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 Winona 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 $5,686 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	51122	161	12605
M	56808	196	14815
Total	54244	357	14130

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

Table 2. Average 2002 Salary by Rank and Gender.

rank	M/F	Mean	N	Std. Dev
professor	F	63489	50	8878
	M	68156	98	9215

associate professor	F	53418	42	7481
	M	52832	32	9510

assistant professor	F	44850	46	5755
	M	45564	43	6193

instructor	F	32590	23	3002
	M	35007	23	5454

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 all other female protected classes, but is lower than the other protected male classes. There are 357 faculty members in the total population.

Table 3. Average 2002 Salary by Ethnicity-Gender

ethnicity-gender	Mean	N	Std. Dev
white female	51035	152	12642
african amer female	*	1
asian female	*	4
hispanic female	*	3
native amer female	*	1
white male	56443	179	15079
african amer male	*	3
asian male	59686	10	8139
hispanic male	*	4

* 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 Winona there are 41 White male Assistant Professors and 30 White male Associate Professors, with corresponding odds of 30/41 (= 0.732) of moving from Assistant to Associate rank. For White females there are 43 Assistants and 39 Associates, with corresponding odds of 39/43 (=0.907) of being Associates. The “odds ratio” of White females to White males getting promoted from Assistant to Associate is then (0.907)/(0.732) = 1.239; that is, White female odds of moving to Associate are 123.9% 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 ratio improved slightly, from 1.239 to 1.328 (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. 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 – Females, White females, Asian 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 are combined into the Under-represented categories.

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

		Sig.	Odds Ratio
Instructor to Assistant	Female to Male	0.318	1.474
	White Female to White Male	0.473	1.328

assistant to associate	White female to White male	0.187	1.581
assistant to associate	Under-rep Female to White male	0.335	2.440
	Asian male to White male	0.543	0.462

associate to professor	White female to White male	0.441	0.768
associate to professor	Under-rep female to White male	0.706	0.691
	Asian male to White male	0.250	3.564
	Under-rep male to White male	0.591	1.899

Table 4 shows the odds of promotion and associated statistical significance levels for five protected classes as compared to White males. The analysis for promotion from Instructor to Assistant Professor, and from Assistant to Associate Professor, indicates that Females, White females, and Under-represented females have better odds than White males, although none of these differences are significant. At the Associate to Professor step, White and Under-represented females have lower odds, while Asian and Under-represented males have better odds, although none of the coefficients are significant.

In general, one or more of the control variables -- Doctoral degree, Length of Service, and Prior experience -- were 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 Model is estimated after the Academic Rank variable has been dropped from the model.

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=357). 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. African American males (N=3) were combined with the Hispanic males (N=4) to form an Under-represented males category (N=7). There are sufficient Asian males (N=10) for a separate variable. Similarly, the African American female (N=1) was combined with the Asian (N=4), Native American (N=1), and Hispanic (N=3) females to form an Under-represented females category (N=9). There are sufficient White (N=152) females 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 an increase in annual salary of $323, all other variables in the regression model being equal. For continuous variables, such as Years since Highest Degree, the corresponding unstandardized coefficient ($95) indicates how much each additional unit (here, a year) is worth, on average.

Table 5. Total Population Model With Ethnicity-Gender Variable.


	Unstandardized		Standardized	t	Sig.	Collinearity
	B	Std. Error	Beta	t	Sig.	Tolerance	VIF
(Constant)	59469	1182		50.326	0.000
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I. Executive Summary