Facebook Pixel

Pay equity analysis is one vital way of embedding equity with data. Not only is it the law in an increasing number of places, but it’s the right thing to do! There are numerous ways to conduct a pay equity analysis, some much better than others. In this series, we’re walking you through the intersectional gender and racial pay equity audit we recommend. In Part 1, we talked about what materials you’ll need to get ready and in Part 2 we discussed how to create job categories and visualize your compensation philosophy. 


In Part I and Part II we collected our data, matched it to our compensation philosophy and assigned job categories to each employee. This gives us a chart that looks like this:

In order to look for pay gaps, we need another axis: pay. Here we look at the differences in Pay in relation to differences in Job Value (which is reflected in Job Category).

Each white circle represents an employee and in our example they can be found in one of four job categories: Sales Staff, Sales Managers, Department Heads, and Executives. It’s important to note that there is variation in Value within Job Categories, this is typical and it might reflect something like seniority, which is in line with our compensation philosophy

The variation in Pay between employees is worth a look, in fact it’s the entire goal behind a Pay Equity Analysis.

Employees doing the same work but receiving different pay signify an equity problem if those gaps reflect differences in race, gender, or whatever protected classes you are focussing on in your Pay Equity Analysis. The only way we can see if there are pay equity gaps (not just pay gaps) is to use some of the data we collected in Part I.

Now we can see which employees identify as men and which as women. Because we are concerned that the women might be being paid systematically less than the men (intentionally or accidentally), we’re going to average the pay of the men within their job categories to get our Pay Equity Line.

Here, we circle all of the male identifying employees (squares) and draw a dotted line that represents their average pay. Calculating this average means that we don’t have to bring every employee up to the pay of the highest paid man in that Job Category – this is how most of the pay equity laws work internationally. Legally, female employees don’t all have to be paid equal to the highest paid man in their job category, they just can’t be below the average of all men. The thinking is that if the average is way below the highest paid man, it also means that there are other men way below that average and this pay gap (though possibly concerning) isn’t based on gender (example: Job category #3). 


The continuous line across the Job Categories still allows for differences in Pay based on your compensation philosophy and the legitimate way you assign value to various employees. 


How do we adjust the pay of employees to correct these gaps? And what if we want to look at more than just one type of pay gap (like race + gender + age + etc.)? That’s all coming in Part IV: Adjusting Pay & Intersectionality, but here’s a sneak peak: