Pay Equity Study

Revised 2021-2022


In 2015, a joint Administration-Academic Senate Committee redesigned our annual campus pay equity study of ladder rank faculty salaries.

The analyses presented in this report focus on the regression models and rate of progression through the ranks, consistent with our campus practice 2015-present. Data are examined at the whole campus level, and for 14 Schools/Units. School of Medicine (SOM) faculty continue to be excluded from this study due to the differences in compensation associated with participation in the COMP plan. SOM faculty are analyzed in a separate study examining pay between Basic Sciences and Clinical areas. Since 2020, Professors of Teaching are included in the analyses with faculty in the Professor series. This occurred with the transition of Lecturers with Security of Employment to Professors of Teaching titles and placement on the same rank/step system employed for the Professor series faculty. For analytical purposes, Professors and Professors of Teaching are treated as a single group.

Analysis of salary data from October 2021 indicated no evidence of systemic disparity in pay associated with gender and/or ethnicity at the campus level when experience, discipline, and rank are included in the model. However, there is further work to do to understand the issues around the 1) low percentage of women and minority faculty at the higher ranks and steps across campus, and 2) differences in the rate of progression through the ranks and salary disparities by gender/ethnicity in some units.

Methodology: Multiple Linear Regression Model

A series of regressions were used to examine potential correlations between gender/ethnicity variables and salary. This approach provided a broad view of faculty employment and pay structure by demographic variables and by experience, discipline, and rank.

  • Demographic factors were entered in the equation as dichotomous variables for Women, Asian, and Underrepresented Minorities (URM). In cases where gender or ethnicity were non-binary, unknown, or declined to state, a missing value was used. This would exclude the faculty member from models that used demographic variables.
  • Experience variables include Years Since Degree, Years of Service, and Decade of Hire. Years Since Degree is the number of years passed from the year the highest degree was earned to the present. Years of Service is the number of years passed since the individual became a Ladder Rank faculty member at UCI. Decade of Hire consists of four binary categorical variables to account for the decade the individual became senate faculty:  2012 to 2021, 2002 to 2011, 1992 to 2001, or prior to 1992.
  • Discipline variables include faculty member school and market salary ratio. Indicator variables were used for each faculty member’s school. The market salary ratio is derived using Association of American Universities Data Exchange (AAUDE) faculty salary data for UCI’s peer institutions connected to each faculty member by Classification of Instructional Program (CIP) code and rank.
  • Rank includes Current Rank and Step, Initial Rank and Step at time of hire, and Progress Rate.

Progress Rate measures number of years the faculty member is ahead or behind normal progression through the ranks. Normative time to achieve each rank is determined by computing the number of years it would take to move from the initial rank to the current rank and step, if the individual is progressing at the campus normative scale. The Progression Matrix shows normative time table and sample calculations. If an individual advanced to the next rank/step in the normative time, then rate of progression is 0. If they took longer than normative time, rate of progression is expressed as a negative number (years). If they took less than normative time then rate of progression is expressed as a positive number (years).

In order to evaluate whether biases exist within progression through the ranks, several box and scatter plots by gender, ethnicity, rank, and school were generated to visualize and investigate the data. Progression rate differences by demographic groups were also tested with t-tests.  Finally, a series of regression models were run to quantify progression rate differences that may exist by gender or ethnicity.

There is a possibility that one or more of the explanatory factors in the salary regression models are correlated; we therefore evaluated the effect of multicollinearity in our models. There was evidence of multicollinearity, therefore, data are presented with and without removal of variables with variance inflation factors (VIF) ≥ 10. Variables were removed in stepwise manner beginning with the variable with the highest VIF. After a one year break in reporting on the faculty equity study related to the pandemic, models for 2021 were re-created using the above methods and adjustments for multicollinearity. In the interest of consistency over time, except in rare circumstances of high levels of collinearity (VIF > 20), variables retained in the final model corrected for collinearity are the same as the previous year.


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