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 2002 yearly salaries across ethnic and gender groupings of Southwest 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 $3,943 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	51080	59	9380
M	55023	90	11568
Total	53462	149	10898

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 and Assistant Professor ranks male salaries are on average $804 and $2,211, respectively, above the female averages. At the Associate Professor females earn on average $1,357 more than males, and at Professor rank males earn on average $4,164 more than females.

Table 2. Average 2002 Salary by Rank and Gender.

Base 02 Salary
rank	M/F	Mean	N	Std. Deviation
professor	F	63572	12	5836
	M	67736	26	8036

associate professor	F	35827	6	7544
	M	34470	5	3505

assistant professor	F	53116	12	4839
	M	55327	20	6100

instructor	F	48223	29	4788
	M	49027	39	6924

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. White males earn more on average than both female groups, but they earn less on average than the under-represented male group.

Table 3. Average 2002 Salary by Ethnicity-Gender

Base 02 Salary
ethnicity-gender	Mean	N	Std. Deviation
white female	50658	52	9819
under-rep female	54215	7	4441
under-rep male	56891	10	10421
white male	54790	80	11743
Total	53462	149	10898

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 Southwest there are 34 White male Assistant Professors and 17 White male Associate Professors, with corresponding odds of 17/34 (=0.50) of moving from Assistant to Associate rank. For White females there are 24 Assistants and 11 Associates, with corresponding odds of 11/24 (=0.458) of being Associates. The “odds ratio” of White females to White males getting promoted from Assistant to Associate is then (0.458/0.50) = 0.916; that is, White female odds of moving to Associate are 91.6% 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 model the odds changed slightly, from 0.916 to 0.795 (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 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 – Under-represented females, White females, 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), all 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	0.698	0.795
assistant to associate	Under-rep female to White male	0.200	0.220
	Under-rep male to White male	0.507	1.918

associate to professor	White female to White male	0.997	0.998
associate to professor	Under-rep male to White male	0.272	0.249

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 is “complete separation” in the data: that is, holding a Doctoral degree completely predicts odds of promotion. White females and Under-represented females have lower odds of promotion from Assistant to Associate than corresponding White male category, although neither coefficient is statistically significant. There are not enough under-represented females in the Associate and Full Professor ranks to estimate odds of promotion at this step.

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 will be examined further below when the Total Population Regression model is estimated after dropping 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=149). 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 not sufficient numbers of Minority males or females to conduct an analyses using any of these variables, these categories were collapsed into Under-represented females (N=7) and Under-represented males (N=10). There are sufficient White females (N=52) 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 $1,791, all other variables in the regression model being equal. For continuous variables, such as Years since Highest Degree, the corresponding unstandardized coefficient ($85) indicates how much each additional unit (here, a year) is worth, on average.

Table 5. Total Population Model with Corrected 2002 Salary data
	Unstandardized		Standardized	t	Sig.	Collinearity
	B	Std. Error	Beta	t	Sig.	Tolerance	VIF
(Constant)	59462	1512		39.316	0.000

ARTFI	-1673	1754	-0.040	-0.954	0.342	0.539	1.857
BUSAD	3130	1396	0.092	2.243	0.027	0.575	1.738
CISCS	6590	2336	0.098	2.821	0.006	0.794	1.259
EDCGN	1029	1377	0.033	0.747	0.456	0.493	2.027
(ENGLS)
HUMNS	-3082	1682	-0.068	-1.833	0.069	0.706

I. Executive Summary