Gender Pay Gap & Workforce Analytics

The situation

You have calculated the gap. Now you need to explain it.

Meridian Professional Services is a fictional organisation created for this example, but the workforce question behind the report is a common one.

Payroll data can provide the headline gender pay figures. What it does not necessarily tell you is why those figures look the way they do.

A workforce might appear relatively balanced overall while men are more heavily represented in senior grades. One department may have a very different workforce profile from another. Bonus participation may also vary between different groups.

So once the calculation has been produced, the more useful question becomes:

What is actually driving the gap?

The approach

Start with the measure. Then open up the workforce.

I built the report so the headline measures provide the starting point rather than the end of the analysis.

Mean and median hourly pay gaps, bonus gaps and bonus participation sit alongside workforce headcount and representation.

From there, the user can explore how men and women are distributed across pay quartiles, grades, departments, locations and levels of seniority.

That makes it possible to see whether a headline difference is associated with the overall shape of the workforce, concentrated within a particular area, or influenced by how reward is distributed.

The point is not simply to produce more HR metrics. It is to give people somewhere useful to look next.

Context matters

A gap is not a diagnosis.

This is one of the reasons I think workforce reporting needs to go beyond publishing a small set of percentages.

A headline gender pay gap may point towards something that warrants attention, but the figure itself does not explain the cause.

An organisation could have a predominantly female workforce and still have more men in its highest-paid roles. Another could have fairly balanced representation at senior levels but differences elsewhere in its workforce.

Looking at those structures does not automatically provide the answer either. What it does provide is context and a much better basis for asking the next question.

Reporting with context

Commentary that changes with what you are looking at.

The report also uses dynamic commentary to help put the selected figures into context.

If someone moves from the organisation-wide position into a particular department, grade or workforce group, the narrative changes with that selection.

That is useful because the explanation accompanying an overall workforce figure may not be relevant when somebody is investigating one particular part of the organisation.

The commentary is not intended to make the judgement for the user. It highlights what the data is showing and helps direct attention towards the areas worth understanding further.

What this is really about

Turning a reporting requirement into something useful.

This example is not really about producing a gender pay gap dashboard.

It is about taking a set of headline figures and building enough context around them to understand what they are telling you about the organisation.

It also shows how something that might otherwise be rebuilt manually each reporting period can become a repeatable process. New workforce and payroll data can be added to the same model, the calculations can be reproduced consistently and changes can be compared over time.

The calculation tells you where you are. The analysis helps you understand why.