Prepared by:

Thomas McMullen

Senior Consultant

Hay Group

Eric Jacobs

Consultant

Hay Group

Malcolm M. Dow

Professor Emeritus

Northwestern University

Table of Contents

I. Executive Summary............................................................................................................... 1

II. Faculty Salary Equity Analysis................................................................................................ 2

A. Brief Description of Average Faculty Salary Differentials by Gender and Ethnicity................... 2

1. Faculty Salary By Gender.................................................................................................. 2

2. Faculty Salary By Gender and Rank.................................................................................... 2

3. Faculty Salary By Gender and Ethnicity............................................................................... 3

B. Promotion to Academic Rank................................................................................................ 3

C. Controlling Salary For Structural Factors: Multiple Regression Analysis................................... 6

2.... Natural Log of Salary Regression Model......................................................................... 11

D. Total Population Model Without Discipline Variables............................................................ 13

E. Individual-level Salary Differences: Regression Residuals...................................................... 14

F. Summary............................................................................................................................. 16

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 St Cloud 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 $5,915 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	51211	271	10822
M	57126	382	12696
Total	54671	653	12296

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

Table 2. Average 2002 Salary by Rank and Gender.

rank	M/F	Mean	N	Std. Dev
professor	F	61867	83	7294
	M	66820	167	8543

associate professor	F	53851	64	5911
	M	56877	75	8726

assistant professor	F	46399	82	5153
	M	47831	110	7782

instructor	F	35526	42	4430
	M	37863	30	5010

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 any other protected class, with the exception of Asian males.

Table 3. Average 2002 Salary by Ethnicity-Gender

ethnicity-gender	Mean	N	Std. Dev
white female	51718	233	11142
african amer female	*	1
asian female	49933	17	8132
hispanic female	45772	8	10439
native amer female	*	3
white male	57570	297	12755
african amer male	53614	19	10330
asian male	61767	39	13026
hispanic male	47285	8	6051
native amer male	*	3
unknown	47484	22	7823
Total	54671	653	12296

* 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 St Cloud there are 77 White male Assistant Professors and 53 White male Associate Professors, with corresponding odds of 53/77 (=0.688) of moving from Assistant to Associate rank. For White females there are 60 Assistants and 60 Associates, with corresponding odds of 60/60 (1.0) of being Associates. The “odds ratio” of White females to White males getting promoted from Assistant to Associate is then (1.0)/(0.688) = 1.453; that is, White female odds are 145.3% 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 entered into the Multinomial logistic regression model the odds ratio improved slightly, from 1.453 to 1.865 (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.

Six minority dummy variables – African American males, White females, Asian males, Asian females, 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. black + 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