II.           Faculty Salary Equity Analysis

A.   Brief Description of Average Faculty Salary Differentials by Gender and Ethnicity

The first three tables reported in this section are intended to provide a very brief indication of the variation in average 1997 yearly salaries across ethnic and gender groupings of Metropolitan State faculty. Explaining as much of this variation in salary as possible, using additional background factors such as academic rank and length of service, is the focus of this report.

1.  Faculty Salary By Gender

Table 1 shows a $624 shortfall in average annual salary for female relative to male faculty. Several factors that account for much of this difference will be discussed below.

2. Faculty Salary By Gender and Rank

One major factor that affects faculty salary differences is Academic Rank. Table 2 reports average annual salaries broken out by Gender and Rank. At the Assistant Professor Females earn on average $4,845 more than Males, but at the Associate Professor and Professor ranks, male salaries are on average $3,116 and $3,906, respectively, above the female averages.

3. Faculty Salary By Gender and Ethnicity

Table 3 reports average salary differences broken out by a combination of Gender and Ethnicity.  Small numbers of protected classes were grouped together.  Comparing the average salary for White males to the other averages reveals that for all other categories earn less on average than the average for White males.

B.   Promotion to Academic Rank

Table 4 shows the estimated odds ratios of promotion from Assistant Professor to Associate Professor and from Associate Professor to Professor obtained using the Multinomial Logistic Regression model.  The odds ratios were calculated after controlling for Highest Degree, Years of Prior Experience, and Length of Service.  Odds ratios greater than 1.0 indicate a correspondingly greater likelihood for individuals in the indicated category in obtaining promotion to the next category. Conversely, odds less than 1.0 indicate less likelihood.

Three minority dummy variables – Females, White females, and All Minorities – were included in predicting odds of promotion to higher rank. Because categorical modeling cannot handle groupings with very low frequency for combinations of attributes (e.g. African American + female + associate professor), all minority groupings had to be combined as separate category.

Table 4 shows the odds of promotion and associated statistical significance levels for three protected classes as compared to White males.  There is no analysis for promotion from Instructor to Assistant Professor since there are no instructors (one instructor was recoded as assistant prof.)  Females and White females have lower odds of promotion from Assistant to Associate than corresponding male categories, although no coefficient is statistically significant.  The All minorities coefficient is greater than one but is not statistically significant. At the Associate to Professor promotion, all protected groups have lower odds than the Male categories, although no coefficient is statistically significant.

In general, at least one of the Highest Degree, Length of Service, and Prior Experience control variables was significant in each equation examined here.

Since none of the promotion odds coefficients for protected classes are statistically significant, there is no statistically significant evidence from this analysis to indicate that the Academic Rank variable is “tainted.”  However, this finding will be re-examined below in the Total Population Model analyses, where the rank variable is omitted to check for bias in salaries for protected classes.

C.   Controlling Salary For Structural Factors: Multiple Regression Analysis

      1. Total Population Salary Analysis – with and without the Academic Rank variable.

Table 5 reports the estimated regression equation and auxiliary statistics for the Total Population Analysis (N=77). In this model, the dependent variable is 1997 Annual Salary, and the predictor variables are all of the structural variables plus a set of dummy variables corresponding to ethnic minority and gender status.  The numbers of individuals within protected classes are generally very small, and so they were collapsed into Under-represented male (N=9) and Under-represented female (N=8) categories.  There are sufficient White females (N=30) for a separate variable. The reference category is thus White males.

The first column of Table 5 shows the labels of each of the variables entered into the regression model. The first term (constant) can be ignored. Each of the other terms in the first column corresponds to either a “structural” variable or one of the ethnicity-gender variables.

The second column in Table 5 shows the “unstandardized coefficient” B associated with each variable, which indicates the average amount by which each faculty member’s salary increases (or decreases) for a one unit change in the corresponding variable, all of the other variables in the equation being held constant.  In the case of a dummy variable, the one unit change is from the omitted reference category (coded as 0) to the corresponding category (coded as 1). So, for example, the coefficient for White females in the second column indicates that an individual moving from the “White male” category (coded as 0) to the “White female” category (coded as 1) would be expected to have an increase (positive coefficient) in annual salary of $1,067, all other variables in the regression model being equal.   For continuous variables, such as Prior Years of Experience, the corresponding unstandardized coefficient ($30) indicates how much each additional unit (here, a year) is worth, on average.