Commonly used factors in Pay Equity Analysis
When setting up your data, or after reviewing the compensation model, certain factors (or variables) may need to be incorporated into your data for iterative refinement towards achieving the optimal compensation model for your organization. In this article, we set out the most common factors, followed by optional other and finally less commonly used factors. The relevant factors for your organization will depend strongly on your pay philosophy and pay structure. Selecting factors to include in your dataset and compensation model is by no means a one-size-fits-all exercise.
Objective factors
Commonly used objective factors are:
Job family
Job level
Grades
Seniority or experience
Divisions
Company/entity
Location
Optional variables
Other optional variables often included are:
Educational level or qualification
Essential skills required for the job
Number of employees under management
Criticality in the job
Employee category (such as Sales/Non sales, Union rates/Non unionized)
Full-time/part-time status (FTE scale 0-1)
Career paths in the company (professionals vs. managers)
Pay scale area or pay scale groups (used as a predictor for pay in unionized populations)
Other factors
Other factors that aren't common, but sometimes are used to explain variability in pay are:
Performance
Secondment (the temporary transfer of an employee to another job role, grade, or employment)
Legacy (for example, former managers moved to professional roles without lowering the pay)
Work schedules - fixed or flexible
Measures of time on the job (such as total hours worked)
Tariff/Non tariff
Collective bargaining agreements/Non CBA
CEO or executive/Non CEO or executive.