Pay equity analysis

The adjusted pay gap measures the difference in pay by gender, race, and others after accounting for objective factors that drive pay, such as job role, education, and experience. 

PayAnalytics uses regression analysis, or log linear regression, to calculate pay gaps.

PayAnalytics helps you quickly identify outliers—employees whose pay is much higher or much lower than it should be. Establish thresholds for outliers and understand the areas of your organization with the greatest concentration.

PayAnalytics’ subgroup analysis makes it easy to run the same analysis on different subsets of the data (for example, countries or markets). Crystal-clear visualizations show you how these groups compare to each other.

After you run an analysis, PayAnalytics provides a detailed overview of the objective variables (education, experience, etc.) that determine pay and their impact. When you look at an individual employee, PayAnalytics helps you make a meaningful comparison by showing you a reference group of similar employees.

PayAnalytics calculates the adjusted pay gap using log-linear regression, or regression analysis. In the solution, measure pay gaps for up to two demographic variables at a time—separately or intersectionally.
Regression analysis, rather than averaging out the pay of all employees, accounts for all the objective factors (like education) that determine pay. Alongside demographic data, our approach shows you whether bias is causing a pay difference—and how big that difference is.