| Abstract: | After analyzing the salary structures of over 20 organizations in the process of implementing the requirements of the Pay Equity Act of the Province of Ontario, I found that one apparently invariant characteristic was the uniqueness of each salary structure — even among organizations with similar organizational structures. In almost every analysis, diagnostics suggested features of the gender gap that were not captured in the initial regression model. In studying a salary structure for the purposes of pay equity, the statistician is expected to help produce gender-neutral measures of the value of jobs and to estimate the shape and size of the gap between salaries of women and men for work of comparable value. We are expected to provide an “objective and unbiased” component to a very complex process fraught with subjectivity and bias. When we analyze real salary data with modern statistical tools, we generally find more questions than answers, yet we are expected to recommend some form of partial remedy for gender inequity within the legislated framework for pay equity plans. One (unsatisfying) solution to the dilemma would be to leave this type of work to analysts whose techniques don't confront them with the inadequacy of their models. This paper describes some experiences and observations gleaned from the analysis of real pay data for pay equity. It provides some answers, but they are, admittedly, much less compelling than the questions. |
