gap – the Pay Determining Characteristics. The adjusted pay gap thus becomes more accurate when you map as many Pay Determining Characteristics as possible in your data. An acceptable adjusted pay gap would be below <|+/-5|%, but ideally you would have an adjusted pay gap of 0%. 

This classification splits your PAGs into groups with pay gaps more than 5%, between 2-5%, and under 2%. These intervals are based on benchmarking in the EU and USA, but you may have different targets for your specific company. You are able to change this classification in your settings. For example, you are able to add another category, edit the names of categories, or change the intervals in pre-existing categories. 

The calculation for an unadjusted pay gap can only be done for employee groups with >10 people. This is to meet the conditions such that a group can be split into four quartiles (as outliers are based on the interquartile range) and that the group must contain both a reference group and comparator group. For example, a Job Level with only men will not have an unadjusted pay gap because there are no women in the Job Level to compare to. 

If the unadjusted pay gap cannot be calculated, the adjusted pay gap also cannot be calculated as the adjusted pay gap is calculated from the unadjusted pay gap. 

The calculation for an adjusted pay gap uses multivariate regression. This method requires at least 25 (+/- 10) employees to work. Additionally, datapoints – such as data on Pay Determining Characteristics – should ideally be distributed across the whole group. If a PAG does not fit the requirements for multivariate regression, you can either look at the regression results for your entire workforce, or look into the individual PAG manually. 

PAG results can be useful for equal pay audits. It helps you identify areas in your workforce where there may be pay parity issues. It also allows you to explain unadjusted pay gaps within specific groups, by looking at the adjusted pay gap and further analysing employees’ Pay Determining Characteristics. If your adjusted pay gap is above 5%, it is then important to either provide more data on the employees’ Pay Determining Characteristics, or take further action in closing your pay gaps. A deeper look into Pay Determining Characteristics can help you understand how employee pay is decided within your organisation, and if pay has been decided fairly. 

We offer consultancy services to help you plan scenarios that can close your pay gap and meet diversity and inclusion targets. Additionally, we have a set of resources on closing the unadjusted and adjusted pay gaps.