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 St Cloud 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 $6,336 shortfall in average annual salary for female relative to male faculty. Several factors that account for much of this difference will be discussed below.

Table 1. Average 1997 Salary by Gender

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 Instructor, Assistant Professor, Associate Professor and Professor ranks, male salaries are on average higher than the female averages.

Table 2.  Average 1997 Salary by Rank and Gender.

3. Faculty Salary By Gender and Ethnicity

Table 3 reports average salary differences broken out by a combination of Gender and Ethnicity. Again, this table shows substantial salary differences in average salaries across these groupings. The average salary for White males is higher than that of any other protected class.

Table 3.  Average 1997 Salary by Ethnicity-Gender

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.  There are no promotions shown for Instructor to Assistant, since there is “complete separation” in the data, meaning that the Doctorate variable completely predicts this promotional step.

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.

Five minority dummy variables – White females, Asian males, African American males, Under-represented females and males – 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), some minority groupings had to be combined into the Under-represented categories.

Table 4 shows the odds of promotion and associated statistical significance levels for five protected classes as compared to White males.  There is no analysis for promotion from Instructor to Assistant Professor since there is no data for Asian male and Under-rep female, and there is “complete separation” in the data: that is, holding a Doctoral degree completely predicts promotion at this step. 

White females and Under-represented males have lower odds of promotion from Assistant to Associate, and from Associate to Professor, than corresponding male categories, although no coefficient is statistically significant.  Asian and African American males have slightly better odds ratios, although none of the odds ratios coefficients are significant. 

In general, one or more of the control variables -- Doctoral degree, Length of Service, and Prior experience -- were highly statistically significant in each equation examined here.

Since none of the promotion odds coefficients for protected classes are statistically significant after the control variables are entered into the equations, there is no statistically significant evidence from this analysis to indicate that the Academic Rank variable is “tainted.”  However, this finding is examined further below, in the discussion of the Total Population Model with the academic rank variable omitted.

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=525). 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. Since there are insufficient numbers of Native American males (N=2) to use as a separate category, they were combined with the Hispanic males to form an Under-represented males category (N=8).  Similarly, the African American females (N=3) were combined with the Hispanic females (N=4) to form an Under-represented females category. There are sufficient White (N=171) and Asian (N=8) females for separate variables.  African American (N=13) and Asian (N=24) males were also entered separately as variables.  The Unknown ethnic category (N=6) was entered into the analyses simply to ensure that the reference category against which all of the protected classes are compared is composed only of 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 a decrease (negative coefficient) in annual salary of $851, all other variables in the regression model being equal.   For continuous variables, such as Years since Highest Degree, the corresponding unstandardized coefficient ($89) indicates how much each additional unit (here, a year) is worth, on average.