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 Moorhead 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 $9,250 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, Associate Professor and Professor ranks, male salaries are on average $1,307, $606, and $5,502, respectively, above the female averages. Female Instructors earn on average $1,027 more than males.

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. Comparing the average salary for White males to the other averages reveals that the Asian Pacific Island  category of females earn more on average than the average for White males, as do the African American males.  However, as a group White females have lower average annual salary than White males.

Data are omitted if less than five incumbents within a grouping.

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.

Four minority dummy variables – Females, White females, Asian males, 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), minority groupings had to be combined as separate category.

Table 4 shows the odds of promotion and associated statistical significance levels for four protected classes as compared to White and All males.  There is no analysis for promotion from Instructor to Assistant Professor since there is complete separation on the Highest Degree variable (it predicts exactly who gets promoted).  All protected classes have lower odds of promotion from Assistant to Associate than corresponding male categories, although no coefficient is statistically significant.  Associate to Professor shows White females having lower odds than the  Male categories, while Under-represented males and Asian males have better odds, although all of these coefficients are  statistically non-significant.

In general, the control variables (Prior experience, Length of Service, Time since highest degree, Highest degree) were 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 is further examined below in the Total Population Model regression analyses where the academic rank variable is omitted.

C.   Controlling Salary For Structural Factors: Multiple Regression Analysis

      1. Total Population Salary Analysis

Table 5 reports the estimated regression equation and auxiliary statistics for the Total Population Analysis (N=270). 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 sufficient numbers of Asian  males (N=10) to conduct an analyses using this variable, this category was entered into the model as a separate variable. The remaining African American, Asian, and Native American males were collapsed into an Under-represented male category (N=6).  The Asian Pacific Island, Native American, African  American, and Asian females were collapsed into an Under-represented females category (N=7).  There are sufficient White females (N=97) 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 a decrease (negative coefficient) in annual salary of $1,217, all other variables in the regression model being equal.   For continuous variables, such as Years since highest degree, the corresponding unstandardized coefficient ($60) indicates how much each additional unit (here, a year) is worth, on average.