I. 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 2002 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 $4,085 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 2002 Salary by Gender

M/F	Mean	N	Std. Dev
F	52909	48	9234
M	56994	55	10324
Total	55091	103	9996

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 $3,422 less than males. At the Associate Professor and Professor ranks, male salaries are on average $315 and $6,162, respectively, above the female averages.

Table 2. Average 2002 Salary by Rank and Gender.

rank	M/F	Mean	N	Std. Dev
professor	F	63657	10	7672
	M	69819	13	8338

associate professor	F	56384	15	6833
	M	56699	21	6367

assistant professor	F	45970	23	4158
	M	49392	20	6255

instructor	F	*	0
	M	*	1

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. There are 103 faculty members in the total population.

Table 3. Average 2002 Salary by Ethnicity-Gender.

ethnicity-gender	Mean	N	Std. Dev
white female	53296	37	10081
african amer female	*	4
asian female	*	3
hispanic female	*	4
white male	58518	40	11327
african amer male	51974	5	4225
asian male	54881	5	5997
hispanic male	*	3
native amer male	*	2

* Data are omitted if less than five faculty members within a grouping.

B. Promotion to Academic Rank

We note that this analysis uses current data patterns within campus to assess odds ratios for promotion. This analysis did NOT examine actual rates of promotion acceptance and rejection within a campus, as this data were not available for analysis. That is, we analyzed only the current distribution of faculty within ranks, broken out by ethnicity-gender. For example, at Metropolitan there are 14 White male Assistant Professors and 14 White male Associate Professors, with corresponding odds of 14/14 (=1.0) of moving from Assistant to Associate rank. For White females there are 18 Assistants and 11 Associates, with corresponding odds of 11/18 (=0.611) of being Associates. The “odds ratio” of White females to White males getting promoted from Assistant to Associate is then (0.611/1.0) = 0.611; that is, White female odds of moving to Associate are only 61.1% of the White male odds. The multinomial Logistic regression model adjusts these odds ratios to take into account the effects of other variables that might factor into promotion decisions, such as highest degree, previous experience, length of service, etc. When these control variables were added to the Multinomial regression model, the odds ratio improved slightly, from 0.611 to 0.638 (see Table 4 below).

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 Total MnSCU years. 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 to five minority dummy variables – Under-represented females, White females, Asian males, African American males, and Under-represented 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 protected classes are combined.

Table 4. Odds of Promotion to Higher Rank by Gender and Ethnicity

		Sig.	Exp(b)=Odds Ratio
assistant to associate	White female to White male	0.487	0.638
assistant to associate	Under-rep female to White male	0.677	0.665
	Under-rep male to White male	0.755	1.391
	Afr Amer male to White male	0.790	1.390
	Asian male to White male	0.869	0.831

associate to professor	White female to White Male	0.372	1.713
associate to professor	Under-rep female to White male	0.604	1.638
	Asian male to White male	0.806	1.310

Table 4 shows the odds of promotion and associated statistical significance levels for 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.) Under-represented females, Asian males, and White females have lower odds of promotion from Assistant to Associate than the corresponding male categories, although no coefficient is statistically significant. The Under-represented and African American male coefficients are greater than one but are not statistically significant. At the Associate to Professor promotion, all protected groups have better odds than the White male categories, although no coefficient is statistically significant.

In general, at least one of the Prior experience, Total MNSCU years, or Years since Highest Degree control variables was highly 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.” This question will be addressed again below in the Total Population without the Rank variable section.

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=103). In this model, the dependent variable is 2002 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 the Hispanic male (N=3) and Native American male (N=2) classes were collapsed into Under-represented male (N=5) category. Similarly, the African American (N=4), Asian (N=3), and African American females (N=4) were collapsed into an Under-represented female (N=11) category. There are sufficient White females (N=37) for a separate variable. The reference category is thus White males (N=40).

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 $615, all other variables in the regression model being equal. For continuous variables, such as Prior Years of Experience, the corresponding unstandardized coefficient ($33) indicates how much each additional unit (here, a year) is worth, on average.

Table 5. Total Population Model with Ethnicity-Gender Variables.


	Unstandardized		Standardized	t	Sig.	Collinearity
	B	Std. Error	Beta	t	Sig.	Tolerance	VIF
(Constant)	54295	2859		18.992	0.000

ARTFI

I. Executive Summary