Equitable compensation requires examination of practices and policies such as market differentials, starting salaries and merit increases. These should be addressed as discriminatory, particularly against members of Indigenous and equity-seeking groups.

CAUT Policy Statement on the Human Right to Equitable Compensation

It is important to be aware of the justification for including and excluding variables. Fundamentally, the specification of the model should reflect the pay structure to which faculty are subject. If the analysis is being done within a committee, specification of the model may be discussed and negotiated between committee members.

There are a multitude of complexities in undertaking regression analysis which have spurred debate. Some of these issues are introduced below. When undertaking analysis, it may be useful to consider how these different perspectives in these debates would shape an analysis.  

Variables that may be influenced by gender discrimination are considered “tainted”. For example, variables related to rank may be tainted if the rank of an academic is influenced by his/her gender. Including tainted variables may reduce the magnitude of the gender coefficient since the tainted variable is to some degree also a measure of gender.²⁴ Some may argue that tainted variables should not be included in a regression because they lead to an under-estimation of the impact of gender on pay, camouflaging the total impact of gender on salaries.

However, Dean and Clifton make the point that excluding tainted variables, specifically rank, may lead to the misattribution or remedies. If rank is not included in a salary regression but is a determinant of pay, the gender coefficient resulting for that regression suggests a financial remedy that “essentially amounts to paying a hypothetical average female as if she had been promoted at the same rate as the hypothetical average male.”²⁵

The problem of tainted variables can be seen as the intersection of pay and employment inequity and shows the importance of setting clear objectives prior to undertaking study. If the objective is to measure pay inequity, then tainted variables relevant to the determination of pay should be included (barring any collinearity problems). If the committee is seeking to determine the overall impact of gender on salaries (pay inequity plus employment inequity), it may wish to exclude tainted variables. The decision of whether to include or exclude tainted variables may depend on the function of the study.

---

²⁴ It may also introduce collinearity, compromising tests of statistical significance.