This blog is the fourth in a series exploring different variables that could be part of pay equity analysis. We have explored time in job, performance, and geography. Here we are looking at length of service.
Use Regression for Pay Analysis
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. If gender explains pay differences, then you have discriminatory pay practices.
It is helpful to express how you expect variables to predict pay as a hypothesis.
Length of Service Could Be A Variable in the Analysis
I hypothesize that if you compare the pay of two employees with the same role, the one who has been with the company longer will have higher pay, all other things being equal.
While this variable might seem straightforward, there is an important consideration. If you have a rehire (someone who left and is returning), does the length of service include their entire time with the company, or just the time since their most recent rehire? This decision should apply to all employees, not select ones.
Stay tuned for our next blog in this series on education.