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Regression analysis - Employment Inequity

Using individual-level salary data, researchers and committees can also investigate the degree to which determinants of salary, like rank, years of experience, or other “tainted” variables are impacted by gender.

In its 2010 study, the University of British Columbia undertook a Linear Probability Model/Probit Regression to determine whether gender impacted the probability of an academic being promoted to full professor rank. The variable for rank was regressed onto variables denoting experience, department, and Canada Research Chair and Distinguished University Professor status. Based on this analysis, gender was found to be a statistically significant determinant of being promoted.  

Similarly, Simon Fraser University’s 2015 study used a Cox Proportional Hazard model to “identify whether or not gender is correlated with the time spent as an Associate before becoming a Full” by estimating the odds of promotion.22 The analysis did not find evidence that gender impacted the odds of promotion, but it did find that “[…] faculty who take medical leaves face greatly reduced odds of promotion to Associate and Full Professor. Further, Assistant Professors who take parental leaves face much lower odds of promotion to Associate Professor. These effects are similar for men and women, but women are much more intensive users of both leave types than are men.”23

22 Kessler A. and Pendakur K., Gender Disparity in Faculty Salaries at Simon Fraser University. Department of Economics, Simon Fraser University. 2015: salary_equity_stu, dy.pdf (

23 Ibid.