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 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 $7,946 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	47955	150	10715
M	55901	194	13130
Total	52436	344	12746

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 $1,077, $725, and $2,164, respectively, above the female averages. Female Instructors earn on average $47 more than males.

Table 2. Average 2002 Salary by Rank and gender.

rank	M/F	Mean	N	Std. Dev
professor	F	63822	27	7540
	M	65986	92	8948

associate professor	F	53691	22	5876
	M	54416	30	7232

assistant professor	F	45520	74	4315
	M	46597	55	6107

instructor	F	34089	27	3889
	M	34042	17	3057

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 African American and Asian males earn more on average than White males. None of the female categories earn more on average than White males. There are a total of 344 faculty members in the total population.

Table 3. Average 2002 Salary by Ethnicity-Gender.

ethnicity-gender	Mean	N	Std. Dev
white female	48430	132	10786
african amer female	*	3
asian female	44594	6	7012
hispanic female	*	1
native amer female	*	3
white male	55986	168	13177
african amer male	56734	7	12761
asian male	56915	12	13586
hispanic male	*	2
native amer male	*	1
unknown	44301	9	10686

* 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 Moorhead there are 45 White male Assistant Professors and 26 White male Associate Professors, with corresponding odds of 26/45 (=0.578) of moving from Assistant to Associate rank. For White females there are 65 Assistants and 19 Associates, with corresponding odds of 19/65 (=0.292) of being Associates. The “odds ratio” of White females to White males getting promoted from Assistant to Associate is then (0.292)/(0.578) = 0.505; that is, White female odds of moving to Associate are only 50.6% 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 Logistic regression model, the odds improved slightly, from 0.505 to 0.632 (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.

Four minority dummy variables – Under-represented Females, White females, Asian females, 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), minority groupings are combined as separate category.

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	0.227	0.632
assistant to associate	Asian female to White male	0.816	1.274
	Under-rep female to White male	0.869	1.247
	Under-rep male to White male	0.255	4.715

associate to professor	White female to Male	0.514	0.765
associate to professor	Under-rep female to White male	0.764	0.616
	Under-rep male to White male	0.891	1.139

Table 4 shows the odds of promotion and associated statistical significance levels for the 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 Doctorate variable; that is, this variable predicts exactly who gets promoted. Only White females have lower odds of promotion from Assistant to Associate and Associate to Professor than the corresponding White male category, although neither of the odds coefficients are statistically significant. Under-represented males and females have better odds of promotion compared to White males at both steps, although again neither coefficient is statistically significant.

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 examined further below when the Total Population Model is estimated without the rank variable.

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=344). 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. Since there are sufficient numbers of Asian males (N=12) 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=10). The Asian Pacific Island female (N=6) was entered as a separate variable. However, the Native American, African American, and Hispanic females were collapsed into an Under-represented females category (N=7). There are sufficient White females (N=132) for a separate variable. The 9 Unknowns were entered as a separate category simply to ensure that the reference category is strictly 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 in annual salary of $692, all other variables in the regression model being equal. For continuous variables, such as Years since highest degree, the corresponding unstandardized coefficient ($104) 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)	56046	903		62.034	0.000

I. Executive Summary