When it comes to Equity, people need to put their money where their mouths are. A key way that businesses can make sure they aren’t unfairly paying some people more than others based on their age, sex, or race is called a ‘pay equity analysis’. 

Undergoing a pay equity analysis can help you correct inequitable (and often illegal) imbalances in employee pay. Its main focus is to separate the good reasons you might be paying an employee more (skill, education, experience, performance, seniority, etc.) from the bad ones: age discrimination, sex discrimination, race discrimination and different-ability discrimination. It doesn’t matter if the discrimination is overt and intentional, or, more commonly, entrenched and unconscious. When a pay equity analysis is well done, it will identify any pay gaps that shouldn’t be there. 

In our 4 part series, We All Count is going to walk you through just how your company might go about it. The process we share is applicable to a gender pay equity analysis, a race pay equity analysis, and an intersectional pay equity analysis.

Part One: Gather Your Materials

To do a good pay equity analysis you need three datasets. 

Dataset #1: Individual wage/salary

Administrative and HR data that records each team member’s hourly wage or salary, and bonuses. This data will give us a clear picture of what each employee makes. You almost certainly have this on a spreadsheet somewhere already.  

Dataset #2: Job titles/positions

Administrative and HR data that records each team member’s job title, department, and, if possible, recent promotions. If your organization has multiple locations, a record of where they work is good too since different sites can legally have different local pay scales. This is another dataset you can assemble yourself, although it can get tricky if you have bad ‘job title hygiene’ where you have lots of different roles and responsibilities that overlap which also vary in pay. Do your best to tidy up this dataset, but don’t worry, this is an area where your pay equity analysts can help. This data will be used to create the job categories that will get compared. We need to identify ‘equal work’ in order to check for ‘equal pay’. 

Dataset #3: Relevant Social Identities

This is where you should have a company from outside yours collect this data. There are privacy and confidentiality issues and we’ve found that employees are more comfortable and trusting sharing sensitive identity information with people they don’t also work with or for. 

If you are only trying to address gender pay inequality, you could only collect that information, but it’s almost always a good idea to look at all of the relevant legally protected social identities (race, age, ability status, religion, gender orientation) as well as any other equity you care about (immigration status, sexual orientation, indigenous status, etc.). By collecting more than one identity factor, you can do an ‘intersectional’ analysis looking at whole people instead of one piece of them at a time (more on that later). It’s not only more fair, it’s much more efficient to do one good pay equity analysis than many incomplete ones over time.  

Once you’re armed with this information, you can move on to the next phase of a pay equity analysis!

If you  are looking for some help getting a gender or race pay equity analysis done, you can contact us here.