This blog is the second in a series exploring different variables that should be part of any pay equity analysis. In the first of this series we explored “time in job”. Here we are looking at performance.
These individual variables are important because the technique for pay equity analysis is regression. Regression works by systematically plotting the variables of interest against the outcome variable. For pay equity analyses, the outcome variable is always pay. The variable you do not want to explain pay differences is gender. In this case, if gender explains pay differences, then you have discriminatory pay practices.
It is good to express how you expect variables to predict pay as a hypothesis.
I hypothesize that if you compare the pay of two employees – one who has been a consistent high performer and one that has been a consistent good performer – the employee that has been a consistent higher performer will have higher pay. For any organization that believes that they pay for performance, they should already be analyzing their pay practices to ensure that this hypothesis is true.
There are other variables that can explain pay differences. Stay tuned for the next variable in a future blog.
The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.