Equal Pay Analysis
Discover how to conduct an Equal Pay Review on Gapsquare and have your equal pay FAQs answered below.
Equal Pay Review FAQs
What is Equal Pay Review?
Equal Pay refers to the fact that employers cannot pay employees doing substantially similar work differently based on protected characteristics. If there are pay differences among employees who do substantially similar work, this must be justifiable using job-related factors such as:
- A system that measures production, and/or
- A bona fide factor other than gender, ethnicity, race i.e., a job-related factor
Gapsquare™ allows you to view a list of employees to investigate for equal pay and compare them with like-for-like employees who perform substantially similar work.
What types of Equal Pay Review are there?
An Equal Pay Review can be conducted on an organizational level or a group level.
To conduct it at an organizational level, navigate to the Equal Pay Review option in the menu. This gives an overview of your company pay gap, the distribution of salaries in your organization, and a master-list of employees with potential pay disparities.
To conduct it at a group level, you should start off with the Pay Analysis Groups (PAGs) feature, then click “Explore” on a group you would like to analyze. The PAGs feature can help you prioritize which groups to analyze first, as the PAGs are sorted based on how wide pay gaps are within the group.
How do I conduct the Equal Pay Review?
Find out how to use Gapsquare™ for an Equal Pay Review based on our guidance here.
What does the Equal Pay Review page show me?
The page gives you an overview of your organization or group in the Summary box, a list of factors that explain your pay gap, a graph showing the distribution of employees’ salaries, and a list of employees to investigate for equal pay issues.
How do I use the Summary on the page?
The Summary box is useful to get an overarching picture of equal pay in your organization or group. It gives you an understanding of how much different employee groups – such as men and women – get paid on average. Additionally, tenure data is shown to give an indication of experience for employees in the group.
How do I use the Actual Salary Vs Predicted Salary graph?
This graph is useful to visualize where employee salaries fall. Employees who appear under the predicted salary will be shown below the line, and those who are most severely underpaid are flagged clearly as red or amber triangles in the graph. You will also be able to see if different types of employee characteristics tend to “clump” together in terms of salaries – for example, you may be able to spot trends where women tend to fall under the predicted salary compared to men.
How do I use the Contribution to Pay Gap graph?
Using the graph of Contribution to Your Pay Gap, you can see factors that impact your pay gap the most – these factors indicate whether different employee groups “score” differently in certain pay determining characteristics. For example, if job level is contributing largely to the gender pay gap, it could mean that men are generally in higher-seniority roles than women.
How do I use the table with Employees to Investigate?
This is a list of employees who are flagged as outliers because they fall significantly below their predicted pay. The table sorts these employees by the most severe to the least severe. You can therefore use this table to prioritize the most severely underpaid employees first. It allows you to view all the pay-determining characteristics mapped in your data, which you can either use to justify their pay or decide to make salary adjustments.
By clicking “Compare” on the table, you can compare an employee with like-for-like employees. This allows you to spot whether an employee counts as underpaid if compared to others that do substantially similar work.
How are employees to investigate flagged?
These are employees that fall under their predicted pay by more than 2 Standardized Differences below the predicted line. Therefore, not all employees that earn less than their predicted pay will be flagged. The severity ratings are described below:
|Red||Standardized difference < -3|
|Amber||-3 ≤ Standardized difference < -2|
|Green||-2 ≤ Standardized difference|
What does Suggested Adjustments show me?
The Gapsquare™ tool calculates the minimum salary adjustment required to bring an employee into a lower severity rating. For red employees, the suggested adjustment is in a range; you can bring an employee up to an amber rating or to a green rating. For amber employees, the suggested adjustment brings the employee to green.
Adjustments can be viewed as either a percentage increase or in dollars.
What are Chosen Adjustments?
You may decide to remediate an employee either more than or less than the suggest adjustment value. To keep a record of these decisions, you can click the Pencil (edit) icon in the chosen adjustment column, an input a chosen value that you have decided on.
What does the Eye icon next to the table do?
The Eye icon allows you to view more pay determining characteristics in the table of employees to investigate. By default, only up to 5 pay determining characteristics are shown when you first navigate to the feature. This allows for better readability. However, you may want to view a full list of columns that you mapped in your data. Click on the Eye icon to toggle the visibility of these columns.
What does the Compare button do?
You can make comparisons between like-for-like employees by clicking “Compare”. The Gapsquare™ app matches an employee with others that have similar pay-determining characteristics. For example, you can match employees with the same location, job level, tenure, and department.
The app automatically matches based on 3 chosen pay-determining characteristics. For example, it could choose tenure, performance, and job level to match. If you would like to change the variables being matched, you can click the Eye icon on the Compare table. The Eye icon allows you to choose columns to match employees by.
How do I know if my results are accurate?
The statistical significance flag on top of the Equal Pay Review page helps quantify whether a result is likely due to chance or to some factor of interest. A result is considered statistically significant when it has a P-value less or equal to 0.05. A smaller P-value indicates that the result is more reliable.
Can I save my results?
By clicking on the triple dots on the top right of the table, you can save it as a CSV file. Additionally, all the graphs on the page can be saved as PNG files.